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All Journal JURNAL TEKNIK INFORMATIKA DAN SISTEM INFORMASI KOMPUTIKA - Jurnal Sistem Komputer
Dhuhita, Windha Mega P
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Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering

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Perbandingan Kinerja Algoritma Multinomial dan Bernoulli Naïve Bayes dalam Mengklasifikasikan Komentar Cyberbullying Dhuhita, Windha Mega P; Zone, Fritz
Komputika : Jurnal Sistem Komputer Vol. 12 No. 2 (2023): Komputika: Jurnal Sistem Komputer
Publisher : Computer Engineering Departement, Universitas Komputer Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34010/komputika.v12i2.9767

Abstract

In today's era, amidst the rapid development of technology, many people misuse technology to do unpleasant things to others, including bullying that is done using social media called cyberbullying. Therefore, researchers classify social media comment data to determine whether it includes bullying or not. The purpose of this study is to classify social media comment data, including cyberbullying or not, by first comparing the performance between Naive Bayes Multinomial and Bernoulli algorithms in classifying such comment data. The researchers compared the Naive Bayes Classifier model, Multinomial and Bernoulli, to obtain the best model. The researchers also compared the use of the Bag of Words and TF-IDF feature extraction methods to improve the accuracy of the algorithms used. The results of the study show that the Naive Bayes Multinomial model algorithm obtained higher accuracy and faster average processing time compared to the Bernoulli model. The use of the Bag of Words feature extraction method can also significantly increase accuracy compared to TF-IDF.
Prediksi Harga Rumah Di Kabupaten Bantul Menggunakan Algoritma Support Vector Regression Dhuhita, Windha Mega P
JATISI Vol 11 No 2 (2024): JATISI (Jurnal Teknik Informatika dan Sistem Informasi)
Publisher : Universitas Multi Data Palembang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35957/jatisi.v11i2.6001

Abstract

House is a dwelling place that is necessary for the survival of people as a basic need. People spend at least half their day at home, such as for eating, bathing, sleeping or just relaxing with family members. The price of a house is influenced by the specifications of the house, such as location, land area, building area, number of bedrooms, number of bathrooms and also number of floors. These variables will affect the determination of the house price.Prediction model was created to estimate the house price from these variables. This study uses the Support Vector Regression algorithm with testing using Linear, RBF and Polynomial kernel functions to predict house prices. The data source for this study was obtained from rumah123.com. The model evaluation of the prediction results used RMSE, R2 and MAPE techniques.The number of data used for this study was 1617 data after preprocessing. The best result of this SVR algorithm was obtained with the RBF kernel function with an RMSE error value of 11.71%.
Co-Authors Zone, Fritz