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INDONESIA
Building of Informatics, Technology and Science
Published by Forum Kerjasama Pendidikan TInggi
ISSN : 26848910     EISSN : 26853310     DOI : -
Core Subject : Science,
Computer Science & IT
Building of Informatics, Technology and Science (BITS) is an open access media in publishing scientific articles that contain the results of research in information technology and computers. Paper that enters this journal will be checked for plagiarism and peer-rewiew first to maintain its quality. This journal is managed by Forum Kerjasama Pendidikan Tinggi (FKPT) published 2 times a year in Juni and Desember. The existence of this journal is expected to develop research and make a real contribution in improving research resources in the field of information technology and computers.
Arjuna Subject : -
Articles 1,062 Documents
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Superioritas Optimizer AdaGrad pada Arsitektur Spasio-Temporal PolyFace-LSTM untuk Prediksi Kepribadian Berbasis Video Muhammad Zidan Alif Oktavian; Salamun Rohman Nudin
Building of Informatics, Technology and Science (BITS) Vol 8 No 2 (2026): September 2026
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v8i2.10945

Abstract

Conventional personality assessment generally relies on psychometric questionnaires, such as the Big Five Inventory (BFI). However, this method is susceptible to self-report bias and requires a relatively long evaluation time. On the other hand, the development of automated video-based personality analysis systems faces high computational challenges in simultaneously integrating facial spatial features and the temporal dynamics of micro-expressions. This study aims to propose a solution in the form of a hybrid spatio-temporal neural network architecture utilizing transfer learning techniques to predict personality more objectively. The proposed system integrates the Multi-Task Cascaded Convolutional Networks (MTCNN) algorithm for automatic face detection, the PolyFace model as a spatial feature extractor, and the Long Short-Term Memory (LSTM) algorithm to model temporal relationships between frames. Experiments were conducted by comparing three optimization algorithms, namely Adam, AdaGrad, and SGD, using the ChaLearn LAP 2017 dataset. The results show that the AdaGrad optimizer achieved the best generalization and prediction performance during the validation phase, with an average accuracy of 89.13%, outperforming SGD (88.34%) and Adam (88.33%). Theoretically and mathematically, the superiority of AdaGrad in this architecture is attributed to its ability to dynamically adjust the learning rate based on the accumulation of historical gradients. This mechanism makes AdaGrad considerably more stable in extracting complex spatio-temporal micro-expression features without becoming trapped in local minima. The highest performance achieved by AdaGrad was observed in the Agreeableness dimension, reaching an accuracy of 90.14%. The main contribution of this study is the development of an efficient (low-latency), precise spatio-temporal personality prediction model that avoids local minima, making it suitable for implementation in real-world automated psychological detection systems.
Analisis Perbandingan Random Forest dan XGBoost dalam Prediksi Pembatalan Pesanan Shopee Menggunakan Interpretasi SHAP Muhammad Bayu Samudra; Mira Afrina; Allsela Meiriza; Rizka Dhini Kurnia; Ardina Ariani
Building of Informatics, Technology and Science (BITS) Vol 8 No 2 (2026): September 2026
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v8i2.10960

Abstract

Order cancellation is a significant issue for e-commerce platforms because it can result in revenue loss, increased operational costs, and reduced transaction management efficiency. This study aims to compare the performance of Random Forest and Extreme Gradient Boosting (XGBoost) in predicting customer order cancellations on Shopee and to interpret the factors contributing to the prediction outcomes using an Explainable Artificial Intelligence approach based on SHapley Additive exPlanations (SHAP). The study employed the Shopee Consumer Behaviour Cancellation Order Analysis dataset obtained from Kaggle. The research process consisted of data preprocessing, dataset partitioning using the Stratified Train-Test Split method with an 80:20 ratio, implementation of both algorithms, model evaluation using Accuracy, Precision, Recall, F1-Score, and Receiver Operating Characteristic–Area Under Curve (ROC-AUC), followed by model interpretation using SHAP. The evaluation results indicate that Random Forest achieved better performance across most evaluation metrics, while XGBoost obtained a slightly higher ROC-AUC value. SHAP analysis identified Total Discount, Buyer-Paid Shipping Cost, Estimated Shipping Cost, and Estimated Shipping Fee Deduction as the variables contributing most to order cancellation predictions.These findings indicate that combining predictive algorithms with an Explainable Artificial Intelligence approach not only supports the classification of order cancellations but also provides insights into the factors influencing prediction outcomes, which can serve as a consideration for reducing order cancellations on e-commerce platforms.

Page 107 of 107 | Total Record : 1062

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