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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

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Klasifikasi Wajah Hewan Mamalia Tampak Depan Menggunakan k-Nearest Neighbor Dengan Ekstraksi Fitur HOG Yohannes Yohannes; Yulya Puspita Sari; Indah Feristyani
Jurnal Teknik Informatika dan Sistem Informasi Vol 5 No 1 (2019): JuTISI
Publisher : Maranatha University Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28932/jutisi.v5i1.1584

Abstract

Mammal is a type of animal that has many diverse characteristics, such as vertebrates and breastfeeding. In this study, the HOG feature and the k-NN method were proposed to classify 15 species of mammals. This study uses the LHI-Animal-Faces dataset which has fifteen species of mammals, where each type of mammal has 50 images measuring 100x100 pixels. The image will be conducted the process by the HOG feature extraction process and continued into the classification process using k-Nearest Neighbor. The performance of the HOG and k-NN features that get the best value is in deer and monkey, the best results for precision, recall, and accuracy are at k=3 where HOG feature extraction provides good vector features to be used in the classification process using the k-NN method.
Penerapan Speeded-Up Robust Feature pada Random Forest Untuk Klasifikasi Motif Songket Palembang Yohannes Yohannes; Siska Devella; Ade Hendri Pandrean
Jurnal Teknik Informatika dan Sistem Informasi Vol 5 No 3 (2019): JuTISI
Publisher : Maranatha University Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28932/jutisi.v5i3.1978

Abstract

Songket is a historical heritage in the city of Palembang. Where Songket has many different types and motifs. Besides having historical value, Palembang's original Songket has high quality and complexity in the manufacturing process. As known Palembang Songket has a lot of motives, one of the ways to recognize Palembang Songket is through its motives, so that research was conducted for the classification of Palembang Songket motifs. The method used to extract features is the Speeded-Up Robust Feature (SURF), while the classification method is Random Forest. The process of forming the SURF feature is divided into two stages, the first stage is Interest Point Detection, which consists of Integral Images, Hessian Matrix Based Interest Points, Scale Space Representation and Interest Point Localization, the second stage of Interest Point Description consists of Orientation Assignment and Descriptor Based on Sum Haar Wavelet Responses. The resulting feature is used for the Random Forest classification. This study used 345 images of Palembang Songket motifs, among others, Bunga Cina, Cantik Manis and Pulir. The images taken are based on 5 colors from each Palembang Songket motif. For the separation of data there are 300 images used as data train and 45 images for testing data. From the tests that have been done the results of the overall overall accuracy are 68.89%, per class accuracy 79.26%, precision 69.27, and recall 68.89%.
Rancang Bangun Edugame "History of Shodanco Supriyadi": Sejarah Perlawanan Pasukan PETA Blitar Terhadap Jepang Philips Denny Azarya; Pandi Pandi; Yohannes Yohannes; Yoannita Yoannita
Jurnal Teknik Informatika dan Sistem Informasi Vol 6 No 1 (2020): JuTISI
Publisher : Maranatha University Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28932/jutisi.v6i1.1979

Abstract

Games are not only for entertainment but games can be a means of learning. Historical subjects are often considered boring and uninteresting lessons because there are no innovations to attract students' curiosity. Therefore, a learning media was created and an introduction to the history of the resistance of the Blitar PETA forces through an adventure edugame. The methodology used is an iteration with three increments, each of which consists of the analysis, design, code, and test phases. The game design uses Unity 3D as a tool. Tests carried out include integration testing, system testing, and acceptance testing. From the results of these tests it was found that the edugame application that had been developed was able to assist students in introducing the history of the resistance figure PETA Blitar named Supriyadi.
Klasifikasi Lukisan Karya Van Gogh Menggunakan Convolutional Neural Network-Support Vector Machine Yohannes Yohannes; Daniel Udjulawa; Febbiola Febbiola
Jurnal Teknik Informatika dan Sistem Informasi Vol 7 No 1 (2021): JuTISI
Publisher : Maranatha University Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28932/jutisi.v7i1.3399

