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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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Ekstraksi Ciri Tekstur Local Ternary Pattern dan Klasifikasi Naive Bayes untuk Deteksi Penggunaan Masker Wajah Hafiz Ari Putra; Randy Cahya Wihandika; Muh. Arif Rahman
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 6 No 8 (2022): Agustus 2022
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Corona Virus Disease is a new outbreak that can transmit infection through close contact or water droplets. Corona virus attacks the human respiratory system so that it can cause illness with symptoms of fever, cough and shortness of breath that can cause death. The use of a mask that covers the nose and mouth can prevent transmission. As a form of prevention, people are starting to be forced by regulations to always use masks in public places and when interacting with other people. However, it will be difficult for the authorities to monitor large groups of people. These problems can be solved with a system to detect masks. Mask detection in this study uses the naive bayes classification to distinguish a face with a mask correctly or incorrectly and also without a mask. The information used for classification is obtained through the histogram of facial image texture feature extraction using Local Ternary Pattern. The extracted image is preprocessed which includes resizing the image width and image grayscaling. The data used are 3,900 face images. Tests were carried out on the size of the image width, the threshold value, the number of bins, and the split of training and testing data. The results of the naive bayes classification produce an optimal accuracy of 68.462% with an image width of 50, a threshold value of 4, the number of bins 32, the distribution of training and testing data are 70%: 30%. Tests with 2 classes, namely correctly masked faces and unmasked faces, obtained an accuracy value of 86.15%. Based on these results, it is known that the naive bayes classification cannot properly classify images in the masked class incorrectly.
Klasifikasi Batik dengan Ekstraksi Fitur Tekstur Local Binary Pattern dan Metode K-Nearest Neighbor Muhammad Tegar Kanugroho; Muh. Arif Rahman; Randy Cahya Wihandika
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 6 No 10 (2022): Oktober 2022
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Indonesia is a country that consists of many islands and has a diversity of tribes that are scattered throughout its archipelago. Diverse ethnicities, various characteristics are also owned in order to distinguish one tribe from another. One of the distinguishing characteristics is batik, which is widely known and has become a cultural heritage. When viewed from the picture, the batik pattern has a texture. In digital image processing, texture can be used as an element that differentiates batik from one another, one of which is the Local Binary Pattern (LBP) method. By using the Local Binary Pattern (LBP) method, the texture of batik will be recognized as a feature of digital image processing, the batik image can be processed to obtain several similar images. The research process on batik begins with pre-processing, then extraction of texture features in the image using the Local Binary Pattern (LBP) method and continues with classification by K-Nearest Neighbor (KNN). In this study was using the normalized LBP value. At normalized values, the best results are using K-Nearest Neighbor with neighbors (K) = 5 by getting an accuracy of 65%
Penentuan Mutu pada Citra Buah Jeruk Keprok menggunakan Metode Local Binary Pattern (LBP) Angelika Trivena Lodong; Agus Wahyu Widodo; Muh. Arif Rahman
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 7 No 4 (2023): April 2023
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Pertumbuhan jumlah produksi jeruk di Indonesia terus meningkat setiap tahunnya. Data dari Kementerian Pertanian pada tahun 2014 menyatakan bahwa dari semua jenis jeruk yang ada di Indonesia, jeruk keprok memiliki hasil produksi yang paling banyak yaitu sekitar 92% dari total hasil produksi buah jeruk seluruhnya. Mutu dari hasil produksi buah jeruk keprok menjadi hal yang sangat penting, terutama dalam persaingan pasar. Pemanfaatan teknologi visual dapat digunakan dalam penentuan mutu jeruk keprok dan dapat menggantikan proses penentuan mutu secara manual oleh manusia, agar sesuai dengan standarisasi mutu buah jeruk keprok. Penelitian ini memanfaatkan hasil dari ekstraksi fitur Local Binary Pattern (LBP) citra jeruk keprok untuk penentuan mutu. Langkah awal dari penelitian ini yaitu mengambilan data citra jeruk keprok. Pada citra jeruk keprok, dilakukan pemotongan citra untuk mendapatkan setiap area yang akan diklasifikasikan menjadi kelas baik atau buruk, selanjutnya dilakukan proses pre-processing yang didalamnya terdapat proses mengubah citra berwarna menjadi citra grayscale. Kemudian dilakukan proses ekstraksi fitur Local Binary Pattern (LBP). Hasil ekstraksi fitur dari potongan citra tersebut akan diklasifikasikan menjadi kelas baik atau buruk. Setelah semua potongan citra telah diklasifikasikan, maka akan didapatkan jumlah potongan yang baik dan buruk dalam sebuah citra, sehingga dapat ditentukan Grade dari buah jeruk keprok. Mutu jeruk keprok dibagi menjadi 3 kelas yaitu, Grade Super, Grade A dan Grade B. Pada penelitian ini diperoleh hasil akurasi terbaik yaitu sebesar 80%, dengan ukuran dimensi citra sebesar 100x100 piksel dan jarak ketetanggaan atau nilai R=1.
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