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All Journal Techno.Com: Jurnal Teknologi Informasi Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) Jurnal Riset Informatika Instal : Jurnal Komputer
Hernawati Gohzali
Mikroskil University
Computer Science & IT Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering

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Peningkatan Kualitas Citra Reduksi Noise Menggunakan Iterative Denoising and Backward Projection-CNN dan TFM-CLAHE Pada Citra 24 Bit Irpan Adiputra Pardosi; Hernawati Gohzali
Techno.Com Vol 20, No 4 (2021): November 2021
Publisher : LPPM Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33633/tc.v20i4.5243

Abstract

Penurunan kualitas yang diakibatkan adanya noise atau kontras yang tidak normal pada citra mengakibatkan objek pada citra menjadi tidak jelas. Masalah itu dapat disebabkan perangkat yang digunakan menimbulkan noise atau tidak bisa menghasilkan kontras yang normal. Adanya noise dan kontras rendah gelap berdampak besar terhadap kualitas citra?, proses reduksi noise yang berukuran besar 45% akan berpengaruh pada informasi didalam citra sehingga kualitas citra hasil reduksi menjadi hal yang perlu dipertimbangkan untuk noise berukuran besar?. Penelitian tahun 2019 menggunakan algoritma Iterative Denoising and Backward Projections with CNN (IDBP-CNN) dinyatakan mampu mereduksi noise hingga 51% dengan kualitas PSNR diatas 30 dB dengan mengabaikan kontras dari citra. Sedangkan algoritma untuk meningkatkan kontras citra menggunakan algoritma Triangular Fuzzy Membership?Contrast Limited Adaptive Histogram Equalization (TFM-CLAHE) juga diklaim mampu meningkatkan kontras citra dengan kualitas PSNR di atas 20 dB, yang lebih baik dibandingkan dengan algoritma CLAHE. Berdasarkan hasil pengujian yang dilakukan pada 10 citra kontras rendah gelap dengan noise 45% didapatkan kombinasi algoritma TFM-CLAHE diikuti IDBP-CNN lebih baik dengan rata-rata hasil PSNR = 31.69 dB, dibandingkan sebaliknya PSNR = 31.01 dB, Namun rata-rata keragaman informasi citra hasil dengan kombinasi IDBP-CNN diikuti TFM-CLAHE lebih kecil selisihnya terhadap citra asli berdasarkan Shanon Entropy sebesar 3.77% dibandingkan sebaliknya 4.75%
Analysis of Combination Algorithms for Denoising and Contrast Enhancement Images Irpan Adiputra Pardosi; Hernawati Gohzali
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol 8, No 2 (2022): June
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v8i2.22216

Abstract

Reducing noise and increasing image contrast is part of the purpose of enhancing image quality; instead, it will impact change the diversity of information in the image based on the Shannon entropy value. Decrease quality caused by noise salt and pepper in this research or abnormal contrast in the image causes objects in the image to become unclear. Low contrast has a major impact on image quality, including noise reduction processes affecting image information so that the quality of the reduced image becomes something to consider for large noise. Iterative Denoising and Backward Projections with CNN (IDBP-CNN) and Different Applied Median Filter (DAMF) is a good solution for denoising a large percentage of noise with good quality results image. In other research for contrast enhancement, Triangular Fuzzy Membership-Contrast Limited Adaptive Histogram Equalization (TFM-CLAHE) and Adaptive Fuzzy Contrast Enhancement Algorithm with Details Preserving (AFCEDP) is claimed to a good solution to solve low contrast of the image. Therefore, this study is to find the best combination of denoising and contrast enhancement to get good image results with step denoising followed by contrast enhancement. Based on the experimental testing is got the best combination is the DAMF + AFCEDP algorithm with an average of PSNR 35dB and an average difference Shannon entropy of 0.0130.
Implementation of Fuzzy K-Nearest Neighbor (K-NN) Algorithm to Identify Grape Plant Diseases Hernawati Gohzali; Syanti Irviantina
Bahasa Indonesia Vol 15 No 01 (2023): Instal : Jurnal Komputer
Publisher : Cattleya Darmaya Fortuna

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

One of the parts of the vine that can be attacked by disease is the leaf. There are 5 types of diseases that can attack grape leaves Black Rot, Leaf Blight (Pseudocercospora Vitis), Black Measles (Phaeomoniellaaleophilum, Phaeomoniella chlamydospora), Powdery Mildew (Ersyphe Necator Burr), and Downy Mildew (Plasmopara Viticola). The symptoms caused by each disease will show different colors and textures of spots on the leaves. For this reason,a system is needed toidentify the type of disease that attacks grape leaves so that appropriate control can be carried out.In this research, a system is built to identify grape leaf disease using a method that is able to recognize the texture on the leaves. The GLCM method is one of the methods for texture extraction in images. In the case of grape disease identification, the selection of color extraction methods is needed in order to achieve good results in the system. One method that is quite good at extracting color features is HSV because the colors in the HSV model are the same as the colors captured by human senses and are able to separate the intensity components of color images. After GLCM and HSV, then theclassification process using the FKNN method by combining Fuzzy and KNN Classifier techniques. The FKNN method has two advantages, where this algorithm is able to consider the ambiguous nature of neighbors, and provide strength in the intances that are in a class, so that the classification process can be done moreobjectively.The results of testing 2720 images as training data with 200 images as testing data show the accuracy value obtained is 92.5%.
IMAGE ENHANCEMENT ON OBJECT DETECTION USING L0 GRADIENT PRIOR Sunario Megawan; Hernawati Gohzali; Apriyanto Halim
Jurnal Riset Informatika Vol. 4 No. 1 (2021): December 2021
Publisher : Kresnamedia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34288/jri.v4i1.142

Abstract

Abstract Object detection is a technique used to retrieve certain parts of the image. The part can be in the form of scenery, people, or other objects. At the time of object detection, the image obtained can experience a decrease in image quality which can be caused by weather factors, namely fog, smoke, dust, rain, and others. A decrease in the quality of the image can result in errors in classification and the inability to recognize objects in the image. Therefore, the process of improving image quality becomes very important to do at the pre-processing stage in detecting image objects. The focus of the problem to be solved in this study is the return of a blurred image using L0 Gradient Prior. The results showed that the application of L0 Gradient Prior in restoring a blurred image can increase the number of objects that can be detected by the object detection system.
Co-Authors Apriyanto Halim Irpan Pardosi Sunario Megawan Syanti Irviantina