PREDIKSI DINI KANKER KULIT MENGGUNAKAN ALGORITMA CONVOLUTIONAL NEURAL NETWORK DEEP LEARNING DAN ANTARMUKA BOT TELEGRAM

MA'WA, QUARTETSYA FAUZIATUL (2022) PREDIKSI DINI KANKER KULIT MENGGUNAKAN ALGORITMA CONVOLUTIONAL NEURAL NETWORK DEEP LEARNING DAN ANTARMUKA BOT TELEGRAM. S1 thesis, Universitas Mercu Buana Jakarta-Menteng.

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Abstract

Angka kematian pada tahun 2020 akibat kanker kulit di seluruh dunia sebesar 7.8% dari seluruh kasus kanker kulit. Penggunaan teknologi informasi dapat menjadi solusi yang ekonomis dalam memprediksi kanker secara dini untuk mengurangi kasus kematian akibat kanker yang telat ditangani, khususnya untuk kanker kulit. Penelitian ini juga menggunakan teknologi informasi untuk memprediksi kanker kulit, yaitu menggunakan algoritma convolutional neural network (CNN). Seluruh data foto lesi kulit yang digunakan berasal dari The International Skin Imaging Collaboration (ISIC). Penelitian ini juga memanfaatkan bot telegram yang difungsikan sebagai antarmuka untuk pengguna dapat berkomunikasi dengan model algoritma CNN yang telah dibuat. Bot Telegram ini memiliki 3 jenis perintah, yaitu perintah prediksi, fitur riwayat, fitur informasi kanker kulit. Hasil percobaan memperoleh f1-Score klasifikasi foto lesi kanker sebesar 93% dan klasifikasi foto non-kanker sebesar 97%. Akurasi yang didapatkan oleh model adalah 97%. Selain itu, model CNN dapat diintegrasikan dengan bot Telegram. Kata kunci: Convolutional Neural Network, Klasifikasi Gambar, Kanker Kulit, Implementasi Bot Telegram The mortality rate in 2020 due to skin cancer worldwide was 7.8% of all cases of skin cancer. The use of information technology can be an economical solution in predicting cancer early to reduce the late cancer death cases, especially for skin cancer. This study uses information technology to predict skin cancer using the convolutional neural network (CNN) algorithm. All photo data of skin lesions used came from The International Skin Imaging Collaboration (ISIC). This research also utilises a telegram bot that functions as an interface for users to communicate with the CNN algorithm model that has been created. This Telegram bot has 3 types of commands: prediction commands, prediction history, and skin cancer information. The experimental results obtained an f1-Score classification of cancerous lesions of 93% and non-cancerous photos classification of 97%. Accuracy obtained by the model is 97%. In addition, the CNN model is tested and can integrate with Telegram bot. Key words: Convolutional Neural Network, Image Classification, Skin Cancer, Telegram Bot Implementation

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