Abstract

Painting is a work of art with various strokes, textures, and color gradations so that a painting that is synonymous with beauty is created. The various paintings created have characteristics, such as the paintings by Van Gogh, which have tightly arranged strokes, creating a repetitive and patterned impression. This study classifies paintings by Van Gogh or not by using the VGG-19 and ResNet-50 feature extraction methods. The SVM method is used as a classification method with two optimizations, namely random and grid optimization in the linear kernel. The data set used consisted of 124 Van Gogh paintings and 207 paintings by other painters. The use of VGG-19 feature extraction using grid optimization has the best value of 93,28% using the use of random optimization which has a value of 92,89%. The use of ResNet-50 using grid optimization with the best value of 90,28% using the use of random optimization which has a value of 90,15%. The extraction feature of VGG-19 is better than ResNet-50 in paintings by Van Gogh or not.
Pemanfaatan Scale Invariant Feature Transform Berbasis Saliency untuk Klasifikasi Sel Darah Putih Yohannes Yohannes; Siska Devella; William Hadisaputra
Jurnal Teknik Informatika dan Sistem Informasi Vol 7 No 2 (2021): JuTISI
Publisher : Maranatha University Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28932/jutisi.v7i2.3707

Abstract

White blood cells are cells that makeup blood components that function to fight various diseases from the body (immune system). White blood cells are divided into five types, namely basophils, eosinophils, neutrophils, lymphocytes, and monocytes. Detection of white blood cell types is done in a laboratory which requires more effort and time. One solution that can be done is to use machine learning such as Support Vector Machine (SVM) with Scale Invariant Feature Transform (SIFT) feature extraction. This study uses a dataset of white blood cell images that previously carried out a pre-processing stage consisting of cropping, resizing, and saliency. The saliency method can take a significant part in image data and. The SIFT feature extraction method can provide the location of the keypoint points that SVM can use in studying and recognizing white blood cell objects. The use of region-contrast saliency with kernel radial basis function (RBF) yields the best accuracy, precision, and recall results. Based on the test results obtained in this study, saliency can improve the accuracy, precision, and recall of SVM on the white blood cell image dataset compared to without saliency.
Implementasi Virtual Reality pada Game Edukasi Protokol Kesehatan Gerry Jeven Timoti; Yohannes Yohannes; Yoannita Yoannita
Jurnal Teknik Informatika dan Sistem Informasi Vol 8 No 1 (2022): JuTISI
Publisher : Maranatha University Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28932/jutisi.v8i1.4065

Abstract

In the year of 2020 until now has been a tough year for almost all industries and the general public due to the coronavirus pandemic. However, the increase in cases per day illustrates that the community has not complied with health protocols optimally. The impact of the pandemic has increased gamers in Asia by more than 75%. With a positive impact on society, the role of games can be used as a medium for education. Virtual Reality will be implemented in the health protocol educational game to see the good role of virtual reality in educational game interactions. The implementation of Virtual Reality in the health protocol educational game is carried out using the GDLC (Game Development Life Cycle) approach with stages starting from initiation, pre-production, production, testing, and release. The results of the recapitulation of the questionnaire on the acceptance and satisfaction of the health protocol educational game application showed the results of 44,9.% strongly agree, 44,9% agree, 9,15% are neutral and 1% are not agree that the application has functioned well and can be selected as one of the learning media especially about health protocol to reduce the growth rate of people infected with the coronavirus.
Implementasi Algoritma $P Point Cloud Recognizer pada Pengenalan Angka Berbasis Game Muhammad Farid Athar; Yohannes Yohannes; Yoannita Yoannita
Jurnal Teknik Informatika dan Sistem Informasi Vol 8 No 3 (2022): JuTISI
Publisher : Maranatha University Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28932/jutisi.v8i3.5472

