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Tri A. Sundara
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tri.sundara@stmikindonesia.ac.id
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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 38 Documents
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Search results for , issue "Vol. 14 No. 3 (2025): The Indonesian Journal of Computer Science" : 38 Documents clear
Machine Learning-Based Security Algorithms for Detecting and Preventing DDoS Attacks on the IoT: State-of-the-Art, Challenges, and Future Directions Baloyi, Coster; Mathonsi, Topside; Du Plessis, Deon; Muchenje, Tonderai; Tshilongamulenzhe, Tshimangadzo
The Indonesian Journal of Computer Science Vol. 14 No. 3 (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.v14i3.4853

Abstract

Abstract - The Internet of Things (IoT) represents a vast network of interconnected devices equipped with software, sensors, and other technologies that enable data exchange and autonomous operation with other devices and systems without human intervention over the internet. IoT applications span across various sectors, including agriculture, education, healthcare, and communication. However, Distributed Denial of Service (DDoS) attacks continue to pose significant risks to the IoT network due to current challenges of classification efficiency and response times by the existing algorithms, such as Decision Tree (DT), Linear Regression (LR), and K-means. This paper provides a comprehensive review of DDoS attack types within the IoT networks. Secondly, the paper critically examines and analyses the challenges and opportunities inherent in leveraging Machine Learning (ML) algorithms for detecting, preventing, and mitigating these attacks. Finally, it presents the categories of IoT performance metrics, and their statistics found in the Literature over the Past decade.
Evaluasi Kualitas Data Pada Daftar Produk Tayang Di Katalog Elektronik Versi 6.0 Pratiwi, Aprilia; Mahsa Elvina Rahmawyanet; Amanda Ghaisani; Tri Broto Siswoyo; Yova Ruldeviyani; Yudho Giri Sucahyo
The Indonesian Journal of Computer Science Vol. 14 No. 3 (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.v14i3.4858

Abstract

Data quality is important aspect in supporting transparency, accountability, and efficiency of the government procurement process. Electronic Catalog data acts as a source of information on products, services, and providers of goods/services. The transition from Electronic Catalog v5 to v6 is form of digital transformation in the government procurement of goods/services as form of improving public services. Measuring the quality of Electronic Catalog v6 data has significant role in providing effective, efficient and accountable information. This study aims to evaluate the quality of data on Electronic Catalog v6 product data using the Total Data Quality Management (TDQM) framework. There are 6 dimensions used in evaluating data quality, completeness, accuracy, data integrity, fairness, consistency, and precision. The results of the study show that the dimensions of consistency, accuracy, completeness, fairness and precision reach above 90% while data integrity reaches below 50% and requires improvement on product data quality.
A Model to Amplify Transmission Quality of Satellite Television Lebogang Maja; Deon du Plessis; Mathonsi, Topside; Tshilongamulenzhe, Tshimangadzo
The Indonesian Journal of Computer Science Vol. 14 No. 3 (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.v14i3.4861

Abstract

One of the various applications of communication satellite technologies is broadcasting satellite television (TV). In TV broadcasting, satellite communication is the easiest way to transmit many services and offers a variety of choices across a varied region, thereby overcoming the need for the complex infrastructure of terrestrial transmitters that a terrestrial network needs to broadcast its signals throughout a wide range area like countries or continents and providing quality digital TV viewing. However, Satellite TV broadcasting has a deficiency of outage effect caused by rain fade that instigate due to bad raining weather which at once will cuts signal transmission from the transmitter satellite to the receiver dish. this study was undertaken to explore the challenges that satellite TV broadcasting faces, which is caused by the rain fade effect. Thereafter, a model to amplify the transmission quality of satellite television is designed. The proposed Gau-satcomm algorithm, ITU-R model, and SAM model had an average BER of 5%, 8%, and 10%, respectively. Additionally, the Gau-Satcomm algorithm, SAM model, and ITU-R model experienced 4%, 9%, and 11% attenuation, respectively.  Furthermore, the study compared outage probability across three algorithms at frequencies over 10 GHz, the proposed Gau-satcomm algorithm, the ITU-R algorithm, and the SAM algorithm minimized outages by 10%, 7%, and 5%, respectively. Therefore, the proposed Gau-Satcomm outperforms these traditional algorithms in regard to average BER, a reduced average attenuation, and outage probability.
Improving Wireless Communication OFDM systems based on image processing Jaber, Ali
The Indonesian Journal of Computer Science Vol. 14 No. 3 (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.v14i3.4863

