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Tri A. Sundara
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The Indonesian Journal of Computer Science
Published by STMIK Indonesia Padang
ISSN : 25497286     EISSN : 25497286     DOI : https://doi.org/10.33022
Core Subject : Science, Engineering,
Computer Science & IT Electrical & Electronics Engineering Engineering
The Indonesian Journal of Computer Science (IJCS) is a bimonthly peer-reviewed journal published by AI Society and STMIK Indonesia. IJCS editions will be published at the end of February, April, June, August, October and December. The scope of IJCS includes general computer science, information system, information technology, artificial intelligence, big data, industrial revolution 4.0, and general engineering. The articles will be published in English and Bahasa Indonesia.
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 1,170 Documents
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The Effect of Virtual Reality Gaming on Developing Computational Thinking Skills Sukirman, Sukirman; Ibharim, Laili Farhana Md; Said, Che Soh; Murtiyasa, Budi
The Indonesian Journal of Computer Science Vol. 13 No. 2 (2024): The Indonesian Journal of Computer Science
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v13i2.3829

Abstract

In the digital age, where programming prowess is increasingly crucial, the enhancement of Computational Thinking (CT) skills becomes essential. This study ventures into the scarcely explored domain of leveraging game-based learning (GBL) within virtual reality (VR) settings to bolster CT skills. Specifically, it introduces "CT Saber," a VR game inspired by the popular "Beat Saber," tailored to cultivate CT competencies. Employing a Design and Development Research (DDR) methodology across five stages—analysis, design, development, implementation, and evaluation—this investigation assessed the game's impact on 37 computer science students (25 male, 12 female) aged 21-24. A quasi-experimental design with pretest-posttest evaluation was utilized, revealing significant enhancements in CT skills post-intervention (Z = -4.496, p < 0.05), as analyzed through Wilcoxon Signed-Rank tests. The findings underscore the VR game's efficacy in CT skill development, suggesting a promising direction for integrating VR technologies in programming education.
Distributed Transactions in Cloud Computing: A Review Reliability and Consistency Ferzo, Barwar; Zeebaree, Subhi R. M.
The Indonesian Journal of Computer Science Vol. 13 No. 3 (2024): The Indonesian Journal of Computer Science
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v13i3.3830

Abstract

The challenges of managing distributed transactions in cloud computing are discussed in this paper. The paper places an emphasis on the critical balance that must be maintained between reliability and consistency in the face of complexities such as hardware failures, network outages, and varying latencies. It sheds light on the delicate balance that must be maintained in order to guarantee that transactions in cloud environments are both reliable and consistent. Cloud environments are prone to hardware glitches and network disruptions. In addition, the paper delves into novel approaches with the objective of cultivating a computing ecosystem that is both resilient and dependable in the face of the ever-changing requirements of cloud computing, also a comparison table is presented for all the literature reviewed.
Crosslingual Transfer Learning for Arabic Story Ending Generation Alhussain, Arwa; Azmi, Aqil
The Indonesian Journal of Computer Science Vol. 13 No. 2 (2024): The Indonesian Journal of Computer Science
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v13i2.3831

Abstract

In the field of natural language processing, the task of generating story endings (SEG) requires not only a deep understanding of the narrative context but also the ability to formulate coherent conclusions. This study delves into the use of crosslingual transfer learning to address the challenges posed by the scarcity of Arabic data in SEG, proposing the utilization of extensive English story corpora as a solution. We evaluated the efficacy of multilingual models, such as mBART, mT5, and mT0, in generating Arabic story endings, assessing their performance in both zero-shot and few-shot scenarios. Despite the linguistic complexities of Arabic and the inherent challenges of crosslingual transfer, our findings demonstrate the potential of these multilingual models to transcend linguistic barriers, significantly contributing to the domain of natural language processing across different languages. This research has significant implications for generating creative text and improving multilingual natural language processing in resource-limited language contexts
Pendekatan Transfer Learning Untuk Klasifikasi Tangisan Bayi Dengan Imbalance Dataset Rochadiani, Theresia Herlina
The Indonesian Journal of Computer Science Vol. 13 No. 2 (2024): The Indonesian Journal of Computer Science
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v13i2.3834

Abstract

Klasifikasi tangisan bayi dapat dimanfaatkan untuk mengidentifikasi masalah kesehatan bayi dan memenuhi kebutuhan bayi dengan cepat. Dalam studi ini, teknik transfer learning, dengan model terlatih YAMNet, diterapkan untuk klasifikasi bayi dengan dataset terbatas dan tidak seimbang. YAMNet, sebuah model Convolutional Neural Network khusus untuk analisis audio, mengatasi keterbatasan metode tradisional yang bergantung pada interpretasi manusia. Dengan mempelajari fitur-fitur audio secara otomatis, memungkinkan kinerja klasifikasi yang lebih akurat. Dalam studi ini, dilakukan eksplorasi dan analisis manfaat penggunaan YAMNet, melalui perbandingan dengan model baseline tanpa teknik transfer learning. Hasilnya menunjukkan bahwa model YAMNet tidak hanya nilai akurasinya yang tinggi 0.8106, namun juga nilai skor-F1nya tinggi yaitu mencapai 0.9831. Terbukti bahwa penggunaan transfer learning dapat meningkatkan kinerja dalam klasifikasi tangisan bayi, terutama dalam mengatasi ketidakseimbangan data dan meningkatkan prediksi untuk kelas minoritas.
Analis Sentimen Aplikasi Maskapai Penerbangan Lion Air Menggunakan Metode SVM dan Naïve Bayes Sulistiawati, Risa; Kamayani, Mia
The Indonesian Journal of Computer Science Vol. 13 No. 3 (2024): The Indonesian Journal of Computer Science
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v13i3.3836

