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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 1,127 Documents
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Analisis Efektivitas Model Pembelajaran Problem Based Learning Dalam Pendidikan Vokasi Berbasis Meta-Analysis Marta, Rizkayeni; Ambiyar; Wulansari, Rizky Ema; Yulianti, Rosi
The Indonesian Journal of Computer Science Vol. 13 No. 3 (2024): The Indonesian Journal of Computer Science (IJCS)
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v13i3.4056

Abstract

The Problem-Based Learning (PBL) model has become a major focus in vocational education, promising an integrated and contextual approach to problem-oriented learning. This research aims to conduct a meta-analysis of existing scientific publications to gain a deeper understanding of the effectiveness of the PBL model in vocational education. The results of the analysis indicate that the success of PBL implementation in vocational education is significantly influenced by several variables, including appropriate learning design, support from instructors/facilitators, and interactions among learners. With a deeper understanding of the factors affecting the effectiveness of PBL in vocational education, it is expected that this learning model can be improved and developed more optimally, thereby making a more significant contribution to enhancing learning outcomes, skills, and the readiness of learners to enter the workforceThe Problem-Based Learning (PBL) model has become a major focus in vocational education, promising an integrated and contextual approach to problem-oriented learning. This research aims to conduct a meta-analysis of existing scientific publications to gain a deeper understanding of the effectiveness of the PBL model in vocational education. The results of the analysis indicate that the success of PBL implementation in vocational education is significantly influenced by several variables, including appropriate learning design, support from instructors/facilitators, and interactions among learners. With a deeper understanding of the factors affecting the effectiveness of PBL in vocational education, it is expected that this learning model can be improved and developed more optimally, thereby making a more significant contribution to enhancing learning outcomes, skills, and the readiness of learners to enter the workforce
Financial Fraud Detection Based on Machine and Deep Learning: A Review Rojan, Zaki
The Indonesian Journal of Computer Science Vol. 13 No. 3 (2024): The Indonesian Journal of Computer Science (IJCS)
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v13i3.4059

Abstract

Financial fraud detection is crucial for protecting the integrity of financial markets and institutions globally. Recent advancements in machine learning (ML) and deep learning (DL) have dramatically enhanced the ability to detect and prevent fraudulent activities across various sectors. This review paper examines the implementation of ML and DL in fraud detection, highlighting the evolution from traditional methods to sophisticated models like neural networks, including Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs). We explore different ML techniques such as supervised, unsupervised, and hybrid approaches, their effectiveness in handling large, imbalanced datasets, and their application in real-world scenarios. Special attention is given to the integration of technologies like blockchain and IoT with AI to innovate fraud detection frameworks. Despite the promising advancements, challenges remain, such as the need for large volumes of labeled data, potential model bias, and the black-box nature of many deep learning models. Future directions focus on enhancing model transparency, addressing privacy concerns, and expanding the use of federated learning. This review aims to demonstrate the effectiveness of current technologies and encourage their adoption in enhancing global financial security
Dampak Kontrol Fokus, Keberanian, Keterbukaan terhadap Kinerja Akademik Siswa Implikasi bagi Dunia Pendidikan Teknik Sisrayanti; Ambiyar; Lapisa, Remon; Sabrina, Elsa
The Indonesian Journal of Computer Science Vol. 13 No. 3 (2024): The Indonesian Journal of Computer Science (IJCS)
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v13i3.4060

Abstract

Education is a crucial pillar in shaping individuals and organizational success. The interconnectedness between them is a primary focus in enhancing the quality of education and human resource development. This research examines the relationship between psychological factors such as Kontrol Fokus, Keberanian, and openness with Kinerja Akademik Siswa. In an ever-evolving era, a deeper understanding of these psychological factors is essential. It is hoped that this study provides insight into how Kontrol Fokus aids in task management, the role of Keberanian in facing challenges, and how openness stimulates innovative thinking and adaptation, thereby facilitating shared goals. The research's conclusion emphasizes the importance of close collaboration in education to tackle complex challenges and achieve future success. Understanding individual psychology is also key to optimizing human potential and organizational goals attainment.
Machine Learning Classification Algorithms-Based Smart Cities Applications: A Review Ismail, Shayma; , Adnan Mohsin Abdulazeez
The Indonesian Journal of Computer Science Vol. 13 No. 3 (2024): The Indonesian Journal of Computer Science (IJCS)
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v13i3.4061

