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SOSIALISASI PEMBELAJARAN AIR CONDITIONING MAINTENANCE BAGI SISWA SMKN 5 SURABAYA Priyo Agus Setyawan; Emie Santoso; Aminatus Sa’diyah; Eky Novianarenti; Mey Rohmadhani; Imah Luluk Kusminah; Invinandri Joko Ahmad; Mayriza Mubarokah Tambas
Jurnal Cakrawala Maritim Vol 6 No 1 (2023): Jurnal Cakrawala Maritim
Publisher : Pusat Penelitian dan Pengabdian Masyarakat (P3M) - PPNS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33863/cakrawalamaritim.v6i1.2727

Abstract

Skills related to safety and maintenance engineering are needed in the industrial world. Challenges in the future require all academics, both high school and vocational levels, to continue honing their soft skills to answer the issue of the industrial revolution 4.0. The idea of ??soft skills training activities related to safety and maintenance for State Vocational High School students in Surabaya was formed. With all the support of the various PPNS academics from lecturers, students, and students together to collaborate to contribute to these activities. The aim is none other than to provide students with knowledge and skills to be ready to face the challenges of the world of work in the future. This service activity is carried out using training and mentoring methods for students of SMKN 5 Surabaya. Training is conducted online and offline (Blended method or hybrid). Some joined online via video conference (Vicon). It is hoped that with this activity, students will have basic skills in the field of safety, including: basic K3 competencies, then skills in the field of air conditioning maintenance are that students are able to carry out periodic maintenance of AC components both from a mechanical and electrical perspective, and are also able to perform maintenance according to schedule by considering the slogan plan, do, check, action.
Application Of Machine Learning Methods in Detecting Anomalies On L.O Cooler Component through Hyperparameter Optimization Nurvita Arumsari; Kelviano Daffa Septiangga; Invinandri joko Ahmad; Edi Haryono; Feby Agung Pamuji
International Conference on Maritime Technology and Its Application Vol. 3 No. 1 (2025): ICOMTA : International Conference on Maritime Technology and Its Application
Publisher : Surabaya State Polytechnic of Shipbuilding

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35991/icomta.v1i1.1

Abstract

LO Cooler is a crucial component in the ship's engine that functions to maintain the temperature of the lubricating oil within safe operational limits. Interference or failure of the LO Cooler can cause serious damage to the vessel's main engine. Early detection of anomalies in LO coolers can help prevent greater damage, thereby improving the reliability and operational efficiency of the vessel. This study proposes a machine learning-based anomaly detection method to monitor the performance of the LO Cooler in real time. In this study, operational data from the RPM, temperature, and pressure, on the LO Cooler were collected over a period of time. Machine learning algorithms, such as Support Vector Machine (SVM) and Decision Tree are applied to detect anomalous patterns in the data that indicate potential failures. The result that Decision tree modeling is the most accurate method for developing models for the LO Cooler of the main engine. With a data split ratio of 90:10 for training and testing and it achieved the highest accuracy, as indicated by the best following metrics MAE, RMSE, RAE, TP Rate and F-measure.