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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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Hepatitis Diagnosis: A Comprehensive Review of Machine Learning Classification Algorithms Saleem, Hayveen
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.v13i3.3966

Abstract

Hepatitis is a liver-related medical disorder caused by inflammation, often caused by hepatitis virus infection or an unknown source. There are five primary hepatitis viruses: A, B, C, D, and E. Machine learning (ML) algorithms have emerged as a promising tool for hepatitis diagnosis, leveraging vast datasets and complex patterns. This review examines the application of ML classification algorithms in hepatitis diagnosis, focusing on challenges faced in traditional diagnostic approaches and the potential of ML techniques to address these. Various ML algorithms, including decision trees, support vector machines, neural networks, Naïve Bayes, random forest, K-nearest neighbor, and logistic regression and ensemble methods, are analyzed for their efficacy in hepatitis classification tasks. Key considerations such as data preprocessing, feature selection, and performance evaluation are also discussed. The review aims to provide clinicians, researchers, and healthcare stakeholders with a comprehensive understanding of ML algorithms' role in hepatitis diagnosis and improving patient outcomes.
Uji Kelayakan Sistem Informasi Berbasis Web Pada Kasus Penyakit Mulut dan Kuku Nazhifah, Sri Azizah; Basri, Fazil; Muslim, Muslim; Putri, Andrini
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.v13i3.3967

Abstract

Foot and Mouth Disease (FMD) is a viral infection in animals that is contagious and acute. However, public access to information and visualization of the spread of FMD and vaccination in Nagan Raya District is still limited. The reporting of FMD cases by the public still relies on village authorities, leading to inaccuracies in information and field validation constraints. Additionally, difficulties in registering livestock vaccinations are caused by inaccuracies in data and livestock location, hindering the Animal Husbandry Department in providing doses and determining the route to these locations. Therefore, this research aims to build a WebGIS that includes visualization of FMD spread, FMD case reporting, and vaccination registration. This WebGIS is developed using Laravel and Leaflet, tested with validation, reliability, and usability questionnaires. Users include the general public, the Animal Husbandry Department, and medical professionals. The results show that the WebGIS has an 89.3% feasibility percentage and is highly suitable. It can facilitate access to FMD information and visualization and streamline reporting and vaccination registration.
Performance Evaluation of Physical Properties on Zinc Sulfide (ZnS)-based Field Effect Transistor Mya Su Kyi; Aye, Maung; 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.v13i3.3968

Abstract

The paper presents the performance evaluation of physical properties on Zinc Sulfide (ZnS)-based Field Effect Transistor. The most famous III-V compound-based semiconductor devices have several affected to the environment and the toxic contents are directly responded to the society. Due to the lack of technology on nontoxic compound-based semiconductor device fabrications, the novel device with II-VI compound materials are challenging issues for the environments. The specific objectives of doing research on fabrication of II-VI compound-based semiconductor devices in advanced laboratories are to emphasize the numerical modeling of the device structure and designing the FET based on ZnS material, to contribute the mathematical model for physical characteristics of the FET structure and the modification of the device structure will be easily established by using numerical simulation. The mathematical analyses on physical properties of device structure with ZnS material are confirmed and observed the several properties of electrical and electronic characteristics. The detailed implementations for ZnS-based FET devices are performed and evaluated the performance of the developed FET devices. There are two steps analyses in physical properties of ZnS-based FET devices with numerical implementation by MATLAB languages. The results observed in this study could be confirmed with the recent works from several research laboratories and the developed ZnS-based FET devices could be utilized in high performance wide band applications on switching in the power electronics and amplification purposes in modern amplifier design in real world applications.
Pengaruh Pengetahuan Kewirausahaan dan Media Sosial Terhadap Motivasi Berwirausaha Mahasiswa Vivi Oktavia; Ganefri; Asmar Yulastri; Nizwardi Jalinus; Jonni Mardizal
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.v13i3.3969

