Pengembangan Model Dynamic Naive Bayes untuk Pemodelan Risiko Akademik Mahasiswa: Integrasi Filsafat Ilmu Kuhn dan Popper dalam Analisis Risiko Pendidikan Tinggi - Ridwan Zulkifli; Dony Waluya Firdaus; Muhamad Nawawi; Agus Nursikuwagus; Usep Mohamad Ishaq; Andrias Darmayadi (2026)
Abstrak - Pemodelan risiko akademik mahasiswa serta prediksi kemungkinan kegagalan studi berdasarkan pola perilaku historis merupakan fokus utama institusi pendidikan tinggi. Model prediksi statis seperti Naive Bayes konvensional sering kali gagal menangkap perubahan perilaku mahasiswa sepanjang waktu pada data longitudinal, sehingga menghasilkan anomali prediksi. Penelitian ini, melalui Tinjauan Literatur Sistematis (Systematic Literature Review/SLR), mengkaji pengembangan Dynamic Naive Bayes (DNB) sebagai perluasan Naive Bayes yang memasukkan dimensi temporal dan transisi antar periode (misalnya perubahan status risiko antar semester), serupa dengan prinsip Dynamic Bayesian Networks. DNB diasumsikan mampu meningkatkan akurasi prediksi sebesar 10–15% dibandingkan model statis. Secara metodologis, pengembangan DNB didasarkan pada kerangka filsafat ilmu Thomas Kuhn dan Karl Popper. Mengacu pada paradigma Kuhn, kegagalan Naive Bayes statis dalam menangani data dinamis mencerminkan tahap crisis dan anomaly dalam normal science pemodelan prediktif risiko akademik, yang mendorong scientific revolution melalui adopsi model dinamis seperti DNB. Sementara itu, dari perspektif Rasionalisme Kritis Popper, hipotesis bahwa DNB memberikan prediksi yang lebih akurat harus terus-menerus diuji secara ketat dan terbuka terhadap falsifikasi empiris menggunakan data baru. Integrasi kedua kerangka ini memastikan bahwa pengembangan DNB tidak hanya bersifat teknis, melainkan juga didasari pada pertumbuhan pengetahuan ilmiah yang kritis, rasional, dan berkelanjutan.Kata kunci: pemodelan risiko akademik; dynamic naive bayes; prediksi kegagalan studi; data longitudinal; tinjauan literatur sistematis; filsafat ilmu,;thomas kuhn; karl popper; Abstract - Student academic risk modeling, the study of predicting the likelihood of failure based on historical behavioral patterns, is a major focus for higher education institutions. Although predictive models have been developed, statistical approaches such as standard Naive Bayes often fail to capture changes in student behavior over time, which are characteristic of longitudinal data. This failure, known as anomalies, highlights the need for new models capable of addressing dynamic and temporal aspects, such as Dynamic Naive Bayes (DNB). This study, through a Systematic Literature Review (SLR), examines the development of a DNB capable of processing or accounting for time factors, such as changes in risk status between semesters. Methodologically, DNB is an extension of Naive Bayes by incorporating transition time, similar to Dynamic Bayesian Networks, which is assumed to improve predictive accuracy by 10-15% compared to statistical models. Philosophically, the DNB development model is interpreted through the framework of Thomas Kuhn and Karl Popper. According to Kuhn, the failure of statistical Naive Bayes on dynamic data led to a crisis and anomaly in normal science (the use of standard predictive models), triggering the need for a scientific revolution (the adoption of DNB). Meanwhile, Popper's perspective (Critical Rationalism) demands that DNB predictive hypotheses must always be open to falsification through aggressive testing against new data. This integration ensures that the DNB development model is based on the critical and continuous growth of scientific knowledge.Keywords: Scientific Revolution; Dynamic Naive Bayes (DNB); Student Academic Risk; Longitudinal Data; Naive Bayes; Philosophy of Science;