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All Journal REKAYASA Jurnal Lebesgue : Jurnal Ilmiah Pendidikan Matematika, Matematika dan Statistika Jurnal Pengabdian Masyarakat Nusantara Bulletin of Information Technology (BIT) Jurnal Abdi Panca Marga Indonesian Business Review Journal of Applied Agricultural Science and Technology Abdimas Indonesian Journal Indonesian Journal of Applied Mathematics Indonesian Journal of Statistics and Its Applications
Nuramaliyah, Nuramaliyah
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Religion Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Mechanical Engineering Physics Social Sciences Other

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Analyzing COVID-19's Educational Impact in Indonesia: K-Means and Self-Organizing Map Approach Fitriana, Ika Nur Laily; Safitri, Emeylia; Faulina, Ria; Nuramaliyah, Nuramaliyah; Leviany, Fonda
Bulletin of Information Technology (BIT) Vol 7 No 1: Maret 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bit.v7i1.2581

Abstract

The COVID-19 pandemic has affected the education sector. This research aimed to investigate the impact of COVID-19 on the education sector in Indonesia, especially on school participation indicators, using cluster analysis. We used fifteen factors related to the involvement indicators of students in elementary, junior secondary, and senior secondary education. The comparison of factors between 2019 and 2020 related to the effects of COVID-19, which began to proliferate in Indonesia in March 2020. Consequently, comparing those periods yields insights into the timeframe before and after the spread of COVID-19. To assess the pandemic's influence on the education sector, we performed an inferential statistical analysis using a nonparametric location test to identify significant changes between variables in 2019 and 2020. Subsequently, we performed cluster analysis using K-Means and Self-Organizing Map (SOM) approaches. The optimal cluster obtained for K-Means and SOM is three clusters. The results indicate that SOM and K-Means exhibit similar performances. Changes in cluster members in 2019 and 2020 indicate an enormous impact due to COVID-19. Cluster 3, which consists of DKI Jakarta, West Java, Central Java, East Java, and North Sumatra, is most affected by the pandemic from the educational sector.
THE BEST GLOBAL AND LOCAL VARIABLES OF THE MIXED GEOGRAPHICALLY AND TEMPORALLY WEIGHTED REGRESSION MODEL Nuramaliyah, Nuramaliyah; Saefuddin, Asep; Aidi, Muhammad Nur
Indonesian Journal of Statistics and Applications Vol 3 No 3 (2019)
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.v3i3.564

Abstract

Geographically and temporally weighted regression (GTWR) is a method used when there is spatial and temporal diversity in an observation. GTWR model just consider the local influences of spatial-temporal independent variables on dependent variable. In some cases, the model not only about local influences but there are the global influences of spatial-temporal variables too, so that mixed geographically and temporally weighted regression (MGTWR) model more suitable to use. This study aimed to determine the best global and local variables in MGTWR and to determine the model to be used in North Sumatra’s poverty cases in 2010 to 2015. The result show that the Unemployment rate and labor force participation rates are global variables. Whereas the variable literacy rate, school enrollment rates and households buying rice for poor (raskin) are local variables. Furthermore, Based on Root Mean Square Error (RMSE) and Akaike Information Criterion (AIC) showed that MGTWR better than GTWR when it used in North Sumatra’s poverty cases.
Co-Authors Abda Abda Abdul Salam Agus Santoso Agus Santoso Akbar, Arya Zufikar Amry Purnama Mauladi ASEP SAEFUDDIN Baehaqi Diah Anggeraini Hasri Fahlia Fahlia Fahlia, Fahlia Faulina, Ria Fendy Maradita Fitriana, Ika Nur Laily Fitriyantari Hanifa Sri Nuryani Hardiansyah, Rian Hartini Hartini Leviany, Fonda Lukmanul Hakim Lukmanul Hakim Maradita, Fendy Mika Alvionita Sitinjak Mikhratunnisa, Mikhratunnisa Muhammad Nur Aidi Oryza Safitri Safitri, Emeylia Safitri, Oryza Sari, Ratna Nurmalita Sri Nuryani, Hanifa