Abstract

Smart cities leverage advanced technologies and data-driven solutions to enhance the quality of life for residents, improve efficiency, and promote sustainable development. Machine learning (ML) plays a crucial role in enabling smart city applications by providing the capability to autonomously analyze complex, unstructured data and make informed decisions. This review paper provides an overview of the use of ML classification algorithms for various smart city applications. It begins by introducing the concept of smart cities and the role of ML in optimizing urban systems. The background theory section discusses the key features of smart cities, such as mobility, economy, people, living, environment, and governance. The paper then delves into specific applications of ML classification algorithms in smart cities, including traffic management, healthcare models, and energy storage systems. It highlights how ML-based approaches can tackle challenges in these domains, such as traffic congestion, emergency response, dynamic hospital environments, and renewable energy integration. Overall, this review underscores the significance of ML classification techniques in enhancing the functionality and efficiency of smart city infrastructure. It also identifies research gaps and areas for further exploration to fully harness the potential of ML in driving sustainable urban development.
A Review on Utilizing Data Mining Techniques for Chronic Kidney Disease Detection Hassan, Shivan Hussein; Abdulazeez, Adnan Mohsin
The Indonesian Journal of Computer Science Vol. 13 No. 3 (2024): The Indonesian Journal of Computer Science (IJCS)
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v13i3.4062

Abstract

This comprehensive study delves into the application of machine learning (ML) and data mining techniques for the prognosis and diagnosis of Chronic Kidney Disease (CKD), a significant global health concern characterized by the gradual loss of kidney function. Through a detailed examination of various predictive models, the research evaluates the efficacy of different ML algorithms and data mining methodologies in classifying and diagnosing CKD. Utilizing datasets from the UCI machine learning repository and other sources, this study explores a range of ML algorithms-including logistic regression, decision trees, support vector machines, random forest, and deep learning networks-alongside feature selection techniques to enhance prediction accuracy and facilitate early diagnosis. Despite facing challenges such as dataset limitations and the need for external validation, the findings reveal remarkable potential in using ML and data mining to improve CKD diagnosis, with some models achieving accuracy rates exceeding 99%. The research underscores the critical role of technology in advancing CKD diagnosis and management, paving the way for more personalized and effective healthcare solutions.
Analisis Sentimen Ulasan Kepuasan Pengguna Aplikasi BSI Mobile Dengan Menggunakan Naive Bayes Syakura, Rais Abdan; Wiranata, Ade Davy
The Indonesian Journal of Computer Science Vol. 13 No. 3 (2024): The Indonesian Journal of Computer Science (IJCS)
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v13i3.4063

Abstract

The BSI mobile application is a digital platform provided by BSI bank for carrying out financial transactions via smartphone. The BSI application does not only carry out transactions, however, it provides sharia features that are adapted to Islamic sharia principles such as sharia investment, zakat and infaq and so on. The methodological process consists of collecting data from 998 reviews which are used as a dataset, preprocessing with the aim of cleaning the data using several stages. Labeling is done by analyzing the sentiment of reviews on the dataset by dividing the data 60% to 40%, resulting in a positive value of 98% and a negative value of 2%. The implementation of the Naive Bayes algorithm in this research resulted in a confusion matrix evaluation with accuracy of 98.71%, precision of 99.22% and recall of 99.48%. This research aims to improve applications through reviews on Google Playstore by analyzing sentiment so that the service and quality of their applications match user feedback.
Deep Learning Classification Algorithms Applications: A Review Kittani, Toreen; Albrifkani, Adnan Mohsin Abdulazeez
The Indonesian Journal of Computer Science Vol. 13 No. 3 (2024): The Indonesian Journal of Computer Science (IJCS)
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v13i3.4064

