cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
Tri A. Sundara
Contact Email
tri.sundara@stmikindonesia.ac.id
Phone
+628116606456
Journal Mail Official
ijcs@stmikindonesia.ac.id
Editorial Address
Jalan Khatib Sulaiman Dalam 1, Padang, Indonesia
Location
Kota padang,
Sumatera barat
INDONESIA
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 1,193 Documents
 116   117   118   119   120 
A Multi-View Anomaly Detection Framework for Elephant Movement Based on GPS Data Hoang Cong Tan Nguyen; The Bao Nguyen; Minh Nguyen Vo; An Phu Tran; Vo Thi Xuan Nhung; Can Huy Vo; Minh Huan Vo
The Indonesian Journal of Computer Science Vol. 15 No. 3 (2026): 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.v15i3.5146

Abstract

Monitoring elephant movement is crucial for wildlife conservation, especially under threats such as poaching and habitat loss. With the availability of large-scale GPS tracking data, anomaly detection can help identify abnormal behaviors linked to critical events. However, challenges such as data imbalance, GPS noise, and real-time deployment constraints remain. This paper proposes an end-to-end framework for anomaly detection in elephant movement using GPS data. The approach combines multi-view anomaly modeling with a weighted scoring mechanism and a lightweight Random Forest model. To address class imbalance, the pipeline integrates SMOTE (Synthetic Minority Over-sampling Technique), under sampling, and class-weighted learning. Feature selection and quantization further optimize the system for edge and FPGA deployment. Experimental results show strong performance, with F1-Macro ≈ 0.98, ROC-AUC ≈ 0.99, and high recall for anomaly detection. The proposed framework provides an efficient and practical solution for real-time wildlife monitoring.
A Novel Deep Learning Framework for Biomedical and Medical Image Classification with Adaptive Feature Learning Approach and Enhanced Diagnostic Performance Mohanaed Ajmi Falih
The Indonesian Journal of Computer Science Vol. 15 No. 3 (2026): 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.v15i3.5148

Abstract

Owing to the significance of early diagnosis and proper clinical decision-making, medical image analysis has now become an essential part of modern healthcare systems. Traditional image analysis techniques weaken for complex patterns, variability in medical data and a high diagnostic accuracy need We therefore propose in this paper a new deep learning-based framework for adaptive feature learning and improved diagnostic accuracy. We then present a convolutional neural network (CNN) based hybrid architecture framed with an adaptive feature learning strategy that learns suitable spatial and semantic characteristics for fine-tuning dynamically. The proposed method will help the model more accurately discover small details in medical images (e.g., lesions, tumors and unusual tissue structures). In addition, a feature fusion strategy is applied to combine multiscale representations which improve the robustness of multiple imaging modalities (MRI, CT and X-ray). Besides that the model implements various optimizations: batch norm, dropout regularization and adaptive learning rate scheduling. Extensive experiments conducted on benchmark medical imaging datasets confirmed the efficacy of the proposed method. The result shows that it outperforms any previous existing state-of-the-art methods by a considerable margin on the accuracy, precision, recall and f1-score metrics. This framework also obtained a greater performance on generalizability, and decreased sensitivity to noise-induced fluctuations over quality of picture changes. We demonstrate that the incorporation of tray-like adaptive feature learning through deep neural networks can lead to substantial improvements on image such as medical imaging. This paper provides a foundation for the development of intelligent, trustworthy and scalable AI based health care systems that assist clinicians in improving decision making with high correct classification rates as fast as possible.
Performance Analysis of Vision Transformer (ViT), ResNet50, and MobileNetV3 Large in Multiclass Bone Fracture Classification Ei Phyu Sin Win; Phyo Thu Zar Tun
The Indonesian Journal of Computer Science Vol. 15 No. 3 (2026): 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.v15i3.5150

Abstract

Automated classification of bone fractures has become a cornerstone of modern emergency radiology, significantly enhancing diagnostic speed and precision. This study evaluates the comparative efficacy of three leading deep learning frameworks ResNet50, MobileNetV3, and Vision Transformer (ViT) using a diverse dataset that includes various fracture modalities, healthy X-rays, and non-radiological images.The experimental data reveals that the Vision Transformer (ViT) attained the highest diagnostic accuracy at 95%, marginally outperforming MobileNetV3 and ResNet50, which both achieved 94%. While all three models demonstrated flawless reliability (100%) in identifying Forteen Classes Bone categories, their performance diverged when analyzing complex fracture patterns.
Ultra-Low Power Soil Sensor Enabling Multi-Year Battery Life Haryono
The Indonesian Journal of Computer Science Vol. 15 No. 3 (2026): 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.v15i3.5151

