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All Journal International Journal of Artificial Intelligence Research Jurnal Ekobistek
Guslendra, Guslendra
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Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Environmental Science Social Sciences

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SISTEM PENUNJANG KEPUTUSAN UNTUK MENGOPTIMALKAN BIAYA PERSEDIAAN DALAM RANGKA MENINGKATKAN KEUNTUNGAN Guslendra, Guslendra
Jurnal Ekobistek Vol 2, No 2 (2013): Jurnal Ekobistek UPI "YPTK" Padang
Publisher : Universitas Putra Indonesia YPTK Padang

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Abstract

Abstrak - Dalam rangka meningkatkan keuntungan dalam suatu proses perdagangan salah satu faktor yang harus diperhatikan adalah dengan meminimalkan biaya-biaya yang harus dikeluarkan. Semakin kecil biaya maka keuntungan yang didapatkan semakin besar. Salah satu biaya yang harus diperhitungkan adalah biaya persediaan barang. Sistem penunjang keputusan yang terdiri dari komponen database, dialog dan model memungkinkan pengambil keputusan dapat menghasilkan keputusan yang tepat dan akurat dengan menggunakan teknik data cleaning, teknik data transformasi dan algoritma association rules. Kata Kunci : Keuntungan, Persediaan, data cleaning, data transformasi, Algorithma Assosiation Rules
K-Means and K-NN Methods For Determining Student Interest Guslendra, Guslendra; Defit, Sarjon; Bastola, Ramesh
International Journal of Artificial Intelligence Research Vol 6, No 1 (2022): June 2022
Publisher : Universitas Dharma Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (687.443 KB) | DOI: 10.29099/ijair.v6i1.222

Abstract

Putra Indonesia University 'YPTK' Padang's Department of Information Systems, Faculty of Computing Science has three specializations, namely Information Technology Management, Business Information Systems, and Industrial Information Systems. In the fifth semester, the acquisition of specializations takes place. In the next semester, the selection of specialist programs will be determined. The option of the degree is adapted to students' needs and capacities. The acquisition of results generated in the previous semester can be seen. The objective of this survey is to provide students with suggestions for the collection of degrees. The study was performed using K-Means and K-Nearest Neighbor methods to obtain the classification of students and the correlation between recent cases and past cases. This analysis uses 13 characteristics, of which 12 are predictors and 1 is the option. The test results can be used as a way to suggest the student preferences based on preset attributes through the K-Means and K-NN methods.
Co-Authors Bastola, Ramesh Sarjon Defit