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All Journal JURNAL ILMIAH MATEMATIKA DAN TERAPAN BAREKENG: Jurnal Ilmu Matematika dan Terapan Jurnal Varian Majalah Ilmiah Matematika dan Statistika (MIMS) JAMBURA JOURNAL OF PROBABILITY AND STATISTICS DedikasiMU: Journal of Community Service Jurnal Pengabdian Masyarakat Bhinneka
Gamayanti, Nurul Fiskia
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Journal : Jurnal Varian

 1 
Pemodelan Jumlah Siswa Putus Sekolah Tingkat SMA di Indonesia Menggunakan Geographically Weighted Generalized Poisson Regression Azizah, Nur; Gamayanti, Nurul Fiskia; Junaidi, Junaidi; Sain, Hartayuni; Fadjriyani, Fadjryani
Jurnal Varian Vol 8 No 1 (2024)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/varian.v8i1.4248

Abstract

In 2022, the high school dropout rate is the highest compared to other levels of education in Indonesia.Seeing the urgency of the 12-year Compulsory Education program, completing education up to the highschool level is an important thing that needs to be considered. Thus, it is necessary to know the factorsthat influence the dropout rate in the hope that this problem can be reduced. This study aims to modelthe high school dropout rate using geographically weighted generalized poisson regression (GWGPR)based on the factors that influence it. GWGPR is used if the response variable is overdispersed anddepends on the location observed. The results of this study indicate that each province has a different regression model. The GWGPR model with the adaptive tricube kernel weighting function is thebest model because it has the smallest AIC value compared to other weighting functions. In CentralSulawesi Province, the GWGPR model with the adaptive tricube kernel weighting function formed isµˆ26 = exp (8, 1267 − 0, 1267X4 + 0, 0344X5 + 0, 0957X6 + 0, 1173X7). With the significant variables are the average length of schooling, the percentage of the population aged 7-17 years who receivePIP, the open unemployment rate, and the percentage of children who do not live with parents.
Mapping of Village Population Profile with Schistosomiasis Cases Using Clustering Large Applications Fajri, Mohammad; Rais, Rais; Gamayanti, Nurul Fiskia; Dg Mabaji, Siti Natazha; Rahman Jati, Shalsa Yunita; Arisandi, Rizwan
Jurnal Varian Vol. 7 No. 2 (2024)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/varian.v7i2.3423

Abstract

Schistosomiasis is a tropical disease caused by Schistosoma mansoni (intestinal schistosomiasis) and Schistosoma haematobium (urogenital schistosomiasis). Schistosomiasis in Indonesia is endemic to Central Sulawesi and is commonly found in the Napu Valley and Bada Valley areas, which are administratively included in Poso District and Sigi District. One approach to obtain information on schistosomiasis endemic areas is by mapping the population profile of villages with schistosomiasis cases. This mapping is intended to provide an overview of the social and demographic conditions of villages with schistosomiasis cases. One of the many analysis methods that can be used is cluster analysis. Cluster analysis is a method for grouping data based on the extent of their similarities. Data with similar characteristics will be grouped together, while data with different characteristics will be placed in different groups. Among several types of methods in cluster analysis is Clustering Large Application (CLARA). CLARA is a clustering method which is more robust to unusual data and can be applied to handle large volumes of data. The results of this study are obtained two optimum clusters, each possessing distinct characteristics as determined by Schistosomiasis cases indicators. Cluster 1 with low schistosomiasis cases and cluster 2 with high schistosomiasis cases.
Pemodelan Jumlah Siswa Putus Sekolah Tingkat SMA di Indonesia Menggunakan Geographically Weighted Generalized Poisson Regression Azizah, Nur; Gamayanti, Nurul Fiskia; Junaidi, Junaidi; Sain, Hartayuni; Fadjriyani, Fadjryani
Jurnal Varian Vol. 8 No. 1 (2024)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/varian.v8i1.4248

