Article Per Year (5 Year)
p-Index From 2021 - 2026
2.461
P-Index
This Author published in this journals
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 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

Published : 14 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 3 Documents
Search
Journal : Jurnal Varian

 1 
Mapping of Village Population Profile with Schistosomiasis Cases Using Clustering Large Applications Mohammad Fajri; Rais Rais; Nurul Fiskia Gamayanti; Siti Natazha Dg Mabaji; Shalsa Yunita Rahman Jati; Rizwan Arisandi
Jurnal Varian Vol 7 No 2 (2024)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/varian.v7i2.3423

Abstract

Schistosomiasis is a tropical disease caused by Schistosoma mansoni (intestinal schistosomiasis) and Schistosoma haematobium (urogenital schistosomiasis). Schistosomiasis in Indonesia is endemic to Central Sulawesi and is commonly found in the Napu Valley and Bada Valley areas, which are administratively included in Poso District and Sigi District. One approach to obtain information on schistosomiasis endemic areas is by mapping the population profile of villages with schistosomiasis cases. This mapping is intended to provide an overview of the social and demographic conditions of villages with schistosomiasis cases. One of the many analysis methods that can be used is cluster analysis. Cluster analysis is a method for grouping data based on the extent of their similarities. Data with similar characteristics will be grouped together, while data with different characteristics will be placed in different groups. Among several types of methods in cluster analysis is Clustering Large Application (CLARA). CLARA is a clustering method which is more robust to unusual data and can be applied to handle large volumes of data. The results of this study are obtained two optimum clusters, each possessing distinct characteristics as determined by Schistosomiasis cases indicators. Cluster 1 with low schistosomiasis cases and cluster 2 with high schistosomiasis cases.
Mapping of Village Population Profile with Schistosomiasis Cases Using Clustering Large Applications Fajri, Mohammad; Rais, Rais; Gamayanti, Nurul Fiskia; Dg Mabaji, Siti Natazha; Rahman Jati, Shalsa Yunita; Arisandi, Rizwan
Jurnal Varian Vol. 7 No. 2 (2024)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/varian.v7i2.3423

Abstract

Schistosomiasis is a tropical disease caused by Schistosoma mansoni (intestinal schistosomiasis) and Schistosoma haematobium (urogenital schistosomiasis). Schistosomiasis in Indonesia is endemic to Central Sulawesi and is commonly found in the Napu Valley and Bada Valley areas, which are administratively included in Poso District and Sigi District. One approach to obtain information on schistosomiasis endemic areas is by mapping the population profile of villages with schistosomiasis cases. This mapping is intended to provide an overview of the social and demographic conditions of villages with schistosomiasis cases. One of the many analysis methods that can be used is cluster analysis. Cluster analysis is a method for grouping data based on the extent of their similarities. Data with similar characteristics will be grouped together, while data with different characteristics will be placed in different groups. Among several types of methods in cluster analysis is Clustering Large Application (CLARA). CLARA is a clustering method which is more robust to unusual data and can be applied to handle large volumes of data. The results of this study are obtained two optimum clusters, each possessing distinct characteristics as determined by Schistosomiasis cases indicators. Cluster 1 with low schistosomiasis cases and cluster 2 with high schistosomiasis cases.
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. 
Co-Authors Aisya, Cici Andri Annisa, Mufitatul Ayu Anggraeni Desy Lusiyanti Dg Mabaji, Siti Natazha Fadjriyani Fadjryani, Fadjryani Gamayanti, Nurul Fiskia Hajra Rasmita Ngemba Hartayuni Sain Iman Al Fajri Iman Setiawan Insany, Annisa Nur Iut Triutami Junaidi Junaidi Lilies Handayani Margareth, Cecilia Muh. Maruf Niftayana, Neli Nur?Eni, Nur?Eni Nurhidayati, Maulida Nurpati Nurpati Nurul Fiskia Gamayanti Phyta Rahima Primajati, Gilang Radi, Aryani Rahman Jati, Shalsa Yunita Rais Rais Rais Rizwan Arisandi Rizwan Arisandi, Rizwan Setiawan, Iman Shalsa Yunita Rahman Jati Siti Natazha Dg Mabaji Siti Soraya Wulansari, Eka Rizky Yandi Ristawan Yunita, Silva