Studi Perbandingan Algoritma - Algoritma Stemming Untuk Dokumen Teks Bahasa Indonesia

Simarangkir, Manase Sahat H

doi:10.46846/jurnalinkofar.v1i1.2

Cited by 10 publications

(8 citation statements)

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“…Some comparative studies have been done before. One of the comparison that has been conducted is [6] that compared stemming Algorithms among Nazief-Adriani, Arifin-Setiono, Tala, and Vega. Another comparison of stemming algorithm conducted by [3] that compare between Porter and Arifin-Setiono [7].…”

Section: Introductionmentioning

confidence: 99%

Comparison of Effectiveness of Stemming Algorithms in Indonesian Documents

Mustikasari¹,

Widaningrum²,

Arifin³

et al. 2021

Proceedings of the 2nd Borobudur International Symposium on Science and Technology (BIS-STE 2020)

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Stemming is a process to determine basic word with some rules. In Bahasa Indonesia, the way is to eliminate prefixes, infixes, suffixes, or combination of prefixes and suffixes in derivative words. Several stemming algorithms for Bahasa Indonesia have been developed. But their effectiveness has not been studied. In this study, these three stemming algorithms will be compared. We used 900 affixes to conduct the comparison. Each word is searched for their basic words using the three algorithms. The basic word resulted then referred to KBBI or Indonesian dictionary to see whether they are right. Comparison process of stemming show that Sastrawi's could do the best stemming that 95,2% of the affix words tested could be root words. The Nazief & Adriani Algorithm resulted 92,4%, while Arifin Setiono's finished at 89%. It could state that Arifin Setiono's needs a lot of improvement because many affixed words could not return to the root word.

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Section: Introductionmentioning

confidence: 99%

Comparison of Effectiveness of Stemming Algorithms in Indonesian Documents

Mustikasari¹,

Widaningrum²,

Arifin³

et al. 2021

Proceedings of the 2nd Borobudur International Symposium on Science and Technology (BIS-STE 2020)

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“…Meanwhile, in another study, Simarangkir [14] tried to compare several stemming algorithms at once. This study uses two dictionary-based stemming algorithms and two stemming algorithms using affix rules.…”

Section: A Stemming Process Based On Confix Strippingmentioning

confidence: 99%

Indonesian Online News Extraction and Clustering Using Evolving Clustering

Alfian

Barakbah

Winarno

2021

JOIV : Int. J. Inform. Visualization

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43,000 online media outlets in Indonesia publish at least one to two stories every hour. The amount of information exceeds human processing capacity, resulting in several impacts for humans, such as confusion and psychological pressure. This study proposes the Evolving Clustering method that continually adapts existing model knowledge in the real, ever-evolving environment without re-clustering the data. This study also proposes feature extraction with vector space-based stemming features to improve Indonesian language stemming. The application of the system consists of seven stages, (1) Data Acquisition, (2) Data Pipeline, (3) Keyword Feature Extraction, (4) Data Aggregation, (5) Predefined Cluster using Automatic Clustering algorithm, (6) Evolving Clustering, and (7) News Clustering Result. The experimental results show that Automatic Clustering generated 388 clusters as predefined clusters from 3.000 news. One of them is the unknown cluster. Evolving clustering runs for two days to cluster the news by streaming, resulting in a total of 611 clusters. Evolving clustering goes well, both updating models and adding models. The performance of the Evolving Clustering algorithm is quite good, as evidenced by the cluster accuracy value of 88%. However, some clusters are not right. It should be re-evaluated in the keyword feature extraction process to extract the appropriate features for grouping. In the future, this method can be developed further by adding other functions, updating and adding to the model, and evaluating.

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“…Algoritma Stemming Algoritma Stemming diciptakan oleh Michael O. Rabin dan Ricard M.Karp pada tahun 1987 yang menggunakan fungsi hashing untuk menemukan pattern 5 . Stemming merupakan salah satu tahapan text pre-processing padasistem temu kembali informasi (STKI) 6 . Algoritma ini memiliki beberapa karakteristik adalah: a. Menggunakan fungsi Hashing merupakan suatu cara untuk mentranformasikan sebuah string menjadi suatu nilai yang unik dengan panjang tertentu yang berfungsi sebagai penanda string.…”

Section: Landasan Teoriunclassified

Pengaruh Algoritma Stemming Porter Terhadap Kinerja Algoritma Rabin Karp Untuk Mendeteksi Plagiarisme Teks Bahasa Indonesia

Darnita

wibowo²,

Toyib

et al. 2020

JTIS

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Plagiat atau plagiarisme adalah perbuatan mengambil ide, tulisan, karangan atau pendapat orang lain tanpa mencantumkan sumber dan menjadikannya seolah-olah tulisan, karangan atau pendapat itu milik sendiri, dalam peraktiknya plagiarisme ini sangat mudah dilakukan, yaitu dengan cara menyalin, menempel dan memodifikasi sebagian isi dokumen. Algoritma Rabin Karp. Algoritma Rabin Karp adalah suatu algoritma pencarian string yangditemukan oleh Michael Rabin dan Richard Karp. Algoritma ini menggunakan hashing untuk menemukan sebuah string dalam sebuah teks. Selain menggunakan algoritma Rabin Karp, untuk mendapatkan hasil presentase yang baik maka diperlukannya preprocessing file yang akan dideteksi, ada banyak tahapan dari preprocessing diantaranya yaitu case folding, tokenizing, filtering, stemming, tagging dan analyzingSistem ini nantinya akan menghasilkan sebuah sistem deteksi plagiat dengan menggunakan dua algoritma yaitu algoritma Stemming Porter yang berfungsi untuk mengubah kata-kata dalamdokumen kedalam bentuk kata dasar dan algoritma Rabin Karp yang berfungsi untuk membandingkan kemiripan dua dokumeyang akan diuji. HasilpengujianAlgoritma Rabin Karp mendeteksi kemiripan tidak berdasarkan dengan kemiripan setiap kata namun dengan melakukan pendeteksian kemiripan setiap Fingerprint antar kedua dokumen, Algoritma Rabin Karp memiliki waktu proses yang lebih cepat, sedangkan pendeteksian plagiat dengan menggunakan algoritma Stemming Porter dan algoritma Rabin Karp memiliki waktu proses yang lebih lama.Kata Kunci :plagiat, robin karp, preprocessing, stemming

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Studi Perbandingan Algoritma - Algoritma Stemming Untuk Dokumen Teks Bahasa Indonesia

Cited by 10 publications

References 0 publications

Comparison of Effectiveness of Stemming Algorithms in Indonesian Documents

Comparison of Effectiveness of Stemming Algorithms in Indonesian Documents

Indonesian Online News Extraction and Clustering Using Evolving Clustering

Pengaruh Algoritma Stemming Porter Terhadap Kinerja Algoritma Rabin Karp Untuk Mendeteksi Plagiarisme Teks Bahasa Indonesia

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