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INDONESIA
Jurnal Linguistik Komputasional
Published by Indonesia Association of Computational Linguistics
ISSN : -     EISSN : 26219336     DOI : -
Core Subject : Science,
Computer Science & IT
Jurnal Linguistik Komputasional (JLK) menerbitkan makalah orisinil di bidang lingustik komputasional yang mencakup, namun tidak terbatas pada : Phonology, Morphology, Chunking/Shallow Parsing, Parsing/Grammatical Formalisms, Semantic Processing, Lexical Semantics, Ontology, Linguistic Resources, Statistical and Knowledge based methods, POS tagging, Discourse, Paraphrasing/Entailment/Generation, Machine Translation, Information Retrieval, Text Mining, Information Extraction, Summarization, Question Answering, Dialog Systems, Spoken Language Processing, Speech Recognition and Synthesis.
Arjuna Subject : -
Articles 5 Documents
 1 
Search results for , issue "Vol 3 No 2 (2020): Vol. 3, No. 2" : 5 Documents clear
Ringkasan Jumlah Aspek Ulasan Hotel untuk Pembentukan Dataset Sentimen Analisis Berbasis Aspek Maulana - Maulana
Jurnal Linguistik Komputasional Vol 3 No 2 (2020): Vol. 3, No. 2
Publisher : Indonesia Association of Computational Linguistics (INACL)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26418/jlk.v3i2.26

Abstract

Mempelajari dan memahami opini orang lain menjadi topik yang cukup banyak dibahas dalam bidang Natural Languge Processing (NLP). Tools yang digunakan dalam memahami opini ini adalah Sentimen Analisis, dalam perkembangan untuk memahami opini lebih jauh mengenai apa yang dibahas dalam suatu teks atau kalimat dan serta melihat sentimen nya apakah menyukai atau tidak terhadap apa yang sedang disampaikan, sentiment analisis berbasis aspek menjadi salah satu pengembangan yang cukup baru untuk saat ini. Dari hasil evaluasi dengan cross validation, dataset tersebut memiliki akurasi masing-masing nya untuk setiap aspek adalah sebagai berikut, untuk aspek kamar memiliki akurasi 79,39%, untuk aspek fasilitas memiliki akurasi 60,14%, untuk aspek lokasi memiliki akurasi 89,53% serta untuk aspek Pelayanan/Umum memiliki akurasi sebesar 78,38%. Dari hasil pengujian yang diperoleh nilai akurasi nya normal, tetapi hanya aspek fasilitas yang memiliki akurasi yang paling rendah yaitu 60,14%. Nilai akurasi ini masih belum maksimal dikarenakan adanya perbedaan jumlah data pada data training dari setiap label pada masing-masing aspek nya, sehingga menurunkan kualitas akurasinya.
Deep Reinforced Model dan Rules-Based untuk Peringkasan Kalimat Bahasa Indonesia Yuniarti Musa'adah; Yudi Wibisono; Yaya Wihardi
Jurnal Linguistik Komputasional Vol 3 No 2 (2020): Vol. 3, No. 2
Publisher : Indonesia Association of Computational Linguistics (INACL)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26418/jlk.v3i2.34

Abstract

The development of technology has an impact on increasing the amount of information released the difficulty of getting information efficiently. This was strengthened by the online media Kapanlagi who claimed to make about 500 news articles per day. Therefore, this study is expected to be able to produce more and provide information in a shorter form so that it requires less time to understand information contained. This study is focused on sentences compression using Deep Reinfoced Model and Rules-Based. Deep Reinforced Model implements the Encoder Decoder algorithm and Long Short Term Memory while Rules-Based is a method for solving problems with rules that are based on knowledge. The data used in this study amounted to 1200 sentences with 3300 tokens. The results obtained from this study are sentence compression using Rules-Based method is produce a better summary seen from the value of Rouge, Rouge-1 of 49.71, Rouge-2 of 33.27, and Rouge-L of 54.33 than the summary produced by Deep Reinfoced Model with a value of Rouge-1 of 14.44, Rouge-2 of 2.82, and Rouge-L of 18.23. In addition, this study also produced a sentences compression dataset that can be used for further study.
Pemanfaatan Epistemic Network Analysis sebagai Pendukung Analisis Sentimen dalam Collaborative Learning Roy Parsaoran; Jonathan Bernad; Tifani Astadini; Hapnes Toba; Maresha Caroline Wijanto; Mewati Ayub
Jurnal Linguistik Komputasional Vol 3 No 2 (2020): Vol. 3, No. 2
Publisher : Indonesia Association of Computational Linguistics (INACL)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26418/jlk.v3i2.36

