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All Journal Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) ComTech: Computer, Mathematics and Engineering Applications
Yanuargi, Bayu
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Computer Science & IT Engineering Mathematics

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Data Clustering for Sentiment Classification with Naïve Bayes and Support Vector Machine Yanuargi, Bayu; Ema Utami; Kusrini; Parikesit, Arli Aditya
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 8 No 6 (2024): December 2024
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v8i6.6139

Abstract

Visitor reviews play a crucial role in determining the success of a business, particularly those offering hospitality and services, such as hotels. The growth of internet technology has made it easier for guests to share their experiences, which can influence potential customers. Google Maps is one of the platforms used for giving and searching reviews This research uses data crawled from Google Maps Review using the playwright library. However, the large volume of reviews can make analysis and topic-based categorization—such as service quality, hotel location, and operational hours—challenging. To address this, DBSCAN is used to cluster reviews based on these topics. Clustering helps improve sentiment classification, making it more targeted and allowing a comparison of two machine learning algorithms: Naïve Bayes and Support Vector Machine (SVM). Naïve Bayes achieved higher accuracy (0.87) in the operational hours cluster, while SVM scored 0.78. However, SVM showed improved accuracy in the location (0.89) and service (0.88) clusters, with Naïve Bayes maintaining a stable 0.86 accuracy in both. Both models demonstrated an average training time of less than one second, excluding preprocessing.
Inception-ResNet-V2 The U-Net Encoder for Road Segmentation using Sentinel 2A Yanuargi, Bayu; utami, ema
ComTech: Computer, Mathematics and Engineering Applications Vol. 16 No. 2 (2025): ComTech (in press)
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/comtech.v16i2.12089

Abstract

Updating road network maps is essential for transportation services, as incomplete or inaccurate maps can lead to inefficiencies and diminish service quality. The online transportation industry generates vast amounts of GPS data as drivers navigate, which is valuable for mapping road networks and improving traffic management. However, since drivers do not cover all roads, satellite imagery plays a crucial role in identifying areas that are not mapped. By combining GPS data as labels with satellite imagery, the extraction of new road networks becomes more accurate. This research employs a deep learning convolutional neural network with the U-Net architecture for road segmentation, allowing for the identification of new paths. Two different encoders are tested in this research: Inception-ResNet-V2 and a pure U-Net encoder. The Inception-ResNet-V2 encoder achieves an accuracy of 91.3%, while the pure U-Net encoder achieves 90.7%. In terms of Dice Loss, the models record values of 0.051 and 0.08, respectively. The research highlights the effectiveness of different U-Net encoders in road network segmentation. With high accuracy and low Dice Loss, this approach provides a reliable method for automatically updating road maps. It has potential applications in navigation systems, urban planning, and AI-driven intelligent transportation systems.
Co-Authors Arli Aditya Parikesit Ema Utami KUSRINI