Article Per Year (5 Year)
p-Index From 2021 - 2026
6.421
P-Index
This Author published in this journals
All Journal Indonesian Journal of Electronics and Instrumentation Systems ELKHA : Jurnal Teknik Elektro JUITA : Jurnal Informatika Annual Research Seminar Sinkron : Jurnal dan Penelitian Teknik Informatika Jusikom : Jurnal Sistem Komputer Musirawas SELAPARANG: Jurnal Pengabdian Masyarakat Berkemajuan JURNAL TEKNIK INFORMATIKA DAN SISTEM INFORMASI Petir KOMPUTIKA - Jurnal Sistem Komputer Jurnal Teknologi Terpadu Jutisi: Jurnal Ilmiah Teknik Informatika dan Sistem Informasi Progresif: Jurnal Ilmiah Komputer Jurnal Ilmiah Betrik : Besemah Teknologi Informasi dan Komputer Algoritme Jurnal Mahasiswa Teknik Informatika Informatics and Enginering Dedication MDP Student Conference Jurnal Ilmiah Betrik : Besemah Teknologi Informasi dan Komputer INOVTEK Polbeng - Seri Informatika JuTISI (Jurnal Teknik Informatika dan Sistem Informasi)
Yohannes, Yohannes
Universitas Multi Data Palembang
Agriculture, Biological Sciences & Forestry Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Medicine & Pharmacology Nursing Public Health Social Sciences Other

Published : 54 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 54 Documents
Search

 1   2   3   4   5 
Verifikasi Kinship Dengan Arsitektur ResNet50 Beni Anthony; Yohannes Yohannes
MDP Student Conference Vol 2 No 1 (2023): The 2nd MDP Student Conference 2023
Publisher : Universitas Multi Data Palembang

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1041.988 KB) | DOI: 10.35957/mdp-sc.v2i1.4320

Abstract

Kinship adalah sistem kekerabatan antara dua orang atau lebih yang menunjukkan hubungan antara kedua orang tersebut dalam silsilah keluarga. Kinship secara biologi yang berkaitan dengan hubungan darah sehingga, fitur wajah yang dimiliki oleh seorang anak akan mengikuti fitur wajah dari orangtuanya. Biasanya kinship dapat dideteksi dengan menggunakan tes DNA dengan akurasi yang sangat tinggi. Namun tes DNA ini memerlukan harga yang mahal dan juga memerlukan waktu yang lama untuk mendapatkan hasil dari tes tersebut. Penelitian ini menggunakan dataset Recognize Faces in Wild berjumlah 20080 data yang dibagi menjadi data train sebesar 80% dan data validation sebesar 20% dengan dimensi 224x224 pixel. Objek atau dataset untuk testing berjumlah 6282 data. Pengujian dilakukan dengan menggunakan ResNet50 dengan optimizer beragam sebagai skenario pengujian. Hasil akurasi pengujian terbesar didapat pada skenario pertama yaitu 93% dengan optimizer Adam, diikuti RMSprop dengan akurasi 88% dan SGD yang memiliki akurasi terkecil sebesar 65%.
Penerapan Algoritma $Q Super Quick Recognizer Untuk Pengenalan Angka Muhammad Yudha Setiawan; Yohannes Yohannes; Yoannita Yoannita
MDP Student Conference Vol 2 No 1 (2023): The 2nd MDP Student Conference 2023
Publisher : Universitas Multi Data Palembang

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (338.398 KB) | DOI: 10.35957/mdp-sc.v2i1.4370

Abstract

Materi pembelajaran pada umumnya dituliskan pada papan tulis di depan kelas sebagai media pembelajaran, namun dengan menggunakan pen tablet sebagai alat bantu dalam pembelajaran layaknya menggunakan pena dan kertas dapat dilakukan lebih mudah dan menarik. Bagaimana tulisan angka anak-anak itu bisa dikenali yaitu menggunakan algoritma pengenalan gestur untuk mendeteksi apakah benar angka yang ditulis oleh anak-anak itu benar. Algoritma $Q Super Quick Recognizer dapat mengenal gerakan 2D yang dirancang untuk pembuatan prototipe cepat antarmuka pengguna berbasis Gerakan. Pada penelitian ini menggunakan algoritma $Q Super Quick Recognizer pada game pengenalan angka. Dengan tingkat rata-rata akurasi sebesar 99.50% , precision sebesar 97.56% dan Recall 95.50%.
KLASIFIKASI MAMALIA MENGGUNAKAN EXTREME GRADIENT BOOSTING BERDASARKAN FITUR HISTOGRAM OF ORIENTED GRADIENT Yohannes; Johannes Petrus
JURNAL ILMIAH BETRIK Vol. 13 No. 03 DESEMBER (2022): JURNAL ILMIAH BETRIK : Besemah Teknologi Informasi dan Komputer
Publisher : P3M Institut Teknologi Pagar Alam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36050/betrik.v13i03 DESEMBER.44

