Klasifikasi Kondisi Pergerakan Palet Menggunakan Metode K-Nearest Neighbors
Keywords:
konveyor, knn, palet, sistem pemantauanAbstract
Tingginya permintaan produk menuntut proses distribusi berjalan efisien dan minim hambatan. Palet merupakan material penting yang berperan dalam mendukung proses tersebut. Namun kondisi operasional yang tidak stabil sering menyebabkan posisi palet tidak sesuai dengan jalur konveyor sehingga terjadi gangguan pergerakan palet yang berdampak pada penurunan efisiensi dan peningkatan waktu henti produksi. Maka dari itu diperlukan adanya penelitian dengan mengembangkan sistem monitoring dan klasifikasi kondisi pergerakan palet menggunakan metode K-Nearest Neighbord (KNN). Sistem menggunakan sensor ultrasonik HC-SR04 untuk mengukur jarak sisi kiri dan kanan palet serta sensor proximity E18-D80NK untuk mendeteksi keberadaan palet. Data dikirim ke mikrokontroler ESP32, disimpan dalam database dan diproses menggunakan algoritma KNN untuk mengklasifikasikan kondisi palet menjadi tiga kelas yaitu normal, miring dan berhenti. Hasil pembacaan sensor, klasifikasi, dan peringatan akan ditampilkan melalui dashboard pemantauan. Hasil pengujian menunjukkan bahwa model KNN dengan k=3 dengan proporsi pembagian dataset 80:20 mampu mengklaisfikasikan kondisi pergerakan palet dengan akurasi sebesar 97,68%, precision 97,21%, recall 97,07% dan F1-score 97,14%. Sistem ini juga mampu menampilkan hasil pembacaan sensor, klasifikasi dan memberikan peringatan apabila terdapat kondisi palet yang tidak normal pada dashboard pemantauan.
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