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All Journal Media Statistika JURNAL MATEMATIKA STATISTIKA DAN KOMPUTASI Jurnal Riset Statistika Bandung Conference Series: Statistics Statistika Jurnal Kajian dan Terapan Matematika
Fitri Amanah
Prodi Statistika, Fakultas Matematika Dan Ilmu Pengetahuan Alam, Universitas Islam Bandung, Indonesia
Computer Science & IT Decision Sciences, Operations Research & Management Education Mathematics

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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.
Peramalan Harga Saham PT Bank Rakyat Indonesia (Persero) Tbk Menggunakan Model Autoregressive Integrated Moving Average (ARIMA) Muhamad Arifianto Darmawan; Fitri Amanah
Bandung Conference Series: Statistics 355-364
Publisher : UNISBA Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29313/bcss.v6i2.26220

Abstract

Abstract. Stock price is an important indicator reflecting a company's performance and serves as a basis for investment decision-making. The fluctuating movement of stock prices requires a forecasting method capable of producing accurate short-term predictions. This study aims to determine the best Autoregressive Integrated Moving Average (ARIMA) model for forecasting the stock price of PT Bank Rakyat Indonesia (Persero) Tbk (BBRI). The data used consist of daily closing stock prices from January 1, 2025 to August 15, 2025, totaling 143 observations. The analysis stages include stationarity testing using the Box-Cox transformation and the Augmented Dickey-Fuller (ADF) test, model identification through the Autocorrelation Function (ACF) and Partial Autocorrelation Function (PACF), parameter estimation using Maximum Likelihood Estimation (MLE), model evaluation based on Residual Mean Square (MS) and the Ljung-Box diagnostic test, and forecasting accuracy measurement using Mean Absolute Percentage Error (MAPE). The results indicate that the ARIMA(0,1,2) model is the best forecasting model with the smallest Residual Mean Square (MS) value of 10,132.8 and residuals satisfying the white noise assumption. Forecasting results show that the stock price of PT Bank Rakyat Indonesia (Persero) Tbk tends to remain stable over the next seven periods with excellent forecasting accuracy based on the MAPE value. Keywords: ARIMA, forecasting, stock price, time series, BBRI. Abstrak. Harga saham merupakan salah satu indikator penting yang mencerminkan kinerja perusahaan serta menjadi dasar pengambilan keputusan investasi. Pergerakan harga saham yang bersifat fluktuatif menyebabkan diperlukan metode peramalan yang mampu menghasilkan prediksi jangka pendek secara akurat. Penelitian ini bertujuan untuk menentukan model Autoregressive Integrated Moving Average (ARIMA) terbaik dalam meramalkan harga saham PT Bank Rakyat Indonesia (Persero) Tbk (BBRI). Data yang digunakan merupakan data harga penutupan harian saham BBRI periode 1 Januari 2025 sampai dengan 15 Agustus 2025 sebanyak 143 observasi. Tahapan analisis meliputi uji stasioneritas menggunakan transformasi Box-Cox dan uji Augmented Dickey-Fuller (ADF), identifikasi model melalui plot Autocorrelation Function (ACF) dan Partial Autocorrelation Function (PACF), estimasi parameter menggunakan Maximum Likelihood Estimation (MLE), evaluasi model berdasarkan Residual Mean Square (MS) dan uji diagnostik Ljung-Box, serta pengukuran akurasi menggunakan Mean Absolute Percentage Error (MAPE). Hasil penelitian menunjukkan bahwa model ARIMA(0,1,2) merupakan model terbaik dengan nilai Residual Mean Square (MS) sebesar 10.132,8 serta memenuhi asumsi residual white noise. Hasil peramalan menunjukkan harga saham PT Bank Rakyat Indonesia (Persero) Tbk cenderung stabil pada tujuh periode mendatang dengan tingkat akurasi peramalan yang sangat baik berdasarkan nilai MAPE. Kata Kunci: ARIMA, harga saham, peramalan, time series, BBRI.
Co-Authors Abdurakhman Abdurakhman Fanny Ahdasabilla Fauziah Roshafara Fitri Rahmawati Jasmine Devanti Bilbina Heriaty Muhamad Arifianto Darmawan Nadya Ayu Aprilia Nafa Nurhanifah Novianda Dwi Tanti Ramdani Nur Sugih Aji Puri Indah Lestari Renita Maharani Retno Subekti Salma Nera Hanifah Salwa Salsabila Sefri Imanuel Fallo