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All Journal Jurnal Sisfokom (Sistem Informasi dan Komputer) JMAI (Jurnal Multimedia & Artificial Intelligence) To Maega: Jurnal Pengabdian Masyarakat Jurnal Teknologi Dan Sistem Informasi Bisnis Jurnal Informa: Jurnal Penelitian dan Pengabdian Masyarakat Jurnal Teknik Informatika (JUTIF) Tamansiswa International Journal in Education and Science (TIJES) Journal Of Information System And Artificial Intelligence Jurnal Indonesia Sosial Sains AMMA : Jurnal Pengabdian Masyarakat
Supatman Supatman
Universitas Mercu Buana Yogyakarta
Humanities Astronomy Biochemistry, Genetics & Molecular Biology Chemistry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Environmental Science Immunology & microbiology Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Neuroscience Nursing Physics Social Sciences Other

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Journal : Journal Of Information System And Artificial Intelligence

 1 
Mendeteksi Salak BerLarva dan Tidak BerLarva Menggunakan Metode Convolutional Neural Network Jati Nugroho; Supatman
Journal Of Information System And Artificial Intelligence Vol. 2 No. 1 (2021): Journal of Information System and Artificial Intelligence Vol II, No I November
Publisher : Universitas Mercu Buana Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (237.648 KB) | DOI: 10.26486/jisai.v2i1.64

Abstract

Salacca fruit is one of the cultivars of the salak pondoh (the young fruits with sweet taste), which has become a leading commodity in Sleman, the Special Region of Yogyakarta Province. The salak madu (honey salacca fruit ~Eng.) became known when it was identified for the first time in Sempu Sub-village (Baterante), Wonokerto Village, Turi District, Sleman Regency. The most prominent feature of this salak madu is the leaves are shorter when compared to other types of salak pondoh. The color of the fruit's skin is blackish-brown when it is young, and it gradually becomes shiny brown after getting old. The arrangement of the scales forms a line pattern. This research was initiated because some people were sometimes deceived by the salak madu that looked good even though it had larvae inside. Therefore, the researcher was willing to create a system to detect which salacca fruits had larvae inside and which did not, using the Convolutional Neural Network (CNN) method. Detecting the salacca fruits with larvae and with no larvae required an image of the salacca fruits with larvae and with no larvae; after that, the image that had been obtained would go through a training process to detect the salacca fruits, whether they have larvae or no larvae; and after that, the image would be tested for the accuracy. The test result obtained by this System was 80%.
Deteksi Tingkat Kematangan Fermentasi Singkong (Tape Singkong) Menggunakan Convolutional Neural Network (CNN) Abdi Subayu; Supatman
Journal Of Information System And Artificial Intelligence Vol. 2 No. 2 (2022): Journal of Information System and Artificial Intelligence Vol II, No II Mei 202
Publisher : Universitas Mercu Buana Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (242.03 KB) | DOI: 10.26486/jisai.v2i2.68

Abstract

Tapay is a food in which the manufacturing process involves yeast. Unlike others, tapay requires fermentation using yeast containing the Kapang Amylomyces Rousi, Mucor sp, Rhizopus sp, Khamir Saccharomycopsis fibuligera, Candida Utilis, Pichia burtonii, Saccharomyces Cerevisiae, Saccharomycopsis Malanga, and the bacteria Pediococcus sp and Bacillus sp. Cassava tapay (Manihot Utilissima) is food containing these elements. Problems arise when the common public has no idea about the ripeness of cassava fermentation. Therefore, an artificial neural system is developed to detect the ripeness of cassava fermentation using the Convolutional Neural Network (CNN) method. The CNN method is one of the Deep Learning methods that can carry out an independent learning process for object recognition that is extracted and classified, then can be applied to high-resolution images with a nonparametric distribution model. The study results by making 45 training data reached 96.88%, and using 30 cassava tapay test data reached 90%. These results aim to reduce community error, especially for consumers, in determining the ripeness of cassava tapay.
Co-Authors Abdi Subayu Ade Sumarsono Ahmad Ramadhan Arita Witanti Christophorus Candra Kusumadewa Enggar Dwi Kharisma Heribertus Christian Agusto Lumban Tobing Indah Susilawati Jati Nugroho Lilis Cahyono Lugas Lukmanul Hakim Mutaqin Akbar Sidiq Purnomo, Agus Sri Ayem Suddin Lada Sumedi Joko Susilo Triana Noor Edwina Dewayani Soeharto Umi Wahidah Wahyu Puspitaningrum Yhoga Heru Pratama