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All Journal FORUM STATISTIKA DAN KOMPUTASI Teknosains: Media Informasi Sains dan Teknologi GRADIEN International Journal of Advances in Intelligent Informatics Jurnal Sains Matematika dan Statistika Jurnal Geocelebes BAREKENG: Jurnal Ilmu Matematika dan Terapan Martabe : Jurnal Pengabdian Kepada Masyarakat Jurnal Pengabdian Masyarakat Bumi Raflesia Jurnal Berdaya Mandiri Dharma Raflesia : Jurnal Ilmiah Pengembangan dan Penerapan IPTEKS JINAV: Journal of Information and Visualization Journal of Applied Data Sciences Pattimura Proceeding : Conference of Science and Technology Prosiding Seminar Nasional Official Statistics Indonesian Journal of Community Empowerment and Service Aceh International Journal of Science and Technology Jurnal Sintak Jurnal Hasil-Hasil Pengabdian dan Pemberdayaan Masyarakat (JHP2M) Journal of Statistics and Data Science Limits: Journal of Mathematics and Its Applications
Pepi Novianti
Program Studi Statistika, FMIPA Universitas Bengkulu
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Ordinal Logistic Regression Model for Human Development Index: A Case Study of Provinces in The southern part of Sumatra Suhaimi, Alus Ahmad; Novianti, Pepi; Pangesti, Riwi Dyah
JURNAL SINTAK Vol. 4 No. 1 (2025): SEPTEMBER 2025
Publisher : LPPM-ITEBA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62375/jsintak.v4i1.723

Abstract

Ordinal logistic regression is a statistical method used to analyze ordinal dependent variables with three or more categories. This study aims to model the Human Development Index (HDI) in the southern Sumatra region, which includes the provinces of Bengkulu, Bangka Belitung, Jambi, South Sumatra, and Lampung. HDI is categorized into three groups: low, medium, and high. The predictor variables used include Gross Regional Domestic Product (GRDP), poverty rate, access to safe drinking water, open unemployment rate (OUR), and labor force participation rate (LFPR). The analysis results indicate that three variables significantly influence HDI: the percentage of the poor population, the proportion of households with access to safe drinking water, and the open unemployment rate (OUR). This study did not conduct a spatial heterogeneity test; therefore, it is recommended that future research incorporate such a test
PELATIHAN ANALISIS DATA MENGGUNAKAN SPSS PADA MAHASISWA PROGRAM KEGURUAN UNIVERSITAS SWASTA DI KOTA BENGKULU Sriliana, Idhia; Dyah Pangesti, Riwi; Setyo Rini, Dyah; Novianti, Pepi; Swita, Baki; Dwi Lorenza, Kenny; Abdul Aziz, Ali
Martabe : Jurnal Pengabdian Kepada Masyarakat Vol 8, No 5 (2025): MARTABE : JURNAL PENGABDIAN KEPADA MASYARAKAT
Publisher : Universitas Muhammadiyah Tapanuli Selatan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31604/jpm.v8i5.2135-2141

Abstract

Kegiatan pengabdian kepada masyarakat ini bertujuan untuk memperluas wawasan serta meningkatkan kemampuan mahasiswa Program Studi S1 Pendidikan Biologi Universitas Muhammadiyah Bengkulu (UM Bengkulu) terhadap analisis data statistik menggunakan software SPSS. Pelatihan ini dilatarbelakangi oleh kebutuhan mahasiswa untuk memahami teknik analisis statistik yang sering kali dianggap sulit bagi mahasiswa yang tidak memiliki latar belakang ilmu statistika. Metode pelaksanaan kegiatan ini mencakup persiapan, impelemntasi, dan evaluasi, diantaranya pembuatan materi edukatif berupa modul dan poster, presentasi, demonstrasi penggunaan SPSS, serta diskusi. Hasil yang diperoleh dari pelatihan ini memperlihatkan peningkatan pemahaman dan keterampilan mahasiswa terhadap penggunaan SPSS dan interpretasi hasil analisis statistik. Evaluasi dilakukan melalui pre-test dan post-test yang memperlihatkan terdapatnya peningkatan pengetahuan serta keterampilan mahasiswa setelah mengikuti pelatihan. Kegiatan pengabdian ini diharapkan mampu berkontribusi dalam mendukung penyelesaian skripsi mahasiswa serta meningkatkan mutu penelitian di lingkungan akademik.
An Analysis of Factors Contributing to Extended Study Duration Among Students of the Faculty of Mathematics and Natural Sciences, University of Bengkulu Using Binary Logistic Regression Wahyuliani, Indah; Novianti, Pepi
Journal of Statistics and Data Science Vol. 2 No. 2 (2023)
Publisher : UNIB Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33369/jsds.v2i2.41287

