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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 1,127 Documents
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Leveraging LSTM Predictions for Enhanced Portfolio Allocation with Markowitz Mean-Variance Optimization Sahid, Irfanda Husni; Budi, Indra
The Indonesian Journal of Computer Science Vol. 13 No. 6 (2024): 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.v13i6.4573

Abstract

This research investigates the application of Long Short-Term Memory (LSTM) networks for predicting expected returns and integrating these predictions into the Markowitz Mean-Variance Optimization (MVO) framework. The study utilized historical data from eight Indonesian stocks: BBCA, BBRI, TLKM, EXCL, UNVR, ICBP, ASII, and SMGR. The dataset covered the period from 2018 to 2024. The LSTM model was employed to predict cumulative returns over a 90-day horizon, and its performance was compared to the Exponentially Weighted Moving Average (EWMA) method. The findings indicate that LSTM achieved lower Root Mean Squared Error (RMSE) than EWMA for four stocks (BBCA, BBRI, UNVR, ICBP), while EWMA demonstrated better performance for the remaining four stocks. MVO results revealed that LSTM-based predictions achieved an average return of 4.285%, surpassing EWMA's 1.856% but falling short of the 12.298% obtained using actual returns. These results highlight the potential of LSTM models to enhance portfolio allocation strategies.
Investigation of Hydrological Drought in Central Dry Zone, Myanmar Poe Zar Ni Aung; Aye, Nilar; Yin Yin Htwe
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.v14i1.4586

Abstract

This study presents the investigation of hydrological drought in Myanmar’s Central Dry Zone using the Streamflow Drought Index (SDI) across various timescales (3, 6, 9, and 12 months) to assess its impact on water resources and agricultural productivity. The Central Dry Zone, which includes the Ayeyarwaddy River and encompasses the regions of Sagaing, Mandalay, and Magway, shows significant vulnerability of hydrological extremes due to its semi-arid climate and dependence on water resources. Monthly discharge data from selected hydrological stations from 1993 to 2022 is analyzed using DrinC 1.7 software to derive SDI values and drought characteristics. The results show that critical drought events in 2005-2006, 2013-2015, and 2019-2021 for all stations are marked by high severity and extended duration. Short-term SDI (SDI 3 and SDI 6) values capture rapid, intense droughts, while long-term SDI (SDI 9 and SDI 12) highlight extended water shortages. From this result, Monywa station shows the highest severity and duration of droughts across all SDI timescales compared to other stations. The results underscore the necessity of strategic water management and drought mitigation measures to protect agriculture and guide planning, establish early warning systems, and support sustainable development in the Central Dry Zone, Myanmar.
Simulasi Perbandingan Motor Listrik dengan Mesin Pembakaran Dalam Sebagai Penggerak Sepeda Motor CVT Abdussalam, Muhammad Zayyana; Arini, Nu Rhahida; Windarko, Novie Ayub
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.v14i1.4425

Abstract

Fuel vehicle conversion to electric vehicle requires electric motor with power equivalently to the vehicle’s fuel engine. Some CVT motorcycle conversion experiment using BLDC motor propulsion resulting 40 km/hour maximum velocity electric vehicle. This study uses computer simulations to compare the performance of electric motor and gasoline fueled internal combustion engine. The results show that fuel vehicle performance overthrow electric vehicle where 200 kg total vehicle mass absorbing power about 4,9 kW for fuel vehicle and 2,8 kW for electric vehicle then 63 km/hour maximum velocity for fuel vehicle and 53 km/hour for electric vehicle. Those result become equal when both propulsion have equal input where absorbed power reach 2,7 kW and maximum velocity reach 50 km/hour.
Exploring Strategies for Optimizing Mobilenetv2 Performance in Classification Tasks Through Transfer Learning and Hyperparameter Tuning with A Local Dataset from Kigezi, Uganda. Turihohabwe, Jack; Ssembatya Richard; Wasswa William
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.v14i1.4436