Abstract

Kewirausahaan di Indonesia mendapat perhatian sebagai cara untuk merangsang pertumbuhan ekonomi dan menciptakan pekerjaan, khususnya di kalangan lulusan perguruan tinggi, dimana tingkat pengangguran masih tinggi. Terlepas dari upaya yang dilakukan, masih ada kesenjangan antara persiapan mahasiswa dan kesiapan berwirausaha yang sesungguhnya. Penelitian ini bertujuan untuk menganalisis pengaruh pengetahuan kewirausahaan, dan penggunaan media sosial, terhadap motivasi berwirausaha mahasiswa di Universitas Negeri Padang. Menggunakan metode survei dengan kuesioner yang disebar secara online kepada 40 mahasiswa, penelitian ini mengumpulkan data yang kemudian dianalisis dengan regresi linear berganda menggunakan SPSS 29. Hasil uji normalitas Kolmogorov-Smirnov menunjukkan data berdistribusi normal, memungkinkan analisis lebih lanjut. Hasil penelitian menunjukkan bahwa pengetahuan kewirausahaan dan penggunaan media sosial secara signifikan meningkatkan motivasi berwirausaha. Dengan pemahaman yang lebih baik tentang faktor-faktor ini, institusi pendidikan dapat merancang program yang lebih efektif untuk mendorong kewirausahaan di kalangan mahasiswa, mengoptimalkan peran media sosial sebagai alat pendorong kesiapan wirausaha.
Efektivitas Pembelajaran Science Technology Engineering and Math (STEM) terhadap Hasil Belajar di Sekolah: Meta Analisis Siregar, Winda Lestari; Syafrijon, Syafrijon; Giatman, Giatman; Ganefri, Ganefri; Krismadinata, Krismadinata; Jalinus, Nizwardi
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.v13i3.3972

Abstract

Education at the high school (SMA) and vocational school (SMK) levels faces challenges in preparing creative and competent students. To address these challenges, a combination of technology is needed in the fields of science, mathematics, and engineering. A study on the implementation of STEM (Science, Technology, Engineering, and Mathematics) in learning in Indonesia. To test its effectiveness, a meta-analysis was conducted which included 25 articles published between 2018 and 2023. The findings showed that STEM learning has a significant role in improving student learning outcomes with an overall effect size of 6,89. Based on the data analysis of the application of the STEM approach, it can develop the ability to solve problems rationally, conduct research, question, collaborate, criticize, analyze and can also improve the ability to access information, adapt to change, make decisions, produce, be responsible, show curiosity, interact in a social and cultural context, and develop leadership and entrepreneurship traits in students. The STEM approach provides a solid foundation for developing a variety of skills and attitudes that are important in facing the challenges and demands of the modern world of students.
Sentiment Analysis on the FIFA U-20 World Cup in Argentina Using Support Vector Machine Warsito Sujatmiko, Achmad; Vitianingsih, Anik Vega; Kacung, Slamet; Cahyono, Dwi; Lidya Maukar, Anastasia
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.v13i3.3973

Abstract

The decision made by FIFA regarding the selection of the soundtrack and the host country for the FIFA U-20 World Cup has sparked emotional reactions among the public and raised concerns about the event, especially on social media platform X. This is due to FIFA’s decision to choose a soundtrack not from the host country, Argentina, but from the previous host, Indonesia. FIFA should advocate for the creation of a soundtrack by the host country to reflect its distinctive characteristics or atmosphere. Concerns about the U-20 World Cup in Argentina have also been fueled by the country’s economic crisis, which is feared to affect the facilities and infrastructure for the young players representing their nations. This research focuses on filtering public responses to FIFA’s decisions regarding the soundtrack selection and the host country for the U-20 World Cup into positive, neutral, and negative categories using the Support Vector Machine (SVM) method. The research aims to provide policy recommendations regarding the host selection process and cultural representation in international sports events. Additionally, this study is expected to provide a deeper understanding of the preferences and values held by the public regarding international sports. The research steps include data collection, pre-processing, labeling, weighting, and classification using a Support Vector Machine. The data for this research were obtained through crawling on social media platform X, totaling 2400 data points. The performance evaluation of the SVM algorithm using a 50:50 ratio of training and testing data yielded an average accuracy of 85.71%, Precision of 85.98%, Recall of 85.71%, and F1-score of 85.58%.
Ocular Disease Recognition Based on Deep Learning: A Comprehensive Review Jameel, Dilan; 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.v13i3.3976

