Herlina Jayadianti
Prodi Sistem Informasi, Jurusan Teknik Informatika, Fakultas Teknik Industri Universitas Pembangunan Nasional ”Veteran” Yogyakarta

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Journal : Science in Information Technology Letters

Knowledge representation of drug using ontology alignment and mapping techniques Herlina Jayadianti; Alisya Amalia Putri Hasanah; Yuli Fauziah; Shoffan Saifullah
Science in Information Technology Letters Vol 2, No 1: May 2021
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/sitech.v2i1.561

Abstract

Drug searches are still based on drug names and brands, making it difficult for patients to come looking for a cure by saying that they feel sick. Likewise, when looking for drugs and information about their content to avoid overdose errors when changing drugs when drugs are supposed to be unavailable. Based on the issues raised, a study was conducted on applying semantic web ontology to search for drugs that can appear based on patients’ names, compositions, or complaints of diseases. Protégé 5.5 serves to represent drug information based on knowledge. The application uses Netbeans with Jena API as a library and creates data and drug information on the semantic web. Drug search also uses similar in-formation meaning based on user knowledge. By representing knowledge on the search for drug and disease information with semantic web ontology technology, it can meet the purpose of research, namely to improve drug and disease information search following the user’s wishes.
Improving sentiment analysis on PeduliLindungi comments: a comparative study with CNN-Word2Vec and integrated negation handling Jayadianti, Herlina; Arianti, Berliana Andra; Cahyana, Nur Heri; Saifullah, Shoffan; Dreżewski, Rafał
Science in Information Technology Letters Vol 4, No 2 (2023): November 2023
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/sitech.v4i2.1184

Abstract

This study investigates sentiment analysis in Google Play reviews of the PeduliLindungi application, focusing on the integration of negation handling into text preprocessing and comparing the effectiveness of two prominent methods: CNN-Word2Vec CBOW and CNN-Word2Vec SkipGram. Through a meticulous methodology, negation handling is incorporated into the preprocessing phase to enhance sentiment analysis. The results demonstrate a noteworthy improvement in accuracy for both methods with the inclusion of negation handling, with CNN-Word2Vec SkipGram emerging as the superior performer, achieving an impressive 76.2% accuracy rate. Leveraging a dataset comprising 13,567 comments, this research introduces a novel approach by emphasizing the significance of negation handling in sentiment analysis. The study not only contributes valuable insights into the optimization of sentiment analysis processes but also provides practical considerations for refining methodologies, particularly in the context of mobile application reviews.