Article Per Year (5 Year)
p-Index From 2021 - 2026
0
P-Index
This Author published in this journals
All Journal BAREKENG: Jurnal Ilmu Matematika dan Terapan
Tamaela, Gysber J.
Unknown Affiliation
Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Energy Engineering Mathematics Mechanical Engineering Physics Transportation

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
SISTEM PAKAR FUZZY UNTUK MANAJEMEN DAUR HIDUP PRODUK Tamaela, Gysber J.
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 1 No 1 (2007): BAREKENG : Jurnal Ilmu Matematika dan Terapan
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (474.111 KB) | DOI: 10.30598/barekengvol1iss1pp20-26

Abstract

Every product has its certain life cycle, whether, for example, it preserve in the market or have to be withdrawed from the market. Some criterias are important to determine those decisions. But they are not in numerical form but categorial one. Fuzzy logic is a tool to transform those categorial form to numerics using membership function. This research was trying to helps management as an expert system to make a decision based on computer software.
EVALUASI KINERJA ALGORITMA ASSOCIATION RULE Tamaela, Gysber J.
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 1 No 1 (2007): BAREKENG : Jurnal Ilmu Matematika dan Terapan
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (147.329 KB) | DOI: 10.30598/barekengvol1iss1pp38-45

Abstract

Association is a technique in data mining used to identify the relationship between itemsets in a database (association rule). Some researches in association rule since the invention of AIS algorithm in 1993 have yielded several new algorithms. Some of those used artificial datasets (IBM) and claimed by the authors to have a reliable performance in finding maximal frequent itemset. But these datasets have a different characteristics from real world dataset. The goal of this research is to compare the performance of Apriori and Cut Both Ways (CBW) algorithms using 3 real world datasets. We used small and large values of minimum support thresholds as atreatment for each algorithm and datasets. As a result we find that the characteristics of datasets have a signifcant effect on the performance of Apriori and CBW. Support counting strategy, horizontal counting, showed a better performance compared to vertical intersection although candidate frequent itemsets counted was fewer.
Co-Authors