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All Journal JurTI (JURNAL TEKNOLOGI INFORMASI) CCIT (Creative Communication and Innovative Technology) Journal
Rizki Mawan
Universitas AMIKOM Yogyakarta
Computer Science & IT

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Analysis of Bali Tourism Data to Map the Potential of Tourism Objects Rizki Mawan; M. Rinandar Tasya; Dimas Wiryatari
CCIT Journal Vol 13 No 1 (2020): CCIT JOURNAL
Publisher : Universitas Raharja

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1153.493 KB)

Abstract

Tourism can not be separated from human life. The last few years tourism has become a culture for modern society both locally and abroad. To increase the potential of tourist attractions can be known by the presence of public facilities around it such as hospitals, restaurants, banks, Gas Stations, bars, shops, hotels and coffee. The purpose of this study was to determine how the influence of these facilities in the potential to increase the number of visitors. To determine the effect of public facilities available on attractions in the potential to increase visitor attraction, it must be known how far the facilities are from attractions and whatfacilities are in the vicinity of these attractions. The distance between facilities on the tourist attraction can be calculated using a predetermined formula. The most visited tourist attraction is Taman Budaya which is located in Denpasar Regency with a total of 7.500 visits from 2013- 2018. The most visited nationalities in Bali in 2018 are Middle East and the State of Turkey which is 9,247. Tourists visit Bali through 2 modes of transportation. The 2 modes of transportation are water and air. The total number of visitors with the mode of water transportation was 20,460,556.00 while the air transport mode was 237,428.00.
Pengaruh Dimensi Gambar pada Klasifikasi Motif Batik Menggunakan Convolutional Neural Network Rizki Mawan; Kusrini Kusrini; Hanif Al Fatta
(JurTI) Jurnal Teknologi Informasi Vol 4, No 2 (2020): DESEMBER 2020
Publisher : Universitas Asahan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36294/jurti.v4i2.1342

Abstract

Abstract – Indonesia is a country with many fascinating cultural assets. Batik is one of the most beautiful cultural assets that should be preserved. Batik existed with many motifs and styles and has been a significant cultural cloth for many regions spread along the Java island. This research proposed the computation for identifying three popular motifs and styles: megamendung, kawung, and parang. This research employed a Convolutional Neural Network classifier to identity those three popular batik motifs. This research used an image size of  64x64, 128x128, and 256x256  for the input images, and the influence of the size or dimension for these inputs have been analyzed. The final result showed that the highest accuracy is reached at 92.85 % on epoch = 240 and batch size = 5.Keywords  - Batik, Convolutional Neural Network, Accuracy, Dimension Abstrak - Banyak budaya di Indonesia yang masih menjadi kebanggan dan dijaga kelestarian nya. Salah satunya adalah batik. Jika berbicara tentang batik sekilas kita mengingat tentang berbagai macam motif yang dimiliki yang tersbar di Indonesia terutama di pulau Jawa. Pada penelitian kali ini motif batik yang diteliti adalah batik megamendung,batik kawung dan batik parang. Alasan pemilihan ketiga motif tersebut karena ketiga motif tersebut sangat diminati oleh khalayak ramai(Populer) , dan ketiga motif tersebut memliki makna tersendiri yang sangat mewakili masyarakat Indonesia. Tujuan dari klasifikasi batik adalah untuk mengetahui keakuratan akurasi motif batik khusus nya motif batik kawung,megamendung, dan parang. Fokus pada penelitian ini  adalah penggunaan dimensi gambar yang dapat mempengaruhi akurasi yang dihasilkan. Dimensi yang digunakan adalah 64x64,128x128, dan 256x256. Akurasi yang dihasilkan dengan menggunakan metode Convolutional Neural Network yang paling tinggi yaitu 92,85% dengan menggunakan  epoch= 240 dan batch_size=5.Kata Kunci - Batik, Convolutional Neural Network, Akurasi, Dimensi
Co-Authors Dimas Wiryatari Hanif Al Fatta Kusrini Kusrini M. Rinandar Tasya