Article Per Year (5 Year)
p-Index From 2021 - 2026
1.456
P-Index
This Author published in this journals
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

Published : 33 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 33 Documents
Search

 1   2   3   4 
Ekstraksi Ciri pada Klasifikasi Tipe Kulit Wajah Menggunakan Metode Local Binary Pattern Diantarakita Diantarakita; Agus Wahyu Widodo; Muh. Arif Rahman
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 3 No 8 (2019): Agustus 2019
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (677.2 KB)

Abstract

Various objects in the form of digital images can be extracted the features. One of the features that can be used in feature extraction is statistical texture features. In this case, the feature extraction is used to identify the characteristics of each type of facial skin because many cases of mistakenly recognize the type of facial skin that has resulted in occurrence of diseases and unwanted things on the face. In this study, the author uses facial skin especially the cheek part as the object of research because the cheek is one part of the T-zone. Local Binary Pattern (LBP) is one of the feature extraction method that uses adjacency/neighboring distance and the number of neighbors that can be used and utilized in the identification process, which can be combined with statistical texture features. The benefit of this study itself is to assist in the initial diagnosis in determining the type of facial skin that is owned. This study uses data as many as 112 female face images obtained by taking data directly in the field (primary data). This study got the highest accuracy result that was equal to 84.62% with adjacency/neighboring distance (R) = 1 and a combination of 3 statistical texture features, that is a combination of mean, skewness and energy.
Pengelompokan Musik berdasarkan Emosi menggunakan Metode Transformasi Haar Wavelet Natassa Anastasya; Agus Wahyu Widodo; Muh. Arif Rahman
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 3 No 11 (2019): November 2019
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (291.564 KB)

Abstract

Music consists of various genres and emotions, therefore it is important to group the music according to what the listener wants both based on the genre and the emotions they feel. As an example of an online music application that can automatically group music that makes it easy for listeners to play the desired music, music grouping is seen based on the similarity of certain characteristics. Another example is the Radio music player, where a common problem with radio broadcasters is to play and choose what songs to play. With the grouping of music automatically this will certainly be very helpful and more efficient for playing these songs automatically. Then this research will cluster songs based on the emotions of Broken Heart and Happy with feature extraction using the Haar Wavelet transformation method, then clustering using K-Means. The results of clustering will be evaluated using purity. The test is based on the song structure, a combination of statistical features, and the Haar Wavelet coefficient. Based on the results of all tests carried out obtained clustering with the highest purity value of 0.62.
Diagnosis Penyakit Mata menggunakan Metode Improved K-Nearest Neighbor Anggita Nurfadilla Mahardika; Agus Wahyu Widodo; Muh. Arif Rahman
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 3 No 11 (2019): November 2019
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (403.29 KB)

Abstract

The eye has a very important for the human body which is as the sense of sight. Humans can do a variety of activities based on visual information received through the eyes. A healthy eye is very supportive to various activities and make out activities without obstacles. So the importance of the part of the eye, health of eye needs to be right considered and cared because the eyes do not escape the threat of disease that can disturb with vision. But, in the fact the people is still underestimate and consider the problem of eye disease is not dangerous. Lack of public awareness about eye diseases can worsen eye conditions if that cannot be handled and resolved to quickly. Beside, the factors that make some people still apathetic to eye diseases, they do not know if they suffer from eye disease and ignore the symptoms that are felt. Public ignorance of the symptoms that arise due to eye disease because people are still reluctant to check eye health to health services, because the cost of the examination, especially for the cost of specialist doctors that are considered quite high. Therefore, in this problem the authors then build an early diagnosis of eye diseases to facilitate the public in recognizing visual disturbances or eye diseases based on symptoms that are felt. In construction this system, the writer uses the Improved K-Nearest Neighbor methods. The improved K-Nearest Neighbor method has been proven to get a good results. The highest accuracy from system using lmproved K-Nearest Neighbor method by 88% in the process of diagnosis of eye disease.
Ekstraksi Ciri untuk Klasifikasi Jenis Kelamin berbasis Citra Wajah menggunakan Metode Compass Local Binary Patterns Muhamad Wahyu Budi Santoso; Randy Cahya Wihandika; Muh. Arif Rahman
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 3 No 11 (2019): November 2019
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (581.112 KB)

