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All Journal JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING JURNAL MEDIA INFORMATIKA BUDIDARMA Jurnal Penelitian Pendidikan IPA (JPPIPA) Indonesian Journal of Artificial Intelligence and Data Mining Building of Informatics, Technology and Science Charity : Jurnal Pengabdian Masyarakat eProceedings of Engineering
Donni Richasdy
Telkom University
Agriculture, Biological Sciences & Forestry Arts Humanities Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Materials Science & Nanotechnology Physics Social Sciences

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Journal : Building of Informatics, Technology and Science

 1 
Chatbot-Based Movie Recommender System Using POS Tagging Muhammad Alwi Nugraha; Z K A Baizal; Donni Richasdy
Building of Informatics, Technology and Science (BITS) Vol 4 No 2 (2022): September 2022
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v4i2.1908

Abstract

The movie recommender system is a technology designed to make it easier for users to provide recommendations quickly and among the many pieces of information. Because the number of movies is huge, it causes a person to be confused in determining the choice of the movie to watch. Many movie recommending systems have been developed, but users cannot interact intensively. Based on these problems, we developed a chatbot-based conversational recommender system, which can interact intensively with the system. The developed chatbot uses normal language handling to permit the framework to comprehend what the user enters as natural language. POS Tagging is used to find tags in the form of movie titles with patterns in the POS Tagging model. However, the algorithm of those used on POS Tagging does not pay attention to the sentence entity, so the predicted title must correspond to the provisions of POS Tagging. Multinomial Naive Bayes looks for similarities of user input to datasets on intents. The dataset with the highest probability value or almost equal to the sentence entered by the user can be used as a response to user input. The test results of the chatbot application showed that the match rate between response and user input was 89.1%. Thus, the developed chatbot can be used well to provide practical and interactive movie recommendations.
Question Answering System Using Semantic Reasoning on Ontology for The History of The Sumedang Larang Kingdom Silvia Atika Anggrayni; Z K A Baizal; Donni Richasdy
Building of Informatics, Technology and Science (BITS) Vol 4 No 2 (2022): September 2022
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v4i2.1910

Abstract

Studying history can train us to understand the sequence of events and increase a sense of nationalism in the younger generation. However, today's young generation views studying history as boring and unimportant. Studying history is considered boring because it has the stereotype of having to learn by reading long writings in books. Therefore, in this study, a Question Answering System (QAS) was built using an ontology to get historical information and get to know the culture. With QAS, users don't have to read long sentences and spend a lot of time searching for historical information, users can also ask questions in natural language without having to pay attention to sentence structure. The ontology was chosen to be able to build a knowledge base on the historical domain and SPARQL was used to find answers in the ontology. The construction of this system is expected not only to help introduce the history of the Sumedang Larang Kingdom but also to be able to introduce the attraction of cultural tourism in Indonesia, especially the Sumedang Larang Kingdom. The results of the evaluation with the system accuracy test showed a result of 87%.
The Organization Entity Extraction Telkom University Affiliated using Recurrent Neural Network (RNN) Muhammad Daffa Regenta Sutrisno; Donni Richasdy; Aditya Firman Ihsan
Building of Informatics, Technology and Science (BITS) Vol 4 No 2 (2022): September 2022
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v4i2.1956

Abstract

In the news portal text, there is a lot of important information such as the name of the person, the name of the organization, or the name of the place. To obtain information in text documents manually, humans must read the contents of the entire news text. To overcome this issue, information extraction, commonly called Named Entity Recognition (NER) was used. The extraction of information expressly for the NER field is used to make it easier to process word or sentence data. It helps search engines and also helps to classify places, times, and organizations. There is a limited number of NER in Indonesian texts using only the Recurrent Neural Network (RNN) method. Similar previous studies only employed other versions of RNN such as LSTM (Long Short Term Memory), BiLSTM (Bidirectional Long Short Term Memory), and CNN (Convolutional Neural Network). NER is one of the answers to the problems that exist in a large number of news portal texts to obtain information effectively and efficiently. The results of this study indicate that the NER system using the RNN method in Indonesian news texts has an F1 -Score of 80%
POS Tagger Improvisation with the Addition of Foreign Word Labels on Telkom University News Winkie Setyono; Donni Richasdy; Mahendra Dwifebri Purbolaksono
Building of Informatics, Technology and Science (BITS) Vol 4 No 2 (2022): September 2022
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v4i2.1983