Abstract

Soil monitoring in large and remote agricultural areas is often limited by manual observation, resulting in low data frequency and reduced decision accuracy. To overcome this, this study presents a next-generation soil sensor system designed for ultra-low power operation and extended battery life. The system utilizes a STM32WLE5CCU6 microcontroller combined with a 6-in-1 RS485 soil sensor (T-H-EC-NPK) to measure soil humidity, temperature, electrical conductivity, and nutrient levels (N, P, K). The system operates using an optimized duty cycle, remaining in deep-sleep mode at approximately 3 μA and periodically activating for a short 600 ms sensing and data transmission phase consuming around 150 mA. This approach significantly reduces average power consumption to approximately 0.028 mA. With a 4000 mAh battery and a transmission interval of one hour, the system achieves a theoretical lifetime exceeding 16 years. However, considering practical factors such as battery self-discharge, sensor overhead, and environmental conditions, the effective operational lifetime is conservatively estimated to exceed 5–10 years without battery replacement. The results demonstrate that the proposed design successfully enables long-term, maintenance-free soil monitoring, making it suitable for large-scale and remote precision agriculture applications where energy efficiency and system reliability are critical.
A Hybrid Deep Learning Framework for Malware Detection Using Metaheuristic Feature Selection and Explainable AI: A Comprehensive Literature Review Dauan Aziz; Firas Amien; Raghad Yousif
The Indonesian Journal of Computer Science Vol. 15 No. 3 (2026): 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.v15i3.5152

Abstract

Malware, including ransomware, trojans, rootkits, spyware, and advanced persistent threats (APTs), poses a growing challenge to modern computing systems. Traditional detection methods, such as signature- and heuristic-based approaches, struggle to detect polymorphic, metamorphic, and zero-day malware. Deep learning has emerged as a powerful solution due to its ability to automatically learn hierarchical features. However, key challenges remain: lack of model transparency, high-dimensional feature redundancy from multimodal analysis, and poor cross-dataset generalization. This paper presents a systematic literature review of state-of-the-art malware detection techniques published between 2020 and 2025, covering static, dynamic, visualization-based, and deep learning approaches (e.g., CNN, LSTM, BiLSTM, and hybrid models), along with metaheuristic feature selection and Explainable AI (XAI) methods such as SHAP, Grad-CAM, and LIME. Analysis of 35 studies identifies critical gaps, particularly the absence of integrated metaheuristic optimization and XAI-driven hybrid frameworks, motivating future research directions.
Treatment of Textile Wastewater with Activated Carbon Produced from Plum Seed Shell Mon Yi Myo Myint; Zin Marlar Tin San; Nway Nway Khaing
The Indonesian Journal of Computer Science Vol. 15 No. 3 (2026): 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.v15i3.5155

Abstract

In this study, the activated carbon derives from plum seed shells are used for the treatment of textile wastewater collected from the Wandwin area which has high concentrations of parameters such as pH, colour, TSS, TDS, COD, and BOD. pH value of textile wastewater is 12.4 that leads to alkaline, so pH adjusts with alum coagulants. After treatment the COD concentration can be reduced from 1280 mg/l to 20 mg/l by using equilibrium concentration of activated carbon, Ce =50 mg/. The adsorption data followed the Langmuir isotherm (R² = 0. 0.97832). Other parameters also significantly decreased from 74.74 % to 98.29%. According to the National Enviromental Quality Guideline (NEQG) Myanmar, all treated water parameters comply with NEQG standards. Therefore, the plum seed shell activated carbon is an effective and sustainable material for the treatment of textile wastewater and treated effluent can be safely discharged into the surrounding water bodies.
Treatment of Sugar Factory Wastewater with Activated Carbon Produced from Plum Seed Shell Mon Yi Myo Myint; Zin Marlar Tin San la; Nway Nway Khaing
The Indonesian Journal of Computer Science Vol. 15 No. 3 (2026): 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.v15i3.5158

Abstract

This study demonstrates that activated carbon produced from plum seed shells is an effective adsorbent for treating highly polluted sugar factory wastewater from the Yeni area. The raw effluent was strongly acid (pH 4.5) and contained elevated TSS, TDS, COD (1280 mg/L), and BOD; pH was corrected using sodium hydroxide coagulants 2 ml/ l prior to adsorption. The effluent of pre-treatment is absorbed by plum seed shell activated carbon AC1, AC2, AC3 and AC4 using 10 gm/l. Among them, AC4 is the most effective and the percent removal of sugar factory wastewater parameters are turbidity (80.91%), TDS (42.86%), Colour (50%),COD (75%) and BOD (97.2%) respectively. The treated effluent met Myanmar’s National Environmental Quality Guideline (NEQG) across all measured parameters, supporting the conclusion that plum seed shell activated carbon is both effective and sustainable for sugar factory wastewater remediation. The research may contribute to civil, enviromental engineering and related domains.
Combination of Hardware and Software Security Mechanisms for Embedded Systems with Internet of Things J S Prasath
The Indonesian Journal of Computer Science Vol. 15 No. 3 (2026): 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.v15i3.5160

