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All Journal International Journal of Electrical and Computer Engineering Seminar Nasional Aplikasi Teknologi Informasi (SNATI) Jurnal Ilmu Komputer dan Informasi Jurnal INKOM Jurnal Simetris TELKOMNIKA (Telecommunication Computing Electronics and Control) JSI: Jurnal Sistem Informasi (E-Journal) JUTI: Jurnal Ilmiah Teknologi Informasi Journal of ICT Research and Applications Jurnal Edukasi dan Penelitian Informatika (JEPIN) JTERA (Jurnal Teknologi Rekayasa) Jurnal Infomedia GUIDENA: Jurnal Ilmu Pendidikan, Psikologi, Bimbingan dan Konseling Jurnal Sistem Cerdas Jurnal Linguistik Komputasional Indonesian Journal of Electrical Engineering and Computer Science Jurnal Sistem Informasi BACA: Jurnal Dokumentasi dan Informasi
Ayu Purwarianti
Institut Teknologi Bandung
Automotive Engineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Education Electrical & Electronics Engineering Industrial & Manufacturing Engineering Library & Information Science Mechanical Engineering Social Sciences Other

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KECENDERUNGAN PENILAIAN PENGGUNA INFORMASI TERHADAP TWEET (KICAUAN) PADA MEDIA SOSIAL TWITTER Pramiyati, Titin; Purwarianti, Ayu; Supriana, Iping
Simetris: Jurnal Teknik Mesin, Elektro dan Ilmu Komputer Vol 7, No 1 (2016): JURNAL SIMETRIS VOLUME 7 NO 1 TAHUN 2016
Publisher : Universitas Muria Kudus

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (324.894 KB) | DOI: 10.24176/simet.v7i1.506

Abstract

Peran media sosial dalam penyebaran informasi, saat ini sudah berada pada tingkat yang sangat berarti, yang ditandai dengan peningkatan jumlah pengguna media sosial, pemanfaatan media sosial untuk kepentingan penyampaian informasi dibidang politik, hukum, ekonomi, sosial dan bidang lainnya. Media sosial seperti facebook dan twitter merupakan media sosial dengan pengguna terbesar dan memiliki rentang usia pengguna yang sangat lebar. Jumlah pengguna yang besar tidak serta merta menjadikan informasi yang disebarkan melalui media sosial tersebut dapat dipercaya, akan tetapi keberadaan pengguna informasi ini dapat dimanfaatkan untuk menentukan tingkat kepercayaan informasi. Penilaian tingkat kepercayaan informasi, diduga dapat dilakukan dengan berdasarkan pada kesesuaian konteks informasi dengan konteks profil sumber informasi. Merujuk pada dugaan yang dibangun, paper ini membahas hasil survey yang dilakukan untuk mengetahui apakah kesesuaian konteks informasi dengan konteks profil sumber informasi dapat digunakan untuk menentukan tingkat kepercayaan informasi. Survey menggunakan kicauan (tweet) dari beberapa tokoh masyarakat, dengan melibatkan 300 responden yang terdiri dari kelompok Mahasiswa dan kelompok Karyawan. Metode yang digunakan adalah analisis induktif yang menggunakan ukuran numerik dan variabilitas untuk mendeskripsikan kecenderungan penilaian terhadap informasi. Ukuran numerik yang digunakan adalah ukuran tendensi sentral, yaitu dengan menghitung nilai rata-rata, median dan modus. Ukuran variabilitas digunakan untuk mengetahui apakah terdapat penyimpangan yang berarti pada pengukuran numerik yang dilakukan. Hasil survey menunjukkan bahwa kecenderungan responden untuk setuju dengan informasi yang dibuat jika konteks informasi memiliki kesamaan dengan konteks profil sumber informasi. Hasil ini selanjutnya akan dijadikan sebagai dasar dalam pembuatan model penentuan tingkat kepercayaan informasi. Kata kunci: media sosial, twitter, kicauan, analisis induktif, kepercayaan informasi.
Entity Recognition of User Profile on Twitter Pramiyati, Titin; Supriana, Iping; Purwarianti, Ayu
INKOM Journal Vol 8, No 2 (2014)
Publisher : Pusat Penelitian Informatika - LIPI

