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Bandung Conference Series: Statistics
Published by Universitas Islam Bandung
ISSN : -     EISSN : 2828206X     DOI : https://doi.org/10.29313/bcss.v2i2
Core Subject : Science, Education,
Decision Sciences, Operations Research & Management Education Mathematics
Bandung Conference Series: Statistics (BCSS) menerbitkan artikel penelitian akademik tentang kajian teoritis dan terapan serta berfokus pada Statistika dengan ruang lingkup sebagai berikut: Alternating Least Square, Analisis Konjoin, Autoregressive, Auxiliary Variabel, Baby Birth, Block Maxima, Churn Distribusi Skellam, Cox Regression, Data spasial, DBD Ordinal Logistic Regression, Diagram kendali, Discrete Choice Experiment Method, Discrete Time Logistic, empirical likelihood, Fisher Scoring, Generalized Structured Component Analysis, Geographically Weighted Regression, GEV, GJR GARCH, Infant Mortality Preferensi, Insurance Claim, Kaplan-Meier, Kernel Bi-Square, Gaussian, Logistic Regression, Maternal Mortality, Mixed Geographically Weighted Regression Model GSTAR, MLE, Model ARIMAX, MSE. Multiple linear regression analysis, Nadaraya Watson, Newton Raphson Method, Nonparametrik Spline Confidence Interval, Optimasi Multi-Objek, orde Spasial, Outlier, Pareto Optimal, Partial Proportional Odds Model, Pemodelan Indeks Pembangunan Manusia. Penduga Rasio dan Produk Tipe Eksponensial, Peramalan, Poisson Bivariate Regression, Poisson Regression, Rata-rata Populasi berhingga, Regresi, Return Period Exogenous Variable, RMSE, Structural Equation Modeling, Survival Analysis, Threshold, Vibrasi Bearing, zero-inflated. Prosiding ini diterbitkan oleh UPT Publikasi Ilmiah Unisba. Artikel yang dikirimkan ke prosiding ini akan diproses secara online dan menggunakan double blind review minimal oleh dua orang mitra bebestari.
Arjuna Subject : Matematika - Statistik dan Probabilitas
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Pemodelan Regresi Gamma pada Data Indeks Pembangunan Manusia Kabupaten/Kota di Provinsi Jawa Barat Tahun 2025 Salma Nera Hanifah; Fitri Amanah
Bandung Conference Series: Statistics 319-328
Publisher : UNISBA Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29313/bcss.v6i2.26122

Abstract

Abstract. Gamma Regression is a method of Generalized Linear Model (GLM) for analyzing the relationship between a positive, non-normally distributed response variable and predictor variables. This study aimed to model the factors affecting the Human Development Index (HDI) of regencies/cities in West Java Province 2025. The study used secondary data from BPS of West Java Province, covering 27 regencies/cities. The response variable was HDI, while the predictor included the percentage of poor population, access to improved drinking water, life expectancy, real expenditure per capita, school participation rate for individuals aged 19–23 years, and the open unemployment rate. The analysis included descriptive statistics, Gamma distribution testing, multicollinearity testing, and Gamma Regression using Maximum Likelihood Estimation (MLE) with the Fisher Scoring algorithm. The results showed that the HDI data followed a Gamma distribution and exhibited no multicollinearity. Partially, the percentage of poor population, real expenditure per capita, and the school participation rate for individuals aged 19–23 years significantly affected HDI at the 5% significance level. Simultaneously, the model was significant (likelihood ratio statistic (= 91.456), p < 0.0001). The coefficient of determination was 96.62%, indicating the model explained most of the HDI variation of regencies/cities in West Java Province in 2025. Abstrak. Regresi Gamma merupakan salah satu metode dalam Generalized Linear Model (GLM) yang digunakan untuk menganalisis hubungan antara variabel respon yang bernilai positif dan berdistribusi tidak simetris dengan variabel prediktor. Penelitian ini bertujuan memodelkan faktor-faktor yang memengaruhi data Indeks Pembangunan Manusia (IPM) Kabupaten/Kota di Provinsi Jawa Barat tahun 2025. Penelitian menggunakan data sekunder dari BPS Provinsi Jawa Barat Tahun 2025 sebanyak 27 kabupaten/kota. Variabel respon dalam penelitian ini adalah IPM, sedangkan variabel prediktor meliputi persentase penduduk miskin, akses air minum layak, usia harapan hidup, pengeluaran riil per kapita, angka partisipasi sekolah usia 19–23 tahun, dan tingkat pengangguran terbuka. Tahapan analisis meliputi analisis deskriptif, uji distribusi gamma, uji multikolinearitas, serta pemodelan regresi gamma menggunakan metode Maximum Likelihood Estimation (MLE) dengan algoritma Fisher Scoring. Hasil penelitian menunjukkan bahwa data IPM mengikuti distribusi gamma dan tidak mengalami masalah multikolinearitas. Secara parsial, variabel persentase penduduk miskin, pengeluaran riil per kapita, dan angka partisipasi sekolah usia 19-23 tahun berpengaruh signifikan terhadap IPM pada taraf signifikansi 5%. Secara Simultan, model yang diperoleh signifikan dengan nilai statistik uji sebesar 91,456 dan p-value < 0,0001. Nilai koefisien determinasi sebesar 96,62% menunjukkan bahwa model mampu menjelaskan sebagian besar variasi IPM Kabupaten/Kota di Provinsi Jawa Barat tahun 2025.

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