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TEPIAN
Published by Politeknik Pertanian Negeri Samarinda
ISSN : 27215350     EISSN : 27215369     DOI : -
Core Subject : Science,
Computer Science & IT
The purpose of TEPIAN is to publish original research studies directly relevant to computer science. TEPIAN encompasses the full spectrum of information technology and computer science, including information system, hardware technology, intelligent system, and multimedia applications. TEPIAN welcomes original papers, reviews and commentaries. Suggestions for special issues covering selected topics may be considered. TEPIAN is devoted to publish manuscripts that advance the knowledge of information technology and communication beyond state-of-the-art. Authors may contact the Editor-in-Chief in advance to inquire about whether their research topic is suitable for consideration by TEPIAN. Through an Open Access publishing model, TEPIAN provides an important forum where computer science researchers in academic, public and private arenas can present the latest results from research on information technology and communication in a broad sense.
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 7 Documents
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Search results for , issue "Vol. 6 No. 4 (2025): December 2025" : 7 Documents clear
Framework for Generating Synthetic Customer Data to Enhance Model Training in Banking Puspa Riri Agustiana; 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.v6i4.3385

Abstract

Data-driven decision-making has taken on a more central role in the banking sector. However, privacy regulations and data security concerns limit the accessibility of real customer data for model training. To address this challenge, synthetic data generation offers a promising solution. This paper presents a framework tool for generating synthetic customer data that closely mimics the statistical properties of real-world data using advanced machine learning techniques, such as Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs), to enhance model training in banking applications. By leveraging advanced machine learning techniques, our framework can replicate the real Production Data to Synthetic Data customer. This synthetic data can be used to augment existing datasets, enhance model training, and improve the accuracy and robustness of predictive models. We demonstrate the effectiveness of our framework through a case study in a banking context, showcasing its potential to address challenges related to data privacy, data scarcity, and model performance.
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.
Internet of Things in Greenhouse Cultivation of Chrysanthemum Flowers in Primadona Tomohon Farmers Group Milytia Christabella Tumengkol; Kofal Hilkiah Lasewa; Jarot S Suroso
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.v6i4.3438

Abstract

The application of the Internet of Things (IoT) in greenhouses provides innovative solutions to enhance the efficiency and quality of chrysanthemum cultivation for the Primadona farmer’s group in Tomohon. The application of the automated system created is a condensation system that activates when the average temperature inside the greenhouse reaches 28 °C during the vegetative phase and 23°C during the generative phase, a drip irrigation system that turns on automatically when the average soil moisture value reaches 50%, as well as UV lights and exhaust fans that operate at night. The application of IoT also enables farmers to monitor and control greenhouse climate conditions in real-time using the Blynk application. The research method employed is experimental, incorporating a literature study to understand the application of IoT in greenhouses for chrysanthemum cultivation, as well as analysis of hardware and software requirements, system design, and system testing for real-world operations. The evaluation of the results provides insights into the effectiveness of IoT implementation in greenhouses for chrysanthemum cultivation, particularly for the Primadona Tomohon farmer group.
Online Shopping Experience in Minimarket and The Impact on Sales Figure Christopher Winson Budiman; Alfa Ryano Yohannis
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.v6i4.3445

Abstract

The rise of digital technology has transformed consumer shopping behavior, with online shopping emerging as a dominant force in retail. This study investigates the online shopping experience in minimarkets, a segment traditionally reliant on in-person transactions and explores its impact on sales figures. As consumer preferences increasingly shift towards convenience and accessibility, minimarkets must adapt to integrate online platforms that cater to these demands. This research employs a mixed-methods approach, combining quantitative analysis of sales data from selected minimarkets with qualitative insights gathered through customer surveys and interviews. Key findings reveal that the online shopping experience significantly influences customer satisfaction and purchasing behavior in minimarkets. Factors such as website usability, product variety, delivery options, and customer service quality are found to be critical determinants of the online shopping experience. Additionally, the study highlights that effective online engagement strategies such as personalized promotions, user-friendly interfaces, and responsive customer support can lead to increased customer loyalty, higher average order values, and improved overall sales performance. The research further demonstrates that the integration of online shopping platforms not only complements traditional brick-and-mortar operations but also attracts new customer segments who prioritize convenience and accessibility. In particular, the ability to shop online allows minimarkets to capitalize on trends such as click-and-collect services and home delivery, which have gained traction during the COVID-19 pandemic and continue to shape consumer expectations. Furthermore, the analysis reveals a direct correlation between enhanced online shopping experiences and improved sales figures, suggesting that minimarkets that invest in their digital presence can achieve significant competitive advantages in a rapidly evolving retail landscape. This paper contributes to the growing body of literature on online retailing by providing empirical evidence on the effectiveness of online shopping experiences in small-format retail environments. It offers actionable insights for minimarket managers and stakeholders looking to optimize their online platforms, enhance customer engagement, and ultimately drive sales growth. The findings emphasize the necessity for minimarkets to embrace digital transformation as an integral component of their business strategy to remain relevant and competitive in today’s consumer-driven market
The Implementation of Cloud Technology for Collaboration and Data Management in Property Developers Maria Veronika; Alfa Ryano Yohannis
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.v6i4.3448

