Article Per Year (5 Year)
p-Index From 2020 - 2025
1.259
P-Index
This Author published in this journals
All Journal Indonesian Journal of Chemistry Jurnal Fisika : Fisika Sains dan Aplikasinya JTM - Jurnal Teknik Mesin J-Icon : Jurnal Komputer dan Informatika Jurnal Pendidikan dan Pengabdian Masyarakat
Bukit, Minsyahril
Unknown Affiliation
Astronomy Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Chemistry Computer Science & IT Control & Systems Engineering Earth & Planetary Sciences Education Electrical & Electronics Engineering Energy Industrial & Manufacturing Engineering Materials Science & Nanotechnology Mechanical Engineering Physics Other

Published : 7 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search
Journal : J-Icon : Jurnal Komputer dan Informatika

 1 
GOOGLE TEACHABLE MACHINE: PEMANFAATAN MACHINE LEARNING BERBASIS CNN UNTUK IDENTIFIKASI CEPAT BATUAN MINERAL KALSIT, KUARSA DAN MAGNETIT Johannes, Albert Zicko; Bukit, Minsyahril; Betan, Agustinus D D; Tarigan, Jonshon
J-Icon : Jurnal Komputer dan Informatika Vol 12 No 2 (2024): Oktober 2024
Publisher : Universitas Nusa Cendana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35508/jicon.v12i2.15170

Abstract

Google Teachable Machine is a web-based application that allows users to create a machine learning model without the need for coding. The application utilizes Convolutional Neural Networks (CNN) in its process. In this study, Teachable Machine was utilized to create a machine learning model capable of identifying specific mineral rocks, particularly magnetite, calcite, and quartz. The research procedure was conducted through several stages: Sample Collection, Sample Classification (Dataset), Model Training, and Evaluation Process. Sample data in the form of mineral rock images were obtained and downloaded from Google. The data was then divided into three mineral classes: magnetite, calcite, and quartz, which were used as inputs in the Teachable Machine. Model training in Teachable Machine used input epochs of 100, batch size of 64, and learning rate of 0.0001. The results of the Teachable Machine modeling were then evaluated, showing that the obtained model could recognize magnetite, calcite, and quartz minerals with an average accuracy, precision, recall, specificity, and F1-score of 91.11% (86.67%), 87.30%, 86.67%, 93.33%, and 86.50% respectively. This research indicates that the utilization of Teachable Machine assists in quick, accurate, and easy identification, thereby contributing to speeding up the process of mineral rock analysis, decision-making, and exploration strategy development. Additionally, these results demonstrate the potential for broader development of this application in various fields of study.
Co-Authors Amaral, Madalena A. Bernandus Bernandus, Bernandus Betan, Agustinus D D Bethan, Agustinus Deka Fidelis Nitti Hery L. Sianturi Johannes, Albert Zicko Ngara, Zakarias S. Pasangka, Bartholomeus Pingak, Redi Kristian Refli, Refli Tanesib, Jehunias Leonidas Tarigan, Jhonson Tarigan, Jonshon Yilu, Siprianus Ndamu Zakarias Seba Ngara