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All Journal Techno.Com: Jurnal Teknologi Informasi MATICS : Jurnal Ilmu Komputer dan Teknologi Informasi (Journal of Computer Science and Information Technology) Prosiding Seminas Competitive Advantage Register: Jurnal Ilmiah Teknologi Sistem Informasi SINTECH (Science and Information Technology) Journal Applied Information System and Management
Hani Nurhayati
Department Of Informatics Engineering, Universitas Islam Negeri Maulana Malik Ibrahim Malang
Computer Science & IT Education Engineering Languange, Linguistic, Communication & Media

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Performance of Multi-Criteria Recommender System Using Cosine-Based Similarity for Selecting Halal Tourism Nadhifah, Rizqi Aulia; Arif, Yunifa Miftachul; Nurhayati, Hani; Angreani, Linda Salma
Applied Information System and Management (AISM) Vol. 5 No. 2 (2022): Applied Information System and Management (AISM)
Publisher : Depart. of Information Systems, FST, UIN Syarif Hidayatullah Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15408/aism.v5i2.25035

Abstract

Tourism is an activity where people or groups travel voluntarily for relaxation, seeking entertainment, or enjoy cultural diversity both within the city, outside the city, or even abroad. For traveling, information about halal tourism is essential that tourists must know. Tourists can contact a tour guide to find information and recommendations for halal tourism. However, it will cost quite a bit and need for a recommendation system to obtain recommendations and make it easier for tourists to determine which halal tourism to visit. This study aims to obtain the Multi-Criteria Recommender System's (MCRS) performance using cosine-based similarity to select halal tourism in Batu City. MCRS extends the traditional approach by using more than one scoring criteria to generate recommendations. The implementation of MCRS using cosine-based similarity succeeded in producing the five highest recommendations for halal tourist attractions, which were implemented in a game-based system. Through recommendation accuracy testing on two items, three items, four items, and five tourist attractions items, we obtained an average accuracy is 77,95%.
Non-Rating Recommender System for Choosing Tourist Destinations Using Artificial Neural Network Arif, Yunifa Miftachul; Wardani, Dyah; Nurhayati, Hani; Diah, Norizan Mat
Applied Information System and Management (AISM) Vol. 6 No. 2 (2023): Applied Information System and Management (AISM)
Publisher : Depart. of Information Systems, FST, UIN Syarif Hidayatullah Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15408/aism.v6i2.26741

Abstract

The development of tourist destinations in Batu City makes it hard for the tourists to decide their destinations. The recommender system is a solution that provides a lot of information or tourist attraction data. Collaborative filtering is often used in recommender systems. However, it has drawbacks; one of which is the cold-start problem, where the system cannot recommend items to new users. It was caused by the new user who had no history of rating on any item, or the system had no information. This study aims to apply a non-rated travel destination recommendation system to address the cold-start problem for new users. We use a multi-layer perceptron or artificial neural networks to overcome the problem by training user preference data to produce high training accuracy. Based on four experiments in the training data, the network architecture shows 5 – 7 – 5 – 3 –14, which is the highest accuracy. The architecture uses five variables as inputs and three hidden layers, with each layer was activated using the ReLU activation function. The output layer produces 14 binary outputs and is activated using the sigmoid activation function. The system can give recommendations to new users using feedforward from test data with updated values in weights and biases. The test results from 46 test data showed an accuracy of 67.235%.
Co-Authors Ahmad Fahmi Karami Diah, Norizan Mat Dyah Wardani Fachrul Kurniawan Fresy Nugroho Linda Salma Angreani M Faisal Mawaridi, Bima Hamdani Medyanti, Wikke Alvina Misbakhul Mustofin Muhammad Faisal Nadhifah, Rizqi Aulia Norizan Mat Diah Nugroho, Fressy Rizqi Aulia Nadhifah Rohma, Salma Ainur Tarranita Kusumadewi Wardani, Dyah Yunifa Miftachul Arif