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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Jurnal Teknik Pertanian Lampung (Journal of Agricultural Engineering)
Hajad, Makbul
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Agriculture, Biological Sciences & Forestry Computer Science & IT Engineering

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Potential Analysis of Biomass Briquettes from Sugarcane Milling Waste for Boiler and Generator Turbines Stations Hajad, Makbul; Syahputra, Muhammad Hafidz; Yulianta, Raditya; Radi, Radi; Markumningsih, Sri; Purwantana, Bambang
Jurnal Teknik Pertanian Lampung (Journal of Agricultural Engineering) Vol. 13 No. 4 (2024): December 2024
Publisher : The University of Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/jtep-l.v13i4.1226-1236

Abstract

The decrease in sugar productivity was due to insufficient process of the sugar production process such as the low efficiency of boiler machine input energy. This study aims to analyze the potential use of Bagasse Briquetting Fuel (BBF) made from sugarcane milling waste at PT Madubaru as an attempt to obtain the optimal efficiency of boiler machine. Analysis of the effect of the adhesive concentration on the BBF quality was carried out to determine the optimal composition of the use of adhesive materials. Economic analysis was also conducted to determine the economic potential of BBF development. The analysis revealed that the BBF from Sugarcane milling waste has Calorific Value of 17,367-19,497 KJ/kg and density of 0.740-0.915 g/cm3. BBF with an adhesive variation of 1.25% is the BBF with the highest efficiency because it meets the needs of boiler fuel with the least amount of 100.8 tons/day for the operation of 1 boiler machine. The development of BBF from sugarcane milling waste has a selling value of Rp1.390.5,-/kg much lower than the existing biomass fuels found in the market. Keywords: Bagasse briquetting fuel; Boiler machine; Energy efficiency; Renewable energy; Sugarcane milling waste.
Classification of single origin Indonesian coffee beans using convolutional neural network Rifai, Achmad Pratama; Sari, Wangi Pandan; Rabbani, Haidar; Safitri, Tari Hardiani; Hajad, Makbul; Sutoyo, Edi; Nguyen, Huu-Tho
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 6: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i6.pp5140-5156

Abstract

This research aims to develop a coffee bean type detection model using convolutional neural networks (CNN), leveraging a dataset of 14,525 images from 116 types of Indonesian coffee beans. Pre-processing steps including resizing, rescaling, and augmentation were applied to improve the dataset quality. The dataset was split into training, validation, and testing sets with proportions of 80%, 10%, and 10%, respectively. Two model development approaches were used: transfer learning with Inception V3 in two scenarios and a model built from scratch. The transfer learning Inception V3 model in scenario 1 achieved the best performance, with a test accuracy of 0.87 and optimal evaluation metrics across precision, recall, and F1-score. This model was fine-tuned using pretrained weights, allowing it to adapt effectively to the coffee bean dataset. The results highlight that transfer learning, especially with Inception V3, provides a robust method for classifying coffee beans, offering potential applications in the coffee industry for improving classification efficiency and accuracy. The study demonstrates how deep learning can enhance the objectivity and precision of coffee bean classification, contributing to greater consistency in product sorting and quality assessment.
Co-Authors Achmad Pratama Rifai Bambang Purwantana Nguyen, Huu-Tho Rabbani, Haidar Radi Radi Safitri, Tari Hardiani Sri Markumningsih, Sri Sutoyo, Edi Syahputra, Muhammad Hafidz Wangi Pandan Sari Yulianta, Raditya