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All Journal Tech-E International Journal of Artificial Intelligence Research bit-Tech
Setyawan Widyarto
Universiti Selangor
Computer Science & IT Electrical & Electronics Engineering

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Comparison of Seven Machine Learning Algorithms in the Classification of Public Opinion Sri Redjeki; Setyawan Widyarto
Tech-E Vol. 5 No. 2 (2022): Tech-E
Publisher : Fakultas Sains dan Teknologi-Universitas Buddhi Dharma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31253/te.v5i1.1046

Abstract

Sentiment analysis is one way that is widely used to identify the beginning of public opinion in various fields of life which are associated with very massive and a lot of information through social media. This study aims to compare several algorithms in machine learning to see the best ability in sentiment classification. The research dataset uses a dataset of public opinion related to tourism in Indonesia. The number of datasets used is 10,228 twitter data that have been cleaned and labelled. The machine learning algorithm used is Logistic Regression, KNN, AdaBoost, Decision Tree, SVM, Random Forest and Gaussian. The seven algorithms for sentiment classification from the Twitter public opinion each produce a Gaussian accuracy of 0.52; SVM 0.78; KNN 0.98; Logistic Regression, Random Forest, Decision Tree, AdaBoost of 0.99. This study shows that the selection of the right machine learning algorithm will have a very good impact on the classification of public opinion through social media
Systematic Literature Review: Smart City Framework Riki Riki; Setyawan Widyarto; Saliyah Kahar
Tech-E Vol 5 No 1 (2021): Tech-E
Publisher : Fakultas Sains dan Teknologi-Universitas Buddhi Dharma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31253/te.v5i1.667

Abstract

Smart cities are currently becoming the trend of large cities in the world and large cities in Indonesia. As the center of human civilization, cities cannot do without the problems of excess capacity and comfort. More and more people are migrating from the countryside to the cities, which brings new problems to the cities. Cities need to change to survive in the future. Strong indicators are needed to support cities, whether in terms of natural environment, society, communities, infrastructure, and education. In this article, we discuss a systematic literature review of research related to smart cities. The systematic literature review is divided into three stages, introduction stage, demographic analysis stage and result analysis. The results reveal important indicators of smart cities based on the conclusions of previous research
Deep Reinforcement-Driven Clustering and Routing Protocol for Smart Vehicular Networks Riki Riki; Setyawan Widyarto
International Journal of Artificial Intelligence Research Vol 9, No 2 (2025): December
Publisher : Universitas Dharma Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29099/ijair.v9i2.1576

Abstract

This study proposes a Deep Reinforcement-Driven Clustering and Routing Protocol (DRCRP) to enhance energy efficiency and routing stability in smart vehicular networks. The protocol integrates an Actor–Critic deep reinforcement learning framework with Proximal Policy Optimization (PPO) to enable adaptive decision-making in dynamic Internet of Vehicles (IoV) environments. Through continuous learning, DRCRP adjusts cluster head selection and routing paths according to real-time vehicular mobility, residual energy, and link quality. Simulation experiments conducted using NS-2 and VanetMobiSim show that DRCRP achieves superior performance compared to benchmark algorithms such as AI-EECR, GWO-CH, and DMCNF. Quantitatively, the proposed model improved the Packet Delivery Ratio (PDR) by up to 4.3%, reduced End-to-End Delay by 18–22%, and lowered Energy Consumption by 12–16%. Moreover, DRCRP effectively minimized communication overhead and extended cluster head and member lifetimes, confirming its ability to balance reliability and energy efficiency. These results demonstrate the capability of reinforcement learning-based architectures to support intelligent, sustainable, and scalable vehicular communication systems under complex mobility conditions
Ontology-Based Semantic Web Model for Cervical Cancer Information Retrieval Sri Rezeki Candra Nursari; Setyawan Widyarto; Amir Murtako; Febri Maspiyanti
bit-Tech Vol. 8 No. 3 (2026): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i3.3916

Abstract

Ontology and Semantic Web technologies have become important approaches for improving the accessibility, integration, and semantic accuracy of medical information, particularly in supporting early awareness and information retrieval related to cervical cancer. This study proposes a hybrid ontology and Semantic Web model to enhance cervical cancer information retrieval by transforming heterogeneous web-based health information into structured and machine-interpretable knowledge. The research was conducted through several stages, including data collection using purposive sampling, preprocessing, data cleaning, labelling, ontology modelling, and Semantic Web implementation. A total of 645 data records were collected from 62 web sources and organized into eight main domain features: symptoms, affected organs, maintenance, treatment, characteristic features, causes, prevention, and types of cervical cancer. The proposed system adopts a layered Semantic Web architecture consisting of XML, RDF, OWL, and logic layers. The XML layer represents the data structure, the RDF layer defines semantic relationships, and the OWL-based ontology layer models domain knowledge and rules. In contrast, the logic layer enables reasoning and knowledge inference. In addition, heuristic-based mapping is applied to connect relational database schemas with ontology models to support semantic interoperability. The results show that the proposed model can represent cervical cancer knowledge more systematically and improve semantic search capabilities in healthcare information systems. Therefore, this study contributes to the development of intelligent, interoperable medical information retrieval systems to support cervical cancer education, prevention, and early detection.
Co-Authors Amir Murtako Febri Maspiyanti Riki Riki RIKI RIKI, RIKI Saliyah Kahar Sri Redjeki