Seleksi Fitur Aroma Teh Kombucha menggunakan ANN untuk Optimasi Kinerja Sistem E-nose

ADHITYA ALVIAN NUGROHO, WAHYU WIJAYA, JANS HENDRY, BUDI SUMANTO

Sari


ABSTRAK

Teh kombucha merupakan hasil fermentasi antara teh manis dengan mikroba yang memiliki khasiat baik bagi kesehatan tubuh. Waktu yang dibutuhkan untuk fermentasi teh ini adalah 7 hingga maksimal 12 hari. Penentuan siap konsumsi dari hasil fermentasi biasanya mengacu dari umur fermentasi dan uji coba rasa oleh human tester. Selain menggunakan 2 cara tersebut, pemanfaatan sistem Electronic Nose (e-nose) dapat digunakan juga untuk melakukan identifikasi terhadap aroma teh kombucha selama proses fermentasi untuk mengetahui matang atau tidaknya. Akan tetapi timbul masalah yaitu hasil pembacaan e-nose menghasilkan data yang cukup banyak sehingga kurang efektif dan dapat menurunkan kinerja sistem, solusinya dapat diterapkan seleksi fitur menggunakan Artificial Neural Network berdasarkan dari Sum of Absolute Errors. Hasil dari penelitian ini mendapatkan 6 fitur terbaik dengan peningkatan nilai akurasi sebesar 97,22%, presisi sebesar 94,74%, dan sensitivitas sebesar 100,00%.

Kata kunci: Teh Kombucha, Seleksi Fitur, E-nose, Artificial Neural Network, Sum of Absolutes Errors

 

ABSTRACT

Kombucha tea is a fermented product of sweet tea with microbes that have good health benefits. The time required to ferment this tea is 7 to a maximum of 12 days. Determination of ready-to-consumption of fermented products usually refers to the age of fermentation and taste testing by a human tester. In addition to using these 2 methods, the use of the Electronic Nose (e-nose) system can also be used to identify the aroma of kombucha tea during the fermentation process to determine whether it is ripe or not. Problems that arise from reading e-nose produce quite a lot of data so that it is less effective and can reduce system performance, the solution can be applied to feature selection using an Artificial Neural Network based on the Sum of Absolute Errors. The results of this study get the best 6 features with an increase in accuracy of 97.22%, precision of 94.74%, and sensitivity of 100.00%.

Keywords: Kombucha Tea, E-nose, Feature Selection, Artificial Neural Network, Sum of Absolute Errors


Kata Kunci


Teh Kombucha; Seleksi Fitur; E-nose; Artificial Neural Network; Sum of Absolutes Errors

Teks Lengkap:

PDF

Referensi


Aini, S. H. A., Sari, Y. A., & Arwan, A. (2018). Seleksi Fitur Information Gain untuk Klasifikasi Penyakit Jantung Menggunakan Kombinasi Metode K-Nearest Neighbor dan Naïve Bayes. Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer, 2(9), 2546–2554.

Challita, N., Khalil, M., & Beauseroy, P. (2016). New feature selection method based on neural network and machine learning. 2016 IEEE International Multidisciplinary Conference on Engineering Technology, IMCET 2016, 81–85. https://doi.org/10.1109/IMCET.2016.7777431

Dutta, H., & Paul, S. K. (2019). Kombucha Drink: Production, Quality, and Safety Aspects. In Production and Management of Beverages (Nomor January). Elsevier Inc. https://doi.org/10.1016/b978-0-12-815260-7.00008-0

Inca, I., Widodo, T. W., & Lelono, D. (2018). Klasifikasi Teh Hijau dan Teh Hitam Tambi-Pagilaran dengan Metode Principal Component Analysis (PCA) Menggunakan E-Nose. IJEIS (Indonesian Journal of Electronics and Instrumentation Systems), 8(1), 61. https://doi.org/10.22146/ijeis.28718

Khaerah, A., & Akbar, F. (2019). Aktivitas Antioksidan Teh Kombucha dari Beberapa Varian Teh yang Berbeda. Prosiding Seminar Nasional LP2M UNM, (pp. 472–476).

