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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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Classification of Coffee Fruit Maturity Level based on Multispectral Image Using Naïve Bayes Method ‘Ulhaq, I’zaz Dhiya; Hidayat, Muhamad Arief; Dharmawan, Tio
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.v17i2.1181

The current research about the classification of coffee fruit ripeness based on multispectral images has been developed using the Convolutional Neural Network (CNN) method to extract patterns from highdimensional multispectral images. The high complexity of CNN allows the model to capture complex features but requires more time and computational resources for model training and testing. Therefore, in this study, classification is performed using a more straightforward method such as Naïve Bayes because its complexity only depends on the number of features and samples. The method only considers each feature independently, so it has high speed and does not require a lot of computational resources. Naïve Bayes is applied to color and texture features extracted from multispectral images of coffee fruit. There are 300 features consisting of 60 color features and 240 texture features. Experiments were conducted based on the comparison of training and testing data and the use of each feature. The combination of color and texture features showed better performance than color or texture features alone, with the highest accuracy reaching 91.01%. In conclusion, using Naïve Bayes is still reasonably good in classifying the ripeness of coffee fruit based on multispectral images.

Note on Algorithmic Investigations of Juosan Puzzles Ammar, Muhammad Tsaqif; Arzaki, Muhammad; Wulandari, Gia Septiana
Jurnal Ilmu Komputer dan Informasi Vol. 17 No. 1 (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.v17i1.1184

We investigate several algorithmic and mathematical aspects of the Juosan puzzle—a one-player pencil-and- paper puzzle introduced in 2014 and proven NP-complete in 2018. We introduce an optimized backtracking technique for solving this puzzle by considering some invalid subgrid configurations and show that this algorithm can solve an arbitrary Juosan instance of size m × n in O(2mn) time. A C++ implementation of this algorithm successfully found the solution to all Juosan instances with no more than 300 cells in less than 15 seconds. We also discuss the special cases of Juosan puzzles of size m × n where either m or n is less than 3. We show that these types of puzzles are solvable in linear time in terms of the puzzle size and establish the upper bound for the number of solutions to the Juosan puzzle of size 1 × n. Finally, we prove the tractability of arbitrary m × n Juosan puzzles whose all territories do not have constraint numbers.

Improving Remote Sensing Change Detection Via Locality Induction on Feed-forward Vision Transformer Fazry, Lhuqita; Mgs M Luthfi Ramadhan; Jatmiko, Wisnu
Jurnal Ilmu Komputer dan Informasi Vol. 17 No. 1 (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.v17i1.1188

The main objective of Change Detection (CD) is to gather change information from bi-temporal remote sensing images. The recent development of the CD method makes use of the recently proposed Vision Transformer (ViT) backbone. Despite ViT being superior to Convolutional Neural Networks (CNN) at modeling long-range dependencies, ViT lacks a locality mechanism, a critical property of pixels that comprise natural images, including remote sensing images. This issue leads to segmentation artifacts such as imperfect changed region boundaries on the predicted change map. To address this problem, we propose LocalCD, a novel CD method that imposes the locality mechanism into the Transformer encoder. Particularly, it replaces the Transformer's feed-forward network using an efficient depth-wise convolution between two $1 \times 1$ convolutions. LocalCD outperforms ChangeFormer by a significant margin. Specifically, it achieves an F1-score of 0.9548 and 0.9243 on CDD and LEVIR-CD datasets.

Improving IT Assets Management with ITIL 4 Framework Harjanto, Andro; Aji, Rizal Fathoni
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.v17i2.1195

IT Asset Management (ITAM) is crucial for organizations as it enables efficient utilization of IT resources, cost reduction, and risk mitigation. Horangi, a startup company, recognizes the importance of asset optimization and aims to enhance its ITAM service. To achieve this, researcher conducts research to identify a suitable framework as a solid foundation. ITIL 4, a widely adopted IT service management framework, is chosen, along with the Continual Service Improvement and Service Value Chain models. These models provide guidelines and recommendations to identify weaknesses and improve current processes while enabling continuous improvement in response to the dynamic IT landscape. The research employs a qualitative approach, utilizing in-depth interviews, document research, and the ITIL 4 guidebook. The study aims to provide recommendations and a foundation for developing guidelines and workflows in ITAM within the company. However, a limitation of this research is not much research related to ITIL 4 in ITAM area and cannot proceed until the implementation of recommendations due to funding constraints and approval processes. To overcome this limitation, it is suggested that future research includes the implementation process to obtain more optimal evaluation results.

