PREDICTION AND REDUCTION OF BACKBREAK IN BLASTING OPERATIONS USING RANDOM FOREST METHOD AT PAMUBULAN LIMESTONE QUARRY

Raden Haris Handayana, Adrian Hartanto, Jihan Farhan Lubis

Abstract


Backbreak pada operasi peledakan merupakan hasil yang tidak diinginkan yang menyebabkan terbentuknya retakan di belakang barisan terakhir peledakan. Hal ini menyebabkan tidak terciptanya smooth freeface, mengurangi efisiensi pengeboran, dan fragmentasi boulder pada peledakan berikutnya. Untuk mengatasi masalah ini, dilakukan upaya untuk memprediksi dan mengurangi backbreak dengan mempertimbangkan berbagai parameter peledakan seperti sifat batuan, burden, spasi, kedalaman lubang, stemming, powder column, powder factor, total lubang, total baris, desain peledakan, interhole timing, interrow timing, back row control, dan time windows. Karena kompleksitas interaksi di antara parameter-parameter tersebut, dalam makalah ini digunakan metode Random Forest yang terdiri dari banyak pohon keputusan. Ditemukan bahwa koefisien determinasi antara backbreak aktual dan prediksi adalah 83,89%, sedangkan kesalahan relatifnya adalah 13,12%

+/- 1,30%. Dengan menerapkan hasil dari penelitian ini, backbreak dapat dikurangi dari 149,38 menjadi 50,04 cm.


Keywords


Blasting, Backbreak, Random Forest, Machine Learning, Blast Optimization

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