Stemming Impact Analysis on Indonesian Quran Translation and Their Tafsir Classification for Ontology Instances

Utomo, Fandy Setyo; Suryana, Nanna; Azmi, Mohd Sanusi

doi:10.31436/iiumej.v21i1.1170

Cited by 12 publications

(15 citation statements)

References 25 publications

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“…The third data pre-processing step in this study was parsing [17]. The aim was to break the document into a string of words and then analyse the collection of words by separating them and determining the syntactic structure of each word.…”

Section: Parsingmentioning

confidence: 99%

The Implementation of the Machine Learning Algorithm for the Sentiment Analysis of Indonesia’s 2019 Presidential Election

Buntoro

Arifin

Syaifuddiin

et al. 2021

IIUMEJ

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In 2019, citizens of Indonesia participated in the democratic process of electing a new president, vice president, and various legislative candidates for the country. The 2019 Indonesian presidential election was very tense in terms of the candidates' campaigns in cyberspace, especially on social media sites such as Facebook, Twitter, Instagram, Google+, Tumblr, LinkedIn, etc. The Indonesian people used social media platforms to express their positive, neutral, and also negative opinions on the respective presidential candidates. The campaigning of respective social media users on their choice of candidates for regents, governors, and legislative positions up to presidential candidates was conducted via the Internet and online media. Therefore, the aim of this paper is to conduct sentiment analysis on the candidates in the 2019 Indonesia presidential election based on Twitter datasets. The study used datasets on the opinions expressed by the Indonesian people available on Twitter with the hashtags (#) containing "Jokowi and Prabowo." We conducted data pre-processing using a selection of comments, data cleansing, text parsing, sentence normalization and tokenization based on the given text in the Indonesian language, determination of class attributes, and, finally, we classified the Twitter posts with the hashtags (#) using Naïve Bayes Classifier (NBC) and a Support Vector Machine (SVM) to achieve an optimal and maximum optimization accuracy. The study provides benefits in terms of helping the community to research opinions on Twitter that contain positive, neutral, or negative sentiments. Sentiment Analysis on the candidates in the 2019 Indonesian presidential election on Twitter using non-conventional processes resulted in cost, time, and effort savings. This research proved that the combination of the SVM machine learning algorithm and alphabetic tokenization produced the highest accuracy value of 79.02%. While the lowest accuracy value in this study was obtained with a combination of the NBC machine learning algorithm and N-gram tokenization with an accuracy value of 44.94%. ABSTRAK: Pada tahun 2019 rakyat Indonesia telah terlibat dalam proses demokrasi memilih presiden baru, wakil presiden, dan berbagai calon legislatif negara. Pemilihan presiden Indonesia 2019 sangat tegang dalam kempen calon di ruang siber, terutama di laman media sosial seperti Facebook, Twitter, Instagram, Google+, Tumblr, LinkedIn, dll. Rakyat Indonesia menggunakan platfom media sosial bagi menyatakan pendapat positif, berkecuali, dan juga negatif terhadap calon presiden masing-masing. Kampen pencalonan menteri, gabenor, dan perundangan hingga pencalonan presiden dilakukan melalui media internet dan atas talian. Oleh itu, kajian ini dilakukan bagi menilai sentimen terhadap calon pemilihan presiden Indonesia 2019 berdasarkan kumpulan data Twitter. Kajian ini menggunakan kumpulan data yang diungkapkan oleh rakyat Indonesia yang terdapat di Twitter dengan hashtag (#) yang mengandungi "Jokowi dan Prabowo." Proses data dibuat menggunakan pilihan komentar, pembersihan data, penguraian teks, normalisasi kalimat, dan tokenisasi teks dalam bahasa Indonesia, penentuan atribut kelas, dan akhirnya, pengklasifikasian catatan Twitter dengan hashtag (#) menggunakan Klasifikasi Naïve Bayes (NBC) dan Mesin Vektor Sokongan (SVM) bagi mencapai ketepatan optimum dan maksimum. Kajian ini memberikan faedah dari segi membantu masyarakat meneliti pendapat di Twitter yang mengandungi sentimen positif, neutral, atau negatif. Analisis Sentimen terhadap calon dalam pemilihan presiden Indonesia 2019 di Twitter menggunakan proses bukan konvensional menghasilkan penjimatan kos, waktu, dan usaha. Penyelidikan ini membuktikan bahawa gabungan algoritma pembelajaran mesin SVM dan tokenisasi abjad menghasilkan nilai ketepatan tertinggi iaitu 79.02%. Manakala nilai ketepatan terendah dalam kajian ini diperoleh dengan kombinasi algoritma pembelajaran mesin NBC dan tokenisasi N-gram dengan nilai ketepatan 44.94%.

