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All Journal BAREKENG: Jurnal Ilmu Matematika dan Terapan Inferensi Jurnal Eurekamatika Journal Of Human And Education (JAHE) Jurnal Ilmu Kesehatan dan Gizi Science and Education Journal Mathematical Journal of Modelling and Forecasting J-KOMA : Jurnal Ilmu Komputer dan Aplikasi Diophantine Journal of Mathematics and Its Applications
Cinta Rizki Oktarina
Universitas Bengkulu
Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Energy Engineering Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mathematics Mechanical Engineering Physics Social Sciences Transportation Other

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Pemetaan Spasial Keterkaitan Faktor Risiko Kematian Neonatal dengan Mixed Geographically Weighted Regression Cinta Rizki Oktarina; Sri Syuhada Putri; Reza Pahlepi; Avrillia Permata Hati4; Dyah Setyo Rini
Jurnal Ilmu Kesehatan dan Gizi Vol. 2 No. 2 (2024): April : Jurnal Imu Kesehatan dan Gizi
Publisher : Pusat Riset dan Inovasi Nasional

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55606/jikg.v2i2.2818

Abstract

Neonatal mortality is a major issue in developing countries, particularly in Indonesia. Data reveals that Neonatal Mortality Rate (NMR) contributes to 59% of infant deaths in Indonesia. Infant mortality rates remain high in Indonesia, at 20 per 1,000 live births. West Java has recorded a significant decline in neonatal mortality rates, dropping from 9.9 per 1,000 live births in 2019 to 9 per 1,000 in 2021. Factors influencing neonatal mortality have been extensively studied, including through the Mixed Geographically Weighted Regression (MGWR) method. The MGWR model combines local and global models, generating parameter estimators that are both local and global according to the observation locations. This research uses secondary data from the health profile of West Java, with the dependent variable being the number of neonatal deaths in 27 districts/cities in the year 2020. MGWR analysis results indicate that congenital anomalies have a local impact, while low birth weight and complete neonatal visits affect the entire West Java region globally. This study offers vital insights into the factors contributing to neonatal mortality in West Java and can serve as a foundation for targeted policy improvements and healthcare interventions
Karakteristik Daerah Rawan Banjir Di Provinsi Bengkulu Rizki Oktarina, Cinta; Pahlepi, Reza; Syuhada Putri, Sri; Agustina, Dian
Science and Education Journal (SICEDU) Vol. 2 No. 3 (2023): Science and Education Journal 2023
Publisher : Faculty Of Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/sicedu.v2i3.139

Abstract

Banjir adalah bencana alam yang sering terjadi di berbagai belahan dunia, menyebabkan kerugian besar baik dalam hal ekonomi maupun sosial. Pemahaman mendalam tentang karakteristik daerah rawan banjir sangat penting dalam upaya mitigasi dan pengelolaan risiko banjir. Oleh karena itu, penelitian ini akan membahas beberapa pengelompokan daerah karakteristik utama dari daerah rawan banjir, di provinsi bengkulu dengan menggunakan analisis Biplot. Hasil dari penelitian ini diperoleh beberapa daerah di Provinsi Bengkulu yang memiliki karakteristik daerah rawan banjir sama antara lain, yang pertama kabupaten bengkulu utara, kabupaten kaur dan kabupaten lebong memiliki kemiripan karakteristik pada jumlah sungai, luas hutan dan perairan serta jumah danau, waduk serta bendungan. Kedua, kabupaten bengkulu selatan, kabupaten rejang lebong, kabupaten seluma dan kabupaten kepahiang memiliki kemiripan karakteristik pada jumlah embung, jumlah saluran irigasi, dan jumlah mata air. Ketiga, kabupaten bengkulu tengah dan kota bengkulu tidak memiliki karakteristik berdasarkan keenam variabel yang ada. Terakhir, kabupaten muko-muko. Kabupaten muko-muko tidak berdekatan dengan kabupaten/kota manapun pada kuadran 4 yang artinya tidak diperoleh jarak antar kabupaten/kota.
Pemodelan IPM di Provinsi Bengkulu dengan Pendekatan Metode Geographically Weighted Regression (GWR) dan Geographically Temporally Weighted Regression (GTWR) Oktarina, Cinta Rizki; Rizal, Jose; Faisal, Fachri; Tasyah, Qhiky Lioni; Pratiwi, Stevy Cahya
Jurnal EurekaMatika Vol 12, No 1 (2024): Jurnal EurekaMatika
Publisher : Universitas Pendidikan Indonesia (UPI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.17509/jem.v12i1.66629