Abstract

Lion Air App is a flight ticket purchase application launched on October 21, 2014. It can be downloaded and used anywhere, anytime. Lion Air App application is available on the Google Play Store and also the Appstore, which aims to facilitate users in the process of purchasing airplane tickets online. online. In several news articles reporting that Lion Air is the world's worst airline. in the world. However, it needs to be realized that the Lion Air application also has many users who give positive, negative and neutral reviews due to several factors. neutral due to the existence of several reviews presented in the Play Store application. This problem was researched for sentiment analysis to get a customer satisfaction rating for the Lion Air application. Lion Air application with the acquisition of 2000 data. In this research, Support Vector Machine (SVM) calculation and Naive Bayes calculation were compared using 80% training ratio and 20% test ratio. In this consideration, 795 positive opinions and 805 negative opinions were used. used, where Support Vector Machine (SVM) with Bigram features became the most superior method with 99.23% precision. method with 99.23% precision, 83.03% recall, 91.75% accuracy, F-1 score of 90.51%.         
A Monte Carlo Simulation study on the Gamma Radiation Shielding Properties of Concrete with PET Plastic Composite using the PHITS Code Aye, Myat Mon; Ko, Thaw Tun
The Indonesian Journal of Computer Science Vol. 13 No. 2 (2024): The Indonesian Journal of Computer Science
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v13i2.3838

Abstract

The gamma radiation shielding properties of four different types of PET concretes, containing 0 %, 5 %, 10 %, and 15 % PET additives, were simulated using the PHITS code. The simulation covered photon energy levels ranging from 0.01 to 1.5 MeV and employed a NaI (Tl) scintillation detector. Parameters such as the linear attenuation coefficient (LAC), mass attenuation coefficient (MAC), half-value layer (HVL), and mean free path (MFP) were calculated to evaluate the gamma-ray attenuation for each photon energy level. The effectiveness of PET plastics as a radiation shield depends on factors like material thickness, the type of radiation, and specific application requirements. However, this research provides valuable insights into repurposing waste PET plastics to enhance the radiation-shielding properties of concrete, contributing to improved waste management practices and the development of radiation-shielding materials. The results obtained from the PHITS code align satisfactorily with both the simulation results and the theoretical XCOM data.
Deep Learning Based Security Schemes for IoT Applications: A Review Othman, Mina; askar, shavan; Ali, Daban; Ibrahim, Media Ali; Abdullah, Nihad
The Indonesian Journal of Computer Science Vol. 13 No. 2 (2024): The Indonesian Journal of Computer Science
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v13i2.3839

Abstract

Due to its widespread perception as a crucial element of the Internet of the future, the Internet of Things (IoT) has garnered a lot of attention in recent years. The Internet of Things (IoT) is made up of billions of sentients, communicative "things" that expand the boundaries of the physical and virtual worlds. Every day, such widely used smart gadgets generate enormous amounts of data, creating an urgent need for rapid data analysis across a range of smart mobile devices. Thankfully, current developments in deep learning have made it possible for us to solve the issue tastefully. Deep models may be built to handle large amounts of sensor data and rapidly and effectively learn underlying properties for a variety of Internet of Things applications on smart devices. We review the research on applying deep learning to several Internet of Things applications in this post. Our goal is to provide insights into the many ways in which deep learning techniques may be used to support Internet of Things applications in four typical domains: smart industrial, smart home, smart healthcare, and smart transportation. One of the main goals is to seamlessly integrate deep learning and IoT, leading to a variety of novel ideas in IoT applications, including autonomous driving, manufacture inspection, intelligent control, indoor localization, health monitoring, disease analysis, and home robotics. We also go over a number of problems, difficulties, and potential avenues for future study that make use of deep learning (DL), which is turning out to be one of the most effective and appropriate methods for dealing with various IoT security concerns. The goal of recent research has been to enhance deep learning algorithms for better Internet of Things security. This study examines deep learning-based intrusion detection techniques, evaluates the effectiveness of several deep learning techniques, and determines the most effective approach for deploying intrusion detection in the Internet of Things. This study uses Deep Learning (DL) approaches to better expand intelligence and application skills by using the large quantity of data generated or acquired. The many IoT domains have drawn the attention of several academics, and both DL and IoT approaches have been explored. Because DL was designed to handle a variety of data in huge volumes and required processing in virtually real-time, it was indicated by several studies as a workable method for handling data generated by IoT.
Image Copyright Protection Based on Blockchain Technology Review Ali, Daban; Askar, Shavan; saleem, mohammed; Othman, Mina; Omer, Saman M.
The Indonesian Journal of Computer Science Vol. 13 No. 2 (2024): The Indonesian Journal of Computer Science
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v13i2.3840

