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All Journal TELKOMNIKA (Telecommunication Computing Electronics and Control) Bulletin of Electrical Engineering and Informatics LEMMA: Letters Of Mathematics Education MUST: Journal of Mathematics Education, Science and Technology BAREKENG: Jurnal Ilmu Matematika dan Terapan JOURNAL OF APPLIED INFORMATICS AND COMPUTING Eksakta : Berkala Ilmiah Bidang MIPA Jurnal Varian Pelita Eksakta Majalah Ilmiah Matematika dan Statistika (MIMS) Journal of Applied Data Sciences Journal of Science and Technology MATHunesa: Jurnal Ilmiah Matematika Journal of Mathematics UNP Mathematical Journal of Modelling and Forecasting Journal of Statistics and Data Science
Devni Prima Sari
Universitas Negeri Padang
Agriculture, Biological Sciences & Forestry Astronomy Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Physics Social Sciences Transportation Other

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University Election Analysis Logistic Regression Approach with Dummy and Ordinal Variables Miftha Delinda; Devni Prima Sari
Mathematical Journal of Modelling and Forecasting Vol. 1 No. 2 (2023): December 2023
Publisher : Universitas Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24036/mjmf.v1i2.13

Abstract

Education has a very important role to advance the development of a country. One of them is the university. Thus, if you continue your studies at university, it is hoped that you will have the knowledge and skills in accordance with the study program you are taking, which will later become the basic capital to be more competent in the world of work. Logistic regression is a statistical method that can be used to determine the factors that influence the choice of university for class XII Phase F students. The dependent variable consists of two categories. This research aims to determine the factors that influence the choice of university for class XII Phase F students. This type of research is applied research and uses primary data obtained from filling out questionnaires. This research was carried out at SMAN 3 Padang. The population in this study were all students in class XII Phase F at SMA Negeri 3 Padang with a sample of 78 students obtained using the Purposive Sampling method. The results of the research show that the factors that influence the choice of university for class XII Phase F students at SMA Negeri 3 Padang are father's work based on educated and trained labor, father's work based on educated labor, father's work based on trained labor, father's work based on uneducated and unskilled labor, convenient university location, easy access to transportation, rent, food and daily living costs -affordable days according to budget, and information from social media and websites.
Penentuan Premi Bersih Tahunan Asuransi Jiwa Dwiguna dengan Hukum De Moivre 1004, Anisa; Sari, Devni Prima
MATHunesa: Jurnal Ilmiah Matematika Vol. 12 No. 2 (2024)
Publisher : Universitas Negeri Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Asuransi jiwa dwiguna merupakan gabungan asuransi jiwa berjangka dan asuransi jiwa dwiguna murni walau jangka waktu asuransi telah berakhir, pemegang polis akan memperoleh uang santunan. Tujuan dari penelitian ini ialah untuk mengidentifikasi premi tahunan bersih untuk asuransi jiwa dwiguna. Premi tahunan dipengaruhi oleh premi tunggal dan nilai tunai anuitas hidup awal. Perhitungan premi dalam konteks asuransi jiwa dwiguna, pendekatan menggunakan hukum De Moivre didasarkan pada distribusi seragam dan diterapkan untuk mengevaluasi mortalitas. Misalnya, dalam kasus seorang karyawan berusia 35 tahun dengan masa asuransi selama 30 tahun dan suku bunga sebesar 2,5%, hukum De Moivre digunakan untuk analisis. Santunan yang diterima Rp.100.000.000,-, didapat premi bersih tahunan asuransi jiwa dwigunanya dengan hukum De Moivre sebesar Rp. 3.154.482,-.
Analysis of Seismic Data in Sumatra using Robust K-Means Clustering Rafflesia, Ulfasari; Rosadi, Dedi; Sari, Devni Prima; Novianti, Pepi
Journal of Applied Data Sciences Vol 6, No 1: JANUARY 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i1.523

Abstract

Indonesia is located within the Pacific Ring of Fire and frequently experiences significant seismic activities, rendering the region susceptible to hazards. Specifically, Sumatra is an island in the western part of the country, near the Eurasian and Indo-Australian tectonic plates. Over the past five years, an observable uptick in seismic events has been recorded in Sumatra. This research aimed to cluster the Sumatra region’s seismic data using the k-means algorithm and its extensions, including trimmed and robust sparse k-means, to determine the characteristics and patterns of seismic events. The k-means clustering algorithm operates effectively on many data but needs to work better in the presence of outliers. Meanwhile, the data identification reports the presence of outliers in the seismic data. The clustering analysis identified two main clusters, supported by multivariate and spatial outlier detection during preprocessing. The first cluster, encompassing 62% of seismic events, is located offshore near the Mentawai seismic gap, characterized by shallow depths (33–41 km) and magnitudes of 4.5–5.0 Ms. The second cluster, representing 28% of events, includes both mainland and offshore regions, associated with the Sumatran Fault system and slab deformation zones, at moderate depths (54–154 km) with magnitudes of 4.3–4.4 Ms. Rare deep-focus events exceeding depths of 214 km were identified as outliers. Evaluation using Silhouette, Davies-Bouldin, and Dunn indices determined that k=2 was the optimal number of clusters. This study contributes by integrating robust clustering methods to handle outliers, enhancing the reliability of seismic data analysis. This study demonstrates the value of applying trimmed and robust sparse k-means algorithms to improve clustering performance in regions with complex tectonic activity.
Application of the Generalized Autoregressive Conditional Heteroskedasticity (GARCH) Model in Forecasting the Volatility of Optimal Portfolio Stock Returns of the MNC36 Index Deswita, Siska; Sari, Devni Prima
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.v2i2.24

