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All Journal Jurnal Teknologi Informasi, Komputer, dan Aplikasinya (JTIKA )
Fahri Aulia Alfarisi Harahap
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Computer Science & IT Decision Sciences, Operations Research & Management Engineering

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IMPLEMENTASI DECISION SUPPORT SYSTEM METODE SIMPLE MULTI ATTRIBUTE RATING TECHNIQUE DALAM PEMILIHAN RUMAH KOST DISEKITAR KAMPUS UNIVERSITAS NEGERI MEDAN Ronaldo Mardianson Sinaga; Fahri Aulia Alfarisi Harahap; Anggi Tasari; Deby Yandra Niska
JTIKA (Jurnal Teknik Informatika, Komputer dan Aplikasinya) Vol 4 No 2 (2022): September 2022
Publisher : Program Studi Teknik Informatika, Fakultas Teknik, Universitas Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/jtika.v4i2.192

Abstract

The need for information on the boarding house and its location at this time is very important. However, due to the large amount of information about boarding houses and their available locations, the general public, especially students studying at universities, are confused about finding the location of a boarding house that suits their needs. Nowadays, many people use websites to find information about boarding houses, but the website only provides or provides information. Therefore, this research was built with the title "Implementation of the Decision Support System Method of Simple Multi Attribute Rating Technique in the Selection of Boarding Houses Around the Medan State University Campus" which aims to create a website-based information system in determining boarding recommendations so that the general public, especially students, can choose boarding houses. - boarding according to their criteria and needs. The method used in this research is the Simple Multi Attribute Rating Technique (SMART).
IMPLEMENTASI ALGORITMA CONVOLUTIONAL NEURAL NETWORK UNTUK MENDETEKSI PENYAKIT GINJAL Fahri Aulia Alfarisi Harahap; Ronaldo Mardianson Sinaga; Khusnul Arifin; Kana Saputra S
JTIKA (Jurnal Teknik Informatika, Komputer dan Aplikasinya) Vol 4 No 2 (2022): September 2022
Publisher : Program Studi Teknik Informatika, Fakultas Teknik, Universitas Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/jtika.v4i2.202

Abstract

There are several types of kidney disease, such as kidney cancer, tumors, etc. Kidney disease can be detected early, to find out what type of disease the patient has. In the world of artificial intelligence, there is a term called Convolutional Neural Network (CNN) which is often used in image data processing. CNN is a category of artificial neural network which is effective in performing image recognition and classification of image data. The purpose of this research is to find out how to apply the CNN algorithm in detecting kidney disease based on existing image data. This research was developed using the Python programming language and will be implemented into a web-based system. The results obtained from this research are the formation of a web-based system, which this website can be used to detect types of kidney disease based on the input images performed. This kidney disease classification website has been successfully created using the Flask Framework with the API from Google Colab which produces the h5 model and Visual Studio Code. Websites can be run on all types of computer operating systems. Image training data using a CNN algorithm derived from 9334 data trains and 3110 data validations. In this case, 4 classes of data image are used, namely cyst kidney data, normal kidney data, tumor kidney data and stone kidney data. It was found that the accuracy of the f1 score was 68%.
Co-Authors Anggi Tasari Kana Saputra S Khusnul Arifin Niska, Debi Yandra Ronaldo Mardianson Sinaga Ronaldo Mardianson Sinaga