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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 Library & Information Science

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.

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Articles 267 Documents

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Traditional Batik Pattern Recognition with MobileNetV2 and Sampling-Based Hyperparameter Optimization Suyahman; Saut Parulian, Onesinus; Prasetyo, Deny; Anwar Fauzi, Muhammad
Jurnal Ilmu Komputer dan Informasi Vol. 19 No. 1 (2026): 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.v19i1.1597

Batik holds significant cultural value in Indonesia, reflecting the nation's historical and artistic heritage through its intricate patterns. Preserving these designs is essential for maintaining cultural identity and supporting artistic and economic communities. With the advancement of technology, deep learning has emerged as an effective approach for recognizing and classifying batik patterns. Convolutional Neural Networks (CNNs), particularly MobileNetV2, are widely recognized for their efficiency and accuracy in image classification. However, the performance of deep learning models is highly influenced by hyperparameter selection, which remains a challenging task. This study investigates the effectiveness of MobileNetV2 in classifying traditional Indonesian batik motifs, including Kawung, Mega Mendung, Parang, and Truntum, by applying different hyperparameter optimization methods such as Treestructured Parzen Estimator (TPE), Gaussian Process Sampler (GPS), Grid Search, and Random Search. The experimental results show that TPE achieved the best overall performance with a test accuracy of 91.94% and an F1 score of 92.09%. GPS and Grid Search obtained identical test accuracy of 90.83% with F1 scores of 90.89% and 90.87%, respectively, while Random Search produced the lowest performance with an accuracy of 88.61% and F1 score of 88.61%. These findings highlight the importance of structured hyperparameter optimization, particularly TPE, in enhancing the robustness of CNN-based batik classification. The results provide valuable insights for the development of automated batik pattern recognition systems that support cultural heritage preservation and related image classification applications.

Continuous Sign Language Recognition for Quranic Recitation by Deaf People Using Deep Learning Brianorman, Yulrio; Munir, Rinaldi; Maulidevi, Nur Ulfa
Jurnal Ilmu Komputer dan Informasi Vol. 19 No. 1 (2026): 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.v19i1.1600

This study proposes a deep learning-based system for recognizing Quranic recitation in the sign language, aimed at enhancing accessibility for the Deaf Muslim community. A central contribution is the construction of a novel dataset comprising videos from three Deaf signers performing Surah Al-Fatihah and Surah Al-Ikhlas, guided by the 2022 official Quranic sign language standard introduced by Indonesia’s Ministry of Religious Affairs. The recognition task is framed as a continuous sign language recognition (CSLR) problem to handle unsegmented input sequences. Five pre-trained convolutional neural networks—EfficientNet, GoogleNet, MobileNetV2, ResNet18, and ShuffleNet—were evaluated as visual feature extractors. These were followed by a temporal encoder composed of 1D CNN and BiLSTM, with sequence alignment performed using the Connectionist Temporal Classification (CTC). The experimental results show that ResNet18 and MobileNetV2 achieved the best performance with Word Error Rates (WER) of 5.00% and 7.92% on the test set, respectively. A cross-participant evaluation was also conducted to assess model generalization, although the results revealed performance gaps likely due to signer variation and limited data. The study highlights the suitability of lightweight and residual architectures for CSLR tasks in religious contexts and provides a benchmark for future research on inclusive sign language technologies. In cross-participant evaluation, the model achieved a validation WER of 8.44% on seen signers and 50.46% on an unseen signer, reflecting generalization challenges commonly observed in low-resource CSLR settings. The proposed system lays the groundwork for AI-assisted Quranic education tools tailored to the Deaf Muslim population.

