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All Journal Sistemasi: Jurnal Sistem Informasi Indonesian Journal Computer Science (ijcs)
M. NURAMINUDIN
STMIK Amikom Yogyakarta
Computer Science & IT Electrical & Electronics Engineering

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Implementation Of MAUT Method for Making Website-based Tourism Recommendations in Yogyakarta based on Maps Location Nuraminudin, M.; Dewi, Melany Mustika; Dahlan, Akhmad
Sistemasi: Jurnal Sistem Informasi Vol 13, No 6 (2024): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v13i6.4653

Abstract

The significance of the tourism sector in contributing to the economy of the Special Region of Yogyakarta, which is a major destination with millions of tourists every year. The rapid growth of tourism demands the integration of information technology, especially in the development of recommendation systems, to improve visitor experience and satisfaction and support sustainable destination management. The main problem faced is the need to develop a recommendation system that is able to provide optimal suggestions for tourist locations and accommodations, considering various attributes such as price, facilities, location, and user personal preferences. The problem-solving approach taken in this study is the application of the Multi-Attribute Utility Theory (MAUT) Method. The research steps involve identifying problems, needs, and key attributes, collecting tourist location and accommodation data through Google Maps and Google Business Data. Furthermore, the raw data is subjected to processing and pre-processing before the MAUT model is developed. This model is implemented into a web-based application which is then tested using blackbox testing to determine whether the system is running properly. With the hope of creating a smart and sustainable tourism ecosystem, this study is innovative in using MAUT to improve the quality of recommendations in the Yogyakarta tourism industry.
Klasifikasi Opini Publik Turnamen PUBG Mobile PMCO Global 2019 Menggunakan Algoritma Naive Bayes Maulina, Dina; Alhamdi, Muhammad Hillal; Nuraminudin, M.
Indonesian Journal Computer Science Vol. 5 No. 1 (2026): April 2026
Publisher : LPPM Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/ijcs.v5i1.11552

Abstract

Analisis sentimen di media sosial penting untuk memahami pendapat orang banyak tentang suatu acara, termasuk dalam konteks turnamen e-sport. Penelitian ini bertujuan untuk mengetahui pendapat masyarakat tentang turnamen PUBG Mobile PMCO Global 2019 berdasarkan komentar pengguna di YouTube dengan menggunakan pendekatan machine learning. Dataset yang digunakan adalah dataset terupdate yang mewakili pendapat publik selama periode waktu yang lebih lama. Proses penelitian mencakup tahapan pra-pemrosesan data teks, penilaian fitur dilakukan dengan metode Term Frequency–Inverse Document Frequency (TF-IDF), serta proses pengklasifikasian sentimen menggunakan algoritma Naive Bayes dan Support Vector Machine (SVM). Hasil analisis menunjukkan bahwa sebagian besar komentar pengguna memiliki sentimen netral, yaitu sebesar 55,5%, kemudian diikuti oleh sentimen negatif sebesar 22,9%, dan sentimen positif hanya mencapai 21,6%. Pengujian kinerja model dilakukan dengan menggunakan confusion matrix. Hasilnya menunjukkan bahwa algoritma SVM menghasilkan akurasi sebesar 87,27%, lebih tinggi daripada algoritma Naive Bayes yang hanya mencapai 81,93%, meskipun Naive Bayes tetap menawarkan efisiensi komputasi yang baik. Temuan penelitian ini menunjukkan bahwa pendekatan klasifikasi sentimen berbasis TF-IDF dan machine learning sangat efektif untuk menganalisis pendapat masyarakat di platform media sosial yang menggunakan video seperti YouTube, khususnya dalam konteks lomba e-sport secara internasional.
Co-Authors Akhmad Dahlan Alhamdi, Muhammad Hillal Dina Maulina