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All Journal Jurnal Simantec Jurnal Teknologi Informasi dan Ilmu Komputer REKAYASA ELINVO (Electronics, Informatics, and Vocational Education) Network Engineering Research Operation [NERO] JATI (Jurnal Mahasiswa Teknik Informatika) Jurnal Aplikasi Teknologi Informasi dan Manajemen (JATIM) Signal and Image Processing Letters
Koeshardianto, Meidya
Universitas Trunojoyo Madura
Agriculture, Biological Sciences & Forestry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Physics

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Akuisisi Foreground dan Background Berbasis Fitur DTC pada Matting Citra secara Otomatis Koeshardianto, Meidya; Yuniarno, Eko Mulyanto; Hariadi, Mochamad
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 7 No 3: Juni 2020
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jtiik.2020732195

Abstract

Teknik pemisahan foreground dari background pada citra statis merupakan penelitian yang sangat diperlukan dalam computer vision. Teknik yang sering digunakan adalah image segmentation, namun hasil ekstraksinya masih kurang akurat. Image matting menjadi salah satu solusi untuk memperbaiki hasil dari image segmentation. Pada metode supervised, image matting membutuhkan scribbles atau trimap sebagai constraint yang berfungsi untuk melabeli daerah tersebut adalah foreground atau background. Pada makalah ini dibangun metode unsupervised dengan mengakuisisi foreground dan background sebagai constraint secara otomatis. Akuisisi background ditentukan dari varian nilai fitur DCT (Discrete Cosinus Transform) yang dikelompokkan menggunakan algoritme k-means. Untuk mengakuisisi foreground ditentukan dari subset hasil klaster fitur DCT dengan fitur edge detection. Hasil dari proses akuisisi foreground dan background tersebut dijadikan sebagai constraint. Perbedaan hasil dari penelitian diukur menggunakan MAE (Mean Absolute Error) dibandingkan dengan metode supervised matting maupun dengan metode unsupervised matting lainnya. Skor MAE dari hasil eksperimen menunjukkan bahwa nilai alpha matte yang dihasilkan mempunyai perbedaan 0,0336 serta selisih waktu proses 0,4 detik dibandingkan metode supervised matting. Seluruh data citra berasal dari citra yang telah digunakan para peneliti sebelumnyaAbstractThe technique of separating the foreground and the background from a still image is widely used in computer vision. Current research in this technique is image segmentation. However, the result of its extraction is considered inaccurate. Furthermore, image matting is one solution to improve the effect of image segmentation. Mostly, the matting process used scribbles or trimap as a constraint, which is done manually as called a supervised method. The contribution offered in this paper lies in the acquisition of foreground and background that will be used to build constraints automatically. Background acquisition is determined from the variant value of the DCT feature that is clustered using the k-means algorithm. Foreground acquisition is determined by a subset resulting from clustering DCT values with edge detection features. The results of the two stages will be used as an automatic constraint method. The success of the proposed method, the constraint will be used in the supervised matting method. The difference in results from In the research experiment was measured using MAE (Mean Absolute Error) compared with the supervised matting method and with other unsupervised matting methods. The MAE score from the experimental results shows that the alpha matte value produced has a difference of 0.336, and the difference in processing time is 0.4 seconds compared to the supervised matting method. All image data comes from images that have been used by previous researchers.
Classification of Corn Seed Quality using Residual Network with Transfer Learning Weight Koeshardianto, Meidya; Agustiono, Wahyudi; Setiawan, Wahyudi
Elinvo (Electronics, Informatics, and Vocational Education) Vol. 8 No. 1 (2023): Mei 2023
Publisher : Department of Electronic and Informatic Engineering Education, Faculty of Engineering, UNY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21831/elinvo.v8i1.55763

Abstract

Corn is one of the main ingredients in farm animal feed. Currently, corn is preferable because widely available and cheaper in the market than others. However, it needs quality control on corn production. The company that manufactures animal feed has certain quality standards to receive corn material. On the other hand, the quality of corn produced varies greatly. Thus, quality control when receiving corn from suppliers greatly affects the quality of animal feed. The quality of feed ingredients is classified into physical properties and analytical values. Physical properties are determined so that the resulting corn can be accepted or rejected, while the analytical value is used as the basis for formulating the diet. The physical properties of corn are determined by the human senses, such as sight and smell, while the analytical value is by chemical analysis. Physical quality control by relying on human senses is certainly limited and takes time. Based on these problems, it needs to make a classification system of corn seeds automatically. This study uses corn seed images as classification data. The system uses public data from Naagar which consists of four classes:  pure, discolored, silk cut, and broken. Image classification uses a Convolutional Neural network (CNN) with ResNet152v2 architecture. The hyperparameters used consist of a learning rate of 0.001, a batch size of 512, and an epoch of 25. Adaptive Moment Estimation (Adam) for the optimizer. Percentage of data training vs validation 80:20. The validation results show an accuracy of 65%, precision of 66%, and recall of 64%.
Co-Authors Dimas Arya Muhajir, Muhammad Eko Mulyanto Yuniarno Eza Rahmanita Fifin Ayu Mufarroha Firmansyah Adiputra Fitrianto, Hambali Liana, Novi Mochamad Hariadi Mulyanto, Eko Novi Prastiti Novi Prastiti, Novi Nuril Hidayah, Alifidyah Reyna Apriliana Rika Yunitarini Soesilo, Budi Wahyudi Agustiono Wahyudi Setiawan