Performa Algoritma User K-Nearest Neighbors pada Sistem Rekomendasi di Tokopedia

Syah, Rama Dian

doi:10.32493/informatika.v5i3.6312

Cited by 2 publications

(3 citation statements)

References 6 publications

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“…The Recommendation System has several methods and algorithms that can be applied. Performance testing of the application of the algorithm on the Recommendation System can measure the algorithm's success in providing recommendations to users [3]. The recommendation system has been widely used today to assist the community in making decisions.…”

Section: Introductionmentioning

confidence: 99%

“…Furthermore, research on the performance of the K-Nearest Neighbors User Algorithm on the Recommendation System at Tokopedia [3] in this study uses an item rating prediction based on the assessment given by the buyer. As for the research on the implementation of the K-Nearest Neighbor Algorithm on the Laptop Recommendation Website [6], in this study, the user is immensely helped by the existence of a website that makes it easy for users to view specification data or images from laptops recommended on the website.…”

Section: Introductionmentioning

confidence: 99%

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Smartphone Purchase Recommendation System Using the K-Nearest Neighbor (KNN) Algorithm

Setiaji

Utama²,

Adiwijaya³

2022

mib

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Indonesia is in the fourth position of the countries with the most smartphone users worldwide. Smartphones are needed in today's modern times. Smartphones are also used not only for long-distance communication but also for carrying out daily work. Smartphones are currently used for study and work and also become entertainment to play. Therefore, smartphones are very much sought after for the suitability of users who carry out their daily activities. So this research is very helpful for users to find smartphones that support their daily activities such as studying, working, and playing. This research is based on a website that can make it easier for users to see their smartphone recommendations directly. The analysis uses the K-Nearest Neighbor (KKN) method to see the ratings reviewed by other users who have tried using their smartphones with different phone brands. The calculation method in the current study uses 3 KNN calculations and uses the concept of combining calculations to find the maximum recommendation results. The result of the recommendation system using the K-Nearest Neighbor method is in the form of a review stating whether the user agrees or disagrees. In the current study, there have been 100 reviews from users, and it has a percentage of 78% for success and 22% for failure.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Smartphone Purchase Recommendation System Using the K-Nearest Neighbor (KNN) Algorithm

Setiaji

Utama²,

Adiwijaya³

2022

mib

View full text Add to dashboard Cite

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“…Collaborative filtering idibagi menjadi dua jenis, yaitu memory-based dan model-based [1], [8] Penelitian tersebut menghasilkan nilai akurasi sebesar 73,53%, nilai precision sebesar 73,64%, dan nilai recall sebesar 99,62%. [10]. Dari penelitian tersebut dapat disimpulkan bahwa algoritma KNN berhasil untuk diterapkan pada pembuatan sistem rekomendasi.…”

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Smartphone Recommendation System Using Model-Based Collaborative Filtering Method

Prayoga¹,

Kusnawi²

2022

J. Tek. Inform. (JUTIF)

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Smartphone are now an importan item that is needed by many people. The rapid development of technology make smartphone companies are competing to release their best smartphones.The many smartphones in online shop cause user to become disoriented about their choice. A recommendation system can help the user in choosing the smartphone that the user likes. In this study, a recommendation system was made using the collaborative filtering method with the K-Nearest Neighbors algorithm and combined with the application of K-Means algorithm to divide the smartphone into several group. The output of collaborative filtering method is that the model can give smartphone rating predictions to user. The prediction results will be used as the basis for giving recommendations to user. The purpose of smartphones groupping is so that the recommendation results are more specific and accurate. The evaluation of the model gets an MAE value is 1.1047 and RMSE value is 1.7579. So it can be concluded that the development of a smartphone recommendation system was successfully implemented.

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Performa Algoritma User K-Nearest Neighbors pada Sistem Rekomendasi di Tokopedia

Cited by 2 publications

References 6 publications

Smartphone Purchase Recommendation System Using the K-Nearest Neighbor (KNN) Algorithm

Smartphone Purchase Recommendation System Using the K-Nearest Neighbor (KNN) Algorithm

Smartphone Recommendation System Using Model-Based Collaborative Filtering Method

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