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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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Enhancing Problem-Solving Learning Models: A Review from the Lens of Independent Learning in the Post-Pandemic Era Elsa Sabrina; Ambiyar; Wulansari, Rizky Ema
The Indonesian Journal of Computer Science Vol. 13 No. 2 (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.v13i2.3868

Abstract

This research aims to explore the optimization of the problem-solving learning model within the context of independent learning in the post-pandemic era. Utilizing a systematic literature review method and the PRISMA model, the study identifies 25 pertinent articles concerning the implementation of the problem-solving learning model in independent learning. The analysis indicates that applying this model positively impacts students' critical thinking abilities, enhances creativity, and reinforces communication and collaboration skills. From an independent learning standpoint, the problem-solving learning model grants students the autonomy to cultivate creative thinking patterns and fosters heightened engagement in the learning process. The study also highlights adapting the model to online learning, with teachers as facilitators. In conclusion, these findings underscore the effectiveness of the problem-solving learning model in independent learning, especially in the post-pandemic era. They also offer valuable insights for educators and policymakers to develop adaptive learning strategies suited to the current educational environment.
Developing a Gantry Robot with Pre-calculated pure S-curve Motion Profiles for Delicate Egg Handling: Utilizing ESP32, FreeRTOS, and AS5600 Encoders Ju Ju Naing; Swe, War War Min; Win, Htay Htay
The Indonesian Journal of Computer Science Vol. 13 No. 2 (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.v13i2.3869

Abstract

The poultry industry faces challenges due to egg breakage during transfer. This paper describes a gantry robot specifically designed for delicate egg handling. The robot utilizes pre-calculated pure S-curve motion profiles to achieve smooth and precise movements, minimizing stress on the eggs. This approach leverages the computational efficiency of pre-calculation, making it suitable for low-power microcontrollers like the ESP32. FreeRTOS(Free Real-Time Operating System) ensures real-time task management for profile execution and data collection every 4 milliseconds from the AS5600 encoders. These encoders provide high-resolution angular position feedback, allowing for comparison with the planned S-curve profile after each movement step. This system offers advantages such as reduced egg breakage, improved transfer efficiency, and a simpler design compared to real-time control. However, limitations include limited adaptability to significant environmental changes and disturbances. Future work may investigate incorporating real-time feedback control for enhanced robustness.
Explainable Sentiment Analysis pada Ulasan Aplikasi Shopee Menggunakan Local Interpretable Model-agnostic Explanations Ninda Rizky Nuraeda; Muhaza Liebenlito; Taufik Edy Sutanto
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.v13i3.3870

Abstract

Seiring dengan perkembangan teknologi, pertumbuhan e-commerce mengalami peningkatan secara signifikan. Hadirnya aplikasi Shopee sebagai salah satu platform e-commerce terkemuka telah mendorong pengguna untuk melakukan transaksi belanja secara online. Dalam konteks ini, perhatian terhadap peningkatan kualitas aplikasi menjadi penting, khususnya melalui evaluasi ulasan pengguna dengan menggunakan analisis sentimen. Analisis sentimen umumnya mengadopsi pendekatan machine learning, meskipun transparansi dalam proses analisis menjadi tantangan utama. Penelitian ini mencoba mengatasi tantangan tersebut dengan menerapkan aspek baru dari Artificial Intelligence (AI), yang dikenal sebagai eXplainable Artificial Intelligence (XAI), khususnya pada analisis sentimen yang disebut Explainable Sentiment Analysis. Metode Local Interpretable Model-agnostic Explanations (LIME) digunakan untuk menjelaskan faktor-faktor yang mempengaruhi prediksi model machine learning. Model yang dievaluasi yaitu Logistic Regression, Random Forest, Support Vector Machine, dan Naïve Bayes. Hasil penelitian memberikan wawasan yang berharga tentang alasan di balik prediksi sentimen pada ulasan, sehingga diharapkan dapat meningkatkan pemahaman tentang bagaimana model machine learning membuat prediksi pada data tertentu.
Feature Selection using Extra Trees for Breast Cancer Prediction Awadelkarim, Shahad
The Indonesian Journal of Computer Science Vol. 13 No. 2 (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.v13i2.3874

