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All Journal BERKALA SAINSTEK JURNAL ILMIAH MATEMATIKA DAN TERAPAN BAREKENG: Jurnal Ilmu Matematika dan Terapan Jurnal Varian Parameter: Journal of Statistics UNP Journal of Statistics and Data Science
Hartayuni Sain
Department Of Statistics Tadulako University
Agriculture, Biological Sciences & Forestry Biochemistry, Genetics & Molecular Biology Chemistry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Energy Engineering Mathematics Mechanical Engineering Physics Social Sciences Transportation Other

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Predicting Drought in East Nusa Tenggara: A Novel Approach Using Wavelet Fuzzy Logic and Support Vector Machines Sain, Hartayuni; Fadri, Firda
Parameter: Journal of Statistics Vol. 4 No. 1 (2024)
Publisher : Fakultas Matematika dan Ilmu Pengetahuan Alam Universitas Tadulako

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22487/27765660.2024.v4.i1.17142

Abstract

The water crisis, or what is hereinafter referred to as drought, has become a systemic and crucial problem in several regions in Indonesia. Indonesia is an agricultural country, where the presence of water is very influential so that drought can become a natural disaster if it starts to cause an area to lose its source of income due to disturbances in agriculture and the ecosystem it causes. Drought forecasting can provide support solutions in preventing the impact of drought. In this paper, we compare the performance of wavelet fuzzy logic and the support vector machine (SVM) as a supervised learning method for drought forecasting in East Nusa Tenggara. This study examines the monthly rainfall data for 1999-2015 which is the basis for calculating the drought index based on the Standardized Precipitation Index (SPI). The SPI value used is SPI-3 at a station in East Nusa Tenggara. The performance of models is compareded on R2. The results showed that R2 of wavelet fuzzy logic is smaller than one of SVMVM is better than the wavelet fuzzy logic for forecasting SPI value of drought in East Nusa Tenggara.
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.
Cluster Analysis of Indonesian Provinces Based on Health Performance Indicators Using Fuzzy C-Means nurul fiskia gamayanti; Mohammad Fajri; Hartayuni Sain; Fadjryani; Iman Setiawan
UNP Journal of Statistics and Data Science Vol. 4 No. 3 (2026): UNP Journal of Statistics and Data Science
Publisher : Departemen Statistika Universitas Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24036/ujsds/vol4-iss3/529

Abstract

In accordance with the Sustainable Development Goals (SDGs), with particular emphasis on Goal 3 which focuses on ensuring a healthy life and improving the welfare of all residents across all stages of life, ranging from infancy to older adulthood, it is closely related to looking at existing health indicators. The Indonesian region consisting of 34 provinces makes policies difficult to generalize due to the different characteristics of each region. So a cluster method is needed where regional objects will be clustered into several groups so that policies will be better implemented in a formed cluster. One clustering technique that may be employed is the Fuzzy C-Means (FCM) algorithm. FCM provides a membership value in the form of degrees ranging from 0 to 1, representing the degree of strength of the relationship of a data with each group, the analysis performed using the Fuzzy C-Means (FCM) method resulted in the formation of two clusters, which represent that cluster 1 is a cluster with indications of areas that have good health, and cluster 2 is a cluster with indications of poor, Cluster 1 comprises 12 provinces, whereas Cluster 2 includes 22 provinces.
Co-Authors Afriza, Dini Aprilia Al Fajri, Iman Anggraini, Nini Armayani Arsal Borahima, Maharani Safira B. Dermawan Lonan, Herdi Fadjriyani Fadjryani, Fadjryani Fadri, Firda Gamayanti, Nurul Fiskia Iman Setiawan Jeffry Kusuma Junaidi Junaidi Mohammad Fajri Mohammad Fajri Nabillah, Chantika Nasria Nacong Nur Azizah Nurul Fiskia Gamayanti Nurwijayanti Rais Rais Rais Selvy Musdalifah Sinaga, Novita Damayanti Soleha, Salma Winarti Winarti