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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 EDUMATIC: Jurnal Pendidikan Informatika Jutisi: Jurnal Ilmiah Teknik Informatika dan Sistem Informasi Progresif: Jurnal Ilmiah Komputer Jurnal Ilmiah Betrik : Besemah Teknologi Informasi dan Komputer JATI (Jurnal Mahasiswa Teknik Informatika) Syntax: Journal of Software Engineering, Computer Science and Information Technology Algoritme Jurnal Mahasiswa Teknik Informatika Informatics and Enginering Dedication DEVICE : JOURNAL OF INFORMATION SYSTEM, COMPUTER SCIENCE AND INFORMATION TECHNOLOGY Jurnal Informatika dan Multimedia MDP Student Conference JRIIN :Jurnal Riset Informatika dan Inovasi Jurnal Ilmiah Betrik : Besemah Teknologi Informasi dan Komputer Applied Information Technology and Computer Science (AICOMS) INOVTEK Polbeng - Seri Informatika JuTISI (Jurnal Teknik Informatika dan Sistem Informasi)

Yohannes, Yohannes

Universitas Multi Data Palembang

Author-ID : 423293

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 Sampah Daur Ulang Menggunakan Dukungan Vektor Machine Dengan Fitur Pola Biner Lokal Leonardo, Leonardo; Yohannes, Yohannes; Hartati, Ery
Algoritme Jurnal Mahasiswa Teknik Informatika Vol 1 No 1 (2020): Oktober 2021 || Algoritme Jurnal Mahasiswa Teknik Informatika
Publisher : Program Studi Teknik Informatika Universitas Multi Data Palembang

Garbage is one of the problems that always arise in Indonesia and even in the world. Increasingly, the production of waste is increased along with the increase in population and consumption. Therefore, need a prevention to stop wasting or producing garbage through recycle. This research do garbage recycle classification of cardboard, glass, metal, paper and plastic by using Local Binary Pattern (LBP) texture feature extraction methode and Support Vector Machine (SVM) as classification methode. For examination technic and dataset distribution is using K-Fold Cross Validation methode type Leave One Out (LOO). From examination result had been done were using fold 5 until fold 10. Polynomial kernel get highest accuracy result from every fold used with mean point 87.82%. Based on SVM classification examination result whether linear kernel, polynomial nor gaussian by using fold 5 until fold 10. The best accuracy point for cardboard garbage is 96.01%. For glass garbage, the best accuracy point is 90.62%. Then, metal garbage get the best accuracy point 89.72%. While paper garbage with highest accuracy point 96.01%. And plastic garbage with highest accuracy point 87.64%.

Rancang Bangun Aplikasi Permainan EscapeMenggunakan Logika Fuzzy Dan Algoritma Floyd Warshall Prabowo, Adrianus; Devella, Siska; Yohannes, Yohannes
Algoritme Jurnal Mahasiswa Teknik Informatika Vol 1 No 2 (2021): April 2021 || Algoritme Jurnal Mahasiswa Teknik Informatika
Publisher : Program Studi Teknik Informatika Universitas Multi Data Palembang

Aplikasi permainan ESCAPE merupakan permainan yang mengandalkan player untuk keluar dari labirin tersebut. Penelitian ini menggunakan Logika Fuzzy untuk membuat perilaku komputer menjadi susah ditebak dan Floyd Warshall untuk membuat item jebakan menghalangi player saat bermain. Aplikasi permainan ini dibangun dan dirancang dengan menggunakan Unity 3D dan menggunakan metodologi prototype. Hasil uji dari data sampel menunjukkan bahwa logika fuzzy berhasil diterapkan dalam menentukan perilaku NPC. Hasil uji dari data sampel yang dilakukan menunjukkan bahwa kemunculan item jebakan berhasil diterapkan pada aplikasi permainan ESCAPE.

