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All Journal EIGEN MATHEMATICS JOURNAL Journal of Mathematics: Theory and Applications
Permatasari, Reni
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Mathematics

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Pemodelan Jumlah Kasus HIV/AIDS di Provinsi Lampung Menggunakan Regresi Binomial Negatif Tri Fatmala, Cici; Hayati, Marufah; Permatasari, Reni; Hudori, Mahfuz; Yuliana Dalimunthe, Desy
Journal of Mathematics: Theory and Applications Vol 6 No 2 (2024)
Publisher : Program Studi Matematika Universitas Sulawesi Barat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31605/jomta.v6i2.4069

Abstract

Tujuan penelitian ini adalah pemilihan metode terbaik untuk memodelkan jumlah kasus penderita HIV/AIDS di propinsi Lampung dengan membandingkan antara regresi Poisson dan Binomial negative. Data yang digunakan dalam penelitian ini diperoleh dari badan pusat statistik (BPS) provinsi Lampung dan Dinas Kesehatan Provinsi Lampung. Generalized Linier Model (GLM) adalah kerangka statistik yang memperluas model regresi linier sehingga memungkinkan variabel dependen memiliki distribusi yang tidak normal. GLM merupakan model yang sangat fleksibel, mencakup berbagai model regresi umum seperti regresi linier, regresi logistik, dan regresi poisson. Hasil analisis menunjukkan bahwa perbandingan antara model regresi Poisson dan model regresi Binomial Negatif diperoleh bahwa model regresi Binomial Negatif memiliki nilai AIC lebih kecil sehingga, model Binomial Negatif lebih baik digunakan dalam memodelkan jumlah kasus HIV/AIDS dibandingkan model regresi Poisson di provinsi Lampung
Prediksi Curah Hujan di Provinsi Lampung Menggunakan Distribusi Tweedie Campuran dengan Reduksi PCA Utami, Sari; Hayati, Ma’rufah; Permatasari, Reni
Eigen Mathematics Journal Vol 8 No 2 (2025): December
Publisher : University of Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/emj.v8i2.280

Abstract

Accurate rainfall prediction is crucial for supporting the agricultural sector in Lampung Province. This research employs the Exponential Dispersion Model (EDM), a special case of the Generalized Linear Model (GLM), incorporating a Tweedie mixture distribution with Principal Component Analysis (PCA) to reduce correlated variables. Rainfall data were obtained from the Meteorology, Climatology, and Geophysics Agency (BMKG) through twelve rain observation posts (2013-2022), and supplemented with precipitation data from the General Circulation Model (GCM) obtained from the European Centre for Medium-Range Weather Forecasts (ECMWF). The Tweedie mixture distribution was selected for its ability to handle non-normally distributed rainfall data containing zero values. The results show that the Root Mean Square Error of Prediction (RMSEP) for the Tweedie mixture-PCA model at the Gisting Atas station is 163.90, while the Normal-PCA model achieved 169.11. Therefore, the Tweedie mixture-PCA approach is more effective and recommended for improving rainfall prediction in Lampung Province, offering potential benefits for agricultural planning and resource management.
Co-Authors Hayati MT, Ma’rufah Hudori, Mahfuz Tri Fatmala, Cici Utami, Sari Yuliana Dalimunthe, Desy