JUTI: Jurnal Ilmiah Teknologi Informasi
type: channel
description: JUTI: Jurnal Ilmiah Teknologi Informasi (Scientific Journal of Information technology) is focusing on publishing original research, communication, and notes as a result of original and high quality research. The journal also invites well known researchers to write a review paper. JUTI accepts high quality papers in the field of engineering, science, and technology. The scope of JUTI encompasses but not limited to the following: Information Technology Computer Science Information System Computer Network Software Engineering Computer Vision JUTI: Jurnal Ilmiah Teknologi Informasi (Scientific Journal of Information Technology) is a scientific journal published by the Department of Informatics, Institut Teknologi Sepuluh Nopember. The journal is published biannually (January and July) and is available in an open-access electronic version. The paper template can be downloaded here. We now also provide a Typst template to facilitate authors in preparing their manuscripts with a structured and professional format. You can start a new Typst project using the template here. If you prepare your manuscript using the Typst template, please submit both the PDF file and the Typst source file. Publication Fees JUTI is fully open-access and free of charge, ensuring that authors can publish their research without any submission or publication fees. We are committed to supporting the academic community by providing a platform for high-quality research dissemination without financial barriers. All accepted papers will be freely accessible to readers worldwide, promoting knowledge sharing and scholarly impact. Accreditation JUTI has been re-accredited by the Ministry of Research, Technology, and Higher Education of the Republic of Indonesia with the decree No. 21/E/KTP/2016, dated July 9, 2016 as Sinta-3 started from Volume 14 Number 1 (2016). The accreditation has been renewed through decree No. 164/E/KPT/2021, dated December 27, 2021. Indexing & Abstract JUTI has been indexed by DOAJ…
publisher: Department of Informatics, Institut Teknologi Sepuluh Nopember
language: en
publication name: JUTI: Jurnal Ilmiah Teknologi Informasi
issn: 1412-6389
copyright: All papers should be submitted electronically. All submitted manuscripts must be original work that is not under submission at another journal or under consideration for publication in another form, such as a monograph or chapter of a book. Authors of submitted papers are obligated not to submit their paper for publication elsewhere until an editorial decision is rendered on their submission. Further, authors of accepted papers are prohibited from publishing the results in other publications that appear before the paper is published in JUTI unless they receive approval for doing so from the Editor-in-Chief. JUTI open access articles are distributed under a Creative Commons Attribution-ShareAlike 4.0 International License. This license lets the audience to give appropriate credit, provide a link to the license, and indicate if changes were made and if they remix, transform, or build upon the material, they must distribute contributions under the same license as the original.
items: Exploring The Effectiveness of In-Context Methods in Human-Aligned Large Language Models Across Languages
items: Gambling Comments Detection on Youtube: A Comparative Study of Tree-Based Boosting, LSTM and GRU Models
items: Topic Modeling for Constructing Learning Profiles Using LDA and Coherence Evaluation
items: Deep Metric Learning with Different Distance Metrics for Enhanced Classification Model in Typing Style
items: Garch Model Hybridization With Feed Forward Neural Network Algorithm Approach For Predicting The Volatility Of The Composite Stock Price Index
items: Gated Recurrent Unit Based Predictive Modeling for Dynamic Obstacle Avoidance in Autonomous Aerial Vehicles
items: DDoS Mitigation in Kubernetes: A Review of ExtendedBerkeley Packet Filtering and eXpress Data Path Technologies
items: Exploring The Role of Augmented Reality in Education: Systematic Literature Review
items: Mixed-Integer Linear Programming for Optimal Operation of Integrated Electricity and Natural Gas System Considering Take or Pay Agreements
items: A Dual-Network iTransformer Model for Robust and Efficient Time Series Forecasting
items: Network Intrusion Detection System with Time-Based Sequential Cluster Models using LSTM and GRU
items: Mandibular Image Segmentation and 3d Reconstruction using U-Net Model
items: Survey on Risks Cyber Security in Edge Computing for The Internet of Things Understanding Cyber Attacks Threats and Mitigation
items: Enhancing Face Detection Performance In 360-Degree Video Using Yolov8 with Equirectangular Augmentation Techniques
items: Evaluating Object Collection In Emergency Simulations Using Virtual And Augmented Reality
items: Evaluation of Synthetic Data Effectiveness using Generative Adversarial Networks (GAN) in Improving Javanese Script Recognition on Ancient Manuscript
items: Audio Feature Analysis and Selection for Deception Detection in Court Proceedings
items: AUTOMATIC TESTING FRAMEWORK BASED ON SERENITY AND JENKINS AUTOMATED BUILD
items: AN IOT-BASED AUTOMATED WATERING SYSTEM FOR PLANTS USING INTEGRATED FUZZY LOGIC AND TELEGRAM BOT
items: DEVELOPMENT OF A MODEL TO EVALUATE USERS' TECHNOLOGY READINESS AND ACCEPTANCE IN USING THE SELF-CHECK-IN KIOSK SERVICE AT SOEKARNO-HATTA INTERNATIONAL AIRPORT
items: ANALYSIS OF USER EXPERIENCE OF DANA E-WALLET USING USER EXPERIENCE QUOSTIONNAIRE (UEQ) AND UX HONEYCOMB
items: OPTIMIZING SENTIMENT ANALYSIS IN EDUCATIONAL YOUTUBE VIDEOS: A COMPARATIVE STUDY OF ROBERTA AND MULTINOMIAL NAIVE BAYES
items: RISK MANAGEMENT ANALYSIS OF E-KOHORTKIA APPLICATION USING ISO 31000 FRAMEWORK IN SOUTH CENTRAL TIMOR DISTRICT HEALTH OFFICE
items: EXPLORING CONSUMER RESPONSE TO TEXT-BASED CHATBOTS IN F-COMMERCE: A QUALITATIVE STUDY ON BANGLADESHI SME’S
items: COAL DEMAND PREDICTION MODEL USING MACHINE LEARNING METHODS
items: CLASSIFICATION OF LUNG AND COLON CANCER TISSUES USING HYBRID CONVOLUTIONAL NEURAL NETWORKS
items: UAV LAND COVER CLASSIFICATION USING CONVOLUTIONAL NEURAL NETWORK FEATURE MAP WITH A COMBINATION OF MACHINE LEARNING
items: OVERSAMPLING HYBRID METHOD FOR HANDLING MULTI-LABEL IMBALANCED
items: SOFTWARE DEFECT PREDICTION USING PCA BASED RECURRENT NEURAL NETWORK
items: MOBILE-BASED ONLINE QUEUE APPLICATION DEVELOPMENT AT GRIBIG PUBLIC HEALTH CENTER IN REALTIME

