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All Journal Mathematics and Applications (MAp) Journal Mathematical Journal of Modelling and Forecasting
Friska, Dina
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Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Mathematics

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PENDEKATAN KOMPARATIF ALGORTIMA MACHINE LEARNING UNTUK PREDIKSI KEMISKINAN GLOBAL Putri, Darvi Mailisa; Friska, Dina
MAp (Mathematics and Applications) Journal Vol 6, No 2 (2024)
Publisher : Universitas Islam Negeri Imam Bonjol Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15548/map.v6i2.10063

Abstract

Kemiskinan masih menjadi masalah sosial yang butuh perhatian khusus untuk ditangani. Banyak dampak yang ditimbulkan, diantaranya permasalahan pertumbuhan otak anak, meningkatnya penyakit jangka panjang, dan meningkatnya konflik sosisal dan keamanan. Maka perlu usaha untuk mengatasi kasus kemiskinan secara efektif dengan pendekatan yang inovatif dan berbasis data yaitu penggunaan Algoritma machine learning. Algoritma ini dapat menganalisis data kemiskinan, mengidentifikasi pola, dan memprediksi risiko kemiskinan dengan lebih akurat. Penelitian ini fokus menganalisis performa algoritm machine learning yaitu Decision Trees dan Naïve Bayes. Hasil penelitian menunjukkan algoritma Decision Trees memiliki akurasi lebih baik (84,2%) dibandingkan akurasi algortima Naïve Bayes (78,9%). Namun performa algoritma Naïve Bayes dalam memprediksi berbagai kelas lebih stabil dibanding algoritma Decision Trees berdasarkan nilai presisi, recall, F1-score dan specificity.
Forecasting the Saudi Riyal to Indonesian Rupiah Exchange Rate Using ARIMA Friska, Dina
Mathematical Journal of Modelling and Forecasting Vol. 3 No. 1 (2025): June 2025
Publisher : Universitas Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24036/mjmf.v3i1.32

Abstract

A Currency exchange rate is an essential indicator in a country's economy. The exchange rate of a country's currency constantly fluctuates against another country's currency at any time, such as the riyal exchange rate against the rupiah. There are several methods to determine the movement of the currency exchange rate and to forecast time series data, such as Autoregressive Integrated Moving Average (ARIMA). ARIMA is a time series data forecasting method that can handle data that is not stationary to the mean and variance, such as the riyal exchange rate against the rupiah, which fluctuates irregularly. This study will forecast the riyal exchange rate against the rupiah at Bank Indonesia. The data used is daily data. The R Studio program studies the minimum AIC value to select the best model. The ARIMA (2,1,0) model is the best in forecasting the Saudi Arabian Riyal exchange rate (SAR) against the Indonesian rupiah (IDR) with an estimated forecast error of 0.26%.
Co-Authors Darvi Mailisa Putri