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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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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.
Navigating the Frontier: Responsible AI in Practice: Governance, Applications, and Future Directions Tiwari, Naresh
The Indonesian Journal of Computer Science Vol. 14 No. 4 (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.v14i3.4883

Abstract

Artificial intelligence systems are increasingly deployed in consequential domains, raising critical questions about governance, domain-specific applications, and emerging challenges. This paper examines the evolving landscape of responsible AI implementation across regulatory frameworks, high-stakes domains, and future research directions. It analyzes diverse regional governance approaches—from the EU's comprehensive risk-based regulation to the US's sectoral framework and East Asian models—alongside industry self-regulation mechanisms including standards, certification programs, and auditing methodologies. The research investigates domain-specific responsible AI practices in healthcare, criminal justice, financial services, and education, identifying tailored approaches to fairness, transparency, privacy, and stakeholder engagement. The paper further explores emerging challenges including foundation model governance, environmental sustainability, global equity, and AI systems reasoning about ethics. It concludes by mapping promising interdisciplinary research directions, addressing persistent knowledge gaps, and identifying essential methodological innovations and infrastructure needed to advance responsible AI practice. This comprehensive analysis offers researchers, practitioners, and policymakers practical frameworks for implementing responsible AI in an era of rapidly expanding capabilities.
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.
Penerapan Algoritma A* dan Edukasi terhadap Anak Usia Dini pada Game Kesiapan bencana Kebakaran Silalahi, Rike; Rorimpandey , Gladly Charen; Maramis, Glenn
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.v14i3.4886

Abstract

Kebakaran merupakan salah satu bencana yang sering terjadi dan dapat menyebabkan kerugian besar, baik secara material maupun psikologis terutama bagi anak anak. Kurangnya edukasi mengenai kesiapsiaagan bencana kebakaran menjadi tantangan yang perlu diatasi. Penelitian ini bertujuan untuk membuat sebuah game edukasi berbasis android menggunakan algoritma A* untuk membantu anak-anak memahami cara menghadapi situasi darurat kebakaran .Metode yang digunakan dalam penelitian ini adalah Multimedia Decelopment Life Cycle (MDLC) yang terdiri dari enam tahap. Game yang dikembangkan berjudul Pici Sang Penakluk Api yang dirancang dalam format 3D menggunakan Unity. Algoritma A* diterapkan untuk menentukan jalur evakuasi yang optimal. Dalam tahap pengujian dilakukan dengan metode Black Box testing yang menunjukkan bahwa game berjalan sesuai dengan fungsionalitas yang dirancang. Hasil penelitian diharakan dapat meningkatkan kesadaran dan pemahaman mengenai kesiapsiaagan terhadap kebakaran.
Optimizing Water and Energy Resources: Forecasting Kariba Dam Water Levels Using the ARIMA Model Amid Load Shedding Challenges in Zambia Zulu, Julius; Gardner Mwansa
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.v14i3.4889

Abstract

The Kariba Dam is a critical source of hydroelectric power for Zambia, but fluctuating water levels have led to recurrent load shedding, impacting economic productivity and daily life. Effective forecasting of water levels using the ARIMA model can help optimize resource management, improve energy planning, and mitigate the adverse effects of power shortages. The study aimed to forecast water levels at Kariba Dam from 2022 to 2035 using the Autoregressive Integrated Moving Average (ARIMA) model. Utilizing historical data from 1924 to 2021, the Box–Jenkins modeling approach was employed to develop the most suitable predictive model for capturing the stochastic variations in water levels at Kariba Dam. The best-fitting ARIMA (6,1,3) model was selected based on statistical criteria, including log likelihood, Sigma, and Akaike and Bayesian information criteria, ensuring robustness and accuracy. The results indicate a fluctuating trend in water levels with a slight overall increase of 4.56% over the forecast period. These findings have significant implications for water resource management, hydropower generation, and climate resilience planning. The study highlights the importance of adaptive strategies to mitigate potential risks associated with water level variability, ensuring sustainable energy production and transboundary water governance for Zambia. Keyword: Forecasting, Kariba Dam, Water Levels, Load Shedding
Analisis Kemiripan Menggunakan Metode BERT dan Jaccard Pada Proses Bisnis Bawaslu RI Abram, Virginia Nasya Daniella
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.v14i3.4890

Abstract

This study aims to calculate and analyze both semantic and structural similarities in the business processes of the Election Supervisory Board of the Republic of Indonesia (Bawaslu RI). For semantic similarity, the BERT (Bidirectional Encoder Representations from Transformers) method is employed, as it provides contextual representations of words and sentences, thereby aiding in understanding the relationships between entities and elements within business processes. Meanwhile, structural similarity is measured using the Jaccard method due to its simplicity and fairness in evaluating the similarity between process models. This analysis offers deeper insights into workflows, inter-unit interactions, and responsibilities in achieving organizational goals. The results of this study are expected to provide recommendations for improving Bawaslu RI’s business processes to better align with organizational needs at various levels, as well as to enhance effectiveness, transparency, and adaptability to change.
An Enhanced Model of the Wireless Multicarrier Communication OFDM Systems Applied on the FPGA Platform Based on Steganography system Y. Jaber, Ali; Fadhil, Ammar
The Indonesian Journal of Computer Science Vol. 14 No. 4 (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.v14i4.4891

