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All Journal Jurnal Edukasi Elektro Science Information System and Technology
Sigit, Firman Matiinu
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Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Industrial & Manufacturing Engineering

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Housing Value Predicted Modelling using Random Forest Regression: Case study California Housing Dataset Sigit, Firman Matiinu; Putra, Haniel Rangga Pramuditya
West Science Information System and Technology Vol. 2 No. 01 (2024): West Science Information System and Technology
Publisher : Westscience Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58812/wsist.v2i01.1021

Abstract

Housing price comes from many factors which are location, population, style of house, age of house, and people income. Many real estate developer companies use this data to predict price of house and give amount of investment for potential housing prices. In this study, we try to help the developer companies to predict price of house based on dataset. We try to build machine learning that can predict for housing price. There are three machine learning models that are used for this study, namely Linier Regression Modelling, Decison Three Regression Modelling, and Random Forest Regression Modelling. Each of those machine learning is trained using California Housing Dataset (1990) which is split into training set and testing set that training set contains 16512 instances and testing set contains 4128 instances. Training dataset is trained into each of machine learning model (Linier Regression, Decison Tree Regression, and Random Forrest Regression) after finished the training followed by evaluting the error prediction using K-Folds Cross Validation and showed by using Root Mean Square Error (RMSE). In this study, Random Forest Regression gives a better performance than two others (Linier Regression and Decision Tree Regression models) with error RMSE =49642.12.
Implementation of Neural Networks in Daily PV Power Output Prediction Using Bayesian Regularization Algorithms to Assist Energy Management Systems Mahmudah, Norma; Delfianti, Rezi; Sigit, Firman Matiinu; Putra, Dimas Panji Eka Jala; Nusyura, Fauzan
Jurnal Edukasi Elektro Vol. 9 No. 2 (2025): Jurnal Edukasi Elektro Volume 9, No. 2, November 2025
Publisher : DPTE FT UNY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21831/jee.v9i2.91044

Abstract

Solar power plants have several advantages, namely continuous energy production, reduced electricity demand, and low photovoltaic maintenance, so that PV power output can be optimized with reliable PV power output predictions. Implementation of Artificial Neural Network (ANN) to predict photovoltaic (PV) power output, using the Bayesian Regularization algorithm. Accurate PV power output prediction is very important in power systems. The data used are solar radiation, PV module temperature, ambient temperature, and actual PV power output, with the target being the PV power output for the next day with the PV power output output for the next day. The architecture used in this study is a Cascade Forward Neural Network (CFNN) and an Elman Neural Network (ENN). Both ANN models use daily data sets and performance evaluation using Mean Square Error (MSE). The results of the study show that ENN is more accurate than CFNN. ENN had the lowest MSE of 0.00664 at a configuration of N=8 and R of 0.9922 with a training time of 6.4 seconds, while CFNN recorded the lowest MSE of 0.024306 with N=25. ENN's ability to capture time series patterns in PV is more reliable and effective. Reliable predictions can assist in energy management systems because they help maintain supply balance, reduce the risk of failure, and improve system stability.
Co-Authors Mahmudah, Norma Nusyura, Fauzan Putra, Dimas Panji Eka Jala Putra, Haniel Rangga Pramuditya Rezi Delfianti