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All Journal Journal of Creative Student Research
Vijay Sitorus
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Humanities Decision Sciences, Operations Research & Management Education Languange, Linguistic, Communication & Media Social Sciences Transportation

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Analisis Prediksi Harga Rumah di Bandung Menggunakan Regresi Linear Berganda Rafif Nauval Tuah Siregar; Vijay Sitorus; Willy Pramudia Ananta
Journal of Creative Student Research Vol. 1 No. 6 (2023): Desember : Journal of Creative Student Research
Publisher : Pusat Riset dan Inovasi Nasional

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55606/jcsrpolitama.v1i6.3038

Abstract

This research aims to develop a model for estimating house prices in the Bandung area using the Multiple Linear Regression approach. House prices play a significant role in the decision-making process for purchases. The ability of this model to predict house prices with high accuracy provides significant benefits for potential buyers, sellers, and various stakeholders in the housing industry. Data on house prices and potential variables such as land area, building area, number of bedrooms, nearby facilities, and geographical location were collected for analysis. The use of Multiple Linear Regression allows for a deeper understanding of the relationships between these variables and the value of the house. The analysis results show a strong correlation between these variables and house prices in Bandung. The developed Multiple Linear Regression model can provide satisfactory predictions of house prices. This model can be used as a tool for both homebuyers and sellers to determine fair prices and assist property developers in identifying key factors influencing house prices in the Bandung region.
Pengenalan Ekspresi Wajah Menggunakan Convolutional Neural Network (CNN) Richard Steven Immanuel Sihombing; Rafif Nauval Tuah Siregar; Vijay Sitorus; Timotius Selar Sitompul
Journal of Creative Student Research Vol. 1 No. 6 (2023): Desember : Journal of Creative Student Research
Publisher : Pusat Riset dan Inovasi Nasional

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55606/jcsrpolitama.v1i6.3046

Abstract

Facial expression recognition is an important research area in the advancement of machine learning. This research uses Convolutional Neural Network (CNN) as a method for recognizing facial expressions with a fairly high level of accuracy. This research uses a dataset obtained from Kaggle in the form of images of facial expressions, including surprised, happy, sad, afraid, angry and neutral. The MobilenetV2 CNN model was trained and tested using this dataset. The research results show that the model is able to recognize facial expressions with 78% accuracy on test data. It can be concluded that the MobilenetV2 model is quite capable of recognizing facial expressions.
Co-Authors Rafif Nauval Tuah Siregar Richard Steven Immanuel Sihombing Timotius Selar Sitompul Willy Pramudia Ananta