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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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Deteksi Penyakit Malaria Menggunakan Convolutional Neural Network Berbasis Saliency Yohannes Yohannes; Siska Devella; Kelvin Arianto
JUITA : Jurnal Informatika JUITA Vol. 8 Nomor 1, Mei 2020
Publisher : Department of Informatics Engineering, Universitas Muhammadiyah Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (844.442 KB) | DOI: 10.30595/juita.v8i1.6671

Abstract

Malaria adalah penyakit mematikan yang menjadi masalah di berbagai negara. Metode yang paling umum untuk mendeteksi malaria adalah dengan memeriksanya secara manual, yang memakan waktu. Convolutional Neural Network (CNN) adalah salah satu solusi untuk deteksi malaria. CNN telah terbukti memberikan hasil yang sangat baik dalam klasifikasi gambar dan telah banyak digunakan dalam penelitian sebelumnya dan memiliki hasil yang baik. Sebelum proses klasifikasi, pra-pemrosesan gambar dapat digunakan untuk mendapatkan hasil klasifikasi yang lebih baik. Salah satu metode dalam pra-pemrosesan adalah arti-penting. Saliency adalah metode yang dapat mengambil bagian penting dari suatu gambar. Pada penelitian ini dilakukanlah pengujian terhadap metode saliency dan CNN untuk masalah pendeteksian penyakit malaria. Skenario pengujian dilakukan dengan membandingkan metode saliency, yaitu Region Contrast Saliency, Frequency-tuned saliency, Spectral Residual, dan Histogram Contrast. Metode saliency terbaik dalam mendeteksi penyakit malaria didapatkan oleh metode frequency-tuned saliency dengan akurasi sebesar 90,32% dibandingkan dengan metode saliency yang lain, yaitu 62,67% untuk region contrast saliency, 50% untuk spectral residual saliency, dan 79,06% untuk histogram contrast saliency.Kata-kata kunci: Klasifikasi; CNN; Malaria; Saliency
Brain Tumor Classification Using Gray Level Co-occurrence Matrix and Convolutional Neural Network Wijang Widhiarso; Yohannes Yohannes; Cendy Prakarsah
IJEIS (Indonesian Journal of Electronics and Instrumentation Systems) Vol 8, No 2 (2018): October
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (792.128 KB) | DOI: 10.22146/ijeis.34713

Abstract

Image are objects that have many information. Gray Level Co-occurrence Matrix is one of many ways to extract information from image objects. Wherein, the extracted informations can be processed again using different methods, Gray Level Co-occurrence Matrix is use for clarifying brain tumor using Convolutional Neural Network. The scope in this research is to process the extracted information from Gray Level Co-occurrence Matrix to Convolutional Neural Network where it will processed as Deep Learning to measure the accuracy using four data combination from TI1, in the form of brain tumor data Meningioma, Glioma and Pituitary Tumor. Based on the implementation of this research, the classification result of Convolutional Neural Network shows that the contrast feature from Gray Level Co-occurrence Matrix can increase the accuracy level up to twenty percent than the other features. This extraction feature is also accelerate the classification process using Convolutional Neural Network.
Analisis Perbandingan Algoritma Fuzzy C-Means dan K-Means Yohannes Yohannes
Annual Research Seminar (ARS) Vol 2, No 1 (2016)
Publisher : Annual Research Seminar (ARS)

