Klasifikasi Jejak Bau Rokok pada Napas Berdasarkan Interval Waktu Pasca-Merokok Menggunakan Electronic Nose dan Pembelajaran Mesin
DOI:
https://doi.org/10.30865/jurikom.v15i5.10146Keywords:
Electronic Nose, Bau Rokok, Bronkopneumonia, Validasi Antar-Sesi, Klasifikasi NapasAbstract
Paparan asap rokok di rumah tangga merupakan faktor risiko bronkopneumonia pada balita, sementara anggapan bahwa napas perokok aman setelah jeda singkat belum diuji pada menit-menit awal pasca-merokok dengan perangkat berbiaya rendah. Penelitian ini menguji electronic nose (E-Nose) berbasis empat sensor gas semikonduktor seri MQ untuk mengklasifikasikan jejak bau rokok pada napas ke dalam empat kelas: perokok 0, 1, dan 10 menit setelah menghabiskan satu batang rokok filter, serta non-perokok. Data berasal dari 12 responden (6 perokok, 6 non-perokok) dengan satu sesi perekaman per responden. Pra-pemrosesan terbaik (Z-Score dan Wavelet Transform) dan ekstraksi ciri statistik per jendela menghasilkan 112 sampel dengan 24 fitur. Random Forest, K-Nearest Neighbor, XGBoost, dan stack deep neural network dievaluasi dengan validasi antar-sesi leave-one-session-out sebagai ukuran utama dan Stratified K-Fold (k=5) tingkat jendela sebagai pembanding. Pada validasi antar-sesi, akurasi tertinggi 74,11% dicapai K-Nearest Neighbor, sedangkan Random Forest mencapai 59,82%, jauh di bawah akurasinya pada Stratified K-Fold (94,64%) yang terbukti terlalu optimistis karena jendela dari sesi yang sama masuk ke data latih dan data uji. Random Forest tetap memisahkan napas perokok dan non-perokok tanpa kesalahan pada kedua skema, tetapi pembedaan lama jeda waktu belum dapat digeneralisasi ke responden baru. Kontribusi utama penelitian ini adalah bukti awal kelayakan E-Nose berbiaya rendah untuk penapisan keberadaan jejak rokok pada napas serta penegasan pentingnya validasi berbasis responden. Dengan dua responden per interval, temuan ini masih bersifat pembuktian konsep.
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