Abstract

This review article represents a major advance in the field of medical imaging and ophthalmology by exploring the critical role of deep learning in the detection and diagnosis of eye diseases. Early and accurate diagnosis becomes essential due to the frequency of ocular disorders that pose a significant risk to vision, including diabetic retinopathy, age-related macular degeneration, glaucoma, and cataracts. The need for more reliable automated solutions is highlighted by the limitations of traditional methods, despite their benefits, which include reliance on small datasets and manual feature analysis. Deep learning, a subset of machine learning, is becoming evident as a powerful tool that can interpret complex medical images and improve diagnostic accuracy without the need to extract human features. This article explores the evolution of deep learning applications in ophthalmology, highlighting the difficulties encountered such as interpretability of models and data quality and the creative solutions that have been found to overcome them. We highlight the revolutionary impact of deep learning in eye disease detection through an in-depth analysis of recent developments, providing insight into potential future research avenues that may further improve patient care in ophthalmology.
The Feasibility Study of a Solar Power Plant at Building B, Universitas Multimedia Nusantara Saputri, Fahmy
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.v13i3.3977

Abstract

The use of environmentally friendly energy resources is becoming increasingly important in reducing the carbon footprint of buildings. One approach is to integrate solar power generation into the building's energy system. This research aims to reduce the carbon footprint by replacing conventional electricity sources with environmentally friendly renewable energy sources and integrating energy sources without disrupting the productivity of activities in Building B of Universitas Multimedia Nusantara (UMN). This study encompasses several key parameters, including the limited use of solar panels in Building B of UMN and simulations conducted using PVsyst software with shadow-free assumptions and solar panel orientation facing north. Additionally, the research considers the potential production of clean energy from available utilization areas, including the roof and fifth-floor balcony of Building B. This feasibility study also includes an analysis of electricity usage over three months, with electricity consumption sourced from PLN. The analysis results show that the use of solar panels can yield long-term economic benefits, with capital costs recoverable within 20 years. However, further review is needed regarding potential system losses, costs, and the addition of energy storage options. Cost calculations need to be adjusted to consider building owner profitability for more accurate results. This study only considers profits without accounting for repair and maintenance costs over the 20-year system operation period.
Enhancing Agricultural Efficiency: Deep Learning-Based Soil Crack Detection for Water Irrigation Myint, Khin Moe; Aye, Maung; 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.v13i3.3979

Abstract

The escalating demand for agricultural precision and environmental monitoring underscores the necessity for effective soil crack detection methods. This study explores the feasibility of employing a Raspberry Pi-powered camera system and deep learning image recognition to detect soil cracks and control agricultural irrigation. The purpose is to develop a soil crack detection system using deep learning techniques, sustain plant growth process, increase productivity, and optimize water irrigation practice. Our approach leverages TensorFlow to craft a convolutional neural network tailored specifically for execution on a Raspberry Pi 3B+. A dataset comprises manually captured images and is trained with the InceptionV3 model categorized into crack or nocrack classes. The accuracy is achieved ranging from 97% to 99%. These results underscore deep learning image recognition models on Raspberry Pi for cost-effective soil crack monitoring and controlling the plants watering system.
Fashion Design Classification Based on Machine Learning and Deep Learning Algorithms: A Review Shushi, Ahmed; Adnan M. 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.v13i3.3980

Abstract

Integration of machine learning algorithms in fashion design classification brought transformational change by allowing the automated analysis, categorization, and prediction of fashion items based on different attributes of the item. This paper reviews the state-of-the-art in fashion design classification through machine learning techniques. Review of literature, methodology, and challenges in this area indicate that an array of algorithms and methods, stretching from traditional machine learning algorithms to convolutional neural networks and further to transfer learning approaches, is being tried and tested. In this paper, I will discuss performance comparison among several machine learning algorithms, pinning their strengths, limitations, and possible applications in the fashion industry. We further elaborate on crucial challenges, such as touching on the issue of data variability, interpretability, and others on ethical consideration issues, all pointing to the need for fairness and sustainability with respect to the representation of reality in algorithmic decision-making. This paper aims to inform researchers, practitioners, and stakeholders of the opportunities and challenges brought about by the use of machine learning in a fast-paced world like fashion, hence demystifying current directions in landscape classification of fashion design.

Page 57 of 113 | Total Record : 1127

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