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All Journal Natural Science: Journal of Science and Technology JURNAL ILMIAH MATEMATIKA DAN TERAPAN Journal of Data Analysis BAREKENG: Jurnal Ilmu Matematika dan Terapan Jurnal Varian Inferensi DedikasiMU: Journal of Community Service Parameter: Journal of Statistics Prosiding Seminar Hukum Aktual Fakultas Hukum Universitas Islam Indonesia Indonesian Journal of Statistics and Its Applications
Mohammad Fajri
Department Of Statistics, Faculty Of Mathematics And Natural Sciences, Tadulako University, Indonesia
Religion Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Mathematics Mechanical Engineering Medicine & Pharmacology Physics Social Sciences Transportation Other

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Ensemble Quick Robust Clustering Using Links for Clustering Hypertension Patients at a Health Center Niftayana, Neli; Fajri, Mohammad; Gamayanti, Nurul Fiskia
Jurnal Varian Vol. 8 No. 3 (2025)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/varian.v8i3.5151

Abstract

Hypertension is a chronic disease with a high risk of cardiovascular complications and requires treatment according to patient characteristics. At the health center, the number of hypertensive patients is 6953, the highest recorded. Therefore, this study aims to classify and determine the characteristics of hypertensive patients at a health center. The method used in this study is Ensemble Quick Robust Clustering Using Links. This method combines the clustering results of Quick Robust Clustering Using Links and Agglomerative Nesting. Where this method is more efficient in clustering. The results of this study show the number of clusters in the Quick Robust Clustering Using Links method is 3, Agglomerative Nesting is 3 and in the Quick Robust Clustering Using Links Ensemble produces 9 clusters with the following distribution: Cluster 1 shows low hypertension, cluster 2 shows high hypertension, cluster 3 to cluster 6 shows high hypertension, cluster 7 shows moderate hypertension, cluster 8 shows high hypertension and cluster 9 shows moderate hypertension. Thus, grouping patients based on a combination of numerical and categorical variables can provide more detailed information about the severity of hypertension. 
Comparison of C4.5 and C5.0 Algorithm Classification Tree Models for Analysis of Factors Affecting Auction: Perbandingan Model Pohon Klasifikasi Algoritma C4.5 dan C5.0 untuk Analisis Faktor yang Mempengaruhi Keberhasilan Lelang Mohammad Fajri; Iut Tri Utami; Muh. Maruf
Indonesian Journal of Statistics and Applications Vol 6 No 1 (2022)
Publisher : Statistics and Data Science Program Study, SSMI, IPB University, in collaboration with the Forum Pendidikan Tinggi Statistika Indonesia (FORSTAT) and the Ikatan Statistisi Indonesia (ISI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29244/ijsa.v6i1p13-22

Abstract

Auction in Indonesia is carried out by the Office of State Assets and Auction Services (KPKNL). Goods auctioned at KPKNL are quite diverse including land, wood, inventory, vehicles, and other goods. However, not all of the items auctioned were sold. Because not a few items have been auctioned but no one has made an offer. The Purpose of this study is to compare two classification methods, C4.5 and C5.0 algorithm and to determine which items were successfully auctioned with those that did not and its factors. The methods that used were comparing the classification tree C4.5 algorithm and C5.0 algorithm with cross validation. From the results of the comparison of the two methods, it was found that the C5.0 Algorithm method was rated better than the C4.5 algorithm in classifying the auction results with an accuracy of 96.43% and 92.86% respectively. In this case, C5.0 has a higher precision than C4.5.
Pelindungan Data Pribadi dalam Transmisi Internasional: Menggagas Indonesian Cross-Border Data Overseer Mohammad Fajri; Alvianto Noval Ade Putra; Fatimah Nada; Damar Sugeng Utomo
Prosiding Seminar Hukum Aktual Fakultas Hukum Universitas Islam Indonesia Vol. 4 No. 4 JULI 2026
Publisher : Fakultas Hukum Universitas Islam Indonesia

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Abstract

The rapid advancement of digital technologies has substantially increased cross-border exchanges of personal data, while Indonesia’s regulatory and supervisory mechanisms for such transmissions remain underdeveloped despite the enactment of Law No. 27 of 2022 on Personal Data Protection. Institutional and procedural gaps create risks of data breaches, misuse, and inadequate protection of data subjects in the international context. This study aims to (1) assess the urgency of establishing a dedicated supervisory authority for cross-border personal data transfers and (2) propose an ideal institutional design compatible with Indonesia’s legal framework. The research employs a normative legal method combined with a statutory approach, historical-comparative study of selected jurisdictions (e.g., EU, Singapore, Brazil), and policy analysis to evaluate regulatory implications and institutional options. Primary legal texts, policy documents, academic literature, and comparative materials are qualitatively analyzed to identify regulatory gaps and formulate institutional recommendations. The findings indicate the necessity of an independent data protection authority with a specialized cross-border transfers unit, investigative and corrective powers, adequacy assessment procedures, and mandatory technical security standards (e.g., encryption, pseudonymization) prior to transfer.
Co-Authors Alvianto Noval Ade Putra Andri Annisa, Mufitatul Ayu Anggraeni Damar Sugeng Utomo Desy Lusiyanti Dg Mabaji, Siti Natazha Fadjriyani Fatimah Nada Gamayanti, Nurul Fiskia Hajra Rasmita Ngemba Hartayuni Sain Iman Al Fajri Iman Setiawan Insany, Annisa Nur Iut Triutami Junaidi Junaidi Lilies Handayani Muh. Maruf Niftayana, Neli Nur?Eni, Nur?Eni Nurhidayati, Maulida Nurpati Nurpati Nurul Fiskia Gamayanti Phyta Rahima Primajati, Gilang Rahman Jati, Shalsa Yunita Rais Rais Rais Rizwan Arisandi Rizwan Arisandi, Rizwan Shalsa Yunita Rahman Jati Siti Natazha Dg Mabaji Siti Soraya Wulansari, Eka Rizky Yandi Ristawan