Abstract

OFDM is a promising technology due to its robustness against multipath fading. Multipath fading distorts a signal propagating in free space due to destructive or constructive interference. The evolution of 5G wireless networks has necessitated the integration of high-throughput, low-latency multi-band modulation schemes, such as orthogonal frequency division multiplexing (OFDM), into real-time hardware-optimized platforms. However, challenges related to spectral efficiency, security, and the maximum-to-average power ratio (PAPR) remain, especially when these schemes are implemented on field-programmable gate arrays (FPGAs). Steganography offers increased information security. Therefore, this paper proposes an improved OFDM model for 5G that incorporates advanced data hiding techniques using steganography to embed secure image data within ORFDM subcarriers. The proposed system is implemented on an FPGA platform, leveraging high-speed pipelines and parallelism to achieve real-time performance at a minimal resource cost. Simulation and synthesis results demonstrate significant improvements in reduced PAPR, BER, and device efficiency compared to conventional OFDM applications. The FPGA platform design takes up approximately 20% of the total available space, with very low energy consumption compared to other traditional implementation methods. The results also showed an improvement in the OFDM system's performance by reducing the BER by 30%, indicating the absence of data loss and the effectiveness of the steganography technique in these systems. This results in improved architecture performance in terms of area, power, and speed. Furthermore, the proposed approach has proven its worth in terms of security and permeability.
Implementasi Privacy-Preserving Record Linkage untuk Meningkatkan Manajemen Hubungan Pelanggan: Studi Kasus pada Perusahaan Manufaktur di Indonesia Muhamad Ikbal; Achmad Nizar Hidayanto; Ni Wayan Trisnawaty; R. Yugo Kartono Isal
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.v14i3.4892

Abstract

As cross-organizational collaboration increases, balancing data utility with privacy protection becomes essential. This study addresses the challenge by implementing Privacy-Preserving Record Linkage (PPRL) using a deterministic hashing approach at an Indonesian manufacturing firm and a leasing company. The system employs a dual-layer hashing technique (MD5 followed by salted SHA-256) to securely link standardized identifiers without revealing raw personal data. The objective was to enhance Customer Relationship Management (CRM) by identifying shared customers for targeted outreach. The approach yielded 2.6 million matched records out of over 36 million, enabling the leasing firm to achieve a 2-4% conversion rate through personalized campaigns. Results demonstrate high efficiency, scalability, and compliance with Indonesia’s data protection law, offering a replicable framework for privacy-conscious data integration in regulated environments.
Causal-Aware Classification of Social Media Hate Speech: Enhancing Robustness and Fairness with BERT Rasul, Pshko
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.v14i3.4895

Abstract

Social media platforms face increasing challenges in moderating hate speech effectively. While deep learning models like BERT have advanced detection performance, they often rely on spurious correlations and may exhibit bias toward marginalized communities. This paper proposes a causal-aware classification framework integrating causal inference techniques with BERT fine-tuning to improve robustness and fairness in hate speech detection. Using the HateXplain dataset, which includes labeled social media posts and annotator rationales, we construct a causal graph identifying potential confounders. Our model incorporates backdoor adjustment and invariant risk minimization (IRM) during training. Experiments demonstrate enhanced accuracy under distribution shifts and reduced demographic bias compared to baseline models.
Development and Performance Analysis of a Human Detection Robot Using YOLOv8 and PWM-Based Speed Control Ni Ni Htay Lwin; Aye, Maung; Tin Tin Hla
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.v14i3.4896

Abstract

This paper presents the design and performance evaluation of a human detection robot using the YOLOv8 model and the COCO dataset for object recognition. The robot is equipped with a Pi camera, Raspberry Pi, four GM25 13CPR motors, an L298 motor driver, and a buck converter, ensuring efficient operation in real-time environments. The human detection accuracy was evaluated at different distances, achieving 99% at 2 feet, 98% at 15 feet, and 96% at 25 feet, demonstrating the effectiveness of the YOLOv8 model in varying conditions.The robot's movement is controlled using a PWM-based speed control technique, where the DC motors operate at different duty cycles. Experimental results show variations in speed accuracy, with error percentages of 7.6% at 20% duty cycle, 5.8% at 40%, 5.1% at 60%, 4.8% at 80%, and 3.8% at 100% duty cycle. These results indicate that higher duty cycles lead to improved speed accuracy, minimizing the deviation from the desired speed. The study highlights the integration of YOLOv8 for object detection and PWM for precise motor control, making the system suitable for applications in autonomous navigation, surveillance, and security.

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