Abstract

Breast cancer is a disease that seriously threatens women's health. It is considering a common death cause in women. Machine learning has made significant progress in recent years to improve the effectiveness of early diagnosis of various diseases. Accurate predication and detection help decrease the death rate of breast cancer. This paper aims to predict breast cancer using several machine-learning techniques. The idea is to lower the number of features in the Wisconsin Breast Cancer Dataset (WCDB) and use it for prediction. The study used the extra trees method for feature selection and Random forest, Logistic regression, and Support Vector Machine (SVM) for testing the dataset. According to the results, SVM achieved the best performance among the other models with 98% accuracy. The proposed method in this study proved its effectiveness in breast cancer prediction.
Predictive Analytics for Water Safety: Data Mining and Supervised Learning in Potability Classification Nanda Aulia Sofiah; Fanny Olivia; Jambak, Muhammad Ihsan
The Indonesian Journal of Computer Science Vol. 13 No. 4 (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.v13i4.3884

Abstract

Water is crucial for survival, especially for consumption, yet its quality is under threat due to human-caused pollution. Contaminated water poses serious health risks, including the transfer of diseases transmitted by water. Therefore, assessing water quality is critical for ensuring its safety for consumption. Data mining and supervised machine learning algorithms can help classify water potability, revealing hidden patterns and correlations between water parameters. This study evaluates the effectiveness of K-Nearest Neighbors (KNN), Naïve Bayes, Support Vector Machine (SVM), and Neural Network methods in categorizing a water quality dataset. The evaluation is aimed at selecting the most accurate procedure, as indicated by the highest accuracy rate. Results show that Neural Network exceeds KNN (81%), Naïve Bayes (63%), and SVM (73%), with a 85% accuracy rate. Keywords : Classification, Data Mining, Supervised Machine Learning, Water Potability
Optimal Sizing and Comparative Analysis of Renewable Energy Integration for the Existing Microgrid System in Kadan Island Htun, Khin Thandar; Swe, Wunna
The Indonesian Journal of Computer Science Vol. 13 No. 2 (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.v13i2.3885

Abstract

The rapid depletion of fossil fuels and the necessity for reduced carbon emissions have led to an increased focus on renewable energy resources. The existing microgrid system comprises solely a diesel generator and a small portion of hydropower. Currently, Kadan Island relies mainly on diesel generators to supply power, resulting in a significantly higher cost of energy in comparison to other areas. Furthermore, there is still not enough electricity available on the entire island of Kadan. However, research has shown that integrating renewable-based systems with storage technologies into existing systems can help mitigate these issues. Therefore, the main objective of this paper is to investigate the optimal size and operation of a hybrid renewable system on the Myanmar Islands. The optimization process will focus on minimizing the net present cost (NPC) and cost of energy (COE) of the selected location. Additionally, the island's network will be analyzed under normal operating conditions with different scenarios, and the best scenario for the existing microgrid on Kadan Island will be recommended.
A Review on Diabetes Classification Based on Machine Learning Algorithms Musa, Jihan; Abdulazeez, Adnan Mohsin
The Indonesian Journal of Computer Science Vol. 13 No. 2 (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.v13i2.3886

Abstract

Diabetes, a chronic metabolic disorder, is a significant global health concern affecting millions of individuals worldwide. Early and accurate diagnosis of diabetes is crucial for effective management and prevention of complications. Machine learning (ML) techniques have emerged as powerful tools for analyzing diabetes-related data, aiding in the classification and prediction of diabetes types. This review provides a comprehensive overview of recent advancements in diabetes classification using ML algorithms, highlighting their strengths, limitations, and future directions. Various ML algorithms, including but not limited to support vector machines, decision trees, random forests, artificial neural networks, and ensemble methods, are discussed in details. Furthermore, data preprocessing techniques, feature selection methods, and evaluation metrics employed in diabetes classification studies are examined. Additionally, challenges such as data imbalance, interpretability, and generalization across diverse populations are addressed. Finally, potential avenues for future research to enhance the accuracy and applicability of ML-based diabetes classification systems are proposed.
A: Sistem Pakar Mendeteksi Kerusakan Mesin Penggiling Jagung Menggunakan Metode Certainty Factor Pada CV. Central Jagung Persada Munawarah, Nadila; Ikhwan, Ali
The Indonesian Journal of Computer Science Vol. 13 No. 2 (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.v13i2.3888

