Pembelajaran Mesin Berbasis E-nose untuk Klasifikasi Daging pada Produk Sosis
DOI:
https://doi.org/10.23887/jstundiksha.v13i1.70307Keywords:
E-Nose, Sosis, Pembelajaran Mesin, Ekstraksi Fitur, HalalAbstract
Sosis adalah produk olahan daging yang digemari masyarakat namun terdapat permasalahan terkait identifikasi jenis daging yang digunakan dalam produk sosis. Hal ini menjadi penting, terutama bagi masyarakat muslim yang perlu memastikan kehalalan suatu produk sebelum dikonsumsi. Tujuan dari penelitian ini adalah menerapkan electronic nose (e-nose) yang tersusun dari enam sensor gas berbasis metal oxide semiconductor (MOS) dalam mendeteksi aroma dari sosis berbahan daging babi, sapi, dan ayam serta menganalisis sinyal respon sensor tersebut dengan metode pembelajaran mesin untuk mengklasifikasi jenis sosis berdasarkan jenis dagingnya. Percobaan dilakukan dengan setiap sampel jenis sosis seberat 2 gram dan pengukuran dilakukan sebanyak 100 kali perulangan untuk tiap sampel menggunakan e-snose. Hasil respon sensor diekstraksi menggunakan ciri maksimum, median, skewnes, kurtosis, standar deviasi, dan varians. Analisis yang dilakukan menggunakan principal component analysis (PCA) sebagai metode pengelompokan sedangkan metode klasifikasi menggunakan metode Linear discriminant analysis (LDA), k-nearest neighbor (k-NN), Quadratic Discriminant Analysis (QDA), Logistic Regresi (LR), dan Classification and Regression Trees (CART). Metode LDA memperoleh hasil yang paling akurat yaitu dengan akurasi internal mencapai 100% dan eksternal sebesar 98,3%. Pengelompokan dengan PCA mampu memisahkan sosis berdasarkan jenis dagingnya dan juga menunjukkan adanya tumpang tindih data antara sosis ayam dan sapi serta sosis babi dan sosis sapi yang mengindikasikan ketiga sampel memiliki kesamaan profil aroma yang hampir sama. Hasil ini menunjukkan bahwa e-nose dapat diterapkan sebagai instrumen untuk mendeteksi dan pengujian dalam mengidentifikasi produk makanan berupa olahan sosis berdasarkan jenis daging yang digunakan.
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