Abstract

In 2022, the high school dropout rate is the highest compared to other levels of education in Indonesia.Seeing the urgency of the 12-year Compulsory Education program, completing education up to the highschool level is an important thing that needs to be considered. Thus, it is necessary to know the factorsthat influence the dropout rate in the hope that this problem can be reduced. This study aims to modelthe high school dropout rate using geographically weighted generalized poisson regression (GWGPR)based on the factors that influence it. GWGPR is used if the response variable is overdispersed anddepends on the location observed. The results of this study indicate that each province has a different regression model. The GWGPR model with the adaptive tricube kernel weighting function is thebest model because it has the smallest AIC value compared to other weighting functions. In CentralSulawesi Province, the GWGPR model with the adaptive tricube kernel weighting function formed isµˆ26 = exp (8, 1267 − 0, 1267X4 + 0, 0344X5 + 0, 0957X6 + 0, 1173X7). With the significant variables are the average length of schooling, the percentage of the population aged 7-17 years who receivePIP, the open unemployment rate, and the percentage of children who do not live with parents.
Analysis of the Relationship Between Net Exports and Gross Regional Domestic Product Using the Panel Vector Correction Model (PVECM) Approach Soleha, Salma; Gamayanti, Nurul Fiskia; Sain, Hartayuni; Fadjryani, Fadjryani
Jurnal Varian Vol. 8 No. 3 (2025)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/varian.v8i3.4898

Abstract

In regional economic growth, various factors play a role, including net exports, a key indicator of international trade. The purpose of this study is to analyze the long-term relationship and causal link between net exports and Gross Regional Domestic Product (GRDP) in Indonesia. The method used in this study is the Panel Vector Error Correction Model (PVECM), applied to panel data from 34 provinces in Indonesia for the period 2010–2023. The results of the study indicate a cointegration relationship between net exports and GRDP, in which a 1-unit increase in net exports decreases GRDP by 5.445139 units. The Granger Causality test shows a significant bidirectional relationship between the variables, indicating that they influence each other. The R² value of 54.99% indicates that the model explains 54.99% of the variation in net exports. The implication of these findings suggests that policymakers need to consider the quality and composition of export and import activities, as well as regional trade structures, to ensure that international trade contributes positively to regional economic growth.
Ensemble Quick Robust Clustering Using Links for Clustering Hypertension Patients at a Health Center Niftayana, Neli; Fajri, Mohammad; Gamayanti, Nurul Fiskia
Jurnal Varian Vol. 8 No. 3 (2025)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/varian.v8i3.5151

Abstract

Hypertension is a chronic disease with a high risk of cardiovascular complications and requires treatment according to patient characteristics. At the health center, the number of hypertensive patients is 6953, the highest recorded. Therefore, this study aims to classify and determine the characteristics of hypertensive patients at a health center. The method used in this study is Ensemble Quick Robust Clustering Using Links. This method combines the clustering results of Quick Robust Clustering Using Links and Agglomerative Nesting. Where this method is more efficient in clustering. The results of this study show the number of clusters in the Quick Robust Clustering Using Links method is 3, Agglomerative Nesting is 3 and in the Quick Robust Clustering Using Links Ensemble produces 9 clusters with the following distribution: Cluster 1 shows low hypertension, cluster 2 shows high hypertension, cluster 3 to cluster 6 shows high hypertension, cluster 7 shows moderate hypertension, cluster 8 shows high hypertension and cluster 9 shows moderate hypertension. Thus, grouping patients based on a combination of numerical and categorical variables can provide more detailed information about the severity of hypertension. 
Co-Authors Abdul Mahatir Najar Afriza, Dini Aprilia Aisya, Cici Andri Andri Anggelina E, Melsy Patricia AULIA, FIRDA Dewi Puji Astuti, Dewi Puji Dg Mabaji, Siti Natazha Difa Shalsabila Fadjriyani Fadjryani, Fadjryani Harismahyanti A., Andi Hartayuni Sain I. Djuru, Moh Syafwan Jamidun, Jamidun Junaidi Junaidi Junaidi, J. Margareth, Cecilia Maulidyani Abu Mohammad Fajri Mu'arif, Zainal Niftayana, Neli Nur Azizah Nur'Eni Nureni, Nureni Nurwahyuni Radi, Aryani Rahman Jati, Shalsa Yunita Rahman, Rezki Rais Rais Rais Resnawati Rizwan Arisandi, Rizwan Setiawan, Iman Soleha, Salma Yunita, Silva