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All Journal The Indonesian Journal of Computer Science
Insani, Faiz Nur Fitrah
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Computer Science & IT Electrical & Electronics Engineering Engineering

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Optimizing E-Commerce in Indonesia: Ensemble Learning for Predicting Potential Buyers Insani, Faiz Nur Fitrah; Denny
The Indonesian Journal of Computer Science Vol. 13 No. 1 (2024): The Indonesian Journal of Computer Science (IJCS)
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v13i1.3690

Abstract

In the competitive Indonesian e-commerce sector, data-driven decisionmaking is crucial for success. This study addresses the challenge faced by a leading e-commerce company, where despite a 134% increase in promotional expenses, active user transactions remained low. Focusing on predicting potential buyers to optimize promotional spending, the research evaluates various ensemble learning methods, including Random Forest, XGBoost, and LightGBM algorithms. Through extensive testing, all three models demonstrated high precision in identifying potential buyers. Remarkably, XGBoost achieved an exceptional precision score of 89.5%. Further enhancement through a soft voting strategy combining XGBoost and LightGBM resulted in the highest precision rate of 89.8%, suggesting a promising approach for targeted marketing and improved promotional strategies in the e-commerce industry
Uncovering the Reasons Behind Abstain Voters' Stances in the 2024 Indonesian Presidential Election: Social Media X Study Cases Putri, Irzanes; Insani, Faiz Nur Fitrah; Budi, Indra; Santoso, Aris Budi; Putra, Prabu Kresna
The Indonesian Journal of Computer Science Vol. 13 No. 4 (2024): The Indonesian Journal of Computer Science (IJCS)
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v13i4.4126

Abstract

The Indonesian Government expects the participation of all Indonesian people in holding General Elections. However, according to the 2019 Political Statistics by BPS, there were 34.75 million people who did not exercise their right to vote or were abstain voters (golput) in the 2019 Election. This research aims to analyze individual attitudes towards abstaining voters using stance analysis and topic modelling. From 9,045 collected tweets, subsequent manual annotation revealed 2,566 pro stances, 5,264 neutral stances, and 1,215 contra stances. The classification models utilized are Random Forest, Decision Tree, Logistic Regression, Support Vector Machine, K-Nearest Neighbor, and Gradient Boosting. The classification outcomes will be analyzed by comparing the accuracy, precision, recall, and F1-score results based on their algorithms and n-grams. The results obtained from the stance analysis show that Random Forest achieved the highest accuracy and precision scores, with values of 84% and 83%, respectively. The discussion topic among those supporting golput due to low trust in the presidential and vice-presidential candidates. Other topics mentioned public feels dissatisfied with the pairs of candidates.
Co-Authors Budi, Indra Denny Prabu Kresna Putra, Prabu Kresna Putri, Irzanes Santoso, Aris Budi