Abstract

Mammals are one type of animal that has many characteristics and characteristics. The shape of the face in each type of mammal has a similar shape. The faces of mammals in the form of frontal images are a challenge in image classification. In this study, the Histogram of Oriented Gradient (HOG) is used as a feature of the facial shape of mammals. HOG is used as a strengthening feature in the classification process using the eXtreme Gradient Boosting (XGBoost) method. The test was carried out using a dataset of frontal facial imagery of mammals consisting of 15 species. The results of the tests show that the XGBoost method with the HOG feature is able to provide better classification results for mammals than without the HOG feature. This is indicated by an increase in the precision value of 0.61; recall of 0.62; and an f1-score of 0.60 on XGBoost with HOG feature which is almost double that of XGBoost without HOG feature.
Pemanfaatan Microsoft Office dan Prezi untuk Membuat Laporan dan Presentasi di Brimob Polda Sumatera Selatan Meiriyama, Meiriyama; Yohannes, Yohannes; Irsyad, Hafiz; Farisi, Ahmad; Devella, Siska; Al Rivan, Muhammad Ezar; Rachmat, Nur
FORDICATE Vol 3 No 1 (2023): November 2023
Publisher : Universitas Multi Data Palembang, Fakultas Ilmu Komputer dan Rekayasa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35957/fordicate.v3i1.6449

Abstract

Perkembangan ilmu pengetahuan dan teknologi suatu bangsa tergantung pada keberhasilan proses belajar mengajar di lembaga pendidikan. Penguasaan ilmu dan teknologi merupakan indikator pembangunan menuju kemajuan bangsa. Microsoft Office, termasuk Excel, Word, dan PowerPoint, adalah perangkat lunak aplikasi perkantoran yang dirancang untuk meningkatkan efisiensi kerja. Microsoft Word memfasilitasi pembuatan dokumen kantor, menghemat waktu, dan mengurangi penggunaan kertas. Microsoft Excel mempermudah pengolahan data numerik dengan fitur formula dan diagram. Microsoft PowerPoint mendukung pembuatan presentasi menarik dengan fitur sisipan teks, grafik, dan animasi. Selain itu, Prezi, alat presentasi berbasis internet, memungkinkan eksplorasi ide dengan konsep Zooming User Interface. Pelatihan ini ditujukan untuk staff dan anggota Brimob Polda Sumatera Selatan agar memiliki keterampilan dalam membuat dokumen, laporan, dan presentasi menggunakan Microsoft Office dan Prezi, sehingga dapat meningkatkan produktivitas dan kualitas pekerjaan.
Klasifikasi Motif Songket Palembang menggunakan Support Vector Machine berdasarkan Histogram of Oriented Gradients Yohannes, Yohannes; Al Rivan, Muhammad Ezar; Devella, Siska; Meiriyama, Meiriyama
Jurnal Teknologi Terpadu Vol 9 No 2 (2023): Desember, 2023
Publisher : LPPM STT Terpadu Nurul Fikri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54914/jtt.v9i2.1032

Abstract

Songket Palembang is one of the intangible cultural heritages with the domain of traditional craftsmanship and crafts. Songket Palembang has several motifs, including Chinese Flowers, Cantik Manis, and Pulir. Preservation efforts are carried out by providing an understanding of Palembang Songket patterns. This study classified Palembang Songket patterns based on shape features using the Histogram of Oriented Gradient (HOG) method. Based on the test results of 45 test data images, the HOG method can become a feature in the image classification of Palembang Songket patterns, namely Chinese Flowers, Cantik Manis, and Pulir. The Support Vector Machine (SVM) method is a classification method that can recognize Palembang Songket patterns with RBF, Linear, and Polynomial kernels. The results showed that the RBF kernel was the best kernel that produced an average accuracy value of 88.1%, a precision of 84.1%, a recall of 82.2%, and an f1-score of 82.6%, and the three Palembang Songket patterns tested, it was found that the Palembang Songket patterns that were easiest to classify well were the Cantik Manis patterns for all types of SVM kernels.
Ekstraksi Fitur Warna dengan Histogram HSV untuk Klasifikasi Motif Songket Palembang Yohannes, Yohannes; Al Rivan, Muhammad Ezar; Devella, Siska; Meiriyama, Meiriyama
JATISI Vol 11 No 2 (2024): JATISI (Jurnal Teknik Informatika dan Sistem Informasi)
Publisher : Universitas Multi Data Palembang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35957/jatisi.v11i2.8110

