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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 257 Documents

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Application of Q-learning Method for Disaster Evacuation Route Design Case Study: Digital Center Building UNNES Alrahma, Hanan Iqbal; Anan Nugroho; Hastawan, Ahmad Fashiha; Arief, Ulfah Mediaty
Jurnal Ilmu Komputer dan Informasi Vol. 17 No. 2 (2024): 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.v17i2.1236

The Digital Center (DC) building at UNNES is a new building on the campus that currently lacks evacuation routes. Therefore, it is necessary to create an evacuation route plan in accordance with the Minister of Health Regulation Number 48 of 2016. Creating a manual evacuation route plan can be inefficient and prone to errors, especially for large buildings with complex interiors. To address this issue, learning techniques such as reinforcement learning (RL) are being used. In this study, Q-learning will be utilized to find the shortest path to the exit doors from 11 rooms on the first floor of the DC building. The study makes use of the floor plan data of the DC building, information about the location of the exit doors, and the distance required to reach them. The results of the study demonstrate that Qlearning successfully identifies the shortest evacuation routes for the first-floor DC building. The routes generated by Q-learning are identical to the manually created shortest paths. Even when additional obstacles are introduced into the environment, Q-learning is still able to find the shortest routes. On average, the number of episodes required for convergence in both environments is less than 1000 episodes, and the average computation time needed for both environments is 0.54 seconds and 0.76 seconds, respectively.

Classification of Clove Leaf Blister Blight Disease Severity Using Pre-trained Model VGG16, InceptionV3, and ResNet Pramesti, Putri Ayu; Supriyadi, Muhamad Rodhi; Alfin, Muhammad Reza; Noveriza, Rita; Wahyuno, Dono; Manohara, Dyah; Melati; Miftakhurohmah; Warman, Riki; Hardiyanti, Siti; Asnawi
Jurnal Ilmu Komputer dan Informasi Vol. 17 No. 2 (2024): 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.v17i2.1237

Clove is one of the precious plants produced in Indonesia. Clove has many benefits for humans, but clove cultivation often experiences problems due to disease attacks, including Leaf Blister Blight Disease(CDC). The handling of CDC disease is carried out based on the severity of the symptoms that can be seen on the affected leaves. This research was conducted to obtain a CDC disease classification model, so appropriate treatment can be carried out. This study used the pre-trained VGG16, InceptionV3, and ResNet models for classification. VGG16 got the highest average accuracy of 96.7%. Aside from that, k-fold cross validation improved the model's accuracy.

Detecting Type and Index Mutation in Cancer DNA Sequence Based on Needleman–Wunsch Algorithm Wisesty, Untari Novia; Mengko, Tati Rajab; Purwarianti, Ayu; Pancoro, Adi
Jurnal Ilmu Komputer dan Informasi Vol. 17 No. 2 (2024): 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.v17i2.1273

Detecting DNA sequence mutations in cancer patients contributes to early identification and treatment of the disease, which ultimately enhances the effectiveness of treatment. Bioinformatics utilizes sequence alignment as a powerful tool for identifying mutations in DNA sequences. We used the Needleman-Wunsch algorithm to identify mutations in DNA sequence data from cancer patients. The cancer sequence dataset used includes breast, cervix uteri, lung, colon, liver and prostate cancer. Various types of mutations were identified, such as Single Nucleotide Variant (SNV)/substitution, insertion, and deletion, locate by the nucleotide index. The Needleman Wunch algorithm can detect type and index mutation with the average F1-scores 0.9507 for all types of mutations, 0.9919 for SNV, 0.7554 for insertion, and 0.8658 for deletion with a tolerance of 5 bp. The F1-scores obtained are not correlated with gene length. The time required ranges from 1.03 seconds for a 290 base pair gene to 3211.45 seconds for a gene with 16613 base pairs.

Deep Image Deblurring for Non-Uniform Blur: a Comparative Study of Restormer and BANet Nugraha, Made Prastha; Rahadianti, Laksmita
Jurnal Ilmu Komputer dan Informasi Vol. 17 No. 2 (2024): 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.v17i2.1274

