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All Journal International Journal of Advances in Data and Information Systems CrossOver:Journal of Adaptation Studies
Fadhillah, Muhammad Alif
Unknown Affiliation
Humanities Computer Science & IT Electrical & Electronics Engineering Languange, Linguistic, Communication & Media

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CHARACTERS’ ENHANCEMENTS IN THE SERIES ADAPTATION OF LEAGUE OF LEGENDS Fadhillah, Muhammad Alif; Faiza, Anjania Muhammad; Subari, Afta Ikhlasul; Biela, Bening Salsa; Ibrahim, Yusuf
CrossOver Vol. 4 No. 2: December 2024
Publisher : UIN Raden Mas Said Surakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22515/crossover.v4i2.10908

Abstract

Arcane is a TV series adaptation from a game named League of Legends and was categorized as the best TV series in 2021. When many TV series adaptations take novel as its source, this adaptation becomes interesting to analyse because the source text is from a game. This research aims to find out the changes applied to the characters in the series adaptation using Linda Hutcheon’s adaptation theory, in which she mentioned that character is one of aspects often changed when a work is adapted into different form.  The Arcane is based on Zaun and Piltover's past, there will be so much to compare from the TV series and the game. Using qualitative method and spradley’s data analysis steps, this research reveal that changes are applied to the characters Vi, Jinx, and Caitlyn. These changes involve appearance, outfit, and ability, which gives more realistic and grounded effect to Arcane’s story. This is due to the different mode of engagement that Hutcheon calls from interacting to showing. With some adjustments made to the characters, the series are able to present engaging story and new audience.
Android Malware Detection with Hybrid Feature Selection and Bayesian Optimization Fadhillah, Muhammad Alif; Saputro, Setyo Wahyu; Muliadi, Muliadi; Faisal, Mohammad Reza; Nugroho, Radityo Adi
International Journal of Advances in Data and Information Systems Vol. 7 No. 1 (2026): April 2026 - International Journal of Advances in Data and Information Systems
Publisher : Indonesian Scientific Journal

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59395/ijadis.v7i1.1526

Abstract

The increasing dimensionality of Android application features poses significant challenges for accurate and efficient malware detection. This study proposes a hybrid feature selection framework that combines Minimum Redundancy Maximum Relevance (mRMR) and correlation filtering to optimize classification performance on the Drebin-215 dataset. A selected configuration of 175 features with a correlation threshold of 0.7 was evaluated using five classifiers: LSTM, Support Vector Machine (SVM), Random Forest, K-Nearest Neighbors (KNN), and XGBoost. The experimental results show that dimensionality reduction improves classification stability and overall predictive performance. SVM exhibits the most notable improvement, with accuracy increasing from 63.05% without feature selection to 98.57% after applying the proposed framework. LSTM achieves 98.57% accuracy with an AUC of 99.86%, while Random Forest, KNN, and XGBoost consistently achieve accuracy above 97%. In addition to performance enhancement, the hybrid feature selection approach substantially improves computational efficiency. SVM training time decreases from 770.75 seconds to 155.88 seconds, and testing time is reduced from 15.581 seconds to 0.3824 seconds. KNN testing time also decreases from 1.623 seconds to 0.4595 seconds..
Co-Authors Biela, Bening Salsa Faiza, Anjania Muhammad Muhammad Reza Faisal, Muhammad Reza Muliadi Radityo Adi Nugroho Saputro, Setyo Wahyu Subari, Afta Ikhlasul Yusuf Ibrahim