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Peningkatan Keamanan Website Menggunakan Metode XML dengan Framework Codeigniter Nur, Rofil M; Na'am, Jufriadif; Nurcahyo, Gunadi Widi; Arlis, Syafri
Indonesian Journal of Computer Science Vol. 8 No. 2 (2019): Oktober 2019
Publisher : STMIK Indonesia Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (323.844 KB) | DOI: 10.33022/ijcs.v8i2.188

Abstract

Website security is needed in the dissemination of every information and data in cyberspace now. On the previous website the security used was not optimal in securing the website so we needed a reliable website security. XML (eXtensible Markup Language) is a markup language that is designed to store and deliver data by forming its own tags as needed. Codeigniter Framework is a php framework that is open source and uses the MVC method (Model, View, Controller). codeigniter is free or not paid if you use it. Codeigniter framework is made with the same purpose as other frameworks, namely to facilitate developers or programmers in building a web-based application without having to create it from scratch. The results of testing of this method are able to help and minimize the entry of hackers and secure data that will later become information. Using this method of combining can safeguard the entry of hackers and help secure the data and information on the website.
Edge Detection on Objects of Medical Image with Enhancement Multiple Morphological Gradient Method Na`am, Jufriadif
Proceeding of the Electrical Engineering Computer Science and Informatics Vol 4: EECSI 2017
Publisher : IAES Indonesia Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1424.066 KB) | DOI: 10.11591/eecsi.v4.997

Abstract

Medical image is an invaluable tool in the detection of diseases or abnormalities in human organs. The low quality of medical images cause difficulty in observing the objects contained in the image, causing errors in detection. This research develops a method for improving the quality of medical images so that the edges of objects more clearly. This method is called Enhancement multiple Morphological Gradient Enhancement (EmMG). The method used at medical images had different formats, that as Computed Tomography Scan (CT-Scan) with format type Windows bitmap (bmp), Chest X-Ray with format type Joint Photographic Experts Group (jpg) and Panoramic X-Ray with format type Portable Network Graphics (png). The method developed produces images that can further clarify the edge of the object in the medical image, making it easier to detect diseases or abnormalities in the human body. This method can used as one of the solutions in medical help to improve the accuracy in detecting objects in medical images because the edge of the objects seen clearly.
Bagian 2: Model Arsitektur Neural Network Dengan Kombinasi K-Medoids dan Backpropagation pada kasus Pandemi Covid-19 di Indonesia Windarto, Agus Perdana; Na`am, Jufriadif; Yuhandri, Yuhandri; Wanto, Anjar; Mesran, Mesran
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 4, No 4 (2020): Oktober 2020
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v4i4.2505

Abstract

The aim of the research is to create a prediction model on the best neural network architecture by combining the k-medoids and backpropagation methods in the case of the COVID-19 pandemic in Indonesia. Data obtained from the Ministry of Health is sampled and processed from covid19.go.id and bnpb.go.id. The case raised was the number of the spread of the COVID-19 pandemic in Indonesia as of July 7, 2020, with 34 records. The variables used in this study are the number of positive cases (x1), the number of cases cured (x2), and the number of deaths (x3) by province. The process of data analysis uses the help of RapidMiner software. The solution provided is to combine the k-medoids and backpropagation methods. Where the k-medoids method is mapping the specified cluster. The cluster labels used are high cluster (C1 = red zone), alert cluster (C2 = yellow zone), low cluster (C3 = green zone). The results of cluster mapping are continued to the backpropagation method to predict the accuracy of the existing cluster results. By using the best architectural model 3-2-1, the accuracy value is 94.17% with learning_rate = 0.696. Cluster mapping results obtained nine provinces are in the high cluster (C1 = red zone), three provinces are in the alert cluster (C2 = yellow zone), and 22 provinces are in the low cluster (C3 = green zone). It is expected that the results of the research can provide information to the government in the form of cluster mapping of regions in Indonesia.
Enlarge Medical Image using Line-Column Interpolation (LCI) Method Jufriadif Na'am; Julius Santony; Yuhandri Yuhandri; Sumijan Sumijan; Gunadi Widi Nurcahyo
International Journal of Electrical and Computer Engineering (IJECE) Vol 8, No 5: October 2018
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (548.955 KB) | DOI: 10.11591/ijece.v8i5.pp3620-3626

