Image of Location Selection Based on Surrounding Facilities in Google Maps using Sort Filter Skyline Algorithm

Artikel Jurnal

Location Selection Based on Surrounding Facilities in Google Maps using Sort Filter Skyline Algorithm



Selecting a good location is an essential task in many location-based applications. Intuitively, a place is better than another if there are many good facilities around it. The most popular location selection platform today is Google Maps. Unfortunately, Google Maps has not provided the location selection based on the number of surrounding facilities. Assume a situation when a college student wants to let a house near his campus. Besides the distance from the campus, the student certainly will consider amenities surrounding it, such as food courts, supermarkets, health clinics, and places of worship. The rent house will become a better choice if there are more of these facilities around. Skyline query is a well-known method to select interesting desirable objects. We applied the Sort Filter Skyline (SFS) Algorithm on Google Maps to get a small number of attractive locations based on the number of nearby facilities. This study has succeeded in developing a web-based application that facilitates Google Maps users to search for places based on the figure of surrounding facilities. The time required to do a location search using SFS in Google Maps will increase with the number of surrounding facility types considered by the user.


Ketersediaan

JKI7-005JKI V7N2 Oktober 2021Perpustakaan FT UPI YAITersedia

Informasi Detil

Judul Seri
Khazanah Informatika : Jurnal Ilmu Komputer dan Informatika
No. Panggil
JKI V7N2 Oktober 2021
Penerbit Universitas Muhammadiyah Surakarta : Surakarta.,
Deskripsi Fisik
hlm : 65-72
Bahasa
English
ISBN/ISSN
2621-038X
Klasifikasi
JKI
Tipe Isi
-
Tipe Media
-
Tipe Pembawa
-
Edisi
Volume 7 Nomor 2 Oktober 2021
Subyek
Info Detil Spesifik
-
Pernyataan Tanggungjawab

Versi lain/terkait

Tidak tersedia versi lain




Informasi


DETAIL CANTUMAN


Kembali ke sebelumnyaXML DetailCite this