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All Journal JURNAL REKAYASA TEKNOLOGI INFORMASI Jurnal Pengabdian kepada Masyarakat Politeknik Negeri Batam Journal of Computing Theories and Applications
Dian Nurdiana
Universitas Terbuka
Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Engineering Mechanical Engineering

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Program Pengembangan Santri Cerdas Melalui Pemanfaatan Media Digital Pada Pesantren Nafidatunjannah Gunung Sindur Dian Nurdiana; Mochamad Bagoes Satria Junianto; Mayang Anglingsari Putri; Denisha Trihapningsari; Irpan Kusyadi
Jurnal Pengabdian kepada Masyarakat Politeknik Negeri Batam Vol. 8 No. 1 (2026): Jurnal Pengabdian kepada Masyarakat Politeknik Negeri Batam
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/abdimaspolibatam.v8i1.11929

Abstract

The rapid development of information and communication technology (ICT) has created significant opportunities for Islamic boarding schools (pesantren) to strengthen their roles in education and da'wah in the digital era. Although santri represent a generation that is highly familiar with technology, its use is still predominantly directed toward entertainment rather than self-development or da'wah activities. This study discusses the urgency of technological proficiency among santri, the challenges faced by pesantren in the process of digital transformation, and the importance of guidance to shape santri into positive content creators capable of spreading Islamic values through digital platforms. The main obstacles include limited technological infrastructure, insufficient training for educators, and a lack of understanding of effective digital da'wah strategies. Therefore, this study emphasizes the need to strengthen technological capacity in pesantren through relevant training, adequate ICT facilities, and the development of a digital curriculum aligned with da'wah needs. The findings are expected to serve as a foundation for pesantren in designing effective and sustainable digital transformation programs.
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.
Explainable Machine Learning for Predicting Indonesian Vocational School Accreditation: Geographic Validation, Probability Calibration, and Subgroup Auditing Muhamad Riyan Maulana; Putu Sudira; Priyanto Priyanto; Yanuar Agung Fadlullah; Dian Nurdiana; Amir Hamzah bin Sofhi @ Subhi
Journal of Computing Theories and Applications Vol. 4 No. 2 (2026): JCTA 4(2) 2026
Publisher : Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/jcta.17721

Abstract

Vocational school accreditation is a high-stakes institutional classification, yet the geographic transferability and probability reliability of models based on routinely collected administrative data remain underexplored. This study developed a reproducible, leakage-controlled machine-learning framework for classifying the recorded accreditation classes C, B, and A among Indonesian vocational schools. Its main contribution is an integrated evaluation design that combines province-disjoint validation, probability calibration, cluster-aware uncertainty, and subgroup auditing to test reliability beyond conventional random-validation performance. A cross-sectional dataset containing 149,376 program-level records was deterministically aggregated into 14,391 schools, including 14,134 with valid labels. Eight provinces comprising 1,536 schools were locked before feature selection, model comparison, tuning, and calibration; 12,598 schools from 31 provinces supported development. The selected CatBoost model used 35 structural and vocational features with grouped out-of-fold temperature scaling. On the locked holdout, the calibrated model achieved balanced accuracy of 0.586, Macro F1 of 0.533, quadratic weighted kappa of 0.499, Macro ROC-AUC of 0.757, and expected calibration error of 0.049. School size, teacher resources, vocational staffing, program diversity, and status were most in-fluential. Class B had the lowest recall, and performance varied across school groups and provinces. The framework supports calibrated, auditable preliminary screening, but not automated accreditation or replacement of professional assessors.
Co-Authors Amir Hamzah bin Sofhi @ Subhi Denisha Trihapningsari Fonda Leviany Irpan Kusyadi Junianto, Mochamad Bagoes Satria Mayang Anglingsari Putri, Mayang Anglingsari Medi Taruk Muhamad Riyan Maulana Priyanto, Priyanto Putu Sudira Septian Nuno Zildjian Yanuar Agung Fadlullah