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All Journal JUTI: Jurnal Ilmiah Teknologi Informasi Jurnal Mandiri IT JEECS (Journal of Electrical Engineering and Computer Sciences) West Science Interdisciplinary Studies West Science Interdisciplinary Studies West Science Social and Humanities Studies NERO (Networking Engineering Research Operation) West Science Nature and Technology INTER TECH
Rifki Fahrial Zainal
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Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Public Health Social Sciences

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ALGORITMA SHARED NEAREST NEIGHBOR BERBASIS DATA SHRINKING Rifki Fahrial Zainal; Arif Djunaidy
JUTI: Jurnal Ilmiah Teknologi Informasi Vol 7, No 1, Januari 2008
Publisher : Department of Informatics, Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (178.689 KB) | DOI: 10.12962/j24068535.v7i1.a56

Abstract

Shared Nearest Neighbor (SNN) algorithm constructs a neighbor graph that uses similarity between data points based on amount of nearest neighbor which shared together. Cluster obtained from representative points that are selected from the neighbor graph. The representative point is used to reduce number of clusterization errors, but also reduces accuracy. Data based shrinking SNN algorithm (SSNN) uses the concept of data movement from data shrinking algorithm to increase accuracy of obtained data shrinking. The concept of data movement will strengthen the density of neighbor graph so that the cluster formation process could be done from neighbor graph components which still has a neighbor relationship. Test result shows SSNN algorithm accuracy is 2% until 8% higher than SNN algorithm, because of the termination of relationship between weak data points in the neighbor graph is done slowly in several iteration. However, the computation time required by SSNN algorithm is three times longer than SNN algoritm computational time, because SSNN algorithm constructs neighbor graph in several iteration.
Analysis of the Indonesian Tourist Destination Recommendation System Using User Profile-Based Collaborative Filtering Hamidah, Mas Nurul; Zainal, Rifki Fahrial; Tias, Rahmawati Febrifyaning; Ardiansyah, Tio Kukuh
JEECS (Journal of Electrical Engineering and Computer Sciences) Vol. 11 No. 1 (2026): JEECS (Journal of Electrical Engineering and Computer Sciences) - In press
Publisher : Fakultas Teknik Universitas Bhayangkara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54732/jeecs.v11i1.6

Abstract

Tourism recommendation systems in Indonesia are challenged by highly heterogeneous user preferences and severe rating sparsity, which undermine the effectiveness of conventional collaborative filtering methods. However, prior studies predominantly rely on rating-based interactions and often utilize generic datasets, limiting their ability to capture the contextual and behavioural diversity of Indonesian tourism. Although user profile information is known to influence preferences, its integration with latent factor models is still fragmented and rarely evaluated in a unified, context-aware framework. Consequently, existing approaches often produce suboptimal accuracy and lack robustness in sparse and imbalanced data environments. This study proposes a unified user profile-enriched collaborative filtering framework that integrates Singular Value Decomposition (SVD), Jaccard similarity, and K-Nearest Neighbor (KNN) to jointly model latent preferences and contextual user characteristics. This integration constitutes the main novelty of this work, enabling simultaneous mitigation of sparsity and enhancement of personalization in a single pipeline. Experiments are conducted on an Indonesian tourism dataset, with performance evaluated using Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and execution time. The results show that the proposed method consistently outperforms the rating-based baseline, achieving lower MAE (1.6994 vs. 1.7355) and RMSE (2.0653 vs. 2.1148), while maintaining comparable computational efficiency. Furthermore, the model demonstrates greater stability across varying neighbor sizes, indicating improved scalability and robustness. Practically, this approach provides a scalable and context-aware recommendation framework that can support more adaptive and personalized tourism services in Indonesia, particularly in real-world scenarios characterized by sparse and heterogeneous data.
Co-Authors Alim, Syariful Ardiansyah, Tio Kukuh Arief Rahman Arif Arizal, Arif Arif Djunaidy bagus teguh santoso, bagus teguh Budi Endarto Hamidah, Mas Nurul Haryono Haryono Rangsang Purnama Tias, Rahmawati Febrifyaning