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All Journal BAREKENG: Jurnal Ilmu Matematika dan Terapan Limits: Journal of Mathematics and Its Applications
Maula, Sugha Faiz Al
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Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Energy Engineering Mathematics Mechanical Engineering Physics Transportation

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MODELING HOUSE SELLING PRICES IN JAKARTA AND SOUTH TANGERANG USING MACHINE LEARNING PREDICTION ANALYSIS Maula, Sugha Faiz Al; Setiawan, Nicoletta Almira Dyah; Pusporani, Elly; Jannah, Sa'idah Zahrotul
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 19 No 1 (2025): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol19iss1pp107-118

Abstract

The increasing demand for housing in urban agglomerations, particularly in areas like Jakarta, has made homeownership a significant challenge for many, especially first-time buyers and the lower-middle class. Post-pandemic shifts have further influenced housing preferences, driving interest towards suburban areas with green spaces. Despite government efforts through mortgage subsidy programs, affordability remains a concern, particularly in peripheral regions. This study aims to analyze housing prices in various Jakarta regions using machine learning models, including Multiple Linear Regression (MLR), Support Vector Regression (SVR), Light Gradient Boosting Machine (LGBM), and Random Forest. A dataset of 554 house prices from West Jakarta, South Jakarta, Central Jakarta, and South Tangerang was used. The analysis focused on key predictors like land area, building area, bedrooms, and carports, with R² and Mean Squared Error (MSE) metrics evaluating model performance. Results showed that LGBM and Random Forest outperformed others with 0.8 R2 and low MSE, with building and land area as the most significant factors influencing prices. The study concludes that property size is a primary determinant of house prices, and there is a need for policy interventions to make housing more affordable. Additionally, apartment rentals offer a viable alternative, especially in central urban areas, where proximity to economic activities and facilities is crucial. The findings suggest that enhancing marketplace features with predictive tools could further assist buyers in making informed decisions.
Pemodelan Angka Harapan Hidup Negara G7 dengan Pendekatan Analisis Regresi Data Longitudinal Farizi, Muhammad Fikry Al; Maula, Sugha Faiz Al; Fajrina, Sofia Andika Nur; Hilma, Dzuria Hilma Qurotu Ain; Suryono, Alda Fuadiyah; Chamidah, Nur
Limits: Journal of Mathematics and Its Applications Vol. 22 No. 1 (2025): Limits: Journal of Mathematics and Its Applications Volume 22 Nomor 1 Edisi Ma
Publisher : Pusat Publikasi Ilmiah LPPM Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/limits.v22i1.3368

Abstract

Life expectancy is the average number of years of life a newborn baby will live in a given year. In general, life expectancy is a tool to evaluate government performance in improving community welfare. The aim of this research is prediction using longitudinal data regression analysis methods, namely Generalized Least Square with a Restricted Maximum Likelihood approach using a uniform correlation structure, Autoregressive (AR) (1), and Gaussian with factors that influence life expectancy, namely Tax to GDP ratio, Gross Domestic Product per Capita (GDPPC) and Health Expenditure per Capita from 2000-2020 in G7 countries. Based on the analysis results, it was found that tax revenues had a negative effect of 0.155 but the effect was not significant, GDP had a positive effect of 0.715 but had a significant effect, while health expenditure had a negative effect of 0.49 on Life Expectancy. The research results found that conditions in the G7 that were not ideal caused negative effects on taxes and health spending that were not in accordance with theory. The suggestions that can be given include tax reform from the source and its implementation, such as cigarette tax and sugary drink tax. In addition, it also provides suggestions to include universal health for a healthier and more prosperous society. This research is also in accordance with the aim of Sustainable Development Goals (SDGs) number 3, namely "Ensuring healthy lives and improving the welfare of all populations of all ages" and can be used as a policy reference for Indonesia.
Co-Authors Elly Pusporani Fajrina, Sofia Andika Nur Farizi, Muhammad Fikry Al Hilma, Dzuria Hilma Qurotu Ain Jannah, Sa'idah Zahrotul Nur Chamidah Setiawan, Nicoletta Almira Dyah Suryono, Alda Fuadiyah