Penggunaan Fitur HOG Dan HSV Untuk Klasifikasi Citra Sel Darah Putih Prasthio, Rial; Yohannes, Yohannes; Devella, Siska
Algoritme Jurnal Mahasiswa Teknik Informatika Vol 2 No 2 (2022): April 2022 || Algoritme Jurnal Mahasiswa Teknik Informatika
Publisher : Program Studi Teknik Informatika Universitas Multi Data Palembang

Sel darah putih (leukosit) merupakan sel pembentuk komponen darah yang diproduksi oleh sumsum tulang dan disebarkan ke seluruh tubuh melalui aliran darah. Sel darah putih merupakan bagian penting dari sistem kekebalan tubuh yang berfungsi untuk menghasilkan antibodi yang dapat membantu tubuh manusia dalam melawan berbagai penyakit. Sel darah putih dibagi menjadi 5 jenis, yaitu neutrofil, limfosit, monosit, eosinofil, dan basophil. Analisis sel darah putih masih dilakukan secara manual yang memakan waktu yang lama dan memiliki tingkat ketelitian dan keakuratan yang rendah. Solusi yang dapat dilakukan salah satunya menggunakan machine learning yaitu SVM (support vector machine) dengan menggunakan fitur HOG dan HSV. Penelitian ini menggunakan dataset hasil mikroskop sel darah putih dari Kaggle yang bersifat public. Jumlah dataset yang digunakan dalam penelitian berjumlah 12.392 gambar dari 4 jenis sel darah putih (Eosinophil, Lymphocyte, Monocyte, dan Neutrophil). Pada perhitungan confusion matrix hasil tertinggi didapatkan oleh Neutrophil dengan accuracy sebesar 88,55%, precision sebesar 100%, dan recall sebesar 54,19%.

Klasifikasi Penyakit Mata Menggunakan Convolutional Neural Network Dengan Arsitektur VGG-19 Marcella, Dewi; Yohannes, Yohannes; Devella, Siska
Algoritme Jurnal Mahasiswa Teknik Informatika Vol 3 No 1 (2022): Oktober 2022 || 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.v3i1.3331

This study raised a topic related to the classification by using eye diseases in humans. This study uses two optimizing options, namely SGD and Adagrad. The data used are 601 images consisting of 430 training images, 50 validation images, and 121 test images with a total of 4 classes. The method used in this study is the Convolutional Neural Network (CNN) method with the VGG-19 architecture, with input in the form of images that have gone through a preprocessing process, namely resizing and the CLAHE (Contrast Limited Adaptive Histogram Equalization) method of eye disease images. The test scenario consisted of 8 scenarios with different Optimizer and ClipLimit. The highest test results were obtained in the first scenario using the Adagrad optimizer and clipLimit of 1.0 with an accuracy value of 65.29%, precision of 66.53%, recall of 65.29%, and f1-score of 65. 40%.

Implementasi Algoritma K-Nearest Neighbor untuk Klasifikasi Cuaca Dandy, Dandy; Udjulawa, Daniel; Yohannes, Yohannes
Algoritme Jurnal Mahasiswa Teknik Informatika Vol 4 No 1 (2023): Oktober 2023 || 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.v4i1.4932

Weather is a brief natural event concerning the atmospheric conditions that take place on Earth which are determined by pressure, wind speed, temperature, and air phenomena. This study classifies 3 weather classes, namely sunny, cloudy, and rainy using the K-Nearest Neighbor algorithm as a weather classification algorithm with K value parameters of 3, 5, 7, and 9. Weather dataset 96.453 data to be examined is data taken from the Kaggle website. The dataset is divided into training data and test data with a ratio of 80:20. The implementation of the K-Nearest Neighbor algorithm produces a confusion matrix and classification report where in the confusion matrix, the largest number of correctly predicted data is at the value K = 9, namely 13.132 correctly predicted data with the largest number of correctly predicted data in the cloudy class, namely 10.865 data. As for the classification report, the highest accuracy value for both the cloudy, rainy, and sunny weather classes is at K = 9, which is 68.073%, and the highest precision, recall, and f1-score values are found in the cloudy class at K = 9, respectively contributed 72.095%, 89.288%, and 79.775%.

Pelatihan Membangun Server DNS Lokal di SMK Negeri 1 Palembang Arman, Molavi; Yohannes, Yohannes; Al Rivan, Muhammad Ezar
FORDICATE Vol 2 No 1 (2022): November 2022
Publisher : Universitas Multi Data Palembang, Fakultas Ilmu Komputer dan Rekayasa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35957/fordicate.v2i1.3393

Pengabdian masyarakat yang dilakukan di SMK Negeri 1 Palembang yaitu berupa pelatihan untuk membangun Server DNS. Pelatihan ini diikuti oleh siswa SMK sehingga siswa memiliki keterampilan dan pengetahuan terkait dengan server DNS. Pelatihan ini diawali dengan melakukan instalasi sistem operasi Linux Debian. Pelatihan ini dilakukan dengan cara praktikum dan tanya jawab. Dari pelatihan ini didapatkan pengetahuan bagaimana melakukan instalasi Linux Debian kemudian dapat membangun Server DNS.

