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Tri A. Sundara
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The Indonesian Journal of Computer Science
Published by STMIK Indonesia Padang
ISSN : 25497286     EISSN : 25497286     DOI : https://doi.org/10.33022
Core Subject : Science, Engineering,
Computer Science & IT Electrical & Electronics Engineering Engineering
The Indonesian Journal of Computer Science (IJCS) is a bimonthly peer-reviewed journal published by AI Society and STMIK Indonesia. IJCS editions will be published at the end of February, April, June, August, October and December. The scope of IJCS includes general computer science, information system, information technology, artificial intelligence, big data, industrial revolution 4.0, and general engineering. The articles will be published in English and Bahasa Indonesia.
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 1,114 Documents
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Deep Learning Applications in Fog Computing Environments : a review Maqdid, Goran; Ibrahim, Media Ali Ibrahim; Shavan Askar; Hussein, Diana Hayder Hussein
The Indonesian Journal of Computer Science Vol. 14 No. 1 (2025): The Indonesian Journal of Computer Science (IJCS)
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v14i1.4654

Abstract

This review investigates the transformation of deep learning in a fog computing environment, strongly emphasizing synergy between these enabled technologies and their real-world consequences across various domains. Fog computing is the decentralized approach to data processing, overcoming certain limitations in traditional cloud systems: it reduces latency up to 50%, minimizes bandwidth usage, and alleviates network congestion. Deep learning, known for pattern extraction from complex datasets, enhances real-time analytics and intelligent decision-making in resource-constrained environments. Together, they enable effective processing and prompt decision-making in applications such as anomaly detection in healthcare-for example, arrhythmias with 50% faster response, traffic flow optimization in smart cities, and predictive maintenance in industrial automation, reducing downtime by 60%. Integrating deep learning with fog computing has numerous advantages, such as reducing dependencies on cloud infrastructure, enhancing data privacy, and increasing real-time processing. Yet, several challenges remain, like the resource-limited computational capacity of fog nodes, security vulnerabilities, and the need for scalable and efficient architecture. Recent lightweight model design, federated learning techniques, and hierarchical frameworks are some promising solutions to such challenges. This review synthesizes the current research findings, identifies sector-specific applications, and addresses critical challenges. It also outlines future directions comprising the development of adaptive architectures, privacy-preserving methodologies, and hybrid approaches in artificial intelligence. Meeting these challenges will unlock the full potential of deep learning and fog computing-driving innovation and efficiency across industries.
Rancang Bangun Aplikasi Kesehatan Mental Berbasis Android untuk Penyandang Disabilitas Della Fitria Lestari; Endah Sudarmilah
The Indonesian Journal of Computer Science Vol. 14 No. 1 (2025): The Indonesian Journal of Computer Science (IJCS)
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v14i1.4656

Abstract

Persons with disabilities often face significant challenges in accessing mental health services due to limited infrastructure and a lack of specialized professionals in Indonesia. This group is particularly vulnerable to mental health issues, with the primary barriers being the absence of inclusive services and difficulties in accessing information and professionals who understand their specific needs, especially in communication. This study focuses on designing and developing an Android-based mental health application tailored for persons with disabilities. The application aims to provide accessible mental health information and consultation services with psychologists, supported by Sign Language Interpreters (SLI) to enhance communication. Developed using the System Development Life Cycle (SDLC) with the waterfall method, the application seeks to address the accessibility gap in mental health services. By improving access to information and support, the application is expected to help persons with disabilities overcome barriers to obtaining essential mental health care and resources.
Parallel Processing in Distributed and Hybrid Cloud-Fog Architectures: A Systematic Review of Scalability and Efficiency Strategies Ihsan, Rasheed; Zeebaree, Subhi R. M.
The Indonesian Journal of Computer Science Vol. 14 No. 1 (2025): The Indonesian Journal of Computer Science (IJCS)
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v14i1.4661