Laavanya, D., Shirkole, S., & Balasubramanian, P. (2021). Current challenges, applications and future perspectives of SCOBY cellulose of Kombucha fermentation. Journal of Cleaner Production, 295, 126454. https://doi.org/10.1016/j.jclepro.2021.126454

Leal, J. M., Suárez, L. V., Jayabalan, R., Oros, J. H., & Escalante-Aburto, A. (2018). A review on health benefits of kombucha nutritional compounds and metabolites. CYTA - Journal of Food, 16(1), 390–399. https://doi.org/10.1080/19476337.2017.1410499

Maibriadi, I., Ratna, & Munawar, A. A. (2019). Deteksi Formalin pada Buah Tomat (Lycopersicum Esculentum Mill) dengan Teknologi Hidung Elektronik (Electronic Nose). Jurnal Ilmiah Mahasiswa Pertanian, 4(2), 359–366.

Purnami, K. I., Anom Jambe, A., & Wisaniyasa, N. W. (2018). Pengaruh Jenis Teh Terhadap Karakteristik Teh Kombucha. Jurnal Ilmu dan Teknologi Pangan (ITEPA), 7(2), 1. https://doi.org/10.24843/itepa.2018.v07.i02.p01

Puspitasari, Y., Palupi, R., & Nurikasari, M. (2017). Analisis Kandungan Vitamin C Teh Kombucha Berdasarkan Lama Fermentasi Sebagai Alternatif Minuman Untuk Antioksidan. Global Health Science (Ghs), 2(3), 245–253. http://jurnal.csdforum.com/index.php/ghs

Raigar, R. K., Upadhyay, R., & Mishra, H. N. (2017). Storage quality assessment of shelled peanuts using non-destructive electronic nose combined with fuzzy logic approach. Postharvest Biology and Technology, 132(April), 43–50. https://doi.org/10.1016/j.postharvbio.2017.05.016

Saputri, R. K., Al-bari, A., Nahdlatul, U., Sunan, U., & Bojonegoro, G. (2020). Pengaruh Konsumsi Teh dengan Tingkat Obesitas Mahasiswa Farmasi Universitas Nahdlatul Ulama Sunan Gir. Jurnal Penjas dan Farmasi, 3, 8–14.

Sitompul, A., Iswanto, B. H., & Indrasari, W. (2020). Analisis Cluster Bahan Herbal Berdasarkan Fitur Respon E-Nose. Prosiding Seminar Nasional Fisika (E-Journal) SNF2020, IX(Cx), 141–146. https://doi.org/doi.org/10.21009/03.SNF2020.01.FA.22

Sun, Y., Wang, J., & Cheng, S. (2017). Discrimination among tea plants either with different invasive severities or different invasive times using MOS electronic nose combined with a new feature extraction method. Computers and Electronics in Agriculture, 143(November), 293–301. https://doi.org/10.1016/j.compag.2017.11.007

Villarreal-Soto, S. A., Beaufort, S., Bouajila, J., Souchard, J. P., Renard, T., Rollan, S., & Taillandier, P. (2019). Impact of fermentation conditions on the production of bioactive compounds with anticancer, anti-inflammatory and antioxidant properties in kombucha tea extracts. Process Biochemistry, 83(April), 44–54. https://doi.org/10.1016/j.procbio.2019.05.004

Villarreal-Soto, S. A., Beaufort, S., Bouajila, J., Souchard, J. P., & Taillandier, P. (2018). Understanding Kombucha Tea Fermentation: A Review. Journal of Food Science, 83(3), 580–588. https://doi.org/10.1111/1750-3841.14068

Yin, Y., & Zhao, Y. (2019). A feature selection strategy of E-nose data based on PCA coupled with Wilks Λ-statistic for discrimination of vinegar samples. Journal of Food Measurement and Characterization, 13(3), 2406–2416. https://doi.org/10.1007/s11694-019-00161-0




DOI: https://doi.org/10.26760/elkomika.v10i2.334

Refbacks

  • Saat ini tidak ada refbacks.


_______________________________________________________________________________________________________________________

ISSN (cetak) : 2338-8323 | ISSN (elektronik) : 2459-9638

diterbitkan oleh :

Teknik Elektro Institut Teknologi Nasional Bandung

Alamat : Gedung 20 Jl. PHH. Mustofa 23 Bandung 40124

Kontak : Tel. 7272215 (ext. 206) Fax. 7202892

Surat Elektronik : jte.itenas@itenas.ac.id________________________________________________________________________________________________________________________

Statistik Pengunjung

Free counters!

Web Analytics Made Easy - StatCounter

Lihat Statistik Jurnal

Jurnal ini terlisensi oleh Creative Commons Attribution-ShareAlike 4.0 International License.

Creative Commons License