Exploring The Effectiveness of In-Context Methods in Human-Aligned Large Language Models Across Languages
type: item
description: Most of past studies about in-context methods like in-context learning (ICL), cross-lingual ICL (X-ICL), and in-context alignment (ICA) come from older, unaligned large language models (LLMs). However, modern human-aligned LLMs are different; they come with chat-style prompt templates, are extensively human-aligned, and cover many more languages. We re-examined these in-context techniques using two recent, human-aligned multilingual LLMs. Our study covered 20 languages from seven different language families, representing high, mid, and low-resource levels. We tested how well these methods generalized using two tasks: topic classification (SIB-200) and machine reading comprehension (Belebele). We found that utilizing prompt templates significantly improves the performance of both ICL and X-ICL. Furthermore, ICA proves particularly effective for mid- and low-resource languages, boosting their f1-score by up to 6.1%. For X-ICL, choosing a source language that is linguistically similar to the target language, rather than defaulting to English, can lead to substantial gains, with improvements reaching up to 21.98%. Semantically similar ICL examples continue to be highly relevant for human-aligned LLMs, providing up to a 31.42% advantage over static examples. However, this gain decreases when using machine translation model to translate query from target language. These results collectively suggest that while modern human-aligned LLMs definitely benefit from in-context information, the extent of these gains is highly dependent on careful prompt design, the language's resource level, language pairing, and the overall complexity of the task.
subject: Articles
  topic: section
creator: Ubaidillah Ariq Prathama
creator: Ayu Purwarianti
creator: Samuel Cahyawijaya
rights: Copyright (c) 2025 Ubaidillah Ariq Prathama, Ayu Purwarianti, Samuel Cahyawijaya https://creativecommons.org/licenses/by-sa/4.0
date: 2025-07-08
publication date: 2025-07-08
starting page: 13
ending page: 31
doi: 10.12962/j24068535.v23i2.a1323

Gambling Comments Detection on Youtube: A Comparative Study of Tree-Based Boosting, LSTM and GRU Models
type: item
description: The exponential growth of online gambling in Indonesia poses significant socio-economic challenges, particularly affecting vulnerable populations through sophisticated digital marketing strategies targeting social media platforms. This study addresses the critical need for automated detection systems to identify gambling-related content in YouTube comments. We scraped and manually labeled 11,673 comments from diverse YouTube videos, creating an extremely imbalanced dataset with gambling comments representing only 10% of the total data. Multiple machine learning approaches were developed and evaluated, comparing traditional gradient boosting methods (LightGBM, XGBoost, CatBoost) using TF-IDF features against deep learning models (LSTM & GRU) with Word2Vec embeddings. The experimental results demonstrate that gradient boosting methods significantly outperform deep learning approaches in generalization capability. LightGBM achieved the highest holdout F1-score with balanced precision (0.8912) and recall (0.8886), while XGBoost followed closely with comparable performance. In contrast, deep learning models exhibited severe overfitting, with GRU and LSTM showing excellent test performance but drastically reduced holdout recall (0.5022 and 0.4844, respectively). The findings indicate that the dataset size was insufficient for deep learning approaches to learn generalizable representations effectively. For practical deployment in YouTube gambling content detection, gradient boosting methods are recommended due to their superior performance with limited, imbalanced datasets.
subject: Articles
  topic: section
creator: Agung Widiyanto
creator: Mayesq Prameswari
creator: Muhammad Abdul Latief
rights: Copyright (c) 2025 Agung Widiyanto, Mayesq Prameswari, Muhammad Abdul Latief https://creativecommons.org/licenses/by-sa/4.0
date: 2025-07-08
publication date: 2025-07-08
starting page: 144
ending page: 160
doi: 10.12962/j24068535.v23i2.a1305

Topic Modeling for Constructing Learning Profiles Using LDA and Coherence Evaluation
type: item
description: Understanding individual learning patterns is important for supporting effective learning strategies in the digital education ecosystem. This study proposes a topic modeling approach using the Latent Dirichlet Allocation (LDA) algorithm to form learning profiles based on student interaction data from EdNet-KT1. The dataset includes 153,824 interactions with 11,613 questions, which were converted into semantic tag-based pseudotexts. Modeling was performed with 20 topics, which were selected as a compromise between semantic quality (coherence score 0.6688) and model readability, although the highest coherence score appeared with a larger number of topics. Each question is linked to a dominant topic, and student accuracy is calculated to form a student-topic performance matrix. The results of the analysis show that 66% of students mastered more than five topics, reflecting a broad range of knowledge. Visualization with heat maps, radar charts, and line charts provides a detailed overview of each individual's strengths and weaknesses. Segmentation was performed using the K-Means algorithm and produced four clusters based on student performance distribution. Adaptive learning recommendations are compiled based on an accuracy threshold of < 0.5 and a number of interactions > 10. Topics_13, topics_10, and topics_12 were identified as the most challenging topics. The results of this study indicate the potential of LDA-based approaches and clustering as analytical tools for shaping more personalized and contextual learning systems. Further research could explore sequential modeling and experimental validation of the effectiveness of recommendations
subject: Articles
  topic: section
creator: Andika Dwi Arko
creator: Muhamad Yusril Helmi Setyawan
creator: Roni Andarsyah
rights: Copyright (c) 2025 Andika Dwi Arko, Muhamad Yusril Helmi Setyawan, Roni Andarsyah https://creativecommons.org/licenses/by-sa/4.0
date: 2025-07-08
publication date: 2025-07-08
starting page: 126
ending page: 143
doi: 10.12962/j24068535.v23i2.a1301

Deep Metric Learning with Different Distance Metrics for Enhanced Classification Model in Typing Style
type: item
description: Writing can be a powerful and unique medium of self-expression for every individual. Therefore, we propound a deep metric learning technique to acquire the vector representation of text, aiming to enhance the performance of deep learning classification models in typing style classification. The study also compared the effect of text pre-processing and distance metrics on model performance using tweet data from six different Twitter users. The outcomes of the study showed that the model without text pre-processing and with deep metric learning using the Cosine distance metric had the optimal result with an accuracy of 0.79, compared to the deep learning model with a categorical cross-entropy loss function which only had an accuracy of 0.76. Additionally, the model with text pre-processing also produced a good performance, with an accuracy of 0.63 using the deep metric learning approach and Cosine distance metric, and an accuracy of 0.64 using deep learning classification with a categorical cross-entropy loss function.
subject: Articles
  topic: section
creator: Hendri Darmawan
creator: Zulfa Muflihah
creator: Tita Karlita
rights: Copyright (c) 2025 Hendri Darmawan, Zulfa Muflihah, Tita Karlita https://creativecommons.org/licenses/by-sa/4.0
date: 2025-07-08
publication date: 2025-07-08
starting page: 111
ending page: 125
doi: 10.12962/j24068535.v23i2.a1292

Garch Model Hybridization With Feed Forward Neural Network Algorithm Approach For Predicting The Volatility Of The Composite Stock Price Index
type: item
description: Stock market volatility is a crucial indicator in measuring investment risk and influencing investor decision-making, where proper understanding of volatility movements can help investors optimize their investment portfolios. Time series data from the stock exchange shows complex heteroscedasticity characteristics, where volatility levels can change dynamically over time, creating distinct challenges in modeling and prediction. The implementation of the hybrid model is carried out by integrating the advantages of both models, where GARCH is used to capture volatility clustering characteristics, while FFNN is utilized to capture complex non-linear patterns in the data. By using evaluation of several comprehensive error measurement metrics, including Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE), to ensure model reliability in various aspects of prediction. The use of the GARCH-FFNN hybrid model is expected to provide more accurate volatility predictions compared to using GARCH or FFNN models separately, with potential improvements in prediction accuracy and adaptability to changing market conditions. These findings provide important contributions to stock market volatility modeling and can serve as a reference for investors, portfolio managers, and financial practitioners in making better investment decisions, as well as paving the way for the development of more sophisticated volatility prediction models in the future
subject: Articles
  topic: section
creator: Rangga Kurnia Putra Wiratama
creator: Ahmad Saikhu
creator: Nanik Suciati
rights: Copyright (c) 2025 Rangga Kurnia Putra Wiratama, Ahmad Saikhu, Nanik Suciati https://creativecommons.org/licenses/by-sa/4.0
date: 2025-07-08
publication date: 2025-07-08
starting page: 88
ending page: 110
doi: 10.12962/j24068535.v23i2.a1278

