Komparasi Algoritma Random Forest dan SVM dalam Klasifikasi Kondisi Aliran Air Berdasarkan Getaran MPU6050

Aknes Tasia Pratama; Ahmad Taqwa; Lindawati

doi:10.30865/json.v7i4.9875

Authors

Aknes Tasia Pratama Politeknik Negeri Sriwijaya
Ahmad Taqwa Politeknik Negeri Sriwijaya
Lindawati Politeknik Negeri Sriwijaya

DOI:

https://doi.org/10.30865/json.v7i4.9875

Keywords:

MPU6050, Machine learning, Random Forest, Support Vector Machine, Distribusi Air

Abstract

Ketersediaan informasi mengenai kondisi aliran air pada sistem distribusi masih menjadi permasalahan di beberapa wilayah, terutama pada jaringan yang tidak beroperasi secara kontinu. Penelitian ini bertujuan mengembangkan model klasifikasi kondisi aliran air pada pipa distribusi berdasarkan data getaran yang diperoleh menggunakan sensor MPU6050. Data getaran direpresentasikan melalui fitur akselerasi tiga sumbu (ax, ay, dan az) serta fitur statistik berupa mean dan Root Mean Square (RMS). Dataset yang digunakan terdiri atas 4.406 sampel yang dikelompokkan ke dalam dua kelas, yaitu kondisi air mengalir dan pipa kosong. Tahapan penelitian meliputi pra-pemrosesan data menggunakan StandardScaler, penanganan ketidakseimbangan kelas menggunakan Synthetic Minority Over-sampling Technique (SMOTE), serta pelatihan model menggunakan algoritma Random Forest dan Support Vector Machine (SVM). Hasil pengujian menunjukkan bahwa Random Forest memperoleh akurasi 95,92%, presisi 96,60%, recall 96,22%, dan F1-score 96,41%, sedangkan Support Vector Machine (SVM) memperoleh akurasi 95,80%, presisi 95,50%, recall 97,21%, dan F1-score 96,35%. Hasil tersebut menunjukkan bahwa kedua algoritma mampu mengklasifikasikan kondisi aliran air dengan baik, dengan Random Forest memberikan performa keseluruhan yang sedikit lebih unggul dibandingkan Support Vector Machine (SVM). Penelitian ini menunjukkan bahwa data getaran dari sensor MPU6050 berpotensi digunakan sebagai solusi pemantauan kondisi aliran air secara non-intrusif pada sistem distribusi air.

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