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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.

Arjuna Subject : -

Articles 257 Documents

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The Optimizing Data Quality in Interagency Data Sharing: A Framework Kurniawati, Monica Vivi; Zulmy, Mohamad Faisal; Ruldeviyani, Yova
Jurnal Ilmu Komputer dan Informasi Vol. 18 No. 1 (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.v18i1.1310

In the modern landscape of government operations, characterized by a shift towards openness, inclusivity, and interagency collaboration driven by the pursuit of public value and evidence-based policy making, the importance of interagency data sharing (IDS) is unmistakable. Despite the evident benefits of information exchange among government agencies, challenges persist, especially concerning nuanced considerations of data quality. This study aims to bridge this critical gap by proposing a specialized framework for IDS within government agencies. This framework, crafted to proactively address data quality considerations throughout the entire lifecycle, transcends traditional approaches and seeks to offer insights for fostering effective practices in interagency data sharing. Positioned at the nexus of evolving government operations, the research underscores the necessity for strategic frameworks prioritizing data quality to support collaborative and effective evidence-driven decision-making.

Estimating Passenger Density in Trains through Crowd Counting Modeling Tjandra, Bryan; Jodrian, Oey Joshua; Handoko, Nyoo Steven Christopher; Wicaksono, Alfan Farizki
Jurnal Ilmu Komputer dan Informasi Vol. 18 No. 1 (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.v18i1.1314

The Greater Jakarta Commuter Rail, also known as the KRL Commuter Line, is one of the primary transportation choices for many people due to its comfort and efficiency. However, the level of user dissatisfaction is still relatively high, particularly regarding the frequent and unpredictable overcrowding of trains. To address this issue, our research develops an Artificial Intelligence-based model to predict train passenger density through crowd counting. By utilizing the proposed k-F1 metric and a constructed dataset of train density, we compare three object detection approaches: bounding box prediction (YOLOv5), density map (CSRNet), and proposal point (P2PNet). Our results show that P2PNet excels in estimating the number of people and predicting their locations in crowded situations. However, for situations that have fewer people and larger object sizes, YOLOv5 demonstrates the best performance. To estimate the density of space, we propose a method that takes into account the region of interest, image perspective transformation, and masking. The proportion between the masked area and the total area provides an estimation of the density level within the train. This method can be applied to real-time image-based CCTV systems in predicting train congestion and facilitating transportation management decisions aligned with Indonesia's sustainable development goals.

E-Government Between Developed and Developing Countries: Key Perspectives from Denmark and Iraq Totonchi, Ahmed; Abd Rahman Ahlan; Hazwani Bt Mohd Mohadis
Jurnal Ilmu Komputer dan Informasi Vol. 18 No. 1 (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.v18i1.1316

E-government involves using technology to provide public information and services digitally. This study examines key factors addressing infrastructure, cultural, political, technical, and social challenges in e-government implementation. By exploring diverse contexts, from citizen engagement to data frameworks, it will elucidate best practices and lessons for overcoming hurdles on the bureaucratic and user sides. The research aims to uncover how states can successfully transition services online. Insights can inform policymakers seeking to digitize governance and leverage information and communication technologies to improve state-citizen relations. Additionally, it aims to compare and analyze e-government systems in a developing country (Iraq) and a developed country (Denmark) to highlight key differences that could inform e-government development efforts in developing nations. Iraq and Denmark were chosen due to the disparity between their e-government systems, enabling the identification of weaknesses in Iraq's e-government initiatives and providing insights from Denmark's more advanced experience. Examining this e-government gap between a developing and developed country will allow developing nations like Iraq to pinpoint areas for improvement and potentially benefit from Denmark's success in this area.

Preprocessing Impact on SAR Oil Spill Image Segmentation Using YOLOv8 Syakrani, Nurjannah; Kurniawan, Dimas; Nugraha, Wili Akbar; Hidayatullah, Priyanto; Firdaus, Lukmannul Hakim; Sholahuddin, Muhammad Rizqi
Jurnal Ilmu Komputer dan Informasi Vol. 18 No. 1 (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.v18i1.1380

