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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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Religion Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Mathematics Mechanical Engineering Medicine & Pharmacology Physics Public Health Social Sciences Transportation Other

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ANALYSIS OF SPATIAL EFFECTS ON FACTORS AFFECTING RICE PRODUCTION IN CENTRAL SULAWESI USING GEOGRAPHICALLY WEIGHTED PANEL REGRESSION Gamayanti, Nurul Fiskia; Junaidi, Junaidi; Fadjryani, Fadjryani; Nur'eni, Nur'eni
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 17 No 1 (2023): BAREKENG: Journal of Mathematics and Its Applications
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (473.617 KB) | DOI: 10.30598/barekengvol17iss1pp0361-0370

Abstract

Fulfillment of rice stock in Indonesia to always be distributed based on demand in the community is certainly closely related to the results of rice production. The results of rice production in various regions of Indonesia are very different. This difference can of course be influenced by geographic location or spatial effects between regions. Central Sulawesi, which is one of the provinces with a large population compared to other provinces on the island of sulwesi, has a responsibility to meet the needs of its community, so it is necessary to take into account and increase the production of rice by relying on production in the province.Modeling of rice production that has spatial effects or heterogeneity between regions is needed as an analytical tool because if the modeling ignores spatial effects and generalizes the model, the modeling predictions will be biased. So we need an analytical model that can accommodate the problem of spatial effects using Geographically Weighted Panel Regression. The purpose of this study was to determine the factors that can affect rice production in central sulawesi. The data used comes from BPS Central Sulawesi province from 2014-2020. This study focus to the spatial effect factors that are considered to be able to affect the rice production production in Central Sulawesi. Tthe results of the study there area 8 districts/cities which are affected by land area, and 4 districts/cities are affected by land area and harvested are.
ANALYSIS OF PRIORITY AREAS FOR HANDLING STUNTING CASES IN SIGI REGENCY USING THE TOPSIS METHOD BASED ON WEB DASHBOARD Mu'arif, Zainal; Afriza, Dini Aprilia; Aulia, Firda; Anggelina E, Melsy Patricia; Gamayanti, Nurul Fiskia
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 18 No 3 (2024): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol18iss3pp1411-1422

Abstract

Stunting is a condition of growth failure in children, where a toddler has a length or height below the average. Stunting is a problem for children because it has the potential to slow down brain development with prolonged effects. Central Sulawesi Province is one of the provinces with the highest stunting prevalence rate and the area with the highest stunting rate is in Sigi Regency at 36.8%. Stunting cases are an important concern for the Sigi Regency government, especially the Health Office and Community Health Centers. To identify and determine areas that are prioritized for handling stunting cases, seven indicators are used, including the number of stunting cases, number of villages covered, number of health workers, number of integrated service posts, number of exclusive breastfeeding, percentage of clean drinking water, and percentage of proper sanitation. To support in reducing the percentage of stunting in Sigi Regency, research was conducted and a web dashboard system application was made to support priority area selection decisions using the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method, a best alternative method that has the shortest distance from the positive ideal solution and the farthest distance from the negative ideal solution. The results obtained in this study are the areas that are prioritized for handling stunting cases in Sigi Regency is the Sigi Biromaru area with a total of 495 stunting cases, the number of coverage villages is 18, the number of integrated service posts is 53, the number of health workers is 96, the number of exclusive breastfeeding is 35, the percentage of proper drinking water is 44%, and the percentage of proper sanitation is 84.00% with the highest preference value through the TOPSIS method analysis of 0.660.
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.
MODELING THE DURATION OF MATERNAL LABOR AT ANUTAPURA HAMMER HOSPITAL USING LIN-YING ADDITIVE HAZARD REGRESSION Fadjryani, Fadjryani; Setiawan, Iman; Sain, Hartayuni; Fajri, Mohammad; Gamayanti, Nurul Fiskia; Radi, Aryani; Aisya, Cici
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 20 No 1 (2026): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol20iss1pp0523-0540

Abstract

The Central Sulawesi government has a Sustainable Development Goals (SDGs) target for 2020-2024, which sets the maternal mortality rate below 70/100,000 KH. However, in 2018-2022, the maternal mortality rate fluctuated by 128/100,000 KH. One of the factors causing maternal mortality is the duration of the labor process. The factors that are thought to have an influence on the duration of labor are gestational age, maternal age, baby height, parity, and hemoglobin levels. Therefore, this study aims to see what modeling and factors affect the duration of birth using Lin-Ying additive hazard regression analysis. Data were obtained from the medical records of normal deliveries between January and December 2023 at Anutapura Palu Hospital. The results showed that the factors that affect the duration of birth are preterm gestational age, aterm gestational age, maternal age 20-35 years, primigravida mothers, multigravida mothers, and mothers who are not anemic. A limitation of this study is the relatively short data collection period of one year, which may not capture variations or trends in labor outcomes over time.
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