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All Journal Jurnal Elementer (Elektro dan Mesin Terapan) JTTM: Jurnal Terapan Teknik Mesin Jurnal Ecotipe (Electronic, Control, Telecommunication, Information, and Power Engineering)
Yunita Umniyati
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Aerospace Engineering Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering Mechanical Engineering

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Crankshaft’s machining process accuracy improvement by integrating DMAIC framework, Shainin Red X, and Taguchi Method Agus Setyo Anto Wibowo; Yunita Umniyati; Aditya Tirta Pratama
JTTM : Jurnal Terapan Teknik Mesin Vol 7 No 1 (2026): JTTM: Jurnal Terapan Teknik Mesin
Publisher : Teknik Mesin - Universitas Muhammadiyah Cileungsi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37373/jttm.v7i1.2103

Abstract

This study investigates the problem of oversized hole diameter defects in crankshaft machining. The problem occurred in fine boring process which accounted for 42.3% of total rejections in the machining process. To systematically overcome this issue, a hybrid methodology was adopted by integrating 3 methods: Six Sigma’s DMAIC framework, Shainin Red X root cause analysis, and Taguchi Design of Experiments (DOE). By using multi-vari analysis and Red X techniques, the dominant cause of variation was identified as inconsistent tool offset input by operators, with secondary factors including clamper piston wear and part positioning misalignment. Taguchi DOE confirmed that depth of cut significantly influenced hole diameter accuracy, while spindle speed had minimal effect. The optimized machining parameters improved process capability, reducing rejection rates and cutting failure costs. The results show the synergy between diagnostic and optimization methods, providing a structured, data-driven framework for sustainable machining improvements. The novelty of this study lies in the integrated application of DMAIC, Shainin Red X, and Taguchi DOE to focus on oversized hole defects in crankshaft fine boring, an approach that has rarely been applied to turning-based off-center boring processes, with implications for both academic research and industrial precision machining practice.
Optimized PID Tuning in Pyrolysis Temperature Control Using Genetic Algorithm and Particle Swarm Optimization Hartono; Yunita Umniyati; Eka Budiarto; Henry Nasution; Mulyadi
Jurnal Ecotipe (Electronic, Control, Telecommunication, Information, and Power Engineering) Vol 13 No 1 (2026): Jurnal Ecotipe, April 2026
Publisher : Jurusan Teknik Elektro, Universitas Bangka Belitung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33019/jurnalecotipe.v13i1.4591

Abstract

Temperature control is crucial for maintaining stable and effective thermal treatment in pyrolysis system. For this application, Proportional-Integral-Derivative (PID) controller is frequently utilized due to its ease of use and efficiency. This study aims to evaluate and compare the performance of classical and metaheuristic tuning methods for PID controllers in pyrolysis temperature control. This work compares conventional Ziegler-Nichols (ZN) and Cohen-Coon (CC) methods with metaheuristic optimization techniques, specifically Genetic Algorithm (GA) and Particle Swarm Optimization (PSO), for tuning PID controller parameters. The main contribution of this research is the demonstration of improved control performance and computational efficiency using PSO-based PID tuning for pyrolysis applications. Simulation results show that PID controllers that the parameters tuned by GA and PSO achieve faster and smoother responses, with small overshoot, compared to classical methods. From both methods, PSO provides balanced performance with the shortest rise time (30.66 s), fastest settling time (50.80 s), and lowest overshoot (1.15%). Although both GA and PSO can maintain the set point of 500 °C with satisfactory transient response, PSO also shows better convergence efficiency, with smaller iteration numbers and lower computational effort. The results indicate that PSO-tuned PID is suitable for pyrolysis temperature control applications.
A Design and Implementation of an IoT-Based Heavy Equipment Engine Overheating Prevention System Using LoRa Communication for Remote Monitoring and Cooling Control Moh Sholeh; Yunita Umniyati; Dena Hendriana; Henry Nasution
Jurnal ELEMENTER (Elektro dan Mesin Terapan) Vol 12 No 1 (2026): Jurnal Elektro dan Mesin Terapan (ELEMENTER)
Publisher : Politeknik Caltex Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35143/elementer.v12i1.6803

Abstract

Bulldozer engines are highly susceptible to overheating during intensive operations, which can lead to severe mechanical damage and costly downtime. This study proposes an Internet of Things (IoT)-based overheat detection and automated cooling system to improve preventive maintenance in heavy machinery. The system utilizes a LilyGo-T Beam (ESP32) microcontroller integrated with a calibrated NTC thermistor sensor and LoRa communication operating at 923 MHz for long-range and low-power data transmission. Temperature data are displayed locally and transmitted to the Blynk IoT platform for real-time remote monitoring and notification when critical thresholds are exceeded. The developed prototype consists of a transmitter–receiver unit and an automated response mechanism that triggers both an alarm and a water-sprayer cooling system. Experimental validation included sensor calibration, thermal response testing, LoRa communication range assessment, and evaluation of cooling performance under various operating conditions. The results demonstrate accurate temperature measurement and reliable data transmission up to 950 meters. The automated water sprayer successfully reduced engine temperature from 80°C to 78.99°C, with an average recovery time of 12 minutes and 28 seconds. Cooling efficiency improved from 16.49% to 22.18%, indicating enhanced heat dissipation capability Overall, the proposed system offers a cost-effective, reliable, and scalable solution for preventing engine overheating in heavy equipment. By integrating IoT-based monitoring with automated cooling, this approach enhances operational performance, minimizes downtime, and extends engine lifespan.
Co-Authors Aditya Tirta Pratama Agus Setyo Anto Wibowo Dena Hendriana Hartono Hartono Henry Nasution Henry Nasution Moh Sholeh MULYADI