Abstract

Quality of medical image has an important role in constructing right medical diagnosis. This paper recommends a method to improve the quality of medical images by increasing the size of the image pixels. By increasing the size of pixels, the size of the objects contained therein is also greater, making it easier to observe. In this study medical images of Brain CT-Scan, Chest X-Ray and Panoramic X-Ray were processed using Line-Column Interpolation (LCI) Method. The results of the treatment are then compared to Nearest Neighbor Interpolation (NNI), Bilinear Interpolation (BLI) and Bicubic Interpolation (BCI) processing results. The experiment shows that Line-Column Interpolation Method produces a larger image with details of the objects in it are not blurred and has equal visual effects. Thus, this method is expected to be a reference material in enlarging the size of the medical image for ease in clinical analysis.
An Automatic ROI of The Fundus Photography Jufriadif Na'am; Johan Harlan; Irawadi Putra; Romi Hardianto; Mutiana Pratiwi
International Journal of Electrical and Computer Engineering (IJECE) Vol 8, No 6: December 2018
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (343.832 KB) | DOI: 10.11591/ijece.v8i6.pp4545-4553

Abstract

The Region of interest (ROI) of the fundus photography is an important task in medical image processing. It contains a lot of information related to the diagnosis of the retinal disease. So the determination of this ROI is a very influential first step in fundus image processing later. This research proposed a threshold method of segmentation to determine ROI of the fundus photography automatically. Data to be elaborated were the fundus photography’s of 13 patients, captured using Nonmyd7 camera of Kowa Company Ltd in Dr. M. Djamil Hospital, Padang. The results of this processing could determine ROI automatically. The automatic cropping successfully omits as much as possible the non-medical areas shown as darkbackground, while still maintaining the whole medical areas, comprised the posterior pole of retina captured through the pupil. Thus, this method is  helpful in further image processing of posterior areas. We hope that this research will be useful for researchers.
Detection of Proximal Caries at The Molar Teeth Using Edge Enhancement Algorithm Jufriadif Na'am; Johan Harlan; Sarifuddin Madenda; Julius Santony; Catur Suharinto
International Journal of Electrical and Computer Engineering (IJECE) Vol 8, No 5: October 2018
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (269.732 KB) | DOI: 10.11591/ijece.v8i5.pp3259-3266

Abstract

Panoramic X-Ray produces produces the most common oral digital radiographic image that it used in dentistry practice. The image can further improve accuracy compared to analog one. This study aims to establish proximal caries edge on enhancement images so they can be easily recognized. The images were obtained from the Department of Radiology, General Hospital of M. Djamil Padang Indonesia. Total file of images to be tested were 101. Firstly, the images are analyzed by dentists who practiced at Segment Padang Hospital Indonesia. They concluded that there is proximal caries in 30 molar teeth. Furthermore, the images were processed using Matlab software with the following steps, i.e. cropping, enhancement, edge detection, and edge enhancement. The accuracy rate of detection of edge enhancement images being compared with that of dentist analysis was 73.3%. In the edge enhancement images proximal caries edge can be found conclusively in 22 teeth and dubiously in eight teeth. The results of this study convinced that edge enhancement images can be recommended to assist dentists in detecting proximal caries. 
Accuracy of Panoramic Dental X-Ray Imaging in Detection of Proximal Caries with Multiple Morpological Gradient (mMG) Method Jufriadif Na`am
JOIV : International Journal on Informatics Visualization Vol 1, No 1 (2017)
Publisher : Politeknik Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (760.082 KB) | DOI: 10.30630/joiv.1.1.13

Abstract

Dental caries is tooth decay caused by bacterial infection. This is commonly known as tooth decay. Classification of caries by location consists of; occlusal caries, proximal caries, root caries and caries enamel. Diagnosis of dental caries in general carried out with the help of radiographic images is called Dental X-Ray. Dental X-Ray consists of bitewing, Periapical and Panoramic. Identification of proximal caries using Dental Panoramic X-Ray lowest precision was compared with both other Dental X-Ray. This study aims to perform sharpening and improving the quality of information contained in the image of Panoramic Dental X-Ray to clarify the edges of the objects contained in the image, making it easier to identify and proximal caries severity. The methods and algorithms used are multiple Morphology Gradient (mMG). The results obtained are increased accuracy in identifying proximal caries 47.5%. Based on the severity of it, that level of enamel = 47.37%; dentin rate = 42.1% and the rate of dentin = 1.3%. Accuracy level of accuracy in identifying proximal caries a higher level of email, so that patients with proximal caries early levels can be tackled early handling by the dentist
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.