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All Journal Indonesian Journal of Electronics and Instrumentation Systems IJCCS (Indonesian Journal of Computing and Cybernetics Systems)
Agus Harjoko
Jurusan Ilmu Komputer dan Elektronika, FMIPA UGM, Yogyakarta
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering

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Pengenalan Spesies Gulma Berdasarkan Bentuk dan Tekstur Daun Menggunakan Jaringan Syaraf Tiruan Herman Herman; Agus Harjoko
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 9, No 2 (2015): July
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.7549

Abstract

AbstrakGulma merupakan tanaman pengganggu yang merugikan tanaman budidaya dengan menghambat pertumbuhan tanaman budidaya. Langkah awal dalam melakukan pengendalian gulma adalah mengenali spesies gulma pada lahan tanaman budidaya. Cara tercepat dan termudah untuk mengenali tanaman, termasuk gulma adalah melalui daunnya. Dalam penelitian ini, diusulkan pengenalan spesies gulma berdasarkan citra daunnya dengan cara mengekstrak ciri bentuk dan ciri tekstur dari citra daun gulma tersebut. Untuk mendapatkan ciri bentuk, digunakan metode moment invariant, sedangkan untuk ciri tekstur digunakan metode lacunarity yang merupakan bagian dari fraktal. Untuk proses pengenalan berdasarkan ciri-ciri yang telah diekstrak, digunakan metode Jaringan Syaraf Tiruan dengan algoritma pembelajaran Backpropagation. Dari  hasil pengujian pada penelitian ini, didapatkan tingkat akurasi pengenalan tertinggi sebesar 97.22% sebelum noise dihilangkan pada citra hasil deteksi tepi Canny. Tingkat akurasi tertinggi didapatkan menggunakan 2 ciri moment invariant (moment  dan ) dan 1 ciri lacunarity (ukuran box 4 x 4 atau 16 x 16). Penggunaan 3 neuron hidden layer pada Jaringan Syaraf Tiruan (JST) memberikan waktu pelatihan data yang lebih cepat dibandingkan dengan menggunakan 1 atau 2 neuron hidden layer. Kata kunci—3-5 gulma, daun ,moment invariant, lacunarity, jaringan syaraf tiruan AbstractWeeds are plants that harm crops by inhibiting the growth of cultivated plants. The first step to take control of weeds is by identifying weed among the cultivating plant. The fastest and easiest way to identify plants, including weeds is by its leaves. This research proposing weed species recognition based on weeds leaf images by extracting its shape and texture features. Moment invariant method is used to get the shape and Lacunarity method for the texturel.  Neural Network with backpropagation learning algorithm are implements for the extracted features recognition proses. The result of this research achievement shows the highest level of recognition accuracy of 97.22% before the noise is eliminated in the image of the Canny edge detection. Highest level of accuracy is obtained using two features from moment invariant (moment  and  ) and 1 lacunarity’s feature (size box 4 x 4 or 16 x 16). The use of 3 neurons in the hidden layer of Artificial Neural Network (ANN) provide training time data more quickly than by using 1 or 2 hidden layer neurons. Keywords— weed, leaf, moment invariant, lacunarity, artificial neural network 
Perbandingan Kapasitas Pesan pada Steganografi DCT Sekuensial dan Steganografi DCT F5 dengan Penerapan Point Operation Image Enhancement Dian Hafidh Zulfikar; Agus Harjoko
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 10, No 1 (2016): January
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.11187

Abstract

Steganographic  process on the DCT transform is generally done on the value of DCT quantization process results that have a value other than 0, this relates to the distribution of the diversity of pixels in the image. Applying point operation of image enhancement (POIE) in the form of histogram equalization, contrast stretching, brigthening and gamma correction on the image of the reservoir is associated with the image histogram . Test parameters used is the number of bits that can be accommodated message, PSNR and MSE value, and the value of DCT coefficients quantization results.    Based on test results that have to be got several conclusions that capacity steganographic message on DCT sequential greater than the DCT F5 steganography either before or after application of the application POIE, stego image quality on DCT steganography F5 better than the sequential DCT steganography well before the application POIE and after application of POIE, both F5 and steganography steganography DCT DCT sequential equally resistant to manipulation of stego image.
Deteksi Perubahan Citra Pada Video Menggunakan Illumination Invariant Change Detection Adri Priadana; Agus Harjoko
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 11, No 1 (2017): January
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.17526

Abstract

 There is still a lot of juvenile delinquency in the middle of the community, especially people in urban areas, in the modern era. Juvenile delinquency may be fights, wild racing, gambling, and graffiti on the walls without permission. Vandalized wall is usually done on walls of office buildings and on public or private property. Results from vandalized walls can be seen from the image of the change between the initial image with the image after a motion.This study develops a image change detection system in video to detect the action of graffiti on the wall via a Closed-Circuit Television camera (CCTV) which is done by simulation using the webcam camera. Motion detection process with Accumulative Differences Images (ADI) method and image change detection process with Illumination Invariant Change Detection method coupled with image cropping method which carried out a comparison between the a reference image or image before any movement with the image after there is movement.Detection system testing one by different times variations, ie in the morning, noon, afternoon, and evening. The proposed method for image change detection in video give results with an accuracy rate of 92.86%.
Co-Authors Adhi Prahara Adri Priadana Afriyani Soraya Sari Ainatul Mardhiyah Bryan Novega Whildan Bimantaka David Ariano Dian Esti Pratiwi Dian Hafidh Zulfikar Dien Rahmawati Handreas Okta Wibawa Raden Sumiharto Rajif Agung Yunmar Triyogatama Wahyu Widodo Yanuar Putu Wiharja Yundari, Yundari