Abstract

Logistic regression is a statistical method used to analyze the relationship between a dichotomous dependent variable and one or more independent variables, which may be numerical or categorical. In this study, binary logistic regression is applied to identify the factors influencing the study duration of students in the Faculty of Mathematics and Natural Sciences at the University of Bengkulu. These factors include both internal and external elements, such as cumulative GPA (Grade Point Average), gender, parents’ occupation, scholarship status, and university admission pathway. The results show that GPA significantly affects the length of study, with an odds ratio of 1102.13, indicating that each one-unit increase in GPA greatly increases the likelihood of graduating on time. This study suggests the use of additional statistical techniques, such as bootstrapping, to enhance parameter estimation accuracy and recommends reporting effect sizes, such as odds ratios, for a more comprehensive interpretation of the relationship between independent and dependent variables.
Perbandingan Metode Regresi Ridge dan Jackknife Ridge Regression pada Data Tingkat Pengangguran Terbuka Andini, Agita; Sunandi, Etis; Novianti, Pepi; Sriliana, Idhia; Agwil, Winalia
Limits: Journal of Mathematics and Its Applications Vol. 22 No. 1 (2025): Limits: Journal of Mathematics and Its Applications Volume 22 Nomor 1 Edisi Ma
Publisher : Pusat Publikasi Ilmiah LPPM Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/limits.v22i1.3374

Abstract

Regression analysis is a statistical technique used to analyze the relationship between predictor and response variables. One of the parameter estimation methods commonly used for regression analysis is Ordinary Least Squares. This method produces unbiased and efficient estimates, known as BLUE (Best Linear Unbiased Estimator). In multiple linear regression analysis involving more than one predictor variable, it is essential to meet model assumptions such as the absence of multicollinearity. Multicollinearity is a condition where predictor variables have a high correlation, which can disrupt the stability of parameter estimates. Therefore, Ridge Regression and Jackknife Ridge Regression methods were used to address this issue. Both methods modify the least squares method by adding a bias constant value. This research uses the Open Unemployment Rate (OUR) data in Sumatra in 2022, and 3 predictor variables exhibit multicollinearity. Based on the analysis comparing the Mean Squared Error (MSE) values, the Jackknife Ridge Regression method yields the smallest MSE value, 0.004. Both methods are effective in addressing multicollinearity and identifying significant predictor variables for OUR in Sumatra Island, namely the Human Development Index (HDI), average years of schooling, number of poor people, Life Expectancy (LE), population density and inactive population
Comparative Analysis of SARIMA, FFNN, and Hybrid Models for Sea Surface Temperature Prediction at Enggano Island (2018–2024) Natisharevi, Raditya Janaloka; Rizal, Jose; Firdaus, Firdaus; Novianti, Pepi; Lestari, Wina Ayu
JURNAL GEOCELEBES Vol. 9 No. 2: October 2025
Publisher : Departemen Geofisika, FMIPA - Universitas Hasanuddin, Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70561/geocelebes.v9i2.46445

Abstract

Sea Surface Temperature (SST) is a key oceanographic variable that influences fish distribution and the livelihoods of coastal communities. On Enggano Island, where most residents rely on fishing, SST is critical for identifying optimal fishing grounds due to limited accessibility and high operational costs. Accurate modeling and forecasting of SST are therefore essential for effective fisheries management and sustainable resource use. This study analyzes and predicts monthly SST patterns in Enggano Island using Seasonal Autoregressive Integrated Moving Average (SARIMA), Feed Forward Neural Network (FFNN), and Hybrid SARIMA-FFNN models. SARIMA effectively captures linear trends and seasonal variations but struggles with nonlinear dynamics and requires statistical assumptions. Conversely, FFNN models nonlinear relationships without such assumptions but is less efficient in representing linear and seasonal structures. The hybrid SARIMA-FFNN combines the strengths of both approaches, integrating linear-seasonal accuracy with nonlinear adaptability. Monthly SST data from January 2018 to December 2024, covering northern, eastern, southern, and western regions of Enggano Island, were analyzed. Results show that all models achieved high predictive accuracy, with MAPE values below 10%. Based on RMSE, FFNN outperformed the other models across all regions (north: 1.173, east: 0.999, south: 1.245, west: 1.049), confirming FFNN as the most accurate model for SST prediction. Predicted SST values across the four regions exhibited only minor differences, offering fishermen flexibility in selecting fishing grounds. Sustainable fishing strategies should also consider species-specific temperature preferences and other ecological factors influencing fish distribution.
Co-Authors Abdul Aziz, Ali Agwil, Winalia Ahmad Ansori Mattjik Andini, Agita Anjani, Retno Tri Baki Swita Crisdianto, Riki Dedi Rosadi Devni Prima Sari Dian Agustina Dian Agustina Dian Agustina Dwi Lorenza, Kenny Dyah Pangesti, Riwi Dyah Setyo Rini Dyah Setyo Rini Dyah Setyorini Eka Angasa Etis Sunandi Fachri Faisal Fadila, Risfa Faeza, Veronnica Noer Fairuzindah, Athaya Fhaeza, Veronnica Noer Firdaus Firdaus Ghufira Hayadi, Ilham Herlin Fransiska Hermansah I Made Sumertajaya Idhia Sriliana Jose Rizal Jose Rizal Jose Rizal Karuna, Elisabeth Evelin Lestari, Wina Ayu Marta, Rezkyan Natisharevi, Raditya Janaloka Nur Afandi Pangesti, Riwi Dyah Rini, Dyah Setyo Riwi Dyah Pangesti Setyo Rini, Dyah Sigit Nugroho Sigit Nugroho Sihombing, Esther Damayanti Suhaimi, Alus Ahmad Sunandi, Etis Susi Wijuniamurti Swita, Baki Ulfasari Rafflesia Ulfasari Rafflesia Ulfasari Rafflesia Ulfasari Rafflesia Wahyuliani, Indah