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All Journal Prosiding Seminar Nasional Sisfotek (Sistem Informasi dan Teknologi Informasi)
Puji Pramudya Marta
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Computer Science & IT

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Prediksi Dinamis Harga Tiket Penerbangan Pesawat Menggunakan Algoritma Regresi Linier Berganda Zacky Muhammad Dinata; Khaerul Anam; Puji Pramudya Marta; Gifthera Dwilestari
Prosiding SISFOTEK Vol 9 No 1 (2025): SISFOTEK IX 2025
Publisher : Ikatan Ahli Informatika Indonesia

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This study aims to develop a dynamic prediction model for airline ticket prices using the Multiple Linear Regression algorithm. The research utilizes the public dataset Flight Price Prediction from Kaggle, which originally contained 116,464 rows and 12 columns. After data cleaning by removing missing values (dropna()) and non-predictive columns (such as Flight_ID), the final dataset used for analysis consisted of 116,463 rows and 10 columns. Data preprocessing included handling missing data, encoding categorical variables, feature engineering, standardization, and multicollinearity testing using the Variance Inflation Factor (VIF). The MLR model achieved an R² of 0.882, MAE of 4573.37, and RMSE of 7797.53, indicating strong predictive performance for a linear model. The most influential factors were airline type, service class, number of stops, duration, and booking lead time. Full-service airlines such as Vistara and Air India tend to have higher ticket prices, while early bookings and economy class tickets significantly lower prices. The findings confirm that MLR remains a reliable baseline for interpretable, efficient, and explainable price forecasting systems. Future research may combine MLR with non-linear algorithms (e.g., Random Forest or Neural Network) to enhance accuracy. This study contributes to integrating data science into predictive information systems for dynamic airline pricing and decision support optimization.
Optimasi Akurasi dan Efisiensi Deteksi Intrusi pada Lingkungan Komputasi Awan dengan Analisis Deret Waktu CNN-LSTM Martanto; Khaerul Anam; Indra Wiguna Marthanu; Puji Pramudya Marta
Prosiding SISFOTEK Vol 9 No 1 (2025): SISFOTEK IX 2025
Publisher : Ikatan Ahli Informatika Indonesia

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Abstract

This study proposes a CNN-LSTM time series analysis-based intrusion detection system (IDS) model to improve accuracy and efficiency in cloud computing environments. With more organizations moving to the cloud, security threats are becoming more sophisticated, rendering traditional detection methods inadequate. The objective of this study is to develop and evaluate a hybrid model that can address these challenges. The methodology used involves an experimental quantitative approach on a representative CSE-CIC-IDS2018 dataset. This dataset underwent rigorous data preprocessing, including data cleaning, conversion to time series format, and feature selection using stationarity and Granger causality tests. The CNN-LSTM model was then trained and evaluated using accuracy and computational efficiency metrics. The results showed superior model performance with an accuracy of 0.910, precision of 0.874, and F1-Score of 0.882. The model also demonstrated good computational efficiency, with a training time of 3.9887 seconds and a prediction time of 0.3607 seconds, making it suitable for real-time detection. This study concludes that the CNN-LSTM model is a viable solution for improving cloud computing security, offering a balance between high accuracy and good computational efficiency. Future research could explore multi-dataset validation and the integration of interpretation methods to improve its application.
Co-Authors Gifthera Dwilestari Indra Wiguna Marthanu Khaerul Anam Martanto Zacky Muhammad Dinata