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All Journal JURNAL MEDIA INFORMATIKA BUDIDARMA Jurnal Ilmiah Matrik Building of Informatics, Technology and Science J-Intech (Journal of Information and Technology)
Haristyawan, Ivan
STMIK Widya Cipta Dharma
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering Library & Information Science

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Journal : Building of Informatics, Technology and Science

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Perbandingan Kinerja Algoritma Klasifikasi Data Mining Untuk Prediksi Penyakit Darah Tinggi Arfyanti, Ita; Bustomi, Tommy; Haristyawan, Ivan
Building of Informatics, Technology and Science (BITS) Vol 6 No 3 (2024): December 2024
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v6i3.6477

Abstract

High blood pressure or hypertension is one of the major health problems in the world. Although this disease can be treated, many individuals are unaware that they have hypertension, because the symptoms are often not visible or felt. Therefore, early detection of high blood pressure is very important to prevent serious complications that can endanger health. In the digital era and advances in information technology, a lot of health data can be used for analysis. One of the rapidly developing approaches to help diagnose disease is by utilizing data mining. Data mining is the process of exploring and analyzing big data to find hidden patterns, information, and knowledge that can be used to support decision making and predictions. One technique in data mining that is often used to predict conditions or diseases is the classification algorithm. However, the comparison of performance between these classification algorithms in the context of hypertension prediction is still limited. This study aims to explore and compare the performance of classification algorithms in predicting hypertension, using a dataset containing medical information about factors that affect a person's blood pressure. The Naive Bayes algorithm is a classification method based on Bayes' theorem and the assumption of independence between features. The C4.5 algorithm is a machine learning algorithm for building decision trees used in data classification. The results of this study are expected to contribute to the development of a data mining-based decision support system that can be used to detect and predict the risk of hypertension. the accuracy value of the Naive Bayes algorithm is 87.01% and the accuracy value of the C4.5 algorithm is 94.72%. From the process that has been carried out, it can be said that the C4.5 algorithm is an algorithm with better performance than the Naive Bayes algorithm. Thus, the model used in the process of diagnosing hypertension is the model of the C4.5 algorithm.
Market Potential Analysis Based on Population and Land Area using K-Means Clustering and MCDM Approaches Arfyanti, Ita; Bustomi, Tommy; Haristyawan, Ivan
Building of Informatics, Technology and Science (BITS) Vol 7 No 1 (2025): June (2025)
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i1.7392

Abstract

In an increasingly competitive global market, accurately identifying untapped market potential in small to medium-sized regions, often overlooked by traditional single-indicator analyses, presents a significant challenge for strategic decision-making. This study addresses this by proposing a hybrid analytical framework integrating K-Means Clustering with Multi-Criteria Decision-Making (MCDM) methods, utilizing population size and land area as core indicators. The primary objective is to develop a robust market potential analysis model capable of systematically classifying regions and providing actionable insights for resource optimization and market expansion. The methodology involves determining the optimal number of clusters using the elbow method (k=3, with a silhouette score of 0.8862), followed by K-Means clustering to segment Asian countries into distinct groups. Subsequently, three MCDM methods SAW, WP, and WASPAS are applied to rank countries within the most relevant cluster (low population and area) under various weighting scenarios. The results consistently demonstrate Turkey's top ranking across all MCDM methods, highlighting its robust market potential regardless of weight variations. Crucially, a very strong agreement in rankings between the MCDM methods was observed, evidenced by Spearman's correlation coefficients consistently above 0.98, with the highest correlation between SAW and WASPAS (0.998379 for [0.3, 0.7] weights). This high correlation confirms the reliability and consistency of the model, concluding that SAW and WASPAS are highly suitable for this analysis, and identifying Turkey as the leading country in market potential among 50 Asian nations based on the criteria studied.
Co-Authors Amelia Yusnita Azahari Bustomi, Tommy Ita Arfyanti Muhammad Jariansyah Raya, Dimas