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Rizky Rachma Putri
Universitas Bina Sarana Informatika
Computer Science & IT

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Comparative Optimization of EfficientNetB3, MobileNetV2, and ResNet50 for Waste Classification Sarifah Agustiani; Haryani Haryani; Agus Junaidi; Rizky Rachma Putri; Meutia Raissa Emiliana
Jurnal Informatika Vol. 12 No. 2 (2025): October
Publisher : Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/

Abstract

Waste management is an important challenge in protecting the environment and public health. Improperly managed waste can cause pollution and hinder the recycling process. This study aims to classify waste based on images by optimizing three deep learning architectures, namely EfficientNetB3, MobileNetV2, and ResNet50, to determine the model with the best performance. The dataset comes from the Kaggle platform, consisting of 4,650 images in six categories: battery, glass, metal, organic, paper, and plastic. The research stages include preprocessing, data augmentation, model development, and evaluation using accuracy, precision, recall, and F1-score metrics. The results show that EfficientNetB3 with the Adam optimizer achieved the best performance with 93% accuracy, followed by ResNet50 with 91%, while MobileNetV2 ranged from 70–73% depending on the optimizer. Variations in optimizers were found to affect model performance, while data augmentation improved generalization capabilities, especially in classes with limited samples. This research confirms the potential of deep learning methods in supporting automatic waste classification systems and provides a basis for the development of technology-based waste management systems in the future.
Perbandingan Linear Regression, K-NN, dan Naive Bayes untuk Prediksi Kategori BMI Adam Wildan Firdaus; Mochamad Danu Rizkyarto; Rizky Rachma Putri
Jurnal Media Informatika Vol. 7 No. 1 (2026): Edisi Januari - Februari
Publisher : Lembaga Dongan Dosen

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55338/jumin.v7i1.7842

Abstract

Penelitian ini bertujuan untuk menganalisis dan membandingkan kinerja tiga algoritma Machine Learning, yaitu Linear Regression, K-Nearest Neighbor, dan Gaussian Naive Bayes dalam memprediksi kategori Indeks Massa Tubuh (BMI) berdasarkan data antropometri. Dataset yang digunakan terdiri dari 111 sampel yang diperoleh dari repositori Zenodo dan diproses melalui tahapan pra-pemrosesan, standarisasi fitur numerik, serta evaluasi menggunakan metode Stratified K-Fold Cross Validation. Hasil pengujian menunjukkan adanya perbedaan karakteristik pada masing-masing algoritma. Gaussian Naive Bayes memberikan akurasi tertinggi dalam klasifikasi kategori BMI sebesar 75,74%, sedangkan Linear Regression menghasilkan prediksi numerik paling presisi dengan nilai Mean Absolute Error 0,362 kg/m². K-Nearest Neighbor memiliki akurasi lebih rendah pada dataset kecil, namun pada uji data pengguna baru justru memberikan hasil prediksi BMI yang paling mendekati nilai aktual. Temuan ini menegaskan bahwa tidak ada satu algoritma yang unggul di semua aspek, melainkan setiap metode memiliki kelebihan sesuai konteks penggunaannya. Oleh karena itu, pemilihan algoritma sebaiknya mempertimbangkan tujuan aplikasi, apakah menekankan ketepatan angka BMI atau klasifikasi kategori secara cepat dan efisien.
Co-Authors Adam Wildan Firdaus Agus Junaidi Agustiani, Sarifah Haryani Meutia Raissa Emiliana Mochamad Danu Rizkyarto