Optimization of 3D U-Net Using Attention Mechanism for Accurate Protein Object Identification in Cryo-Electron Tomography Theniana, Ghessa; Setiawan, Sendi; Syakrani, Nurjannah; Yudi Widhiyasana
Jurnal Ilmu Komputer dan Informasi Vol. 19 No. 1 (2026): 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.v19i1.1622

Object segmentation in 3D tomograms is a key problem in Cryo-Electron Tomography (Cryo-ET) analysis. In this work, performance for the 3D U-Net architecture, and its variants with three attention mechanisms (Attention Gate (AG), Squeeze-and-excitation (SE), and Convolutional Block Attention Module (CBAM)) was evaluated. Experiments were conducted on a publicly available Cryo-ET dataset comprising two tomogram samples using a combination of (16,32,64,128) and (32,64,128,256) channel configurations, and with patch sizes of (32,64,64), (32,96,96), and (32,128,128) respectively. Model performance was evaluated with the F-Beta Score metric. The results of the analysis show that larger patch sizes significantly improve performance, and deep channel configurations do not always lead to better performance. Compared to the baseline 3D U-Net, which achieved a best score of 0.670, 3D UNet + SE led to the best model performance with the highest F-Beta Scores at 0.718, representing an improvement of 0.048. 3D U-Net + CBAM was second with F-Beta Scores at 0.707, improving by 0.037 over the baseline, while 3D U-Net + AG exhibited prediction inconsistency, with its accuracy falling below the baseline in multiple settings. Overall, these results show that incorporating either SE or CBAM, is a better approach to improve segmentation accuracy for 3D tomogram analysis.

Face Gender Recognition Optimization Using VGG-16 With Integration of Spatial Attention Block and Channel Attention Block Dharmawan, Tio; Putra, Leonardus Virmus Danar Kusuma; Hidayat, Muhamad Arief
Jurnal Ilmu Komputer dan Informasi Vol. 19 No. 1 (2026): 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.v19i1.1627

Face gender recognition plays a critical role in applications such as security systems, personalized services, and human-computer interaction. Although VGG-16 is commonly used in this domain, it struggles to retain important spatial information under varying lighting conditions, facial expressions, and viewing angles. This study enhances the VGG-16 model by integrating the Convolutional Block Attention Module (CBAM), which consists of spatial and channel attention mechanisms. Several training scenarios were explored, including applying CBAM to all convolutional blocks and fine-tuning blocks 2 to 5. Experiments conducted on the Labeled Faces in the Wild (LFW) Gender dataset showed a notable improvement in performance. The best configuration achieved an accuracy of 91.78%, outperforming the baseline model (82.13%–88.72%). Other evaluation metrics such as Precision, Recall, and F1-Score also improved, confirming the effectiveness of attention mechanisms in enhancing feature extraction and classification accuracy in face gender recognition tasks.

Nominal Detection of Rupiah Banknotes with Audio Output Using MobileNetV2 Transfer Learning Method Farid Abdillah, Rachmat; Ramadhani, Dian
Jurnal Ilmu Komputer dan Informasi Vol. 19 No. 1 (2026): 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.v19i1.1674

Banknotes are widely used all over the world. Banknotes are a means of payment used by the public, including the visually impaired. The visually impaired still depend on others to recognize the nominal rupiah banknotes. One of the efforts that can help the visually impaired is creating a machine-learning model that can recognize the nominal rupiah banknotes. This research aims to assist the visually impaired in independently identifying the nominal rupiah banknotes. In this study, the MobileNetV2 pre-trained model was used to learn how to make a model that can detect the nominal amount of rupiah banknotes. The dataset consisted of 1,400 images of rupiah banknotes, divided into 80% for training data and 20% for testing data. The evaluation carried out on the model using the confusion matrix resulted in a model accuracy value of 99.2%.

