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All Journal Building of Informatics, Technology and Science JIKA (Jurnal Informatika) Jurnal Pendidikan dan Teknologi Indonesia Jurnal Ilmiah Computer Science Jurnal Informatika dan Rekayasa Perangkat Lunak
Ariany, Fenty
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Engineering

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Journal : Building of Informatics, Technology and Science

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Decision Support System for Tourist Attraction Recommendations Using Reciprocal Rank and Multi-Objective Optimization on the basis of Ratio Analysis Ariany, Fenty; Suryono, Ryan Randy; Setiawansyah, Setiawansyah
Building of Informatics, Technology and Science (BITS) Vol 5 No 3 (2023): December 2023
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v5i3.4663

Abstract

A tourist attraction is a destination or place visited by tourists to enjoy a variety of attractions, natural beauty, culture, history, or recreation. Attractions can be beaches, mountains, lakes, national parks, historical buildings, museums, amusement parks, and much more. One common problem is confusion in choosing the right attraction, where the information available is incomplete or inaccurate, causing tourists difficulty in making the right decision. Therefore, there needs to be a holistic and integrated approach in choosing tourist attractions, taking into account these aspects so that the tourist experience becomes more meaningful and meaningful for all parties involved. The research objective of the Attraction Recommendation Decision Support System Using Reciprocal Rank and MOORA is to develop a system that can provide optimal attraction recommendations to users based on their preferences against diverse criteria, such as distance, cost, travel time, and cleanliness level. By using the Reciprocal Rank approach to take into account the user's subjective preferences towards each criterion. Meanwhile, by applying MOORA, this study aims to optimize the relative performance of alternative attractions based on the relationship between criteria. Thus, this research is to provide useful tools for users to make better and more informed decisions. The ranking results provide recommendations for alternative krui beach with a final value of 0.3752 to rank 1, alternative tanjung setia beach with a final value of 0.3558 to rank 2, alternative klara beach with a final value of 0.3512 to rank 3
Komparasi Algoritma Naive Bayes dan K-Nearest Neighbor untuk Analisis Sentimen Pengguna Dompet Digital pada Google Play Store Akbar, M Adhe; Ariany, Fenty
Building of Informatics, Technology and Science (BITS) Vol 7 No 4 (2026): March 2026
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i4.9285

Abstract

The rapid growth of digital wallet users in Indonesia, reaching millions of active users, has generated a massive volume of reviews on the Google Play Store. This textual data contains crucial insights regarding customer satisfaction but is often underutilized due to challenges in processing unstructured data. This study aims to perform a comparative performance analysis between the probabilistic Naive Bayes algorithm and the distance-based K-Nearest Neighbor (KNN) in classifying user sentiment for DANA, OVO, DOKU, and LinkAja applications. This study utilizes a dataset of 18,869 reviews which exhibits a mild class imbalance with a negative sentiment dominance of 57.54%. To preserve the representation of the large original data, this research applies Stratified Sampling without synthetic data balancing techniques (such as SMOTE), followed by comprehensive preprocessing stages aided by the Sastrawi library and Term Frequency-Inverse Document Frequency (TF-IDF) feature extraction. Model optimization was systematically conducted using GridSearchCV for Naive Bayes and the Elbow Method to determine the optimal k value for KNN. Empirical test results show that the Naive Bayes algorithm with a smoothing parameter alpha of 0.1 achieved the best performance with an accuracy of 88.5% and an AUC of 0.9237, outperforming KNN at k=27 which obtained an accuracy of 87.4%. The validity of this performance difference was confirmed to be significant through the McNemar statistical test with a p-value of 0.0045. Another crucial finding is computational efficiency, where Naive Bayes proved to be 129 times faster in the prediction process compared to KNN. Based on the significant advantages in accuracy and time efficiency, Naive Bayes is recommended as the superior method for real-time sentiment analysis in the financial technology ecosystem.
Co-Authors Akbar, M Adhe Dedi Darwis DENY KURNIAWAN Gunawan, Rakhmat Dedi Khusairi, Ahmad Andi Ryan Randy Suryono Setiawansyah Setiawansyah tarisa, ekanofi Wang, Junhai