Abstract

One of the most common methods used in modern communication systems is orthogonal frequency division multiplexing (OFDM) to reduce the resistance to selective frequency fading. Another important reason is the possibility of reducing interference between symbols. However, there are also drawbacks, including the high peak-to-average power ratio (PAPR), which affects the power loss, increasing the complexity of transmitters. In the proposed study, we reduce PAPR in OFDM-based systems by hiding information in the image to maintain data security. In this steganography method, we insert digital text into the image and hide it, thus providing greater reliability while simultaneously reducing the PAPR required in OFDM. Text steganography is a method for hiding a large amount of information, which is helpful in OFDM. This also helps keep the system free from noise. Therefore, sending hidden data over communication channels is a good way to avoid interference, as image transmission is less affected by interference, and upon receipt, the main structure can be reconstructed to extract the hidden data and the original image. This is a way to preserve communication channels, which is the main objective of this study. The proposed method was evaluated by comparing the results with other methods, and it was found that the amount of data loss is reduced to 50% compared to the conventional method, which is the most important part. Also, the transmitted signal power has improved PAPR to 5.9 dB compared to the conventional method, which helps improve the quality of wireless transmission of the OFDM signal
Mengeksplorasi Faktor-Faktor Penentu Berbagi Pengetahuan di Sektor Publik: Tinjaun Sistematis-PRISMA Rizky, Fajar; Altino, Iqbal Caraka; Sensuse, Dana Indra; Lusa, Sofian; Safitri, Nadya; Elisabeth, Damayanti
The Indonesian Journal of Computer Science Vol. 14 No. 3 (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.v14i3.4864

Abstract

Knowledge sharing (KS) plays is essential for improving organizational performance and innovation, enabling faster problem-solving and collaboration. However, in the public sector, participation in KS remains low, hindering organizational learning and development. As Indonesia adopts knowledge management systems in its public sector, challenges emerge in fostering knowledge-sharing behaviors. This study uses the systematic review method, following PRISMA 2020 guidelines, to identify key factors influencing knowledge-sharing intention (KSI) and propose solutions. Through a rigorous selection process, 20 relevant studies were analyzed, categorizing factors into individual, organizational, and technological groups. The results indicate that attitudes, perceived behavioral control, subjective norms, reward systems, and the perceived usefulness of technology have a significant impact on KSI. This study offers a comprehensive reference for future research on KS in the public sector and provides insights for policymakers to design initiatives that enhance organizational learning.
High-Precision GPS Tracker for Monitoring Agricultural Sprayer Drone Operations Haryono
The Indonesian Journal of Computer Science Vol. 14 No. 3 (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.v14i3.4871

Abstract

Efficient and precise pesticide application is critical in modern agriculture, especially when using drone technology. To ensure that spraying operations are carried out accurately in the intended locations, a reliable tracking system is essential. This newly developed Agriculture Sprayer Drone Tracker addresses this need through several key improvements over traditional methods. Previously, monitoring was performed using wired connections to check the tank, but the new system employs a wireless LoRa solution housed in a robust IP67-rated enclosure for better durability and flexibility.The tracker features a custom-designed GPS board based on the u-blox F9P module, providing high-precision location data. The GPS antenna has been optimized for a more compact form factor, resulting in a smaller, more portable device. Data is logged directly to an SD card and can be quickly accessed via a USB connection. This method offers a significant improvement over previous systems that relied on web APIs, which were often slow and dependent on internet speed. With dedicated software, users can now efficiently retrieve and save tracker data to a PC. Overall, these advancements make the tracker more versatile, reliable, and user-friendly, enhancing the effectiveness of agricultural drone spraying operations.
Implementasi Agile Project Management pada Proyek TI Sektor Publik: Studi Kasus Direktorat Jenderal Pendidikan Islam Mutiara Nur Insani Yuwantara; Bob Hardian Syahbuddin
The Indonesian Journal of Computer Science Vol. 14 No. 3 (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.v14i3.4877