Federated Learning for Privacy-Preserving IoT Intrusion Detection under Extreme Non-IID Conditions Riyadi, Michael Angello Qadosy; Dewi, Adinda Mariasti
Jurnal Ilmu Komputer dan Informasi Vol. 19 No. 1 (2026): 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.v19i1.1687

The rapid growth of IoT devices has expanded attack surfaces, making intrusion detection critical. Traditional centralized IDS compromise privacy and strain bandwidth by requiring raw data transfer. Federated learning (FL) offers a privacy-by-design solution, enabling collaborative training across IoT clients while sharing only model updates. However, FL is highly sensitive to non-IID data. Extreme heterogeneity, prevalent in real-world IoT IDS datasets due to device-specific traffic patterns and severe class imbalances, causes significant convergence challenges and accuracy degradation. This study benchmarks four advanced FL algorithms (FedAvg, SCAFFOLD, FedYogi, and AdaFedAdam) on the RT-IoT2022 dataset (123,117 samples, 12 attack classes) under extreme nonIID conditions (Dirichlet α = 0.01, average JSD = 0.5677, three heterogeneous clients). Using a multilayer neural network with 10-fold cross-validation nested in the FL loop, SCAFFOLD achieves the most stable performance (Round 100: accuracy 0.7981, F1-score 0.7451, ROC-AUC 0.9396), while FedAvg converges slowly (accuracy 0.6959). FedYogi and AdaFedAdam fail due to gradient starvation and second-moment explosion. Compared to centralized baselines (accuracy up to 1.000), FL incurs a 20% accuracy trade-off, an acceptable cost for enhanced privacy in edge-IoT environments. Contributions include the first validation of SCAFFOLD under extreme non-IID IoT IDS and a reproducible evaluation protocol.

Predicting Flight Departure Delay Durations Using Ensemble Learning: A Case Study of Soekarno-Hatta International Airport Saputri, Zuyina Ayuning; Denny
Jurnal Ilmu Komputer dan Informasi Vol. 19 No. 1 (2026): 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.v19i1.1755

Flight delays at primary hubs like Soekarno-Hatta International Airport (CGK) can disrupt national connectivity and incur substantial operational costs. While existing research often relies on binary classification, tactical airport management requires precise temporal granularity in minutes to optimize resource allocation, such as gate and stand management. This study develops a robust duration prediction model using ensemble learning (XGBoost and Random Forest) integrated with a cost-sensitive learning strategy to address the severe skewness in delay duration distribution. The methodology incorporates advanced preprocessing, including Winsorizing to stabilize gradients and cyclical encoding to capture temporal continuity. Experimental results using 2024 operational data show that the optimized XGBoost model achieves superior performance with a Mean Absolute Error (MAE) of 6.39 minutes and an R² score of 0.70. Feature importance analysis identifies scheduled turnaround and ground infrastructure readiness as the primary determinants of delays, highlighting a significant "knock-on effect" where narrow transition windows fail to absorb inbound disruptions. These findings facilitate a transition from reactive reporting to proactive analytics, enabling the Airport Operation Control Center (AOCC) to optimize gate assignments and mitigate delay propagation.

Grade and Disease Detection of Dragon Fruit based on Modified VGGNet with Identity Mapping Dinial Utami Nurul Qomariah; Ade Irma Elvira; Syifa Nurhayati
Jurnal Ilmu Komputer dan Informasi Vol. 19 No. 2 (2026): 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.v19i2.1516

In the agricultural sector, dragon fruit is one of the most frequently harvested fruits throughout the year, regardless of the season. To ensure optimal quality during the harvest period, fruit monitoring should begin from the immature stage. However, dragon fruit is highly susceptible to various diseases, particularly as it approaches ripeness. Therefore, there is a growing need for an automated system that can classify the quality grade of dragon fruit. In addition, such a system should also be able to detect potential diseases affecting fruit. In this study, we propose a modified VGGNet architecture integrated with Identity Mapping. The original VGGNet serves as the backbone. Identity Mapping is then incorporated into each block to improve training efficiency and computational performance. At the final stage, the fully connected and classification layers are fine-tuned to enhance overall accuracy. The proposed method is evaluated using a dragon fruit dataset collected from Bondowoso. Experimental results demonstrate that the proposed approach achieves superior performance, with 99.9% accuracy, recall, and precision. It also outperforms baseline models, including VGG16, AlexNet, and EfficientNet variants B0–B3 and B5–B6.

