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All Journal EKSAKTA: Journal of Sciences and Data Analysis JURNAL PENGABDIAN KEPADA MASYARAKAT JNPM (Jurnal Nasional Pendidikan Matematika)
Sekti Kartika Dini
Department Of Statistics, Faculty Of Mathematics And Natural Sciences, Universitas Islam Indonesia
Arts Humanities Chemical Engineering, Chemistry & Bioengineering Chemistry Earth & Planetary Sciences Education Languange, Linguistic, Communication & Media Materials Science & Nanotechnology Mathematics Social Sciences

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Journal : EKSAKTA: Journal of Sciences and Data Analysis

 1 
Clustering Provinces in Indonesia based on Community Welfare Indicators Sekti Kartika Dini; Achmad Fauzan
EKSAKTA: Journal of Sciences and Data Analysis VOLUME 1, ISSUE 1, February 2020
Publisher : Fakultas Matematika dan Ilmu Pengetahuan Alam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20885/EKSAKTA.vol1.iss1.art9

Abstract

The Preamble of the 1945 Constitution of the Republic of Indonesia explicitly states that the main task of the government of the Republic of Indonesia is to advance general prosperity, to develop the nation's intellectual life, and to realize social justice for all Indonesian people. Social inequality is a problem that is still faced by Indonesian people today. To solve the problem required supporting data analysis as a basis for policy formulation. This research was conducted with the aim of clustering provinces in Indonesia based on community welfare indicators using K-Means cluster analysis. K-Means cluster analysis is chosen based on the variance value (0.101), which is smaller than the variance value in the average linkage cluster analysis (0.152). Based on data analysis, provinces in Indonesia are clustered into three where the first cluster consists of 21 provinces, the second cluster consists of 3 provinces, and the third cluster consists of 10 provinces. Each cluster has different characteristics that can be of concern to the parties concerned to overcome the social welfare gap. Besides, in order cluster results are more easily understood, visualization of results is added with a Geographic Information System (GIS) using Indonesian maps accompanied by differences in color gradations for each cluster
Clustering of PDQ Participant Student in Faculty of Mathematics and Natural Sciences UII using the ROCK Method Rahmadi Yotenka; sekti kartika dini
EKSAKTA: Journal of Sciences and Data Analysis VOLUME 3, ISSUE 2, August 2022
Publisher : Fakultas Matematika dan Ilmu Pengetahuan Alam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20885/EKSAKTA.vol3.iss2.art7

Abstract

The Qur’anic Self-Development (PDQ)-Ta'lim Program is one of the student activities that must be followed by diploma and bachelor program students in Universitas Islam Indonesia (UII). The implementation of PDQ is coordinated by each faculty which is carried out for 4 semesters with 12 meetings for each semester. After carrying out PDQ activities, it is necessary to know the student profiles that can be used as the basis for policy making in the implementation of PDQ activities in the next period. In order to find out the profile of students after participating in PDQ activities, it is necessary to group these students based on related variables. This study uses the ROCK method to group students participating in the PDQ Faculty of Mathematics and Natural Sciences (FMIPA) UII batch 2020. The ROCK method is a robust agglomerative hierarchical-clustering algorithm based on the notion of links. The ROCK method is a suitable clustering method for grouping data with categorical variables. Based on the results of the analysis of the ROCK method of student data for the batch 2020 FMIPA UII, obtained three optimum clusters (k=3) at a threshold value of θ of 0.20. Threshold 0.20 has the smallest SW/SB ratio value of 0.0514 or 5.14% and the largest R-squared value is 61.76% compared to other thresholds.
Co-Authors ACHMAD FAUZAN Edy Widodo Muthia Citra Safira Novendri Isra Asriny Novendri Isra Asriny Rahmadi Yotenka Riefyal Arshyzha Mustain Rohmatul Fajriyah Sheilta Alphenia