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Journal : Infotech: Journal of Technology Information

 1 
PENERAPAN CONVOLUTIONAL NEURAL NETWORK (CNN) DALAM KLASIFIKASI KAYU GUNA BAHAN BAKU MEBEL Fadhillah, Dian Agil; Sumarlinda, Sri; Lestari, Wiji
Infotech: Journal of Technology Information Vol 10, No 2 (2024): NOVEMBER
Publisher : ISTEK WIDURI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37365/jti.v10i2.282

Abstract

Currently, the need for furniture is increasing and developing into something more attractive so that many people are interested in it. Furniture has unique characteristics that can become the owner's identity. Furniture buying and selling transactions are also widespread among local and foreign people. Apart from the uniqueness of the furniture, the difference in material and wood quality is an important factor in determining the price of furniture. This research aims to build a machine learning model using CNN to classify good wood and bad wood through images of wood types. In this research, the Convolution Neural Network (CNN) technique and direct source interview data are used and supported by relevant secondary data. So the results of classification using a CNN model that is uniquely created can produce 85% accuracy in classifying good wood and bad wood. And it produces a precision for good wood of 0.50 and a recall of 1.00, while the precision and recall for bad wood is 0.00. Accuracy with this CNN model has its own benefits, such as being able to help users classify good wood and bad wood for furniture raw materials, besides that it can be used as a reference in developing subsequent machine learning applications. ABSTRAKSaat ini kebutuhan furniture semakin meningkat dan berkembang menjadi yang lebih menarik sehingga banyak peminat. Furniture memiliki keunikan yang bisa menjadikan karakteristik sebagai identitas pemiliknya. Transaksi jual beli furniture juga marak dikalangan masyarakat lokal maupun mancanegara. Selain keunikan dari furniture perbedaan bahan dari kualitas kayu menjadi faktor penting dalam menetukan harga furniture.Penelitian ini bertujuan untuk membangun model machine learning menggunakan CNN untuk mengklasifikasikan antara kayu baik dan kayu tidak baik melalui citra jenis kayu. Dalam penelitian ini, teknik Convolution Neural Network (CNN) dan dari data wawancara narasumber langsung serta didukung dengan data sekunder yang relevan. Sehingga hasil dari klasifikasi dengan model CNN yang dibuat dengan keunikan tersendiri dapat menghasilkan akurasi 85% dalam klasifikasi kayu baik dan kayu tidak baik dan menghasilkan precision kayu baik sebesar 0.50 dan recall sebesar 1.00 sedangkan hasil dari precision dan recall untuk kayu tidak baik sebesar 0.00. Akurasi dengan  model CNN ini memiliki manfaat tersendiri seperti dapat membantu pengguna mengklasifikasikan antara kayu baik  dan kayu tidak baik guna bahan baku mebel, selain itu dapat dijadikan acuan dalam pengembangan aplikasi machine learning berikutnya.
Co-Authors Afifa, Nadhira Nuraini Agustin, Heidy Akmal, Baasith Khoiruddin Al-am, Maftah Rozani Anissa, Lidya Ariasti, Dinar Asa Wahyu Purnama, Theodorus Atho’urrohman, Wifqi Atima, Vihi Awang Long, Zalizah binti Azizah, Nikmatul binti Rahmat, Azizah Cahya, Bambang Dwi Chandra, Dian Novita Dhiana, Azis Sukma Diana Sunardi Fadhillah, Dian Agil Farid Faqih, Muhammad Fariz, Nurwidya Florencia Grifit Joiner, Da Costa Hornay Hanafi, Christi Giovani Anggasta Hasanah, Herliyani Helena Fabiani Herawati Herawati Irawan, Rosyid Rafi Ismawati, Mega Yuniar Istiqomariyah, Ernita Jamaludin, Bani Lin, Kenzie Hara Maria Kanan Maulindar, Joni Muhtarom, Moh Mulyanto, Hari Nibras Faiq Muhammad Ninik Mudjihartini Novemy Triyandari Nugroho Pipin Widyaningsih Pradana, Afu Ichsan Prasetiyo, Dwi Pratiwi, Sandi Puspita Prawirohajo, Pukovisa Putra, Fendi Ardya Rahmawati, Anni Ray March Syahadat Reni Widyastuti Rifa'i, Rifan Ronaldi, Abon Rubeda, Syukril Agaba Kalis Rudi Susanto Saidek, Abdul Rahim Shirly Rizki Kusumaningrum Silviana Pebruary Soe, Calvin Johnson Cannavaro Sondang Ratnauli Sianturi Steffi Sonia Sudarsa, Caca Sulisyowati, Sulisyowati Sumarlinda, Sri Surya, Clarissa Winata Syahputra, Paisal Syam, Shaogi Tai, Jacky Anderson Thantawi, Tubagus Rifqy Victoria, Chelsea zulfa, nely