Comparative Analysis on Data Balancing and Augmentation in Skin Cancer Image Classification Using Multiple Datasets with Explainable AI Dimas Fanny Hebrasianto Permadi; Annisaa Utami; Muhammad Raafi’u Firmansyah
Jurnal Ilmu Komputer dan Informasi Vol. 19 No. 2 (2026): 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.v19i2.1598

Skin cancer is one of the most common types of cancer worldwide, with a continuously increasing incidence rate. Early detection and accurate classification of skin lesions are crucial for improving patient survival, particularly for melanoma. This study presents a comparative analysis of data-balancing strategies for skin cancer image classification across multiple publicly available datasets, including ISIC 2024, ISIC 2019, ISIC 2020, HAM10000, and PROVe-AI. The ConvNeXt-Tiny architecture is employed as the classification model and evaluated under three dataset configurations, namely RAW without balancing, Undersampling, and Oversampling via data augmentation, combined with two Learning Rate (LR) settings of 0.001 and 0.0001. In addition to reporting classification performance, this study emphasizes a comparative evaluation of accuracy, training stability, and computational efficiency across different balancing strategies. The experimental results indicate that the RAW dataset with an LR=0.0001 provides the best trade-off between performance and efficiency, achieving a validation accuracy of 99.88% and an F1-score of 0.9988. Oversampling via augmentation achieves the highest performance, with a validation accuracy of 99.93% and an F1-score of 0.9993, but requires substantially higher computational resources. Undersampling enables faster training with lower resource consumption, although it results in a slight performance degradation. Furthermore, Explainable Artificial Intelligence techniques, including Grad-CAM and LIME, demonstrate that models trained with an appropriate learning rate consistently focus on clinically relevant lesion regions, thereby improving interpretability and trustworthiness.

Endoscopic Image Classification Using ConvNeXt for GERD and Polyp Identification Muhammad Faqih; Okta Qomaruddin Aziz; Ajib Hanani
Jurnal Ilmu Komputer dan Informasi Vol. 19 No. 2 (2026): 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.v19i2.1702

Early and accurate detection of gastrointestinal abnormalities, such as gastroesophageal reflux disease (GERD) and intestinal polyps, is essential for preventing severe clinical complications. However, manual interpretation of endoscopic images is often constrained by inter-observer variability and time limitations. This study proposes a ConvNeXt-Tiny-based deep learning framework for multi-class classification of gastrointestinal endoscopic images. Experiments were conducted using the GastroEndoNet v3 dataset, which contains 4,006 images categorized into four classes: GERD, GERD Normal, Polyp, and Polyp Normal. A total of twelve experimental scenarios were designed to systematically evaluate the effects of dataset-provided augmentation, ImageNet-based normalization, and batch size on model performance. The optimal configuration, combining augmentation, normalization, and a batch size of 64, achieved a test accuracy of 99.75% and a macro-averaged F1- score of 0.9977, indicating stable convergence and strong generalization on unseen data. The results demonstrate that ConvNeXt-Tiny effectively captures disease-relevant visual patterns in endoscopic images while maintaining consistent performance across varying training conditions. Comparative evaluation with a transformer-based baseline further indicates that modern convolutional architectures remain competitive for gastrointestinal image classification tasks. The proposed framework establishes a reliable and lightweight baseline for automated gastrointestinal disease detection. Extensions to video-based endoscopy would require incorporating temporal information across consecutive frames, which is beyond the scope of the current image-based study.

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Contact Name Nurul Fazriah

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Contact Name
Nurul Fazriah

Contact Email
jiki@cs.ui.ac.id

Phone
+62217863419

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