Gated Recurrent Unit Based Predictive Modeling for Dynamic Obstacle Avoidance in Autonomous Aerial Vehicles
type: item
description: The entry of Autonomous Aerial Vehicles (AAVs) has reshaped multiple industries through novel solutions such as transport, monitoring, and deliveries. Nevertheless, the existence of dynamic operating environments, and the unpredictability of barrier emergence, constitutes a complicated path planning challenge that is difficult to cope with. Current methods of dynamic obstacle avoidance, e.g. Recurrent Neural Networks (RNNs) and Long Short-term Memory (LSTM) networks, accomplished the task and became the essential part of AAV navigation systems development. These techniques may work, but they have a disadvantage of being slow in processing and less energy efficient, which are important for a real-time operation and for a mission which lasts for a long time. The purpose of the research is to fill up the identified gaps by introducing a GRU-based predictive model for dynamic obstacle avoidance in AAV’s. While the previous models concentrate on the improvement of reaction time and energy consumption without the degradation of computational efficiency, the recent GRU model is particularly designed for such purpose. It is realized through a streamlined design that facilitates rapid and precise object trajectory predictions, thus, making AAVs be able to rethink their paths in advance of any obstacles lurking. We show that the RNN-based GRU model is benchmarked significantly better than the RNN and LSTM models in simulated settings. In the Eco mode, the model GRU responded in 0.35 seconds in low-speed and its energy consumption never exceeded 130 units even in the high-speed scenarios with maximum load. Path efficiency was preserved and the path length was kept to the minimum in most cases, which indicates the model's capability in finding the most direct paths. Additionally, computer loads were at a tolerable level, thus further showing the applicability of this model for systems on-board having inducted limits for their processing capabilities. GRU- based model comes out …
subject: Articles
  topic: section
creator: Parthasarathi Periasamy
creator: Joemax Agu Maxwell Thompson
creator: Biju Johnson
creator: Sridar Krishnan
rights: Copyright (c) 2025 Parthasarathi Periasamy, Joemax Agu Maxwell Thompson, Biju Johnson; Sridar Krishnan https://creativecommons.org/licenses/by-sa/4.0
date: 2025-07-08
publication date: 2025-07-08
starting page: 74
ending page: 87
doi: 10.12962/j24068535.v23i2.a1271

DDoS Mitigation in Kubernetes: A Review of ExtendedBerkeley Packet Filtering and eXpress Data Path Technologies
type: item
description: Kubernetes, as a widely adopted container orchestration platform, is increasingly targeted by sophisticated cyber threats, including Distributed Denial of Service (DDoS) attacks, which can severely compromise the stability, availability, and operational integrity of Kubernetes clusters by overwhelming the cluster’s control plane, disrupting pod scheduling, or saturating network resources. Emerging Linux kernel technologies, such as the Extended Berkeley Packet Filter (eBPF) and eXpress Data Path (XDP), offer innovative and efficient solutions to mitigate these challenges by enabling high-performance packet filtering, real-time traffic monitoring, and advanced intrusion detection directly within the kernel. These capabilities help reduce latency, enhance resource efficiency, and strengthen the security posture of modern cloud-native environments. This review explores advancements in network security by examining the integration of eBPF and XDP for defending Kubernetes environments against DDoS attacks. By analyzing existing studies and identifying their limitations, this review highlights the potential of these technologies to establish efficient, scalable, and adaptive mitigation frameworks. The insights gained from this research can guide the development of robust security policies tailored for modern containerized infrastructures.
subject: Articles
  topic: section
creator: Mircea Ţălu
rights: Copyright (c) 2025 Mircea Ţălu https://creativecommons.org/licenses/by-sa/4.0
date: 2025-07-08
publication date: 2025-07-08
starting page: 60
ending page: 73
doi: 10.12962/j24068535.v23i2.a1268

Exploring The Role of Augmented Reality in Education: Systematic Literature Review
type: item
description: The development of digital technology drives innovation in education, one of which is through the implementation of augmented Reality (AR), which increases interactivity and understanding of abstract concepts in learning. This study employs a Systematic Literature Review (SLR) with the PRISMA method to analyze the implementation of AR in education. Of the 3,225,372 articles reviewed, 30 journals met the research criteria, with Marker-Based Tracking as the most commonly used AR method because of its stability and accuracy. The study results showed that AR increases students' interactivity, facilitates understanding of abstract concepts, increases student engagement, improves information retention and memory, facilitates simulation and practice, develops creativity and collaboration, adapts learning to individual needs, and improves cost and resource efficiency, although it still faces challenges in the infrastructure and technical skills of teachers. Therefore, further development in AR applications at various education levels is recommended to improve understanding and adaptation to scientific developments. Keywords: Augmented Reality, Learning Media, Marker-Based Tracking.
subject: Articles
  topic: section
creator: Passion Timothy Gerald Sianipar
creator: Wilonotomo
creator: Priati Assiroj
rights: Copyright (c) 2025 Passion Timothy Gerald Sianipar, Wilonotomo, Priati Assiroj https://creativecommons.org/licenses/by-sa/4.0
date: 2025-07-08
publication date: 2025-07-08
starting page: 48
ending page: 59
doi: 10.12962/j24068535.v23i2.a1267

Mixed-Integer Linear Programming for Optimal Operation of Integrated Electricity and Natural Gas System Considering Take or Pay Agreements
type: item
description: This paper is proposed to demonstrate the implementation of Mixed-Integer Linear Programming (MILP) for solving the optimal operation of the Integrated Electricity and Natural Gas System (IENGS). The MILP is used to realize an economical and reliable power electricity system based on Dynamic Optimal Power and Gas Flow (DOPGF) considering Take or Pay (TOP) agreements for natural gas. This method is simulated on the integrated 6-bus electricity and 6-node natural gas systems. By using MILP, the best costs for optimal operation of IENGS are obtained in three scenarios. The superiority of the MILP is validated by suppressing the increasing best cost for optimal operation to be below 10%. In the first case, the best cost is $735,405.37 without the TOP agreement. In the second scenario, the best cost ranges from $748,399.30 to $760,320.57 with the TOP agreement implemented in one-by-one generators, which is 1.77% to 3.39% higher than the first scenario. In the third case, the best cost is $791,833.04 with the TOP agreement in all of the generators, which is 7.67% higher than the first scenario. In addition, the MILP can perform the DOPGF for IENGS without violating the problem constraints regarding the load demand fulfillment and power system limitations in both coal-fired and gas-fired generators.
subject: Articles
  topic: section
creator: Ervina Nooraini
creator: Mohamad Almas Prakasa
creator: Muhammad Ruswandi Djalal
creator: Rony Seto Wibowo
creator: Imam Robandi
rights: Copyright (c) 2025 Ervina Nooraini, Mohamad Almas Prakasa, Muhammad Ruswandi Djalal, Rony Seto Wibowo, Imam Robandi https://creativecommons.org/licenses/by-sa/4.0
date: 2025-07-08
publication date: 2025-07-08
starting page: 32
ending page: 47
doi: 10.12962/j24068535.v23i2.a1265