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

Abstract

Klasterisasi merupakan teknik pengelompokkan data berdasarkan kemiripan data. Teknik klasterisasi ini banyak digunakan pada bidang ilmu komputer khususnya pengolahan citra, pengenalan pola, dan data mining. Banyak sekali algoritma yang digunakan untuk klasterisasi data. Algoritma yang sering digunakan untuk klasterisasi data  pada umumnya adalah Fuzzy C-Means dan K-Means. Algoritma Fuzzy C-Means merupakan algoritma klasterisasi dimana data dikelompokkan ke dalam suatu pusat cluster data dengan derajat keanggotaan masing-masing cluster. Sedangkan algoritma K-Means merupakan teknik mengelompokkan data dengan mempartisi data ke dalam beberapa cluster dengan menetapkan sejumlah objek data terdekatnya. Pada penelitian ini akan dilakukan perbandingan algoritma Fuzzy C-Means dan K-Means dalam hal klasterisasi data dengan jumlah klaster dan jumlah data yang berbeda.
PELATIHAN PEMBUATAN GAME MENGGUNAKAN GDEVELOP UNTUK SISWA/I SMA NEGERI 6 PALEMBANG Yohannes Yohannes; Siska Devella; Meiriyama Meiriyama
SELAPARANG: Jurnal Pengabdian Masyarakat Berkemajuan Vol 5, No 1 (2021): Desember
Publisher : Universitas Muhammadiyah Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31764/jpmb.v5i1.6333

Abstract

ABSTRAKKegiatan pengabdian kepada masyarakat dalam bentuk pelatihan pembuatan game dengan menggunakan GDevelop memiliki tujuan yaitu untuk memberikan wawasan kepada siswa dan siswi SMA Negeri 6 Palembang dalam menggunakan aplikasi GDevelop yang dapat digunakan untuk membuat game sehingga dapat menambah keterampilan dan wawasan. Terdapat tiga tahapan utama yang dilakukan dalam pengabdian ini antara lain perencanaan, implementasi dan terakhir adalah evaluasi dengan memberikan tautan kuesioner berupa google form. Hasil kuesioner menunjukkan bahwa   didapatkan rata-rata 96,8% peserta merasakan manfaat dan sangat setuju dengan pelatihan yang telah dilakukan. Kata kunci: game; gdevelop; pelatihan; pengabdian kepada masyarakat. ABSTRACTCommunity service activities in the form of training in making games using GDevelope aim to provide insight to students of SMA Negeri 6 Palembang in using the GDevelop application for making of the video games, so the students can add skills and insights. There are three main stages carried out in this service, including planning, implementation and finally evaluation by providing a questionnaire link in the form of a google form. The results of the questionnaire showed that an average of 96.8% of participants feel the benefits and strongly agreed with the training that had been carried out. Keywords: community service activities; game; gdevelop training.
PELATIHAN PEMBUATAN WEBSITE SEKOLAH MENGGUNAKAN WORDPRESS UNTUK GURU TIK SMA NEGERI 17 PALEMBANG Siska Devella; Yohannes Yohannes; Nur Rachmat
SELAPARANG Jurnal Pengabdian Masyarakat Berkemajuan Vol 4, No 2 (2021): April
Publisher : Universitas Muhammadiyah Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (726.821 KB) | DOI: 10.31764/jpmb.v4i2.4488

Abstract

ABSTRAKKegiatan pengabdian kepada masyarakat dalam bentuk pelatihan pembuatan website sekolah dengan menggunakan WordPress bertujuan untuk meningkatkan kompetensi dan pemahaman serta mengasah keterampilan Guru TIK di SMA Negeri 17 Palembang dalam membuat dan mengelola website. Terdapat tiga tahapan utama yang dilakukan dalam pengabdian ini antara lain tahap perencanaan yang dibagi menjadi studi lapangan, analisis kebutuhan serta perizinan, tahap kedua yaitu pelaksanaan kemudian dilanjutkan dengan tahap evaluasi. Hasil evalusi terhadap kegiatan pengabdian yang telah dilakukan adalah pelatihan pembuatan website sekolah tersebut mampu meningkatkan pengetahuan dan keterampilan guru TIK di SMA Negeri 17 Palembang dalam menghasilkan tampilan website sekolah yang lebih menarik dan interaktif. Kata kunci: pengabdian kepada masyarakat; guru TIK; website; wordpress ABSTRACTCommunity service activities in the form of training on creating school websites using WordPress aim to improve competence and understanding as well as hone the skills of ICT Teachers at SMA Negeri 17 Palembang in creating and managing websites. There are three main stages carried out in this service, including the planning stage which is divided into field studies, needs analysis, and licensing, the second stage is implementation then followed by the evaluation stage. The result of the evaluation of the community service activities that have been carried out is that the school website creation training can increase the knowledge and skills of ICT teachers at SMA Negeri 17 Palembang and can produce a more attractive and interactive appearance of the school website. Keywords: community service activities; ict teachers; website; wordpress
Klasifikasi Mamalia Berdasarkan Bentuk Wajah Dengan k-NN Menggunakan Fitur CAS dan HOG Muhammad Ezar Al Rivan; Yohannes Yohannes
JATISI (Jurnal Teknik Informatika dan Sistem Informasi) Vol 5 No 2 (2019): JATISI
Publisher : Lembaga Penelitian dan Pengabdian pada Masyarakat (LPPM) STMIK Global Informatika MDP

