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All Journal JURNAL REKAYASA TEKNOLOGI INFORMASI Bulletin of Information Technology (BIT)
Fonda Leviany
Universitas Terbuka
Computer Science & IT

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Analyzing COVID-19's Educational Impact in Indonesia: K-Means and Self-Organizing Map Approach Ika Nur Laily Fitriana; Emeylia Safitri; Ria Faulina; Nuramaliyah Nuramaliyah; Fonda Leviany
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.
Studi Komparatif IndoBERT dan SVM-TF-IDF untuk Analisis Sentimen Program Makan Bergizi Gratis Nasional Septian Nuno Zildjian; Dian Nurdiana; Fonda Leviany; Medi Taruk
Jurnal Rekayasa Teknologi Informasi (JURTI) Vol 10, No 2 (2026): Jurnal Rekayasa Teknologi Informasi (JURTI)
Publisher : Universitas Mulawarman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30872/jurti.v10i2.28221

Abstract

Program Makan Bergizi Gratis (MBG) merupakan kebijakan sosial yang memicu beragam respons masyarakat, khususnya di media sosial. Penelitian ini bertujuan menganalisis opini publik terhadap Program MBG melalui komentar TikTok, mengidentifikasi distribusi sentimen, serta membandingkan kinerja model IndoBERT dan Support Vector Machine (SVM) berbasis TF-IDF dalam klasifikasi sentimen berbahasa Indonesia. Data komentar diproses melalui tahapan preprocessing, meliputi pembersihan teks, penghapusan duplikasi, normalisasi bahasa, pengolahan emoji, case folding, dan penyesuaian format teks. Hasil penelitian menunjukkan bahwa IndoBERT memberikan performa yang lebih baik dibandingkan SVM-TF-IDF, dengan nilai accuracy sebesar 89,61% dan F1-makro 83,47%, sedangkan SVM-TF-IDF memperoleh accuracy 78,45% dan F1-makro 64,67%. Distribusi sentimen menunjukkan dominasi sentimen negatif (71,7%), diikuti sentimen netral (14,3%) dan positif (14,1%). Temuan ini mengindikasikan adanya kritik dan kekhawatiran masyarakat terhadap pelaksanaan Program MBG, namun tidak dapat dimaknai sebagai penolakan secara menyeluruh. Penelitian ini menunjukkan bahwa analisis sentimen berbasis Natural Language Processing dapat menjadi pendekatan yang efektif untuk memahami dinamika opini publik dan mendukung evaluasi komunikasi kebijakan berbasis data.
Co-Authors Dian Nurdiana Emeylia Safitri Faulina, Ria Ika Nur Laily Fitriana Medi Taruk Nuramaliyah Nuramaliyah Septian Nuno Zildjian