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All Journal JURNAL MEDIA INFORMATIKA BUDIDARMA Building of Informatics, Technology and Science
Rice Novita
Universitas Islam Negeri Sultan Syarif Kasim Riau, Pekanbaru
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering

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Journal : JURNAL MEDIA INFORMATIKA BUDIDARMA

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Analisis Sentimen Pengguna Transportasi Online Maxim Pada Instagram Menggunakan Naïve Bayes Classifier dan K-Nearest Neighbor Dzul Asfi Warraihan; Inggih Permana; Mustakim Mustakim; Rice Novita
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 7, No 3 (2023): Juli 2023
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v7i3.6336

Abstract

Online transportation is a form of internet-based transportation that covers all aspects of the transaction process, including booking, route tracking, payment, and service assessment of the online transportation. Maxim is one of the popular online transportation providers in Indonesia so it will continue to improve its services to serve the needs of the entire community. In making developments, Maxim needs user opinions regarding its application or services. This research conducts sentiment analysis of Maxim users' opinions on Instagram using Naive Bayes Classifier (NBC) and K-Nearest Neighbor (KNN) algorithms. Opinions are divided into 3 classes: negative, neutral, and positive. This research also uses the Random Over Sampling method and data sharing with 10-Fold Cross Validation. The accuracy results on sentiment data related to applications using the NBC algorithm are 81.03% and in the KNN algorithm with a value of k = 3 which is 80.72%. Meanwhile, sentiment data related to services produces an accuracy value in the NBC algorithm, namely 94% and the KNN algorithm with k = 3, namely 84%. It can be concluded that the NBC model is better than the KNN model in testing application-related sentiment data and service-related sentiment data after the Random Over Sampling method.
Analisis Perbandingan Pembelajaran Online Dan Offline Terhadap Mahasiswa UIN SUSKA Riau Menggunakan Naive Bayes Nur Asiah; Idria Maita; Rice Novita; Eki Saputra; Arif Marsal
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 7, No 4 (2023): Oktober 2023
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v7i4.6701

Abstract

Online lectures have become a common method used in education to deliver course materials to students. However, the exucution of Online lectures is not always smooth and often faces various challenges. One of the main issues is the limited internet access frequently experienced by students, particularly in regions where internet connectivity is limied, making it difficult for them to parcipate in lecturesnseamlessly. Addtionally, some students encounter difficulties in time management and self-motivation for independent learning. This research aims to analyst the conditions and issues that arise during the implementation of Online lectures and compare them with the traditional Offline lecture delivery at UIN SUSKA Riau. The Naïve Bayes algorithm is applied for the analysis, with a focus on Accuracy, Precision, Sensitivity, and specificity. The findings and analysis using this algorithm demonstrate a remarkable accuracy rate of 66,67%, precision rate of 70%, sensitivity rate of 77,78% and specificity rate of 50%. By looking at the results obtained, the Naïve Bayes method was successfully used in analyzing comparisons of Online and Offline learning for students of UIN SUSKA Riau.
Co-Authors Akhas Rahmadeyan Arif Marsal Dzul Asfi Warraihan Eki Saputra Inggih Permana Mustakim Mustakim Nur Asiah Vina Vamilina