Peningkatan Kinerja K-Nearest Neighbor menggunakan Bagging pada Permasalahan Ragam Kelas terhadap Pemeliharaan Prediktif Permesinan
DOI:
https://doi.org/10.26418/justin.v12i2.78503Keywords:
pembelajaran mesin, permasalahan ragam kelas, nearest neighbor, metode baggingAbstract
Dalam beberapa dekade terakhir, aplikasi pembelajaran mesin menjadi sangat diminati dalam menyelesaikan permasalahan tertentu. Bidang ini memberikan andil dalam menawarkan solusi terhadap banyak disiplin ilmu yang berkaitan dengan masalah klasifikasi atau prediksi. Salah satu dari sekian banyaknya algoritma pembelajaran mesin, K-nearest Neighbor masih menjadi algoritma favorit yang relevan untuk saat ini. Banyak dari penelitian terdahulu berlomba-lomba untuk mengoptimalkan algoritma KNN dan studi ini juga terinspirasi akan hal itu. Sehingga, studi ini akan berfokus pada upaya peningkatan algoritma KNN dalam penyelesaian permasalahan ragam kelas pada dataset. Lebih lanjut, adopsi metode bagging akan dipadukan dengan algoritma terkait dalam membantu mengklasifikasikan ragam tipe kerusakan mesin pada dataset permesinan. Adapun sebelum proses pemodelan berlanjut, metode yang diusulkan juga menerapkan beberapa metode klasik terlebih dahulu layaknya normalisasi dan tuning grid-search. Selanjutnya, studi ini juga akan menyajikan tentang perbandingan model dengan atau tanpa metode bagging, akurasi, precision, dan recall sebagai bagian dari matriks evaluasi. Hasil akhir eksperimen akan disajikan melalui beberapa matriks dengan menunjukkan akurasi sebesar 95.68%, rata-rata makro precision sebesar 96.28%, dan rata-rata makro recall sebesar 94.07%.
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