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Author: search.filters.author.Surasak, Thattapon

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    Enhancing Property Management Efficiency via an Automated Notification System for Expenses and Deliveries
    (2026-01-01)
    Trakunsathitman, Phanthika
    ;
    Bandurat, Patipan
    ;
    Surasak, Thattapon
    This paper presents the design, development, and evaluation of an Automated Expense and Parcel Notification System (AEPNS) for residential property management. The system integrates a web-based application, a PHP-based backend, a MySQL database, and third-party notification services (LINE Notify and SendGrid) to automate billing and delivery alerts. The evaluation combined functional, integration, and user acceptance testing with affective assessment using the International Positive and Negative Affect Schedule Short Form (I-PANAS-SF). Results from real-world deployment demonstrated reductions in administrative workload and communication latency, alongside high levels of user satisfaction and positive engagement. In addition to reporting operational gains, this work contributes (1) a lightweight and deployable architecture tailored for small to medium-sized residential communities, (2) dual evaluation metrics that combine operational key performance indicators with affective user responses, and (3) practical insights into system scalability, maintainability, and compliance with security and privacy requirements. Identified limitations, such as procedural code constraints and third-party service quotas, inform a clear roadmap for future development that includes migration to a modular framework (Laravel), multilingual notification support, and a resident-facing mobile application. These findings indicate that even low-cost, API-driven solutions can act as scalable enablers of digital transformation in emerging digital economies.
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    Machine Health Diagnosis and Prognosis: A Predictive Maintenance Approach for Fiberboard (MDF) Production
    (2025-01-01)
    Surasak, Thattapon
    ;
    Trakunsathitman, Phanthika
    ;
    Wannaboon, Chatchai
    ;
    Jiteurtragool, Nattagit
    Predictive maintenance (PdM) leverages machine learning (ML) to enhance equipment reliability and production efficiency in Medium-Density Fiberboard (MDF) manufacturing. By analyzing operational data - such as temperature, vibration, and pressure - PdM forecasts equipment failures, enabling proactive maintenance that minimizes downtime and reduces costs. This study introduces an ML-driven maintenance framework utilizing historical failure logs and performance records to predict the Remaining Useful Life (RUL) of critical components in MDF production. Focusing on key processes like chip refining, fiber drying, resin mixing, and hot pressing, the framework employs supervised learning models, including Random Forest and Gradient Boosting, to analyze maintenance trends and operational parameters. The implementation of PdM in MDF manufacturing presents challenges, such as high initial investments, sensor calibration, and the necessity for skilled personnel to interpret predictive insights. Despite these hurdles, the proposed framework demonstrates the potential to transition from reactive to proactive maintenance strategies, thereby enhancing production efficiency and equipment longevity. The study's findings suggest that integrating PdM into MDF production can lead to significant operational improvements, though careful consideration of implementation challenges is essential for success.
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    Integrating Blockchain and Smart Contracts for Carbon Credit Management in Thailand: A Preliminary Multidisciplinary Analysis
    (2025-01-01)
    Trakunsathitman, Phanthika
    ;
    Chaisamran, Chiratt
    ;
    Surasak, Thattapon
    Blockchain and smart contracts have emerged as transformative technologies for enhancing transparency, security, and automation in carbon credit management. This study explores the integration of these technologies within the context of Thailand's carbon markets, focusing on system design, user interface development, and blockchain architecture. A UX testing phase was conducted to evaluate usability, security perception, and transaction efficiency. The results demonstrate strong stakeholder trust in blockchain's security benefits while identifying areas for improvement in user guidance and error feedback. While blockchain-based carbon credit platforms exist globally, this study examines their feasibility in Thailand and highlights potential challenges, including policy considerations and stakeholder engagement. The findings suggest that blockchain and smart contracts can enhance market transparency and compliance mechanisms, paving the way for further research into their broader adoption in sustainability initiatives.