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All Journal Journal of Student Research Exploration Journal of Information System Exploration and Research Journal of Electronics Technology Exploration (JOeTEx)
Sundari, Putri Susi
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering

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The Classification of Hate Comments on Twitter Using a Combination of Logistic Regression and Support Vector Machine Algorithm Damayanti, Nabila Putri; Prameswari, Della Egyta; Puspita, Wiyanda; Sundari, Putri Susi
Journal of Information System Exploration and Research Vol. 2 No. 1 (2024): January 2024
Publisher : shmpublisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52465/joiser.v2i1.229

Abstract

This research was conducted to increase accuracy in classifying sentences containing hate speech and non-hate speech on Twitter. This is important to do because, as technology develops, it also comes with negative impacts, one of which is hate speech. This classification is carried out using a combination of Logistic Regression (LR) and Support Vector Machine (SVM) methods. This combination is based on the ease of implementation and speed of LR as well as SVM's ability to handle more complex and non-linear data. In this context, LR is used to model the probability that a comment on Twitter contains hate elements or not. The model can then provide probability predictions for each class, and a threshold can be set to determine the final class. This research shows that combining these methods can build a good classification model with an accuracy of 96%.
Application of Passive Infrared Sensor to Improve the Quality of CCTV in Maintaining Home Security Ananda, Mohammad Nabiel Dwi; Shabaha, Achmad Rozin; Sundari, Putri Susi
Journal of Electronics Technology Exploration Vol. 2 No. 1: June 2024
Publisher : SHM Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52465/joetex.v2i1.366

Abstract

Artificial intelligence, or AI, is a simulation technology that runs through human intelligence demonstrated by machines or tools. Artificial intelligence can overcome and provide a sense of comfort, especially in the application of CCTV devices that use this passive infrared sensor method. This method can detect thieves or people moving in the area of the house where CCTV is installed, by detecting human objects using IR filters. If it detects an object that has the minimum temperature possessed by humans, it will immediately direct the alarm indicator. With the existence of CCTV that applies AI, it is hoped that human life will be safe, and crime will be reduced in an area, especially quiet areas with high crime rates.  The application of Passive Infrared Sensor (PIR Sensor) in this anti-theft CCTV tool can detect and be able to work with a high level of accuracy.
The Optimization house price prediction model using gradient boosted regression trees (GBRT) and xgboost algorithm Sundari, Putri Susi; Khafidz Putra, Mahardika
Journal of Student Research Exploration Vol. 2 No. 1: January 2024
Publisher : SHM Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52465/josre.v2i1.176

Abstract

In this rapidly advancing technological era, the demand for the real estate industry has also increased, including in the field of house price prediction. House prices fluctuate every year due to several factors such as changes in land prices, location, year of construction, infrastructure developments, and other factors. Numerous studies have been conducted on this issue. However, the challenge lies in building a proven accurate and effective model for predicting house prices with the abundance of features present in the dataset. The objective of this research is to develop a predictive model that can accurately estimate house prices based on relevant features or variables. The researcher utilizes ensemble learning techniques, combining the Gradient Boosted Regression Trees (GBRT) and XGBoost algorithms. The dataset used in this article is titled "Ames Housing dataset" obtained from Kaggle. The predictive model is then evaluated using the Root Mean Squared Error (RMSE) method. The RMSE result from a previous study that used the combination of Lasso and XGBoost was 0.11260, while the RMSE result from this research is 0.00480. This indicates a decrease in the RMSE value, indicating a lower level of error in the model. It also means that the combination of GBRT and XGBoost algorithms successfully improves the prediction accuracy of the previous research model.
Co-Authors Ananda, Mohammad Nabiel Dwi Damayanti, Nabila Putri Khafidz Putra, Mahardika Prameswari, Della Egyta Puspita, Wiyanda Shabaha, Achmad Rozin