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This Author published in this journals
All Journal JTT (Jurnal Teknologi Terpadu) International Journal Science and Technology (IJST)
Deko Trio Desembara
Politeknik Negeri Manado
Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Materials Science & Nanotechnology Mechanical Engineering

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Computer Vision-Based Automated Waste Sorting System for Plastic and Organic Waste Classification Using Color and Shape Features Rick Resa Wahani; Michael Edward G. Kimbal; Deko Trio Desembara; Leonardo Frando Pasla; Firmansyah Reskal Motulo
International Journal Science and Technology Vol. 4 No. 3 (2025): November: International Journal Science and Technology
Publisher : Asosiasi Dosen Muda Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56127/ijst.v4i3.2384

Abstract

The increasing volume of municipal solid waste demands low-cost, real-time sorting solutions to improve recycling efficiency and reduce landfill burden. Objective: This study develops and evaluates a low-cost, real-time computer vision system to classify plastic waste and organic leaf waste for automated sorting. Methodology: The system uses a standard RGB camera (640×480, 30 fps) and OpenCV-based processing, including Gaussian blurring, HSV color-space conversion, morphological operations, contour detection, and geometric feature extraction (circularity, solidity, aspect ratio, and extent). Classification is performed using a hierarchical rule-based logic that combines HSV color masks with a proposed overlap ratio to quantify the spatial correspondence between object contours and leaf-color regions. Findings: Experimental testing under controlled illumination (500–1000 lux) achieved 89% overall accuracy with an average processing time of 45 ms/frame and an operational throughput of approximately 7 objects/min. The system correctly classified 8 plastic items and 7 leaf samples in the initial test set. Implications: The proposed approach supports practical deployment in small-scale or resource-constrained waste management facilities by enabling real-time sorting without large, labeled datasets or GPU hardware. Originality: This work introduces an interpretable hybrid decision framework that integrates a mask-based overlap ratio with multiple geometric shape descriptors, improving discrimination between plastic and leaf waste while maintaining computational efficiency.
Intelligent Robotic Arm Control System with Adaptive Learning Algorithm Based on Motion Pattern Recognition Excellsdeo Ndahawali; Jonah Mekel; Jaqlin Tamaka; GheridsDipipi GheridsDipipi; Rick Resa Wahani; Michael Edward G. Kimbal; Deko Trio Desembara; Firmansyah Reskal Motulo
International Journal Science and Technology Vol. 4 No. 3 (2025): November: International Journal Science and Technology
Publisher : Asosiasi Dosen Muda Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56127/ijst.v4i3.2386

Abstract

Robotic-arm deployment beyond specialized facilities is often constrained by time-intensive programming and the need for expert operators, while gesture-based control can lose reliability due to sensor noise, drift, and inter-user variability. Objective: This study develops a low-cost, embedded robotic arm control system that learns from human demonstrations. Methodology: A quantitative experimental prototyping approach was used by building a 3-DOF robotic arm with an MPU6050 IMU and an Arduino Mega 2560. Multi-user gesture trials were collected, and system performance was analyzed through end-to-end evaluation of recognition accuracy, response time, learning efficiency, and motion replication error. Findings: The system achieved 85% gesture recognition accuracy, a 195 ms average response time, and a 4.2° mean absolute joint-angle error (SD = 2.1°), reaching target performance within ≤5 adaptation iterations while operating within microcontroller memory limits. Implications: The results support the feasibility of real-time, gesture-driven robotic arm control on resource-constrained embedded hardware for educational and light industrial use, enabling faster setup and user personalization without extensive pre-training. Originality: This work integrates embedded motion pattern recognition with error-based adaptive learning in a low-cost 3-DOF platform and reports consolidated end-to-end evidence (accuracy–latency–learning convergence–replication fidelity) to demonstrate practical feasibility.
Retrofit Kontrol Kemiringan Pisau Mesin Pemotong Pelat Merek Colgar Berbasis HMI Dan PLC Omron Deko Trio Desembara; Moody Noldy Tumembow; Priyono Priyono
Jurnal Teknologi Terpadu Vol 14 No 2 (2026): JTT (Jurnal Teknologi Terpadu)
Publisher : Pusat Penelitian dan Pengabdian Kepada Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

A Colgar brand plate cutting machine (Guillotine Shear) manufactured in 1995 located at the Manufacturing Laboratory at Manado State Polytechnic is not functioning properly because the blade (cutter) angle adjustment mechanism is no longer working, thereby limiting the cutting capacity and quality of the results; currently, only the up-and-down movement of the cutting blade is controlled by a relay. This research employs the Research and Development (R&D) method with a design-and-build approach, which includes analyzing the machine’s initial condition, designing the mechanical and electrical systems, programming the PLC using CX-Programmer, and designing the HMI interface. Implementation was carried out by integrating hardware and software components, followed by functional testing to measure machine performance. The results of the study indicate that the retrofit system successfully improved machine performance, where the blade tilt angle can be adjusted using the HMI interface by controlling the movement of the solenoid connected to the hydraulic system. The system is also equipped with an audible alert (buzzer) when starting operation. It is recommended that the system be further developed by adding an automatic sensor capable of adjusting the angle according to the thickness of the material being used.
Co-Authors Excellsdeo Ndahawali Firmansyah Reskal Motulo GheridsDipipi GheridsDipipi Jaqlin Tamaka Jonah Mekel Leonardo Frando Pasla Michael Edward G. Kimbal Moody Noldy Tumembow Priyono Priyono Rick Resa Wahani