Article Per Year (5 Year)
p-Index From 2019 - 2024
0.61
P-Index
This Author published in this journals
All Journal TELEKONTRAN: Jurnal Ilmiah Telekomunikasi, Kendali dan Elektronika Terapan BERNAS: Jurnal Pengabdian Kepada Masyarakat Journal of Mechanical and Manufacture
Ari Ajibekti Masriwilaga
Universitas Subang
Religion Aerospace Engineering Agriculture, Biological Sciences & Forestry Humanities Automotive Engineering Computer Science & IT Control & Systems Engineering Dentistry Education Electrical & Electronics Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Mathematics Mechanical Engineering Neuroscience Nursing Public Health Social Sciences Veterinary Other

Published : 3 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search
Journal : Journal of Mechanical and Manufacture

 1 
CNN-BASED ARTIFICIAL INTELLIGENCE (AI) IMPLEMENTATION TO IDENTIFY BASMATI RICE IN SUBANG DISTRICT Ari ajibekti Masriwilaga; ari wibowo; dian susanto
Jurnal Mekanika dan Manufaktur Vol 3 No 1 (2023)
Publisher : Universitas Majalengka

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31949/jmm.v3i1.6371

Abstract

The existence of Basmati rice among the upper middle class in Indonesia is increasingly popular. Unfortunately, this rice is only grown in northern India and Pakistan. Fulfillment of rice must be imported and the price in Indonesia is relatively expensive. Responding to this phenomenon, the Center for Rice Research (BB Padi), the Agricultural Research and Development Agency succeeded in assembling a special rice variety Basmati. And given the name Baroma, an abbreviation of type Basmati Aromatic rice. And Baroma rice was launched in Subang in 2019 until now it has been recorded that several agricultural lands in Subang have planted this type. The more types of rice varieties, the more types of rice will be found. So that it will make consumers difficult to distinguish between types of rice with one another. Therefore, we need a solution to overcome this problem. And one solution that can be used is to use AI technology, as in the research we did. Using the CNN algorithm produces very good accuracy for detecting types of rice such as the type of data used for training data and test data. From the results of the model training carried out, it produces an accuracy rate of 98,52% while model testing to see how well the model predicts the label correctly is 97,80%.
Co-Authors Agus Subagja Ari Wibowo Dian Susanto Kasda, Kasda Muhtar Kosim Novandri Tri Setioputro Sopian Septiana Sugeng Sutikno Tubagus Abdul Jabar Malik Al-hadi