Analisis Optimasi Klasifikasi Citra Awan Berdasarkan Nilai Hyperparameter Pada Teachable Machine untuk Pengembangan Aplikasi Web Mobile
DOI:
https://doi.org/10.25126/jtiik.2025126Kata Kunci:
teachable machine, deteksi objek, jenis awan, convolutional neural networks, optimalisasi citraAbstrak
Pengamatan cuaca menjadi aspek penting dalam berbagai bidang seperti meteorologi, penelitian lingkungan dan penerbangan. Identifikasi jenis awan memainkan peran kunci dalam memprediksi perubahan cuaca dan mengevaluasi dampak lingkungan. Tujuan dari penelitian ini adalah untuk mengembangkan sebuah aplikasi web mobile sistem cerdas yang mampu membantu masyarakat dalam mendeteksi jenis awan secara mandiri di sekitar, memberikan edukasi tentang jenis awan dan yang paling penting adalah mencari nilai optimasi hyperparameter epoch, batch size dan learning rate dalam Teachable Machine. Penelitian ini menggunakan nilai untuk parameter-parameter yang diteliti, yaitu nilai epoch yang bervariasi antara 10, 50, 100, 250, 750 dan 1000. Kemudian nilai batch size yang bervariasi antara 16, 32, 64, 128, 256 dan 512 serta learning rate yang bervariasi antara 0,00001; 0,0001; 0,001; 0,01; 0,1 dan 1. Total dataset sebanyak 4.000 sampel data latih (400 sampel dalam 10 kelas) digunakan dalam Teachable Machine. Metode yang digunakan adalah dengan memanfaatkan framework TensorFlow pada layanan Teachable Machine untuk melatih data citra atau gambar. Framework ini menyediakan algoritma Convolutional Neural Networks (CNN) yang dapat melakukan klasifikasi citra atau gambar dengan tingkat akurasi yang tinggi. Hasil pengujian menunjukkan bahwa nilai optimal tertinggi tercapai pada nilai epoch ke-50, dengan nilai batch size 16 dan learning rate 0,00001 yang menghasilkan tingkat akurasi antara 70% hingga 98%. Aplikasi web mobile ini diharapkan dapat diimplementasikan secara luas untuk kepentingan masyarakat agar mengenali jenis awan yang menyebabkan potensi hujan.
Abstract
Weather observation is becoming an increasingly important aspect in various fields, such as Meteorology, Environmental Research, and aviation. The identification of cloud types plays a key role in predicting weather changes and evaluating environmental impacts. The purpose of this study is to develop a mobile web application intelligent system that is able to help people detect the type of cloud independently around, provide education about the type of cloud, and most importantly, find the value of optimization hyperparameter epoch, batch size and learning rate in Teachable Machine. This study uses the values for the parameters studied, namely the epoch values that vary between 10, 50, 100, 250, 750, and 1000. Then the value of batch size varies between 16, 32, 64, 128, 256, and 512, and the learning rate varies between 0.00001; 0.0001; 0.001; 0.01; 0.1, and 1. A total of 4,000 training data samples (400 samples per class) were used in the Teachable Machine. The method used is to utilize the TensorFlow framework in the Teachable Machine Service to train image or image data. This Framework provides Convolutional Neural Networks (CNN) algorithms that can classify images with a high degree of accuracy. The test results showed that the highest optimal value was reached at the 50th epoch value, with a batch size value of 16 and a learning rate of 0.00001 which resulted in an accuracy rate of 70% to 98%. This application is expected to be widely implemented for the benefit of the community in order to recognize the type of cloud that causes the potential for rain.
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- Penulis menyimpan hak cipta dan memberikan jurnal hak penerbitan pertama naskah secara simultan dengan lisensi di bawah Creative Common Attribution-ShareAlike 4.0 International (CC BY-SA 4.0) yang mengizinkan orang lain untuk berbagi pekerjaan dengan sebuah pernyataan kepenulisan pekerjaan dan penerbitan awal di jurnal ini.
- Penulis bisa memasukkan ke dalam penyusunan kontraktual tambahan terpisah untuk distribusi non ekslusif versi kaya terbitan jurnal (contoh: mempostingnya ke repositori institusional atau menerbitkannya dalam sebuah buku), dengan pengakuan penerbitan awalnya di jurnal ini.
- Penulis diizinkan dan didorong untuk mem-posting karya mereka online (contoh: di repositori institusional atau di website mereka) sebelum dan selama proses penyerahan, karena dapat mengarahkan ke pertukaran produktif, seperti halnya sitiran yang lebih awal dan lebih hebat dari karya yang diterbitkan. (Lihat Efek Akses Terbuka).