Abstract

The rapid advancement of technology in society has driven government agencies to transform public service delivery through the initiation of information technology (IT) projects. In executing these projects, government institutions must adapt swiftly to changing circumstances. However, the complexity of bureaucratic environments often hinders the adoption of dynamic project management methodologies such as Agile Project Management (APM). This study examines the challenges of implementing APM in IT projects at Indonesia’s Directorate General of Islamic Education (Ditjen Pendis) and identifies strategies to address them. Using a mixed-methods approach (quantitative and qualitative), the research highlights challenges across three dimensions: human resources (HR), processes, and organizational structure. The findings of this study show that the distribution of challenges faced by Ditjen Pendis is dominated by the process category (37%), followed by the HR category (32%) and the organization category (31%). To overcome these, Ditjen Pendis implemented strategies such as fostering open communication, systematizing project documentation, and classifying stakeholders. The results demonstrate that the success of APM in governmental contexts depends not only on technical execution but also on balancing agility with structured bureaucratic governance. The practical implications of this study are expected to guide other government agencies in enhancing their readiness to adopt APM in IT projects, particularly in designing APM-driven digital transformation strategies.
Systematic Literature Review on Crime Prediction using Machine Learning Techniques Esan, Omobayo; Bester Chimbo
The Indonesian Journal of Computer Science Vol. 14 No. 3 (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.v14i3.4881

Abstract

Abstract contains problem statement, approaches/problem solving method, objectives and resulTo lower the crime rate in the community, many governments around the world have made preventive security measures their top priority. Thus, a major and extensively studied field is the use of machine learning in crime prediction. To investigate crime prediction using machine learning approaches, this study carried out a systematic literature review. The review assesses performance evaluation criteria, forecast methods, present issues, and potential future directions. From 2018 to 2024, a total of 100 research papers covering machine learning techniques for crime prediction were reviewed. The supervised learning approach is the most often used crime prediction technology, according to the review. The evaluation and performance criteria, the tools used to construct the models, and the difficulties they face in predicting crime were also covered. Machine learning approaches for crime prediction are an interesting area of research, and academics have used a number of machine learning models.
A Klasifikasi Penyakit Tumor Ginjal Menggunakan SVM dengan Ekstraksi Ciri HOG dan GLCM Affandy, Muhammad Eric; Mohamad Sofie; Muhammad Rofi’i
The Indonesian Journal of Computer Science Vol. 14 No. 3 (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.v14i3.4882

Abstract

Early detection of kidney tumors is essential to increase the chances of a patient's recovery. This study aims to develop a classification system for kidney CT scan images to distinguish between normal kidneys and kidneys containing tumors. The classification method used is Support Vector Machine (SVM) with three types of kernels, namely linear, polynomial, and radial basis function (RBF). Previously, feature extraction was performed using two approaches, namely Histogram of Oriented Gradients (HOG) to obtain shape values, and Gray Level Co-occurrence Matrix (GLCM) to obtain texture characteristics of the image. The test results show that SVM with a linear kernel gives the highest accuracy of 90%, followed by polynomial at 85%, while the RBF kernel only reaches 50%. Based on these results, it can be concluded that the combination of HOG and GLCM feature extraction followed by classification using linear kernel SVM is effective for distinguishing normal kidney images and kidney tumors. This research makes a positive contribution to the development of a medical image-based kidney disease diagnosis support system.
AI-Enhanced Multi-Modal Emotion and Personalized Responding System for Undergraduates Karunathilaka, Chamoda; De Vass Gunawardane, Dilun; Athuluwage, Tharaka; Marasinghe, Chamalka; Vidanaralage, Anjana Junius; Fernando, Harinda
The Indonesian Journal of Computer Science Vol. 14 No. 3 (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.v14i3.4884

Abstract

Undergraduate students face increasing academic and personal pressures, often leading to stress and emotional distress. Traditional single-modal emotion recognition systems, relying solely on facial or vocal analysis, struggle with accuracy due to environmental variations and limited contextual awareness. This research proposes a multi-modal AI-driven emotion recognition system that integrates facial and vocal data for enhanced real-time emotional detection and response. The system leverages Vision Transformers (ViTs) for facial feature extraction and Mel-Frequency Cepstral Coefficients (MFCC) for speech-based emotion analysis, ensuring improved classification through confidence-weighted temporal fusion. Additionally, an adaptive response generation module utilizes natural language processing (NLP) and text-to-speech (TTS) synthesis for human-like interactions. To enable scalable mobile deployment, the model is optimized with quantized lightweight transformers, achieving sub 300ms inference latency. Bias mitigation techniques ensure fairness across demographic groups. This research contributes to affective computing, human-computer interaction, and AI-driven emotional intelligence, offering a scalable and ethically responsible solution for virtual counseling, AI-assisted tutoring, and mental health support.

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