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (349.752 KB) | DOI: 10.35957/jatisi.v5i2.139

Abstract

Object classification has been done to various images. Animal classification has been done using segmentation and non-segmentation approach as initial stage. Context Aware Saliency (CAS) is a method that able to make the object area more dominant than the background in saliency mode so that it can be an alternative object segmentation process. The shape feature will taken based on saliency results using the Histogram of Oriented Gradient (HOG). The K-Nearest Neighbors (K-NN) used to classify mammal species based on HOG features from saliency images. The dataset used in this study is LHI-Animal-Faces. The results obtained show that animal species that can be recognized well are cats and tigers, while sheep, dogs, and pigs have not been able to be recognized properly.
Implementasi Random Forest Untuk Klasifikasi Motif Songket Palembang Berdasarkan SIFT Siska Devella; Yohannes Yohannes; Firda Novia Rahmawati
JATISI (Jurnal Teknik Informatika dan Sistem Informasi) Vol 7 No 2 (2020): JATISI (Jurnal Teknik Informatika dan Sistem Informasi)
Publisher : Lembaga Penelitian dan Pengabdian pada Masyarakat (LPPM) STMIK Global Informatika MDP

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35957/jatisi.v7i2.289

Abstract

Indonesia has a variety of intangible cultural heritage, one of which is songket. Songket has a lot of variety according to the characteristics of each region, especially Songket Palembang. Songket Palembang has more features compared to songket from other regions. Besides having historical value, Songket Palembang has a high motive, quality, and complexity in the manufacturing process. In this study, the Random Forest method was used to classify the Songket Palembang motif image by using Scale-Invariant Feature Transform (SIFT) feature extraction. The process of feature formation using the SIFT method is through the stages of extrema detection scale space, keypoint localization, orientation assignment, and keypoint descriptor. The resulting feature is used for the Random Forest classification. Songket motif images used in this study were 115 images of each type of motif, namely Chinese Flowers, Beautiful Flowers, and Pulir. Image selection is taken from 5 colors of each Songket Palembang motif. Training data and test data used were 100 and 15 for each Songket Palembang motif, respectively. The test results show that the SIFT and Random Forest methods for the classification of Songket Palembang motifs can provide a pretty good accuracy, where the SIFT and Random Forest methods can produce an overall accuracy of 92.98%, per class accuracy of 94.07%, precision 92.98%, and recall 89.74%.
Klasifikasi Makna Tangisan Bayi Menggunakan CNN Berdasarkan Kombinasi Fitur MFCC dan DWT Yohannes Yohannes; Ricky Wijaya
JATISI (Jurnal Teknik Informatika dan Sistem Informasi) Vol 8 No 2 (2021): JATISI (Jurnal Teknik Informatika dan Sistem Informasi)
Publisher : Lembaga Penelitian dan Pengabdian pada Masyarakat (LPPM) STMIK Global Informatika MDP

