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All Journal Journal of Information Systems and Informatics Zonasi: Jurnal Sistem Informasi
Alinda, Yelli Nur
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Humanities Computer Science & IT Decision Sciences, Operations Research & Management Environmental Science Languange, Linguistic, Communication & Media

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ANALISIS K-MEANS DENGAN RAPIDMINER UNTUK KLASIFIKASI KUALITAS PENDIDIKAN SEKOLAH DASAR DI INDONESIA Irwansyah, Muhammad Aziiz; Alinda, Yelli Nur; Nur’Aini, Risma; Alfitrah, Intan Aidita; Khairani, Annisa; Tania, Ken Dhita; Meiriza, Allsela; Rifai, Ahmad
ZONAsi: Jurnal Sistem Informasi Vol. 7 No. 3 (2025): Publikasi artikel ZONAsi: Jurnal Sistem Informasi Periode September 2025
Publisher : Universitas Lancang Kuning

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31849/a0hhsc57

Abstract

Penelitian ini menerapkan metode K-Means Clustering untuk mengelompokkan 38 provinsi di Indonesia berdasarkan kualitas pendidikan dasar. Tujuan penelitian ini adalah mengidentifikasi pola distribusi pendidikan dengan mempertimbangkan faktor tenaga pendidik, angka putus sekolah, kondisi infrastruktur sekolah, serta tingkat kesejahteraan guru. Dataset yang digunakan berasal dari Kaggle SD tahun 2023-2024 dan data Upah Minimum Provinsi (UMP) tahun 2024, kemudian dianalisis melalui tahapan Knowledge Discovery in Database (KDD) menggunakan RapidMiner. Hasil klasterisasi menghasilkan tiga kelompok provinsi dengan karakteristik berbeda: Klaster 0 dengan jumlah sekolah dan siswa tinggi serta angka putus sekolah sedang; Klaster 1 dengan tenaga pendidik dan ruang kelas terbanyak serta angka putus sekolah terendah; dan Klaster 2 dengan angka putus sekolah tertinggi meskipun UMP tertinggi. Evaluasi kualitas klasterisasi menggunakan Davies-Bouldin Index (DBI = 0,162) menunjukkan hasil yang baik. Berdasarkan analisis magnitudo vektor Euclidean, faktor dominan dalam pembentukan klaster adalah Kepala Sekolah dan Guru (1,376), Putus Sekolah (1,368), Ruang Kelas (baik) (1,324), Sekolah (1,312), Siswa (1,286), dan UMP (1,214). Penelitian ini menyimpulkan bahwa faktor tenaga pendidik dan kondisi infrastruktur memiliki dampak lebih besar terhadap kualitas pendidikan dasar dibandingkan faktor ekonomi.
Sentiment Analysis of Indonesian Netizens toward Vasectomy on X Using the IndoBERT Model Alinda, Yelli Nur; Meiriza, Allsela; Hardiyanti, Dinna Yunika
Journal of Information System and Informatics Vol 8 No 1 (2026): February
Publisher : Asosiasi Doktor Sistem Informasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63158/journalisi.v8i1.1425

Abstract

Vasectomy-related conversations on X (Twitter) frequently generate polarized pro–contra debates that can shape public understanding of male contraception, yet evidence on Indonesian netizens’ sentiment remains limited. This study maps and classifies sentiment toward vasectomy during April–June 2025 using a descriptive quantitative text-mining and NLP pipeline. After preprocessing (cleaning and deduplication), 9,817 posts were analyzed. Semi-supervised labeling was performed using the teacher model taufiqdp/indonesian-sentiment with confidence-based refinement, supported by a rule-based sarcasm_flag that identified 330 potentially sarcastic texts. A 20% manually verified GOLD subset (1,963 samples) served as ground truth, and IndoBERT (indolem/indobert-base-uncased) was fine-tuned with weighted cross-entropy and early stopping. Evaluation on the GOLD test set (n = 393) showed strong performance (accuracy = 0.8168; macro F1 = 0.8141), with most errors concentrated in short, ambiguous, or humor/sarcasm-leaning posts. Full-corpus predictions produced 3,957 negative, 3,520 positive, and 2,340 neutral texts, indicating a contested and polarized discourse with a slightly higher negative share. These findings support the need for evidence-based digital communication strategies to address misconceptions and stigma surrounding male contraception.
Co-Authors Ahmad Rifai Alfitrah, Intan Aidita Allsela Meiriza, Allsela Dinna Yunika Hardiyanti Irwansyah, Muhammad Aziiz Ken Dhita Tania, Ken Dhita Khairani, Annisa Nur’Aini, Risma