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All Journal Jurnal Informatika dan Teknik Elektro Terapan JurTI (JURNAL TEKNOLOGI INFORMASI) Jurnal Nasional Komputasi dan Teknologi Informasi Jurnal Teknologi Terpadu Journal of Information System, Applied, Management, Accounting and Research Journal of Applied Data Sciences HOAQ (High Education of Organization Archive Quality) : Jurnal Teknologi Informasi Jurnal Teknik Informatika dan Teknologi Informasi
Yohanes Suban Belutowe
STIKOM Uyelindo Kupang
Aerospace Engineering Automotive Engineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering Neuroscience Transportation

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Journal : Journal of Applied Data Sciences

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Teachable Machine: Optimization of Herbal Plant Image Classification Based on Epoch Value, Batch Size and Learning Rate Malahina, Edwin Ariesto Umbu; Saitakela, Mardhalia; Bulan, Semlinda Juszandri; Lamabelawa, Marinus Ignasius Jawawuan; Belutowe, Yohanes Suban
Journal of Applied Data Sciences Vol 5, No 2: MAY 2024
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v5i2.206

Abstract

Herbal plants are a source of natural materials used in alternative medicine and traditional therapies to maintain health. The purpose of this research is to develop an intelligent system application that is able to assist people in independently detecting herbal plants around them, provide education, and most importantly, find the optimal value based on certain parameters. This research uses several values for the parameters studied, namely the epoch value which varies between 10, 50, 100, 250, 750, and 1000; the batch size value which varies between 16, 32, 64, 128, 256, and 512; and the learning rate value which varies between 0.00001, 0.0001, 0.001, 0.01, 0.1, and 1. A total of 10,000 training data samples (1,000 samples in 10 classes) were used in Teachable Machine. The method used is to utilize the TensorFlow framework in the Teachable Machine service to train image data. This framework provides Convolutional Neural Networks (CNN) algorithms that can perform image classification with a high degree of accuracy. The test results for more than three months showed that the highest optimal value was achieved at the 50th epoch value, with a learning rate of 0.00001, and a batch size of 32, which resulted in an accuracy rate between 98% and 100%. Based on these results, a mobile web-based intelligent system application service was developed using the TensorFlow framework in Teachable Machine. This application is expected to be widely implemented for the benefit of the community. However, the challenges and limitations in training this test data are the large number of data classes that will be very good so that machine learning can learn to recognize objects but will take hours to train, then the training image object data has a clean background from other objects so that when tested it is not detected and influenced as another object or can result in a decrease in the percentage value.
A Grid-search Method Approach for Hyperparameter Evaluation and Optimization on Teachable Machine Accuracy: A Case Study of Sample Size Variation Malahina, Edwin Ariesto Umbu; Iriane, Gregorius Rinduh; Belutowe, Yohanes Suban; Katemba, Petrus; Asmara, Jimi
Journal of Applied Data Sciences Vol 5, No 3: SEPTEMBER 2024
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v5i3.290

Abstract

This study aims to evaluate the effectiveness of the grid-search method in hyperparameter optimization on Teachable Machine (TM) using a varying number of image samples. The hyperparameters studied include epoch (e), batch size (b), and learning rate (l). A structured grid-search method approach will be applied to test 216 hyperparameter combinations across 6 categories of sample size per class, namely 10, 25, 50, 100, 250, and 500. The results showed that the optimal combination findings were obtained based on variations in the number of samples as follows: 10 samples using e:100, b:256, l:0.001 get an accuracy range of ≥ 90%; for 25 samples using e:500, b:16, l:0.001 get an accuracy range ≥ 97%; for 50 samples using e:100, b:512, l:0.001 get an accuracy range ≥ 88%; for 100 samples using e:500, b:32, l:0.001 get an accuracy range ≥ 88%; for 250 samples using e:50, b:16, l:0.001 get an accuracy range ≥ 92%, and finally 500 samples using e:500, b:256, l:0.001 get an accuracy range ≥ 96% and on average are able to achieve 100% accuracy from the detection test results of the best value performed for each sample variation of the image object. This research provides significant contributions or benefits in finding the optimal hyperparameter configuration, minimizing overfitting, and shortening the search time for TM accuracy in image classification, particularly in human face recognition. The findings support the development of more efficient and accurate TMs and provide practical guidance for finding better hyperparameter optimization using the grid-search method approach. The results of this study have implications for improving the effectiveness and accuracy of TM models and their development in mobile web applications
Co-Authors Dewi Anggraini Edwin Ariesto Umbu Malahina Erna Rosani Nubatonis Huan Arthur Dominicus Ado Jimi, Asmara Kale, Swingli Setiawan Ratu Linus Evrianus Ama Kean Magsyar, Ahmad Fajar Maifa, Oknel Tabita Mardhalia Saitakela Martin Reynaldi Unfeto Muhammad Agus Muljanto Petrus Katemba Priambudi, Silvester Baiyo Revansa Eli Manafe Semlinda Juszandri Bulan Tikneon, Febrianus Yohanes Karmani