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All Journal Buletin Ilmiah Sarjana Teknik Elektro IJMST
Nakib, Arman Mohammad
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Computer Science & IT Electrical & Electronics Engineering

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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.
Understanding Time Series Forecasting: A Fundamental Study Furizal, Furizal; Ma’arif, Alfian; Kariyamin, Kariyamin; Firdaus, Asno Azzawagama; Wijaya, Setiawan Ardi; Nakib, Arman Mohammad; Ningrum, Ariska Fitriyana
Buletin Ilmiah Sarjana Teknik Elektro Vol. 7 No. 3 (2025): September
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/biste.v7i3.13318

Abstract

Time series forecasting plays a vital role in economics, finance, engineering, etc., due to its predictive power based on past data. Knowing the basic principles of time series forecasting enables wiser decisions and future optimization. Despite its importance, some researchers and professionals find it difficult to use time series forecasting techniques effectively, especially with complex data settings and selection of methods for a particular problem. This study attempts to explain the subject of time series forecasting in a comprehensive and simple manner by integrating the main stages, components, preprocessing steps, popular forecasting models, and validation methods to make it easier for beginners in the field of study to understand. It explains the important components of time series data such as trend, seasonality, cyclical components, and irregular components, as well as the importance of data preprocessing steps, proper model selection, and validation to achieve better forecasting accuracy. This study offers useful material for both new and experienced researchers by providing guidance on time series forecasting techniques and approaches that will help in enhancing the value of decision making.
Co-Authors Abdul Syukur Alfian Ma’arif Ariska Fitriyana Ningrum Asno Azzawagama Firdaus Furizal, Furizal Jayed, A K M Kariyamin, Kariyamin Murni Murni Nusantara Habibi, Ahmad Rizky Sufiyandi, Ilham Wijaya, Setiawan Ardi