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Jurnal Ilmu Komputer dan Informasi

JIKI
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Published by Universitas Indonesia

ISSN : 20887051 EISSN : 25029274 DOI : 10.21609

Core Subject : Science,

Computer Science & IT

Jurnal Ilmu Komputer dan Informasi is a scientific journal in computer science and information containing the scientific literature on studies of pure and applied research in computer science and information and public review of the development of theory, method and applied sciences related to the subject. Jurnal Ilmu Komputer dan Informasi is published by Faculty of Computer Science Universitas Indonesia. Editors invite researchers, practitioners, and students to write scientific developments in fields related to computer science and information. Jurnal Ilmu Komputer dan Informasi is issued 2 (two) times a year in February and June. This journal contains research articles and scientific studies. It can be obtained directly through the Library of the Faculty of Computer Science Universitas Indonesia.

Arjuna Subject : -

Articles 247 Documents

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Application of Machine Learning Methods for Classification of Gamma and Hadron Signals in High Energy Particle Detection Wibowo, Firdaus Andi; Yulianto, Tomi; Malun, Nicholaus Ola; Rionaldy, Rizqy; Yasin, Verdi; Siagian, Ruben Cornelius
Jurnal Ilmu Komputer dan Informasi Vol. 18 No. 2 (2025): Jurnal Ilmu Komputer dan Informasi (Journal of Computer Science and Informatio
Publisher : Faculty of Computer Science - Universitas Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21609/jiki.v18i2.1489

A major challenge in particle physics is the binary classification of high-energy gamma signals against a complex hadron background. Accurate identification of these gamma signals is critical for particle detection, especially as the volume and complexity of data increases as technology advances. The research developed a machine learning-based classification model to efficiently and accurately distinguish gamma signals from hadrons. Logistic Regression, Decision Trees, Random Forests, and Artificial Neural Networks are used for classification. Principal Component Analysis (PCA) and correlation analysis identified dominant features, while Monte Carlo simulations validated the distribution of gamma and hadron spectra. This study focuses on geometric parameters such as fLength, fWidth, fAlpha, as well as photon distribution and distance effects (fDist) in gamma signal identification using K-Means clustering. The Random Forest algorithm achieved the highest accuracy of 87.96%, with an F1-score of 0.91, which defines its robustness in the classification task. PCA and correlation analysis showed fSize, fLength, and fWidth as the most influential factors in classification. Monte Carlo simulations successfully replicated the spectral distribution pattern with high experimental validation. The research presents a novel integration of geometric analysis, clustering techniques, and simulation validation in the classification of high-energy particles. Machine learning methods, in particular Random Forest, effectively distinguish the gamma signal from the hadron background. The combination of PCA and Monte Carlo simulations improves the understanding of data distribution patterns and key classification factors. This research contributes to the development of a more reliable astrophysical signal classification system with potential applications in large-scale astronomical data management.

YOLOv11 Model as a Smart Solution for Waste Identification and Classification in Automated Waste Management System Permana, Muhammad Fajar Jati; Gani, Julio Caesar Ray Bakar; Fauzan, Naufal Ahmad; Adiwilaga, Anugrah
Jurnal Ilmu Komputer dan Informasi Vol. 18 No. 2 (2025): Jurnal Ilmu Komputer dan Informasi (Journal of Computer Science and Informatio
Publisher : Faculty of Computer Science - Universitas Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21609/jiki.v18i2.1490

Urbanization and population growth present significant challenges for efficient and sustainable waste management. This research develops an IoT-based intelligent system for waste classification and management utilizing RFID technology, ESP32, a camera, an ultrasonic sensor, and the YOLOv11 object detection model. The system accurately identifies three categories of waste: organic, inorganic, and hazardous. The classification process is automated, incorporating user identification via RFID, servo-controlled bin lid operation, and capacity monitoring through an ultrasonic sensor. Data management is facilitated through a mobile application and a website, which provide user guidance and support for administrators. Test results indicate that the system achieves an average accuracy of 87.5% in the mAP50-95 evaluation, with specific accuracies of 89.0% for inorganic waste, 86.0% for hazardous waste, and 87.0% for organic waste. Despite these results, challenges remain, including object detection errors related to background interference. Future research should focus on enhancing the dataset and implementing data encryption to improve model accuracy and information security. This system demonstrates significant potential for enhancing waste management efficiency and promoting sustainable environmental practices.

