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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) snimed Jurnal Teknoin Seminar Nasional Aplikasi Teknologi Informasi (SNATI) TELKOMNIKA (Telecommunication Computing Electronics and Control) Proceeding of the Electrical Engineering Computer Science and Informatics JOIN (Jurnal Online Informatika) International Journal of Artificial Intelligence Research JURNAL MEDIA INFORMATIKA BUDIDARMA Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Jurnal Linguistik Komputasional Jurnal Informatika dan Rekayasa Elektronik Journal of Computer System and Informatics (JoSYC) Automata
Ahmad Fathan Hidayatullah, Ahmad Fathan
Jurusan Teknik Informatika, Fakultas Teknologi Industri, Universitas Islam Indonesia
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Mechanical Engineering

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Discovering Computer Science Research Topic Trends using Latent Dirichlet Allocation Kartika Rizqi Nastiti; Ahmad Fathan Hidayatullah; Ahmad Rafie Pratama
JOIN (Jurnal Online Informatika) Vol 6 No 1 (2021)
Publisher : Department of Informatics, UIN Sunan Gunung Djati Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15575/join.v6i1.636

Abstract

Before conducting a research project, researchers must find the trends and state of the art in their research field. However, that is not necessarily an easy job for researchers, partly due to the lack of specific tools to filter the required information by time range. This study aims to provide a solution to that problem by performing a topic modeling approach to the scraped data from Google Scholar between 2010 and 2019. We utilized Latent Dirichlet Allocation (LDA) combined with Term Frequency-Indexed Document Frequency (TF-IDF) to build topic models and employed the coherence score method to determine how many different topics there are for each year’s data. We also provided a visualization of the topic interpretation and word distribution for each topic as well as its relevance using word cloud and PyLDAvis. In the future, we expect to add more features to show the relevance and interconnections between each topic to make it even easier for researchers to use this tool in their research projects.
Named Entity Recognition on Tourist Destinations Reviews in the Indonesian Language Hidayatullah, Ahmad Fathan; Putra, Muhammad Fakhri Despawida Aulia; Wibowo, Adityo Permana; Nastiti, Kartika Rizqi
Jurnal Linguistik Komputasional Vol 6 No 1 (2023): Vol. 6, NO. 1
Publisher : Indonesia Association of Computational Linguistics (INACL)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26418/jlk.v6i1.89

Abstract

To find information about tourist destinations, tourists usually search the reviews about the destinations they want to visit. However, many studies made it hard for them to see the desired information. Named Entity Recognition (NER) is one of the techniques to detect entities in a text. The objective of this research was to make a NER model using BiLSTM to detect and evaluate entities on tourism destination reviews. This research used 2010 reviews of several tourism destinations in Indonesia and chunked them into 116.564 tokens of words. Those tokens were labeled according to their categories: the name of the tourism destination, locations, and facilities. If the tokens could not be classified according to the existing categories, the tokens would be labeled as O (outside). The model has been tested and gives 94,3% as the maximum average of F1-Score.
Mi-Botway: a Deep Learning-based Intelligent University Enquiries Chatbot Windiatmoko, Yurio; Hidayatullah, Ahmad Fathan; Fudholi, Dhomas Hatta; Rahmadi, Ridho
International Journal of Artificial Intelligence Research Vol 6, No 1 (2022): June 2022
Publisher : Universitas Dharma Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (478.614 KB) | DOI: 10.29099/ijair.v6i1.247

Abstract

Intelligent systems for universities that are powered by artificial intelligence have been developed on a large scale to help people with various tasks. The chatbot concept is nothing new in today's society, which is developing with the latest technology. Students or prospective students often need actual information, such as asking customer service about the university, especially during the current pandemic, when it is difficult to hold a personal meeting in person. Chatbots utilized functionally as lecture schedule information, student grades information, also with some additional features for Muslim prayer schedules and weather forecast information. This conversation bot was developed with a deep learning model adopted by an artificial intelligence model that replicates human intelligence with a specific training scheme. The deep learning implemented is based on RNN which has a special memory storage scheme for deep learning models, in particular in this conversation bot using GRU which is integrated into RASA chatbot framework. GRU is also known as Gated Recurrent Unit, which effectively stores a portion of the memory that is needed, but removes the part that is not necessary. This chatbot is represented by a web application platform created by React JavaScript, and has 0.99 Average Precision Score.
Discovering Computer Science Research Topic Trends using Latent Dirichlet Allocation Nastiti, Kartika Rizqi; Hidayatullah, Ahmad Fathan; Pratama, Ahmad Rafie
JOIN (Jurnal Online Informatika) Vol 6 No 1 (2021)
Publisher : Department of Informatics, UIN Sunan Gunung Djati Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15575/join.v6i1.636

Abstract

Before conducting a research project, researchers must find the trends and state of the art in their research field. However, that is not necessarily an easy job for researchers, partly due to the lack of specific tools to filter the required information by time range. This study aims to provide a solution to that problem by performing a topic modeling approach to the scraped data from Google Scholar between 2010 and 2019. We utilized Latent Dirichlet Allocation (LDA) combined with Term Frequency-Indexed Document Frequency (TF-IDF) to build topic models and employed the coherence score method to determine how many different topics there are for each year’s data. We also provided a visualization of the topic interpretation and word distribution for each topic as well as its relevance using word cloud and PyLDAvis. In the future, we expect to add more features to show the relevance and interconnections between each topic to make it even easier for researchers to use this tool in their research projects.
Co-Authors Adityo Permana Wibowo Ahmad Raf'ie Pratama Ahmad Rafie Pratama Amiin Majiid Nugroho Anas Satria Lombu Annisa Zahra Azmiardhy Zulkifli Farmadiansyah Cahyaningtyas, Siwi Chairullah Naury Chanifah Indah Ratnasari Dantik Puspita Sari, Dantik Puspita Dhomas Hatta Fudholi Dimas Bintang Prasetyo Fayruz Rahma Febrianto Eko Saputro Fudholi, Dhomas Hatta Habib Faizal Fadli Kartika Rizqi Nastiti Muhammad Fakhri Despawida Aulia Putra Muhammad Rifqi Ma'arif Nassharih Abdulloh Nastiti, Kartika Rizqi Naury, Chairullah Nuli Giarsyani Pamungkas, Rheza Daffa Prasetyo, Alan Dwi PRATIWI, INTAN Purwoko, Agus Putra, Muhammad Fakhri Despawida Aulia Rheza Daffa Pamungkas Ridho Rahmadi Ridho Rahmadi Satrio Wisnugroho Septia Rani Siwi Cahyaningtyas Yurio Windiatmoko, Yurio