Abstract

The Geographically Temporally Weighted Regression (GTWR) method is a development of the Geographically Weighted Regression (GWR) method, namely by considering elements of location and time. This research aims to obtain the best estimation results between the GWR and GTWR methods applied to human development index data in Bengkulu Province for 2018–2022. There are three variables modelled, namely three independent variables: life expectancy, average years of schooling, and open unemployment rate, while the dependent variable is the Human Development Index. The research results show that the three independent variables significantly influence the dependent variable and have spatial heterogeneity in the modelled data. In addition, the coefficient of determination value for GTWR is 99.98%, while for GWR it is 99.74%, so the GTWR method is better for modelling the Human Development Index in Bengkulu Province for 2018–2022.Keywords: Coefficient of Determination, GWR Method, GTWR Method, Human Development Index, Spatial heterogeneity.AbstrakMetode Geographically Temporally Weighted Regression (GTWR) merupakan pengembangan dari metode Geographically Temporally Weighted Regression (GWR), yakni dengan mempertimbangkan unsur lokasi dan waktu. Penelitian ini bertujuan untuk mendapatkan hasil estimasi terbaik antar metode GWR dan GTWR yang diterapkan pada data indeks pembangunan manusia di Provinsi Bengkulu Tahun 2018-2022. Terdapat tiga variabel yang dimodelkan, yakni tiga variabel bebas: angka harapan hidup, rata-rata lama sekolah, dan tingkat pengangguran terbuka, sedangkan variabel takbebas adalah Indeks Pembangunan Manusia. Hasil penelitian menunjukkan bahwa ketiga variabel bebas tersebut mempengaruhi variabel takbebas secara signifikan dan terdapat sifat heterogenitas spasial pada data yang dimodelkan. Sebagai tambahan, nilai koefisien determinasi untuk GTWR sebesar 99.98%, sedangkan untuk GWR sebesar 99.74%, jadi metode GTWR lebih baik untuk memodelkan Indeks Pembangunan Manusia di Provinsi Bengkulu tahun 2018-2022.
Modelling the Number of Stunting Cases in Indonesia in 2022 Using Negative Binomial Regression to Address Overdispersion Oktarina, Cinta Rizki; Pahlepi, Reza
Mathematical Journal of Modelling and Forecasting Vol. 2 No. 2 (2024): December 2024
Publisher : Universitas Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24036/mjmf.v2i2.27

Abstract

This study models the incidence of stunting in toddlers in Indonesia in 2022 using negative binomial regression to address the overdispersion issue often present in count data. The Poisson regression model, typically used for count data, showed less accurate results due to the variance exceeding the mean, indicating overdispersion. By adopting a negative binomial regression approach, this study accommodates higher variability in the data, leading to more accurate estimates. The results reveal that the percentage of pneumonia cases and low birth weight are significant factors in stunting incidence. In contrast, other variables, such as complete basic immunization and poverty levels, are insignificant in the final model. The final negative binomial model yielded a lower AIC value than the initial model, indicating an improved model fit, with an R-squared (Nagelkerke's R²) of 50.50%. This study offers enhanced insights into the factors influencing stunting, supporting more targeted health policy decisions to reduce stunting rates in Indonesia.
BIRESPONSE SPLINE TRUNCATED NONPARAMETRIC REGRESSION MODELING FOR LONGITUDINAL DATA ON MONTHLY STOCK PRICES OF THREE PRIVATE BANKS IN INDONESIA Pahlepi, Reza; Sriliana, Idhia; Agwil, Winalia; Oktarina, Cinta Rizki
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 19 No 4 (2025): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol19iss4pp2467-2480

