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All Journal JSiI (Jurnal Sistem Informasi) bit-Tech JUTECH : Journal Education and Technology KOMMAS: Jurnal Pengabdian Kepada Masyarakat Jurnal Pengabdian Masyarakat Information Technology (JPM ITech) Jurnal Pengabdian Masyarakat Bhinneka AMMA : Jurnal Pengabdian Masyarakat
Amin Hidayat
Universitas Pamulang
Agriculture, Biological Sciences & Forestry Humanities Chemistry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Nursing Public Health Other

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Journal : bit-tech

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Comparative Analysis of Machine Learning Algorithms for Predicting LQ45 Stock Index Prices Amin Hidayat; Ade Putra Prima Suhendri
bit-Tech Vol. 8 No. 1 (2025): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i1.2853

Abstract

An essential metric for assessing the success of the country's capital markets is the LQ45 index, which is made up of 45 stocks with the biggest market capitalization and liquidity on thb e Indonesia Stock Exchange. Stock price prediction, particularly in volatile markets, remains complex challenge that benefits from advanced analytical approaches. While machine learning (ML) techniques have demonstrated significant promise in financial forecasting, comprehensive comparative evaluations across multiple algorithms and preprocessing strategies remain limited. In order to evaluate the predictive performance of nine machine learning algorithms Random Forest, Decision Tree, AdaBoost, Support Vector Classifier (SVC), XGBoost, Naive Bayes, K-Nearest Neighbors (KNN), Logistic Regression, and Artificial Neural Networks (ANN) in predicting the direction of movements of the LQ45 index, this study presents a structured comparative framework. The models are trained using a 10-year historical dataset, incorporating both continuous and binary representations of technical indicators. Three data preprocessing approaches are explored: raw trading data, unsmoothed indicators, and smoothed indicators. Accuracy, precision, recall, F1-score, and ROC AUC are all important factors in model evaluation. The findings show that when applied to continuous data with smoothed technical indications, Random Forest and XGBoost produce the best prediction results. For binary classification tasks, Naive Bayes emerges as the most effective model. These results demonstrate how important data representation and preprocessing in particular, smoothing are to enhancing the accuracy and robustness of models.  Research aids in the creation of trustworthy, data-driven stock prediction tools that are suited for developing markets.  Financial analysts, portfolio managers, and algorithmic traders looking to improve investment strategies through well-informed model selection and preprocessing design can benefit from the findings.
Spice Image Classification Based on Content-Based Image Retrieval Meidy Fajar Wahyu; Lely Panca Andriyanto; Amin Hidayat; Achmad Sehan; Eko Sutono
bit-Tech Vol. 8 No. 3 (2026): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i3.4149

Abstract

Indonesia possesses exceptional spice biodiversity, yet public familiarity with the original visual characteristics of many spices is declining because of packaged processing, reduced direct exposure, and changing food-consumption patterns. This study develops RempahID, a web-based spice identification system that integrates Content-Based Image Retrieval (CBIR) with machine-learning classification. The study addresses the limited availability of Indonesian spice recognition systems that simultaneously provide class predictions and visually similar reference images for user verification. The system uses a dataset comprising ten major spice categories, including ginger, turmeric, galangal, aromatic ginger, cinnamon, cloves, nutmeg, coriander, candlenut, and star anise. Each image is preprocessed through resizing, normalization, noise reduction, and Otsu-based segmentation. Visual representation combines 24 Hue-Saturation-Value color histogram features, four Gray-Level Co-occurrence Matrix texture descriptors, and seven Hu Moment shape features, producing a 35-dimensional feature vector. Euclidean Distance is employed to rank visually similar database images, while K-Nearest Neighbors, Support Vector Machine, and Random Forest are compared for classification. Performance is evaluated using accuracy, precision, recall, and F1-score. The Support Vector Machine with a radial basis function kernel achieved the best result, with 92.1% accuracy, 0.91 precision, 0.92 recall, and a 0.91 F1-score. Retrieved reference images also supported transparent visual comparison rather than presenting an isolated predicted label alone. These findings demonstrate that integrating complementary color, texture, and shape descriptors within a CBIR framework provides an effective and interpretable approach for Indonesian spice identification.
Co-Authors Achmad Sehan Ade Putra Prima Suhendri Ade Putra Prima Suhendri Ade Putra Prima Suhendri Aji Khoirunas Dea Khoirunnisa Dede Handayani Edi Mariyanto Eko Sutono Lely Andriyanto Lely Panca Andriyanto Lely Panca Andriyanto Lely Panca Andriyanto Andri Meidy Fajar Wahyu Meidy Fajar Wahyu Meidy Fajar Wahyu Meidy Nanang nanang nanang Nanang Nanang nanang nang Nurjanah, Elin Suraya, Gina Wahyu, Meidy Fajar Yuda Samudra