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All Journal The Journal of Experimental Life Sciences (JELS) International Journal Science and Technology (IJST) Seminar Nasional Produk Terapan Unggulan Vokasi Frontier Advances in Applied Science and Engineering
Motulo, Firmansyah Reskal
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Journal : international journal science and technology ijst

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Computational Analysis of Bioethanol Production from Arenga Pinnata Sap using Rice Husk Biomass Heating: Statistical Modeling of Fermentation Time Effects on Alcohol Yield Saroinsong, Tineke; Mekel, Alfred Noufie; Motulo, Firmansyah Reskal
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.v4i3.2348

Abstract

This study presents a comprehensive computational analysis of sustainable bioethanol production from Arenga pinnata sap using rice husk biomass as a renewable heating source. The research investigated fermentation time effects on alcohol yield through systematic experimentation and Python-based statistical modeling across four conditions: fresh sap, 1-day, 3-day, and 18-day fermentation periods. Distillation processes utilized 8.5 kg rice husk biomass at 80°C for 1.42 hours, producing 600 ml bioethanol per batch. Statistical analysis revealed a highly significant inverse correlation (r = -0.965, p < 0.05) between fermentation duration and alcohol content. Fresh palm sap yielded optimal alcohol concentration of 39.67 ± 7.76%, while 18-day fermentation reduced yield to 2.50 ± 2.50%, representing 93.7% decrease. The exponential decay model (R² = 0.984) demonstrated superior predictive accuracy compared to linear regression. The integrated system achieved 70.6 ml bioethanol per kg rice husk with positive energy balance (1.23 MJ output per MJ input), confirming commercial viability for rural renewable energy applications. This computational framework establishes optimal processing parameters for agricultural waste-powered biofuel systems, supporting circular economy principles and rural energy independence through effective biomass utilization in tropical regions.
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; Motulo, Firmansyah Reskal
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; Motulo, Firmansyah Reskal
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.
Co-Authors Abdul Rouf Alfred Noufie Mekel Deko Trio Desembara Excellsdeo Ndahawali GheridsDipipi, GheridsDipipi Jaqlin Tamaka Jedithjah Naapia Tamedi Papia Jerry Heisye Purnama Jonah Alfred Mekel Jonah Mekel Leonardo Frando Pasla Mekel, Alfred Noufie Michael Edward G. Kimbal Oldi Malfri Lambonan Rick Resa Wahani Runtuwene, Steven Johny Saroinsong, Tineke Tammy Tinny V. Pangow Tineke Saroinsong Trisilowati Trisilowati, Trisilowati