Implementasi Metode K-Nearest Neighbor pada Sistem Deteksi Kelelahan Mental berbasis Sinyal Electroencephalogram
Kata Kunci:
Electroencephalography, K-Nearest Neighbor, Kelelahan MentalAbstrak
Kelelahan mental dapat dialami individu dari berbagai latar belakang pekerjaan, baik pelajar maupun pekerja. Kelelahan mental dapat berdampak negatif terhadap individu, salah satunya menjadi penyebab terjadinya kecelakaan lalu lintas. Menurut Victorian Transport Resources, sekitar 10-40% kecelakaan lalu lintas di Australia disebabkan kelelahan mental yang dialami pengemudi. Deteksi kelelahan mental dapat dilakukan dengan tes psikologis dan fisiologis, namun penggunaan tes psikologis membutuhkan waktu untuk melakukan deteksi karena pada prosesnya membutuhkan lebih dari satu jenis kuesioner psikometri yang digunakan, serta data yang dihasilkan bersifat subjektif. Deteksi kelelahan mental dengan tes fisiologis dapat menjadi validator tambahan untuk mendukung data hasil tes psikologis dan mengurangi waktu deteksinya, salah satu tes fisiolgis ialah pengukuran sinyal electroencephalogram menggunakan teknik Electroencephalography. Penelitian ini membahas pengembangan sistem deteksi kelelahan mental berbasis sinyal electroencephalogram dengan gelombang Theta (4-8Hz) yang mengimplementasikan metode K-Nearest Neighbor. Penelitian ini mengklasifikasi tingkat kelelahan mental ke dalam kelas Alert, Slight, dan Severe. Hasil pengujian mendapatkan akurasi 83,33% dengan rata-rata waktu komputasi 0,5656 detik. Klasifikasi kelelahan mental ditampilkan pada (graphical user intercace) GUI MATLAB. Sistem ini diharapkan mampu mengukur tingkat kelelahan mental melalui tiga kelas klasifikasi dan menjadi validator tambahan yang membantu psikolog.
Referensi
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Chalder, T., Berelowitz, G., Pawlikowska, T., Watts, L., Wessely, S., Wright, D. and Wallace, E.P., 1993. DEVELOPMENT OF A FATIGUE SCALE. Journnl of Psychosomatic Research, 37(2), pp.147–153.
Gao, J., Wang, W. and Zhang, J., 2016. Explore Interregional EEG Correlations Changed by Sport Training Using Feature Selection. Computational Intelligence and Neuroscience, 2016. https://doi.org/10.1155/2016/6184823.
Henderi , Wahyuningsih , T. and Rahwanto, E., 2021. Comparison of Min-Max normalization and Z-Score Normalization in the K-nearest neighbor (kNN) Algorithm to Test the Accuracy of Types of Breast Cancer. International Journal of Informatics and Information System, [online] 4(1), pp.13–20. Available at: <http://archive.ics.uci.edu/ml.>.
Hendrawan, M.A., 2021. DETEKSI KELELAHAN MENTAL DENGAN MENGGUNAKAN SINYAL EEG SATU KANAL. Jurnal Sistem Informasi Dan Bisnis Cerdas (SIBC), 14(2), pp.78–87.
Hendrawan, M.A., Pane, E.S., Wibawa, A.D. and Purnomo, M.H., 2017. Investigating Window Segmentation on Mental Fatigue Detection Using Single-channel EEG. 2017 5th International Conference on Instrumentation, Communications, Information Technology, and Biomedical Engineering (ICICI-BME), pp.173–178.
Kunasegaran, K., Ismail, A.M.H., Ramasamy, S., Gnanou, J.V., Caszo, B.A. and Chen, P.L., 2023. Understanding mental fatigue and its detection: a comparative analysis of assessments and tools. PeerJ, 11. https://doi.org/10.7717/peerj.15744.
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Purnamasari, P.D., Yustiana, P., Sudiana, D. and Ratna, A., 2019. Mobile EEG Based Drowsiness Detection using K-Nearest Neighbor. 2019 IEEE 10th International Conference on Awareness Science and Technology (iCAST), pp.1–5.
Risqiwati, D., Wibawa, A.D., Pane, E.S., Islamiyah, W.R., Tyas, A.E. and Purnomo, M.H., 2020. Feature Selection for EEG-Based Fatigue Analysis Using Pearson Correlation. In: 2020 International Seminar on Intelligent Technology and Its Applications (ISITIA). pp.164–169. https://doi.org/10.1109/ISITIA49792.2020.9163760.
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Su, M., Li, W., Peng, F., Zhou, W., Zhang, R. and Wen, Y., 2022. EEG-Based Mental Fatigue Detection Using CNN-LSTM. 2022 16th ICME International Conference on Complex Medical Engineering, CME 2022, pp.302–305. https://doi.org/10.1109/CME55444.2022.10063316.
Suparmam, A., Wibowo, S., Kekalih, A., Ilyas, M. and Agustina, A., 2022. Indonesian Version of Swedish Occupational Fatigue Inventory (SOFI): Validity and Reliability Test of Worker’s Fatigue Assessment Instrument. ndonesian Journal of Community and Occupational Medicine (IJCOM) 2022, pp.89–95.