A Dual-Network iTransformer Model for Robust and Efficient Time Series Forecasting
type: item
description: Time-series forecasting plays a crucial role in various fields, including economics, healthcare, and meteorology, where accurate predictions are essential for informed decision-making. As data volume and complexity continue to grow, the need for efficient and reliable forecasting methods has become more critical. iTransformer, a recent innovation, improves interpretability while effectively handling multivariate data. In this study, the author proposes Dual-Net iTransformer, a novel approach that integrates iTransformer with a dual-network framework to enhance both accuracy and efficiency in time-series forecasting. This research aims to evaluate and compare the performance of traditional methods, iTransformer, and Dual-Net iTransformer, highlighting the advantages of the proposed model in improving forecasting outcomes.
subject: Articles
  topic: section
creator: Ary Mazharuddin Shiddiqi
creator: Bagaskoro Kuncoro Ardi
creator: Bilqis Amaliah
creator: I Komang Ari Mogi
creator: Agung Mustika Rizki
creator: Bintang Nuralamsyah
creator: Ilham Gurat Adillion
creator: Moch. Nafkhan Alzamzami
rights: Copyright (c) 2025 Ary Mazharuddin Shiddiqi, Bagaskoro Kuncoro Ardi, Bilqis Amaliah, I Komang Ari Mogi, Agung Mustika Rizki, Bintang Nuralamsyah, Ilham Gurat Adillion, Moch. Nafkhan Alzamzami https://creativecommons.org/licenses/by-sa/4.0
date: 2025-07-08
publication date: 2025-07-08
starting page: 1
ending page: 12
doi: 10.12962/j24068535.v23i2.a1264

Network Intrusion Detection System with Time-Based Sequential Cluster Models using LSTM and GRU
type: item
description: Technological development and the growth of the internet today have a positive and revolutionary impact in various areas of human life, such as banking, health, science, and more. The presence of Open Data and Open API also facilitates the exchange of data and information between entities without the restrictions imposed by different regions and geographical areas. However, information openness not only has a positive impact but also makes data vulnerable to data theft, viruses, and various other types of cyber attacks. The large-scale data exchange that occurs across the network poses a challenge in detecting unusual activity and new cyber attacks. Therefore, the existence of an Intrusion Detection System (IDS) is urgently essential. The IDS helps system administrators detect cyber attacks and network anomalies, thus minimizing the risk of data leaks and intrusions. The research developed a new approach using time-based sequential clustered data sets in the Long Short Term Memory (LSTM) and Gated Recurrent Unit (GRU) models. This IDS model was implemented using the CIC-IDS 2018 data set, which has more than 4 million data lines. The capabilities and uniqueness of the LSTM and GRU models are used to classify and determine various attacks in IDS based on sequential data sets ordered by time and clustered according to the destination ports and protocols, such as TCP and UDP. The model was evaluated using the accuracy, precision, recall, and F-1 scores matrix, and the results showed that the time-based sequential clustered models in LSTM and GRU have an accurities of up to 97.21%. This suggests that this new approach is good enough to be applied to the future IDS models.
subject: Articles
  topic: section
creator: Ravi Vendra Rishika
creator: Baskoro Adi Pratomo
creator: Shintami Chusnul Hidayati
rights: Copyright (c) 2025 JUTI: Jurnal Ilmiah Teknologi Informasi https://creativecommons.org/licenses/by-sa/4.0
date: 2025-02-25
publication date: 2025-02-25
starting page: 1
ending page: 12
doi: 10.12962/j24068535.v23i1.a1241

Mandibular Image Segmentation and 3d Reconstruction using U-Net Model
type: item
description: Penelitian ini bertujuan untuk meningkatkan presisi dan efisiensi dalam segmentasi citra mandibula dan rekonstruksi 3D menggunakan model U-Net. Segmentasi otomatis dengan U-Net menangani tantangan metode manual yang memakan waktu. Struktur Encoder-Decoder pada U-Net memungkinkan pembelajaran fitur citra medis yang kompleks dengan akurasi tinggi, menghasilkan segmentasi yang konsisten dan presisi. Hasil penelitian menunjukkan bahwa Res U-Net mencapai performa segmentasi yang unggul dengan Dice Similarity Coefficient (DSC) sebesar 95,37%, meskipun memerlukan waktu komputasi yang lebih lama. Sementara itu, U-Net standar menawarkan efisiensi komputasi yang lebih tinggi dan cocok untuk aplikasi real-time meskipun akurasinya sedikit lebih rendah. Integrasi segmentasi dengan rekonstruksi 3D meningkatkan visualisasi anatomi mandibula, memperbaiki efektivitas perencanaan bedah, serta menyediakan alat simulasi interaktif untuk perawatan personal dan pelatihan profesional. Penggunaan standar DICOM memfasilitasi aksesibilitas antar perangkat medis, mendukung interoperabilitas sistem perawatan kesehatan. Studi ini menyimpulkan bahwa Res U-Net optimal untuk kebutuhan presisi tinggi, sedangkan U-Net lebih cocok untuk aplikasi dengan pemrosesan cepat. Temuan ini diharapkan dapat memajukan teknologi segmentasi dan visualisasi medis yang andal dan efektif dalam praktik klinis.
subject: Articles
  topic: section
creator: Mambaul Izzi
creator: Chastine Fatichah
creator: Hadziq Fabroyir
rights: Copyright (c) 2025 JUTI: Jurnal Ilmiah Teknologi Informasi https://creativecommons.org/licenses/by-sa/4.0
date: 2025-02-25
publication date: 2025-02-25
starting page: 51
ending page: 64
doi: 10.12962/j24068535.v23i1.a1245

Survey on Risks Cyber Security in Edge Computing for The Internet of Things Understanding Cyber Attacks Threats and Mitigation
type: item
description: Dalam era pesatnya perkembangan teknologi, penggunaan IoT terus meningkat, terutama dalam konteks edge computing. Makalah survei ini secara teliti menjelajahi tantangan keamanan yang muncul dalam implementasi IoT pada edge computing. Fokus utama penelitian ini adalah potensi serangan dan ancaman siber yang dapat mempengaruhi keamanan sistem. Melalui metode survei literatur, makalah ini mengidentifikasi risiko keamanan siber yang mungkin timbul dalam lingkungan IoT di edge computing. Pendekatan metodologi penelitian digunakan untuk mengklasifikasikan serangan berdasarkan dampaknya pada infrastruktur, layanan, dan komunikasi. Keempat dimensi klasifikasi, yaitu Network Bandwidth Consumption Attacks, System Resources Consumption Attacks, Threats to Service Availability, dan Threats to Communication, memberikan dasar untuk memahami dan mengatasi risiko keamanan. Makalah ini diharapkan memberikan landasan pemahaman yang kokoh tentang keamanan pada IoT dalam edge computing, serta kontribusi untuk pengembangan strategi keamanan yang efektif. Dengan fokus pada pemahaman mendalam tentang risiko keamanan, makalah ini mendorong pengembangan solusi keamanan yang adaptif di masa depan untuk mengatasi tantangan keamanan yang berkembang seiring dengan pesatnya adopsi teknologi IoT di edge computing.
subject: Articles
  topic: section
creator: Tiara Rahmania Hadiningrum
creator: Resky Ayu Dewi Talasari
creator: Karina Fitriwulandari Ilham
creator: Royyana Muslim Ijtihadie
rights: Copyright (c) 2025 JUTI: Jurnal Ilmiah Teknologi Informasi https://creativecommons.org/licenses/by-sa/4.0
date: 2025-02-25
publication date: 2025-02-25
starting page: 29
ending page: 50
doi: 10.12962/j24068535.v23i1.a1210