Implementation Genetic Algorithm for Optimization of Kotlin Software Unit Test Case Generator Satria Permana, Mohammad Andiez; Muhammad Johan Alibasa; Sri Widowati
Jurnal Ilmu Komputer dan Informasi Vol. 17 No. 1 (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.v17i1.1198

Unit testing has a significant role in software development and its impacts depend on the quality of test cases and test data used. To reduce time and effort, unit test generator systems can help automatically generate test cases and test data. However, there is currently no unit test generator for Kotlin programming language even though this language is popularly used for android application developments. In this study, we propose and develop a test generator system that utilizes genetic algorithm (GA) and ANTLR4 parser. GA is used to obtain the most optimal test cases and data for a given Kotlin code. ANTLR4 parser is used to optimize the mutation process in GA so that the mutation process is not totally random. Our model results showed that the average value of code coverage in generated unit tests against instruction coverage is 95.64%, with branch coverage of 76.19% and line coverage of 96.87%. In addition, only two out of eight generated classes produced duplicate test cases with a maximum of one duplication in each class. Therefore, it can be concluded that our optimization with GA on the unit test generator is able to produce unit tests with high code coverage and low duplication.

A Dynamic-Bayesian-Network-Based Approach to Predict Immediate Future Action of an Intelligent Agent Kridalukmana, Rinta; Eridani, Dania; Septiana, Risma
Jurnal Ilmu Komputer dan Informasi Vol. 17 No. 1 (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.v17i1.1199

Predicting immediate future actions taken by an intelligent agent is considered an essential problem inhuman-autonomy teaming (HAT) in many fields, such as industries and transportation, particularly toimprove human comprehension of the agent as their non-human counterpart. Moreover, the results of suchpredictions can shorten the human response time to gain control back from their non-human counterpartwhen it is required. An example case of HAT that can be benefitted from the action predictor is partiallyautomated driving with the autopilot agent as the intelligent agent. Hence, this research aims to develop anapproach to predict the immediate future actions of an intelligent agent with partially automated drivingas the experimental case. The proposed approach relies on a machine learning method called naive Bayesto develop an action classifier, and the Dynamic Bayesian Network (DBN) as the action predictor. Theautonomous driving simulation software called Carla is used for the simulation. The results show that theproposed approach is applicable to predict an intelligent agent’s three-second time-window immediate futureaction.

Enhancing Assault Maneuvers in Simulated Scenarios of Multiple Invader Kamikaze Drones through the Utilization of a Modified Adaptive Elforce Algorithm Triditya, Gregory; Ramadhan, Mgs M Luthfi; Jatmiko, Wisnu
Jurnal Ilmu Komputer dan Informasi Vol. 17 No. 1 (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.v17i1.1202

The development of autonomous drone technology has led in their widespread deployment, especially in combat scenarios. One instance of this is the utilization of kamikaze drones, as seen in the Ukraine war. Autonomous defense drones have been used to counter these invading kamikaze drones. This study focuses on simulating scenarios involving invader vs. defender drones, primarily exploring invader drone maneuver motions to maximize damage inflicted on chosen targets. The work we conducted presents an enhanced el-force algorithm that employs Coulomb's Law-based maneuver techniques to improve the effectiveness of multiple kamikaze invader drones when engaging target defended by defender drones. We aim to improve traditional el-force by addressing key challenges such as siege tendencies and unproductive conduct. In addition, we explore various attacking formations to determine the most effective formation. To evaluate the performance of our proposed algorithm, we conducted simulation in a dynamic 3D environment, employing damage inflicted as the evaluation metric. Through rigorous testing, we conclusively demonstrate that our proposed method combining with a circular formation, outperforms alternative attacking maneuvers and formations. Our findings provide insights into optimal maneuver movements and attacking formations, improving the effectiveness of invader drones in engaging and damaging designated targets.

