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All Journal Nusantara Hasana Journal Jurnal Informatika Progres
Habi Talib, Emil Agusalim
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Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Economics, Econometrics & Finance Education Nursing

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COMPARATIVE ANALYSIS OF THE APPLICATION OF FEATURE SELECTION IN RANDOM FOREST REGRESSION FOR STOCK PRICE PREDICTION Habi Talib, Emil Agusalim; Alvina Felicia Watratan; Saharuddin, Saharuddin
Nusantara Hasana Journal Vol. 5 No. 3 (2025): Nusantara Hasana Journal, August 2025
Publisher : Yayasan Nusantara Hasana Berdikari

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59003/nhj.v5i3.1641

Abstract

The rapid development of information technology and data mining has encouraged the use of machine learning algorithms in various fields, including the financial sector and capital markets. One of the main challenges in stock price prediction is the large number of available variables, not all relevant to the target variable, potentially reducing accuracy and causing overfitting. This study aims to analyze the benefits of applying feature selection in improving the performance of the Random Forest Regression algorithm for stock price prediction. The dataset used in this research consists of ten years of historical stock price data from PT Aneka Tambang Tbk (ANTM). The research was conducted using an experimental approach by developing two models: (1) Random Forest Regression without feature selection and (2) Random Forest Regression with feature selection using the Spearman Correlation method. Model performance was evaluated using Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Square Error (RMSE), Coefficient of Determination (R²), and Mean Absolute Percentage Error (MAPE). The experimental results show that the model with feature selection achieved better performance, with improvements in all evaluation metrics, such as reduced error values (MAE: 26.22; RMSE: 51.82; MAPE: 1.32%) and increased R² (0.9895). These findings confirm that integrating feature selection with Random Forest Regression can improve prediction accuracy, reduce model complexity, and minimize overfitting risk. Therefore, feature selection plays a significant role in enhancing the effectiveness of machine learning models in stock price prediction.
IMPLEMENTASI METODE HYBRID FUZZY JARO WINKLER DAN COSINE SIMILARITY PADA SISTEM PENCARIAN AYAT AL-QURAN BERBASIS TRANSLITERASI LATIN Tahir, Gempar Perkasa; Habi Talib, Emil Agusalim; Rachman, Fahrim Irhamna
PROGRESS Vol 17 No 2 (2025): September
Publisher : P3M STMIK Profesional Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56708/progres.v17i2.482

Abstract

This research addresses the challenge of retrieving Qur’anic verses in Latin transliteration, which is hindered by the absence of a standardized orthography, leading to diverse spelling variations. The study aims to design and implement a hybrid information retrieval system that integrates Fuzzy Jaro-Winkler for lexical similarity and Cosine Similarity on fine-tuned DistilBERT embeddings for semantic relevance. The system workflow begins with preprocessing and normalization of the dataset, followed by initial candidate selection using Jaro-Winkler, and final reranking through semantic similarity scoring. Evaluation was conducted using black-box testing across scenarios including ideal queries, spelling variations, incomplete queries, and varying query lengths. Results show high accuracy for ideal (96%) and varied spelling queries (92%), with performance improving as query length increases, reaching 96% for four-word queries. The hybrid approach effectively bridges lexical and semantic gaps, outperforming single-method baselines, and demonstrates robustness in handling non-standard transliteration in Qur’anic text retrieval.
Co-Authors Alvina Felicia Watratan Rachman, Fahrim Irhamna Saharuddin Tahir, Gempar Perkasa