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All Journal Jurnal Nasional Ilmu Komputer
A.Haidar Mirza
Universitas Bina Darma
Computer Science & IT

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Web Scraper Dan Graphql API Untuk Data Perguruan Tinggi Di Indonesia Berdasarkan Website Kementerian Ristekdikti (Studi Kasus: Website Kementerian Ristekdikti) Lingga Tiara; Hadi Syaputra; Widya Cholil; A.Haidar Mirza
Jurnal Nasional Ilmu Komputer Vol. 2 No. 3 (2021): Jurnal Nasional Ilmu Komputer
Publisher : Training and Research Institute Jeramba Ilmu Sukses (TRI - JIS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47747/jurnalnik.v2i3.533

Abstract

The urge to obtain and transmit information is one of the reasons for current technological developments, such as the need for students to access higher education reporting forums or Forlap Dikti. Forlap Dikti is a website page developed by the Ministry of Research, Technology and Higher Education that contains data on student academic activities based on reporting data from universities in Indonesia. Therefore we need a support application to be able to facilitate and expedite the need for access to information on the Forlap Dikti website page using the android application. This study aims to: (1) Help collect student data as material for presenting the required information, (2) Design an application as a medium for finding information on student data at Bina Darma University, (3) With an efficient web scraping function it also helps in data analysis. This application uses puppeteer for scraping data from websites, App Inventor and GraphQL API to display student data.
Objek Deteksi Makanan Khas Palembang Menggunakan Algoritma YOLO (You Only Look Once) Lusiana Rahma; Hadi Syaputra; A.Haidar Mirza; Susan Dian Purnamasari
Jurnal Nasional Ilmu Komputer Vol. 2 No. 3 (2021): Jurnal Nasional Ilmu Komputer
Publisher : Training and Research Institute Jeramba Ilmu Sukses (TRI - JIS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47747/jurnalnik.v2i3.534

Abstract

Deep learning is a part of machine learning method that uses artificial neural network (ANN). The type of learning in deep learning can be supervised, semi-supervised, and unsupervised [7] . CNN & RNN (Supervised) and RBM & Autoencoder (Unsupervised) are deep learning algorithms. All of the above algorithms have uses in their respective fields, depending on what we want to use them for. One of the most frequently used cases for deep learning is object detection and classification. The Convolutional Neural Network (CNN) algorithm is the most widely used algorithm for object detection cases, one of the reasons because it is supported by Google's Tensorflow framework, but it turns out that there is one object detection algorithm that has a higher level of accuracy and processing speed, namely You Only Look Once (YOLO) which can run on 2 frameworks (Darknet & Darkflow) and is supported by GPU. That's why here the author prefers to do object detection with the You Only Look Once (YOLO) method. The research data with the title Palembang Food Detection Object Using the YOLO (You Only Look Once) Algorithm is a sample photo of food from Google Image. There are 31 types of Palembang specialties, each type consists of approximately 50 to 70 images, so the total images used are from 31 types of Palembang foods, namely 1955 images with jpeg format for training data, and 31 images with jpeg format typical Palembang foods for test data.
Co-Authors Hadi Syaputra Lingga Tiara Lusiana Rahma Susan Dian Purnamasari Widya Cholil