Synthetic Aperature Radar (SAR) is a sensory equipment used in marine remote sensing that emits radio waves to capture a representation of the target scene. SAR images have poor quality, one of which is due to speckle noise. This research uses SAR images containing oil spills as objects that are detected using machine learning with the YOLOv8 model. The dataset was obtained from MKLab by preprocessing to improve the quality of SAR images before processing. Preprocessing involves annotating the dataset, augmenting it with flip augmentation, and filtering it using threshold and median filters in addition to a sharpen kernel that finds the optimal midway value. The default value of the YOLOv8 hyperparameter is used with addition of delta as well as subtraction of the same delta. The implementation of preprocessing and combination of hyperparameters is examined to optimize the YOLOv8 model in detecting oil spills in SAR images. Based on 10 experimental scenarios, initial results with the original MKLab image provide an mAP50 of 49.7%. Implementing Flip augmentation alone on the data set increases the mAP50 value by 18.8%. Followed by the sharpen 1.2 kernel filter increasing the mAP50 value to 68.89%, while the median and thresholding filters tend to reduce the mAP50 value. The combination of experiments with the best results was preprocessing with flip augmentation and sharpen 1.2 kernel filter with hyperparameters: epoch 200, warmup 4.0, momentum 0.9, warmup bias lr 0.01, weight decay 0.005, and learning rate 0.000714, resulting in an mAP50 value of 68.89%. In addition, it was found that the sharpening kernel with a real number midpoint of 1.2 and combination with flipping augmentation had the greatest impact on increasing the MAP50 value in SAR oil spill image segmentation by YOLOv8.

Utilizing X Sentiment Analysis to Improve Stock Price Prediction Using Bidirectional Long Short-Term Memory Pasaribu, Marcella Komunita; Alghany, Fahrel Gibran; Simanullang, Benhard; Khasanah, Hilma Nur; Zain, Hanan Nurul Hardyana; Khadijah; Rismiyati
Jurnal Ilmu Komputer dan Informasi Vol. 18 No. 1 (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.v18i1.1428

The capital market is one of the important factors that influence the national economy. However, the stock price in capital market fluctuates over time. Therefore, the investors strongly need an accurate prediction of stock price for making profitable decision. However, with the pervasive influence of the internet, investors and investment institutions have started incorporating online opinions and news, including those found on social media platforms like X. This research aims to enhance stock price prediction by utilizing X sentiment analysis. The sentiment of tweets from X related to IHSG stock price is predicted by using BERT (Bidirectional Encoder Representations from Transformers), then its result isintegrated with the historical stock price data for predicting future stock price by using BiLSTM (Bidirectional Long Short-Term Memory). The experiment results show that the RMSE and MAPE of the proposed model with sentiment analysis is decreased by 0.042 and 0.595, resepectively, compared to the model without sentiment analysis. Therefore, it can be concluded that the inclusion of X sentiment analysis in conjunction with BiLSTM succeeded in improving the performance of stock price prediction. The study's outcome is expected to be valuable for investors to make profitable decisions, leveraging the information available on social media.

Multilabel Hate Speech Classification in Indonesian Political Discourse on X using Combined Deep Learning Models with Considering Sentence Length Angger Saputra, Revelin; Sibaroni, Yuliant
Jurnal Ilmu Komputer dan Informasi Vol. 18 No. 1 (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.v18i1.1440

Hate speech, as public expression of hatred or offensive discourse targeting race, religion, gender, or sexual orientation, is widespread on social media. This study assesses BERT-based models for multi-label hate speech detection, emphasizing how text length impacts model performance. Models tested include BERT, BERT-CNN, BERT-LSTM, BERT-BiLSTM, and BERT with two LSTM layers. Overall, BERT-BiLSTM achieved the highest (82.00%) and best performance on longer texts (83.20% ) with high and , highlighting its ability to capture nuanced context. BERT-CNN excelled in shorter texts, achieving the highest (79.80%) and an of 79.10%, indicating its effectiveness in extracting features in brief content. BERT-LSTM showed balanced and across text lengths, while BERT-BiLSTM, although high in r, had slightly lower on short texts due to its reliance on broader context. These results highlight the importance of model selection based on text characteristics: BERT-BiLSTM is ideal for nuanced analysis in longer texts, while BERT-CNN better captures key features in shorter content.

Efficient Design and Compression of CNN Models for Rapid Character Recognition Parulian, Onesinus Saut
Jurnal Ilmu Komputer dan Informasi Vol. 18 No. 1 (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.v18i1.1443

Convolutional Neural Networks (CNNs) are extensively utilized for image processing and recognition tasks; however, they often encounter challenges related to large model sizes and prolonged training times. These limitations present difficulties in resource-constrained environments that require rapid model deployment and efficient computation. This study introduces a systematic approach to designing lightweight CNN models specifically for character recognition, emphasizing the reduction of model complexity, training duration, and computational costs without sacrificing performance. Techniques such as hyperparameter tuning, model pruning, and post-training quantization (PTQ) are employed to decrease model size and enhance training speed. The proposed methods are particularly well-suited for deployment on edge computing platforms, such as Raspberry Pi, or embedded systems with limited resources. Our results demonstrate a reduction of over 80% in model size, decreasing from 43.73 KB to 6.25 KB, and a reduction of more than 45% in training time, decreasing from over 150 seconds to less than 80 seconds. This research highlights the potential for achieving a balance between efficiency and accuracy in CNN design for real-world deployment, addressing the increasing demand for streamlined deep learning models in resource-constrained environments.

