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All Journal Jurnal Riset Informatika
Endang Palupi
Universitas Bina Sarana Informatika
Computer Science & IT

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House Price Prediction Using Data Mining with Linear Regression and Neural Network Algorithms Endang Palupi
Jurnal Riset Informatika Vol. 6 No. 1 (2023): December 2023
Publisher : Kresnamedia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1009.756 KB) | DOI: 10.34288/jri.v6i1.262

Abstract

The need for housing in big cities is very high because most offices and economic centers are in big cities. Limited land and high demand cause house prices to rise. Many developers build housing on the outskirts of big cities with access to trains and toll roads to make transportation easier. Property developers compete by providing the best prices, various choices of house specifications, ease of the mortgage process, and attractive promotions such as no down payment. A house is a long-term investment whose price increases yearly, so proper analysis is needed to buy a place to live in. Several factors influence the price of a house, including location, land area, building area, building type, and so on. This research aims to create a house price prediction model using the Linear Regression Algorithm and Neural Network so that the results can be useful for property agents in predicting house sales or from the buyer's side in predicting house prices. The results of this research use the Linear Regression Algorithm RMSE 0.775, while the Neural Network Algorithm uses RMSE 0.645. From this research, modeling using the Linear Regression Algorithm has better results. Still, the Linear Regression Algorithm and Neural Network Algorithm have RMSE results that are close to accurate and have small errors.
Divorce Factor Classification Uses The C4.5 Algorithm Based On Particle Swarm Optimization Endang Palupi
Jurnal Riset Informatika Vol. 6 No. 3 (2024): June 2024
Publisher : Kresnamedia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34288/jri.v6i3.307

Abstract

Cases of household divorce increased in the West Java area during the Covid-19 pandemic. The pandemic has increased personal relationships and interactions between family members, and some families are using this opportunity to strengthen their relationships. However, increased family interaction can also result in increased conflict, leading to divorce. The author classifies divorce factors that have increased during the pandemic using the C4.5 Algorithm based on Particle Swarm Optimization (PSO). The main factors for divorce are economic factors that have hit during the pandemic coupled with unstable mental conditions resulting in poor communication and continuous fighting. So that the husband/wife leaves one of the parties, infidelity, and adultery, then domestic violence and ending in divorce. The dataset was taken from the West Java BPS website, and the author split the data, namely 80% training data and 20% testing data, to avoid overfitting. Research results on the classification of divorce factors during the pandemic using the C4.5 algorithm based on particle swarm optimization are an accuracy value of 87.50% and an AUC (Area Under Curve) value of 0.807, which is included in the good classification category.
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