Claim Missing Document
Check
Articles

Found 3 Documents
Search
Journal : Faktor Exacta

PEMBENTUKAN WORD GRAPH PREPOSISI BAHASA INDONESIA MENGGUNAKAN METODE KNOWLEDGE GRAPH Wulan Anggraeni
Faktor Exacta Vol 3, No 2 (2010): Faktor Exacta
Publisher : LPPM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30998/faktorexacta.v3i2.23

Abstract

Knowledge Graph (KG) is a new method of knowledge representation, belongs to the category of semantics network. In principle, the composition of a knowledge graph is including concept and relationship. In KG we can expressed the meaning of word with word graph. Word graph is a graph contains concept and relationship to describe the meaning of word. In researched we expressed the meaning of preposition with word graph. Keyword: graph, knowledge graph, word graph, preposition.
APLIKASI ALGORITMA SOLLIN DALAM PENCARIAN POHON PERENTANG MINIMUM PROVINSI JAWA TENGAH WULAN ANGGRAENI
Faktor Exacta Vol 8, No 4 (2015)
Publisher : LPPM

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (679.576 KB) | DOI: 10.30998/faktorexacta.v8i4.508

Abstract

The purpose of this study is to find the minimum range tree of Central Java province by using sollin algorithm. This was research study by using literature study. Tree range minimum is a tree range which has minimum point from a graft. Looking for minimum range tree of Centre Java Province by using algorithm sollin with the solution by using matlab (matrix laboratory). Sollin algorithm is a combination between algorithm prim and kruscal. The way of working was choosing the left side from one point. Every point was identified. After identification process. Next step was checking cutset sides, and the most minimum cutset, so one tree with another tree was connected. After doing the identification by using algorithm sollin so it was got point of minimum tree range was 1251 km. Keywords: graf, minimum spanning tree, algoritma sollin.
PENENTUAN NILAI PANGKAT PADA ALGORITMA FUZZY C-MEANS WULAN ANGGRAENI
Faktor Exacta Vol 8, No 3 (2015)
Publisher : LPPM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30998/faktorexacta.v8i3.327

Abstract

The purpose of this study was to determine the value of rank of the fuzzy c-means algorithm that generates cluster with a good accuracy. The selection value of the square of the yield rate of 92% with central point is . the selection of the value of the cube produce an accuracy rate of 85% with central point is . While election rank value of 4 produce an accuracy rate of 62% with central point is . Every selection the rank was value produce first cluster for the student with tendency IPS and the second group for the student with tendency IPA. It can be concluded that the rank value of 2 yields the highest accuracy. Keyword: clustering, fuzzy, fuzzy c-means