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All Journal TELKOMNIKA (Telecommunication Computing Electronics and Control) Bulletin of Electrical Engineering and Informatics International Journal of Advances in Intelligent Informatics Jurnal Teknik Pertanian Lampung (Journal of Agricultural Engineering) Jurnal Teknologi Terapan Journal of Tropical AgriFood Indonesian Journal of Electrical Engineering and Computer Science
Radi Radi
Universitas Gadjah Mada
Agriculture, Biological Sciences & Forestry Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering Mechanical Engineering

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Potential Analysis of Biomass Briquettes from Sugarcane Milling Waste for Boiler and Generator Turbines Stations Makbul Hajad; Muhammad Hafidz Syahputra; Raditya Yulianta; Radi Radi; Sri Markumningsih; Bambang Purwantana
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.
Performance of electronic nose based on gas sensor-partition column for synthetic flavor classification Radi Radi; Joko Purwo Leksono Yuroto Putro; Muhammad Danu Adhityamurti; Barokah Barokah; Luthfi Fadillah Zamzami; Andi Setiawan
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 20, No 5: October 2022
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v20i5.22358

Abstract

Electronic nose (e-nose) has been developed and implemented in a wide area, included in food industries. This study was conducted to investigate the performance of an e-nose that utilizes a packed gas chromatography column and a gas sensor for classification of synthetic flavor products. There were six aroma variants of synthetic flavor evaluated, namely durian, jackfruit, ambonese banana, melon, orange and lemon. The e-nose was designed with four main parts, namely aroma provider, column and detector room, microcontroller, and data acquisition system. The device was operated automatically at a stable temperature of 60 °C. Collected data consisted of ten data of each sample was preprocessed by baseline equalization and normalization, extracted its distinctive feature and then were analyzed through pattern recognition analysis. There were two kinds of methods used to analyzed the patterns of the data, namely a fuzzy c-means clustering and an artificial neural network (ANN). With the fuzzy c-means clustering, the result was six data clusters with an unbalanced number of members, indicated that this analysis could not classify samples properly. Meanwhile, analysis with the ANN could classify properly the samples with the level of accuracy of 70%.
Development of Android application for coffee roast level classification using CNN mobilenetev3 based on digital image analysis Isran Mohamad Pakaya; Radi Radi; Agus Dharmawan; Melda Nurmaisari
Journal of Tropical AgriFood Volume 8 Nomor 3 Tahun 2026
Publisher : Department of Agricultural Products Technology, Mulawarman University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35941/jtaf.8.3.2026.26928.202-212

Abstract

Coffee is one of Indonesia's key commodities with high economic value in both domestic and international markets. Roasting is a crucial stage in coffee processing, as it directly influences the final taste and aroma. However, until now, the roasting level has been determined visually and subjectively, making it prone to human error. This study aimed to develop an automatic classification system for coffee roasting levels using a Convolutional Neural Network (CNN) approach integrated into an Android application. The CNN model was built using the MobileNetV3 architecture and applied with a transfer learning method on Google Colab. The dataset consisted of 1,600 coffee images with three roasting levels: light, medium, and dark. The trained model was then converted into TensorFlow Lite (TFLite) format for integration into an Android application called RoastScan. This app features automatic classification through the camera or gallery and provides real-time predictions with confidence levels. The evaluation results show that the model has a classification accuracy of 97% on the test data, with high precision, recall, and F1-score across all classes. Further testing via the application showed that the best accuracy reached 93.55%. These findings suggest that integrating CNN with mobile applications has the potential to be a practical, efficient, and accurate solution for determining coffee roasting degrees, as well as supporting the standardization of coffee product quality at both industrial and MSME levels.
Co-Authors Agus Dharmawan Anang Kukuh Adisusilo Andi Muh. Saldan Andi Setiawan Arifin Dwi Saputro, Arifin Dwi Bambang Purwantana Barokah Barokah Dwi Noor Rohmah Eka Wahyudi Eko Mulyanto Yuniarno Icha Syahrotul Anam Isran Mohamad Pakaya Isran Mohamad Pakaya Joko Purwo Leksono Yuroto Putro Lilik Sutiarso Luthfi Fadillah Zamzami Makbul Hajad Melda Nurmaisari Mochamad Hariadi Muhammad Danu Adhityamurti Muhammad Hafidz Syahputra Raditya Yulianta Redika Ardi Kusuma Sri Markumningsih, Sri Yuwan Nanda Adyatma