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All Journal Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer
Muh. Arif Rahman
Fakultas Ilmu Komputer, Universitas Brawijaya
Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Engineering

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Pengenalan Citra Jajanan Pasar Berdasarkan Ekstraksi Ciri Warna HSV Dan Ciri Tekstur Full Neighbor Local Binary Pattern Ferry Jiwandhono; Muh. Arif Rahman; Bayu Rahayudi
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 4 No 13 (2020): Publikasi Khusus Tahun 2020
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Artikel dipublikasikan di Jurnal Ilmu Komputer dan Informasi (JIKI) Universitas Indonesiahttps://jiki.cs.ui.ac.id/
Implementasi Metode Haar Wavelet Untuk Penentuan Mutu Pada Citra Digital Jeruk Keprok (Citrus Reticulata Blanco) Nur Faiqoh Laely Ambarwati; Agus Wahyu Widodo; Muh. Arif Rahman
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 4 No 10 (2020): Oktober 2020
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Tangerine (Keprok) is one of well known horticultural commodities products in Indonesia. Tangerine fruit quality is determined manually, so far. The tangerine's skin texture can be used as an indication of the tangerine image quality determination. This study implements the Haar Wavelet method to determine the quality of tangerines in standard and automatic ways. The processes carried out in this study are preprocessing, Haar Wavelet feature extraction, and classification of tangerine fruit images using the k-NN algorithm. Statistical calculations of mean, standard deviation, and skewness are used to represent the Haar Wavelet features. The result of the Haar Wavelet extraction process is an image of tangerine that has been classified into Grade Super, Grade A, or Grade B. The Haar Wavelet decomposition process in this study was carried out at level 1 and level 2. The test results showed that level 1 Haar Wavelet decomposition produces a higher accuracy than Level 2. The highest accuracy obtained is 90%.
Penerapan Ekstraksi Ciri Local Binary Pattern pada Pengenalan Karakter Kode Peti Kemas Aisha Laras; Muh. Arif Rahman; Agus Wahyu Widodo
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 4 No 13 (2020): Publikasi Khusus Tahun 2020
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Untuk dipublikasikan di Indonesian Journal of Computing and Cybernetics Systems (IJCCS)
Penerapan Metode Ekstraksi Fitur Tekstur Local Binary Pattern Histograms pada Proses Pengenalan Wajah untuk Sistem Presensi Otomatis Faiz Abiyandani; Muh. Arif Rahman; Bayu Rahayudi
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 4 No 13 (2020): Publikasi Khusus Tahun 2020
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Untuk dipublikasikan di Indonesian Journal of Computing and Cybernetics Systems (IJCCS)
Klasifikasi Amfibi di Asia Tenggara dengan Penjajaran Sekuen DNA Menggunakan Algoritme Genetika: Classification of Amphibian in Southeast Asia by DNA Sequence Alignment using Genetic Algorithm Yunita Kristanti Emilia; Agus Wahyu Widodo; Muh. Arif Rahman
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 5 No 2 (2021): Februari 2021
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Analysis of DNA sequences is the main thing in the process of classification of amphibians at the family level. One method in sequence analysis is sequence alignment but it requires a large amount of time and cost. The problem of adding gaps and how long gaps are also a problem in the sequence alignment. The amphibian classification process uses genetic algorithms for inserting gaps with several modifications at each stage so that the DNA structure does not change. This study uses two cut point crossover and reciprocal exchange mutation. Fitness scores are used for the classification process by calculating proportions and then convert to evolutionary distance values. The data used were 363 amphibian DNA with 16s rRNA genes divided into five families namely Bufonidae, Dicroglossidae, Megophyridae, Ranidae, Rhacophoridae. Testing the accuracy of the system using the parameters crossover rate = 0.7, mutation rate = 0.3, popsize = 700, number of generations = 900, and multiplier factor = 1.2. System accuracy testing for five families was done 10 times each and got an average accuracy of 94%. With an average accuracy of the system, the genetic algorithm can carry out the sequence alignment process for amphibian classification.
Pengenalan Citra Wajah menggunakan Ekstraksi Fitur Ruang Warna YCbCr dan Metode Principle Component Analysis (PCA) untuk Presensi Mahasiswa Otomatis Angga Wahyudi Kurniawan Pratama; Muh. Arif Rahman; Bayu Rahayudi
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 5 No 3 (2021): Maret 2021
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Face is a part of the human body that is very influential in everyday life. Most humans recognize a person by face. From the uniqueness of the face, there are innovations about automatic presence using faces that are more efficient than manual presence which is prone to manipulation. However, in reality, automatic presence based on facial recognition has a disadvantage of high light intensity, which makes facial images look biased. This problem can be overcome by approaching the YCbCr color space which is able to overcome the high light intensity in the image. The essence of automatic presence is the process of detecting and recognizing human facial images. In this study, only focus on facial recognition with the YCbCr color space feature extraction method and the Principle Component Analysis method. The results from the PCA value will be calculated using the Eucledian Distance to determine the closeness between the training image and the test image. In this test using two scenarios, the first test scenario uses a test image without light that is able to get an accuracy of 84%. The second test scenario uses a test image with a light beam capable of obtaining an accuracy of 52%.