Abstract

Palembang Songket is a type of traditional woven cloth that has been registered as Indonesia's intangible cultural heritage since 2013. Palembang Songket has many motifs including Bunga Cina, Cantik Manis, and Pulir. The motifs on Palembang Songket have different meanings which can influence the selling price of the Songket. Recognition and classification of Palembang Songket types and motifs can be done by utilizing computer technology such as digital image processing and machine learning. In this research, the classification of Palembang Songket motifs was carried out using color features with histograms in Hue, Saturation, and Value (HSV) space and the Support Vector Machine (SVM) machine learning algorithm. Testing was carried out on a classification system using 45 test images. The histogram of HSV and SVM methods with the best kernel, namely RBF, were able to classify Palembang Songket motifs with an accuracy of 0.956; precision of 0.94; recall of 0.933; and f1-score of 0.931.
Pengenalan Motif Songket Palembang Menggunakan Convolutional Neural Network dengan Arsitektur ResNet-50 Cahyati, Imelia Dwinora; Devella, Siska; Yohannes, Yohannes
Algoritme Jurnal Mahasiswa Teknik Informatika Vol 5 No 1 (2024): Oktober 2024 || Algoritme Jurnal Mahasiswa Teknik Informatika
Publisher : Program Studi Teknik Informatika Universitas Multi Data Palembang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35957/algoritme.v5i1.9404

Abstract

Songket fabric is a cultural heritage of Indonesia woven with gold or silver threads, creating textiles that are not only visually captivating but also rich in cultural significance. Each motif on Palembang Songket reflects the traditions and beliefs of the community, where the selection of motifs is often tailored to specific event contexts. However, the recognition of several motifs with similar patterns presents unique challenges in the identification process. This study aims to implement a Convolutional Neural Network (CNN) method for classifying Palembang Songket motifs. The dataset used consists of images of Songket motifs, including Bintang Berantai, Naga Besaung, Nampan Perak, and Pulir. The ResNet-50 architecture is utilized as the classification model. The results indicate that the implemented model achieves an accuracy of 96% in recognizing these motifs, thereby contributing to the preservation and enhancement of understanding regarding the cultural richness of Palembang Songket.
Penerapan Model U-Net untuk Segmentasi Gigi pada Citra Radiografi Panoramik Orang Dewasa Sonia, Sonia; Yohannes, Yohannes
Algoritme Jurnal Mahasiswa Teknik Informatika Vol 5 No 2 (2025): April 2025 || Algoritme Jurnal Mahasiswa Teknik Informatika
Publisher : Program Studi Teknik Informatika Universitas Multi Data Palembang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35957/algoritme.v5i2.10965

Abstract

Dental and oral health play a crucial role in maintaining overall bodily health. Panoramic radiography serves as a primary diagnostic tool for analyzing dental and oral conditions; however, the complexity of its images often complicates manual analysis. This study aims to implement a Convolutional Neural Network (CNN) architecture for segmenting panoramic radiographic images, utilizing U-Net as the chosen model. The dataset used consists of panoramic radiographic images. The test results indicate that the implemented model achieved an IoU score of 0.8335 and a dice coefficient of 0.9092, demonstrating strong segmentation capability. These findings suggest that the proposed method can serve as a supportive tool for diagnosis and treatment planning in dental and oral healthcare.
Deteksi Teks Secara Otomatis Pada Natural Image Berbasis Superpixel Menggunakan Maximally Stable Extremal Regions dan Stroke Width Transform Yohannes Yohannes
Jurnal Teknik Informatika dan Sistem Informasi Vol 3 No 2 (2017): JuTISI
Publisher : Maranatha University Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28932/jutisi.v3i2.676