Abstract

This study explores the impact of cloud technology implementation on collaboration, data management, and information security within a property development company. Prior to the implementation, the company faced significant challenges, including fragmented data storage, limited cross-team collaboration, and inadequate data security measures. Through a case study approach, this research analyzes the transition from a fragmented system to a centralized cloud infrastructure. The findings indicate that cloud adoption improved real-time collaboration across dispersed teams, reduced data duplication by 30%, and enhanced data security through role-based access control and encryption. Additionally, the centralized data storage system improved the accuracy of project information and sped up decision-making processes by 25%. The research also highlights how cloud technology helped the company meet compliance standards like ISO 27001 while enhancing operational efficiency. This study contributes valuable insights into how cloud solutions can streamline operations in complex industries like property development, emphasizing the need for continuous adaptation of technological solutions to meet evolving business demands.
Enhancing Business Intelligence with Explainable AI: Evaluating Transparency, Interpretability, and User Trust Aji Jayaloka; Alfa Ryano Yohannis
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.v6i4.3462

Abstract

The integration of Artificial Intelligence (AI) into Business Intelligence (BI) systems has significantly advanced data analysis and decision-making capabilities. However, the inherent "black box" nature of many sophisticated AI models poses considerable challenges to transparency, interpretability, and user trust, hindering their full adoption in critical business contexts. Explainable AI (XAI) emerges as a crucial field to address these challenges by rendering AI decision-making processes understandable and verifiable. This paper investigates the impact of different XAI methodologies on transparency, interpretability, and user trust within BI systems through a mixed-methods study. We specifically evaluate the effectiveness of feature importance techniques (LIME, SHAP) and rule extraction methods (Decision Tree Surrogates) in enhancing user understanding and confidence when interacting with an AI-driven BI prototype focused on customer churn prediction. Our findings reveal that while a baseline black-box model achieved high predictive accuracy, XAI-enhanced scenarios significantly improved user trust and perceived interpretability. Notably, a Decision Tree Surrogate model achieved the best balance between explainability, user trust, and decision accuracy. This research provides empirical insights into tailoring XAI explanations for varying user needs in BI, offering guidelines for integrating XAI to build more ethical, transparent, and trustworthy BI solutions, ultimately fostering greater user acceptance and more informed decision-making.
A Business Architecture Framework for Streamlining Post-Production Workflows in the Film Industry Wilson; Alfa Ryano Yohannis; Erick Dazki
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.v6i4.3483

Abstract

The post-production phase of filmmaking is a complex sequence of activities that directly impacts artistic quality, cost-efficiency, and time-to-market. Despite advancements in editing suites and asset repositories, many studios face fragmented systems, manual bottlenecks, and version-control conflicts. This paper presents a comprehensive Business Architecture framework, modeled using ArchiMate and aligned with the TOGAF Architecture Development Method (ADM), to transform post-production into an integrated, transparent ecosystem. We systematically map the current ("as-is") and envisioned ("to-be") states across business, application, and technology layers to identify critical inefficiencies, such as redundancy in asset handling and gaps in process standardization. Key recommendations, derived directly from this architectural analysis, include deploying a cloud-based Digital Asset Management (DAM) solution, instituting standardized handoff and approval workflows, and embedding granular metadata strategies to enhance searchability. These targeted interventions not only streamline collaboration and minimize manual rework but also accelerate overall delivery timelines. By systematically applying a robust enterprise architecture framework to a creative industry often characterized by ad-hoc solutions, this research provides a practical blueprint for studios to achieve sustained operational excellence and strategic agility.

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