cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
Tri A. Sundara
Contact Email
tri.sundara@stmikindonesia.ac.id
Phone
+628116606456
Journal Mail Official
ijcs@stmikindonesia.ac.id
Editorial Address
Jalan Khatib Sulaiman Dalam 1, Padang, Indonesia
Location
Kota padang,
Sumatera barat
INDONESIA
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
 62   63   64   65   66 
Sentiment Analysis Based on Machine Learning Techniques: A Comprehensive Review Hamid, Ari Ibrahim; Abdulazeez, Adnan Mohsin
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.v13i3.4049

Abstract

In the landscape of digital communication, sentiment analysis stands out as a pivotal technology for deciphering the vast troves of unstructured text generated online. When integrated with machine learning, sentiment analysis transforms into a powerful tool capable of distilling insights from complex human emotions and opinions expressed across social media, reviews, and forums. This review paper embarks on a thorough exploration of the integration of machine learning techniques with sentiment analysis, shedding light on the latest advancements, challenges, and applications spanning various sectors including public health, finance, and consumer behavior. It meticulously examines the role of machine learning in elevating sentiment analysis through improved accuracy, adaptability, and depth of analysis. Furthermore, the paper discusses the implications of these technologies in understanding consumer sentiment, tracking public health trends, and forecasting market movements. By synthesizing findings from seminal studies and cutting-edge research, this review not only charts the current landscape but also forecasts the trajectory of sentiment analysis. It underscores the necessity for ongoing innovation in machine learning models to keep pace with the evolving digital discourse. The insights presented herein aim to guide future research endeavors, highlight the transformative impact of machine learning on sentiment analysis, and outline the potential for new applications that could benefit society at large.
Perancangan Perancangan Evaluasi Proses Pembelajaran Untuk Kurikulum Merdeka Fitri Rahmadani, Ade; Yudhi Diputra
The Indonesian Journal of Computer Science Vol. 13 No. 4 (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.v13i4.4054

Abstract

This study aims to produce a learning evaluation system on the independent curriculum for State Junior High School 5 Pelepat. This system can help schools in the process of capturing and processing value data that still uses tools and uses manual processing methods. In the development of this learning evaluation system, the SDLC (System Development Lifecycle) method is used with a prototype model. Field study methods and literature studies are used for data collection. This information system is created using Microsoft Excel. System testing is carried out by testing aspects of functionality and usability using the black box testing test method. The results of the information system test developed obtained a functionality value of 1 (Very Good), and usability aspect testing obtained results with a percentage of 85% (Very Decent). So it can be concluded that the system built has succeeded in helping schools overcome manual problems in processing independent and feasible curriculum values and ready to use based on the results of the tests carried out.
Perbandingan Algoritma Naïve bayes Dan Support Vektor Machine Untuk Klasifikasi Status Stunting Pada Balita Muh. Faried Muchtar; Rahma Laila; Dwi Shinta; H. M. Yazdi Pusadan
The Indonesian Journal of Computer Science Vol. 13 No. 4 (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.v13i4.4055

Abstract

Penelitian ini bertujuan untuk membandingkan efektivitas algoritma Naïve Bayes dan Support Vector Machine (SVM) dalam klasifikasi status stunting pada balita. Stunting merupakan kondisi pertumbuhan terhambat pada balita akibat kekurangan gizi yang memiliki dampak serius terhadap kesehatan dan perkembangan anak. Dengan menggunakan data dari Puskesmas Tawaeli Kecamatan Tawaeli, penelitian ini mengimplementasikan kedua algoritma untuk mengidentifikasi balita yang mengalami stunting. Metode penelitian meliputi pengumpulan data, preprocessing, dan pengujian menggunakan metrik evaluasi yang sesuai. Hasil penelitian diharapkan dapat memberikan kontribusi dalam pengembangan metode klasifikasi stunting pada balita serta memberikan wawasan baru dalam penanganan masalah stunting pada tingkat populasi. Diharapkan penelitian ini dapat menjadi referensi bagi peneliti selanjutnya dalam pengembangan sistem informasi serupa.
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
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
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
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
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
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
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
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.

Page 64 of 117 | Total Record : 1170

 62   63   64   65   66 

Filter by Year

2022 2026


Reset
Filter By Issues
All Issue Vol. 15 No. 2 (2026): The Indonesian Journal of Computer Science Vol. 15 No. 1 (2026): The Indonesian Journal of Computer Science Vol. 14 No. 6 (2025): The Indonesian Journal of Computer Science Vol. 14 No. 5 (2025): The Indonesian Journal of Computer Science Vol. 14 No. 4 (2025): The Indonesian Journal of Computer Science Vol. 14 No. 3 (2025): The Indonesian Journal of Computer Science Vol. 14 No. 2 (2025): The Indonesian Journal of Computer Science Vol. 14 No. 1 (2025): The Indonesian Journal of Computer Science Vol. 13 No. 6 (2024): The Indonesian Journal of Computer Science Vol. 13 No. 5 (2024): The Indonesian Journal of Computer Science Vol. 13 No. 4 (2024): The Indonesian Journal of Computer Science Vol. 13 No. 3 (2024): The Indonesian Journal of Computer Science Vol. 13 No. 2 (2024): The Indonesian Journal of Computer Science Vol. 13 No. 1 (2024): The Indonesian Journal of Computer Science Vol. 12 No. 6 (2023): The Indonesian Journal of Computer Science Vol. 12 No. 5 (2023): The Indonesian Journal of Computer Science Vol. 12 No. 4 (2023): The Indonesian Journal of Computer Science Vol. 12 No. 3 (2023): The Indonesian Journal of Computer Science Vol. 12 No. 2 (2023): The Indonesian Journal of Computer Science Vol. 12 No. 1 (2023): The Indonesian Journal of Computer Science Vol. 11 No. 3 (2022): The Indonesian Journal of Computer Science Vol. 11 No. 2 (2022): The Indonesian Journal of Computer Science Vol. 11 No. 1 (2022): The Indonesian Journal of Computer Science More Issue