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All Journal JOURNAL OF MECHANICAL ENGINEERING, MANUFACTURES, MATERIALS AND ENERGY Formosa Journal of Computer and Information Science
Siburian, Rivaldo
Unknown Affiliation
Computer Science & IT Energy Industrial & Manufacturing Engineering Mechanical Engineering

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Damage Analysis and Repair of a Small-Scale ORC (Organic Rankine Cycle) Prototype Turbine During Testing Siburian, Rivaldo; Antonius, Dikky; Abadi , Surjo
JOURNAL OF MECHANICAL ENGINEERING MANUFACTURES MATERIALS AND ENERGY Vol. 9 No. 1 (2025): June 2025 Edition
Publisher : Universitas Medan Area

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31289/jmemme.v9i1.12316

Abstract

This research discusses the damage analysis and repair of an Organic Rankine Cycle (ORC) prototype turbine that experienced failure during testing. The damage occurred due to a collision between the rotor blades and stator blades, causing the rotor blade to break. The primary cause was identified as a manufacturing error in the turbine stator, where the flange holes were misaligned with the shaft. Additionally, the use of improper locking flange bolts and loose fitting on the coupling and stator shaft contributed to this issue. The research methodology included failure analysis using Process Failure Modes and Effects Analysis (PFMEA) to identify potential failures and prioritize corrective actions. The results indicate that adjustments in the manufacturing process, the use of high-precision measurement technology, improved material quality control, and better technician training can significantly reduce the risk of future failures. Furthermore, evaluating operational conditions and conducting regular inspections are recommended to ensure optimal turbine performance. With the implementation of these corrective actions, the ORC turbine prototype is expected to operate more reliably and efficiently during testing and use. This research is expected to make a significant contribution to enhancing the reliability and efficiency of ORC system turbines, and provide guidance for the future development and testing of turbines.
The Application of Naive Bayes in Analyzing Public Sentiment Toward the Performance of the North Sumatra Regional Government in Handling Flash Floods Siburian, Rivaldo; Tampubolon, Rikki Josua; Surbakti, Valentino; Haris, M. Irvandy; Rahmansyah, Rizky
Formosa Journal of Computer and Information Science Vol. 5 No. 1 (2026): March 2026
Publisher : PT FORMOSA CENDEKIA GLOBAL

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55927/fjcis.v5i1.16417

Abstract

This study analyzes public sentiment towards the performance of the North Sumatra Regional Government in handling flash floods using the Multinomial Naive Bayes algorithm. A total of 1,132 opinion data points were collected from social media and news portals through web crawling from November 2025 to February 2026. Sentiment labeling was performed using a lexicon-based approach with the InSet dictionary. Classification results showed a dominance of negative sentiment at 88.4%, focusing on slow emergency response. Model evaluation with an 80:20 data split yielded 89.43% accuracy and an F1-Score of 0.844 for Naive Bayes, while SVM achieved the highest F1-Score (0.855). This study concludes that AI-based sentiment analysis can serve as an objective instrument for government performance auditing.
Co-Authors Abadi , Surjo Antonius, Dikky Haris, M. Irvandy Rahmansyah, Rizky Surbakti, Valentino Tampubolon, Rikki Josua