Pengembangan Aplikasi Deteksi Kematangan Buah Pisang Berbasis Web Menggunakan Model CNN-LSTM

Cindy Sintiya, Erin Gunawan, Dhea Romantika Marpaung, Farrell Rio Fa, Frans Mikael Sinaga

Abstract


Klasifikasi tingkat kematangan buah merupakan salah satu tantangan dalam penerapan teknologi kecerdasan buatan di sektor pertanian. Penelitian ini mengusulkan sistem deteksi tingkat kematangan pisang menggunakan arsitektur deep learning berbasis Convolutional Neural Network (CNN) dan Long Short-Term Memory (LSTM). Dataset citra pisang diproses melalui tahapan preprocessing yang mencakup normalisasi, segmentasi warna (mask kuning dan hijau), serta deteksi tepi, untuk menonjolkan fitur visual yang relevan. Model yang diimplementasikan mampu mengklasifikasikan tingkat kematangan pisang ke dalam kategori "matang" dan "mentah". Sistem ini diintegrasikan dengan antarmuka berbasis web menggunakan Streamlit, memungkinkan prediksi dilakukan secara real-time. Hasil pengujian menunjukkan bahwa model mencapai akurasi 100% pada dataset uji, dengan precision, recall, dan F1-score sempurna. Penelitian ini membuktikan efektivitas pendekatan CNN-LSTM dalam klasifikasi tingkat kematangan buah yang diharapkan dapat membantu memberikan kontribusi terhadap otomatisasi di sektor pertanian.


Keywords


CNN-LSTM; Klasifikasi Kematangan Buah; Pembelajaran Mendalam

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DOI: https://doi.org/10.55601/jsm.v26i1.1500

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