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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 Inferensi JINAV: Journal of Information and Visualization Journal of Applied Data Sciences MATHunesa: Jurnal Ilmiah Matematika 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
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.
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.
Estimation of Stunting and Wasting in Sumatra 2022 with Nadaraya-Watson Kernel and Penalized Spline Oktarina, Cinta Rizki; Nugroho, Sigit; Sriliana, Idhia; Novianti, Pepi; Sunandi, Etis; Pahlepi, Reza
Inferensi Vol 8, No 3 (2025)
Publisher : Department of Statistics ITS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j27213862.v8i3.23330

Abstract

This study aims to estimate the prevalence of Stunting and Wasting in Sumatra in 2022 using nonparametric regression methods, specifically the Nadaraya-Watson Kernel and Penalized Spline regression models. Both models were applied to assess the relationship between these two correlated response variables and various predictor variables, such as low birth weight, sanitary facilities, poor population, and exclusive breastfeeding. The results showed that the Nadaraya-Watson Kernel regression, particularly using the Gaussian kernel, provided the best fit with minimal prediction error, as indicated by its low Generalized Cross-Validation (GCV) value of 0.024 and high R-squared values (0.9992 for Stunting and 0.9995 for Wasting). In contrast, the Epanechnikov kernel and Biweight kernel produced higher GCV values (0.110 and 0.356, respectively), indicating less optimal performance. For the Penalized Spline model, optimal parameters were determined with a smoothing parameter λ of 5 and 3 knots, which balanced model flexibility and smoothness. This research underscores the potential of nonparametric regression techniques in capturing complex relationships in health data and provides insights for improving interventions aimed at addressing child malnutrition in Indonesia.
Pemodelan log linier tiga dimensi dalam tingkat penyelesaian pendidikan di Indonesia pada tahun 2020 sampai 2023 Hayadi, Ilham; Anggraini, Ranisyah; Aria, Aurel Giovani; Sunandi, Etis; Novianti, Pepi
Teknosains Vol 20 No 1 (2026): Januari-April
Publisher : Fakultas Sains dan Teknologi Universitas Islam Negeri Alauddin Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24252/teknosains.v20i1.51709

Abstract

Kesenjangan pendidikan di Indonesia masih menjadi isu penting yang memengaruhi berbagai kelompok demografis. Penelitian ini bertujuan untuk menganalisis hubungan antara tingkat penyelesaian pendidikan, jenis kelamin, dan tahun di Indonesia untuk periode 2020–2023 menggunakan pemodelan log-linear tiga dimensi, metode statistik untuk memodelkan frekuensi sel dalam tabel kontingensi multiarah dan mengidentifikasi efek utama dan interaksi antar variabel kategorikal. Penelitian ini didasarkan pada persistensi kesenjangan dalam penyelesaian pendidikan di berbagai kelompok demografis, khususnya berdasarkan jenis kelamin dan waktu. Data yang digunakan adalah data kategorikal tentang tingkat pendidikan, jenis kelamin, dan tahun yang dianalisis menggunakan model log-linear saturnated dan homogeneous, kemudian dibandingkan melalui nilai deviasi, uji chi-square, dan kriteria AIC. Hasil penelitian menunjukkan bahwa model saturnated merupakan model terbaik dengan nilai AIC sebesar 510,04 dan deviasi residual mendekati nol, serta terdapat interaksi tiga arah yang signifikan antara tingkat penyelesaian pendidikan, jenis kelamin, dan tahun, yang mengindikasikan bahwa ketiga variabel tersebut saling memengaruhi secara simultan. Tahun 2023 menunjukkan peningkatan yang signifikan dalam tingkat penyelesaian pendidikan, sedangkan tahun 2020 menunjukkan penurunan. Terdapat pula perbedaan pola penyelesaian pendidikan antara laki-laki dan perempuan pada setiap tingkat dan tahun.
PREDIKSI HARGA SAHAM PT BANK NEGARA INDONESIA (PERSERO) TBK MENGGUNAKAN MODEL STOKASTIK GEOMETRIC BROWNIAN MOTION : (STUDI KASUS: DATA HARGA SAHAM BBNI 2024) Rizal, Jose; Rahma Sholeha, Tari; Hidayati, Nurul; Novianti, Pepi; Sriliana, Idhia
MATHunesa: Jurnal Ilmiah Matematika Vol. 14 No. 1 (2026)
Publisher : Universitas Negeri Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26740/mathunesa.v14n1.p227 - 234

Abstract

The Geometric brownian motion (GBM) model is widely used in predicting financial instruments such as stocks, because it can overcome the weakness of Brownian motion (BM) which can produce negative values. This study aims to apply the GBM model to predict the daily stock price of PT Bank Negara Indonesia (Persero) Tbk (BBNI) for the period from January to December 2024. The data used is secondary data on daily closing prices obtained from Investing.com, with a distribution of 95% training data and 5% testing data. Parameter drift and volatility are estimated using the Maximum Likelihood Estimation (MLE) method, while model accuracy is evaluated using MAPE and RMSE. The results show that a data proportion of 95%:5% provides the best prediction performance with a MAPE value of 5.724% and an RMSE of 0.267, indicating a high level of accuracy. Thus, the GBM model is reliable enough to describe the price movements of BBNI shares. Future research could develop models that take external factors into account or compare them with other stochastic models.
Co-Authors Abdul Aziz, Ali Ahmad Ansori Mattjik Anggraini, Ranisyah Anjani, Retno Tri Aria, Aurel Giovani Baki Swita Cinta Rizki Oktarina 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 Karuna, Elisabeth Evelin Lestari, Wina Ayu Marta, Rezkyan Natisharevi, Raditya Janaloka Nur Afandi Nurul Hidayati Pangesti, Riwi Dyah Rahma Sholeha, Tari Reza Pahlepi, Reza 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 Wahyuliani, Indah