Article Per Year (5 Year)
p-Index From 2020 - 2025
2.959
P-Index
This Author published in this journals
All Journal International Journal of Electrical and Computer Engineering IJCCS (Indonesian Journal of Computing and Cybernetics Systems) TELKOMNIKA (Telecommunication Computing Electronics and Control) Proceeding of the Electrical Engineering Computer Science and Informatics JOIV : International Journal on Informatics Visualization Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) JURNAL MEDIA INFORMATIKA BUDIDARMA Indonesian Journal of Artificial Intelligence and Data Mining PROCESSOR Jurnal Ilmiah Sistem Informasi, Teknologi Informasi dan Sistem Komputer MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer Jurnal Ilmiah Media Sisfo Systematics Zonasi: Jurnal Sistem Informasi Jurnal Informasi dan Teknologi Jurnal Informatika Ekonomi Bisnis Jurnal Computer Science and Information Technology (CoSciTech) AJARCDE (Asian Journal of Applied Research for Community Development and Empowerment) Prosiding Seminar Nasional Sisfotek (Sistem Informasi dan Teknologi Informasi) Jurnal Komtekinfo Jurnal Informatika Ekonomi Bisnis Journal Medical Informatics Technology
Jufriadif Na`am, Jufriadif
Universitas Putra Indonesia YPTK
Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Languange, Linguistic, Communication & Media Library & Information Science Medicine & Pharmacology Public Health

Published : 49 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 4 Documents
Search
Journal : TELKOMNIKA (Telecommunication Computing Electronics and Control)

 1 
Filter technique of medical image on multiple morphological gradient (MMG) method Jufriadif Na'am; Johan Harlan; Rosda Syelly; Agung Ramadhanu
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 17, No 3: June 2019
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v17i3.9722

Abstract

Filter technique is supportive for reducing image noise. This paper presents a study on filtering medical images, i.e., CT-Scan, Chest X-ray and Panoramic X-ray collected from two of the most prominent public hospitals in Padang City, Indonesia. The aim of this study preserved to facilitate in diagnosing objects in x-ray medical images. This study used filter technique, i.e. Blur, Emboss, Gaussian, Laplacian, Roberts, Sharpen, or Sobel techniques as pre-processing step. The filter process performed before edge detection and edge clarification. MMG method used in this study to clarify the edge detection. Thus, this research showed the hesitation decline (confidence increase) of the diagnosis of objects contained in medical images.
Detection of Infiltrate on Infant Chest X-Ray Jufriadif Na'am; Johan Harlan; Gunadi Widi Nurcahyo; Syafri Arlis; Sahari Sahari; Mardison Mardison; Larissa Navia Rani
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 15, No 4: December 2017
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v15i4.3163

Abstract

Currently, Chest X-ray is still widely used around the world for disease examination. This is due to its low cost, low radiation and a lot of disease information. The commonly detected disease using chest x-rays is lung disease. The characteristic of this disease is infiltrate. However, the accuracy of Chest X-ray observations is still low. Therefore, this research offers a method to perform Chest X-ray image processing in clarifying the information contained therein. This research used Chest X-ray of infant patients who treated at Central Public Hospital (RSUP) Dr. M. Djamil Padang. The total of the images tested were 17 images. In these images, there were some suspected infiltrates after being analyzed by doctors. Software used was Matlab which is conducted by applying image processing method. The method used consisted of 4 parts, that was Cropping, Filtering, Detecting Edge, and Sharpening Edge. The results of the research showed that the method could clarify edge detection of the objects contained in the image, so that the infiltrate could be more easily recognized. With this easiness, it will help the doctor to remove doubts for infiltrate observations in the Infant's lungs.
Image Processing of Panoramic Dental X-Ray for Identifying Proximal Caries Jufriadif Na'am; Johan Harlan; Sarifuddin Madenda; Eri Prasetyo Wibowo
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 15, No 2: June 2017
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v15i2.6856

Abstract

This study aims to facilitate the identification of proximal caries in the Panoramic Dental X-Ray  image. Twenty-seven X-Ray images of proximal caries were elaborated. The images in digital form were processed using Matlab and Multiple Morphological Gradient. The process produced sharper images and clarifies the edges of the objects in the images. This makes the characteristics of the proximal caries and the caries severity can be identified precisely.
An artificial neural network approach for detecting skin cancer Sugiarti Sugiarti; Yuhandri Yuhandri; Jufriadif Na'am; Dolly Indra; Julius Santony
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 17, No 2: April 2019
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v17i2.9547

Abstract

This study aims to present diagnose of melanoma skin cancer at an early stage. It applies feature extraction method of the first order for feature extraction based on texture in order to get high degree of accuracy with method of classification using artificial neural network (ANN). The method used is training and testing phases with classification of Multilayer Perceptron (MLP) neural network. The results showed that the accuracy of test image with 4 sets of training for image not suspected of melanoma and melanoma with the lowest accuracy of 80% and the highest accuracy of 88.88%, respectively. The 4 sets of training used consisted of 23 images. Of the 23 images used as a training consisted of 6 as not suspected of melanoma images and 17 as suspected melanoma images.
Co-Authors Adiddo Restiady ADIF, RIANDY MARDHIKA Agung Ramadhanu Agus Perdana Windarto AHMADI Ainul Mardiah Alya Shafra Hewiz Apriade Voutama Asep Kurniawan Bob Subhan Riza Bob Subhan Riza Catur Suharinto Catur Suharinto Dika Putri Metalica Dodi Andre Putra Dolly Indra Dwiza Riana Elfina Novalia Elvin Syahrin Eri Prasetyo Wibowo, Eri Prasetyo Faruq Aziz Feriantano Sundang Pranata Feriantano Sundang Pranata Gunadi Bin Senitio Gunadi Nurcahyo Gunadi Widi Nurcahyo Iman Priyadi Irawadi Putra Johan Harlan Johan Harlan Johan Harlan Julius Santony Julius Santony Julius Santony Julius Santony Kurnia, Hezy Larissa Navia Rani Mardison Mardison Maulia Rahman Mesran, Mesran Montesna Muhammad Abrar Masril Mutiana Pratiwi Nasir Hamzah Novizar Nazir Nur, Rofil M Puspitaningtyas, Mery Oktaviyanti Raju Wandira Rakhmad Kuswandhie Riandy Mardhika Adif Romi Hardianto Rosda Syelly Sahari Sahari Saputri, Daniati Uki Eka Saputri Sarifuddin Madenda Sarjon Defit Saut Parulian, Onesinus Sri Hadianti Sugiarti, Sugiarti Sumidjan Sumidjan Sumijan Sumijan Sumijan Sumijan Sumijan Sumijan Sumijan Sumijan Sumijan Sumijan Suryadi Putra Liawatimena Syafri Arlis Teguh Herwanto Waeisul Bismi Wanto, Anjar Yanto Saputra Yuhandri Yuhandri Yuhandri Yuhandri Yuhandri, Yuhandri Yundari, Yundari