Abstract

This paper examines the recent articles on classification tasks, particularly focusing on deep learning Algorithms. The process of categorizing data into distinct classes based on specific features is essential for tasks such as image recognition, sentiment analysis, disease diagnosis, and more. This article the fundamental concepts of deep learning, including neural network architectures like Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and their variants. It explores the significance of feature selection techniques in improving classification model performance. Additionally, this article provides a detailed literature review, aiming to foster the development of more effective and efficient classification algorithms and methodologies and highlighting their applications in fields such as healthcare, agriculture, disaster response, and beyond. Through this review, this article underscores the transformative impact of deep learning approaches in enabling automated decision-making, pattern recognition, and data-driven insights, offering valuable insights for researchers, practitioners, and policymakers involved in classification tasks, this article aims to facilitate the development of more effective and efficient classification algorithms and methodologies.
Design and Analysis of Load Frequency Control for a Two-Area Power System Using Conventional PID and FPID Controllers Win, Khin Thu Zar; Hlaing, May Su; Hla, Tin Tin
The Indonesian Journal of Computer Science Vol. 13 No. 3 (2024): The Indonesian Journal of Computer Science (IJCS)
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v13i3.4066

Abstract

Power system stability is required to maintain a continuous balance between power generation and load demand. Frequency control is also a major function of automatic generation control and one of the most important control problems in power system design and operation. So, a robust control system must be implemented for controlling the actual power and frequency. This paper mainly focuses on the design of a two-area power system with a rated frequency of 50 Hz, which is used in Myanmar, using the fuzzy-PID method to control the load frequency. It also aims to imply from one area to another when demand suddenly increases or decreases. It involves applying mathematical formulas and models to analyze how an automatic generation control works. The plan is first implemented in a single area before being modified to a two-area power system with and without controllers. The output results are simulated with MATLAB/ SIMULINK.
Transfer Learning in Machine Learning: A Review of Methods and Applications Ali, Ali Hamad; Abdulazeez, Adnan Mohsin
The Indonesian Journal of Computer Science Vol. 13 No. 3 (2024): The Indonesian Journal of Computer Science (IJCS)
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v13i3.4068

Abstract

Transfer learning has gained significant traction and popularity in the field of machine learning due to its wide range of potential applications. This review article offers a thorough examination of transfer learning techniques and their wide-ranging applications in several fields. This text provides a thorough evaluation of the literature, focusing on important research and the methodology used. Furthermore, a comparative table highlighting transfer learning research across several areas provides valuable insights into the wide range of applications. The inclusion criteria were centred on recent articles published within the past five years that comprehensively examined transfer learning methodologies, applications, frameworks, problems, and future directions. The review articles highlight the widespread use of transfer learning models, the effectiveness of data augmentation strategies, and the capability of transfer learning to tackle issues particular to different domains. Nevertheless, some constraints like as biases in the dataset, difficulties in interpreting the model, and problems with scalability have been recognised. These limitations provide opportunities for future research to focus on creating transfer learning algorithms that are more resilient and easier to read.
Dinamika Opini Publik Indonesia terhadap Krisis Rohingya dalam Perspektif Waktu menggunakan Traditional Machine Learning dan Deep Learning Istiqomah, Relaci Aprilia; Budi, Indra
The Indonesian Journal of Computer Science Vol. 13 No. 3 (2024): The Indonesian Journal of Computer Science (IJCS)
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v13i3.4069

Abstract

The Rohingya are an ethnic minority who currently still face persecution and discrimination in Myanmar, so they have to flee to neighboring countries, such as Indonesia. However, the polemic regarding the issue of the existence of Rohingya refugees in Indonesia still shows that there are differences of opinion between groups who support and oppose it. For this reason, this research aims to determine the dynamics of Indonesian public opinion regarding the Rohingya from 2015-2023 via Twitter, as well as find out the topics that are often discussed each year using LDA. This research compares classification methods using traditional machine learning algorithms (NB, SVM, LR, and DT) and deep learning algorithms (LSTM, GRU, LSTM-GRU, and GRU-LSTM). The research results show that the traditional machine learning algorithm, LR, has the highest accuracy. There has been a change in sentiment from initially being dominated by positive sentiment to negative sentiment which is more dominant in the last five years. The topics that are often discussed for positive sentiment are the support of the Indonesian people for the Rohingya in providing assistance and shelter, while the negative topics are related to concerns about the social, economic, and security impacts that may be caused by the presence of Rohingya refugees.

Page 62 of 113 | Total Record : 1127

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