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All Journal Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Jurnal Sistem Informasi dan Sistem Komputer Jurnal Saintekom : Sains, Teknologi, Komputer dan Manajemen Jurnal Teknologi Informasi untuk Masyarakat (Jurnal Teknokrat)
Hirmayanti
Universitas Hamzanwadi
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Environmental Science Languange, Linguistic, Communication & Media Library & Information Science Other

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Perbandingan Algoritma Machine Learning Menggunakan Pemilihan Fitur Chi-square dalam Pengklasifikasian Penyakit Jantung Hirmayanti, Hirmayanti; Utami, Ema
Jurnal Saintekom : Sains, Teknologi, Komputer dan Manajemen Vol 15 No 1 (2025): Maret 2025
Publisher : STMIK Palangkaraya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33020/saintekom.v15i1.815

Abstract

Heart disease is one of the deadliest diseases worldwide. This condition often presents symptoms that do not immediately cause severe effects on the sufferer, making early anticipation crucial. To reduce fatalities caused by heart disease or cardiovascular disorders, a system is required to identify its primary causes so that these factors can be minimized. Therefore, this study applies the Chi-square feature selection method to determine the key features influencing the accuracy of Machine Learning models. A comparison is conducted between K-Nearest Neighbor, Naïve Bayes, Logistic Regression, Support Vector Machine, and Random Forest algorithms. This comparison aims to obtain the most accurate results, as a higher algorithm accuracy leads to a more precise classification system for heart disease. The study’s findings indicate that eight key features selected using the Chi-square method yield the highest accuracy, specifically 93.51% with the KNN algorithm. These results demonstrate that using relevant features improves classification accuracy and system efficiency compared to utilizing all available features. Consequently, this research contributes to the selection of essential features in Machine Learning algorithms through the Chi-square technique, ensuring a more effective and optimized heart disease classification system.
Enhanced Heart Disease Diagnosis Using Machine Learning Algorithms: A Comparison of Feature Selection Hirmayanti; Ema Utami
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 9 No 2 (2025): April 2025
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v9i2.6175

Abstract

Heart disease or cardiovascular disease is one of the leading causes of death in the world. Based on WHO data, in 2019, as many as 17.9 million people died from cardiovascular disease. If early prevention is not carried out immediately, of course, the victims will increase every year. Therefore, with the increasingly rapid development of technology, especially in the health sector, it is hoped that it can help medical personnel in treating patients suffering from various diseases, especially heart disease. So in this study, it will be more focused on the selection of relevant features or attributes to increase the accuracy value of the Machine Learning algorithm. The algorithms used include Random Forest and SVM. Meanwhile, for feature selection, several feature selection techniques are used, including information gain (IG), Chi-square (Chi2) and correlation feature selection (CFS). The use of these three techniques aims to obtain the main features so that they can minimize irrelevant features that can slow down the machine process. Based on the results of the experiment with a comparison of 70:30, it shows that CFS-SVM is superior by using nine features, which obtain the highest accuracy of 92.19%, while CFS-RF obtains the best value with eight features of 91.88%. By using feature selection and hyperparameter techniques, SVM obtained an increase of 10.88%, and RF obtained an increase of 9.47%. Based on the performance of the model using the selected relevant features, it shows that the proposed CFS-SVM shows good and efficient performance in diagnosing heart disease.
Pendampingan LMS Berbasis Outcome-Based Education dan Conditional Access pada Dosen PGSD Universitas Hamzanwadi Hirmayanti; Harianto; Zaenul Amri
Jurnal Teknologi Informasi untuk Masyarakat Vol. 4 No. 1 (2026): Jurnal Teknologi Informasi untuk Masyarakat (Teknokrat)
Publisher : Universitas Hamzanwadi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29408/jt.v4i1.35087

Abstract

The advancement of digital technology in higher education has encouraged the utilization of Learning Management Systems (LMS) not only as platforms for delivering learning materials but also as tools for supporting competency-based and adaptive learning. However, LMS implementation in the Elementary School Teacher Education Program at Hamzanwadi University has primarily focused on basic learning features and has not fully utilized adaptive learning functionalities such as conditional access. This community service program aimed to enhance lecturers’ competencies in developing and managing an Outcome-Based Education LMS integrated with conditional access features to support adaptive learning. The program was conducted through several stages, including needs assessment, training material development, workshops, digital course design practices, LMS implementation mentoring, and activity evaluation. The results indicated that all participants successfully developed Outcome-Based Education courses within the LMS, while 15 lecturers successfully implemented the conditional access feature in their courses. In addition, 85% of participants reported that the mentoring activities improved their ability to develop digital assessments, design adaptive learning activities, and utilize LMS features more effectively. The implementation of the Outcome-Based Education LMS also facilitated more systematic and measurable monitoring of student learning outcomes. Therefore, the mentoring program contributed positively to strengthening lecturers’ digital competencies and supporting the development of adaptive learning environments oriented toward student learning outcomes
Prediksi Kegagalan Build Real-Time Pada Pipeline CI/CD Menggunakan Pendekatan Machine Learning Berbasis Data Streaming Hirmayanti Hirmayanti; Harianto Harianto; Rooy Marthen Thaniket
Jurnal Sistem Informasi dan Sistem Komputer Vol 11 No 2 (2026): Vol 11 No 2 - 2026
Publisher : STIMIK Bina Bangsa Kendari

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51717/simkom.v11i2.1414

Abstract

Kegagalan proses build pada lingkungan Continuous Integration/Continuous Deployment (CI/CD) dapat menghambat rilis perangkat lunak, meningkatkan biaya perbaikan, dan menurunkan efisiensi pengembangan. Hal ini menunjukkan perlunya sistem prediksi yang cepat, adaptif, serta mampu menangani data yang terus mengalir secara real-time. Penelitian ini bertujuan untuk menganalisis dan membandingkan kinerja beberapa algoritma online machine learning dalam memprediksi kegagalan build pada pipeline CI/CD. Dataset yang digunakan berasal dari TravisTorrent yang mencakup aktivitas repositori, perubahan kode, metrik pengujian, serta riwayat build. Tahapan penelitian meliputi preprocessing data, pembentukan skema data streaming, penerapan model Hoeffding Tree, Adaptive Random Forest (ARF), dan Online Logistic Regression, serta evaluasi menggunakan metrik accuracy. Hasil eksperimen menunjukkan model terbaik mencapai accuracy 97,35%, lebih tinggi dibandingkan penelitian sebelumnya sebesar 95,9%. Peningkatan absolut sebesar 1,45 poin persentase atau sekitar 1,51% ini membuktikan bahwa pendekatan yang digunakan lebih efektif dalam mengenali pola kegagalan build pada lingkungan CI/CD berbasis streaming data.
Co-Authors Ema Utami Harianto Harianto Rooy Marthen Thaniket Zaenul Amri