Abstract

In distributed computing, hybrid cloud-fog architectures have become a revolutionary concept for tackling the pressing issues of latency, scalability, and energy efficiency. These systems allow real-time data processing closer to end users by fusing the localized capabilities of fog computing with the centralized capacity of cloud computing. This makes them especially useful for latency-sensitive applications like smart cities, healthcare, and the Internet of Things. The technological developments, application areas, and difficulties related to hybrid systems are all examined in this study's methodical analysis of the body of existing research. With a focus on utilizing technologies like SDN, NFV, and AI-driven optimization frameworks, key focus areas include resource management, dynamic job allocation, privacy-preserving procedures, and scaling tactics. Although hybrid designs show great promise for increasing system responsiveness and efficiency, unresolved problems including resource allocation complexity, privacy concerns, and interoperability underscore the need for more study. This work offers actionable recommendations to address these gaps, including standardization of communication protocols, integration of advanced AI techniques, and the development of energy-efficient designs. The findings lay a strong foundation for advancing hybrid cloud-fog systems and ensuring their broader adoption across diverse industries.
Pemodelan Indeks Kualitas Udara PM2.5 di Kemayoran, Jakarta, dengan Faktor Meteorologi Menggunakan ARFIMAX Oktavia Laras Dianingati; Mahmudi; Dhea Urfina Zulkifli
The Indonesian Journal of Computer Science Vol. 14 No. 1 (2025): The Indonesian Journal of Computer Science (IJCS)
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v14i1.4663

Abstract

Poor air quality that significantly impacts on health due to fine particles PM2.5 has become one of the serious problems in urban areas such as Kemayoran, Jakarta. This study aims to predict the Air Quality Index of PM2.5 pollutant in Kemayoran, Jakarta, with the ARFIMAX model which will be compared with the ARFIMA model. The ARFIMAX modeling involves meteorological factors as exogenous variables. The research results showed that the ARFIMAX(1,0,33,1) model with significant exogenous variables, namely minimum temperature, average temperature, and wind direction at maximum speed provides better prediction accuracy with a Mean Absolute Percentage Error (MAPE) value of 23.69%, compared to ARFIMA with a MAPE value of 25.76%. This decrease in MAPE value indicates that the addition of exogenous variables in the model can improve the accuracy of air quality forecast.
APT Winnti Panda as a Power-Gathering Tool in International Cyberspace Septiasari, Rycka; Kurniawan, Yandry; Arifandy, Mohamad; Putri, Erika Husna Nabila
The Indonesian Journal of Computer Science Vol. 14 No. 2 (2025): The Indonesian Journal of Computer Science
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v14i2.4687

Abstract

This study provides an analysis of the Advanced Persistent Threat (APT) Winnti Panda impact on Indonesian infrastructure in 2022, which is a tool for gathering power in international cyberspace. The cybersecurity dilemma concept is utilized to explain the phenomena that occur using a deductive qualitative method. This study highlights how Indonesia perceives the cyber threats posed by the APT Winnti Panda. The data used in this study are primary data sourced from the Indonesian Cyber Security Agency (BSSN), which was taken through interviews. In addition, secondary data is also used using the archival and desk research methods from various online and offline sources. The main argument of this study is that the APT Winnti Panda, which attacked Indonesia in 2022, is a tool used to gather power in international cyberspace.
Deep Learning for Dynamic Resource Management in 5G Networks: A Review Diana Hayder Hussein; Abdulwahab, Sara; Shavan Askar; Media Ali Ibrahim
The Indonesian Journal of Computer Science Vol. 14 No. 1 (2025): The Indonesian Journal of Computer Science (IJCS)
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v14i1.4688

Abstract

Dynamic resource management is important for 5G wireless networks to ensure they are efficient, scalable, and can handle growing connectivity demands while maintaining quality service. The aim of this review is to discuss how deep learning has changed the way complex challenges are being addressed in resource allocation, frequency spectrum management, energy efficiency, and runtime decision-making over 5G wireless networks. It combines the very best of leading-edge research insights into showing, through advanced deep learning techniques like supervised learning, and federated learning, how to allow for intelligent, adaptive solutions that go beyond conventional approaches. The manuscript describes this through a review that compares the strengths of these methodologies in network performance optimization while pointing out some limitations related to computational complexity or lack of extensive real-world testing. It further elaborates on promising future directions, ranging from federated learning for decentralized resource management to enhancing the interpretability of deep learning models and leveraging diverse datasets for improving robustness. The discussion also covers the arrival of 6G networks, which will introduce refined and AI-driven approaches for resource optimization. By establishing the logical links between theoretical developments and practical uses, the presented review will pinpoint the transforming potential of deep learning in re-shaping both the wireless communication networks of the future, but also opening new frontiers well beyond 5G.
Machine Learning for Network Anomaly Detection A Review Mahmood, Nawzad Hamad; Diana Hayder Hussein; Shavan Askar; Media Ali Ibrahim
The Indonesian Journal of Computer Science Vol. 14 No. 1 (2025): The Indonesian Journal of Computer Science (IJCS)
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v14i1.4703

