Stemming javanese affix words using  nazief and adriani modifications

Wibawa, Aji Prasetya; Dwiyanto, Felix Andika; Zaeni, Ilham Ari Elbaith; Nurrohman, Rizal Kholif; Afandi, A.N.

doi:10.26555/jifo.v14i1.a17106

Cited by 10 publications

(4 citation statements)

References 2 publications

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“…3) Stemming: Pada tahapan ini, setiap kata berimbuhan di dalam teks diubah menjadi bentuk kata dasar, sesuai definisi stemming itu sendiri, yaitu proses pemisahan kata dasar dari imbuhannya [10], [11]. Contohnya, kata berimbuhan 'membantu' diubah menjadi bentuk dasar 'bantu' dan 'dirancang' diubah menjadi bentuk dasar 'rancang'.…”

Section: B Prapengolahan Teksunclassified

Regresi Linear untuk Mengurangi Bias Sistem Penilaian Uraian Singkat

Fauziati

Permanasari

Hidayah

et al. 2021

JNTETI

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Makalah ini bertujuan untuk memperbaiki kinerja sistem penilaian tes uraian singkat. Perbaikan kinerja tersebut dilakukan dengan menambahkan regresi linear sederhana pada keluaran gabungan metode cosine similarity (dengan pembobotan frekuensi kata berbasis metode Term Frequency-Inverse Document Frequency (TF-IDF)) dan mekanisme pencocokan kata. Regresi linear dilakukan dengan menjadikan nilai uraian singkat (hasil cosine similarity dan pencocokan kata) sebagai variabel regressor. Untuk mengetahui efektivitas sistem penilaian yang diusulkan, diukur kinerja sistem penilaian relatif terhadap nilai manual yang dilakukan oleh dosen. Diperoleh bahwa sebelum dilakukan regresi linear, sistem penilaian cenderung mengeluarkan nilai lebih tinggi (nilai mengalami bias) dibandingkan nilai manual yang dilakukan dosen. Regresi linear memperbaiki kinerja sistem penilaian tersebut dengan mengurangi bias penilaian secara signifikan, yaitu nilai yang diberikan tidak cenderung lebih tinggi maupun lebih rendah daripada nilai manual oleh dosen. Bahwa bias penilaian dapat diturunkan secara signifikan dengan metode yang sederhana, yaitu regresi linear, diharapkan dapat memberikan kontribusi terhadap akselerasi proses penerapan sistem penilaian otomatis untuk tes uraian pada teknologi pembelajaran dalam jaringan seperti e-learning.

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Section: B Prapengolahan Teksunclassified

Regresi Linear untuk Mengurangi Bias Sistem Penilaian Uraian Singkat

Fauziati

Permanasari

Hidayah

et al. 2021

JNTETI

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“…Beberapa penelitian telah membahas algoritma Stemming untuk bahasa jawa. Metode Nazief dan Adriani dimodifikasi sesuai aturan morfologi bahasa agar dapat digunakan untuk Stemming teks dalam bahasa Jawa [15]. ECS stemmer juga pernah digunakan untuk menghasilkan kata dasar Jawa [2], [16].…”

Section: Pendahuluanunclassified

Stemming Bahasa Jawa Menggunakan Damerau Levenshtein Distance (Dld)

Wibawa

Hakim

2021

j. Teknik inform.

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Stemming is one of the essential stages of text mining. This process removes prefixes and suffixes to produce root words in a text. This study uses a string matching algorithm, namely Damerau Levenshtein Distance (DLD), to find the basic word forms of Javanese. Test data of 300 words that have a prefix, insertion, suffix, a combination of prefix and suffix, and word repetition. The results of this study indicate that the Damerau Levenshtein Distance (DLD) algorithm can be used for Stemming Javanese text with an accuracy value of 49.6%.

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“…The Nazief & Adriani algorithm is a basic word search algorithm in Indonesian using Indonesian morphological rules by removing prefixes and word endings according to the Indonesian dictionary [8]. Nazief & Adriani's algorithm is often used for stemming the Java language, as was done in research [9]- [11], while other languages are used in research [12]- [16]. In the stemming process will find understemming, namely the reduction of words that are too few than they should be understemming and overstemming [17], namely the occurrence of fragmentation of words that are more excessive than they should be so that they have different meanings [18].…”

Section: Introductionmentioning

confidence: 99%

ANoM STEMMER: Nazief & Andriani Modification for Madurese Stemming

Enni Lindrawati,

Ema Utami,

Yaqin

2023

J. RESTI (Rekayasa Sist. Teknol. Inf.)

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Madurese is one of the regional languages in Indonesia. This is a cultural property that needs to be preserved. With various uniqueness and word formation rules, the Madurese language can be used in information retrieval, namely stemming. The Madurese language has a close relationship with the Javanese language; in several studies, the stemming method is often used, such as the modification of the Nazief and Adriani method, which has good performance for the Javanese language, but there has never been any research on the Madurese language and it has not been proven successful. Previous studies also have not used morphophonemic rules that influence word formation in Madurese. Therefore, this research was developed by modifying Nazief and Adriani's algorithm for Madurese based on Madurese language morphology by removing affixes, namely ter-ater (prefix), panoteng (suffix), and morphophonemic rules. Corpus uses 1000 words from the Madurese language dictionary that have received affixes. The accuracy of the algorithm is 89% with 890 words that match; the prefix has an accuracy of 93.81%; the suffix has an accuracy of 83.78%; and the confix has an accuracy of 80.07%. As for the overall performance, it produces an accuracy of 89.0% with an error rate of 11%. Understemming is found in 104 words, and overstemming in 6 words. The time it takes to compile is 31.31 seconds.

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Stemming javanese affix words using nazief and adriani modifications

Cited by 10 publications

References 2 publications

Regresi Linear untuk Mengurangi Bias Sistem Penilaian Uraian Singkat

Regresi Linear untuk Mengurangi Bias Sistem Penilaian Uraian Singkat

Stemming Bahasa Jawa Menggunakan Damerau Levenshtein Distance (Dld)

ANoM STEMMER: Nazief & Andriani Modification for Madurese Stemming

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