Journal of Computers and Digital Business

Beyond Binary Classification: Time-to-Event Modeling for Player Retention Using Cox Proportional Hazards and Ensemble Learning

2025-12-02T07:51:40+07:00

Player retention is the primary economic driver in the Free-to-Play (F2P) gaming industry, yet traditional churn prediction methodologies often rely solely on binary classification, neglecting the critical temporal dimension of when a player is likely to leave. Furthermore, the scarcity of open-source behavioral datasets restricts the development of reproducible academic frameworks. This study addresses these gaps by proposing a hybrid analytical framework that integrates Ensemble Learning (XGBoost) for predictive precision and Survival Analysis (Cox Proportional Hazards) for time-to-event risk modeling, utilizing a realistically simulated dataset of 5,000 players. Experimental results indicate that while the XGBoost model achieves robust discriminative stability with an AUC of roughly 0.90, the Survival Analysis provides deeper explanatory insights, revealing that game progression (level_reached) is a significantly more dominant determinant of retention (Hazard Ratio < 1.0) than short-term recency metrics. These findings suggest that depth of commitment acts as a stronger buffer against churn than login frequency, offering game developers a quantifiable basis to shift retention strategies from generic daily incentives to progression-based milestones. By openly providing the simulated dataset and full analytical pipeline, this work also contributes a reproducible methodological template for future game analytics research in data-scarce environments.

Conceptual Framework for Designing an Expert Advisor System Based on Technical Indicators: Evidence from Malaysian Forex Traders

2025-12-02T09:17:00+07:00

The evolution of algorithmic trading (AT) has dramatically transformed the Foreign Exchange (Forex) market by integrating computational intelligence into trading and decision-making processes. Despite these advancements, Malaysian traders remain challenged in adopting such systems, particularly due to limited technical expertise, inadequate adaptation to local trading practices, and a lack of customized automated tools. This concept paper proposes a framework for designing Expert Advisors (EAs) that incorporate technical indicators (TIs) aligned with Malaysian traders' preferences and prevailing market conditions. The framework integrates three core components: trader competency assessment, indicator-based strategy development, and EA system architecture design, aimed at improving trade accuracy, profitability, and risk management. A qualitative approach grounded in literature synthesis and contextual analysis is employed to construct the proposed framework. The resulting model offers a structured and context-sensitive approach that combines trader preferences, technological innovation, and ethical considerations, with practical implications for system developers, educators, and regulators. The originality of this study lies in its localization of EA design to Malaysian traders' needs, bridging the gap between advanced algorithmic tools and local market readiness, while providing a replicable model for other emerging markets adopting AT solutions.

Machine Learning in Fraud Detection for Financial Services in Real time Data

2025-12-17T07:47:17+07:00

Fraud detection has become a critical concern for financial institutions seeking to safeguard their assets and maintain client trust in an increasingly digitized financial landscape. This study examines the application of machine learning (ML) techniques to enhance fraud detection systems within financial institutions. By leveraging computational algorithms and data analytics, organizations can identify patterns and anomalies in transaction data that conventional rule-based approaches often fail to detect. The efficacy of multiple ML paradigms, including supervised, unsupervised, and reinforcement learning, in identifying fraudulent activities is evaluated through a systematic review of existing literature and comparative analysis of model performance across benchmark datasets. The study highlights the critical role of feature engineering and data preprocessing in building robust ML models, as the quality of input data significantly influences predictive accuracy. The integration of real-time data processing, which enables organizations to respond to emerging threats promptly, is also examined. Key challenges are discussed, including high false positive rates, class imbalance inherent in fraud datasets, and the necessity for continuous model adaptation to track evolving fraud patterns. The findings indicate that ML-based approaches not only improve fraud detection rates but also enhance operational efficiency and customer satisfaction. This paper serves as a foundational reference for practitioners and researchers aiming to advance the application of machine learning for fraud detection in the financial sector.

Implementasi Metode Weighted Product untuk Menentukan Produk Perabotan Unggulan pada Winnie Houseware

2025-12-28T06:20:11+07:00

Perkembangan industri ritel, khususnya pada sektor perlengkapan rumah tangga (houseware), mendorong meningkatnya persaingan pasar sehingga menuntut pelaku usaha untuk menerapkan strategi pengelolaan produk yang lebih efektif dan berbasis data. Winnie Houseware menghadapi permasalahan dalam menentukan produk unggulan yang layak dijadikan prioritas etalase, karena proses pengambilan keputusan masih didominasi oleh pendekatan subjektif tanpa analisis yang terstruktur. Penentuan produk unggulan yang tepat memerlukan integrasi berbagai kriteria yang saling berkaitan, antara lain tingkat penjualan, ketersediaan stok, margin keuntungan, tingkat retur atau keluhan, serta daya tahan produk. Ketidakseimbangan dalam mempertimbangkan kriteria tersebut berpotensi menimbulkan keputusan yang kurang optimal dan berdampak pada kinerja finansial serta reputasi toko. Oleh karena itu, penelitian ini menerapkan Sistem Pendukung Keputusan menggunakan metode Weighted Product (WP), yaitu salah satu pendekatan Multi-Attribute Decision Making yang mampu mengolah kriteria benefit dan cost secara simultan melalui proses pembobotan relatif. Hasil penelitian menunjukkan bahwa metode WP mampu menghasilkan peringkat produk unggulan yang objektif dan terukur berdasarkan seluruh kriteria yang ditetapkan, sehingga dapat dijadikan dasar pengambilan keputusan manajerial di Winnie Houseware dalam menentukan prioritas produk etalase.

Classification of Korean Drama Popularity Based on Ratings Using Naïve Bayes

2025-12-31T15:10:15+07:00

This study aims to classify the popularity of Korean dramas based on ratings obtained from the MyDramaList website. With the rapid growth of digital entertainment platforms, evaluating drama popularity has become increasingly important for understanding audience preferences and supporting decision-making in the content industry. The Naive Bayes algorithm is employed as the classification method due to its computational efficiency and suitability for handling categorical and numerical features. The dataset comprises 351 Korean dramas with attributes including title, year of release, genre, tags, number of episodes, cast information, synopsis, and user ratings. Ratings serve as the primary label for categorizing dramas into three classes: Top Dramas (rating ≥ 8.5), Popular (7.5–8.4), and Less Popular (< 7.5). The classification pipeline involves data preprocessing, feature encoding, and model training using Naive Bayes. Evaluation results yield an overall accuracy of 79%, with per-class performance assessed through precision, recall, and F1-score metrics. Supplementary visualizations, including pie charts, bar charts, and word clouds, are employed to analyze the distribution of dominant genres and tags across popularity categories. The findings indicate that the proposed approach provides a viable baseline for drama popularity classification while revealing content patterns, such as the prevalence of specific genres and thematic tags among top-rated dramas, that may inform content curation strategies on digital platforms.