Abstract

Text detection in natural image is something to do before performing character recognition. The process of text detection plays an important role in the acquisition of information in an image. This research aims to detect text automatically in natural image based on superpixels with Maximally Stable Extremal Regions (MSER) and Stroke Width Transform (SWT). The superpixel method used is Simple Linear Iterative Clustering (SLIC). The SLIC method is used for segmenting text images into superpixel spaces. Image segmentation to superpixel aims to group pixels into homogeneous regions that capture redundant images. SLIC is a technique that effectively divides images into homogeneous regions (superpixels). Furthermore MSER is used as a feature to locate the text candidate region in a segmented image with superpixel. Then edge detection is done to validate the text area that has been found. Next, the SWT method is used to distinguish both text and non-text image regions. The dataset used is ICDAR 2003. Based on test result, MSER with superpixel is able to detect region of text in natural image. SWT is also able to recover the region which is the candidate of the text in natural image.
Perbandingan Performa Algoritma Minimax dan Breadth First Search Pada Permainan Tic-Tac-Toe Jerry Setiawan; Farhan Agung Famerdi; Daniel Udjulawa; Yohannes Yohannes
Jurnal Teknik Informatika dan Sistem Informasi Vol 4 No 1 (2018): JuTISI
Publisher : Maranatha University Press

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

Abstract

Tic-Tac-Toe is one of the board games that can hone the motor skills of the brain. This game uses 2 pawns, there are X and O. The game started with X’s pawn as the player who first turns, the game got win condition if the player or the enemy put the 3 pawns in a diagonal, vertical or horizontal line. While the game got draw if there is no player or enemy who put 3 pawns in a diagonal, vertical or horizontal line. The game’s problems are the player should think about the next best step to win and defend with put pawn to block enemy’s steps to win. To solve the problems, the game needs some algorithms, there are Minimax algorithm and Breadth First Search algorithm. Minimax algorithm explores node from deepest level and evaluates the scores using minimum or maximum value. Breadth First Search algorithm is an algorithm which explores node widely and compares evaluation scores to the deepest level. In this research, each algorithm is tested to response time and number of nodes needed on a game board with 3×3, 5×5, 7×7, and 9×9 size as much as 16 scenarios. Based on the test results, Breadth First Search algorithm is superior to Minimax on 3×3 board size in terms of response time and the number of nodes required. While the Minimax algorithm is superior to Breadth-First Search on 5×5 and 9×9 board size in terms of response time and the number of nodes required. In the first turn, the algorithm will trace the number of nodes larger than the next step so that the placement of the algorithm for the first turn affects the final result of the node number parameter.
Co-Authors Ade Hendri Pandrean Adhytio Mahendra Andreas, Kevin Azarya, Philips Denny Beni Anthony Bobby Jaya Saputra Cahyati, Imelia Dwinora Calvin Oliver Saputra Celvine Adi Putra Cendy Prakarsah Dafid Dafid Dandy, Dandy Daniel Udjulawa Daniel Udjulawa Devella, Siska Dody, Muhammad Famerdi, Farhan Agung Farhan Agung Famerdi Farisi, Ahmad Febbiola Febbiola Feristyani, Indah Firda Novia Rahmawati Gerry Jeven Timoti Glen, Billy Hafiz Irsyad Hartati, Ery Indah Feristyani Jericho Jericho Jerry Setiawan Jimmy Aprilyanto Johannes Petrus Julian Rusli Tee Baldi Juliana Nasution Kelvin Arianto Laksana, Jovansa Putra Leo Chandra Leonardo Leonardo M Ezar Al Rivan Marcella, Dewi Meiriyama Meiriyama, Meiriyama Meiriyama, Meiriyama Molavi Arman Muhammad Farid Athar Muhammad Rizky Pribadi Muhammad Yudha Setiawan Muhdhor, Umar Novan Wijaya Nur Rachmat Pandi Pandi Pandi Pandi, Pandi Pandrean, Ade Hendri Philips Denny Azarya Prabowo, Adrianus Prasthio, Rial Putra, Lipi Amanda Ricky Wijaya RR. Ella Evrita Hestiandari Sahpira, Mulia Saputra, Dika Sari, Yulya Puspita Selvie, Selvie Setiawan, Jerry Sonia Sonia, Sonia Tanuwijaya, William Timoteus Ivan Sariyo Wijang Widhiarso William Hadisaputra Yoannita Yoannita Yoannita Yoannita, Yoannita Yulya Puspita Sari