Abstract

On a daily basis, a significant number of individuals distribute several photos and videos that have been marginally modified from the original material produced by copyright owners, such as photographers, graphic designers, and video producers. Individuals that infringe upon the rights of others, lacking the legal authority to access multimedia content, employ various digital image and picture manipulation techniques, it involves converting to gray scale, trimming, rotating, contracting the frame, and adjusting the background speed, to modify said content. Blockchain technology obviates the necessity of an intermediary, hence circumventing the possibility of a singular point of failure. Infractions to copyright poses a significant barrier to protecting commercial image and video information. The IPFS blockchain technology offers on-chain preservation for copyright information and off-chain storing for distinct multimedia files. The enhanced perceptual hashing algorithm significantly enhances the precision of identifying connections to identify digital image piracy. The photographers and designers that submit their photographs on websites are experiencing significant dissatisfaction due to a prevalent practice in which others attempt to claim credit and profit from the initial creator's effort.
The Industrial Internet of Things (IIoT) and its roles in the Fourth Industrial Revolution: A review Saleem, Mohammed; askar, shavan; Ibrahim, Media Ali; Othman, Mina; Abdullah, Nihad
The Indonesian Journal of Computer Science Vol. 13 No. 2 (2024): The Indonesian Journal of Computer Science
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v13i2.3841

Abstract

The Industrial Internet of Things and Industry 4.0 are now two highly sought-after areas of research and development, attracting significant interest from both academic and industrial sectors. The two ideas, Industry 4.0 and IIoT, share significant similarities, with Industry 4.0 being seen as the use of IIoT specifically in the automation and manufacturing sectors. Within the framework of the present Industry 4.0 paradigm, many growth pathways have emerged, collectively leading to notable enhancements in terms of efficiency, flexibility, communication, adaptability, customization, and modularity in the industrial sector. The Industry 4.0 is rapidly evolving within the framework of the Industrial Internet of Things (IIoT), and the authors are recognizing the necessity for a comprehensive and in-depth overview of the many research areas that are currently expanding. The area will remain intriguing in the foreseeable future due to its significant potential for enhancing the existing industrial technologies. An exhaustive evaluation of the current systems in the automotive sector, emergency response, and chain management on IIoT has been conducted, revealing that IIoT has been widely adopted across several technological domains. Industry 4.0 is the term used to describe the present automation and data sharing trend in businesses. Presently, there is a dearth of agreement about the assessment of an organization's readiness for Industry 4.0. Industry 4.0 encompasses a diverse array of digital technologies that profoundly influence industrial enterprises. The literature on Industry 4.0 has had significant exponential growth during the previous decade. The results of our research confirm the idea of Industry 4.0 as a concept that goes beyond the Smart Manufacturing sector, hence opening up possibilities for collaboration with other interconnected disciplines.
Deep Learning in Medical Image Analysis Article Review Ibrahim, Media Ali; askar, shavan; saleem, Mohammad; Ali, Daban; Abdullah, Nihad
The Indonesian Journal of Computer Science Vol. 13 No. 2 (2024): The Indonesian Journal of Computer Science
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v13i2.3842

Abstract

Transfer learning, in evaluation to common deep studying strategies which include convolutional neural networks (CNNs), stands proud due to its simplicity, efficiency, and coffee education value, efficaciously addressing the venture of restricted datasets. The importance of scientific picture analysis in both scientific research and medical prognosis can't be overstated, with image techniques like Computer Tomography (CT), Magnetic Resonance Image (MRI), Ultrasound (US), and X-Ray playing a crucial function. Despite their utility in non-invasive analysis, the scarcity of categorized medical images poses a completely unique challenge in comparison to datasets in other pc imaginative and prescient domains, like facial reputation. Given this shortage, switch getting to know has won reputation amongst researchers for medical photo processing. This complete evaluation draws on one hundred amazing papers from IEEE, Elsevier, Google Scholar, Web of Science, and diverse sources spanning 2000 to 2023 It covers vital components, which includes the (i) shape of CNNs, (ii) foundational know-how of switch learning, (iii) numerous techniques for enforcing transfer mastering, (iv) the utility of switch gaining knowledge of throughout numerous sub-fields of medical photo analysis, and (v) a dialogue at the future potentialities of transfer studying within the realm of medical image analysis. This evaluate no longer handiest equips beginners with a scientific understanding of transfer mastering applications in medical image analysis but additionally serves policymakers by means of summarizing the evolving trends in transfer learning within the scientific image domain. This insight might also encourage policymakers to formulate advantageous rules that support the continued development of Transfer learning knowledge of in medical image analysis.

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