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All Journal Sistemasi: Jurnal Sistem Informasi Prosiding Seminar Nasional Official Statistics
Wijaya, Yuliagnis Transver
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Humanities Computer Science & IT Economics, Econometrics & Finance Electrical & Electronics Engineering Social Sciences

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Pola Migrasi dan Variabel yang Memengaruhinya di Provinsi Jawa Barat Fauziyah, Syifa; Wijaya, Yuliagnis Transver
Seminar Nasional Official Statistics Vol 2024 No 1 (2024): Seminar Nasional Official Statistics 2024
Publisher : Politeknik Statistika STIS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34123/semnasoffstat.v2024i1.2081

Abstract

Migration is one of the demographic components of population growth in a region. A problem phenomenon in population growth that occurs at this time is that the population is not evenly distributed and only concentrated in one region. The purpose of this study is to determine the distribution pattern of migrant population working in the formal sector between districts / cities in West Java Province and the factors that influence it. This study uses multiple linear regression analysis methods to determine what variables affect the occurrence of migration, both in-migration, out-migration, and between the two that occur between districts / cities in West Java Province. The variables that significantly affect the occurrence of out-migration are the population size variable and the proportion of higher education; the variables that affect the occurrence of in-migration are the Gross Regional Domestic Product value variable and the proportion of higher education; the variables that affect migration between in-migration and out-migration include the neighboring variable, the stock of lifetime migrants, the GRDP ratio, and the distance ratio of the capital to the Province.
Handling of Data Imbalance in Classification of Regencies/Municipalities in Eastern Indonesia Japany, Adham Malay; Wijaya, Yuliagnis Transver
Sistemasi: Jurnal Sistem Informasi Vol 13, No 1 (2024): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v13i1.2862

Abstract

Imbalance of data between classes can result in incorrect predictions in classification, which can cause problems in decision making. Eastern Indonesia (KTI) is one of the regions that has a Human Development Index (HDI) below the national HDI, so increasing human potential in the production process in KTI must be focused on. In the categorization of regencies/municipalities in KTI there is imbalanced data. This shows that human development between regions in KTI is still uneven. For this reason, a classification of regencies/municipalities based on HDI into certain categories is carried out accurately and quickly. The classification results are expected to help the government in determining future strategic steps to improve the quality of human resources in KTI. One method that can handle data imbalance is Synthetic Minority Over-sampling Technique (SMOTE), using three classification algorithms, namely Support Vector Machine (SVM), K-Nearest neighbors (KNN), and Random Forest (RF). It was found that with the handling of data imbalance and the application of the k-fold cross validation method, the three algorithms showed a significant increase in accuracy. Therefore, handling data imbalance is proven to be able to improve the performance of the applied classification algorithms.
Co-Authors Japany, Adham Malay Syifa Fauziyah, Syifa