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All Journal G-Tech : Jurnal Teknologi Terapan
Marcelena Vicky Galena
Universitas Airlangga, Indonesia
Computer Science & IT Decision Sciences, Operations Research & Management Energy Engineering

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Analisis Sentimen Masyarakat terhadap Keamanan Penggunaan E-Commerce B2C Menggunakan Pendekatan Naïve Bayes Berbasis Text Mining untuk Mencegah Penipuan Marcelena Vicky Galena; Adnan Syawal Adilaha Sadikin; Aprilia Prastyaningrum; Reza Febrian Nugroho; M. Fariz Fadillah Mardianto
G-Tech: Jurnal Teknologi Terapan Vol 8 No 3 (2024): G-Tech, Vol. 8 No. 3 Juli 2024
Publisher : Universitas Islam Raden Rahmat, Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33379/gtech.v8i3.4846

Abstract

Perkembangan teknologi di Era Society 5.0 telah mengubah interaksi pemasaran dari tatap muka menjadi interaksi melalui layar, seperti belanja online menggunakan e-commerce. Menurut Statista Market Insights, jumlah pengguna e-commerce di Indonesia mencapai 178,94 juta orang pada tahun 2022, dengan nilai transaksi sebesar Rp476,3 triliun. Namun, e-commerce rentan terhadap cybercrime. Ditipideksus Polri menerima 16.845 laporan kejahatan siber antara 2017 dan 2020. Penelitian ini bertujuan menganalisis sentimen masyarakat mengenai keamanan e-commerce melalui komentar di Play Store dan App Store menggunakan pendekatan Naïve Bayes yang sederhana, tetapi memiliki tingkat akurasi yang tinggi. Hasil analisis menunjukkan model Naïve Bayes memiliki akurasi 80% di Play Store dan 87% di App Store, dengan AUC masing-masing 0,864 dan 0,942, menunjukkan kinerja yang sangat baik dalam klasifikasi sentimen. Diharapkan penelitian ini dapat membantu e-commerce B2C untuk meningkatkan keamanan, seperti implementasi teknologi keamanan yang lebih kuat, edukasi pengguna, serta transparansi terhadap insiden keamanan guna meningkatkan kepercayaan pengguna.
Prediction Analysis of Jakarta Composite Index Movement Using Support Vector Regression Method Marcelena Vicky Galena; Sediono Sediono; M. Fariz Fadillah Mardianto; Elly Pusporani
G-Tech: Jurnal Teknologi Terapan Vol 9 No 1 (2025): G-Tech, Vol. 9 No. 1 January 2025
Publisher : Universitas Islam Raden Rahmat, Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70609/gtech.v9i1.5879

Abstract

The JCI is an important indicator that reflects the performance of the Indonesian stock market. In recent times, the JCI has faced significant fluctuations due to complex factors, including global economic conditions and market sentiment, which make predicting its movements challenging. Good prediction is needed to support market stability and sustainable economic development as per SDGs point 8. This study applies a modern nonparametric regression method, namely Support Vector Regression (SVR), to predict a dataset in the form of weekly JCI data from the period April 2022 to October 2024 obtained from the investing.com website. The analysis shows that the SVR model with RBF kernel function provides the best performance, with MAPE of 1.43%, RMSE of 121.6196, and MAE of 104.65. The findings also reveal that the fluctuation pattern of the JCI cannot be fully explained based solely on historical data. External variables, such as global economic conditions and market sentiment, have a significant influence on the prediction results. Therefore, the SVR method can be utilized to optimize portfolio allocation based on weekly JCI predictions. In addition, the results of this study provide guidance for policymakers in designing proactive economic policies to mitigate market volatility and increase investor confidence.
Co-Authors Adnan Syawal Adilaha Sadikin Aprilia Prastyaningrum Elly Pusporani M. Fariz Fadillah Mardianto Reza Febrian Nugroho Sediono, Sediono