Abstract

A corn grinding machine is a combustion engine that is used to drive a propeller which functions to knock out corn kernels from the cob, so that the release of corn kernels from the cob is very fast and time efficient. This really helps corn farmers and also corn refinery owners to speed up the process to the processing factory. However, in the process of threshing corn from the cobs, the machine often experiences damage, so it is necessary to detect damage to the corn grinding machine with an expert system using the Certainty Factor method in order to help farmers quickly understand what damage is occurring to the machine without having to bring the machine or call someone. machine repair expert, where the system created can also provide solutions related to machine damage. Corn grinding machines that experience fan damage with damage code P01 have a certainty level of damage of 67.2% or Certainty Factor=0.672.
STUDI PEMETAAN SPEKTRUM KEAHLIAN LULUSAN PROGRAM STUDI PENDIDIKAN TEKNIK ELEKTRO UNIVERSITAS NEGERI PADANG Siregar, Winda Lestari; Yudhi Diputra; Jalius, Nizwardi Jalinus; Ridwan; Rijal Abdullah; Nurhasan Syah
The Indonesian Journal of Computer Science Vol. 13 No. 2 (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.v13i2.3891

Abstract

Upon graduation, the aim of education graduates is to become qualified educators capable of meeting the needs of education, society, and industry. Graduates of the Electrical Engineering Education Study Programme are expected to possess superior competence and provide solutions to societal problems. The research conducted is descriptive in nature, utilising a qualitative approach. The study was carried out at the Faculty of Engineering, State University of Padang. The research instrument comprised documents such as course outlines, course synopses, and syllabi in Electrical Engineering Education at Padang State University. Data was collected through interviews and documentation. The study aimed to create a mapping of Electrical Engineering Education courses at Padang State University. The UNP Electrical Engineering Education program requires students to complete a set number of courses each semester. In semester 1, students must complete 22 SKS; in semester 2, 22 SKS; in semester 3, 22 SKS; in semester 4, 19 SKS; in semester 5, 22 SKS; in semester 6, 20 SKS; in semester 7, 8 SKS; and in semester 8, 10 SKS of final courses. The requirement for Semester Credit Units for Electrical Engineering Education students at Padang State University is 145 credits.
Aplikasi POS Berbasis Web Terintegrasi Dengan Whatsapp Pada Fuku Petshop Hanggaraxsha, Irwan; Gunawan, Dedi
The Indonesian Journal of Computer Science Vol. 13 No. 2 (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.v13i2.3893

Abstract

The Point Of Sale (POS) application is a service created to simplify the business activity process for entrepreneurs. Every transaction, whether sales or purchases, is important to record to ensure the accuracy of transaction reports. However, at Fuku Petshop, the recording method still uses conventional transaction books, which are prone to data loss and inaccuracies. Fuku Petshop, a shop located in Madiun, felt the need to adopt a computerized POS system to monitor sales, purchase and stock transactions more efficiently. Apart from that, integration with the WhatsApp application is also part of the goal of this system. In developing the POS system, utilizing the PHP programming language and MySQL database, using the CodeIgniter framework. The development approach adopted is the waterfall method. To ensure the quality of the system being developed, testing is carried out using black box techniques and also using the System Usability Scale (SUS) method. The results of testing using the black box technique state that the POS application at Fuku Petshop runs well and for SUS testing the average value is 81, which indicates Excellent qualifications and is included in the Acceptable classification, indicating the system is ready for use.

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