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Teddy Mantoro
Computer Science, Nusa Putra University
Computer Science & IT

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AI for Enhanced Efficiency in Business Waste Sorting Strategies Ama Muzni Mahmudi; Teddy Mantoro
TEPIAN Vol. 6 No. 3 (2025): September 2025
Publisher : Politeknik Pertanian Negeri Samarinda

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51967/tepian.v6i3.3379

Abstract

As the global waste crisis grows, businesses are under pressure to improve waste management. AI, especially through machine learning and image recognition, offers innovative solutions for optimizing waste sorting. By using Convolutional Neural Networks (CNNs) and deep learning models trained on extensive datasets of waste images, companies can automate the classification of materials such as plastic, glass, and metal with high accuracy. This reduces reliance on manual labor, minimizes human error, and improves the speed and precision of sorting. Cameras capture images of waste items on conveyor belts, which are then analyzed by AI algorithms in real time. These systems continuously improve through feedback loops and reinforcement learning, leading to more efficient sorting over time. The result is higher recycling rates, reduced operational costs, and enhanced sustainability outcomes. AI-based systems enable businesses to decrease waste sent to landfills, recover valuable materials, and lower costs associated with waste management. With continuous updates to their training data and the use of edge computing for real-time processing, these solutions represent a major advancement in sustainable business practices.
Improving Meta Ads Efficiency through Multi-Level Campaign Structuring and Budget Optimization Mario Sutardiman; Teddy Mantoro
TEPIAN Vol. 6 No. 2 (2025): June 2025
Publisher : Politeknik Pertanian Negeri Samarinda

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51967/tepian.v6i2.3386

Abstract

The rise of digital advertising has transformed the way businesses interact with consumers, making platforms like Meta Ads a cornerstone of marketing strategies. However, achieving optimal efficiency in Meta Ads remains challenging due to the complexity of campaign setups and budget allocation. This study addresses the issue by examining key configurations at three levels: campaigns, ad sets, and individual ads. The research explores how advertisers can tailor campaigns to specific objectives, such as driving traffic or increasing sales, while leveraging ad set customization for audience targeting, placement optimization, and A/B testing. To improve ad performance, this study emphasizes the importance of refining content at the ad level, ensuring alignment with campaign goals. Budget management is also highlighted, contrasting Campaign Budget Optimization (CBO) with Ad Set Budget Optimization (ABO), and offering insights into leveraging these tools to maximize returns. The study further recommends adjusting budgets based on audience behavior patterns, such as spikes in purchasing activity during twin dates or paydays. By providing actionable strategies for configuring Meta Ads, this study contributes to the field of digital marketing by bridging practical implementation and theoretical insights. Evaluation of these strategies is supported through examples of best practices, with recommendations for advertisers to enhance their Meta Ads efficiency through continual testing and strategic budgeting.
Predicting Loan Delinquency in Installment Loans Using LightGBM for Enhanced Credit Risk Management Hanif Han; Teddy Mantoro; Handri Santoso
TEPIAN Vol. 6 No. 4 (2025): December 2025
Publisher : Politeknik Pertanian Negeri Samarinda

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51967/tepian.v6i4.3423

Abstract

Credit risk assessment is essential for financial institutions to effectively manage loan portfolios, especially for installment loans. Predicting delinquency is challenging due to the complex interplay of borrower behavior, loan characteristics, and repayment pattern. Traditional models often fail to capture non-linear relationships in data and require significant preprocessing to address imbalanced datasets, feature scaling, and diverse data distributions, resulting in inefficiencies. This research predicts installment loan delinquency using LightGBM, a gradient-boosting algorithm tailored for complex, imbalanced financial datasets. Unlike previous studies focusing on general credit risk or credit card defaults, this work specifically addresses the temporal and behavioral dynamics of installment loans. The model uses a real-world dataset from financial institutions, integrating borrower demographics, loan attributes, and engineered repayment features. LightGBM's histogram-based binning and inherent handling of heterogeneous feature scales both reduce preprocessing complexity and improve performance. Evaluation results show significant improvements over traditional models, achieving an AUC-ROC of 0.91 and strong precision and recall. This approach demonstrates scalability and effectiveness for modern credit risk management. Future work could incorporate macroeconomic factors and assess real-time deployment to further expand the model’s applicability.
Co-Authors Ama Muzni Mahmudi Handri Santoso Hanif Han Mario Sutardiman