Garuda - Garba Rujukan Digital

N P Dharshinni

Universitas Prima Indonesia

Author-ID : 1587623

Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Engineering Languange, Linguistic, Communication & Media Physics

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Menu Package Recommendation Menu Package Recommendation Using Combination of K-Means and FP-Growth Algorithms at Bakery Stores N P Dharshinni; Elvana Bangun; Sarah Karunia; Ruth Damayanti; Gabriel Rophe; Roy Pandapotan
Jurnal Mantik Vol. 4 No. 3 (2020): November: Manajemen, Teknologi Informatika dan Komunikasi (Mantik)
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/mantik.Vol4.2020.897.pp1582-1587

Bakery shop is a shop that sells variants menu like bread, cakes, and drinks. The main problem with this store's sales is still not knowing which product items are best sellers and the shop still markets a lot of non-selling menus, causing the shop to lose money. So it takes the right strategy to increase the sales of bakery shop menus by making a menu package recommendations from the menus most frequently purchased by customers. The k-means algorithm performs grouping on menus to get menu packages. Furthermore, the fp-growth algorithm looks for linkages between frequently purchased menus to get menu package recommendations. The results of the research that the dominant items often purchased in cluster0 packages are hotdogs, pancakes, milk, garlic breadsticks with a confidence value of 92%, cluster1 packages are garlic breadsticks, hotdogs, chicken sand, pancakes with a confidence value of 92% and the last cluster2 packages are garlic breadstick, pastry, milk with a confidence value of 79%.

Menu Package Recommendation using Combination of K-Means and FP-Growth Algorithms at Bakery Stores: Menu Package Recommendation using Combination of K-Means and FP-Growth Algorithms at Bakery Stores N P Dharshinni; Elvana Bangun; Sarah Karunia; Ruth Damayanti; Gabriel Rophe; Roy Pandapotan
Jurnal Mantik Vol. 4 No. 2 (2020): Augustus: Manajemen, Teknologi Informatika dan Komunikasi (Mantik)
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/mantik.Vol4.2020.931.pp1272-1277

Bakery shop is a shop that sells variants menu like bread, cakes, and drinks. The main problem with this store's sales is still not knowing which product items are best sellers and the shop still markets a lot of non-selling menus, causing the shop to lose money. So it takes the right strategy to increase the sales of bakery shop menus by making a menu package recommendations from the menus most frequently purchased by customers. The k-means algorithm performs grouping on menus to get menu packages. Furthermore, the fp-growth algorithm looks for linkages between frequently purchased menus to get menu package recommendations. The results of the research that the dominant items often purchased in cluster0 packages are hotdogs, pancakes, milk, garlic breadsticks with a confidence value of 92%, cluster1 packages are garlic breadsticks, hotdogs, chicken sand, pancakes with a confidence value of 92% and the last cluster2 packages are garlic breadstick, pastry, milk with a confidence value of 79%.

Designing Applications For Damage Reporting Of Public Facilities Using K-Means Clustering Algorithm Rika Saljuni; Muhammad Sholahuddin; Fanema Putra Hartaret Harefa; Thines Raman; Juliansyah Putra Tanjung; N P Dharshinni
Jurnal Mantik Vol. 6 No. 2 (2022): August: Manajemen, Teknologi Informatika dan Komunikasi (Mantik)
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/mantik.v6i2.2900

Public facilities are facilities provided for public purposes such as roads, street lighting, bus stops, sidewalks, and pedestrian bridges. The facilities provided are facilities that provide convenience for the community so that they must be maintained properly. Data mining is a process of dredging or collecting important information from large data. The data mining process often uses statistical, mathematical methods, to utilize artificial intelligence technology. The application designed uses 50 datasets which, after normalization, the number of data becomes 350 data, and after preprocessing the data used in the study is 81 data, with 4 attributes and 3 clusters. The results of the data processing resulted in the first data clustering based on the facility attributes produced as many as 29 data, the second data clustering based on the year attribute produced was 12 data, the third data clustering based on the attribute the resulting amount was 40 data

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