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All Journal Indonesian Journal of Electrical Engineering and Computer Science Engineering, Mathematics and Computer Science Journal (EMACS) JGGAG (Journal of Games, Game Art, and Gamification)
Novika, Andien Dwi
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Arts Civil Engineering, Building, Construction & Architecture Computer Science & IT Engineering Industrial & Manufacturing Engineering Mathematics Other

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Multi-layer perceptron hyperparameter optimization using Jaya algorithm for disease classification Novika, Andien Dwi; Girsang, Abba Suganda
Indonesian Journal of Electrical Engineering and Computer Science Vol 35, No 1: July 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v35.i1.pp620-630

Abstract

This study introduces an innovative hyperparameter optimization approach for enhancing multilayer perceptrons (MLP) using the Jaya algorithm. Addressing the crucial role of hyperparameter tuning in MLP’s performance, the Jaya algorithm, inspired by social behavior, emerges as a promising optimization technique without algorithm-specific parameters. Systematic application of Jaya dynamically adjusts hyperparameter values, leading to notable improvements in convergence speeds and model generalization. Quantitatively, the Jaya algorithm consistently achieves convergences at first iteration, faster convergence compared to conventional methods, resulting in 7% higher accuracy levels on several datasets. This research contributes to hyperparameter optimization, offering a practical and effective solution for optimizing MLP in diverse applications, with implications for improved computational efficiency and model performance.
Cost-Sensitive Learning with LightGBM for Class Imbalance in Intrusion Detection Systems Novika, Andien Dwi; Mujhid, Almuzhidul
Engineering, MAthematics and Computer Science Journal (EMACS) Vol. 7 No. 2 (2025): EMACS
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/emacsjournal.v7i2.13435

Abstract

Imbalanced data is a common challenge in classification problems, where standard models tend to be biased toward majority classes, leading to poor detection of minority instances. This paper presents a comparative study of Light Gradient Boosting Machine (LightGBM) and eXtreme Gradient Boosting (XGBoost) models, enhanced with cost-sensitive learning to address class imbalance at the algorithmic level. The objective is to evaluate the impact of cost-sensitive loss adjustments on model performance using various evaluation metrics. Experimental results show that both models achieved high cross-validation and test accuracies, with LightGBM and XGBoost recording over 99.9% accuracy. However, only cost-sensitive LightGBM achieved perfect scores in precision, recall, and F1-score, indicating its ability to handle minority class identification effectively. In contrast, XGBoost exhibited lower recall and F1-score despite similar accuracy, reflecting limitations in sensitivity to minority instances. Models without cost-sensitive learning demonstrated further drops in performance across minority-related metrics. The findings suggest that cost-sensitive LightGBM is a more robust solution for imbalanced classification tasks, outperforming both its baseline and the cost-sensitive XGBoost variant. This approach is particularly beneficial for critical applications such as fraud detection, cybersecurity, and medical diagnostics, where class imbalance is prevalent and misclassification costs are high
Low-resolution image quality enhancement using enhanced super-resolution convolutional network and super-resolution residual network Riftiarrasyid, Mohammad Faisal; Halim, Rico; Novika, Andien Dwi; Zahra, Amalia
Indonesian Journal of Electrical Engineering and Computer Science Vol 39, No 1: July 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v39.i1.pp634-643

Abstract

This research explores the integration of enhanced super-resolution convolutional network (ESPCN) and super-resolution residual network (SRResNet) to enhance image quality captured by low-resolution (LR) cameras and in internet of things (IoT) devices. Focusing on face mask prediction models, the study achieves a substantial improvement, attaining a peak signal-to-noise ratio (PSNR) of 28.5142 dB and an execution time of 0.34704638 seconds. The integration of super-resolution techniques significantly boosts the visual geometry group-16 (VGG16) model’s performance, elevating classification accuracy from 71.30% to 96.30%. These findings highlight the potential of super-resolution in optimizing image quality for low-performance devices and encourage further exploration across diverse applications in image processing and pattern recognition within IoT and beyond.
Remake of Survival Horror Video Game and How It’s Implemented: Comparative Analysis on Original and Remake Version of Resident Evil 4 Pratama, Galih Dea; Shiddiqi, Hafizh Ash; Hermawan, Eric Savero; Novika, Andien Dwi; Ardiyanto, Elshad Ryan
Journal of Games, Game Art, and Gamification Vol. 9 No. 1 (2024)
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/jggag.v9i1.10999

Abstract

Video game is particularly known as an interactive media designed to present entertainment among its targeted users. While being released from time to time, there comes the desire to provide the outdated games to the modern audiences, hence surfaces the trend of video game remakes, which recently presented through Resident Evil 4 that serves as the newer version to the original one released in 2005. To give better understanding on the games, there is Elemental Tetrad which emphasizes on four elements like Mechanics, Story, Aesthetics and Technology. In this research, Elemental Tetrad is utilized to analyze both versions of Resident Evil 4, diving deep into how the game elements are implemented by the games while also highlighting the advances presented in the remake version compared to the original one. There are various differences of both games, such as the existence of more new mechanics, the expanded story and characterizations, slight change in the aesthetics to accommodate the realism of the remake, with all are enabled through the usage of latest RE Engine.
Co-Authors Abba Suganda Girsang, Abba Suganda Ardiyanto, Elshad Ryan Halim, Rico Hermawan, Eric Savero Mujhid, Almuzhidul Pratama, Galih Dea Riftiarrasyid, Mohammad Faisal Shiddiqi, Hafizh Ash Zahra, Amalia