Pengaruh Aksi Boikot Terhadap Harga Saham Unilever: Pendekatan Prediktif Dengan Neural Network Dan Linear Regression - Yani, Ririn Yuli; Nidaa, Syafiqotun; Suseno, Akrim Teguh; Wulandari, Umi Meganinditya (ROUTERS: Jurnal Sistem dan Teknologi Informasi, 2025)
PT Unilever Indonesia Tbk is a  multinational company that produces and markets various consumer goods in various countries to fulfill needs ranging from health, nutrition, daily care and so on. PT Unilever Indonesia Tbk is facing a crisis of calls for a boycott of products due to pro-Israel which has an impact on the Company’s reputation and performance. In the face of this situation, stock price prediction analysis is important to help investors in making decisions. To overcome this problem, this research applies Data Mining Techniques in predicting the share price of PT Unilever Tbk. The two algorithms used are Neural Network and Linear Regression, which are then tested using the Root Mean Squared Error (RMSE) evaluation method. Data processing is done using RapidMiner with historical data period from December 2023 to May 2024. Based on the analysis results, the Linear Regression algorithm produces an RMSE value of 22,745, showing a more accurate prediction compared to the Neural Network algorithm which has an RMSE value of 44,830. The test results show that predicting stock prices using Linear Regression has a lower error rate than the Neural Network. Thus, in this study, the Linear Regression algorithm is superior in predicting the stock price of PT Unilever Indonesia Tbk compared to the Neural Networj. The results of this study are also compared with previous research which shows thaht the accuracy of the stock price prediction model depends on the characteristics of the dataset and the method used. Some previous studies concluded that Neural Network is superior in capturing complex patterns in certain stocks, while Linear Regression is more suitable for data with linear relationships. Therefore, although Linear Regression is better in this study, model selection still needs to be tailored to the characteristics and objectives of the analysis.