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All Journal JOURNAL OF APPLIED INFORMATICS AND COMPUTING Journal of Information Systems and Informatics
Pardede, Chandro
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Computer Science & IT

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Advancing Diversity in Recommendation Systems Through Collaborative Filtering: A Focus on Media Content Pardede, Chandro; Togatorop, Parmonangan R.; Panjaitan, Permana Gabriel
Journal of Information System and Informatics Vol 7 No 1 (2025): March
Publisher : Universitas Bina Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51519/journalisi.v7i1.1045

Abstract

A recommendation system provides suggestions based on user preferences, interests, or behavior. However, a major challenge is its tendency to generate monotonous recommendations, reducing diversity and limiting new user experiences. Therefore, increasing diversity is essential to enhance user experience and satisfaction while maintaining recommendation accuracy. This research proposes to apply collaborative filtering method, which focuses on item-based filtering using KNN. This method focuses on item similarity using cosine similarity. To enhance diversity, the system filters results based on similarity and rating thresholds. The evaluation results confirm that applying a similarity threshold increases recommendation diversity, as indicated by consistently higher individual diversity values. Clustering further enhances individual diversity. Findings show that the highest individual diversity with clustering reaches 0.5719, compared to 0.5706 without clustering. These improvements suggest potential applications in domains such as e-commerce and music recommendation systems.
Comparative Study of Manual and Generated Data Transfer Object Implementation Performance Pardede, Chandro; Sihombing, Wilson; Nainggolan, Winfrey
Journal of Applied Informatics and Computing Vol. 9 No. 5 (2025): October 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i5.10818

Abstract

The Data Transfer Object (DTO) is a fundamental component in Flutter application development, particularly in managing data serialization and deserialization. This study compares two DTO implementation methods—manual and generated—focusing on execution speed and memory efficiency. Testing was conducted across three levels of data complexity (Small, Medium, and Large) over 100 iterations using Flutter DevTools. The findings reveal that the generated approach (utilizing libraries such as json_serializable) consistently outperforms the manual approach. Specifically, it achieves a 1:1.147 ratio in parsing speed and a 1:1.42 ratio in memory efficiency compared to manual DTOs. Although the manual method provides greater flexibility for implementing conditional parsing logic, it tends to be more error-prone and less efficient when handling large datasets. In contrast, the generated approach offers faster performance, better scalability, and reduced human error potential, making it the preferred option for projects demanding technical efficiency and rapid development cycles. Consequently, this study recommends adopting generated DTOs for applications dealing with large-scale and complex data, while reserving manual DTOs for cases requiring highly dynamic or conditional data parsing.
Co-Authors Nainggolan, Winfrey Panjaitan, Permana Gabriel Sihombing, Wilson Togatorop, Parmonangan R.