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35957/jatisi.v8i2.470

Abstract

Crying is a baby's way of communicating. The sound of crying can be used to identify problems in babies, such as hunger, pain, drowsiness, fatigue, discomfort, coldness or heat, and others. However, not everyone can recognize the meaning of the crying baby. The combination of MFCC and DWT features was used in this study as an extraction feature in baby crying sounds. In this study, the Convolutional Neural Network (CNN) method is used to classify the meanings of sounds from babies crying. The dataset used in this study is a public dataset consisting of a total of 61 training data and 30 testing data. The types of crying babies used in this study were hunger, fatigue, discomfort, and stomach ache. Based on the test results, the MFCC and CNN features obtained a precision of 32.76%, a recall of 32.63%, and an accuracy of 73.33%. The combination of MFCC and DWT features (Mean, Standard Deviation, Range, Max) and CNN obtained a precision of 50.91%, a recall of 44.23%, and an accuracy of 73.33%.
Klasifikasi Video Olahraga Berdasarkan Citra Berbasis Konten Menggunakan Segmentasi Superpixel Yohannes Yohannes; Nur Rachmat; Bobby Jaya Saputra
Komputika : Jurnal Sistem Komputer Vol 11 No 1 (2022): Komputika: Jurnal Sistem Komputer
Publisher : Computer Engineering Departement, Universitas Komputer Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34010/komputika.v11i1.4542

Abstract

Sport has a large number and its number is always increasing as the time goes by. Many people like to watch sports competitions or tournaments on the field live. Nowadays, sports tournaments can now be watched digitally via video without the need to watch them live on the field by technological advances. Video classification needs to be done to differentiate the number of sports videos currently into the sports category according to the content. This study classifies sports videos of the types of Baseball, Basketball, Boxing, Gymnastic, Hockey, Swimming, Tennis, Volleyball, and Wrestling based on image content. The classification is done with the image content as a he, then the superpixel segmentation and superpixel colorization processes are carried out on the images to form a model. The model is formed using the CNN method with ResNet-50 architecture. Various numbers of superpixel segmentation are used as a comparison in the modelling to get the best results. The number of superpixel segmentation with the best result is 25.000 with an average rate of accuracy 0.97, precision 0.87, and recall 0.86 so that it is used for testing sports videos. The test result is the superpixel segmentation can be used to classify sports videos well with an average rate of accuray 0.91, precision 0.64 and recall 0.61, although some sports that had poor results, such as basketball and wrestling.
Penggunaan Fitur Saliency-SURF untuk Klasifikasi Citra Sel Darah Putih dengan Metode SVM Siska Devella; Yohannes Yohannes; Celvine Adi Putra
JATISI (Jurnal Teknik Informatika dan Sistem Informasi) Vol 8 No 4 (2021): JATISI (Jurnal Teknik Informatika dan Sistem Informasi)
Publisher : Lembaga Penelitian dan Pengabdian pada Masyarakat (LPPM) STMIK Global Informatika MDP

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35957/jatisi.v8i4.1547

Abstract

Sel darah putih merupakan sel pembentuk komponen darah yang berfungsi melawan berbagai penyakit dari dalam tubuh (sistem kekebalan tubuh). Sel darah putih dibagi menjadi lima jenis, yaitu basofil, eosinofil, neutrofil, limfosit, dan monosit. Pendeteksian jenis sel darah putih dilakukan di laboratorium yang memerlukan seorang spesialis serta usaha yang lebih, waktu, dan biaya. Solusi yang dapat dilakukan salah satunya adalah menggunakan machine learning seperti support vector machine (SVM) dengan ekstraksi fitur SURF. Penelitian ini menggunakan dataset citra sel darah putih yang sebelumnya dilakukan tahap pre-processing yang, terdiri dari crop, resize, dan saliency. Metode saliency mampu memberikan bagian yang bermakna pada sebuah citra. Metode ekstraksi fitur SURF mampu memberikan keypoint yang dapat digunakan SVM dalam mengenali jenis sel darah putih. Penggunaan region-contrast saliency dengan kernel radial basis function (RBF) mendapatkan hasil akurasi, presisi, dan recall yang baik di bandingkan dengan penggunaan kernel lain dalam penelitian ini. Berdasarkan hasil pengujian yang didapat pada penelitian ini, saliency dapat meningkatkan hasil akurasi, presisi, dan recall dari SVM untuk dataset citra sel darah putih dibandingkan dengan tanpa saliency.
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