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Journal : Journal of Dinda : Data Science, Information Technology, and Data Analytics

Comparative Analysis of Linear Regression, Decision Tree and Gradient Boosting for Predicting Stock Price of Bank Rakyat Indonesia Rahma Dwi Ningsih; Sarwido Sarwido; Gentur Wahyu Nyipto Wibowo
Indonesian Journal of Data Science, IoT, Machine Learning and Informatics Vol 4 No 2 (2024): August
Publisher : Research Group of Data Engineering, Faculty of Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20895/dinda.v4i2.1566

Abstract

An investment is the placement of a current amount of funds in the hope of generating a profit in the future. There are several types of investments, including stocks, which are attractive options as they can bring a huge return to investors. However, rapidly fluctuating stock prices are influenced by various factors, such as company performance, interest rates, economic conditions, and government policies. In Indonesia, PT Bank Rakyat Indonesia Tbk (BBRI) had the largest profit among the 10 largest banks by the end of March 2024, with a profit of IDR 13.8 trillion. The higher the bank's return, the greater the investor's interest in purchasing the stock, influencing the stock price. The goal of stock price prediction is to forecast the stock's future price in order to increase investors' potential profits. Various methods, such as Linear Regression, Decision Tree, and Gradient Boosting, have been developed for stock price prediction. Comparative analysis is needed to determine the most accurate method in predicting the stock price of People's Bank of Indonesia, using Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), Root Mean Squared Error (RMSE), and R-squared metrics (R2). The analysis showed that Linear Regression achieved the highest accuracy with R2 of 0.96, MAE of 65.72 and RMSE of 86.74 compared to the Decision Tree and Gradient Boosting models.
Integration of RFM Method and K-Means Clustering for Customer Segmentation Effectiveness Nafissatus Zahro; Nadia Annisa Maori; Gentur Wahyu Nyipto Wibowo
Indonesian Journal of Data Science, IoT, Machine Learning and Informatics Vol 5 No 1 (2025): February
Publisher : Research Group of Data Engineering, Faculty of Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20895/dinda.v5i1.1649

Abstract

Penelitian ini bertujuan untuk mengintegrasikan metode RFM dan K-Means Clustering untuk segmentasi pelanggan. Rumusan masalah yang diajukan adalah bagaimana mengintegrasikan kedua metode ini agar segmentasi pelanggan lebih efektif. Data transaksi pelanggan ADAPTA.Id tahun 2022 yang meliputi 2.252 transaksi pelanggan dianalisis untuk menghasilkan nilai RFM, dinormalisasi, dan diklaster menggunakan K-Means. Dua klaster optimal diidentifikasi dengan skor silhouette sebesar 0,8511. Dari total 2.252 transaksi pelanggan, terdapat dua klaster utama: klaster pertama berisi 10 pelanggan dengan frekuensi pembelian tinggi dan nilai transaksi signifikan, sedangkan klaster kedua terdiri dari 918 pelanggan dengan frekuensi dan nilai transaksi lebih rendah. Mayoritas pelanggan berada di klaster kedua. Segmentasi ini memungkinkan perusahaan untuk merancang strategi pemasaran yang lebih efektif dengan memfokuskan sumber daya untuk mempertahankan pelanggan bernilai tinggi dan meningkatkan aktivitas pembelian di klaster bernilai rendah. Pendekatan ini menawarkan wawasan mendalam untuk strategi bisnis yang lebih efisien, serta meningkatkan kepuasan dan loyalitas pelanggan. Skor silhouette yang tinggi menegaskan validitas klaster.
Heart Failure Classification Using a Hybrid Model Based on SVM and Random Forest Abdilllah, Muh Sajid; Mulyo, Harminto; Wibowo, Gentur Wahyu Nyipto
Indonesian Journal of Data Science, IoT, Machine Learning and Informatics Vol 5 No 2 (2025): August
Publisher : Research Group of Data Engineering, Faculty of Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20895/dinda.v5i2.2001

Abstract

This study discusses the development of a model to classify heart failure disease by combining two algorithms in the field of data mining: Support Vector Machine (SVM) and Random Forest (RF). The dataset used is the Heart Failure Prediction Dataset, consisting of 918 patient records containing medical information such as blood pressure, cholesterol levels, and heart rate. The research process began with data cleaning, normalization using MinMaxScaler, and data balancing with the SMOTE technique to equalize the number of cases between heart failure patients and non-patients. The data was then split into training and testing sets. Each model (SVM and RF) was tested individually and also combined into a hybrid model. Validation was performed using 5-Fold Cross Validation to ensure consistent results. The results show that SVM performed better in terms of precision for detecting heart failure after applying SMOTE, while RF remained stable even without data balancing. The hybrid model combining both algorithms achieved the best performance, with an accuracy of 91.20%, precision of 90.85%, recall of 92.44%, and an AUC score of 0.961. These results indicate that the hybrid model can detect heart failure more accurately and in a more balanced manner. With its high and consistent performance, this model is suitable for use as a decision support system in the medical field, particularly for early detection of heart failure.