Abstract

More devices use gestures as their input method. Recognizing these gestures becomes more important for app development. One of the methods used for gesture recognition is Point Cloud Recognizer or $P. Gesture recognition can be used to recognize written characters like numbers or letters. Result of this recognition can be used for education involving apps, like games. This study is done by implementing $P in games to show that $P can be used as one of the methods for gesture recognition when developing games that need such features. In this study $P is implemented with the help of the game engine Unity with C# programming language. 3 sets of numerals 1 to 10 are used as data with $P configured to use 32 points. Total of 100 tests are done in the game resulting in 99% accuracy, showing $P is able to recognize the gesture well.
DETEKSI PLAT NOMOR KENDARAAN MENGGUNAKAN METODE YOLOv8 Putra, Lipi Amanda; Yohannes, Yohannes
JATISI Vol 12 No 2 (2025): 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.v12i2.11261

Abstract

Motorized vehicles play a crucial role in daily life, making vehicle management and monitoring increasingly necessary. One common issue arises in parking systems, where current systems only capture photos of vehicles and still require manual input of license plate numbers upon vehicle exit. These systems are not yet capable of automatically detecting and recognizing license plates. Therefore, this study aims to design an application for license plate recognition using the YOLOv8 method to automatically and accurately detect license plates. YOLOv8 is a fast and accurate object detection model. The dataset used consists of 764 images of vehicle license plates, divided into 70% training data, 20% validation data, and 10% test data. he results of the study show a detection accuracy with a precision value of 94.3%, recall of 87.3%, and mAP of 95.3%.
Perangkat Lunak Pendeteksi Jenis Seragam Siswa Jenjang Pendidikan Menengah Menggunakan Yolov8 Dody, Muhammad; Yohannes, Yohannes
JATISI Vol 12 No 2 (2025): 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.v12i2.11356

Abstract

In a school environment, policies and regulations play a vital role in teaching students discipline, particularly in adhering to uniform rules. School uniforms help instill discipline by requiring students to dress according to the rules, without modifications, and in compliance with set standards. These regulations foster equality among students, reduce social differences, and support character and moral education. However, enforcing uniform policies can pose challenges for schools. Schools need to regularly monitor compliance to ensure every student follows the uniform rules, a process that often requires significant time and effort. To address this issue, this study developed a student uniform detection system using the You Only Look Once Version 8 (YOLOv8) method. YOLOv8 is a convolutional neural network-based object detection method capable of identifying objects in real-time with high accuracy. The aim of this study is to create a system that can automatically detect student uniforms, improve record-keeping accuracy, and reduce excessive time and energy spent monitoring detection results through cameras. The research methodology includes image data collection, YOLOv8 model training, and system testing. The testing results showed that the developed model achieved a precision of 95.%, a recall of 85%, a mean Average Precision (mAP) of 92.2%.
PELATIHAN PENGGUNAAN WORDPRESS UNTUK MEDIA INFORMASI KPCDI PALEMBANG Al Rivan, Muhammad Ezar; Irsyad, Hafiz; Meiriyama, Meiriyama; Yohannes, Yohannes; Devella, Siska; Wijaya, Novan; Rachmat, Nur
FORDICATE Vol 4 No 2 (2025): April 2025
Publisher : Universitas Multi Data Palembang, Fakultas Ilmu Komputer dan Rekayasa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35957/fordicate.v4i2.11572

Abstract

Penguasaan teknologi informasi menjadi kebutuhan penting bagi organisasi berbasis komunitas dalam menyebarluaskan informasi secara cepat dan terstruktur. Komunitas Pasien Cuci Darah Indonesia (KPCDI) Palembang membutuhkan sarana digital yang dapat menunjang komunikasi dan edukasi antaranggota. Kegiatan pengabdian ini bertujuan untuk memberikan pelatihan penggunaan WordPress sebagai media informasi komunitas. Pelatihan dilaksanakan di Rumah Sakit RK Charitas Palembang dengan metode ceramah, demonstrasi, dan praktik langsung. Materi pelatihan mencakup pengelolaan konten situs, pengunggahan media, dan pengaturan tampilan dasar website. Peserta dibimbing secara bertahap agar mampu memahami penggunaan platform meskipun berasal dari latar belakang non-teknis. Hasil kegiatan menunjukkan bahwa peserta antusias dan mampu mengikuti alur pelatihan dengan baik. Kegiatan ini diharapkan dapat memperkuat kapasitas digital KPCDI Palembang dalam pengelolaan media informasi secara mandiri dan berkelanjutan
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