Attention-based Residual Long Short-Term Memory for Earthquake Return Period Prediction in the Sulawesi Region Bachmid, Muhdad; Sengkey, Daniel; Manoppo, Fabian
Jurnal Ilmu Komputer dan Informasi Vol. 18 No. 2 (2025): Jurnal Ilmu Komputer dan Informasi (Journal of Computer Science and Informatio
Publisher : Faculty of Computer Science - Universitas Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21609/jiki.v18i2.1506

Indonesia, particularly the Sulawesi region, experiences significant seismic activity due to its position at the convergence of three major tectonic plates. This study seeks to construct a model for predicting earthquake return periods in the Sulawesi area by employing the Residual Long Short-Term Memory (Residual LSTM) architecture integrated with an attention mechanism. The dataset utilized originates from the United States Geological Survey (USGS), focusing on the Sulawesi Island region within the coordinates of latitude -6.184° to 2.021° and longitude 118.433° to 125.552°, spanning the years 1975 to 2024. The research methodology is structured into three primary phases: (1) data collection and preprocessing, including data cleaning, missing value handling, and normalization, (2) exploratory data analysis to understand seismic data characteristics, and (3) development of the Residual LSTM model with an attention mechanism. The evaluation results show excellent model performance with Train Loss 0.0090, Test Loss 0.0091, Training MAE 0.0698, Testing MAE 0.0717, Training RMSE 0.0947, Testing RMSE 0.0951, and stable Huber Loss of 0.0045 for both training and testing data. The implementation of residual connections successfully addressed the vanishing gradient problem, while the attention mechanism enhanced prediction interpretability. The small discrepancy between the training and testing metrics confirms the model's robust generalization ability, indicating its strong potential for applications in predicting earthquake return periods.

Comparing ASM and Learning-Based Methods for Satellite Image Dehazing Steven Christ Pinantyo Arwidarasto; Rahadianti, Laksmita
Jurnal Ilmu Komputer dan Informasi Vol. 18 No. 2 (2025): Jurnal Ilmu Komputer dan Informasi (Journal of Computer Science and Informatio
Publisher : Faculty of Computer Science - Universitas Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21609/jiki.v18i2.1521

Recent advancements in optical satellite technologies have significantly improved image resolution, providing more detailed information about Earth's surface. However, atmospheric interference, such as haze, is still a major factor in image capture. The interference results in visibility degradation of the acquired images, hindering computer vision tasks. Numerous studies have proposed various methods to recover haze-affected regions in satellite images, highlighting the need for more effective solutions. Motivated by this, this paper compares different atmospheric dehazing methods, including Atmospheric Scattering Model (ASM)-based and deep learning-based. The results show that SRD is the best ASM-based method, with a PSNR value of 19.09 dB and an SSIM of 0.908. Among deep learning models, DW-GAN achieves the best restoration results with a PSNR value of 26.22 dB and an SSIM of 0.959. SRD offers faster inference times, but still suffers from residual haze and noticeable color degradation compared to DW-GAN. In contrast, DW-GAN provides a more complete haze removal at the cost of higher computational demands than ASM-based methods.

Context-Aware Detection of Deceptive Design Patterns in E-Commerce Websites Using Word Embedding Based Deep Learning Paradigms Premathilaka, Rukshika
Jurnal Ilmu Komputer dan Informasi Vol. 18 No. 2 (2025): Jurnal Ilmu Komputer dan Informasi (Journal of Computer Science and Informatio
Publisher : Faculty of Computer Science - Universitas Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21609/jiki.v18i2.1530

Deceptive designs (DDs) are a hidden technological tactic that manipulates the user's consumer behavior in a way that benefits website vendors without them knowing. Proper identification of deceptive designs is essential to prevent users from being misled by hidden tactics. To fulfill this requirement, this study assesses Word2Vec word embedding based deep learning models for text based deceptive design detection. Models trained consist of Convolutional Neural Networks (CNN), Bidirectional Long Short-Term Memory (BiLSTM), and a hybrid model (CNN + BiLSTM) that combines the two aforementioned models. These four key score indices of accuracy, precision, sensitivity, and F1-score are computed to compare the performance of each proclaimed model. When compared to the existing DD detection techniques, all three of these approaches attain state-of-the-art performance. The results of this evaluation illustrate that the hybrid model achieves the highest accuracy of 95% in capturing the nuanced text context of deceptive designs. Furthermore, even when other metrics are considered, the hybrid model performs more effectively. To guarantee the independence and security of user activities, intelligent deep learning paradigms are integrated to identify hidden deceptive activities automatically. This allows for the accurate detection and classification of deceptive designs in intricate e-commerce environments.

