Article Per Year (5 Year)
p-Index From 2021 - 2026
0
P-Index
This Author published in this journals
All Journal Jurasik (Jurnal Riset Sistem Informasi dan Teknik Informatika) Jurnal Rekayasa elektrika
Siti Dara Fadilla
Universitas Sumatera Utara
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Energy Engineering

Published : 3 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 3 Documents
Search

 1 
Analisis Algoritma Rabin-Karp Pada Kamus Umum Berbasis Android Herriyance Herriyance; Handrizal Handrizal; Siti Dara Fadilla
Jurasik (Jurnal Riset Sistem Informasi dan Teknik Informatika) Vol 2 (2017): Edisi Juli
Publisher : STIKOM Tunas Bangsa Pematangsiantar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/jurasik.v2i1.20

Abstract

Development of the era had a considerable impact on the existence of a language. To overcome this there are some efforts to be made, one of which is to create a dictionary, a dictionary that was made to be practical and quick in use. Dictionary in question is a dictionary based on Android. To create a dictionary-based android can use string matching algorithm, one of the string matching algorithm is the Rabin-Karp algorithm, Rabin-Karp algorithm perform string matching hash value based on the text and the pattern hash value. The study produced an android based dictionary application which the base number is used to generate a hash value greatly affects the speed of search words. Average running time of 10 attempts to search for words is 14.9 ms.
Perbandingan Metode Klaster dan Preprocessing Untuk Dokumen Berbahasa Indonesia Amalia Amalia; Maya Silvi Lydia; Siti Dara Fadilla; Miftahul Huda
Jurnal Rekayasa Elektrika Vol 14, No 1 (2018)
Publisher : Universitas Syiah Kuala

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (847.705 KB) | DOI: 10.17529/jre.v14i1.9027

Abstract

Clustering is an unsupervised method to group multiple objects based on the similarity automatically. The quality of clustering accuracy is determined by the number of similar objects in a correct cluster group. The robust preprocessing process and the choice of cluster algorithm can increase the efficiency of clustering. The objective of this study is to observe the most suitable method to cluster document in Bahasa Indonesia. We performed tests on several cluster algorithms such as K-Means, K-Means++ and Agglomerative with various preprocessing stages and collected the accuracy of each algorithm. Clustering experiments were conducted on a corpus containing 100 documents in Bahasa Indonesia with a commonly used preprocessing scenario. Additionally, we also attach our preprocessing stages such as LSA function, TF-IDF function, and LSA / TF-IDF function. We tested various LSA dimension reductions values from 10% to 90%, and the result shows that the best percentage of reduction rates between 50%-80%. The result also indicates that K-Means++ algorithm produces better purity values than other algorithms.
Perbandingan Metode Klaster dan Preprocessing Untuk Dokumen Berbahasa Indonesia Amalia Amalia; Maya Silvi Lydia; Siti Dara Fadilla; Miftahul Huda
Jurnal Rekayasa Elektrika Vol 14, No 1 (2018)
Publisher : Universitas Syiah Kuala

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.17529/jre.v14i1.9027

Abstract

Clustering is an unsupervised method to group multiple objects based on the similarity automatically. The quality of clustering accuracy is determined by the number of similar objects in a correct cluster group. The robust preprocessing process and the choice of cluster algorithm can increase the efficiency of clustering. The objective of this study is to observe the most suitable method to cluster document in Bahasa Indonesia. We performed tests on several cluster algorithms such as K-Means, K-Means++ and Agglomerative with various preprocessing stages and collected the accuracy of each algorithm. Clustering experiments were conducted on a corpus containing 100 documents in Bahasa Indonesia with a commonly used preprocessing scenario. Additionally, we also attach our preprocessing stages such as LSA function, TF-IDF function, and LSA / TF-IDF function. We tested various LSA dimension reductions values from 10% to 90%, and the result shows that the best percentage of reduction rates between 50%-80%. The result also indicates that K-Means++ algorithm produces better purity values than other algorithms.
Co-Authors Amalia Amalia Handrizal Handrizal Herriyance Herriyance Maya Silvi Lydia Miftahul Huda