Predicting Earthquake Magnitudes in Indonesia: Exploring the Potential of the Prophet Algorithm Nurindahsari, Susi; Wiyono, Slamet; Dairoh
Jurnal Ilmu Komputer dan Informasi Vol. 17 No. 1 (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.v17i1.1203

Research on earthquakes has been extensively conducted by previous studies using various methods and specific discussions. Similarly, research to predict the magnitude of earthquakes that will occur in the future has also been conducted. This study employs the Prophet algorithm to test its capability in predicting a case study's magnitude using data with numerous missing values and outliers. The study is conducted without transformation and with Box-Cox and log-transformations. Transformations are applied to handle outliers. The results indicate that across the three experiments, the difference between the predicted and actual data ranges from 0.1 to 0.5 or even more. Performance metrics reveal that the log-transform is superior to the other two experiments, with a smaller MAE of 0.27 and a MAPE of 5.96%. Nevertheless, the use of the Prophet algorithm in this case study needs further investigation with different treatments to achieve more accurate results.

Land Cover Segmentation of Multispectral Images Using U-Net and DeeplabV3+ Architecture Herlawati; Handayanto, Rahmadya Trias
Jurnal Ilmu Komputer dan Informasi Vol. 17 No. 1 (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.v17i1.1206

The application of Deep Learning has now extended to various fields, including land cover classification. Land cover classification is highly beneficial for urban planning. However, the current methods heavily rely on statistical-based applications, and generating land cover classifications requires advanced skills due to their manual nature. It takes several hours to produce a classification for a province-level area. Therefore, this research proposes the application of semantic segmentation using Deep Learning techniques, specifically U-Net and DeepLabV3+, to achieve fast land cover segmentation. This research utilizes two scenarios, namely scenario 1 with three land classes, including urban, vegetation, and water, and scenario 2 with five land classes, including agriculture, wetland, urban, forest, and water. Experimental results demonstrate that DeepLabV3+ outperforms U-Net in terms of both speed and accuracy. As a test case, Landsat satellite images were used for the Karawang and Bekasi Regency areas.

Rethinking Smart Keyboard Layout to Aid Strong Password Creation Hossain, Md. Faruk; Rahman, Md. Mizanur; Ahmed Rumee, Sarker Tanveer; Zaber, Moinul Islam
Jurnal Ilmu Komputer dan Informasi Vol. 17 No. 1 (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.v17i1.1235

In an era marked by increasing digitization and the omnipresence of smartphones, the importance of robustpassword security cannot be overstated. With the ever-growing threat of cyberattacks, there is a pressing needfor user-friendly tools that facilitate the creation of strong and unique passwords. Traditional alphanumerickeyboard layouts (physical or virtual) have remained largely unchanged for decades, relying on the sameQWERTY layout initially designed for typewriters. However, these layouts may not be optimal for generatingstrong passwords. This paper focuses on tailoring virtual keyboard layouts on smartphones specifically forstrong password creation. For this, we have performed extensive user surveys to see if the presence ofdedicated rows for digits and special characters (essential in any strong password) allows users to createstronger passwords compared to regular smartphone keyboard layout. Apart from that, we also investigatedthe optimal assignment of characters, digits, and special characters and their groupings in a single soft key.The findings from the detailed user experiment suggested optimal settings for a smartphone virtual keyboard(for Android) like- diagonal length for good typing speed (approximately between 8.38 and 9.41 cm), andkey density (0.88 to 1.21 keys/cm2) which produces the least error without sacrificing the strength ofpasswords created using those layouts. We hope the outcome of this paper will help designers to aid virtualkeyboard layouts for smartphones that can motivate and create strong passwords without sacrificing usability.

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Nurul Fazriah

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Home Page

OAI Link

Editorial Team

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Reviewer

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