Enhancing Face Detection Performance In 360-Degree Video Using Yolov8 with Equirectangular Augmentation Techniques
type: item
description: This study aims to enhance face detection performance in 360-degree videos by utilizing advanced image augmentation techniques with the YOLOv8 algorithm, which is effective for real-time object detection. Acknowledging the unique challenges posed by equirectangular projection, this research introduces a novel equirectangular augmentation method specifically designed for this medium. Our findings demonstrate a remarkable 1.346% improvement in detection accuracy in Equirectangular Projection (ERP) settings compared to default YOLOv8 augmentation strategies. This significant enhancement not only addresses the geometric distortions inherent in panoramic video formats but also emphasizes the critical need for tailored augmentation approaches to improve face detection in complex environments. By showcasing the effectiveness of these customized methods, this research contributes to the growing field of deep learning applications for immersive video technologies, with implications for sectors like security, virtual reality, and interactive media. Ultimately, this work highlights the potential of innovative augmentation techniques to ensure robust face detection in challenging visual contexts.
subject: Articles
  topic: section
creator: Rizky Damara Ardy
creator: Anny Yuniarti
creator: Christy Atika Sari
rights: Copyright (c) 2025 JUTI: Jurnal Ilmiah Teknologi Informasi https://creativecommons.org/licenses/by-sa/4.0
date: 2025-02-25
publication date: 2025-02-25
starting page: 81
ending page: 94
doi: 10.12962/j24068535.v23i1.a1255

Evaluating Object Collection In Emergency Simulations Using Virtual And Augmented Reality
type: item
description: Virtual Reality (VR) and Augmented Reality (AR) are two technologies that have received significant attention in recent years. While both hold immense potential, they offer distinct ways for users to interact with digital content and their physical surroundings. This research aims to evaluate the interaction between users and a collection of objects in both VR and AR settings. To achieve this, a user study was conducted with 24 participant using Meta Quest 3 headset to run simulation in both environments. The study focused on tasks related to object collection and emergency management while utilizing combination of objective and subjective metrics to evaluate user interactions in both VR and AR environments. Despite the relatively close scores for both result, research shows that participants prefer AR for emergency simulations over VR. Even considering participants' first-time use of the applications, AR remains more popular, supported by lower symptom rates reported in the sickness than VR. Additionally, participants tended to focus more on collecting small objects, though VR users often forgot these items, while medium-sized objects were more frequently overlooked in AR. Although VR users experienced more human errors related to collisions with real objects, the overall impact on immersion during simulations was not significant enough to favor one technology over the other. Based on this result, it can be said that while VR is better for showing immersion, it is generally better for a first-time user to engage in AR first since it will give less incidence of virtual sickness.
subject: Articles
  topic: section
creator: Matthew Vieri Suriawan
creator: Hadziq Fabroyir
creator: Darlis Herumurti
rights: Copyright (c) 2025 JUTI: Jurnal Ilmiah Teknologi Informasi https://creativecommons.org/licenses/by-sa/4.0
date: 2025-02-25
publication date: 2025-02-25
starting page: 65
ending page: 80
doi: 10.12962/j24068535.v23i1.a1252

Evaluation of Synthetic Data Effectiveness using Generative Adversarial Networks (GAN) in Improving Javanese Script Recognition on Ancient Manuscript
type: item
description: The imbalance of Javanese script data in ancient manuscript recognition poses a challenge due to the limited availability of data. A potential approach to addressing this issue is the use of Generative Adversarial Networks (GAN). This study evaluates the effectiveness of synthetic data generated using Enhanced Balancing GAN (EBGAN) in mitigating data imbalance. Various evaluation scenarios are conducted, including: (i) assessing the impact of syn-thetic data as augmentation, (ii) evaluating the sufficiency of synthetic data for recognition models, (iii) analyzing minority class oversampling with different selection strategies, and (iv) evaluating model generalization through cross-validation. Quantitative analysis of the generated synthetic data, based on Fréchet Inception Distance (FID) and Structural Similarity Index (SSIM), as well as visual assessment, indicates that the quality of synthetic data closely resembles real data. Additionally, experimental results demonstrate that combining real and synthetic data improves accuracy, precision, recall, and F1-score. The oversampling strategy for synthetic data has proven effective in meeting the data sufficiency requirements for training recognition models. Meanwhile, selecting minority classes and determining threshold values based on percentage, distribution, and model performance in oversampling can serve as guidelines for enhancing script recognition performance. Compared to previous methods, the use of EBGAN has been shown to produce more diverse synthetic data with better visual quality. However, further research is still needed to optimize GAN performance in supporting script recognition.
subject: Articles
  topic: section
creator: Muhammad 'Arif Faizin
creator: Nanik Suciati
creator: Chastine Fatichah
rights: Copyright (c) 2025 JUTI: Jurnal Ilmiah Teknologi Informasi https://creativecommons.org/licenses/by-sa/4.0
date: 2025-02-25
publication date: 2025-02-25
starting page: 95
ending page: 109
doi: 10.12962/j24068535.v23i1.a1256

Audio Feature Analysis and Selection for Deception Detection in Court Proceedings
type: item
description: Deception detection is a method to determine whether a person is lying or not. One lie detector is a polygraph that measures human physiology, such as pulse and blood pressure. However, polygraphs have a problem in that they cannot be measured based on human psychology, such as speech and intonation. Therefore, audio deception detection is required, and this can be measured based on human psychology. This research will extract audio features, such as the Mel Frequency Cepstral Coeffi-cient (MFCC), Jitter, Fundamental Frequency (F0), and Perceptual Linear Prediction (PLP), from the Real-Life Trial dataset, which comprises 121 audio data. From the extraction results in the form of numerical data totaling 6387 features, various feature-selection methods are employed, such as Feature Importance (FI), Principal Component Analysis (PCA), Information Gain, Chi-Square, and Recursive Feature Elimination (RFE). After feature selection, the selected features are input to machine learning models, such as random forest and support vector machine (SVM). After model testing, metrics such as accuracy, precision, recall, and F1 score were evaluated, as well as statistical evaluation, to assess the developed model. Results from this experiment show that the deception detection model is improved after a feature selection process to reduce irrelevant features. Comparing the accuracy, Chi-Square achieves a significantly higher result, reaching up to 92% with an improvement of 24.32%, surpassing the SVM model's accuracy of 67.57% before feature selection. In contrast, the RFE technique yielded the best accuracy of 86%, with an increase of 13.52%, building upon its baseline accuracy of 72.97%.
subject: Articles
  topic: section
creator: Muhammad Meftah Mafazy
creator: Chastine Fatichah
creator: Anny Yuniarti
rights: Copyright (c) 2025 JUTI: Jurnal Ilmiah Teknologi Informasi https://creativecommons.org/licenses/by-sa/4.0
date: 2025-02-25
publication date: 2025-02-25
starting page: 13
ending page: 28
doi: 10.12962/j24068535.v23i1.a1250

