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Bambang Sugiarto
Indonesian Institute of Sciences (LIPI)
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Journal : Jurnal Teknologi Informasi dan Ilmu Komputer

 1 
Kayu7net: Identifikasi dan Evaluasi F-Measure Citra Kayu berbasis Deep Convolutional Neural Network (DCNN) Erwin, Iwan Muhammad; Risnandar, Risnandar; Prakarsa, Esa; Sugiarto, Bambang
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 7 No 6: Desember 2020
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jtiik.2020712663

Abstract

Identifikasi kayu salah satu kebutuhan untuk mendukung pemerintah dan kalangan bisnis kayu untuk melakukan perdagangan kayu secara legal. Keahlian khusus dan waktu yang cukup dibutuhkan untuk memproses identifikasi kayu di laboratorium. Beberapa metodologi penelitian sebelumnya, proses identifikasi kayu masih dengan cara menggabungkan sistem manual menggunakan anatomi DNA kayu. Sedangkan penggunaan sistem komputer diperoleh dari citra penampamg melintang kayu secara proses mikrokopis dan makroskopis. Saat ini, telah berkembang teknologi computer vision dan machine learning untuk mengidentifikasi berbagai jenis objek, salah satunya citra kayu. Penelitian ini berkontribusi dalam mengklasifikasi beberapa spesies kayu yang diperdagangkan menggunakan Deep Convolutional Neural Networks (DCNN). Kebaruan penelitian ini terletak pada arsitektur DCNN yang bernama Kayu7Net. Arsitektur Kayu7Net yang diusulkan memiliki tiga lapisan konvolusi terhadap tujuh spesies dataset citra kayu. Pengujian dengan merubah citra input menjadi berukuran 600×600, 300×300, dan 128×128 piksel serta masing-masing diulang pada epoch 50 dan 100. DCNN yang diusulkan menggunakan fungsi aktivasi ReLU dengan batch size 32. ReLU bersifat lebih konvergen dan cepat saat proses iterasi. Sedangkan Fully-Connected (FC) berjumlah 4 lapisan akan menghasilkan proses training yang lebih efisien. Hasil eksperimen memperlihatkan bahwa Kayu7Net yang diusulkan memiliki nilai akurasi sebesar 95,54%, precision sebesar 95,99%, recall sebesar 95,54%, specificity sebesar 99,26% dan terakhir, nilai F-measure sebesar 95,46%. Hasil ini menunjukkan bahwa arsitektur Kayu7Net lebih unggul sebesar 1,49% pada akurasi, 2,49% pada precision, dan 5,26% pada specificity dibandingkan penelitian sebelumnya. AbstractWood identification is one of the needs to support the government and the wood business community for a legally wood trading system. Special expertise and sufficient time are needed to process wood identification in the laboratory. Some previous research works show that the process of identifying wood combines a manual system using a wood DNA anatomy. While, the use of a computer system is obtained from the wood image of microscopic and macroscopic process. Recently, the latest technology has developed by using the machine learning and computer vision to identify many objects, the one of them is wood image. This research contributes to classify several the traded wood species by using Deep Convolutional Neural Networks (DCNN). The novelty of this research is in the DCNN architecture, namely Kayu7Net. The proposed of Kayu7Net Architecture has three convolution layers of the seven species wood image dataset. The testing changes the wood image input to 600×600, 300×300, and 128×128 pixel, respectively, and each of them repeated until 50 and 100 epoches, respectively. The proposed DCNN uses the ReLU activation function and batch size 32. The ReLU is more convergent and faster during the iteration process. Whereas, the 4 layers of Fully-Connected (FC) will produce a more efficient training process. The experimental results show that the proposed Kayu7Net has an accuracy value of 95.54%, a precision of 95.99%, a recall of 95.54%, a specificity of 99.26% and finally, an F-measure value of 95.46%. These results indicate that Kayu7Net is superior by 1.49% of accuracy, 2.49% of precision, and 5.26% of specificity compared to the previous work.  
Co-Authors Achmad, Zubaidi Adawiah, Lulu Robiatul Ade Kurniawan Agama, Askar Adika Ahmad Hasyim Amaria Amaria Anggarani, Riesta Ardiyani, Anita Nur Auzani, Ahmad Syihan Bagusputra, Argan Imam Bustanul Arifin Christy Atika Sari Dendi, Dede Dewanata, Rachman Pandu Dewi, Cicilia Tri Marantika Dhiani Dyahjatmayanti Dwi Mulya, Okta Endah Wahyurini, Endah ENDANG SUSANTINI Erwin, Iwan Muhammad Fatchurohman, Dedi Fatman, Yenni Frans Setiawan, Frans G.M. Lucki Junursyah, G.M. Lucki Gunawan Gunawan Gunawansyah Gunawansyah, Gunawansyah Handayani, Sri Hanifuddin, M Hapsari, Nani Sarah Hasibuan, Juana Hizkia Haviani Laluma, Riffa Hendranto, Rahadian Yogi Hendrawan, Rizqi Ainur Hendro Widjanarko Hermawan, Angga Dimas Heru Sigit Purwanto Ikhsan, Akhmad Fauzi Ilham Ramadhan, Muhammad Ghiffaari Indra Sakti Indyo Pratomo Iqbal, Fahmi Mohamad Irfandi, Fauzan Isnaini Nur Azizah, Isnaini Ivany Sarief, Ivany Kusuma, Heri Septya Lucki Junursyah, G.M Mariska Aulia Putri, Indah Merdeka Putri, Wulandari Pancadasa Mokhtar, Mokhtar Muchlis Muchlis Nathaniel, Adriel Ningsih, Ristati Nugraha, Muhammad Fauzi Nur, Salsabila Nuraini Sukmana, Rini Nurfitriani, Nisa Nurholis Majid, Nurholis Nyamiati, Retno Dwi PRABOWO Prakarsa, Esa Praromadani, Zulimatul Safa'ah Prasetya, Angga Proklamagita, Angela Merici Herdyana Putri, Sari Rahmawati Ramadhan, Zulqy Fazrie Ramdhani, Muhamad Deris Riffa Haviani Laluma Rirung, Yustin Risnandar, Risnandar Rizqon Fajar Rokhis, Tria Ainur Rukmana, Ade Samidi Samidi, Samidi Santosa Utomo, Humam Saputra, Krisna Arga Septian Chairulsyah, Defti Setyawan, Tri Aji Sifa Nurpadillah Sri Poedjiastoeti Sulistyowati, RR Endang Suranto Suranto SUYONO Suyono Suyono Syahrizal, Adyanto Syech Ahmad, Mochamad Taufik Ali Taufik Suryantoro, Taufik Taufiqurrahman, Muhammad Faja Tiara, Dinda Raihan Tunjung Wahyu Widayati Tuqa, Eka Tina Nur Ula Ummi Kalsum Wellia Shinta Sari Wibowo, Cahyo Setyo Widayati, Tunjung Wahyu Wiharko, Teguh Wiharso, Tri Arif Wirawan, Kristuaji Andre Wulandari, Amelia Puspita Yuliana, Siska Yulianto Sulistyo Nugroho