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All Journal IJIIS: International Journal of Informatics and Information Systems Infolitika Journal of Data Science
Safhadi, Aulia Al-Jihad
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Computer Science & IT Decision Sciences, Operations Research & Management Engineering

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Backpropagation Neural Network-Based Prediction of Kovats Retention Index for Essential Oil Compounds Safhadi, Aulia Al-Jihad; Noviandy, Teuku Rizky; Irvanizam, Irvanizam; Suhendra, Rivansyah; Karma, Taufiq; Idroes, Rinaldi
Infolitika Journal of Data Science Vol. 2 No. 1 (2024): May 2024
Publisher : Heca Sentra Analitika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.60084/ijds.v2i1.197

Abstract

The identification of chemical compounds in essential oils is crucial in industries such as pharmaceuticals, perfumery, and food. Kovats Retention Index (RI) values are essential for compound identification using gas chromatography-mass spectrometry (GC-MS). Traditional RI determination methods are time-consuming, labor-intensive, and susceptible to experimental variability. Recent advancements in data science suggest that artificial intelligence (AI) can enhance RI prediction accuracy and efficiency. However, the full potential of AI, particularly artificial neural networks (ANN), in predicting RI values remains underexplored. This study develops a backpropagation neural network (BPNN) model to predict the Kovats RI values of essential oil compounds using five molecular descriptors: ATSc1, VCH-7, SP-1, Kier1, and MLogP. We trained the BPNN on a dataset of 340 essential oil compounds and optimized it through hyperparameter tuning. We show that the optimized BPNN model, with an epoch count of 100, a learning rate of 0.1, a hidden layer size of 10 neurons, and the ReLU activation function, achieves an R² value of 0.934 and a Root Mean Squared Error (RMSE) of 76.98. These results indicate a high correlation between predicted and actual RI values and a low average prediction error. Our findings demonstrate that BPNNs can significantly improve the efficiency and accuracy of compound identification, reducing reliance on traditional experimental methods.
Classifying Driver Behavior Using Machine Learning: A Simple Approach to Detect Distracted and Aggressive Driving Purba, Ika Christine; Safhadi, Aulia Al-JIhad
International Journal of Informatics and Information Systems Vol 9, No 2: Regular Issue: March 2026
Publisher : International Journal of Informatics and Information Systems

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/ijiis.v9i2.299

Abstract

This study explores the use of ML models to classify driver behavior as either Distracted or Aggressive, using data derived from real-world driving scenarios. Two ML algorithms, Random Forest (RF) and Support Vector Machine (SVM), were applied to classify driver behavior based on key features such as brake_pressure, lane_deviation, and headway_distance. The RF model outperformed the SVM model, achieving an accuracy of 95% compared to 94% for SVM. The study demonstrates that brake_pressure and headway_distance are the most important features for detecting Aggressive driving, while lane_deviation is crucial for identifying Distracted driving. The findings suggest that RF is particularly effective in handling complex, high-dimensional data, providing accurate and reliable predictions. The results contribute to the advancement of road safety technologies by enhancing the detection of unsafe driving behaviors, which can be integrated into Advanced Driver Assistance Systems (ADAS) and autonomous vehicles. Future work should focus on expanding the dataset, integrating more diverse sensor data, and exploring more complex ML models, such as deep learning, to further improve classification accuracy and real-time performance in real-world applications.
Co-Authors Irvanizam, Irvanizam Purba, Ika Christine Rinaldi Idroes Suhendra, Rivansyah Taufiq Karma Teuku Rizky Noviandy