Tyas, A.E., Dharma Wibawa, A. and Purnomo, M.H., 2020. Theta, Alpha and Beta Activity in the Occipital Based on EEG Signals for Mental Fatigue in High School Students. Proceeding - ICoSTA 2020: 2020 International Conference on Smart Technology and Applications: Empowering Industrial IoT by Implementing Green Technology for Sustainable Development. https://doi.org/10.1109/ICoSTA48221.2020.1570614141.
Victorian Transport Resources, 2019. Crash statistics: VicRoads. [Online] Available at: https://www.vicroads.vic.gov.au/safety-and-road-rules/safety-statistics/crash-statistics [Diakses 10 Desember 2023].
Bansal, M., Goyal, A. and Choudhary, A., 2022. A comparative analysis of K-Nearest Neighbor, Genetic, Support Vector Machine, Decision Tree, and Long Short Term Memory algorithms In Machine Learning. Decision Analytics Journal. https://doi.org/10.1016/j.dajour.2022.100071.
Chalder, T., Berelowitz, G., Pawlikowska, T., Watts, L., Wessely, S., Wright, D. and Wallace, E.P., 1993. DEVELOPMENT OF A FATIGUE SCALE. Journnl of Psychosomatic Research, 37(2), pp.147–153.
Gao, J., Wang, W. and Zhang, J., 2016. Explore Interregional EEG Correlations Changed by Sport Training Using Feature Selection. Computational Intelligence and Neuroscience, 2016. https://doi.org/10.1155/2016/6184823.
Henderi , Wahyuningsih , T. and Rahwanto, E., 2021. Comparison of Min-Max normalization and Z-Score Normalization in the K-nearest neighbor (kNN) Algorithm to Test the Accuracy of Types of Breast Cancer. International Journal of Informatics and Information System, [online] 4(1), pp.13–20. Available at: <http://archive.ics.uci.edu/ml.>.
Hendrawan, M.A., 2021. DETEKSI KELELAHAN MENTAL DENGAN MENGGUNAKAN SINYAL EEG SATU KANAL. Jurnal Sistem Informasi Dan Bisnis Cerdas (SIBC), 14(2), pp.78–87.
Hendrawan, M.A., Pane, E.S., Wibawa, A.D. and Purnomo, M.H., 2017. Investigating Window Segmentation on Mental Fatigue Detection Using Single-channel EEG. 2017 5th International Conference on Instrumentation, Communications, Information Technology, and Biomedical Engineering (ICICI-BME), pp.173–178.
Kunasegaran, K., Ismail, A.M.H., Ramasamy, S., Gnanou, J.V., Caszo, B.A. and Chen, P.L., 2023. Understanding mental fatigue and its detection: a comparative analysis of assessments and tools. PeerJ, 11. https://doi.org/10.7717/peerj.15744.
Md Isa, N.E.Z., Amir, A., Ilyas, M.Z. and Razalli, M.S., 2017. The Performance Analysis of K-Nearest Neighbors (K-NN) Algorithm for Motor Imagery Classification Based on EEG Signal. In: MATEC Web of Conferences. EDP Sciences. https://doi.org/10.1051/matecconf/201714001024.
Purnamasari, P.D., Yustiana, P., Sudiana, D. and Ratna, A., 2019. Mobile EEG Based Drowsiness Detection using K-Nearest Neighbor. 2019 IEEE 10th International Conference on Awareness Science and Technology (iCAST), pp.1–5.
Risqiwati, D., Wibawa, A.D., Pane, E.S., Islamiyah, W.R., Tyas, A.E. and Purnomo, M.H., 2020. Feature Selection for EEG-Based Fatigue Analysis Using Pearson Correlation. In: 2020 International Seminar on Intelligent Technology and Its Applications (ISITIA). pp.164–169. https://doi.org/10.1109/ISITIA49792.2020.9163760.
Shabiyya, S.H. and Widasari, E.R., 2023. Implementasi Algoritme Learning Vector Quantization untuk Deteksi Kelelahan Mental berbasis Sinyal Electroencephalography. Jurnal Pengembangan Teknologi Informasi Dan Ilmu Komputer, 7(13).
Su, M., Li, W., Peng, F., Zhou, W., Zhang, R. and Wen, Y., 2022. EEG-Based Mental Fatigue Detection Using CNN-LSTM. 2022 16th ICME International Conference on Complex Medical Engineering, CME 2022, pp.302–305. https://doi.org/10.1109/CME55444.2022.10063316.
Suparmam, A., Wibowo, S., Kekalih, A., Ilyas, M. and Agustina, A., 2022. Indonesian Version of Swedish Occupational Fatigue Inventory (SOFI): Validity and Reliability Test of Worker’s Fatigue Assessment Instrument. ndonesian Journal of Community and Occupational Medicine (IJCOM) 2022, pp.89–95.
Tyas, A.E., Dharma Wibawa, A. and Purnomo, M.H., 2020. Theta, Alpha and Beta Activity in the Occipital Based on EEG Signals for Mental Fatigue in High School Students. Proceeding - ICoSTA 2020: 2020 International Conference on Smart Technology and Applications: Empowering Industrial IoT by Implementing Green Technology for Sustainable Development. https://doi.org/10.1109/ICoSTA48221.2020.1570614141.
Victorian Transport Resources, 2019. Crash statistics: VicRoads. [Online] Available at: https://www.vicroads.vic.gov.au/safety-and-road-rules/safety-statistics/crash-statistics [Diakses 10 Desember 2023].
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