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All Journal Bulletin of Electrical Engineering and Informatics Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) Khazanah Informatika: Jurnal Ilmu Komputer dan Informatika INTENSIF: Jurnal Ilmiah Penelitian dan Penerapan Teknologi Sistem Informasi Buletin Ilmiah Sarjana Teknik Elektro Mobile and Forensics Journal of Robotics and Control (JRC) International Journal of Robotics and Control Systems International Journal of Social Service and Research Control Systems and Optimization Letters IJMST
Furizal, Furizal
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Aerospace Engineering Automotive Engineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Engineering Environmental Science Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mechanical Engineering Neuroscience Social Sciences

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Journal : IJMST

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Classification of Heart (Cardiovascular) Disease using the SVM Method Abidin, Minhajul; Munzir, Misbahul; Imantoyo, Adi; Bintang Grendis, Nuraqilla Waidha; Hadi San, Ahmad Syahrul; Mostfa, Ahmed A.; Furizal, Furizal; Sharkawy, Abdel-Nasser
Indonesian Journal of Modern Science and Technology Vol. 1 No. 1 (2025): January
Publisher : CV. Abhinaya Indo Group

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64021/ijmst.1.1.9-15.2025

Abstract

Cardiovascular disease is one of the leading causes of death worldwide, with a high mortality rate, especially in developing countries like Indonesia. This highlights the importance of developing systems to identify and detect heart disease at an early stage. In this study, the Support Vector Machine (SVM) algorithm was used to classify cardiovascular diseases by utilizing a dataset consisting of 303 patient records obtained from Kaggle. The dataset was divided into 70% for training and 30% for testing. Before optimization using GridSearchCV, the SVM model achieved an accuracy of 79%, precision of 79%, recall of 73%, and F1-score of 76%. However, after adjusting the hyperparameters with GridSearchCV, the model's accuracy slightly decreased to 77%, with precision remaining at 79%, recall dropping to 66%, and F1-score at 72%. Despite this decline in performance after optimization, the results indicate that although SVM has potential for classifying heart disease, its performance is highly influenced by data quality and the selection of appropriate hyperparameters. Even with the performance decrease postoptimization, the model still provides useful predictions, showing consistent results and a proportional class distribution.
Classification of Stunting in Toddlers using Naive Bayes Method and Decision Tree Maulana, Adrian; Ilham, Muhammad; Lonang, Syahrani; Insyroh, Nazaruddin; Sherly da Costa, Apolonia Diana; B. Talirongan, Florence Jean; Furizal, Furizal; Firdaus, Asno Azzawagama
Indonesian Journal of Modern Science and Technology Vol. 1 No. 1 (2025): January
Publisher : CV. Abhinaya Indo Group

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64021/ijmst.1.1.28-33.2025

Abstract

Child stunting is a health problem that has a major impact on their physical growth and brain development. This study aims to create a model that can predict the risk of stunting using machine learning technology, in order to provide assistance quickly. Using data from 7,573 children, which included information such as age, weight, height gender and breastfeeding status, we tried two methods, Naive Bayes and Decision Tree. As a result, Naive Bayes was more accurate and the success rate reached 92%, compared to Decision tree which was only 88%. With this model, it is hoped that health workers will find it easier to find children at risk of stunting, so that preventive action can be taken earlier. This research aims to provide technology-based solutions to overcome the problem of stunting in the community.
Diabetes Mellitus Disease Analysis using Support Vector Machines and K-Nearest Neighbor Methods Nusantara Habibi, Ahmad Rizky; Sufiyandi, Ilham; Murni, Murni; Jayed, A K M; Nakib, Arman Mohammad; Syukur, Abdul; Furizal, Furizal
Indonesian Journal of Modern Science and Technology Vol. 1 No. 1 (2025): January
Publisher : CV. Abhinaya Indo Group

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64021/ijmst.1.1.22-27.2025

Abstract

Diabetes Mellitus (DM) is a chronic disease characterized by high blood sugar levels and can cause various serious complications if not treated properly. This study aims to analyze the effectiveness of Support Vector Machines (SVM) and K-Nearest Neighbor (KNN) methods in classifying diabetes mellitus patient data. The methodology used includes collecting diabetes datasets, preprocessing data, and applying SVM and KNN algorithms to perform classification. The performance of both methods is analyzed using evaluation metrics such as accuracy, precision, recall, and F1-score. The experimental results show that the SVM method provides more optimal performance in classifying diabetes data compared to KNN, with higher accuracy and lower error rate. This finding indicates that SVM is more suitable for early detection of diabetes mellitus in the dataset used in this study.
Co-Authors A. Salah, Wael Abdul Syukur Abidin, Minhajul Adriyanto, Feri Ahmad Azhari Al Madani Kurniawan, Aqsha Aldi Bastiatul Fawait Fawait Alfian Ma’arif ALYA MASITHA Anton Yudhana Ariska Fitriyana Ningrum Arqam, Mhd Lailan Asno Azzawagama Firdaus Atmojo, Ahmad Muslih B. Talirongan, Florence Jean Bintang Grendis, Nuraqilla Waidha D.E Purwadi Putra, Izzan Julda Dianda Rifaldi Faresta, Rangga Hadi San, Ahmad Syahrul Imam Riadi Imantoyo, Adi Insyroh, Nazaruddin Jayed, A K M Kariyamin, Kariyamin Kartoirono, Suprihatin Lora Khaula Amifia Maghfiroh, Hari MAJDOUBI, Rania Maulana, Adrian Mawarni, Syifa’ah Setya Moch. Iskandar Riansyah Mostfa, Ahmed A. Muhammad Ilham Muhammad Mujahid Dakwah Muis, Alwas Munzir, Misbahul Murinto Murinto Murni Murni Nakib, Arman Mohammad Natawangsa, Hari Nusantara Habibi, Ahmad Rizky Palahuddin, Palahuddin Ramelan, Agus REKIK, Chokri Ritonga, Asdelina Sagita, Muhamad Rian Sharkawy, Abdel-Nasser Sherly da Costa, Apolonia Diana Son Ali Akbar Sufiyandi, Ilham Sunardi Sunardi Suprihatin Suprihatin Suwarno, Iswanto Syahrani Lonang Wahyu Caesarendra Wijaya, Setiawan Ardi