Abstract

This study investigates the application of a truncated spline nonparametric regression model for biresponse analysis of longitudinal data, focusing on modeling monthly stock prices specifically opening and closing prices of three private banks in Indonesia: Bank Mayapada, Bank Mega, and Bank Sinar Mas. The data used in this research are secondary data sourced from the website Id.Investing.com and monthly financial statement publications of three private banks in Indonesia. Longitudinal data, combining cross-sectional and time-series dimensions, are utilized to capture trends and patterns not detectable in traditional cross-sectional analysis. The truncated spline method is selected for its adaptability to nonlinear relationships and abrupt data behavior changes. The model incorporates three predictor variables traded stock volume, total assets, and total liabilities and evaluates their influence on stock prices. Assumptions of longitudinal data are validated using the Ljung-Box autocorrelation test, Bartlett’s sphericity test, and Pearson correlation. Results confirm significant within-subject correlations, independence between subjects, and strong interdependence between response variables. The optimal configuration is determined using Generalized Cross Validation (GCV), with up to three knots considered for segmentation. Weighted Least Squares (WLS) is employed for parameter estimation, accounting for within-subject correlations. Model evaluation based on Mean Absolute Percentage Error (MAPE) indicates high accuracy, with all MAPE values below 5%. The highest MAPE value is 4.41% for the closing price of Bank Mayapada, while the lowest is 2.65% for the opening price of the same bank. The segmentation analysis reveals that traded stock volume and total assets positively influence stock prices, while total liabilities exhibit a predominantly negative impact. The model is limited to internal financial indicators and does not include external macroeconomic factors such as interest rates or inflation. This study is the first to apply a biresponse truncated spline nonparametric regression approach to analyze stock prices of private banks in Indonesia by simultaneously modeling both opening and closing prices, providing a flexible and effective method for capturing complex patterns in longitudinal financial data.
Desa Cantik, Desa Cakap Statistik Agwil, Winalia; Sriliana, Idhia; Rini, Dyah Setyo; Supianti, Filo; Oktarina, Cinta Rizki; Famuji, Ahmad
Journal Of Human And Education (JAHE) Vol. 4 No. 1 (2024): Journal Of Human And Education (JAHE)
Publisher : Universitas Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/jh.v4i1.708

Abstract

Dalam pembangunan desa tentunya diperlukan pengetahuan terkait dengan potensi desa yang dimiliki. Pengembangan potensi desa dapat dilakukan dengan penggalian data awal serta pengumpulan data untuk pemetaan potensi desa. Sayangnya, sering kali terdapat desa yang memiliki sumber daya dengan kompetensi yang masih belum memadai sehingga di perlukannya peningkatan kompetensi perangkat desa tentang pengumpulan dan pemanfaatan data. Program studi S1 Statistika dan Pojok Statistik Universitas Bengkulu bermitra bersama BPS melakukan pengembangan peningkatan kompetensi terhadap perangkat desa dengan melakukan pengabdian Desa Cantik yang diharapkan dapat meningkatkan kemampuan perangkat desa dalam memanfaatkan data. Pengabdian ini dilakukan pada Kelurahan Tanah Panah Kota Bengkulu. Melalui pengabdian ini, diharapkan pihak terkait dapat memanfaatkan data yang ada. Hasil dari pengabdian ini mampu memberikan pemahaman terkait dengan pemanfaatan data desa serta pengolahan data Microsoft excel dan Canva. Dimana perangkat desa mengetahui tipe-tipe data dan pengaplikasiannya dalam memvisualisasi data pada Microsoft Excel, mengetahui fitur-fitur pada canva yang dapat digunakan pada elemen-elemen infografis. Sebagai rekomendasi bentuk kegiatan pengabdian Desa Cantik patut dilakukan untuk desa-desa yang lain untuk meningkatkan kemampuan dalam memanfaatkan data-data desa.
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.
Statistical Modelling of Rainfall Data Using Robust Kriging with Gaussian Semivariogram in Bengkulu Province Rizki Oktarina, Cinta; Pahlepi, Reza
Mathematical Journal of Modelling and Forecasting Vol. 3 No. 2 (2025): December 2025
Publisher : Universitas Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24036/mjmf.v3i2.46