Transfer Learning dengan MobileNetV2 untuk Klasifikasi Motif Jumputan Palembang Sahpira, Mulia; Yohannes, Yohannes
Algoritme Jurnal Mahasiswa Teknik Informatika Vol 6 No 1 (2025): Oktober 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.v6i1.10998

Palembang jumputan fabric is one of Indonesia's cultural heritages that is unique in its motifs and manufacturing techniques. However, the lack of public understanding of the meaning of motifs and competition with other traditional fabrics are challenges in its preservation. This research aims to develop a classification model of Palembang jumputan fabric motifs using the Convolutional Neural Network method with MobileNetV2 architecture and transfer learning approach. The dataset used consists of 800 images of four types of motifs, namely Bintik Tujuh, Pola, Tabur, and Terong. The data is divided into 80% training, 10% validation, and 10% testing. The model was trained using four types of optimisers, namely AdamW, Adagrad, Nadam, and SGD, with training parameters of 100 epochs, batch size 32, and learning rate 0.001. The test results showed that AdamW gave the highest accuracy of 97%, followed by Nadam 96%, Adagrad 95%, and SGD 90%. The model recognised the motifs well, especially the Bintik Tujuh and Tabur motifs which achieved 100% accuracy. With these results, artificial intelligence can be utilised to support the preservation of Palembang jumputan fabrics through motif recognition technology.

Implementasi Arsitektur Half-UNet untuk Mendeteksi Kanker Payudara pada Citra Ultrasonografi Glen, Billy; Yohannes, Yohannes
Progresif: Jurnal Ilmiah Komputer Vol 20, No 1: Februari 2024
Publisher : STMIK Banjarbaru

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35889/progresif.v20i1.1595

Breast cancer is one of the biggest causes of death for women worldwide. Breast cancer is a metastatic cancer and can spread to other organs, such as bones, liver, lungs and brain. Breast cancer can be detected at an early stage, but it is difficult to find and cases of breast cancer are on the rise. Therefore, this study uses the Half-UNet architecture for breast cancer sonogram dataset. The dataset used consists of 780 breast sonograms which are divided into training data and test data with a ratio of 80:20. The Dice Coefficient results obtained on the Half-UNet architecture is 0.7063. The U-Net value can provide better Dice Coefficient results, but the Half-UNet architecture has comparable values and provides results in a relatively faster time.

Residual-Gated Attention U-Net with Channel Recalibration for Polyp Segmentation in Colonoscopy Images Tanuwijaya, William; Yohannes
INOVTEK Polbeng - Seri Informatika Vol. 10 No. 3 (2025): November
Publisher : P3M Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35314/4qmfa987

This study proposed a modification to the Attention U-Net architecture by integrating a Residual-Gated mechanism and Squeeze-and-Excitation (SE) Block-based channel recalibration within the Attention Gate to enhance feature selectivity in polyp segmentation. This integration reinforces both spatial and channel attention, enabling the model to better highlight polyp regions while suppressing irrelevant background features. Experiments were conducted on three colonoscopy datasets, CVC-ClinicDB, CVC-ColonDB, and CVC-300, using IoU and DSC metrics. Compared to the Attention U-Net baseline, the proposed model achieves noticeable improvements, with performance gains of mIoU 0.0043 and mDSC 0.0094 on CVC-ClinicDB, mIoU 0.0012 on CVC-ColonDB, and a larger margin of mIoU 0.0224 and mDSC 0.0127 on CVC-300. The best results were obtained on CVC-ClinicDB (mIoU 0.8889, mDSC 0.9412). Although the absolute scores on CVC-ClinicDB and CVC-ColonDB are lower than those reported in several recent studies, these datasets contain higher variability in polyp size, boundary ambiguity, and illumination, contributing to more challenging segmentation conditions. Visual evaluation further shows smoother and more coherent boundaries, especially on small or low-contrast polyps. Overall, the integration of the residual-gated mechanism and SE block within the attention gate effectively improves model accuracy and generalization, particularly in challenging scenarios.

