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All Journal Journal of System and Computer Engineering Tadulako Social Science and Humaniora Journal
Nur Azizah Janad
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Religion Arts Humanities Computer Science & IT Decision Sciences, Operations Research & Management Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Social Sciences

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ESTIMATOR NADARAYA-WATSON DENGAN FUNGSI KERNEL NORMAL DAN FUNGSI KERNEL KUADRATIK Saskia Amalia Putri; Ayudita Rahmi Aristya; Nur Azizah Janad; Yudy Novindri Tadale; Lilies Handayani
Journal of System and Computer Engineering (JSCE) Vol 3 No 1 (2022): JSCE: JANUARI 2022
Publisher : Universitas Pancasakti

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47650/jsce.v2i2.353

Abstract

The Human Development Index (HDI) is a comparative measurement of life expectancy, literacy, education and living standards for all countries around the world. The human development index is used to classify whether a country is a developed country, a developing country or a backward country and also to measure the effect of economic policies on the quality of life. The purpose of this study is to find out how the percentage of poor people and the unemployment rate are related to the human development index in Central Sulawesi. In this study, the Nadaraya-Watson kernel regression method was used with a regression comparison between normal kernel functions and quadratic kernel functions. Based on the results of the study, the best model on X1 (percentage of poor people) with the smallest MSE value is the CV.LS method with a bandwidth value of 1.369349, and for the best model on X2 (open unemployment rate) with the smallest MSE value, namely the CV.AIC method. with a bandwidth value of 1.331878.
APPLICATION OF THE EXTREME LEARNING MACHINE (ELM) METHOD IN PREDICTING THE COMBINED STOCK PRICE INDEX (IHSG) IN INDONESIA Nur Azizah Janad; Junaidi; Iman Setiawan
Tadulako Social Science and Humaniora Journal Vol. 2 No. 2 (2022): Tadulako Social Science and Humaniora Journal
Publisher : LPPM Universitas Tadulako

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22487/sochum.v2i2.17382

Abstract

The stock market is one area that continues to attract the attention of investors and financial researchers. This research explores the application of the Extreme Learning Machine (ELM) method to predict the Composite Stock Price Index (IHSG) in Indonesia. ELM is known for its fast learning capabilities with minimal prerequisite network architecture. In this research, three types of activation functions, namely Sigmoid, ReLU, and Tanh, are applied to ELM to compare their performance in predicting IHSG. Monthly IHSG data is used for model training and testing. Data preprocessing steps, such as dividing the data into Training and Test sets, are applied before feeding it into the model. Model performance was evaluated using Root Mean Square Error (RMSE) and compared for each activation function. The research results show that each activation function has a different impact on the IHSG prediction performance. In this research, the ReLU activation function showed the best performance in predicting IHSG compared to other activation functions, with a Root Mean Square Error (RMSE) of 1 x 10-16. These results show that the model's predictive performance in estimating actual values is very good.
Co-Authors Ayudita Rahmi Aristya Iman Setiawan Junaidi Junaidi Lilies Handayani Saskia Amalia Putri Yudy Novindri Tadale