Abstract

Humans can quickly and make accurate predictions from visual images. Among facial tasks, gender classification is one that has an important role and is probably the easiest and fastest way to achieve. This time, the use of computers continues to grow so that a system similar to human capabilities is built, namely the gender recognition based on facial images. Some applications that require a gender recognition system such as, application of human-computer interaction interface (adjusting software behavior concerning the gender of the user), and demographic data collection to determine trends and product recommendations in the store based on gender. The use of accessories on facial images such as glasses, earrings, and hats that can make a person's gender difficult to recognize is a challenge in doing gender classification based facial image by the system. Compass Local Binary Patterns (CoLBP) as one of the image processing methods used in feature extraction for gender classification based face images. CoLBP utilizes the Kirsch Compass Mask to improve the performance of Local Binary Patterns (LBP) in the feature extraction process. In this research using the Color FERET dataset containing photos of faces (with accessories and without accessories) and the Random Forest classification method for the evaluation process. In the test results, the best accuracy average is 91.8%. From this research, can be concluded that the CoLBP method provides good feature extraction performance and accessories on the face give an influence on the reducing quality of the feature extraction by the CoLBP method.
Prediksi Jumlah Hablur Gula menggunakan Metode Backpropagation (Studi Kasus : Pabrik Gula Pesantren Baru) Anne Diane Rachmadani; 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

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

Abstract

Untuk dipublikasikan di INTENSIF: Jurnal Ilmiah Penelitian dan Penerapan Teknologi Sistem Informasi
Prediksi Jumlah Pengunjung Wisata di Kota Kediri menggunakan Metode Backpropagation Ayuda Dhira Pramadhari; 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

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

Abstract

Untuk dipublikasikan di INTENSIF: Jurnal Ilmiah Penelitian dan Penerapan Teknologi Sistem Informasi
Deteksi Rambu Lalu Lintas menggunakan Algoritma Moore Neighbour Contour Following dan Simplifikasi Poligon dalam HSV Color Space Achmad Dewanto Aji Wibisono; 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

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

Abstract

Traffic Signs Recognition System (SPRLL) is needed to map and repair road signs, driver assistance systems, and autonomous cars. As one of the important parts of SPRLL, the introduction of traffic signs has some difficulties in dealing with real traffic conditions due to changes in illumination, partial occlusion, too much noise and a small sign size compared to other objects. The program flow from the detection system usually uses known features, extracts from the region that is promoted by the program, and filters negative regions. Derived from the above requirements, we need a system that can be used to detect traffic signs that exist in an image. This traffic sign detection system is applied by the writer to the German Traffic Sign Detection Benchmark GTSDB dataset. Some images taken in poor conditions such as foggy can reduce the accuracy of the detection system. In evaluating the system, an evaluation method is used to determine the accuracy and accuracy of the system. A value of 0.75 is obtained for accuracy which states that the system is accurate enough to detect traffic signs on the dataset.
Pengenalan Wajah Menggunakan Ruang Warna HSV Dengan Ekstraksi Fitur LBP Untuk Presensi Kehadiran Mahasiswa Laviana Agata; 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

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

Abstract

Artikrel dipublikasikan di JITeCS
Ekstraksi Fitur Momen Warna HSV Dan Fitur Ciri Tekstur Gray Level Co-Occurrence Matrix Untuk Pengenalan Jenis Jajanan Pasar Arya Agung Andika; 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

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

Abstract

Artikel dipublikasikan di JITeCS
Pengenalan Karakter Kode Peti Kemas dengan Memanfaatkan Haar Wavelet sebagai Metode Ekstraksi Ciri Putu Satya Cahyani; 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

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

Abstract

Untuk dipublikasikan di Knowledge Engineering and Data Science (KEDS)
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