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Journal : Infolitika Journal of Data Science

Artificial Neural Network–Particle Swarm Optimization Approach for Predictive Modeling of Kovats Retention Index in Essential Oils Kurniadinur, Kurniadinur; Noviandy, Teuku Rizky; Idroes, Ghazi Mauer; Ahmad, Noor Atinah; Irvanizam, Irvanizam; Subianto, Muhammad; Idroes, Rinaldi
Infolitika Journal of Data Science Vol. 2 No. 2 (2024): November 2024
Publisher : Heca Sentra Analitika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.60084/ijds.v2i2.220

Abstract

The Kovats retention index is a critical parameter in gas chromatography used for the identification of volatile compounds in essential oils. Traditional methods for determining the Kovats retention index are often labor-intensive, time-consuming, and prone to inaccuracies due to variations in experimental conditions. This study presents a novel approach combining Artificial Neural Networks (ANN) with Particle Swarm Optimization (PSO) to predict the Kovats retention index of essential oil compounds more accurately and efficiently. The ANN-PSO hybrid model leverages the strengths of both techniques: the ANN's capacity to model complex nonlinear relationships and PSO's capability to optimize hyperparameters by finding the global optimum. The model was trained using a dataset of 340 essential oil compounds with molecular descriptors, with the performance evaluated based on Root Mean Squared Error (RMSE) and Mean Absolute Percentage Error (MAPE). Results indicate that a simpler ANN configuration with one hidden neuron achieved the lowest RMSE (80.16) and MAPE (5.65%), suggesting that the relationship between the molecular descriptors and the Kovats retention index is not overly complex. This study demonstrates that the ANN-PSO model can serve as an effective tool for predictive modeling of the Kovats retention index, reducing the need for experimental procedures and improving analytical efficiency in essential oil research.
A Convolutional Neural Network Model for Mushroom Toxicity Recognition Irvanizam, Irvanizam; Subianto, Muhammad; Jamil, Muhammad Salsabila
Infolitika Journal of Data Science Vol. 3 No. 2 (2025): November 2025
Publisher : Heca Sentra Analitika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.60084/ijds.v3i2.359

Abstract

Mushroom poisoning remains a public health concern, often caused by misidentifying toxic species that visually resemble edible ones. This study investigates the feasibility of using a Convolutional Neural Network (CNN) to classify five mushroom species, Amanita caesarea, Amanita phalloides, Cantharellus cibarius, Omphalotus olearius, and Volvariella volvacea into toxic and non-toxic categories based on image data. A dataset of 137 images was collected and preprocessed through resizing, normalization, and data augmentation. A modified AlexNet-based CNN was trained and evaluated using accuracy, precision, recall, and F1-score. The best-performing model achieved a validation accuracy of 0.40, indicating limited discriminative capability. These findings highlight that the dataset size is insufficient for training a CNN from scratch and that the model cannot reliably distinguish species with subtle morphological differences. The study concludes that larger datasets, improved image quality, and transfer learning approaches are essential for achieving practical and deployable mushroom classification performance.
Co-Authors . Zulfan Afidh, Razief Perucha Fauzie Ahmad, Noor Atinah Ainal Mardhiah Akhyar, Fikrul ALFIAN FUTUHUL HADI Almunir Sihotang Asep Rusyana Azzahra, Syarifah Fathimah Baehaqi Bagus Sartono Cut Morina Zubainur Cut Mulyawati Cut Rina Rossalina Dwi Fadhiliani Earlia, Nanda Essy Harnelly EVI RAMADHANI Farsiah, Laina Fitriana AR Furqany, Nuwairy El Ghazi Mauer Idroes Hijriyana P., Meildha Hizir Sofyan Husdayanti, Noviana Idroes, Ghalieb Mutig Idroes, Ghazi M. Idroes, Ghifari M. INA YATUL ULYA Indah Manfaati Nur Irnanda , Irnanda, Irnanda Irvanizam, Irvanizam Jamil, Muhammad Salsabila Kairupan, Tara S. Kurniadinur, Kurniadinur M. Ikhsan M. Ikhsan M. Ikhsan Maulana, Aga Miftahuddin Miftahuddin Miftahuddin Miftahuddin Miftahuddin Mikyal Bulqiah, Mikyal Misbullah, Alim Muhammad Al Agani Muhammad Iqbal Muhammad Irfan Mukhamad Najib Mursyida, Waliam Nazaruddin Niode, Nurdjannah Jane Nisya Fajri Noviandy, Teuku R. Nurbaiti Nurbaiti Nurdjannah J. Niode Nurjani Nurjani Nurjannah Nurjannah Nurleila, Nurleila Prakoeswa, Cita RS. Purnama Mulia Farib Rahmah Johar Razief Perucha Fauzie Afidh Reza Wafdan Rika Fitriani Rika Siviani Rinaldi Idroes Rizal Munadi RR. Ella Evrita Hestiandari S.Pd. M Kes I Ketut Sudiana . Salmawaty Salmawati Salmawaty Salmawaty Sasmita, Novi Reandy sufriani, sufriani Sugara, Dimas Rendy Suhartono Suhendra, Rivansyah Suryadi Suryadi Teuku Rizky Noviandy Tuti Asmiati Vivi Dina Melani Vivi Dina Melani Vivi Dina Melani Widya Sari Wira Dharma Wisnu Ananta Kusuma Yusrizal Yusrizal Zahriah, Zahriah Zainal Abidin Zainal Abidin Zhilalmuhana, Teuku Zulfan