AUTOMATIC TESTING FRAMEWORK BASED ON SERENITY AND JENKINS AUTOMATED BUILD
type: item
description: Software Testing plays an important role in making high quality products and the right time. The process of testing done manually is often inaccurate, unreliable, and needed more than automatic testing. This research proposes a new framework for automation testing. This framework will help developers to create applications with better quality and shorten testing time. This framework offers a solution for developers so that the testing process is carried out easily and quickly. Our proposed concept consists of an automated test script based on Serenity Framework and can be done as a background process using Jenkins. Input of the system is a testing scenario, then mapped into Java Programming Language. Output of this system are test reports that represent the scenario that has been carried out. the results of implementation system prove that developers are helped by this framework in the software testing process. So that in this study it can be concluded that the automated testing framework that has been developed can improve the quality of application products through effective and efficient work methods.
subject: Articles
  topic: section
creator: Umi Sa'adah
creator: Jauari Akhmad Nur Hasim
creator: Andhik Ampuh Yunanto
creator: Desy Intan Permatasari
creator: Fadilah Fahrul Hardiansyah
creator: Irma Wulandari
creator: Hazna At Thooriqoh
rights: Copyright (c)
date: 2021-07-31
publication date: 2021-07-31
starting page: 102
ending page: 110
doi: 10.12962/j24068535.v19i2.a1017

AN IOT-BASED AUTOMATED WATERING SYSTEM FOR PLANTS USING INTEGRATED FUZZY LOGIC AND TELEGRAM BOT
type: item
description: The development of automatic plant watering systems has recently gained popularity due to the need to conserve water and ensure healthy plant growth. This study focuses on integrating fuzzy logic, sensors, and algorithms to provide an automatic watering system. Fuzzy logic is a powerful tool that allows the system to interpret sensor data and make informed decisions. The sensors measure soil moisture, humidity, temperature, and light intensity. The data collected from these sensors is analyzed using algorithms to determine the appropriate watering schedule. The system’s ability to analyze and interpret data ensures that the plants receive the necessary moisture without over-watering or under-watering. Integrating the Telegram Bot is a significant feature of the system, enabling users to monitor and control the system remotely. The Telegram Bot sends users notifications when the system is activated, or the plants require attention. The system can also be controlled remotely through the Bot, enabling users to adjust the watering schedule or turn the system on or off. This research shows that the designed features of the system function effectively and can be used on a daily household scale. The system’s automated features reduce the need for constant monitoring and manual watering, making it ideal for those who engage in gardening at home. This innovation is particularly relevant in increasing the productivity of plants. In addition, the system’s ability to be controlled remotely through the Telegram Bot is a significant advantage, making it accessible and convenient for users.
subject: Articles
  topic: section
creator: Ary Shiddiqi
creator: Muhammad Raihan Anindita
creator: Wahyu Suadi
creator: Rully Soelaiman
creator: Suhadi Lili
creator: Ilham Gurat Adillion
rights: Copyright (c)
date: 2024-07-31
publication date: 2024-07-31
starting page: 116
ending page: 124
doi: 10.12962/j24068535.v22i2.a1191

DEVELOPMENT OF A MODEL TO EVALUATE USERS' TECHNOLOGY READINESS AND ACCEPTANCE IN USING THE SELF-CHECK-IN KIOSK SERVICE AT SOEKARNO-HATTA INTERNATIONAL AIRPORT
type: item
description: The self-check-in kiosk is one of the digital technologies used by the aviation industry to help passengers check in on passenger flights independently and efficiently without the need for a conventional check-in counter at the airport. However, the phenomenon on the ground indicates that many users have not yet used the service. As a result, the check-in area in some of the flight masks often has a long wait. Studies conducted by several airports in campsites such as Malaysia, South Africa, and Switzerland show that self-check-in kiosks do not meet the echoes of users. The same thing happened at Indonesian airports, where the use of self-check-in kiosks was still below 20% of total passenger traffic in 2022–2023. The study introduces the User Experience Technology Readiness and Acceptance Model (UX TRAM), which is used to evaluate user readiness and acceptance of the application of new technologies in the airport environment. The Partial Least Squares Structural Equation Modeling (PLS-SEM) method is used to analyze the research model and the proposed hypothesis. Based on the results of the test of significance and relevance of the relationship in this study, the structural model proposed by the majority is of significant value, except for the variables Innovativeness and Insecurity versus Perceived Ease of Use. Based on the results of the test of the hypothesis carried out, out of 15 hypotheses tested, there are 13 accepted and 2 rejected hypotheses related to the readiness and acceptance of users in the use of new technology on the Self-Check-in Kiosk service at Soekarno-Hatta International Airport. The results of this study show that the proposed research model has varying explanatory strengths (near moderate to substantial/high) as well as predictive strengths that offer better predictable performance.
subject: Articles
  topic: section
creator: Muhammad Faisal Fanani
creator: Umi Laili Yuhana
creator: Ary Mazharuddin Shiddiqi
rights: Copyright (c)
date: 2024-07-31
publication date: 2024-07-31
starting page: 125
ending page: 139
doi: 10.12962/j24068535.v22i2.a1238

ANALYSIS OF USER EXPERIENCE OF DANA E-WALLET USING USER EXPERIENCE QUOSTIONNAIRE (UEQ) AND UX HONEYCOMB
type: item
description: In the era Society 5.0, economic growth and digital products have mushroomed, leading to digital transaction product innovation becoming a daily necessity for society. Easy, cashless payment are made through an E-wallet application with various options, including the DANA application. However, in several reviews, user have complained about the performance and issues with the DANA application such as, the display is unclear when logging into the application, and it loads slowly when entering the amount of money. This research was conducted to evaluate DANA application users and provide recommendation for improvements and enhancements to enhance the DANA application user experience. From the result of the value proposition on the implementation of the UX Honeycomb method in the DANA application between the people of Palembang City, users generally agree that DANA deserves a good above-average rating. Meanwhile, the User Experience Questionnaire (UEQ) method show that the DANA application received positive scores on the attractive, perspicuity, dependability, efficiency, and stimulation variables, but it obtained a negative score on the novelty variable. Thus, the DANA application need to design and create more creative, intentional, and innovative products.
subject: Articles
  topic: section
creator: Nyimas Silvia
creator: Allsela Meiriza
creator: Nabila Rizky Oktadini
creator: Pacu Putra
rights: Copyright (c)
date: 2024-07-31
publication date: 2024-07-31
starting page: 91
ending page: 100
doi: 10.12962/j24068535.v22i2.a1207

