SISTEM REKOMENDASI KOMIK MENGGUNAKAN ALGORITMA TYPICALITY BASED COLLABORATIVE FILTERING

KURNIAWAN, RUDI (2022) SISTEM REKOMENDASI KOMIK MENGGUNAKAN ALGORITMA TYPICALITY BASED COLLABORATIVE FILTERING. S1 thesis, Universitas Mercu Buana Jakarta.

Preview	Text (HAL COVER) 01 COVER.pdf Download (525kB) | Preview
Preview	Text (ABSTRAK) 02 ABSTRAK.pdf Download (24kB) | Preview
	Text (BAB I) 03 BAB 1.pdf Restricted to Registered users only Download (22kB)
	Text (BAB II) 04 BAB 2.pdf Restricted to Registered users only Download (250kB)
	Text (BAB III) 05 BAB 3.pdf Restricted to Registered users only Download (22kB)
	Text (BAB IV) 06 BAB 4.pdf Restricted to Registered users only Download (599kB)
	Text (BAB V) 07 BAB 5.pdf Restricted to Registered users only Download (19kB)
	Text (DAFTAR PUSTAKA) 08 DAFTAR PUSTAKA.pdf Restricted to Registered users only Download (71kB)
	Text (LAMPIRAN) 09 LAMPIRAN.pdf Restricted to Registered users only Download (737kB)

Abstract

Comics are defined as illustrated stories (in magazines, newspapers or book form) which are generally easy to digest and funny. In the Popular Scientific Dictionary, comics are illustrated stories (about activities and so on) that are definitely funny. With the development of the comic era, it also began to experience development, namely towards the digital era. With this comic recommendation system, it can help us get recommendations for which comics we want the most and maybe read the most. However, this recommendation system has a weakness, so a new method was developed from the User Based CF method, namely Typical Based Collaborative Filtering (TyCo). TyCo works by searching from searches based on typicality. In addition, this method can overcome the Cold Start Problem. The results of testing 50 users got an average score of 302.14 comic recommendations from the 1000 available comics. Maybe in the future there will be more comic data collected and the number of trials. in order to get greater results from this test.. Keywords: Digital Comics, Recommendation System, Typicality Komik diartikan sebagai cerita bergambar (dalam majalah, surat kabar, atau bentuk buku) yang umumnya mudah dicerna dan lucu. Dalam Kamus Ilmiah Populer, komik adalah cerita bergambar (tentang aktivitas dan sebagainya) yang pasti lucu. Dengan berkembangnya jaman komik pun juga mulai mengalami perkembangan yaitu menuju era digital. Dengan adanya system rekomendasi komik ini bisa membantu kita untuk mendapatkan rekomendasi komik mana yang paling kita inginkan dan mungkin paling banyak dibaca. Akan tetapi system rekomendasi ini memiliki sebuah kelemahan , Sehingga dikembangkanlah metode baru dari metode User Based CF yaitu Typicality Based Collaborative Filtering (TyCo). Cara kerja TyCo ini dengan mencari dari pencarian berdasarkan tipikalitas . Selain itu metode ini dapat mengatasi Cold Start Problem. Hasil dari pengujian 50 user mendapat nilai rata rata sebesar 302.14 hasil rekomendasi komik dari 1000 komik yang tersedia. Mungkin kedepannya akan diperbanyak data komik yang dikumpulkan dan jumlah percobaan nya . agar mendapatkan hasil yang lebih besar dari pengujian ini. Kata Kunci : Komik Digital, Sistem Rekomendasi, tipikaslitas

Item Type:	Thesis (S1)
Call Number CD:	FIK/INFO. 22 198
NIM/NIDN Creators:	41517120052
Uncontrolled Keywords:	Komik Digital, Sistem Rekomendasi, tipikaslitas
Subjects:	000 Computer Science, Information and General Works/Ilmu Komputer, Informasi, dan Karya Umum > 000. Computer Science, Information and General Works/Ilmu Komputer, Informasi, dan Karya Umum 000 Computer Science, Information and General Works/Ilmu Komputer, Informasi, dan Karya Umum > 000. Computer Science, Information and General Works/Ilmu Komputer, Informasi, dan Karya Umum > 003 Systems/Sistem-sistem 000 Computer Science, Information and General Works/Ilmu Komputer, Informasi, dan Karya Umum > 000. Computer Science, Information and General Works/Ilmu Komputer, Informasi, dan Karya Umum > 004 Data Processing, Computer Science/Pemrosesan Data, Ilmu Komputer, Teknik Informatika 000 Computer Science, Information and General Works/Ilmu Komputer, Informasi, dan Karya Umum > 000. Computer Science, Information and General Works/Ilmu Komputer, Informasi, dan Karya Umum > 004 Data Processing, Computer Science/Pemrosesan Data, Ilmu Komputer, Teknik Informatika > 004.2 Systems Analysis and Computer Design, Computer Architecture, Computer Performance Evaluation/Sistem Analis dan Desain Komputer, Arsitektur Komputer, Evaluasi Daya Guna dan Performa Komputer 500 Natural Science and Mathematics/Ilmu-ilmu Alam dan Matematika > 510 Mathematics/Matematika > 518 Numerical Analysis/Analisis Numerik, Analisa Numerik > 518.1 Algorithms/Algoritma
Divisions:	Fakultas Ilmu Komputer > Informatika
Depositing User:	ADELINA HASNA SETIAWATI
Date Deposited:	24 Mar 2023 03:25
Last Modified:	24 Mar 2023 03:25
URI:	http://repository.mercubuana.ac.id/id/eprint/75399

Actions (login required)

View Item