Abstract

Abstract Background Deep learning has proved to very vital in numerous applications in recent years. However, the development of a model may require access to datasets. Training models on datasets may impose numerous challenges in terms of computational constraints, making it inefficient for limited computational environments and in this study a local dataset from Kigezi Uganda will be used. The study will also explore the strategies of optimizing the MobilenetV2 through transfer learning and hyper-tuning. Main Objective: This study explored the strategies for optimizing MobileNetV2 performance in performing classification tasks through transfer learning, data augmentation, and hyper parameter tuning with local data from Kigezi, Uganda. A total of 2,415 images is the dataset used and 9,660 images were obtained after data augmentation. Methodology The methodology used is experimentation using transfer learning and hyper-tuning of the model. Results The model layer freezing. Freezing All Layers except Final Dense Layer: Training accuracy: 90%, Testing accuracy: 85%. The model was not flexible enough to adapt to the new dataset, Unfreezing Top 10 Layers: Training accuracy: 92%, Testing accuracy: 88%. Moderate improvement observed, but still underperforming. Unfreezing Top 20 Layers: Training accuracy: 95%, Testing accuracy: 91%. Significant improvement, suggesting that more layers need to be fine-tuned. Unfreezing Entire Network: Training accuracy: 98%, Testing accuracy: 96%. The model showed substantial improvement in learning task-specific features. Hyper tuning the Learning Rate. The optimal configuration was found by unfreezing the entire network, which allowed the model to fine-tune all layers, thus improving the model’s ability to generalize to the new dataset. Learning Rate Tuning: Learning rate is one of the most crucial hyper parameters. An extensive grid search was performed over the following values: 0.1, 0.01, 0.001, 0.0001, and 0.00001, Batch Size Tuning: Different batch sizes (16, 32, 64, and 128) were tested to determine the most efficient size for gradient updates, Optimizer Selection: Various optimizers were tested, including SGD, RMSprop, and Adam. The Adam optimizer was selected for its adaptive learning rate capabilities. Epochs and Early Stopping: The number of epochs was tuned along with early stopping criteria to prevent overfitting. Epochs were tested in the range of 10 to 100 with a patience of 5 for early stopping The results of the learning rate 0.1: Training accuracy: 60%, Testing accuracy: 55%. The model was unable to converge 0.01: Training accuracy: 80%, Testing accuracy: 75%. Improved but still underperforming. 0.001: Training accuracy: 90%, Testing accuracy: 88%. Further improvement, but overfitting observed. 0.0001: Training accuracy: 99%, Testing accuracy: 98%. Optimal performance achieved.0.00001: Training accuracy: 95%, Testing accuracy: 92%. Learning was too slow. Hyper-tuning using the batch-size: 16: Training accuracy: 97%, Testing accuracy: 94%. Good performance but higher computational cost32: Training accuracy: 99%, Testing accuracy: 98%. Optimal balance between performance and efficiency, 64: Training accuracy: 95%, Testing accuracy: 93%. Slightly reduced performance, 128: Training accuracy: 90%, Testing accuracy: 87%. The model struggled with larger batch sizes. Hyper-tuning using by different optimizers SGD: Training accuracy: 85%, Testing accuracy: 80%. Slower convergence. RMSprop: Training accuracy: 92%, Testing accuracy: 88%. Moderate performance. Adam: Training accuracy: 99%, Testing accuracy: 98%. Best performance due to adaptive learning rate. The final customized model, after applying transfer learning and extensive hyper parameter tuning, achieved outstanding results: Training Accuracy: 99%Testing Accuracy: 98%,Training Loss: 0.02,Testing Loss: 0.04.
Evaluating the Impact of Deep Learning Model Architecture on Sign Language Recognition Accuracy in Low-Resource Context Moape, Tebatso; Muzambi, Absolom; Chimbo, Bester
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.v14i1.4493

Abstract

Deep learning models are well-known for their reliance on large training datasets to achieve optimal performance for specific tasks. These models have revolutionized the field of machine learning, including achieving high accuracy rates in image classification tasks. As a result, these models have been used for sign language recognition. However, the models often underperform in low-resource contexts. Given the country-specific nature of sign languages, this study examines the effectiveness and performance of Convolutional Neural Networks (CNN), Artificial Neural Networks (ANN), hybrid model (CNN + Recurrent Neural Networks (RNN)), and VGG16 deep learning architectures in recognizing South African Sign Language (SASL) under a data-constrained context. The models were trained and evaluated using a dataset of 12420 training images representing 26 static SASL alphabets, and 4050 validation images. The paper's primary objective is to determine the optimal methods and settings for improving sign recognition models in low-resource contexts. The performance of the models was evaluated across multiple image dimensions trained for 60 epochs to analyze each model's adaptability and efficiency under varying computational parameters. The experiments showed that the ANN and CNN models consistently achieved high accuracy with lower computational requirements, making them well-suited for low-resource contexts.
Evaluasi Aksesibilitas Website Akademik bagi Pengguna Disabilitas Menggunakan Website Accessibility Conformance Evaluation Methodology (WCAG-EM) Aryantoputri, Biandra; Suranto, Beni
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.v14i1.4515

Abstract

This study evaluates the accessibility of the academic website of Universitas Islam Indonesia (UII) using AChecker and WAVE tools based on the Web Content Accessibility Guidelines (WCAG) standards. The results indicate that most pages have potential accessibility issues, such as "use of color," "keyboard operability," and "flashing content." The WAVE tool provides more detailed insights, identifying significant issues in elements like "Non-text Content," "Contrast (Minimum)," and "Link Purpose (In Context)." Recommendations for improvement include adding alternative text, enhancing color contrast ratios, and optimizing keyboard navigation. Implementing these evaluation results is expected to improve accessibility and foster an inclusive digital environment. For further development, the study recommends direct testing by users with disabilities to ensure the effectiveness of the implemented improvements..
Analisis Metrik Hibrida untuk Deteksi Emosi (Studi Kasus: Ulasan Game) Wardani, Deyana Kusuma; Mayangsari, Mustika Kurnia
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.v14i1.4535