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Section: Parsingmentioning

confidence: 99%

The Implementation of the Machine Learning Algorithm for the Sentiment Analysis of Indonesia’s 2019 Presidential Election

Buntoro

Arifin

Syaifuddiin

et al. 2021

IIUMEJ

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“…Tabel 6. Analisis Metode Representasi Teks dalam Arabic Natural Language Processing Metode Untuk Representasi Teks Peneliti Jumlah TF-IDF [4], [9], [48], [30], [23], [24], [29], [53], [54], [7], [38], [55], [32], [56], [25], [31], [59], [1], [51], [60] 20 Word2Vec [34], [24], [49], [7], [57], [39], [41], [43], [45], [46] 10 AraVec [34], [35], [37], [40], [41], [42] 6 FastText [34], [47], [35], [57], [41], [42], [50], [46] 8 mBERT [27], [22], [41], [46] 4 AraBERT [36],…”

Section: Tahap Pembuatan Rencana Awalmentioning

confidence: 99%

“…Untuk lebih mempertimbangkan konteks kata dan makna kalimat dapat diatasi dengan dynamic atau contextual embedding yang mempelajari representasi kalimat secara universal [27]. Contextual embedding yang pertama kali diusulkan adalah Embeddings from Language Model (ELMO) [68] [33], [34], [47], [48], [30], [23], [24], [54], [38], [27], [56], [31], [57], [39], [1], [42], [51], [46], [9] 20…”

Section: Tahap Pembuatan Rencana Awalunclassified

“…Berdasarkan penelitian yang dilakukan oleh Rostam dan Malim [1] untuk mengetahui keterkaitan atau interelasi antara dua sumber utama dalam ajaran Islam yaitu Al-Qur'an dan hadis dapat diatasi dengan menggunakan klasifikasi dokumen teks melalui penggunaan pembobotan kata TF-IDF (Term Frequency Inverse Document Frequency) dan algoritma klasifikasi Support Vector Machine (SVM), yang memperoleh hasil akurasi lebih baik daripada dengan menggunakan algoritma klasifikasi yang lain seperti K-Nearest Neighbours (KNN) maupun Naïve Bayes. Penelitian Utomo et al [9] juga menggunakan pembobotan kata TF-IDF sebagai metode representasi teks dan algoritma SVM digunakan sebagai metode klasifikasi. Kombinasi dari dua metode tersebut bertujuan untuk melakukan interelasi antar ayat-ayat Al-Qur'an.…”

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Analisis Metode Representasi Teks Untuk Deteksi Interelasi Kitab Hadis: Systematic Literature Review

Ariyanto

fatichah²,

Arifin³

2021

RESTI

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Hadith is the second source of reference for Islamic law after the Qur'an, which explains the sentences in the Qur'an which are still global by referring to the provisions of the Prophet Muhammad SAW. Classification of text documents can also be used to overcome the problem of interrelation between the Qur'an and hadith. The problem of interrelation between books of hadith needs to be done because some hadiths in certain hadith books have the same meaning as other hadith books. This study aims to analyze the development of text representation and classification methods suitable to overcome similarity meaning problems in detecting interrelationships between hadith books. The research method used is Systematic Literature Review (SLR) sourced from Google Scholar, Science Direct, and IEEE. There are 42 pieces of literature that have been studied successfully. The results showed that contextual embedding as the newest text representation method considered word context and sentence meaning better than static embedding. As a classification method, the ensemble method has better performance in classifying text documents than using only a single classifier model. Thus, future research can consider using a combination of contextual embedding and ensemble methods to detect interrelationships between books of hadith.

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“…phenotype ontologies [9][10][11][12]. The concepts, relationships, axiomatic constraints and the domain knowledge's formal representation are provided by ontology.…”

Section: Introductionmentioning

confidence: 99%

Research on Machine Translation of Deep Neural Network Learning Model Based on Ontology

Tian

Khanna²,

Pljonkin

2021

IJCAI

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To align different ontologies, it is necessary to find effective ways to achieve interoperability of information in the context of the Semantic Web. The development of accurate and reliable techniques to automatically perform this task, it is becoming more and more crucial as overlap between ontologies grows proportionally. In order to solve the problem that traditional machine translation cannot meet the needs of users because of the slow translation speed. According to the characteristics of Ontology's domain knowledge concept system, deep neural network learning model based machine translation method is proposed. Through the experimental design, we examine the translation time and BLEU score and other indicators. After junior translators use the tools, the translation time is reduced by 34.0% and the BLEU score increases by 7.59; after the senior translators use the tools, the translation time is reduced by 11.3%, and the BLEU score is increased by 1.67. Analysis of the experimental results shows that the essence of this method is to complement translation skills, so it is more effective for junior translators who are not good enough in translation skills. The machine translation method based on deep neural network learning can significantly improve the quality and efficiency of translation.Povzetek: Predstavljena je metoda prevajanja s pomočjo globokih nevronskih mrež in ontologij.

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Stemming Impact Analysis on Indonesian Quran Translation and Their Tafsir Classification for Ontology Instances

Cited by 12 publications

References 25 publications

The Implementation of the Machine Learning Algorithm for the Sentiment Analysis of Indonesia’s 2019 Presidential Election

The Implementation of the Machine Learning Algorithm for the Sentiment Analysis of Indonesia’s 2019 Presidential Election

Analisis Metode Representasi Teks Untuk Deteksi Interelasi Kitab Hadis: Systematic Literature Review

Research on Machine Translation of Deep Neural Network Learning Model Based on Ontology

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