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (532.021 KB) | DOI: 10.14203/j.inkom.411

Abstract

Atribut trust scope sebagai atribut untuk menentukan tingkat kepercayaan sumber informasi, akan diisi dengan data yang terdapat pada user profile Twitter yang dikenal sebagai Bio Twitter. Hanya saja, data tersebut harus sesuai dengan karakteristik dan fungsi dari masing-masing atribut trust scope, seperti atribut pendidikan harus diisi dengan informasi yang berkaitan dengan latar belakang pendidikan dari pemilik profil tersebut. Untuk mendapatkan data yang sesuai dengan atribut, kami melakukan named entity recognition, yang merupakan salah satu kegiatan pada proses ekstraksi informasi. Oleh karena itu, paper ini menjelaskan hasil proses pengenalan entitas yang dilakukan terhadap data yang terdapat pada user profile. Perangkat lunak yang digunakan untuk mengenali data sebagai entitas adalah IndonesiaNetagger. IndonesiaNettagger, merupakan perangkat lunak untuk mengenali entitas yang ditulis dalam bahasa Indonesia. Kami melakukan penelitian dalam empat tahap, yaitu pengenalan entity dengan data Bio twitter yang asli,identifikasi kesalahan proses pengenalan, formalisasi data dan pengujian pengenalan entitas akhir. Hasil penelitian menunjukkan keberhasilan sebagai berikut; entitas Person dikenali dengan benar adalah sebesar 71% dari total data entitas yang tersedia, entitas Organization dikenali dengan benar sebasar 50%, entitas Position 20% dikenali denganbenar, dan 50% entitas Location dikenali dengan benar.
Model dan Metoda Arsitektur pada Sistem Tanya Jawab Medis suwarningsih, wiwin; Supriana, Iping; Purwarianti, Ayu
INKOM Journal Vol 8, No 2 (2014)
Publisher : Pusat Penelitian Informatika - LIPI

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (808.565 KB) | DOI: 10.14203/j.inkom.303

Abstract

Pada makalah ini, akan dilakukan survey beberapa penelitian yang membahas mengenai sistem tanya jawab dengan domain pada bidang medis (medical question answering = MedQuAn). Sistem MedQuAn mengolah pertanyaan yang diajukan dalam bentuk teks bahasa alami dan kemudian sistem akan memberikan jawaban yang relevan. Makalah ini mencoba menelaah modul konseptual MedQuAn, bahwa sistem tanya jawab terdiri dari tiga komponen inti yang berbeda beserta metoda/ pendekatan yang digunakan. Ketiga komponen inti tersebut adalah klasifikasi pertanyaan, pencarian dokumen, dan ekstraksi jawaban. Hasil akhir dari survey ini adalah sebuah kontribusi untuk pengembangan penelitian di masa mendatang di domain MedQuAn khususnya untuk sistem tanya jawab medis dengan menggunakan bahasa Indonesia.
Monitoring Gadget Usage Behavior Among Adolescents Using Machine Learning Iskandar, Yanti Rubiyanti; Purwarianti, Ayu; Lestari, Dessi Puji; Hendradjaja, Bayu
GUIDENA: Jurnal Ilmu Pendidikan, Psikologi, Bimbingan dan Konseling Vol 8, No 2 (2018)
Publisher : Universitas Muhammadiyah Metro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24127/gdn.v8i2.1633