Abstract

Investment is a capital investment made by investors through the purchase of several stocks that are usually long-term with the hope that investors will benefit from increased stock prices. The most commonly used risk indicator in investing is volatility. Therefore, it is necessary to carry out modeling that can overcome the effects of heteroscedasticity to predict future volatility. Efforts are made to overcome the effects of heteroscedasticity by applying the Generalized Autoregressive Conditional Heterossexicity (GARCH) Model in Forecasting the Volatility of Optimal Portfolio Stock Returns on the MNC36 Index. This type of research is applied research that begins with reviewing the problem, analyzing relevant theories, and reviewing the problem and its application. Based on the results of data analysis using the residual normality test through the Jarque-Bera test, it was obtained that the GARCH model has a normal residual and is not heteroscedasticity so that it can be used as a forecasting model. BNGA shares obtained the most stable forecast results with almost constant volatility, indicating that this stock has the lowest risk compared to BBCA and BMRI stocks.
Achievement Cluster of Covid-19 Vaccination at the South Bengkulu Health Center Using Agglomerative Hierarchical Clustering Sari, Devni Prima; Sumita, Nurmaya
Journal of Statistics and Data Science Vol. 1 No. 2 (2022)
Publisher : UNIB Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33369/jsds.v1i2.24082

Abstract

The concerns of many people and the lack of vaccine information are significant obstacles to achieving the Covid-19 vaccination target. The government and health groups must be ready to provide correct vaccine information to reduce public doubts. To evaluate the vaccine implementation, this is necessary to cluster the area regarding the achievement of the vaccination target. Clustering this area can be done using the Agglomerative Hierarchical Clustering method. In this study, clustering was carried out using Covid-19 vaccination data at the South Bengkulu Health Center involving six variables. Three clusters were formed for the clustering process: the first dose of Covid-19 vaccination, the second dose of Covid-19 vaccination, and the first Booster vaccination. Each cluster is represented by low, medium, and high clusters
Factors Affecting The Open Unemployment Rate in West Sumatra Province Using Spatial Autoregressive (SAR) Adellia, Clara Febby; Sari, Devni Prima
Journal of Statistics and Data Science Vol. 3 No. 2 (2024)
Publisher : UNIB Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33369/jsds.v3i2.36514

Abstract

This paper proposes a Spatial Autoregressive (SAR) model to analyze the significant factors affecting the open unemployment rate in West Sumatra during 2023. The main advantage of the method is its ability to accurately capture spatial interactions between neighboring regions, such that it can provide a comprehensive understanding of regional unemployment patterns efficiently. By introducing the K Nearest Neighbor (KNN) weighting matrix and spatial lag parameter to the model, the effect of regional proximity on unemployment rates is more accurately captured. The viability of the SAR model is assessed by analyzing its ability to produce the lowest Akaike’s Information Criterion (AIC) value, indicating its suitability for modeling regional unemployment patterns. The result indicates that the SAR model is more effective than the multiple linear regression model in capturing regional unemployment patterns, with an AIC value of 52.756. The factors that influence the open unemployment rate are gross regional domestic product, labor force participation rate and the percentage of poor people.
Analysis of the Quality of Health Service at the Air Haji Hearth Center Using the Ordinal Logistics Regression Method Soleha, Annisa; Sari, Devni Prima
Journal of Statistics and Data Science Vol. 3 No. 2 (2024)
Publisher : UNIB Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33369/jsds.v3i2.36516

Abstract

Improving the quality of public services has become a major concern in government agencies as an effort to provide optimal public services. The quality of service can be affected by various factors. Therefore, it is necessary to conduct an analysis to find out the relationship between factors that affect service quality and service quality itself. Efforts are made to analyze the relationship between factors that affect service quality and service quality itself by using the ordinal logistic regression method in analyzing the relationship between influencing factors and influencing factors. This type of research is applied research that begins with theoretical analysis and data collection then ordinal logistic regression analysis. Based on the results of data analysis, it was found that the variables that significantly affected the quality of service were direct evidence variables, guarantee variables, and empathy variables. This research is useful for the Air Haji health center in an effort to improve the quality of health services.
Comparison of Poverty Clustering Results based on Distance Measurement with the Complete Linkage Method in Indonesia Anggraini, Fira; Devni Prima Sari
Journal of Statistics and Data Science Vol. 4 No. 1 (2025)
Publisher : UNIB Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33369/jsds.v4i1.40554