Abstract

The rising popularity of online gaming has positioned Steam as a leading platform for accessing diverse games. Beyond gameplay, Steam enables users to submit reviews, offering valuable data for analyzing emotional tone and classifying feedback as positive or negative. This study analyzed 8,000 reviews from Steam across four games: The Sims 4, Counter-Strike 2, FIFA 23, and Dead by Daylight. Plutchik’s emotion theory, with its eight basic emotions served as the foundation for classification, utilizing the NRC Lexicon as an emotional dictionary. A hybrid algorithm combining cosine similarity (70%) and Euclidean distance (30%) with a threshold mechanism was employed to label emotions. Reviews exceeding the threshold received specific emotion labels, while others were classified as "unknown." Positive reviews, associated with joy, trust, fear, and surprise, were predominant for The Sims 4. Conversely, Counter-Strike 2, FIFA 23, and Dead by Daylight garnered largely negative reviews, highlighting the utility of emotional analysis in evaluating user feedback.
Evaluasi Tingkat Kapabilitas Teknologi Informasi pada STAI Auliaurrasyidin Tembilahan Menggunakan Farmework COBIT 2019 Ansyari, Muhammad Fadli; Megawati; Saputra, Eki; Rozanda, Nesdi Evrilyan
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.v14i1.4553

Abstract

Information Technology (IT) plays a vital role in supporting operational efficiency and innovation, particularly in higher education. XYZ University has implemented the Integrated Academic Information System (AKSIA) to manage academic data and student services. However, technical challenges in system management require further evaluation using the COBIT 2019 framework. The evaluation focuses on DSS01 (Managed Operations), APO04 (Managed Innovation), and APO09 (Managed Service Agreements) domains, as determined through design factor analysis. Based on interviews, observations, and questionnaires analyzed using the Guttman scale and gap analysis, it was found that all domains are at Capability Level 1 with largely achieved performance: DSS01 at 75%, APO04 at 60%, and APO09 at 66%. The recommendations include improving process documentation, conducting routine monitoring of activities, and aligning services more closely with strategic needs. These steps are expected to elevate IT capability to higher levels and better support the institution's strategic objectives.
ANALISIS KEPUASAN PENGGUNA TERHADAP APLIKASI MOBILE LAYANAN TELEKOMUNIKASI BIMAPLUS MENGGUNAKAN METODE EUCS Fadillah, Muhammad Rezky; Megawati; Saputra, Eki; Rozanda, Nesdi Evrylian
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.v14i1.4554

Abstract

The Bima Plus application, launched by the Tri Provider, was the subject of a study aimed at analyzing the factors influencing user satisfaction. The End User Computing Satisfaction (EUCS) model was employed for this research. Seven variables were identified as influencing user satisfaction: Ease of Use (EOU), Timeliness (TIM), Security (SEC), Speed of Response (SOR), Content (CON), Accuracy (ACC), and Format (FOR). Analysis results indicated that four variables—Ease of Use, Timeliness, Security, and Speed of Response—had a significant impact on user satisfaction, with Security being the most dominant factor (coefficient of 0.497). Conversely, Content, Accuracy, and Format did not demonstrate significant influence. These findings suggest that security is the primary factor influencing user satisfaction, followed by response speed, timeliness, and ease of use.
PENGGUNAAN YOLO UNTUK DETEKSI ROBOT DAN GAWANG PADA ROBOT SEPAK BOLA BERODA Surya, Muhammad; Toscany, Afrizal; Saputra, Chindra; Pratama, Yovi; Bustami, M Irwan
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.v14i1.4575

Abstract

The ability to detect objects in real-time is a crucial factor in enhancing a robot's performance in understanding and adapting to dynamic environments. This research aims to develop and implement an object detection system on a wheeled soccer robot using the YOLOv11 algorithm, applied to images generated by omnidirectional and front-facing cameras. The system leverages deep learning technology for data labeling, model training, and performance evaluation. Testing was conducted by comparing the object detection results from both types of cameras, as well as analyzing performance metrics such as precision, recall, F1-score, and accuracy. The results show that the YOLOv11 model is effective in detecting objects in real-time, with a detection accuracy of 95.91% for the front camera and 96.7% for the omnidirectional camera. The highest precision and recall were recorded in the robot class, with precision of 99.12% and recall of 97.40% for the front camera, and precision of 96.5% and recall of 97.8% for the omnidirectional camera. The use of a combination of cameras proved to expand the robot's field of vision, enhancing object detection accuracy in dynamic environments. This research contributes to the implementation of object detection systems in robotics, particularly in the context of robot soccer competitions.

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