Abstract

The aim study has a long-term goal, namely to reduce negative impact of gadget use among adolescents. By giving awareness and the ability for teens to control the use of gadgets, adolescents are expected will be more productive and act as users of information technology intelligent. From economic products, the software developed can be marketed to various educational institutions such as junior high school or university, or where parents or schools will get a monitoring report on the use of gadgets from adolescents users. The method used in this study includes artificial intelligence techniques (machine learning) for various development models of text/speech / video/type classification user; User Centered Design techniques for application development; and multiple techniques social humanities such as desk study activities, focus group discussions, survey/questionnaire/interview. The results of the first year research to date are software development to monitor user behavior on the gadget, collecting user behavior data adolescents on gadgets, interviewing gadget use on teenage respondents, development. The hate learning model based on deep learning, the development of the rude classification model words based on deep learning and the development of Indonesian parsers.
Representasi Kata Padat pada Pengurai Dependensi Bahasa Indonesia Arief Rahman; Ayu Purwarianti
Jurnal Linguistik Komputasional Vol 3 No 1 (2020): Vol. 3, No. 1
Publisher : Indonesia Association of Computational Linguistics (INACL)

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (593.788 KB) | DOI: 10.26418/jlk.v3i1.33

Abstract

Available Indonesian dependency parsers can be considered worse than other languages’ parsers that have been researched thoroughly. Currently, Indonesia dependency parsers can’t reliably parse sentences with gerund(s) and/or ellipsis correctly. This is because of the sparse feature representation that causes difficulty in parsing these types of sentences. In this research, dense representation is proposed for Indonesian dependency parser. The use of dense word representation may allow better generalization and gives more information regarding the words to be parsed, which allows a more accurate parsing. The scope of the dependency parsing in this research is limited to well-formed Indonesian sentences, using the local transition-based parsing. Based on our experiments, we found that using word embedding instead of sparse word representation increases parsing accuracy significantly.
Employing Dependency Tree in Machine Learning Based Indonesian Factoid Question Answering System Irfan Afif; Ayu Purwarianti
Jurnal Linguistik Komputasional Vol 2 No 1 (2019): Vol. 2, No. 1
Publisher : Indonesia Association of Computational Linguistics (INACL)

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (577.209 KB) | DOI: 10.26418/jlk.v2i1.9

Abstract

We proposed the usage of dependency tree information to increase the accuracy of Indonesian factoid question answering. We employed MSTParser and Universal Dependency corpus to build the Indonesian dependency parser. The dependency tree information as the result of the Indonesian dependency parse is used in the answer finder component of Indonesian factoid question answering system. Here, we used dependency tree information in two ways: 1) as one of the features in machine learning based answer finder (classifying each term in the retrieved passage as part of a correct answer or not); 2) as an additional heuristic rule after conducting the machine learning technique. For the machine learning technique, we combined word based calculation, phrase based calculation and similarity dependency relation based calculation as the complete features. Using 203 data, we were able to enhance the accuracy for the Indonesian factoid QA system compared to related work by only using the phrase information. The best accuracy was 84.34% for the correct answer classification and the best MRR was 0.954.
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.
Supervised Entity Tagger for Indonesian Labor Strike Tweets using Oversampling Technique and Low Resource Features Ayu Purwarianti; Lisa Madlberger; Mochammad Ibrahim
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 14, No 4: December 2016
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v14i4.3876

Abstract

We propose an entity tagger for Indonesian tweets sent during labor strike events using supervised learning methods. The aim of the tagger is to extract the date, location and the person/organization involved in the strike. We use SMOTE (Synthetic Minority Oversampling Technique) as an oversampling technique and conducted several experiments using Twitter data to evaluate different settings with varying machine learning algorithms and training data sizes. In order to test the low resource features, we also conducted experiments for the system without employing the word list feature and the word normalization. Our results indicated that different treatment of different types of machine learning algorithms with low resource features can lead to a good accuracy score. Here, we tried Naïve Bayes, C4.5, Random Forest and SMO (Sequential Minimal Optimization) algorithms using Weka as the machine learning tools. For the Naïve Bayes, due to the data distribution based of the class probability, the best accuracy was achieved by removing data duplication. For C4.5 and Random Forest, SMOTE gave higher accuracy result compared to the original data and the data with data duplication removal. For SMO, there is no significant difference among various sizes of training data.
Determining Trust Scope Attributes Using Goodness of Fit Test: A Survey Titin Pramiyati; Iping Supriana; Ayu Purwarianti
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 13, No 2: June 2015
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v13i2.649