Abstract

Every year, population growth in Indonesia increases and has the potential to trigger poverty.Poverty indicators include the number of poor people, per capita expenditure, humandevelopment index, average years of schooling, and unemployment. The clustering of regionsis necessary for the government to be more effective in development. One of the methodsused is cluster analysis, a statistical technique that groups objects based on similarcharacteristics. This research compares the results of clustering poverty in Indonesia'sRegency/City in 2023 using the complete linkage method, which is based on the farthestdistance. The distances analyzed include Euclidean, Square Euclidean, Manhattan, andMinkowski, resulting in two clusters at each distance. Minkowski proved to be the bestdistance with the smallest standard deviation ratio, which was 1.518 for cluster 1 and 2.225for cluster 2, compared to the other distances. These results show that the Minkowski methodis superior in clustering poverty areas in Indonesia.  
ESTIMATION OF BENEFIT RESERVES IN ENDOWMENT INSURANCE USING THE INDONESIAN MORTALITY TABLE IV AND ZILLMER METHOD Sadli, Wanda Hamidah; Sari, Devni Prima
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 19 No 3 (2025): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol19iss3pp1737-1746

Abstract

This study focuses on determining the benefit reserves for endowment life insurance using the Zillmer method, an extension of the prospective reserve approach. Benefit reserves are crucial as they represent the funds insurance companies must set aside to cover future claims. Traditionally, reserves can be calculated retrospectively or prospectively. Still, the Zillmer method introduces an innovative approach by incorporating a Zillmer rate and time to account for loading costs, particularly at the beginning of the policy period. This research's novelty lies in applying the Zillmer method using the most recent Indonesian Mortality Table (TMI) IV, which provides updated and accurate life expectancy data for calculating reserves. The study reveals that the reserve values ​​calculated using the Zillmer method are initially lower than those derived from the conventional prospective method due to the inclusion of the Zillmer rate. However, as the policy progresses, the reserve values ​​gradually align with the prospective reserves after the Zillmer time period concludes. This study not only applies the Zillmer method in a local context with updated mortality data but also demonstrates how insurance companies can manage reserves more effectively, particularly in the early years of the policy.
The ROCK Ensemble Cluster Method for People's Welfare Analysis A Mixed Data Approach : Metode Ensemble Cluster untuk Analisis Kesejahteraan Rakyat Pendekatan Data Campuran Arsilla Uswatunnisa; Devni Prima Sari
Journal of Statistics and Data Science Vol. 4 No. 1 (2025)
Publisher : UNIB Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33369/jsds.v4i1.40859

Abstract

This study clusters districts/cities in West Sumatra based on public welfare indicators using the Ensemble Cluster Method with the ROCK algorithm. This approach handles mixed data, where numeric data is clustered with Hierarchical Agglomerative Clustering, while categorical data uses ROCK. The clustering results are combined through Cluster Ensemble to improve accuracy. Secondary data from BPS 2023 includes eight indicators of people's welfare. Clustering was validated using Compactness (CP). Results showed five optimal clusters, with a CP value of 0.44. Cluster 1 has the greatest welfare challenges, while Cluster 5 shows the highest welfare. These findings can be used as a basis for formulating more targeted regional development policies.
Co-Authors 1004, Anisa Abda Abda Adellia, Clara Febby Adhitya Ronnie Effendie, Adhitya Ronnie Afifah Humayrah Afifah, Wardah Hasna Anggraini, Fira Anis, Mei Syarah arsilla uswatunnisa Danardono Danardono Dedi Rosadi Deswita, Siska Dina Agustina Dina Fakhriyana Emmelia Safani Fadhilah Fitri Febri, Fera Malianis Filfiqri, Hamimatul Gulo, Trimodesman Hardinsyah Gusvira Widuri irwandi irwandi Jayawarsa, A.A. Ketut Jeri Jefrianto Lestari, Adika Risky Media Rosha Medika, Amelia Nurul Miftha Delinda Muhammad Rashif Hilmi nur hafizah Pepi Novianti Prastika, Ifa Pratama, Tasya Putri Puja Ermiati Putri, Elisa Ananda Putri, Muthiara Hazimah Putri, Rahma Dana Putriyana Nursal Rara Sandhy Winanda Riri Indriani Rohadatul Aisy Sadli, Wanda Hamidah Soleha, Annisa Sumita, Nurmaya Tessy Octavia Mukhti Tri Rahmayani Ulfasari Rafflesia Wardana, Yuni Yolandafitri Zulvia