Garuda - Garba Rujukan Digital

Lita Karlitasari

Universitas Pakuan

Author-ID : 295872

Agriculture, Biological Sciences & Forestry Humanities Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Medicine & Pharmacology Neuroscience Public Health Social Sciences

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Search Of Favorite Books As A Visitor Recommendation of The Fmipa Library Using CT-Pro Algorithm Maryana, Sufiatul; Karlitasari, Lita
Journal of Science Innovare Vol 1, No 01 (2018): Journal of Science Innovare
Publisher : Universitas Pakuan

The library of Faculty of Mathematics and Natural Science (FMIPA) has a collection of books and other print media, total of 2,678 books with 7237 visitors and 2148 borrowers. The available book search system was very helpful for visitors to find the required books. Especially if the system has features recommended of books. In the provision of book recommendations used one of the data mining techniques, namely association rule mining techniques or excavation of association rules. In the development of this recommendation system, KDD (Knowledge Discovery from Database) model was used. The data used was the transaction history of borrowing book with the category of "chemistry", for the last 5 (five) months, that is September 2014 - February 2015. The excavation technique of this association rule has 2 (two) main process, they are: frequent patterns and rules. To find frequent patterns, a CT-PRO algorithm was used. The minimum value of support used was 1 and 2. Once the pattern is found, the confidence value of each pattern was calculated. The minimum value of confidence used ranges from 10% to 100%. The recommendation rule was based on calculating the value of this confidence. The comparison of minimum support values indicates that the greater value of minimum support then the less borrowing pattern was generated, and vice versa. The comparison of minimum confidence value shows that the greater of minimum confidence value then the less recommended rule given.. Keywords: Library, Recommended System, Knowledge Discovery from Database (KDD), Association Rule Mining, CT-PRO Algorithm

Search Of Favorite Books As A Visitor Recommendation of The Fmipa Library Using CT-Pro Algorithm Sufiatul Maryana; Lita Karlitasari
Journal of Science Innovare Vol 1, No 01 (2018): Journal of Science Innovare
Publisher : Universitas Pakuan

The library of Faculty of Mathematics and Natural Science (FMIPA) has a collection of books and other print media, total of 2,678 books with 7237 visitors and 2148 borrowers. The available book search system was very helpful for visitors to find the required books. Especially if the system has features recommended of books. In the provision of book recommendations used one of the data mining techniques, namely association rule mining techniques or excavation of association rules. In the development of this recommendation system, KDD (Knowledge Discovery from Database) model was used. The data used was the transaction history of borrowing book with the category of "chemistry", for the last 5 (five) months, that is September 2014 - February 2015. The excavation technique of this association rule has 2 (two) main process, they are: frequent patterns and rules. To find frequent patterns, a CT-PRO algorithm was used. The minimum value of support used was 1 and 2. Once the pattern is found, the confidence value of each pattern was calculated. The minimum value of confidence used ranges from 10% to 100%. The recommendation rule was based on calculating the value of this confidence. The comparison of minimum support values indicates that the greater value of minimum support then the less borrowing pattern was generated, and vice versa. The comparison of minimum confidence value shows that the greater of minimum confidence value then the less recommended rule given.. Keywords: Library, Recommended System, Knowledge Discovery from Database (KDD), Association Rule Mining, CT-PRO Algorithm

Search Of Favorite Books As A Visitor Recommendation of The Fmipa Library Using CT-Pro Algorithm Sufiatul Maryana; Lita Karlitasari
Journal of Science Innovare Vol 1, No 1 (2018): Journal of Science Innovare, Volume 01 Number 01 2018
Publisher : Universitas Pakuan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33751/jsi.v1i01.677

The library of Faculty of Mathematics and Natural Science (FMIPA) has a collection of books and other print media, total of 2,678 books with 7237 visitors and 2148 borrowers. The available book search system was very helpful for visitors to find the required books. Especially if the system has features recommended of books. In the provision of book recommendations used one of the data mining techniques, namely association rule mining techniques or excavation of association rules. In the development of this recommendation system, KDD (Knowledge Discovery from Database) model was used. The data used was the transaction history of borrowing book with the category of "chemistry", for the last 5 (five) months, that is September 2014 - February 2015. The excavation technique of this association rule has 2 (two) main process, they are: frequent patterns and rules. To find frequent patterns, a CT-PRO algorithm was used. The minimum value of support used was 1 and 2. Once the pattern is found, the confidence value of each pattern was calculated. The minimum value of confidence used ranges from 10% to 100%. The recommendation rule was based on calculating the value of this confidence. The comparison of minimum support values indicates that the greater value of minimum support then the less borrowing pattern was generated, and vice versa. The comparison of minimum confidence value shows that the greater of minimum confidence value then the less recommended rule given.. Keywords: Library, Recommended System, Knowledge Discovery from Database (KDD), Association Rule Mining, CT-PRO Algorithm

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Journal : Journal of Science Innovare