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Journal : PIKSEL : Penelitian Ilmu Komputer Sistem Embedded and Logic

Android-Based Shortest Path Finding Using A-Star (A*) Algorithm in Bekasi City Herlawati Herlawati; Prima Dina Atika; Ajif Yunizar Pratama Yusuf; Fata Nidaul Khasanah; Endang Retnoningsih; Beno Aditya Sanusi; Gedhe Hilman Wakhid
PIKSEL : Penelitian Ilmu Komputer Sistem Embedded and Logic Vol 9 No 2 (2021): September 2021
Publisher : LPPM Universitas Islam 45 Bekasi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33558/piksel.v9i2.3227

Abstract

Getting information on routes can be he main problem for visitors. For example in determining the route to a proper place for eating and how to find the closest route to a mall. Based on the existing problems, this study proposes an application for finding information about places that visitors want to go based on the closest route. Algorithm A-Star (A*) was implemented that uses the distance estimation by finding the closest path to the destination using a heuristic function as a basis to select from several alternatives effectively. The result showed that an android application can give the information about the location of places to visit for eating and malls by calculating the distance from the starting point to the end point.
Sentiment Analysis of On-Demand Ride-Hailing Systems using Support Vector Machine and Naïve Bayes Bhagaskara Farhan Wiguna; Herlawati Herlawati; Ajif Yunizar Pratama Yusuf
PIKSEL : Penelitian Ilmu Komputer Sistem Embedded and Logic Vol 11 No 2 (2023): September 2023
Publisher : LPPM Universitas Islam 45 Bekasi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33558/piksel.v11i2.7384

Abstract

Gojek is one of Indonesia's most popular online transportation, founded in 2010. The Gojek application has been downloaded one hundred forty-two million times with more than two million drivers and four hundred thousand partners in food delivery services. Due to the increasing use of the Gojek application and the importance of knowing user views about the services provided by the application. In this research, the sentiment analysis is using Support Vector Machine and the Naïve Bayes method to classify positive sentiment and negative sentiment. The target label focus on positive and negative labels to aims avoid the bias that exists in neutrally labeled reviews on the Gojek Application. The research process includes data collection, pre-processing the data, weighting with Term Frequency-Invers Document Frequency, Support Vector Machine, and Naïve Bayes training by dividing the data into 90% training data and 10% testing data and then evaluating the results using a confusion matrix. The results of testing using the Support Vector Machine algorithm resulted in 90% accuracy, 94% recall, 91% precision, and 94% f1-score, therefore the Naïve Bayes algorithm produces 77% accuracy, 96% recall, 77% precision, and 85% f1-score.