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All Journal JUTI: Jurnal Ilmiah Teknologi Informasi Jurnal Ilmiah Kursor Prosiding SNATIKA Vol 01 (2011) Jurnal Teknologi Informasi dan Ilmu Komputer REKAYASA Proceedings Konferensi Nasional Sistem dan Informatika (KNS&I) ELINVO (Electronics, Informatics, and Vocational Education) Proceeding of the Electrical Engineering Computer Science and Informatics Network Engineering Research Operation [NERO] Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control JSiI (Jurnal Sistem Informasi) Multitek Indonesia : Jurnal Ilmiah Jambura Journal of Mathematics ComTech: Computer, Mathematics and Engineering Applications JoMMiT : Jurnal Multi Media dan IT JUSIFOR : Jurnal Sistem Informasi dan Informatika International Journal of Integrative Sciences Sains Data Jurnal Studi Matematika dan Teknologi
Devie Rosa Anamisa
Universitas Trunojoyo Madura
Agriculture, Biological Sciences & Forestry Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Education Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Library & Information Science Mathematics Physics Social Sciences

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Journal : ComTech: Computer, Mathematics and Engineering Applications

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K-Nearest Neighbors Method for Recommendation System in Bangkalan’s Tourism Devie Rosa Anamisa; Achmad Jauhari; Fifin Ayu Mufarroha
ComTech: Computer, Mathematics and Engineering Applications Vol. 14 No. 1 (2023): ComTech
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/comtech.v14i1.7993

Abstract

The more tourist objects are in an area, the more challenging it is for local governments to increase the selling value of these attractions. The government always strives to develop tourist attraction areas by prioritizing the beauty of tourist attractions. However, visitors often have difficulty in determining tourist objects that match their criteria because of the many choices. The research developed a tourist attraction recommendation system for visitors by applying machine learning techniques. The machine learning technique used was the K-Nearest Neighbor (KNN) method. Several trials were conducted with a dataset of 315 records, consisting of 11 attributes and 21 tourist attractions. Based on the dataset, the preprocessing stage was previously carried out to improve the data format by selecting data where the data were separated based on existing criteria, then calculating the closest distance and determining the value of k in the KNN method. The results are divided into five folds for each classification method. The highest system accuracy obtained at KNN is 78% at k=1. It shows that the KNN method can provide recommendations for three tourist attraction classes in Bangkalan. Applying the KNN method in the recommendation system determines several alternative tourist objects that tourists can visit according to their criteria in natural, cultural, and religious tourist objects.
Prediction Model for Tourism Object Ticket Determination in Bangkalan, Madura, Indonesia Fifin Ayu Mufarroha; Akhmad Tajuddin Tholaby; Devie Rosa Anamisa; Achmad Jauhari
ComTech: Computer, Mathematics and Engineering Applications Vol. 14 No. 2 (2023): ComTech
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/comtech.v14i2.7992

Abstract

One of the regencies in Madura, namely Bangkalan, with its local wisdom and beautiful landscapes has the potential to become a tourism center. However, there may be a decrease in the number of visits caused by some factors. The research used the time series method to build a prediction model for tourist attraction entrance tickets. The model development aimed to estimate the number of tourist attraction visits in the future. The right model was needed to get the best prediction results. Least square, Holt-Winter, Seasonal Autoregressive Integrated Moving Average (SARIMA), and Rolling were chosen as the models. Data collection related to the number of tourist objects was carried out directly at the Tourism Office to obtain valid data. Using data on visitors to tourist attractions in Bangkalan Regency from 2015 to 2019, the results of measuring errors using Mean Absolute Percentage Error (MAPE) and Root Mean Squared Error (RMSE) are obtained. The error measurement results show that the Holt-Winter model has the lowest error rate of 5% and RMSE of 307,1198. Based on these calculations, the Holt-Winter model is the best model for determining tourist attraction entrance tickets. The ranking of the error measurement results from the highest to the lowest are Holt-Winter, Rolling, SARIMA, and Least Square methods.
Co-Authors Achmad Jauhari Achmad Jauhari, Achmad Admojo, Fadhila Tangguh Aeri Rachmad Agustiono, Wahyudi Ahmad Sahru Romadhon Akhmad Tajuddin Tholaby Ana Tsalitsatun Ni'mah Arif Djunaidy Bachtiar, Dimas Bain Khusnul Khotimah Bain Khusnul Khotimah Bain Khusnul Khotimah Faikul Umam Fathul Qorib Yusfila Fifin Ayu Mufarroha Firli Irhamni Ika Oktavia Suzanti Indah Pratiwi Intan Rofika Putri Khusnul Khotimah, Bain Mohammad Syarief Mohammad Yanuar Hariyawan Muhammad Yusuf Muhammad Yusuf Nuraini, Dinah Salsabila, Unik Hanifah Septiyanto, Triyas Setiani Syakur, Muhammad Ali Syarief, Mohammad Violina, Dina Wahyu Indra Kustina Wahyudi Agustiono Wahyudi Agustiono Wanda, Isnaini Septyana Wulandari, Ana Yuniasti Retno Yeni Kustiyahningsih