OPTIMIZING SENTIMENT ANALYSIS IN EDUCATIONAL YOUTUBE VIDEOS: A COMPARATIVE STUDY OF ROBERTA AND MULTINOMIAL NAIVE BAYES
type: item
description: YouTube has evolved into a globally influential platform, engaging over 2.1 billion users worldwide and serving as a prominent medium for sharing, consuming, and creating diverse video content. Particularly popular among younger demographics, YouTube stands as a multifaceted hub spanning various genres and has significantly impacted education by providing extensive educational materials, fostering independent learning, and supporting a wealth of educational resources. This research conducts an in-depth investigation into sentiment analysis specifically within the context of educational YouTube videos. Leveraging advanced machine learning techniques, notably RoBERTa, this research conducts a comparative analysis with Multinomial Naive Bayes (MNB). The primary focus is on exploring RoBERTa's adaptability and performance across a spectrum of educational video content, revealing its commendable accuracy of 91.21%, surpassing MNB's accuracy of 79.59%. However, it is observed that RoBERTa's performance is notably affected by smaller datasets, highlighting the critical importance of ample and diverse training data for achieving optimal results. These findings highlight the pivotal role of dataset characteristics and size in developing robust sentiment analysis models, especially with advanced natural language processing methods like RoBERTa.
subject: Articles
  topic: section
creator: Ulima Inas Shabrina
creator: Muhammad Iskandar Java
creator: Siti Rochimah
rights: Copyright (c)
date: 2024-07-31
publication date: 2024-07-31
starting page: 83
ending page: 90
doi: 10.12962/j24068535.v22i2.a1204

RISK MANAGEMENT ANALYSIS OF E-KOHORTKIA APPLICATION USING ISO 31000 FRAMEWORK IN SOUTH CENTRAL TIMOR DISTRICT HEALTH OFFICE
type: item
description: The E-KohortKIA application is a web-based application launched by the Indonesian Ministry of Health for use in Puskesmas and Health Offices throughout Indonesia as a solution to various problems caused by manual use of the KIA Cohort. This application can be accessed via computer or smartphone and is currently being implemented by the South Central Timor District Health Service for Maternal and Child Health (KIA) services. In using this application, various possible risks can occur that can disrupt application performance, therefore it is necessary to carry out a risk management analysis. The aim of this research is to determine various possible risks that could occur in implementing the E-KohortKIA application and to carry out risk treatment for these possible risks. This research uses qualitative methods by collecting data through interviews and observations, as well as data processing and risk management analysis using the ISO 31000 framework including the risk assessment stage and risk treatment stage. The results of this research found 23 possible risks, most of which were due to system and infrastructure factors. These possible risks include 13 possible low level risks, 8 possible medium level risks, and 2 possible high level risks. There are 2 possible risks that have a high and maximum level of risk severity so that they have the potential to disrupt or inhibit or even stop application performance. This research also provides recommendations for risk treatment proposals for various possible risks and can be used by users to maintain application performance.
subject: Articles
  topic: section
creator: Roy Armus Allu
creator: Kristoko Dwi Hartomo
rights: Copyright (c)
date: 2024-07-31
publication date: 2024-07-31
starting page: 101
ending page: 115
doi: 10.12962/j24068535.v22i2.a1201

EXPLORING CONSUMER RESPONSE TO TEXT-BASED CHATBOTS IN F-COMMERCE: A QUALITATIVE STUDY ON BANGLADESHI SME’S
type: item
description: This qualitative study examines the consumer response to text-based chat bots in F-commerce, specifically in the context of Bangladeshi SMEs. The study aims to explore the benefits and challenges of using chat bots in F-commerce and identify the factors that influence consumer response to chat bots. The study uses semi-structured interviews to collect data from 15 Bangladeshi consumers who have experience using chat bots in F-commerce. The findings suggest that chat bots can improve customer service, save time and effort, and provide convenience for consumers, but they also face challenges such as technical issues, language barriers, and privacy concerns. The study also identifies several factors that influence consumer response to chat bots, including perceived usefulness, perceived ease of use, trust, familiarity, and personalization. The study concludes by discussing the practical implications of the findings for SMEs in Bangladesh and suggesting directions for future research.
subject: Articles
  topic: section
creator: Md Mehedi Hasan Emon
creator: Tahsina Khan
creator: Md. Adnan Rahman
rights: Copyright (c)
date: 2024-07-31
publication date: 2024-07-31
starting page: 78
ending page: 82
doi: 10.12962/j24068535.v22i2.a1181

COAL DEMAND PREDICTION MODEL USING MACHINE LEARNING METHODS
type: item
description: Forecasting coal demand needs is important to minimize operational costs. Forecasting will help companies determine the right amount and time to order coal from suppliers. Research on coal forecasting in Indonesia generally uses a statistical approach and has not analyzed the performance of other forecasting models. This research aims to forecast coal demand using statistical and machine learning methods, namely ARIMA, Exponential Smoothing, Support Vector Regression (SVR), Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM). The evaluation methods used to analyze forecasting performance are Mean Absolute Error (MAE) and Mean Absolute Percentage Error (MAPE). The new coal demand data used is 1097 daily data taken from January 2021 to December 2022 in the form of a timeseries and is stationary which has been tested using Augmented Dickey-Fuller (ADF). The test results show that the ARIMA model has MAPE value of 5.11%, MAE 2.91 and R-Square 0.925, Exponential Smoothing MAPE 1.07%, MAE 0.55 and R-Square 0.997, SVR with MAPE value of 5.48%, MAE 3.16 and R-Square 0.88, RNN with MAPE value of 5.19%, MAE 2.91 and R-Square 0.896, LSTM with MAPE value of 4.83%, MAE 2.84 and R-Square 0.897. From the test results it was found that exponential smoothing had the smallest error values among the other models. With forecasting results that have a small error rate, it can help management in making decisions to minimize costs in coal ordering and warehouse management.
subject: Articles
  topic: section
creator: Kristina Febriani
creator: Chastine Fatichah
rights: Copyright (c)
date: 2024-01-31
publication date: 2024-01-31
starting page: 65
ending page: 77
doi: 10.12962/j24068535.v22i1.a1209

CLASSIFICATION OF LUNG AND COLON CANCER TISSUES USING HYBRID CONVOLUTIONAL NEURAL NETWORKS
type: item
description: Colon and lung cancers are two highly lethal kinds of cancer which can often coexist and pose a new challenge for accurate diagnosis. While research often concentrates on detecting a single cancer in a specific organ, this study proposes an innovative machine-learning approach to identify both colon and lung cancers. The objective is to create a hybrid machine learning classification model to enhance diagnostic precision. The LC25000 dataset comprises 25,000 color histopathological image samples of lung and colon cell tissues, indicating the presence or absence of cancer (adenocarcinoma). Image features are extracted using the pre-trained VGG-16 model. The cancer type is identified through three machine learning classification algorithms: Stochastic Gradient Descent (SGD), Random Forest (RF), and K-Nearest Neighbor (KNN). The model's evaluation employed a 10-fold cross-validation technique, with CNN-SGD exhibiting the highest performance based on evaluation metrics. On a scale of 0 to 100, it scored 99.8 for Area Under Curve (AUC) and 98.88 for Classification Accuracy (CA). CNN-RF, a model with performance closely following CNN-SGD, demonstrates training times 58.3 seconds faster than CNN-SGD. Meanwhile, CNN-KNN ranks last among the models evaluated in this study based on its F1, recall, AUC, and CA scores.
subject: Articles
  topic: section
creator: Chilyatun Nisa'
creator: Nanik Suciati
creator: Anny Yuniarti
rights: Copyright (c)
date: 2024-01-31
publication date: 2024-01-31
starting page: 56
ending page: 64
doi: 10.12962/j24068535.v22i1.a1225

