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All Journal International Journal of Informatics and Computing International Journal of Machine Learning (IJOML)
Rajiansyah Rajiansyah
Department of Computer Science and Systems Engineering, Wroclaw University of Science and Technology, Poland
Computer Science & IT Decision Sciences, Operations Research & Management Electrical & Electronics Engineering

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Support Vector Machine Based Machine Learning for Sentiment Analysis of User Reviews of the Bibit Application on Google Play Store Ega Shela Marsiani; Fauzan Natsir; Redo Abeputra Sihombing; Millati Izzatillah; Rajiansyah Rajiansyah
JICO: International Journal of Informatics and Computing Vol. 1 No. 2 (2025): November 2025
Publisher : IAICO

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

The increasing use of financial technology (fintech) applications has changed the investment patterns of users in Indonesia. Bibit, as one of the popular fintech investment platforms, receives many user reviews through the Google Play Store that reflect user perceptions and satisfaction levels. Although the volume of user reviews continues to increase, systematic analysis of user sentiment is still limited, making it difficult for developers to understand the needs and experiences of users. Therefore, an artificial intelligence-based approach is needed to efficiently and objectively extract and analyze user opinions. This study aims to conduct sentiment analysis of user reviews of the Bibit application using a Machine Vector Machine (SVM) based machine learning model. The research methodology includes data collection, pre-processing of texts, extraction of features using TF-IDF, as well as classification of sentiment into positive, negative, and neutral categories. Of the total review data, 7,801 data (79.99%) were used as training data, and 1,561 data (20.01%) were used as test data with a division ratio of 80:20 according to general standards in machine learning. The purpose of this study was to identify the dominant user sentiment and evaluate the classification performance of the SVM algorithm. The results of the experiment showed that the SVM model achieved high accuracy and was able to capture user opinions effectively, thus providing valuable input for developers in improving the quality of applications and user engagement on fintech platforms.
Stability-Aware Hierarchical Forecasting: Synergizing Conformal Prediction with Decomposition Ensembles Damar Nurcahyono; Rajiansyah Rajiansyah; Hamdani Hamdani
International Journal of Machine Learning (IJOML) Vol. 1 No. 1 (2026): IJOML Volume 1, Number 1, June 2026
Publisher : APJIKOM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.66472/ijoml.v1i1.6

Abstract

Accurate retail demand forecasting is frequently impeded by high-dimensional hierarchies and intermittent sales patterns, which destabilize traditional models and compromise operational decision-making. To address these challenges, this study develops a stability-aware forecasting framework that unifies global machine learning ensembles with hierarchical reconciliation and conformal uncertainty calibration. Utilizing the large-scale M5 dataset, the methodology synergizes decomposition-based feature engineering with a global Light Gradient Boosting Machine (LightGBM), reinforced by a robust Bottom-Up reconciliation strategy and Centered Conformalized Quantile Regression (CQR). Empirical results based on rolling-origin cross-validation demonstrate that the proposed framework achieves a superior Weighted Root Mean Squared Scaled Error (WRMSSE) of 8.7723, significantly outperforming both the standalone LightGBM (9.4846) and the Seasonal Naïve baseline (10.1740). Furthermore, the Centered CQR mechanism effectively balances predictive sharpness with coverage, attaining a Scaled Pinball Loss (SPL) of 0.2347, thereby mitigating error degradation often observed in sparse data regimes. These findings confirm that integrating structural decomposition with rigorous reconciliation acts as a potent regularizer, offering a scientifically robust solution for managing non-stationarity and signal sparsity in complex retail supply chains.
Co-Authors Damar Nurcahyono Ega Shela Marsiani Fauzan Natsir Hamdani Hamdani Millati Izzatillah Redo Abeputra Sihombing