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All Journal TELKOMNIKA (Telecommunication Computing Electronics and Control) JOIN (Jurnal Online Informatika) JOIV : International Journal on Informatics Visualization Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) JITK (Jurnal Ilmu Pengetahuan dan Komputer) MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer Jurnal Pelayanan dan Pengabdian Masyarakat (Pamas) Indonesian Journal of Electrical Engineering and Computer Science Indonesian Journal of Computing, Engineering, and Design TEPIAN Journal of Applied Data Sciences Komunita: Jurnal Pengabdian dan Pemberdayaan Masyarakat Journal of Community Services: Sustainability and Empowerment CSRID
Teddy Mantoro
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Arts Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Engineering Environmental Science Industrial & Manufacturing Engineering Materials Science & Nanotechnology Mechanical Engineering Public Health Social Sciences Other

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Journal : TEPIAN

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Improving Meta Ads Efficiency through Multi-Level Campaign Structuring and Budget Optimization Sutardiman, Mario; Mantoro, Teddy
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 Han, Hanif; Mantoro, Teddy; Santoso, Handri
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 Abdul Rahman Wijaya Putra Abdullah Alkalbani Achmad Farid Wadjdi Adamu Abubakar Ibrahim Adi Putra Andriyandi Agung Nugroho Akeem Olowolayemo Al Qodri Maarif, Haris Alfian Dody Andri Zakariya Andri Zakariya Anggy Pradiftha Junfithrana Aris Gunaryati Aris Gunaryati, Aris Arisantoso Arisantoso Ben Rahman Benrahman Dholah Rosita, Moneyta Dian Sa’adillah Maylawati Dian Sa’adillah Maylawati Dimas Erlangga Edi Irawan Effendi, Nicholas Lasca Hamdan Sugilar Han, Hanif Haris Al Qodri Maarif Haris Al Qodri Maarif I Made Alan Priyatna Ibrahim, Adamu Abubakar Jelita Asean Jelita Asian Lumbia, Mega Lutfil Khakim Media A. Ayu Media Anugerah Ayu, Media Anugerah Mega Lumbia Mohammad Iwan Wahyuddin Moneyta Dholah Rosita Muchlis Nurseno Muhamad Rausyan Fikri Muhammad Agni Catur Bhakti Muhammad Ali Ramdhani Muhammad Anton Permana Nugroho, Agung Nurseno, Muchlis Putra, Abdul Rahman Wijaya Raffliansyah, Alfarizky Esah Rahman, Ben Santoso, Handri Septi Andryana Septya Egho Pratama Sony Surya Wijaya Sutardiman, Mario Tarigan, William Hazkel Umar Aditiawarman Wahyuddin, Mohammad Iwan Wahyudin Darmalaksana Wandy Wandy