Abstract

Embedded systems are fast growing technologies and it is widely used in varieties of applications. Embedded devices are susceptible to variety of attacks due to its large number of deployment, resource limitations and increased complexity. Intruders try to acquire the process information from the embedded devices through variety of security attacks. It is essential to protect the embedded system along with wireless networks from unauthorized access and modification of process parameters. The novelty of this proposed work is the implementation of hybrid security algorithm along with the exclusive hardware key using embedded system which performs secure transmission and monitoring of process variables over internet. This proposed hybrid security algorithm is a mixture of asymmetric, symmetric and hash function algorithms which assures strong security. The process data security is strengthened by generating large key size of 2048-bit in this proposed asymmetric encryption and 256-bit key size for symmetric encryption. It also encrypts the hardware key along with the hybrid encryption. The encryption algorithm is performed at the master node and the decryption algorithm is performed at the two slave nodes. When the master node fails due to some fault, then one of the slave node acts as master and sends the encrypted data to another slave node. The slave node acts as redundant node as well as it performs hybrid decryption. The hardware key is essential to access and modify the encryption code as well as to monitor the cipher text both at the transmitter and the receiver. This proposed security algorithm is cost effective and it prevents the sensitive process information from illegal access and alteration.
Multi-Sensor IoT Smart Home Anomaly Detection Using Random Forest Algorithm Juarisman; Kamarudin; Netci Hesvindrati
The Indonesian Journal of Computer Science Vol. 15 No. 4 (2026): The Indonesian Journal of Computer Science
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

The increasing adoption of Internet of Things (IoT) technology in smart home environments generates large volumes of dynamic and heterogeneous multi-sensor data, creating challenges in accurately detecting anomalous conditions. This study aims to implement and evaluate the Random Forest algorithm for anomaly detection in IoT-based smart home systems using multi-sensor data. The dataset consists of temperature, humidity, light, motion, carbon monoxide (CO), liquefied petroleum gas (LPG), smoke, door/window status, and energy consumption collected from three IoT devices. Data preprocessing included cleaning, labeling, and Min-Max Scaling normalization, followed by an 80:20 training-testing split. Model performance was evaluated using a confusion matrix, accuracy, precision, recall, and F1-score. The experimental results achieved an accuracy of 96.75%, precision of 94.65%, recall of 93.82%, and F1-score of 94.23%, demonstrating that Random Forest is effective for identifying anomalous conditions in smart home IoT environments.
English English: English May Su Hlaing; Tin Tin Hla
The Indonesian Journal of Computer Science Vol. 15 No. 3 (2026): 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.v15i3.5180

Abstract

DC-DC buck converters are widely employed in power electronic systems to provide efficient voltage regulation for various applications. However, the converter performance is significantly influenced by the duty cycle, which determines the output voltage and current characteristics. This study presents a comparative analysis of the voltage and current responses of a DC-DC buck converter operating at duty cycles of 0.5 and 0.25 using MATLAB/Simulink. The objective is to evaluate the effect of duty cycle variation on the converter's dynamic and steady-state performance. A simulation model was developed and tested under identical operating conditions, and the resulting output waveforms were analyzed. The results indicate that increasing the duty cycle enhances both the average output voltage and load current, while a lower duty cycle reduces the output magnitude. The simulated responses closely agree with the theoretical principles of buck converter operation, demonstrating the effectiveness of MATLAB/Simulink for performance evaluation and design optimization.

Page 119 of 120 | Total Record : 1193

 116   117   118   119   120 

Filter by Year

2022 2026


Reset
Filter By Issues
All Issue Vol. 15 No. 4 (2026): The Indonesian Journal of Computer Science Vol. 15 No. 3 (2026): The Indonesian Journal of Computer Science Vol. 15 No. 2 (2026): The Indonesian Journal of Computer Science Vol. 15 No. 1 (2026): The Indonesian Journal of Computer Science Vol. 14 No. 6 (2025): The Indonesian Journal of Computer Science Vol. 14 No. 5 (2025): The Indonesian Journal of Computer Science Vol. 14 No. 4 (2025): The Indonesian Journal of Computer Science Vol. 14 No. 3 (2025): The Indonesian Journal of Computer Science Vol. 14 No. 2 (2025): The Indonesian Journal of Computer Science Vol. 14 No. 1 (2025): The Indonesian Journal of Computer Science Vol. 13 No. 6 (2024): The Indonesian Journal of Computer Science Vol. 13 No. 5 (2024): The Indonesian Journal of Computer Science Vol. 13 No. 4 (2024): The Indonesian Journal of Computer Science Vol. 13 No. 3 (2024): The Indonesian Journal of Computer Science Vol. 13 No. 2 (2024): The Indonesian Journal of Computer Science Vol. 13 No. 1 (2024): The Indonesian Journal of Computer Science Vol. 12 No. 6 (2023): The Indonesian Journal of Computer Science Vol. 12 No. 5 (2023): The Indonesian Journal of Computer Science Vol. 12 No. 4 (2023): The Indonesian Journal of Computer Science Vol. 12 No. 3 (2023): The Indonesian Journal of Computer Science Vol. 12 No. 2 (2023): The Indonesian Journal of Computer Science Vol. 12 No. 1 (2023): The Indonesian Journal of Computer Science Vol. 11 No. 3 (2022): The Indonesian Journal of Computer Science Vol. 11 No. 2 (2022): The Indonesian Journal of Computer Science Vol. 11 No. 1 (2022): The Indonesian Journal of Computer Science More Issue