Abstract

This study aims to predict rainfall in Bengkulu Province for January 2024 using the Robust Kriging method, an advanced geostatistical approach designed to handle outliers and non-ideal spatial characteristics. The novelty of this study lies in integrating Robust Kriging with a Gaussian semivariogram for short-term rainfall prediction in Bengkulu Province. This combination has not been explored in previous hydrometeorological studies. Rainfall data were obtained from the Meteorology, Climatology, and Geophysics Agency (BMKG) and analysed to identify spatial dependency and variation. The analysis began with descriptive statistics, assumption testing, and outlier detection, followed by the construction of robust empirical and theoretical semivariogram models. Three semivariogram models, Spherical, Exponential, and Gaussian, were compared to determine the most suitable model based on Mean Squared Error (MSE) and Mean Absolute Percentage Error (MAPE) values. The results indicate that the Gaussian model produced the smallest MSE and MAPE values, showing the best fit to the empirical semivariogram. The Robust Kriging interpolation generated spatial predictions of rainfall intensity across Bengkulu, showing higher rainfall in the north and lower rainfall in the south. The findings demonstrate that Robust Kriging effectively improves prediction accuracy by minimizing the influence of outliers and optimizing spatial weighting. These results provide valuable insights for water resource management, agricultural planning, and hydrometeorological disaster mitigation in Bengkulu Province.
Outlier Handling in Applied Regression: Performance Comparison Between Least Trimmed Squares and Maximum Likelihood-Type Estimators Oktarina, Cinta Rizki; Andini Setyo Anggraeni; Muhammad Arib Alwansyah; Reza Pahlepi
J-KOMA : Jurnal Ilmu Komputer dan Aplikasi Vol 8 No 02 (2025): J-KOMA : Jurnal Ilmu Komputer dan Aplikasi
Publisher : Universitas Negeri Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21009/JKOMA.082.01

Abstract

Poverty analysis often relies on regression models whose performance can deteriorate in the presence of outliers, leading to biased estimates and unreliable conclusions. This study aims to evaluate the effectiveness of robust regression methods compared with Ordinary Least Squares (OLS) when modeling poverty levels across 154 regions in Sumatra. Four socioeconomic indicators were used as predictors, and outlier detection was conducted using the DFFITS approach. After identifying deviations from normality and the presence of influential observations, two robust estimation techniques M-estimation and Least Trimmed Squares (LTS) were applied to improve model stability. The results show that while all predictors significantly influence poverty, the LTS estimator provides the most accurate and robust performance, yielding the smallest Mean Squared Error (MSE) and an R-squared value of 53.37%. These findings demonstrate that LTS is better suited than OLS and M-estimation for handling data contamination and offers a more reliable approach for modeling poverty determinants
Comparison of Robust Regression Methods: Least Trimmed Squares and Maximum Likelihood for Handling Outliers Kurniawan, Andro; Oktarina, Cinta Rizki; Sabarinsyah, Sabarinsyah
Diophantine Journal of Mathematics and Its Applications Vol. 4 No. 2 (2025): Vol. 4 No. 2 (2025)
Publisher : UNIB Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33369/diophantine.v4i2.46149

Abstract

This study investigates the determinants of per capita expenditure in 154 regencies and cities across Sumatra Island. The use of the Ordinary Least Squares method is deemed inappropriate due to violations of classical assumptions and the presence of outliers within the dataset. To address these issues, robust regression approaches are applied, specifically M-estimation and Least Trimmed Squares (LTS). The dependent variable in the analysis is per capita expenditure, while the explanatory variables include poverty line, human development index, average years of schooling, and expected years of schooling. The estimation procedures are performed using both raw and standardized data. The empirical results demonstrate that each independent variable significantly influences per capita expenditure under both robust estimation techniques. To determine the most reliable method, the residual standard error is used as the evaluation criterion. The outcomes indicate that the LTS estimator applied to standardized data provides the lowest error value, suggesting that it is the most suitable approach for estimating the regression parameters associated with per capita expenditure in Sumatra.
Co-Authors Agwil, Winalia Anggraeni, Andini Setyo Avrillia Permata Hati4 Dian Agustina Dyah Setyo Rini Fachri Faisal Famuji, Ahmad Idhia Sriliana Jose Rizal Kurniawan, Andro Muhammad Arib Alwansyah Pepi Novianti Pratiwi, Stevy Cahya Reza Pahlepi Reza Pahlepi, Reza Rini, Dyah Setyo Sabarinsyah, Sabarinsyah Sigit Nugroho Sri Syuhada Putri Sunandi, Etis Supianti, Filo Syuhada Putri, Sri Tasyah, Qhiky Lioni