Abstract

This research aims to investigate the application of machine learning (ML) techniques in network anomaly detection to enhance security in the face of evolving cyber threats. Employing a systematic review of existing literature and experimental evaluation, the study explores the effectiveness of various ML algorithms and their capacity to detect anomalies in network traffic. Unlike traditional rule-based methods, ML algorithms analyze extensive traffic data to distinguish normal from abnormal behavior, adapting dynamically to new threats in real-time. Key methodologies include feature engineering to optimize model performance, focusing on attributes like packet size and flow duration. The research evaluates detection accuracy, reduction of false positives, and the adaptability of ML-based systems to changing conditions. Main outcomes demonstrate that ML offers significant advantages over heuristic approaches, with improved detection rates, minimized human intervention, and enhanced responsiveness to emerging threats. The findings underscore the importance of real-time detection capabilities and highlight challenges such as computational complexity and dataset quality. By addressing these challenges, the study contributes valuable insights into strengthening network defense mechanisms through advanced ML applications.
Quality of Service (QoS) Optimization in 5G Using Machine Learning Diana Hayder Hussein; Mahmood, Nawzad Hamad; Shavan Askar; Media Ali Ibrahim
The Indonesian Journal of Computer Science Vol. 14 No. 1 (2025): The Indonesian Journal of Computer Science (IJCS)
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v14i1.4706

Abstract

The emergence of 5G networks has revolutionized communication systems by providing unprecedented speed, connectivity, and reliability. This breakthrough technology enables diverse applications such as autonomous vehicles, smart cities, and industrial automation through higher bandwidth and ultra-low latency. However, maintaining consistent Quality of Service (QoS) across these varied applications presents significant challenges due to their conflicting demands. Traditional QoS management methods struggle to address the dynamic and complex requirements of 5G, prompting the adoption of Machine Learning (ML) techniques. ML offers intelligent, adaptive solutions for traffic prediction, network slicing, and real-time decision-making, ensuring improved resource allocation and seamless service delivery.
Deteksi Anomali Hasil Pengukuran Penakar Hujan Otomatis Menggunakan Metode Long Short Term Memory Wahyudi, Niko
The Indonesian Journal of Computer Science Vol. 14 No. 1 (2025): The Indonesian Journal of Computer Science (IJCS)
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v14i1.4707

Abstract

Perkembangan teknologi memungkinkan Badan Meteorologi, Klimatologi, dan Geofisika (BMKG) untuk melakukan pengamatan curah hujan secara otomatis menggunakan peralatan penakar hujan otomatis. Namun, peralatan ini berpotensi menghasilkan data curah hujan yang tidak valid akibat kerusakan sensor atau gangguan lingkungan. Penelitian ini bertujuan untuk mendeteksi anomali hasil pengukuran penakar hujan otomatis dengan metode Long Short Term Memory (LSTM) untuk memastikan validitas data dan mempercepat perbaikan peralatan yang mengalami malfungsi. Deteksi anomali dilakukan melalui metode quality control (QC) berbasis range dan step check, spatial check, serta error check yang menghasilkan label Total Anomali QC. Label ini kemudian ditransformasikan menggunakan one-hot encoding dan digunakan sebagai input dalam model klasifikasi berbasis LSTM. Hasil pengujian menunjukkan bahwa model mampu mengklasifikasikan anomali data dengan akurasi lebih dari 90% pada kluster barat, timur, dan pesisir, sehingga memungkinkan deteksi anomali yang lebih akurat dan efisien. Hasil penelitian ini berkontribusi dalam meningkatkan keandalan pengukuran curah hujan otomatis dan mendukung upaya BMKG dalam menjaga kualitas data cuaca dan iklim.
Integrasi Sistem Pakar Deteksi Penyakit Tanaman Bawang Merah Pada E-Commerce Untuk Pemberian Rekomendasi Obat Nur Phasya Aryanto
The Indonesian Journal of Computer Science Vol. 14 No. 2 (2025): The Indonesian Journal of Computer Science
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v14i2.4708

Abstract

The problem of disease in shallot plants is a major challenge for farmers because it has a direct impact on decreasing crop yields. One of the contributing factors is the mistake in choosing the right medicine or pesticide to overcome pest attacks. This study was designed to develop a web-based system that can help farmers detect diseases in shallots while providing appropriate treatment recommendations. This system utilizes a forward chaining algorithm combined with the certainty factor method to identify the type of disease based on symptoms entered by the user. The research data comes from the experience of farmers in Madiun Regency and relevant literature studies, and has been verified by local farmer groups. Evaluation of the system quality was carried out using the black box testing method and System Usability Scale (SUS) measurement. As a result, the system obtained an average SUS score of 81.6, indicating that this system is easy to use and well accepted by users.

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