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Sanwani Sanwani
Universitas Bina Sarana Informatika, Jakarta
Computer Science & IT

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Sistem Pendukung Keputusan Pemilihan Content Creator TikTok Terbaik Menggunakan Metode Simple Additive Weighting (SAW) dengan Pembobotan Rank Order Centroid (ROC) Ratu Adnin Jahraini; Sanwani Sanwani; Besus Maula Sulthon; Syamsimahara Siregar; Pahotton Hutabarat; Mesran Mesran
Bulletin of Computer Science Research Vol. 6 No. 1 (2025): December 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v6i1.918

Abstract

In this selection of the best TikTok Content Creator using the Rank Order Centroid (ROC) and Simple Additive Weighting (SAW) methods. ROC and SAW were chosen because they are simple and efficient and can allow proper weighting of criteria and proper ranking. Many factors must be considered in choosing TikTok Content Creators. The selection of the best TikTok content creators is useful for helping audiences assess and select quality content and can add insight and entertainment that is useful for the future. Judging must also be based on predetermined criteria. There are five criteria that are assessed such as creativity, originality, content quality, interaction, ethics. The results of the assessment of each criterion will be normalised and the total weight calculated. The system will sort from the highest value as the first and last rank. With the existence of a decision support system that uses the ROC and SAW methods, it can make it faster and easier to make decisions to choose the best TikTok Content Creator according to predetermined criteria. Ranking results in applying the SAW method shown in table 5 above, the best alternative is the first rank produced, namely A10 with a final acquisition value of 0.956. The second rank is alternative A3 with a final value of 0.935 and the third rank is alternative A1 with a final value of 0.909.
Deteksi Penipuan pada Transaksi Keuangan Digital Menggunakan Ensemble Learning: Studi Komparatif Random Forest, Gradient Boosting, dan XGBoost Nurul Akbar Tanjung; Sugeng Hary Purnomo; Sanwani Sanwani
Bulletin of Computer Science Research Vol. 6 No. 4 (2026): June 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v6i4.1155

Abstract

Digital payment fraud in Indonesia has grown alongside the dramatic expansion of mobile money services, creating a detection problem that conventional rule-based systems are increasingly ill-equipped to handle. This paper examines whether a soft-voting ensemble of Random Forest, Gradient Boosting, and XGBoost can offer a more effective solution. The model was trained on the PaySim synthetic dataset, consisting of 6.36 million mobile money transactions in which fraudulent cases account for just 0.129 percent of all records. SMOTE was used exclusively on the training data to address the extreme class imbalance before model fitting. Five-fold cross-validated Grid Search determined the hyperparameter configuration for each constituent model. On the held-out test set, the ensemble achieved 94.7 percent precision, 91.3 percent recall, 93.0 percent F1-score, and 0.987 AUC-ROC figures that consistently exceeded those of any single algorithm. Examining feature contributions revealed that the sender balance difference and transaction amount carried the most discriminative weight, a finding that aligns with known fraud behavior in mobile payment datasets. A local streaming latency test across 5,000 consecutive transactions produced an average response time of 147.3 ms, with the 99th percentile remaining below the 200 ms operational threshold. Taken together, the results indicate that the ensemble approach is not only statistically superior but also practically deployable within the real-time constraints of digital banking environments.
Co-Authors Besus Maula Sulthon Mesran Mesran Nurul Akbar Tanjung Pahotton Hutabarat Ratu Adnin Jahraini Sugeng Hary Purnomo Syamsimahara Siregar