72 BAB VI
PENUTUP
6.1. Kesimpulan
Dari hasil yang didapat, kesimpulan yang dapat ditarik dari penelitian
tentang algoritma rekomendasi yang memanfaatkan Pearson-Correlation dalam
mencari neighbor ini adalah :
Penyaringan neighbor dengan memanfaatkan Pearson-Correlation dalam
sistem rekomendasi dan menaikkan batas korelasi-nya dapat
mempengaruhi nilai akurasi maupun presisi rekomendasi pada porsi data
rating yang semakin bertambah di lingkungan yang bersifat offline.
6.2. Saran
Beberapa saran yang dapat diterapkan pada algoritma rekomendasi yang
memanfaatkan Pearson-Correlation dalam pencarian neighbor ini antara lain :
1. Menambahkan atau menggabungkan algoritma rekomendasi UCF pada
penelitian ini dengan algoritma rekomendasi lainnya untuk
meningkatkan keakuratan rekomendasi.
2. Mengganti lingkungan sistem dari yang bersifat offline menjadi on-line
dengan cara mengimplementasikan sistem rekomendasi yang
memanfaatkan algoritma rekomendasi pada penelitian ini kedunia
nyata dengan data real-time. 6.1. Kesimpulan
Dari hhasil yang didapaatt, kkesesimmpupulalan yayang dapat ditarariki dari penelitian tentangg algoritma reekkomemendn asi yang memanffaatkakan n PPearson-Correlelataion dalam me
mencari neeigighbhboror ini adadalalah :
P
Penyarariingan neighbor dengan memanfaatkan r Pearson-n-Corrr elelatation dalalam siststem rekomendasi dan menaikkan batas korelelasa i-nynya a dad paat t m
mempengaruhi nilai akurasi maupun presisi rekomendasi paadad pororsisi data rating yang semakin bertambah di lingkungan yang bersifat g offffline.
6.2.2. Saaran
Beberapa saran yang dadapapat t didterarapkpkaan pada algoritma rekomendasi yayangng me
memam nfaatkan Pearson-Correlation dalam pencarian neighbor ini antara llainn :
1. MMenambmbahahkakan atau mmenenggggababunungkgkan algororititmama rekomendasi Uk UCFCF pada pe
p nenelilititian ini dengagan algogoritma rekomendndasasi lalaininnnya untuk meningkatkan keakurratan rekomendasi.
73 3. Mengelompokkan terlebih dahulu para pengguna / rating / film
berdasarkan sifat-sifat atau pola yang ada didalamnya untuk
menyeleksi neighbor dengan tujuan meningkatkan akurasi dan presisi
rekomendasi
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