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Journal : SISFOTENIKA

Sistem Rekomendasi dan Peminjaman Buku Menggunakan Algoritma Hybrid Based Filtering Lily Aprilyani; Ransi, Natalis; Saputra, Rizal Adi; Isnawaty, Isnawaty
SISFOTENIKA Vol. 14 No. 2 (2024): SISFOTENIKA
Publisher : STMIK PONTIANAK

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Abstract

SMAN 1 Ladongi in East Kolaka has gained a reputation as an educational institution that is highly committed in providing quality education for the younger generation. SMA Negeri 1 Ladongi in East Kolaka Regency has a diverse collection of books, but the library service is still done manually. This process is not only time-consuming, but also less efficient, especially considering the large number of book collections. Implementing an online book recommendation and lending system can be a solution to improve the effectiveness and efficiency of library services. With a mobile web-based system, students and library staff can easily access book recommendations and perform the loan process online. This research uses a Hybrid Recommendation System that combines Item-Based Collaborative Filtering and User-Based Collaborative Filtering methods. The combination of these two methods aims to obtain better recommendation results. Based on the results of testing the accuracy of the recommendations that have been carried out, it is obtained for the MAE value of 4.52, then the MSE value is 0.02 and for the MAPE value obtained is 0.76%.
Sistem Rekomendasi Produk UMKM Menggunakan Algoritma User-Based Collaborative Filtering Berbasis Website Esy Anugerah Rahayu Kasim; Statiswaty, Statiswaty; Ransi, Natalis; Isnawaty, Isnawaty
SISFOTENIKA Vol. 14 No. 2 (2024): SISFOTENIKA
Publisher : STMIK PONTIANAK

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Kolaka Regency is one of the regions in Southeast Sulawesi Province that is rich in quality MSME products, but faces obstacles in promoting these products to a wider market. This research aims to adapt and optimise the UBCF method for the Kolaka MSME context and improve the algorithm to overcome the challenges faced, especially data limitations and variations in customer preferences that change quickly. This research method uses a recommendation engine approach with the UBCF method, which is applied through the stages of data preparation, UBCF implementation on a web-based system, and recommendation accuracy testing. The data used is product rating data from users, which is then processed using UBCF. The test results show that this recommendation system is able to provide fairly accurate rating predictions. Based on the results of testing the accuracy of the recommendations that have been carried out, it is obtained for the MAE value of 1.11, then the MSE value of 0.0649 and for the MAPE value obtained of 1.65%. This research contributes to improving the competitiveness of MSME products in Kolaka through UBCF technology, and provides a model that can be applied in other areas with similar characteristics.