Comparative Evaluation of Database Systems for High-Volume Seismic Prediction Data Management in Real-Time Applications Wibisono, Ari; Naufal Rahmadika, Rafif
Jurnal Ilmu Komputer dan Informasi Vol. 18 No. 2 (2025): Jurnal Ilmu Komputer dan Informasi (Journal of Computer Science and Informatio
Publisher : Faculty of Computer Science - Universitas Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21609/jiki.v18i2.1539

The Earthquake Early Warning System (EEWS) plays a pivotal role in mitigating structural damage and minimizing casualties by issuing alerts prior to the arrival of destructive seismic waves (S-waves), through the detection of the earlier and faster P-waves. The operational effectiveness of EEWS depends not only on the accuracy of its predictive algorithms but also on the efficiency of the underlying data storage and management infrastructure. This study presents a comparative evaluation of three data storage approaches i.e. MongoDB, MongoDB with sharding, and InfluxDB, as well as the MiniSEED (mseed) binary format, with a focus on their performance in managing real-time seismic prediction data. Benchmarking was conducted based on two key metrics: Input/Output Operations Per Second (IOPS) and data throughput. The results indicate that both MongoDB and InfluxDB offer strong performance in high-ingestion scenarios, with MongoDB demonstrating higher IOPS, while InfluxDB exhibits better scalability and consistency as data volume increases. Conversely, the mseed format achieves exceptionally high throughput due to its flat-file structure but lacks the responsiveness and query capabilities required for real-time analytics. These findings suggest that MongoDB and InfluxDB are well-suited for integration into scalable EEWS infrastructures, offering a balance between performance and flexibility. Future work will extend this evaluation to larger-scale datasets and alternative architectures such as data lake systems to improve disaster response readiness.

A Hybrid Vision Transformer Model for Efficient Waste Classification Amir Mahmud Husein; Baren Baruna Harahap; Tio Fulalo Simatupang; Karunia Syukur Baeha; Bintang Keitaro Sinambela
Jurnal Ilmu Komputer dan Informasi Vol. 18 No. 2 (2025): Jurnal Ilmu Komputer dan Informasi (Journal of Computer Science and Informatio
Publisher : Faculty of Computer Science - Universitas Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21609/jiki.v18i2.1545

The rapid and accurate sorting of municipal waste is essential for efficient recycling and sustainable resource recovery. Most existing AI solutions focus only on four common materials (plastic, paper, metal, and glass), overlooking many other routinely encountered waste types and losing accuracy when applied to the mixed waste compositions seen in operational environments. We introduce HR-ViT, a hybrid network that combines ResNet50 residual blocks, which capture fine-grained local cues, with Vision Transformer global self-attention. Trained on a balanced six-class benchmark of about 775 images per class (plastic, paper, organic, metal, glass, batteries), HR-ViT attains 98.27 % accuracy and a macro-averaged F1-score of 0.98, outperforming a pure ViT, VT-MLH-CNN, and Garbage FusionNet by up to five percentage points in both metrics. Gains arise from selective fine-tuning of the last ten ResNet layers, lightweight ViT hyper-parameter optimisation, and targeted data augmentation that mitigates cluttered backgrounds, uneven lighting, and object deformation. These results show that hybrid attention-residual architectures provide reliable predictions under complex imaging conditions. Future work will extend the method to multi-object scenes and domain-adaptive deployment in smart-city recycling systems.

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Nurul Fazriah

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Home Page

OAI Link

Editorial Team

Contact

Reviewer

Google Scholar

Contact Name Nurul Fazriah

Contact Email jiki@cs.ui.ac.id

Phone -

Journal Mail Official jiki@cs.ui.ac.id

Editorial Address "Faculty of Computer Science Universitas Indonesia Kampus Baru UI Depok - 16424"

Location Kota depok, Jawa barat INDONESIA

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Contact Name
Nurul Fazriah

Contact Email
jiki@cs.ui.ac.id

Phone
-

Journal Mail Official
jiki@cs.ui.ac.id

Editorial Address
"Faculty of Computer Science Universitas Indonesia Kampus Baru UI Depok - 16424"

Location
Kota depok,

Jawa barat

INDONESIA