Article Per Year (5 Year)
p-Index From 2020 - 2025
0
P-Index
This Author published in this journals
All Journal Journal of Big Data Analytic and Artificial Intelligence
Obert
Unknown Affiliation
Computer Science & IT

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
Desain Aplikasi Pencarian Kontrakan Kota Tarakan Berbasis Mobile Menggunakan Metode Algoritma Djikstra Evi Marliyani; Moh. Masduki Syahlan; Obert; Gusmana, Roman
Journal of Big Data Analytic and Artificial Intelligence Vol 5 No 1 (2019): JBIDAI Juni 2019
Publisher : STMIK PPKIA Tarakanita Rahmawati

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

Abstract

Nowadays, searching for rented accommodation in Tarakan city for students, employees, and the general public, in their search currently most still use word of mouth and social media. With this system it will be difficult to find information such as, in the process of searching for the address it also takes a very long time in searching for rented accommodation in Tarakan city usually students, employees, and the general public usually only listen to or know the information conveyed from one community to another so that the information obtained is not accurate. Dijkstra's algorithm is an algorithm for determining routes with short distances. It is assumed that all distances traveled are positive. The idea of ​​this algorithm is based on the fact that each minimum distance has more than one, but in fact there is only one distance to travel. This happens because all distances are positive. According to the results of the analysis obtained by the author in conducting research on, the Dijkstra Algorithm Method is that the method used is still very inefficient in determining the shortest route because this method does not calculate from all existing paths but only calculates the closest node from the starting point and will calculate when the node has branches and will choose the smallest value from the node that has branches.
Implementasi Metode K-Nearest Neighbor (KNN) dalam Pengelompokan Status Ekonomi Warga Riski Fadilla Karunia Dewi; Obert; Roman Gusmana
Journal of Big Data Analytic and Artificial Intelligence Vol 4 No 1 (2018): JBIDAI Juni 2018
Publisher : STMIK PPKIA Tarakanita Rahmawati

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

Abstract

Pengelompokan data berdasarkan keputusan yang telah ditetapkan sangat penting karena akan sangat berpengaruh terhadap hasil akhir dari pengelompokan yang di lakukan. Contoh kasus salah satunya adalah Semakin meningkatnya jumlah masyarakat di dalam suatu wilayah akan disertai dengan perubahan jumlah penduduk maupun status ekonominya. Semakin banyaknya data yang akan di kelompokkan maka akan membutuhkan waktu yang lebih banyak, untuk mempermudah dan mempercepat proses tersebut maka diperlukan suatu metode yang mampu mengolah data banyak dengan hasil yang akurat salah satu caranya adalah dengan data mining. Data mining merupakan mengelompokkan data yang terdiri dari berbagai macam metode pengelompokkan dengan harapan dapat mempermudah proses pengolahan data hingga pengelompokkan data. Salah satu metode dari data mining yaitu K-Nearest Neighbor atau biasa di sebut algoritma KNN. Pada penelitian ini penulis membuat aplikasi pengelompokan status ekonomi warga dengan mengimplementasikan metode KNN di dalamnya. Aplikasi yang di buat ini dapat melakukan input data, panggil data dan perbandingan data hingga memperoleh hasil berupa keterangan status ekonomi warga.
Co-Authors Evi Marliyani Gusmana, Roman Moh. Masduki Syahlan Riski Fadilla Karunia Dewi Roman Gusmana