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All Journal Jurnal Lebesgue : Jurnal Ilmiah Pendidikan Matematika, Matematika dan Statistika Bulletin of Information Technology (BIT) SENTRI: Jurnal Riset Ilmiah Smatika Jurnal : STIKI Informatika Jurnal
Leviany, Fonda
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Aerospace Engineering Humanities Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Law, Crime, Criminology & Criminal Justice Mathematics

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MAPPING INDONESIA'S AGRICULTURAL DIVERSITY: CLUSTERING PROVINCES WITH SELF-ORGANIZING MAPS Fitriana, Ika Nur Laily; Leviany, Fonda; Faulina, Ria; Nuramaliyah, Nuramaliyah; Safitri, Emeylia
Jurnal Lebesgue : Jurnal Ilmiah Pendidikan Matematika, Matematika dan Statistika Vol. 5 No. 3 (2024): Jurnal Lebesgue : Jurnal Ilmiah Pendidikan Matematika, Matematika dan Statistik
Publisher : LPPM Universitas Bina Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46306/lb.v5i3.844

Abstract

The agricultural sector has an important role in national economic development in Indonesia. Based on data from the 2023 Agricultural Census from the Central Bureau of Statistics, it was found that the quantity and quality of the agricultural sector in various provinces in Indonesia still varies greatly. Hence, the suitable statistical methods are needed, namely cluster analysis, to group 38 provinces in Indonesia based on similar characteristics in the agricultural sector. Cluster analysis in this research uses the Self-organizing Maps (SOM) method. Before cluster analysis is carried out, Principal Component Analysis (PCA) is carried out to reduce the dimensions of the variables so that the data is easier to process and avoids the curse of dimensionality. The PCA results obtained 2 main components formed from 9 agricultural sector variables, which were then used as input data for clustering analysis with SOM. The results of clustering with SOM showed that the optimal number of provincial groups was 3 with a Davies-Boulden Index (DBI) value of 0.544 and a Silhouette of 0.623. The results of grouping the provinces can then be categorized into cluster 1 with a high average value of agricultural sector variables, cluster 2 with a medium average value of agricultural sector variables, and cluster 3 with a low average value of agricultural sector variables.
Feature Selection pada Indikator Indeks Ekonomi Hijau di Indonesia dengan Machine Learning Leviany, Fonda; Fitriana, Ika Nur Laily; Amin, Nurul Nisa’a
SENTRI: Jurnal Riset Ilmiah Vol. 4 No. 9 (2025): SENTRI : Jurnal Riset Ilmiah, September 2025
Publisher : LPPM Institut Pendidikan Nusantara Global

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55681/sentri.v4i9.4615

Abstract

Green economy policies are crucial for all countries to ensure that economic activities progress while preserving environmental sustainability. The success of such policies is measured by the Green Economy Index, which in 2020 recorded a national score of 59.17 with 15 indicators, while provincial-level indicators are still being developed. This study analyzes 18 provincial indicators to identify the main factors influencing the Green Economy Index using LASSO regression. This method was chosen for its ability to efficiently perform feature selection, address multicollinearity, and reduce overfitting risks. The dataset includes 18 indicators and index values from 34 provinces. The results show that 15 indicators significantly affect the index. The developed model demonstrates good performance with an RMSE of 1.23 for the training set and 2.29 for the testing set. The R² values of 95.6% (training) and 85.98% (testing) indicate strong predictive capability. Moreover, surface water quality is identified as the most influential indicator. These findings are expected to support data-driven policymaking in strengthening the green economy at the provincial level.
Measuring Service Quality and User Satisfaction of Universitas Terbuka’s Ruang Baca Virtual (RBV) Using SERVQUAL and EUCS Trihapningsari, Denisha; Astuti Aprijani, Dwi; Anglingsari Putri , Mayang; Leviany, Fonda
SMATIKA JURNAL : STIKI Informatika Jurnal Vol 16 No 01 (2026): SMATIKA Jurnal : STIKI Informatika Jurnal
Publisher : LPPM Universitas Bhinneka Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32664/smatika.v16i01.2183

Abstract

This study aims to measure the service quality and user satisfaction of the Ruang Baca Virtual (RBV) application within Universitas Terbuka’s (UT) digital library and to identify priority dimensions requiring improvement. A quantitative survey approach was employed using two structured questionnaires based on the SERVQUAL and End User Computing Satisfaction (EUCS) models. SERVQUAL was applied to assess five dimensions of service quality (tangibles, reliability, responsiveness, assurance, and empathy), while EUCS evaluated five dimensions of user satisfaction (content, accuracy, format, ease of use, and timeliness). The SERVQUAL gap analysis was conducted using a percentage comparison approach against the ideal score to determine priority improvement areas. The research included 100 participants, where 50 respondents evaluated service quality using the SERVQUAL method, and the remaining 50 respondents measured user satisfaction using the EUCS approach. The results indicate that all SERVQUAL and EUCS dimensions fall within the “satisfied” category. Empathy and reliability emerged as key strengths in service quality, while ease of use and accuracy were identified as main strengths in user satisfaction. However, responsiveness and assurance (SERVQUAL), as well as format and timeliness (EUCS), exhibited relatively larger gaps, indicating areas requiring improvement. These findings provide a comprehensive descriptive evaluation of RBV performance and offer strategic insights for enhancing digital library services in distance learning environments.
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.
Co-Authors Amin, Nurul Nisa’a Anglingsari Putri , Mayang Astuti Aprijani, Dwi Faulina, Ria Fitriana, Ika Nur Laily Nuramaliyah, Nuramaliyah Safitri, Emeylia Trihapningsari, Denisha