Indonesian License Plate Detection and Recognition System using Gaussian YOLOv7 Wijaya, Juan Thomas; Arymurthy, Aniati Murni
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.v18i2.1320

In recent years, Automatic License Plate Recognition (ALPR) systems have garnered attention in computer vision research. However, practical applications face challenges such as inconsistent lighting, diverse license plate designs, and environmental variations, which increase the complexity of the task and lead to more false detections. To address these issues, we proposed Gaussian YOLOv7 for license plate detection and character recognition within ALPR systems, along with the Spatial Transformer Network (STN) for rectifying license plate orientation, aiming to enhance performance and adaptability to real-world scenarios. Additionally, we introduced a novel dataset for Indonesian ALPR systems to ensure robust detection and a balanced class distribution. Evaluation results indicate that Gaussian YOLOv7 improves precision and reduces false positives by 37.5% in the detection stage, albeit with poorer performance in other metrics. Conversely, the implementation of STN results in decreased character recognition accuracy, underscoring its limited effectiveness. Despite these challenges, Gaussian YOLOv7 excels in license plate rectification, achieving a recall of 83.8% and reducing false positives by 50.13% compared to YOLOv7. Moreover, post-processing techniques introduced by our approach further enhance precision by 5.3% and recall by 1%. Overall, our approach offers promising advancements in Indonesian ALPR systems, addressing fundamental challenges and enhancing performance.

Classification of Economic Activities in Indonesia Using IndoBERT Language Model Syazali, Muhammad Rizki; Yulianti, Evi
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.v18i2.1446

Classification of economic activities plays a vital role in understanding, analyzing, and managing complex economic processes in a society or country. It facilitates economic analysis, data collection, policy formulation, and informed decision-making. In Indonesia, economic activities are classified according to the Indonesian Standard Industrial Classification (KBLI). This classification process requires in-depth knowledge about KBLI, and this process is still performed manually, which is therefore time-consuming. To address this challenge, this paper proposes to use a transformer-based language model that was pretrained using a large Indonesian corpus, i.e., IndoBERT, to better understand the contextual meanings of text in order to improve the accuracy of automatic economic activity classification. Our results show that the finetuned IndoBERTLARGE model achieves superior results, with an F1 score of 96.82% and a balanced accuracy of 96.10%, outperforming other recent methods used for similar task, i.e., CatBoost and DistilBERT models.

Analysis of Coding Stress Impact on Students Programming Skills with Random Forest and C4.5 Algorithms Azkiya, M. Akiyasul; Ifriza, Yahya Nur
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.v18i2.1487

Students' stress often impedes their advancement in programming, which demands logical reasoning, an understanding of algorithms, and a firm grasp of basic concepts. This research intends to pinpoint the elements that affect students' programming abilities, explore their connection to stress levels, and assess the effectiveness of the Random Forest and C4.5 algorithms in classifying data. Information was gathered through an online questionnaire involving 744 students in 2024 at various leading universities in Islamabad, Pakistan. The dataset used in this study was sourced from Kaggle, which provides insights into factors affecting students' programming performance and stress levels. The analysis utilized a Confusion Matrix and evaluation metrics like accuracy, precision, recall, and F1-Score. The analysis results indicate that the C4.5 algorithm has a higher accuracy of 68.04% compared to Random Forest, which achieved 65.54%. Additionally, C4.5 outperforms Random Forest in terms of precision, scoring 71.7% versus 65.2%. However, in terms of recall, Random Forest performs better with a score of 66.3%, while C4.5 only reaches 59.6%. This study confirms that interest in programming, debugging skills, mathematical and analytical abilities, and perceptions of programming significantly impact students' performance and stress levels. Students with strong logical abilities and adequate support demonstrate better performance and lower stress levels, whereas those with weak technical skills and negative perceptions are more vulnerable to stress, which adversely affects their performance. These findings emphasize the importance of creating a positive learning environment through interactive methods, structured problem-solving, and additional support.

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Nurul Fazriah

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Home Page

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Google Scholar

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

Abstract

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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