UAV LAND COVER CLASSIFICATION USING CONVOLUTIONAL NEURAL NETWORK FEATURE MAP WITH A COMBINATION OF MACHINE LEARNING
type: item
description: In geographic analysis, land cover plays an important role in everything from environmental analysis to sustainable planning methods and physical geography studies. The Indonesian National Standard (SNI 7645:2014) classifies vegetation analysis based on density. There are four categories of vegetation density index: non-vegetation, bare, medium, and high. Technically, vegetation data can be obtained through remote sensing. Satellite and UAV data are two types of data used in remote sensing to collect information. This research will analyze land cover based on vegetation density information that can be collected through remote sensing. Based on vegetation density information from remote sensing, the information can help in land processing, Land Cover Classification is carried out based on vegetation density. Convolutional neural networks (CNN) have been trained extensively to apply their properties to land cover classification. This research will evaluate features extracted from Convolutional Neural Networks (ResNet 50, Inception-V3, DenseNet 121) which have previously been trained and continued with Decision Tree algorithms, Random Forest, Support Vector Machine and eXtreme Gradient Boosting to perform classification. From the comparison results of classification tests between machine learning methods, Support Vector Machine is superior to other machine learning methods. This is proven by the accuracy results obtained at 85% with feature extraction using ResNet-50 where the processing time is 8 minutes. Followed by the second-best model, namely ResNet-50 with XGBoost which obtained accuracy results of 82% with a processing time of 55 minutes. Meanwhile, the use of feature extraction using the DenseNet-121 method was obtained using a combination of the Support Vector Machine method and the XGBoost method with the accuracy obtained being 81%.
subject: Articles
  topic: section
creator: Erika Maulidiya
creator: Chastine Fatichah
creator: Nanik Suciati
creator: Fajar Baskoro
rights: Copyright (c)
date: 2024-01-31
publication date: 2024-01-31
starting page: 45
ending page: 55
doi: 10.12962/j24068535.v22i1.a1214

OVERSAMPLING HYBRID METHOD FOR HANDLING MULTI-LABEL IMBALANCED
type: item
description: Data and information continue to increase along with the development of digital technology. Data availability is becoming increasingly numerous and complex. The existence of unbalanced data causes classification errors due to the dominance of majority-class data over the minority class. Not only limited to the binary class, but data imbalance is also often encountered in multi-label data, which become increasingly important in recent years due to its vast application scope. However, the problem of class imbalance has been a characteristic of many complex multi-label datasets, making it the focus of this research. Handling unbalanced multi-label data still has a lot of potential for development. One approach, Synthetic Oversampling of Multi-Label Data Based on Local Label Distribution (MLSOL) and Integrating Unsupervised Clustering and Label-specific Oversampling to Tackle Imbalanced Multi-Label Data (UCLSO), has been developed. UCLSO's attention only focuses on the majority class, which can lead to data imbalance and excessive overfitting. Although effective in preventing majority class domination, this approach cannot overcome the lack of variation within the minority class. By contrast, MLSOL focuses on minority classes, allowing for variations in multi-label data and significantly improving classification performance. This research aims to overcome the problem of data imbalance by combining the MLSOL and UCLSO oversampling methods. Combining these two approaches is expected to exploit the strengths and reduce the weaknesses of each, resulting in significant performance improvements. The trial results show that the hybrid oversampling method produces the highest value on biological data with an F-1 score of 88%. Meanwhile, the single oversampling methods UCLSO and MLSOL on biological data produce an F-1 score of 67% and 62%, respectively.
subject: Articles
  topic: section
creator: Dara Tursina
creator: Sherly Rosa Anggraeni
creator: Chastine Fatichah
creator: Misbakhul Munir Irfan Subakti
rights: Copyright (c)
date: 2024-01-31
publication date: 2024-01-31
starting page: 32
ending page: 44
doi: 10.12962/j24068535.v22i1.a1208

SOFTWARE DEFECT PREDICTION USING PCA BASED RECURRENT NEURAL NETWORK
type: item
description: Software quality is one of the important phases in software development. Software quality assesses the usability and quality of the software developed. Defect prediction early in software development helps in software quality assurance by reducing software defects that may occur. With good predictions, it will provide additional benefits in terms of resource and cost efficiency. The researchers in this study have proposed a software defect prediction method that utilizes a Recurrent Neural Network (RNN) based on Principal Component Analysis (PCA). The dataset used is the PROMISE dataset, namely JM1, CM1, PC1, KC1, and KC2. The test results showed that the PCA-RNN method was successfully applied. For the highest accuracy on the PC1 dataset, with an accuracy of 93.99% with the division of training data by testing data (70:30).
subject: Articles
  topic: section
creator: Eka Alifia Kusnanti
creator: Lauretha Devi Fajar Vantie
creator: Umi Laili Yuhana
rights: Copyright (c)
date: 2024-01-31
publication date: 2024-01-31
starting page: 23
ending page: 31
doi: 10.12962/j24068535.v22i1.a1199

MOBILE-BASED ONLINE QUEUE APPLICATION DEVELOPMENT AT GRIBIG PUBLIC HEALTH CENTER IN REALTIME
type: item
description: Puskesmas is one of the health service facilities that organizes community and individual health efforts at level one. Gribig Health Center is one of the health centers in the city of Malang, East Java, which has several health services. To get services from the Puskesmas, each patient is required to register and complete the required files for further processing by the administration. However, the imbalance between the number of patients and the availability of services is the cause of queues. This study aims to create a mobile-based queuing application at the Gribig Health Center in real-time. The ar-chitectural concept used in developing applications is client-server. The queuing method used in the system is a combina-tion of FCFS (First Come First Served) and PS (priority service) methods. In system development, the development method used in this research is the waterfall method. For system testing, the author uses the Black Box Testing method to ensure that all application functionality is appropriate. The purpose of developing this application is to make it easier for pa-tients to get queue numbers for Gribig Health Center services anywhere and anytime, make it easier for patients to make registration bookings for other days in advance, exchange queue numbers, notifications when their turn is approaching, find out the estimated time to get service, and queue information. up-to-date for each service at the Puskesmas. The results of this study are successful in developing an online queuing application at the Gribig Health Center in real-time by utiliz-ing the QR Code to verify the queue and there is also a notification feature as a patient reminder.
subject: Articles
  topic: section
creator: Dwiky Satria Hutomo
creator: Chaulina Alfianti Oktavia
rights: Copyright (c)
date: 2024-01-31
publication date: 2024-01-31
starting page: 11
ending page: 22
doi: 10.12962/j24068535.v22i1.a1197