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All Journal BAREKENG: Jurnal Ilmu Matematika dan Terapan Imajiner: Jurnal Matematika dan Pendidikan Matematika
Putro, Suzatmo
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Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Energy Engineering Languange, Linguistic, Communication & Media Mathematics Mechanical Engineering Physics Transportation Other

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PREDICTION OF ECONOMIC GROWTH RATE OF TUBAN REGENCY WITH ADAPTIVE NEURO-FUZZY INFERENCE SYSTEM ALGORITHM Muaziza, Maya; Arifin, Ahmad Zaenal; Putro, Suzatmo
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 19 No 3 (2025): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol19iss3pp1699-1710

Abstract

This research aims to implement and evaluate the accuracy of the Adaptive Neuro Fuzzy Inference System (ANFIS) forward stage method to predict the economic growth rate of the Tuban Regency. In the application of ANFIS, two types of variables are required, namely, input variables which include road length, the number of electricity customers, the number of health workers, the number of high schools, and the number of cases of ordinary theft. Meanwhile, the predicted output variable is the economic growth rate. The fuzzification process uses a triangular membership function to map the input values. The data used in this study were obtained from the Central Bureau of Statistics (BPS) of Tuban Regency for 2014-2024. The prediction results show a very low Mean Absolute Percentage Error (MAPE) value of 0.14%, which reflects a very high level of accuracy. With MAPE < 10%, the accuracy of this model reaches 99.86% based on calculations made through the Matlab GUI. This research shows that the Adaptive Neuro Fuzzy Inference System (ANFIS) method can be used effectively and accurately to predict the economic growth rate of the Tuban Regency.
Peramalan Curah Hujan di Kabupaten Tuban Menggunakan Algoritma KNN Fatimah, Vita; Arifin, Ahmad Zaenal; Putro, Suzatmo
Imajiner: Jurnal Matematika dan Pendidikan Matematika Vol 7, No 4 (2025): Imajiner: Jurnal Matematika dan Pendidikan Matematika
Publisher : Universitas PGRI Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26877/imajiner.v7i4.23526

Abstract

This study aims to predict rainfall using historical and current data by applying the K-Nearest Neighbors (KNN) method. These predictions are useful for supporting various socio-economic activities, including public safety, agriculture, plantations, fisheries, and aviation. The research method refers to literature studies using data published by the Central Statistics Agency (BPS) of Tuban Regency. The analysis results show an RMSE value of 36.3942 and an R-square of 0.6214 based on 36 monthly data samples covering variables such as temperature, air pressure, wind speed, air humidity, and rainfall. 
Co-Authors Arifin, Ahmad Zaenal Fatimah, Vita Muaziza, Maya