Evaluation of MobileNet-Based Deep Features for Yogyakarta Traditional Batik Motif Classification Muhdhor, Umar; Yohannes
Sinkron : jurnal dan penelitian teknik informatika Vol. 10 No. 1 (2026): Article Research January 2026
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v10i1.15668

Batik is an Indonesian intangible cultural heritage that embodies profound philosophical, aesthetic, and cultural values. Yogyakarta batik motifs, such as Parang, Kawung, and Truntum, reflect Javanese wisdom and identity through distinctive geometric and floral patterns. In the digital era, artificial intelligence based image processing provides a promising approach to support the preservation and automatic recognition of traditional batik motifs. The objective of this study is to evaluate the effectiveness of MobileNet-based feature extraction combined with Support Vector Machine (SVM) classification for Yogyakarta batik motif recognition. The proposed method employs MobileNet as a convolutional feature extractor and SVM as a decision model to separate motif classes in the feature space. Experiments were conducted on 685 batik images consisting of three motif classes, with class imbalance handled using Synthetic Minority Over-sampling Technique (SMOTE). Model performance was evaluated using weighted accuracy, precision, recall, and F1-score under five-fold cross validation. The results show that MobileNetV3Large achieved the best performance with a weighted accuracy of 98.36%, followed by MobileNetV3Small and MobileNetV4Small. Statistical significance tests using the Friedman test and Wilcoxon signed-rank analysis confirm that the performance differences among the evaluated models are statistically significant. These findings indicate that MobileNetV3 architectures provide robust and discriminative feature representations for batik motif classification on limited yet structured datasets. This study contributes a validated MobileNet–SVM framework for batik recognition and supports ongoing efforts in the digital preservation of Indonesia’s cultural heritage. Future work will explore larger motif sets and cross-dataset evaluation to further improve generalization performance.

Co-Authors Ade Hendri Pandrean Adhytio Mahendra Adrian Chandra Albert Cahayadi Andreas, Kevin Ariel Sudarsono Azarya, Philips Denny Beni Anthony Bobby Jaya Saputra Cahyati, Imelia Dwinora Calvin Oliver Saputra Candra Candra Celvine Adi Putra Cendy Prakarsah Daffa Yudha Musyaffa Dafid Dafid Dandy, Dandy Daniel Udjulawa Daniel Udjulawa Devella, Siska Dody, Muhammad Fadhel Muhammad Famerdi, Farhan Agung Farhan Agung Famerdi Farisi, Ahmad Febbiola Febbiola Felix Gunawan Femmy Johan Feristyani, Indah Firda Novia Rahmawati Gerry Jeven Timoti Glen, Billy Hafiz Irsyad Hafiz Irsyad Hartati, Ery Inayatullah Inayatullah Indah Feristyani Jaysen Stephanus Jendraja Husin Kotan Jennifer Verty Jericho Jericho Jerry Setiawan Jimmy Aprilyanto Johannes Petrus Jonathan Tanujaya Joseph Eduard Uly Loni Julian Rusli Tee Baldi Juliana Nasution Kelvin Arianto Kevin Andreas Klaudius Audie Irsansaputra Laksana, Jovansa Putra Leo Chandra Leonardo Leonardo M Dhafa Adjie Saputra M. Zaky Naufal Farisky Marcella, Dewi Meiriyama Meiriyama, Meiriyama Migel Orvin Febryan Molavi Arman Muhammad Ezar Al Rivan Muhammad Farid Athar Muhammad Radja Juang Jamemiko 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 Raphael Lee Ricky Wijaya RR. Ella Evrita Hestiandari Sahpira, Mulia Saputra, Dika Sari, Yulya Puspita Selvie Selvie Serenity Devina Suryanto Setiawan, Jerry Siska Amelia Siska Devella Siti Fatimah Az Zahrah Sonia Sonia, Sonia Tanuwijaya, William Timoteus Ivan Sariyo Veraldo Verrino Adityya Wijang Widhiarso William Hadisaputra William Tanuwijaya Yeremia Agung Chandra Yoannita Yoannita Yoannita Yoannita, Yoannita Yulya Puspita Sari

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