Abstract

Indonesian, as one of the countries with high number of internet users has the potential to serve as the place with great information resources. However, these resources must be accompanied by the availability of dependable information. Information trustworthiness can be obtained by assessing the confidence level (trust) of the source of information. This can be determined by using trust scope attributes. Hence, in this study, we intended to establish the trust scope attributes by means of utilizing the ones contained in the User Profile provided by social media; in this case Facebook, Google+, Twitter, and Linkedin. We carried out the research by conducting four stages namely data collection, attributes grouping, attribute selection, and surveys. A survey was then distributed to 257 randomly selected respondents (divided into two clusters: civilians and military officers) to seek for their opinions in terms of what attributes were considered to be crucial in defining the believability of an information source. Chi-square Goodness of fit Test was conducted to compare observed data with data we would expect to obtain. The results of the research suggested that there was similar judgment in terms of dictating source of information trustworthiness chosen by the research participants with the attributes provided by trust scope category. In this research, both civilians and military officer clusters concurrently perceived that educational background was the most dependable attribute.
Toward a Framework for Indonesian Medical Question Generator Wiwin Suwarningsih; Iping Supriana; Ayu Purwarianti
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 13, No 1: March 2015
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v13i1.648

Abstract

Question generating is the task of automatically generating questions from various inputs such as raw text, database, or semantic representation. In this paper, we attempt to describe a general framework that could help develop and characterize efforts to medical Indonesian generates questions medical text. We propose a new style of question generation that actively uses sentences within a document as a source of answer. We use manually written rules to perform a sequence of general purpose a syntactic transformation (e.g. identification of keywords or key phrase to NER based on PICO frame) to turn a declarative sentence into questions. The final result of this research is a pattern of question and answer pairs, where the test results show the pattern matching algorithm precision value of 0.101 and a recall of 0.712.
Co-Authors Adhitia, Rama Adhitya, Maulana Krisna Afrida Helen Alit Kesatria Mendala Aminuddin, Amir Andri Fachrur Rozie Andria Arisal Antoni, Jefry Arief Rahman Arry Akhmad Arman Arry Akhmad Arman Aswin Juari Dessi Puji Lestari Devi Munandar Diana Permata Sari Diana Sari Dianadewi Riswantini Dwi H. Widyantoro Dwi Hendratmo Widyantoro Ekasari Nugraheni Hadi Saputra Halim, Ismail Syababun Hari Bagus Firdaud Haryono Putro, Ibnu Prastowo Hendradjaja, Bayu Herawati, Neng Ayu Herry Sujaini Herry Sujaini Hibatullah, Muhammad Helmi Imamah Imamah Intan Jelita Saragih Iping S Suwardi Iping Supriana Iping Supriana Iping Supriana Iping Supriana Iping Supriana Iping Supriana Iping Supriana Irfan Afif Iskandar, Yanti Rubiyanti Kridanto Surendro Krishna Aurelio Noviandri KUSPRIYANTO Lisa Madlberger Martalia, Anastasia Mia Miranti Jatnika Riski Mochammad Ibrahim Muh Habibi Haidir Neng Ayu Herawati Nila Nurmala Novi Yusliani Nugraha Priya Utama Prathama, Ubaidillah Ariq Purnomo Husnul Khotimah Putra, Helmy Satria Martha Rama Adhitia Rama Adhitia Rama Adhitia Rifky Raymond, Rifky Ririn Dwi Agustin Ruskanda, Fariska Zakhralativa Samuel Cahyawijaya Saputra, Neva Supanto Supanto Titin Pramiyati Titin Pramiyati Titin Pramiyati Titin Pramiyati Tupamahu, Meldrin Utama, Nugraha Priya Wiwin Suwarningsih Wiwin Suwarningsih Wiwin Suwarningsih wiwin suwarningsih Wiwin Suwarningsih Yoga Hanggara Yuhana, Umi Laili Zakiy Firdaus Alfikri Zakiy Firdaus Alfikri