Abstract

A lot of blended learning methods have been applied to modern learning system. One of the most used learning methods is collaborative learning which combines and extends group discussion. The recorded data during a collaborative learning session could be useful to enhanced the interaction among the class members, including the lecturer. Using sentiment analysis, the discussion can be categorized whether the discussion goes well or not, it can also be seen which group members are most active and have a positive impact on the work assigned to the group. In this preliminary research, sentiment analysis approach will be combined with Epistemic Network Analysis (ENA) so that it can see a graphical depiction of each member's contribution in a group discussion. Our experimental results show that ENA displays better insights of the students activities than only using the sentiment analysis.
Klasifikasi Kepribadian Myres-Briggs Type Indicator Berdasarkan Cuitan di Twitter Menggunakan Metode TF-IDF dan Naive Bayes Classifier Maya Cendana; Alfin Wijaya
Jurnal Linguistik Komputasional Vol 3 No 2 (2020): Vol. 3, No. 2
Publisher : Indonesia Association of Computational Linguistics (INACL)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26418/jlk.v3i2.37

Abstract

Tipe kerpibadian seseorang biasanya dapat diketahui dengan menggunakan psikotes. Salah satu jenis psikotes yang sering digunakan adalah Myres-Briggs Type Indicator (MBTI). Sebuah aplikasi berbasis web yang dapat melakukan klasifikasi tipe kepribadian seseorang berdasarkan cuitan di Twitter diharapkan dapat menjadi salah satu alternatif untuk mengetahui tipe kepribadian seseorang tanpa perlu mengikuti psikotes secara konvensional. Selain itu, hasil psikotes juga dapat diketahui secara langsung. Aplikasi ini dibuat dengan menggunakan teknik text mining, yaitu dengan menggunakan metode Term Frequency-Inverse Document Frequency (TF-IDF) dan metode klasifikasi Naive Bayes Classifier. Metode TF-IDF digunakan untuk memberikan pembobotan kata dari data training dan metode Naive Bayess Classifier digunakan untuk mengklasifikasikan data testing berdasarkan data yang telah dilatih sebelumnya. Data akan dibersihkan terlebih dahulu sebelum dilatih maupun diuji melalui proses preprocessing sehingga informasi yang dihasilkan dapat menjadi lebih akurat. Hasil dari penelitian ini adalah dari 100 data yang diuji, 71 data termasuk kategori klasifikasi benar sesuai dengan labelnya, sedangkan 29 data termasuk kategori klasifikasi salah karena hasil prediksi tidak sesuai dengan labelnya. Tingkat akurasi adalah sebesar 71% dengan rincian precision dan recall untuk kelas artisan sebesar 63% dan 68%, untuk kelas guardian sebesar 77% dan 68%, untuk kelas rational sebesar 64% dan 72%, dan untuk kelas idealist sebesar 83% dan 76%.
Short Answer Grading Using Contextual Word Embedding and Linear Regression Muh Habibi Haidir; Ayu Purwarianti
Jurnal Linguistik Komputasional Vol 3 No 2 (2020): Vol. 3, No. 2
Publisher : Indonesia Association of Computational Linguistics (INACL)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26418/jlk.v3i2.38

Abstract

Abstract—One of the obstacles in an efficient MOOC is the evaluation of student answers, including the short answer grading which requires large effort from instructors to conduct it manually. Thus, NLP research in short answer grading has been conducted in order to support the automation, using several techniques such as rule and machine learning based. Here, we’ve conducted experiments on deep learning based short answer grading to compare the answer representation and answer assessment method. In the answer representation, we compared word embedding and sentence embedding models such as BERT, and its modification. In the answer assessment method, we use linear regression. There are 2 datasets that we used, available English short answer grading dataset with 80 questions and 2442 to get the best configuration for model and Indonesian short answer grading dataset with 36 questions and 9165 short answers as testing data. Here, we’ve collected Indonesian short answers for Biology and Geography subjects from 534 respondents where the answer grading was done by 7 experts. The best root mean squared error for both dataset was achieved by using BERT pretrained, 0.880 for English dataset dan 1.893 for Indonesian dataset.

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