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All Journal Idealis : Indonesia Journal Information System
Wiwit Agus Triyanto
Sistem Informasi, Fakultas Teknik, Universitas Muria Kudus, Kudus, Indonesia
Computer Science & IT Decision Sciences, Operations Research & Management

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Optimasi Hiperparameter Extreme Learning Machine Menggunakan Particle Swarm Optimization dan Indikator Teknikal untuk Peramalan Bitcoin Bagus Putra Sulung; Wiwit Agus Triyanto; Pratomo Setiaji
IDEALIS : InDonEsiA journaL Information System Vol. 9 No. 2 (2026): Jurnal IDEALIS Juli 2026
Publisher : Universitas Budi Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36080/idealis.v9i2.3812

Abstract

Highly volatile price changes in crypto assets like Bitcoin complicate precise forecasting. The Extreme Learning Machine (ELM) artificial neural network approach offers high computational speed but is prone to performance instability due to random weight initialization and manual hyperparameter determination. To overcome this problem, this study proposes a hybrid PSO-ELM algorithm to automate the search for optimal parameters, namely the number of hidden neurons and the regression regularization penalty. Evaluated using a five-fold Walk-Forward Validation to prevent data leakage, the model comparatively tested five input feature scenarios based on technical indicators, which are mathematical calculations from historical prices to identify market patterns. Results demonstrate the hybrid PSO-ELM outperforms conventional static models, reducing average error (MAPE) by 18.67 percent. The cross-scenario comparison reveals that applying the Simple Moving Average technical indicator yields the best forecasting model, achieving a 26.65 percent error reduction and a final MAPE accuracy of 2.15 percent. The contribution of this research is providing empirical evidence that automatic parameter optimization combined with a random fluctuation filtering feature is proven to be more robust and accurate in responding to extreme volatility compared to the stacking of various complex derivative momentum indicators.
Analisis Sentimen Ulasan Mobile Legends: Bang Bang dalam Bahasa Indonesia Menggunakan Random Forest, KNN, TF-IDF, dan SMOTE Ahmad Alif Candra Selamet; Pratomo Setiaji; Wiwit Agus Triyanto
IDEALIS : InDonEsiA journaL Information System Vol. 9 No. 2 (2026): Jurnal IDEALIS Juli 2026
Publisher : Universitas Budi Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36080/idealis.v9i2.3819

Abstract

Mobile Legends: Bang Bang (MLBB) is one of the most popular mobile games, generating a large number of user reviews on the Google Play Store. The large volume of reviews makes manual sentiment analysis impractical, requiring an automated machine learning approach. This study compares the performance of Random Forest and K-Nearest Neighbors (KNN) for classifying sentiment in Indonesian-language MLBB reviews. A total of 6,994 reviews were obtained through web scraping and preprocessing. The proposed framework includes text preprocessing, rating-based sentiment labeling, TF-IDF feature extraction, SMOTE-based class balancing, model training, and evaluation using Accuracy, Precision, Recall, F1-score, Macro-F1, Weighted-F1, and 5-fold cross-validation. Random Forest with SMOTE achieved the best performance, with an Accuracy of 84.0% and a Macro-F1 score of 80.8%, outperforming KNN with SMOTE, which achieved 73.8% Accuracy and 71.9% Macro-F1. The ablation study demonstrates that the effectiveness of SMOTE is model-dependent, improving Random Forest but degrading KNN performance. This study provides empirical evidence of SMOTE impact on different classifiers and employs cross-validation-based K selection to prevent test data leakage.
Co-Authors Ahmad Alif Candra Selamet Bagus Putra Sulung Pratomo Setiaji