Penerapan Ruang Warna HSV dan Ekstraksi Fitur Tekstur Local Binary Pattern untuk Tingkat Kematangan Sangrai Biji Kopi Muhammad Fahmi Wibawa; Muh. Arif Rahman; Agus Wahyu Widodo
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 5 No 7 (2021): Juli 2021
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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The popularity of coffee-based dishes is now being favored by many people, especially Indonesians. Not a few people also want to try to make coffee dishes (roasted coffee beans) with their own flavors but are constrained by such expensive equipment for cooking coffee beans with sophisticated systems purchased in the market. With so many color standards for the level of ripeness of coffee beans available, an innovation has emerged to determine the ripeness level of the coffee bean roast automatically, more efficiently, and as desired. However, in taking pictures there are constraints on excessive light intensity so that the resulting image looks biased. From these problems, an approach can be made using the Hue and Saturation color features which are able to detect and reduce excess light intensity from the outside and become the main focus of this research. The features used are the Hue and Saturation color spaces using the values ​​from the normalization results of the Red, Green, and Blue (RGB) color features, and using 500 coffee roast images. This study uses two methods, the first uses RGB values ​​without normalizing them by using the features Hue and Saturation which are able to get 76% accuracy with 3 classes of data matching results (Green Roast, Yellow Roast, and Dark Roast), and the second feature Hue and Saturation normalization are able to get an accuracy of 70% with data matching results of 4 classes (Green Roast, Yellow Roast, Light Roast, and Dark Roast).
Perbandingan Ruang Warna RGB dan HSV dalam Klasifikasi Kematangan Biji Kopi Haidar Azmi Rabbani; Muh. Arif Rahman; Bayu Rahayudi
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 5 No 6 (2021): Juni 2021
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Coffee is one of the most popular drinks in the world, and is favored by many group, including in Indonesia. The taste image of coffee has the characteristics of each type of coffee. One thing that influences the image of the taste of coffee is the maturity of the coffee beans. For this reason, an introduction to the level of maturity of coffee beans is carried out by utilizing the color space of the coffee bean image. The research conducted utilizes a comparison of the extraction of RGB and HSV color spaces as the first test and the addition of LBP texture extraction in the second test. Based on tests carried out on 500 images of coffee beans, the best accuracy was 99.2% in the first test and 98.2% in the second test. Testing using a backpropagation neural network.
Identifikasi Fertilitas Telur Ayam Kampung Menggunakan Metode Learning Vector Quantization (LVQ) Dan Ekstraksi GLCM Feris Adi Kurnia Sadiva; Muh. Arif Rahman; Agus Wahyu Widodo
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 5 No 13 (2021): Publikasi Khusus Tahun 2021
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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The high consumption of native chickens by people in Indonesia indirectly demands the field of native chicken farming so that the need for the amount of meat can be met. In the initial stage, namely breeding, breeders need to produce chicken eggs to be used as seeds, where the success of hatching eggs can be identified by monitoring at the age of 8 days of the incubation period. These eggs can be identified earlier at the age of 5 days of incubation but require good eye ability because the chicken embryos that are formed are still small. A fertility classification system is needed so that eggs can be identified whether they are capable of hatching or not. The system in this study uses the Gray Level Co-occurrence Matrix (GLCM) method for the feature extraction process from an egg image. The extraction results are reprocessed using the Learning Vector Quantization (LVQ) method so that the results of the classification of the egg image classification are fertile or infertile. The data used are images of eggs that have gone through an incubation period of 5 days and are photographed with the aid of a flashlight. The results of this study, the GLCM method was able to extract features from the image of native chicken eggs, either fertile or infertile, with a significant value. Whereas for the LVQ method, the best test results are using learning rate of 0.05 and for GLCM using 5 features with 0 degree for angle orientation, with an accuracy value of 88.13%.
Segmentasi Citra pada Kue Tradisional berbasis Clustering dengan menggunakan Algoritme DBSCAN Fatwa Reza Rizqika; Yuita Arum Sari; Muh. Arif Rahman
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 5 No 12 (2021): Desember 2021
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Traditional cakes or generally referred to as market snacks are one of the many traditional specialties originating from Indonesia and are usually traded in traditional markets. The cakes that are traded are of various types and have a distinctive taste and are not inferior to modern food. The traditional cake is usually packaged in an attractive and unique form, the wrapper can come from leaves that have fallen or are still alive to be wrapped in plastic in order to attract consumers or buyers. Even though in today's era, there are not a few instant and practical food products, even some imported products from abroad whose packaging is more attractive. And this traditional cake is one of many cultural heritages that should be more commensurate with other Indonesian cultural assets. Therefore, as the color of Indonesia, we should maintain and preserve and further introduce to all levels of society that traditional cakes are no less delicious than modern food, especially children today who are foreign to these traditional cakes. Therefore, a system is needed to identify traditional foods, especially traditional cakes, by utilizing the sophistication of technology that exists in the current digital era. This study proposes the application of image segmentation on traditional cakes using the DBSCAN algorithm to obtain cake image segmentation results with an average Intersection over Union (IoU) accuracy of 91.3% and a maximum value of 99.8%. This shows that the proposed method is able to provide the best results.
Co-Authors Achmad Dewanto Aji Wibisono Agus Wahyu Widodo Aisha Laras Angelika Trivena Lodong Angga Wahyudi Kurniawan Pratama Anggita Nurfadilla Mahardika Anne Diane Rachmadani Annisa Amalia Nur'aini Arya Agung Andika Ayuda Dhira Pramadhari Bayu Rahayudi David Bernhard Diantarakita Diantarakita Dwi Retnoningrum Faiz Abiyandani Fatwa Reza Rizqika Femilia Nopianti Feris Adi Kurnia Sadiva Ferry Jiwandhono Grace Theresia Situmorang Hafiz Ari Putra Haidar Azmi Rabbani Laviana Agata Muhamad Wahyu Budi Santoso Muhammad Fahmi Wibawa Muhammad Tanzil Furqon Muhammad Tegar Kanugroho Natassa Anastasya Nur Faiqoh Laely Ambarwati Putri Bunga Rahmalita Putri Harnis Putu Satya Cahyani Randy Cahya Wihandika Ruri Armandhani Witriana Sumarni Yosafat Vincent Saragih Yuita Arum Sari Yunita Kristanti Emilia