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All Journal beta (Biosistem dan Teknik Pertanian) Jurnal Ilmiah Rekayasa Pertanian dan Biosistem Dinamika Teknik Mesin Jurnal Sains Teknologi dan Lingkungan (JSTL) JMM (Jurnal Masyarakat Mandiri) Jurnal Agrotek UMMat JTIM : Jurnal Teknologi Informasi dan Multimedia
Sukmawaty Sukmawaty
Program Studi Teknik Pertanian, Fakultas Teknologi Pangan dan Agroindustri, Universitas Mataram
Agriculture, Biological Sciences & Forestry Automotive Engineering Biochemistry, Genetics & Molecular Biology Computer Science & IT Energy Engineering Industrial & Manufacturing Engineering Materials Science & Nanotechnology Mechanical Engineering Physics Public Health Other

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Journal : JTIM : Jurnal Teknologi Informasi dan Multimedia

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Sistem Pendeteksi Kerusakan Buah Mangga Menggunakan Sensor Gas Dengan Metode DCS - LCA Murad Murad; Sukmawaty Sukmawaty; Ansar Ansar; Rahmat Sabani; Syahroni Hidayat
JTIM : Jurnal Teknologi Informasi dan Multimedia Vol 3 No 4 (2022): February
Publisher : Puslitbang Sekawan Institute Nusa Tenggara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35746/jtim.v3i4.169

Abstract

Fruits, including mangoes, produce a wide variety of volatile organic compounds that give them their distinct aroma. Characteristics of fruit aroma is one of the important keys in determining consumer acceptance in the commercial fruit market based on individual preferences. So a possible way to determine the level of ripeness/damage is to feel the distinctive aroma presented by the fruit using a gas sensor. This study aims to build a system that can detect mango damage based on its aroma. The sensors used are TGS 2600, MQ3, MQ4, MQ2, and MQ8 which are connected to the Arduino Mega 2560. The learning model used is an ensemble learning model of Dynamic Classifier Selection (DCS) with Local Class Accuracy (LCA)/DCS-LCA. This algorithm combines Logistic Regression, Selection Tree, Support Vector Machine (SVM), Naïve Bayes, Random Forest, and Neural Networks. The model was then tested with a comparison of the amount of test data and training data of 70%:30%. The test results showed that the overall system Accuracy was 75% and the ability to detect mango fruit damage was 71%. The DCS-LCA classifier model outperforms each of its constituent base classifiers.
Co-Authors Ansar Ansar Ansar Ansar Asih Priyati Diah Ajeng Setiawati Diah Ajeng Setiawati Guyup Mahardhian Dwi Putra Guyup Mahardhian Dwi Putra Hary Kurniawan Hary Kurniawan Kurniawan Yuniarto Kurniawan Yuniarto M. Mirmanto, M. Murad, Murad Rahmat Sabani Rahmat Sabani S. Romdani S. Syahrul Sirajuddin Haji Abdullah Sopiyan Iqbal Syahroni Hidayat Zamharir, Zamharir