Sistem Pakar Mendiagnosa Penyakit Akibat Penggunaan Vape Menggunakan Metode Adaptive Neuro Inference System
DOI:
https://doi.org/10.64366/adajisr.v2i2.74Keywords:
Vape; ANFIS; Expert SystemAbstract
Vape is an electric cigarette that is widely used as a temporary escape for those who have a little difficulty in trying to quit smoking. Vape produces steam into the air which contains fine nicotine and other harmful substances, the nicotine contained in the vape will be absorbed by the body. The vapors that emerge from vape cigarettes are not water vapor, but they contain nicotine and other harmful substances that are clearly not good for health and can also pollute the air. Several studies have shown that chemicals in e-cigarettes can damage lung tissue and reduce the ability of lung cells to protect the lungs from germs and other harmful substances. ANFIS method is an adaptive system based on fuzzy logic inference that combines two methods, namely adaptive (neural metwork) and fuzzy methods. This adaptive network is used to adapt the fuzzy logic inference system to represent the desired fuzzy interference system. The principle of the working method is to combine two methods, namely the adaptive method and the fuzzy method where both use the same two sources of information, but the representation is different. The results of diagnosing vape-related diseases are lung infection, hypotension, and heart failure, but the disease that is often caused by vape is lung infection, with an expert system diagnosing vape-related diseases that make it easier to find out the effects of vape use.
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References
D. M. Gaba, “Artificial intelligence and expert systems,” in Control and Automation in Anaesthesia, Springer, 2022, hal. 22–36. doi: https://doi.org/10.1007/978-3-642-79573-2_3.
D. Janjanam, B. Ganesh, dan L. Manjunatha, “Design of an expert system architecture: An overview,” in Journal of Physics: Conference Series, 2021, vol. 1767, no. 1, hal. 1–7. doi: 10.1088/1742-6596/1767/1/012036.
B. Kaplan, “Revisiting health information technology ethical, legal, and social issues and evaluation: telehealth/telemedicine and COVID-19,” Int. J. Med. Inform., vol. 143, hal. 104239, 2020, doi: https://doi.org/10.1016/j.ijmedinf.2020.104239.
B. M. Rashed dan N. Popescu, “medical image-based diagnosis using a hybrid adaptive neuro-fuzzy inferences system (ANFIS) optimized by GA with a deep network model for features extraction,” Mathematics, vol. 12, no. 5, hal. 1–32, 2024, doi: https://doi.org/10.3390/math12050633.
A. M. Glasser et al., “Overview of electronic nicotine delivery systems: a systematic review,” Am. J. Prev. Med., vol. 52, no. 2, hal. e33–e66, 2017, doi: https://doi.org/10.1016/j.amepre.2016.10.036.
L. F. Chun, F. Moazed, C. S. Calfee, M. A. Matthay, dan J. E. Gotts, “Pulmonary toxicity of e-cigarettes,” Am. J. Physiol. Cell. Mol. Physiol., vol. 313, no. 2, hal. L193–L206, 2017, doi: https://doi.org/10.1152/ajplung.00071.2017.
L. J. England, R. E. Bunnell, T. F. Pechacek, V. T. Tong, dan T. A. McAfee, “Nicotine and the developing human: a neglected element in the electronic cigarette debate,” Am. J. Prev. Med., vol. 49, no. 2, hal. 286–293, 2015, doi: https://doi.org/10.1016/j.amepre.2015.01.015.
J.-S. R. Jang, “ANFIS: adaptive-network-based fuzzy inference system,” IEEE Trans. Syst. Man. Cybern., vol. 23, no. 3, hal. 665–685, 1993, doi: 10.1109/21.256541.
L. Priyadarshini dan L. Shrinivasan, “Design of an ANFIS based decision support system for diabetes diagnosis,” in 2020 International Conference on Communication and Signal Processing (ICCSP), 2020, hal. 1486–1489. doi: 10.1109/ICCSP48568.2020.9182163.
E. Kasson, A. K. Singh, M. Huang, D. Wu, dan P. Cavazos-Rehg, “Using a mixed methods approach to identify public perception of vaping risks and overall health outcomes on Twitter during the 2019 EVALI outbreak,” Int. J. Med. Inform., vol. 155, hal. 104574, 2021, doi: https://doi.org/10.1016/j.ijmedinf.2021.104574.
M. L. Goniewicz et al., “Comparison of nicotine and toxicant exposure in users of electronic cigarettes and combustible cigarettes,” JAMA Netw. open, vol. 1, no. 8, hal. e185937–e185937, 2018, doi: 10.1001/jamanetworkopen.2018.5937.
D. Arisandi dan I. P. Sari, Sistem Pakar Dengan Fuzzy Expert System. Gracias Logis Kreatif, 2021.
A. Kondinski, J. Bai, S. Mosbach, J. Akroyd, dan M. Kraft, “Knowledge engineering in chemistry: From expert systems to agents of creation,” Acc. Chem. Res., vol. 56, no. 2, hal. 128–139, 2022, doi: https://doi.org/10.1021/acs.accounts.2c00617.
H. Pruvost, A. Wilde, dan O. Enge-Rosenblatt, “Ontology-based expert system for automated monitoring of building energy systems,” J. Comput. Civ. Eng., vol. 37, no. 1, hal. 4022054-1-4022054–11, 2023, doi: https://doi.org/10.1061/(ASCE)CP.1943-5487.0001065.
J. Junaidi dan R. Said, “Pemberdayaan Kesehatan Anak Usia Sekolah: Edukasi Bahaya Rokok Elektrik di Sekolah Indonesia Kuala Lumpur Malaysia,” PaKMas J. Pengabdi. Kpd. Masy., vol. 4, no. 2, hal. 322–330, 2024, doi: https://doi.org/10.54259/pakmas.v4i2.3022.
R. F. Hasani, B. Nixon, A. Humairah, dan A. Yasmin, “Rancang Bangun Alat Pendeteksi Uap Vaping dan Asap Rokok Berbasis Internet of Things Terintegerasi Aplikasi Android,” Spektral, vol. 4, no. 1, hal. 151–158, 2023, doi: https://doi.org/10.32722/spektral.v4i1.5629.
M. Deif, R. Hammam, dan A. Solyman, “Adaptive neuro-fuzzy inference system (ANFIS) for rapid diagnosis of COVID-19 cases based on routine blood tests,” Int. J. Intell. Eng. Syst., vol. 14, no. 2, hal. 178–189, 2021, doi: https://doi.org/10.22266/ijies2021.0430.16.
S. Rahmah, W. Witanti, dan P. N. Sabrina, “Prediksi Penjualan Obat Menggunakan Metode Adaptive Neuro-Fuzzy Inference System (ANFIS),” JIMP-Jurnal Inform. Merdeka Pasuruan, vol. 7, no. 3, hal. 109–114, 2023, doi: DOI http://dx.doi.org/10.51213/jimp.v7i3.733.
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