Image blur is one of the common degradations on an image. The blur that occurs on the captured images is sometimes non-uniform, with different levels of blur in different areas of the image. In recent years, most deblurring methods have been deep learning-based. These methods model deblurring as an imageto-image translation problem, treating images globally. This may result in poor performance when handling non-uniform blur in images. Therefore, in this paper, the author compared two state-of-the-art supervised deep learning methods for deblurring and restoration, e.g. BANet and Restormer, with a special focus on the non-uniform blur. The GOPRO training dataset, which is also used in various studies as a benchmark, was used to train the models. The trained models were then tested on the GOPRO testing test, the HIDE testing set for cross-dataset testing, and GOPRO-NU, which consists of specifically selected non-uniform blurred images from the GOPRO testing set, for the non-uniform deblur testing. On the GOPRO testing set, Restormer achieved an SSIM of 0.891 and PSNR of 27.66 while BANet obtained an SSIM of 0.926 and PSNR of 34.90. Meanwhile, for the HIDE dataset, Restormer achieved an SSIM of 0.907 and PSNR of 27.93 while BANet obtained an SSIM of 0.908 and PSNR of 34.52. Finally, on the non-uniform blur GOPRO dataset, Restormer achieved an SSIM of 0.911 and PSNR of 29.48 while BANet obtained an SSIM of 0.935 and PSNR of 35.47. Overall, BANet shows the best result in handling non-uniform blur with a significant improvement over Restormer.

Hand Sign Interpretation through Virtual Reality Data Processing Tju, Teja Endra Eng; Shalih, Muhammad Umar
Jurnal Ilmu Komputer dan Informasi Vol. 17 No. 2 (2024): 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.v17i2.1280

The research lays the groundwork for further advancements in VR technology, aiming to develop devices capable of interpreting sign language into speech via intelligent systems. The uniqueness of this study lies in utilizing the Meta Quest 2 VR device to gather primary hand sign data, subsequently classified using Machine Learning techniques to evaluate the device's proficiency in interpreting hand signs. The initial stages emphasized collecting hand sign data from VR devices and processing the data to comprehend sign patterns and characteristics effectively. 1021 data points, comprising ten distinct hand sign gestures, were collected using a simple application developed with Unity Editor. Each data contained 14 parameters from both hands, ensuring alignment with the headset to prevent hand movements from affecting body rotation and accurately reflecting the user's facing direction. The data processing involved padding techniques to standardize varied data lengths resulting from diverse recording periods. The Interpretation Algorithm Development involved Recurrent Neural Networks tailored to data characteristics. Evaluation metrics encompassed Accuracy, Validation Accuracy, Loss, Validation Loss, and Confusion Matrix. Over 15 epochs, validation accuracy notably stabilized at 0.9951, showcasing consistent performance on unseen data. The implications of this research serve as a foundation for further studies in the development of VR devices or other wearable gadgets that can function as sign language interpreters.

Temporal Action Segmentation in Sign Language System for Bahasa Indonesia (SIBI) Videos Using Optical Flow-Based Approach I Dewa Made Bayu Atmaja Darmawan; Linawati; Sukadarmika, Gede; Wirastuti, Ni Made Ary Esta Dewi; Pulungan, Reza
Jurnal Ilmu Komputer dan Informasi Vol. 17 No. 2 (2024): 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.v17i2.1284

Sign language (SL) is vital in fostering communication for the deaf and hard-of-hearing communities. Continuous Sign Language Translation (CSLT) is a work that translates sign language into spoken language. CSLT translation is done by changing continuous forms into isolated signs. Segmenting morpheme signs from phrase signs has several challenges, such as the availability of annotated datasets and the complexity of continuous gesture movements. The Indonesian Sign Language (SIBI) system follows Indonesian grammatical norms, including word formation, in contrast to other sign languages with rules derived from their spoken language. In SIBI, a word can consist of a root word and an affix word. Therefore, temporal action segmentation in SIBI is important to reconstruct the results of translating each sign into spoken Indonesian sentences. This research uses an optical flow approach to segment temporal actions in SIBI videos. Optical flow methods that calculate changes in intensity between adjacent frames can be used to determine the occurrence of sign movement or vice versa to determine the delay between sign movements. The absence of intensity differences between the two frames indicates the boundary between sign gestures. This study tested the use of dense optical flow on videos containing SIBI sentences taken from 3 signers. Evaluation is done on several parameters in the dense optical flow algorithm, such as threshold size, PyrScale, and WinSize, to obtain the best accuracy. This paper shows that the optical flow algorithm successfully performs segmentation, as measured by Perf and F1r. The experimental results showed that the highest Perf and F1r yields were 0.8298 and 0.8524, respectively.

