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All Journal Building of Informatics, Technology and Science Bhakti Nagori (Jurnal Pengabdian kepada Masyarakat) Journal of Social Computer and Religiosity
Arriyanti, Eka
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Religion Agriculture, Biological Sciences & Forestry Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Education Environmental Science Languange, Linguistic, Communication & Media Social Sciences

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Journal : Building of Informatics, Technology and Science

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Determining the Best E-Commerce Using the Multi Criteria Decision Making (MCDM) Method Salmon, Salmon; Lailiyah, Siti; Arriyanti, Eka
Building of Informatics, Technology and Science (BITS) Vol 7 No 1 (2025): June (2025)
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i1.7395

Abstract

The rapid expansion of e-commerce has positively influenced lifestyles and fueled economic growth, as evidenced by rising transaction volumes and government revenue. However, challenges persist, especially in consumer security, logistics infrastructure, and taxation. The quality of e-commerce websites is crucial, serving as a primary source of customer information and ensuring secure transactions. Selecting the right e-commerce platform is also essential for business development. Despite the proliferation of e-commerce platforms offering diverse features and user-friendly interfaces, issues like product quality discrepancies, fraudulent activities, and incomplete features continue to frustrate consumers. To address these challenges and aid consumers in selecting optimal platforms, Multi-Criteria Decision Making (MCDM) methods are employed. This study explores various MCDM techniques to rank 8 major e-commerce platforms based on 5 criteria. The analysis consistently identifies Shopee as the top-performing platform. While Tokopedia, Bukalapak, Lazada, and TikTok Shop show some variations in rankings depending on the MCDM method used, Blibli, JD.ID, and OLX Indonesia maintain consistent rankings across all methods. This suggests that while Shopee demonstrates clear superiority, the subtle differences in MCDM methodologies can influence the relative rankings of other platforms.
Speech Emotion Classification Using MFCC Feature Extraction and Bagging-Based Ensemble Learning Haristyawan, Ivan; Arriyanti, Eka; Wahyuni, Wahyuni
Building of Informatics, Technology and Science (BITS) Vol 7 No 3 (2025): December 2025
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i3.8878

Abstract

Speech emotion classification, also known as Speech Emotion Recognition (SER), has become increasingly important with the growing prevalence of human–machine interaction, particularly in the domains of healthcare, online education, and customer service. This study aims to develop a robust speech emotion classification system by employing Mel-Frequency Cepstral Coefficients (MFCC) for feature extraction and a Decision Tree–based Bagging algorithm for classification. The proposed approach is designed to address the challenges of low classification accuracy, especially under speaker-independent conditions and limited availability of labeled emotional speech data. The research workflow includes speech signal preprocessing, MFCC feature extraction, dataset partitioning through bootstrapping, ensemble model training, and performance evaluation using accuracy, precision, recall, and F1-score metrics. Experimental results on a balanced dataset comprising five emotion classes (anger, disgust, fear, happy, and sad) demonstrate that the proposed model achieves an overall accuracy of 61.04%. While the fear and happy emotions are classified effectively with recall values of 0.75, the anger class exhibits the lowest performance with an F1-score of 0.49. Confusion matrix analysis further reveals substantial acoustic overlap among several emotion categories, particularly the frequent misclassification of sad as disgust or anger. In conclusion, the integration of MFCC features with the Bagging algorithm improves model stability and robustness; however, further optimization of acoustic features and hyperparameters is required to enhance overall classification accuracy.
Co-Authors Fachry Abda El Rahman Haristyawan, Ivan Putri, Ariyanthi Rismawati Rangan, Andi Yusika Salmon Salmon, Salmon Siti Qomariah, Siti wahyuni wahyuni