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All Journal SAINTEK Tadulako Science and Technology Journal Tadulako Social Science and Humaniora Journal
Iman Setiawan
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Religion Agriculture, Biological Sciences & Forestry Arts Humanities Automotive Engineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Social Sciences

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Journal : Tadulako Science and Technology Journal

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Comparison of Nonparametric Regression Nadara - Watson Estimator Kernel Function And Local Polynomial Regression In Predicting USD Against IDR Isni Rahma; Junaidi; Iman Setiawan
Tadulako Science and Technology Journal Vol. 2 No. 2 (2022): Tadulako Science and Technology Journal
Publisher : LPPM Universitas Tadulako

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22487/sciencetech.v2i2.17300

Abstract

Introduction: Macroeconomic problems such as inflation and exchange rates are often highlighted as benchmarks for achieving economic progress. The stability of both must be monitored by the government in order to control the inflation rate and exchange rate. This instability is a phenomenon of fluctuation, namely the phenomenon of the rise and fall of the exchange rate of a currency based on demand and supply. Given the large impact of exchange rate fluctuations on the economy, the prediction of the wage exchange rate against the US dollar is considered necessary because it is useful to anticipate and minimize bad possibilities that arise. Method: Methods that can be used to analyze fluctuating currency exchange rate data are nonparametric regression, Nadaraya-Watson estimator, Gaussian kernel function, and Local Polynomial Regression. Results and Discussion: The results of a nonparametric regression comparison between the Nadaraya-Watson estimator, Gaussian kernel function, and local polynomial regression were obtained by MAPE of 2.508% and 0.179%, respectively. This shows that the best model uses the local polynomial regression method and predicted USD exchange rate data against IDR using the best model, namely Local polynomial Regression where the MAPE value is less than 10%, which means the prediction rate is very good. Conclusion: The nonparametric regression method of the Nadaraya-Watrson estimator, Gaussian kernel function, and local polynomial regression shows that the best model uses the local polynomial regression method.
Grouping Districts / Cities in Central Sulawesi Province Based on Poverty Indicators Using the Fuzzy Geographically Weighted Clustering -Artificial Bee Colony Method Nafiul Agristya; Rais; Iman Setiawan
Tadulako Science and Technology Journal Vol. 2 No. 2 (2022): Tadulako Science and Technology Journal
Publisher : LPPM Universitas Tadulako

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22487/sciencetech.v2i2.17301

Abstract

Introduction: Poverty is the main problem that is the focus of attention of the government in Indonesia. In general, poverty is a person's inability to meet basic basic needs in every aspect of life. Cluster analysis is a solution to map this problem. Method: Fuzzy Geographically Weighted Clustering-Artificial Bee Colony (FGWC-ABC) is one clustering method that is an integration of classical fuzzy clustering methods and geodemographic elements. Artificial Bee Colony is a metaheuristic algorithm that is used as a global optimization to increase cluster accuracy. Artificial Bee Colony can efficiently and effectively solve various function optimization problems in various cases. Result and Discussion: The research results obtained 3 optimum clusters with each cluster characteristic relatively different based on poverty indicators. Cluster 1 with low poverty, cluster 2 with high poverty, and cluster 3 with moderate poverty. Conclusion: By using the IFV validity index, 3 optimum clusters were obtained with different characteristics of each cluster based on its indicators. Cluster 1 consists of three regencies/cities with low poverty status, cluster 2 consists of seven regencies/cities with high poverty status, and cluster 3 consists of six regencies/cities with moderate poverty status.
Co-Authors Abid Husein Ahmad Syukron Delfian Ruly Havatilla Ilham Maulana Cakra Isni Rahma Junaidi Junaidi Nabilah Ananda Putri Nafiul Agristya Nur Azizah Janad Rais