Automated Ultrasound Object Segmentation Using Combinatorial Active Contour Method Anan Nugroho; Sunarko, Budi; Wibawanto, Hari; Mulwinda, Anggraini; Fauzi, Anas; Oktaviyanti, Dwi; Savitri, Dina Wulung
Jurnal Ilmu Komputer dan Informasi Vol. 17 No. 2 (2024): 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.v17i2.1298

Active Contour (AC) is an algorithm widely used in segmentation for developing Computer-Aided Diagnosis (CAD) systems in ultrasound imaging. Existing AC models still retain an interactive nature. This is due to the large number of parameters and coefficients that require manual tuning to achieve stability. Which can result in human error and various issues caused by the inhomogeneity of ultrasound images, such as leakage, false areas, and local minima. In this study, an automatic object segmentation method was developed to assist radiologists in an efficient diagnosis process. The proposed method is called Automatic Combinatorial Active Contour (ACAC), which combines the simplification of the global region-based CV (Chan-Vese) model and improved-GAC (Geodesic Active Contour) for local segmentation. The results of testing with 50 datasets showed an accuracy value of 98.83%, precision of 95.26%, sensitivity of 86.58%, specificity of 99.63%, similarity of 90.58%, and IoU (Intersection over Union) of 82.87%. These quantitative performance metrics demonstrate that the ACAC method is suitable for implementation in a more efficient and accurate CAD system.

Transformative Insights into Corrosion Inhibition: A Machine Learning Journey from Prediction to Web-Based Application Dzaki Asari Surya Putra; Nicholaus Verdhy Putranto; Nibras Bahy Ardyansyah; Gustina Alfa Trisnapradika; Muhamad Akrom
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.v18i1.1303

This study focuses on the exploration and evaluation of machine learning (ML) models to analyze expired pharmaceutical data for their potential use as corrosion inhibitors. Additionally, the entire modeling process is integrated into a user-friendly platform through a Streamlit service-assisted corrosion inhibitor website, facilitating broader accessibility and practical application. The models are trained offline to ensure accurate performance, eliminating the need for users to retrain the models themselves. This approach simplifies the user experience by offering a ready-to-use prediction service directly on the website platform. Among the various ML models implemented, XGB demonstrated the highest performance with an R2-score of 0.99999999. Given that many chemists are not familiar with informatics coding, the researchers developed a Streamlit-based website that includes tools to customize the models. The end product of this work is a corrosion inhibitor experimentation tool that eliminates the need for users to code, making advanced ML techniques accessible to a broader audience within the chemistry community.

Forest and Land Fire Vulnerability Assessment and Mapping using Machine Learning Method in East Nusa Tenggara Province, Indonesia Wijaya, Hans Timothy; Arymurthy, Aniati Murni
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.v18i1.1304

Forest and land fires are severe disasters for forest ecosystems, diminishing their functionality. Accurate prediction of fire-prone areas aids in effective management and prevention. Machine learning methods have shown promise in this regard. By 2022, East Nusa Tenggara (NTT) had the highest incidence of such fires. This study aims to assess NTT's forest and land fire vulnerability using seven machine learning methods: Gaussian Naive Bayes, Support Vector Machine, Logistic Regression, Artificial Neural Network, Random Forest, Gradient Boosting Machine, and Extreme Gradient Boost. A geospatial dataset integrating NTT's 2022 fire data and fourteen fire-related factors were created using ArcGIS. Feature selection, employing the Information Gain Ratio, identified nine key features: Degree of Slope, Land Cover, NDVI, Annual Rainfall, Distance to Road, Distance to River, Distance to Buildings, Wind Speed, and Solar Radiation. The Random Forest model emerged as optimal, with AUC values of 0.864 and 0.742 for training and testing, respectively. The resulting vulnerability map highlighted factors contributing to NTT's forest fires, including gentle slopes, forest cover, unhealthy vegetation, low rainfall, human activities, remote water access, soil moisture, distant firefighting facilities, low wind speeds, and high solar radiation. Recommendations include land management, fire-resistant vegetation, policy enforcement, community education, and infrastructure enhancement.

The Conceptual Design e-Wallet for Rupiah Digital Zulmy, Mohamad Faisal; Kurniawati, Monica Vivi; Yazid, Setiadi
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.v18i1.1309

This research study the advancement of Central Bank Digital Currencies (CBDCs) spurred by fi-nancial technology progress. It focuses on Rupiah Digital, Indonesia's CBDC initiative led by the Bank of Indonesia (BI). The study explores the technical aspects of Wholesale and Retail Digital Rupiah, proposes an e-wallet system for seamless digital transactions in related to blockchain technology, specifically Permissioned Distributed Ledger Technology (DLT). The objective of this research to provide recommendations to BI regarding appropriate e-wallet conceptual design based on study literature review (LR) methods and qualitative research method by conducting interviews throughs forum group discussion (FGD) and e-mail with leading economic (banks), legal (BI and Government), and technical experts (banks, academic expert on this field, BI and Government) to get reviews and input regarding the e-wallet conceptual design that was proposed. As result, we recommended the architecture for Rupiah Digital using Hyperledger Fabric blockchain with two-tiered distribution and user layer backed by digital token using ID on mobile apps to enhance the security of the system. The FGD with experts and executor result in approval on those conceptual design to be part of the option on development of CBDC in Indonesia.

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Contact Name Nurul Fazriah

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

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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