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All Journal BAREKENG: Jurnal Ilmu Matematika dan Terapan Electronic Integrated Computer Algorithm Journal
Lase, Yuyun
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Energy Engineering Mathematics Mechanical Engineering Physics Social Sciences Transportation

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Uncovering the Secrets of the 2023 Box Office: Analysis of Factors Affecting the Success of the Top 200 Films Using the Linear Regression Method Lase, Yuyun; Claudia, Sindi; Lingga, Ichan; Ariyudha, Dimas
Electronic Integrated Computer Algorithm Journal Vol. 2 No. 1 (2024): VOLUME 2, NO 1: OCTOBER 2024
Publisher : Yayasan Asmin Intelektual Berkah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62123/enigma.v2i1.45

Abstract

The film industry plays a crucial role in the global economy and popular culture, realizing creative outcomes through complex processes involving production to marketing. This research analyzes the factors influencing the success of the top 200 films at the Box Office in 2023 using linear regression. Independent variables such as film ratings, number of theaters, and distributors are examined in relation to total gross revenue. The dataset processed for this research consists of 200 data points specifically for the year 2023, sourced from Kaggle and processed with Python in Google Colab. The analysis revealed that films with a rating of 7.0 and above averaged a total gross revenue of approximately $100 million, while those rated below 7.0 averaged around $50 million, indicating a negative correlation between film ratings and gross revenue. Additionally, films shown in an average of 2,000 theaters grossed approximately $150 million, demonstrating a positive correlation between the number of theaters and gross income. The analysis also indicated that films distributed by major companies tend to have higher grosses, with the top distributors achieving an average gross of $120 million compared to $70 million for smaller distributors. Nonetheless, this analysis highlights the complexity of other factors influencing a film's success. Further research is needed for a better understanding. The relevance of these findings for the film industry lies in supporting strategic decision-making and the development of more sophisticated analytical methodologies.
A COMPARATIVE STUDY OF PIPELINE-VALIDATED MACHINE LEARNING CLASSIFIERS FOR PERMISSION-BASED ANDROID MALWARE DETECTION Lubis, Arif Ridho; Wulandari, Dewi; Adha, Lilis Tiara; Ariyani, Tika; Lase, Yuyun; Lubis, Fahdi Saidi
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 20 No 2 (2026): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol20iss2pp1675-1692

Abstract

The growing prevalence of Android malware distributed through third-party APK sideloading poses a significant security threat to users and developers. This study aims to evaluate the effectiveness of three machine learning algorithms—Logistic Regression (LR), Random Forests (RF), and Gradient Boosting Machine (GBM)—for static Android malware detection based on permission features. The experiment employs the publicly available Android Malware Prediction Dataset (Kaggle, accessed 2025), containing 4,464 application samples with 328 binary permission attributes. A leakage-free CRISP-DM workflow was implemented, integrating data cleaning, automated feature selection via SelectKBest (Mutual Information), and hyperparameter optimisation using GridSearchCV with stratified 5-fold cross-validation. Results on the unseen hold-out test set show that GBM achieved the best performance, with 96.05% accuracy and 0.9924 ROC-AUC, outperforming LR and RF. In addition, GBM exhibited superior probability calibration (Brier Score = 0.0344) and interpretability, as confirmed through SHAP analysis. The ablation study further validated that optimal model performance saturates at 30–40 selected features. This research contributes a reproducible and pipeline-validated comparative framework for static Android malware detection, addressing prior studies’ limitations regarding feature selection bias and data leakage. Nevertheless, the study is limited by its reliance on static permission features and the absence of dynamic behavioural data, which may restrict generalisation to evolving malware families.
Co-Authors Adha, Lilis Tiara Ariyani, Tika Ariyudha, Dimas Claudia, Sindi Dewi Wulandari Lingga, Ichan Lubis, Arif Ridho Lubis, Fahdi Saidi