Abstract

News is a medium of daily information usually obtained by the public. The news consists of a lot of information in it and is composed of sentence structures. Each language is unique with its own sentence structure, like Indonesian and other foreign languages. But nowadays, many media mix Indonesian with foreign languages, making the sentence structure different from Bahasa Indonesia. To classify these words, Part Of Speech Tagging needed to determine the class of words composed of sentences by learning from the Corpus of each language. With the new sentence structure, POS Tagger requires a larger Corpus to learn. The language structure can determine the results of tagging from the POS Tagger. If there are words that are not in the Corpus, it can reduce the accuracy of the POS Tagger. We conducted to enhance the research results by adding data with a different sentence structure from the Indonesian Language Corpus using sentences from online media. Added about 242 sentences with 7,043 tokens on Corpus focused on Foreign Word tags, which total 3819 tags. After doing some testing and scenarios, the results of the accuracy of POS Tagger show an accuracy of 94.7% using the Hidden Markov Model method with the F1-Score tag FW 78%.
Telkom University News Topic Modeling Using Latent Semantic Analysis (LSA) Method on Online News Portal Amala, Ihsan Ahsanu; Richasdy, Donni; Purbolaksono, Mahendra Dwifebri
Building of Informatics, Technology and Science (BITS) Vol 4 No 1 (2022): June 2022
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (415.709 KB) | DOI: 10.47065/bits.v4i1.1584

Abstract

In this day and age, the development of online news portals regarding news is quite easy to access, online news portals are information that explains an event that has occurred or is happening with electronic media intermediaries, as well as news about Telkom University which is quite easily accessible through online news portals. A system has been designed that is capable of modeling Telkom University news topics. Modeling news topics is very interesting to be used as research material because the process of understanding each individual on the topics contained in the news is different, therefore topic modeling is needed to find out what topics are news about Telkom University. In this study, a Latent Semantic Analysis (LSA) model has been designed to carry out a topic modeling process that aims to make it easier for readers to understand news topics related to Telkom University, Latent Semantic Analysis (LSA) is a mathematical method in finding hidden topics by analyzing the structure semantics of the text. After doing several research scenarios, the best coherence score was 0.524 with a total of six topics.
Co-Authors Ade Romadhony Aditya Firman Ihsan Amala, Ihsan Ahsanu Arfive Gandhi Arie Ardiyanti Suryani Aroman, Andika Djatnika Widia Nugraha Elsa Rachel Dementieva Erbina Selvia Br Perangin-Angin Firmansyah, Nanda H Hasmawati Hasmawati Hasmawati Hutari Gani, Prati Irfan Budi Prakoso Jofardho Adlinnas Kemas Muslim Lhaksmana Mahendra Dwifebri Purbolaksono Meillano, Zico Fachreza Moch Arif Bijaksana Muhammad Alwi Nugraha Muhammad Daffa Regenta Sutrisno Muhammad Faris Abdussalam Nazar Azmi Pratama, Reyhan Ramanti Dharayani Rendhy Al-Farrel Rendy Andrian Saputra Rheza Ramadhan Putra Ridho Akbar Rimbawan, Awan Rinaldi Jasmi Saiful Akbar Salsabila Putri Adityani Sa’adah, Siti Sean Akbar Ryanto Silfi Nur Amalia Silvia Atika Anggrayni Sri Widowati Tandya Rizky Pratama Widi Astuti Wijaya, Kurniadi Ahmad Winkie Setyono ZK Abdurahman Baizal