DOI: https://doi.org/10.14710/jtsiskom.2022.14450

Prapemrosesan pada Klasifikasi Status Mutu Air Sungai Menggunakan Random Oversampling dan Outlier Remover Clustering

1Department of Informatics Magister, Universitas Islam Negeri Sunan Kalijaga Yogyakarta. Jl Marsda Adisucipto No 1 Depok Sleman Yogyakarta 55281, Indonesia

2Department of Biology, Universitas Islam Negeri Sunan Kalijaga, Indonesia

3Department of Informatics Magister UIN Sunan Kalijaga Yogyakarta, Indonesia

Received: 16 Apr 2022; Published: 24 Sep 2024.
Open Access Copyright (c) 2024 Shofwatul Uyun
Creative Commons License This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

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Abstract
Ketidakseimbangan jumlah data pada setiap kelasnya serta adanya data outlier seringkali menjadi masalah dalam proses klasifikasi, hal tersebut tentu akan mempengaruhi performa kinerja pembelajaran mesin yang menurun. Oleh karena itupada penelitian ini diusulkan penggunaan teknik Random Oversampling (ROs) untuk mengatasi ketidakseimbangan data serta teknik Outlier Removal Clustering (ORC) untuk mengatasi data outlier pada penentuan status mutu air. Kedua teknik tersebut digunakan pada tahapan prapemrosesan. Penelitian ini terdiri dari beberapa tahapan, yaitu penentuan kelas status mutu air menggunakan teknik indeks pencemaran, prapemrosesan, pembagian data, klasifikasi serta evaluasi kinerja. Ada tiga algoritma klasifikasi yang digunakan sebagai perbandingan, yaitu KNN, CART dan random forest. Berdasarkan hasil penelitian menunjukkan peningkatan rerata akurasi dari penggunaan ketiga algoritma klasifikasi tersebut dengan tanpa dilakukan prapemrosesan, penggunaan ROs serta integrasi ROs dan ORC secara berurutan sebagai berikut 83,81%; 94,87% dan 95,51%. Jadi penggunaan teknik Ros dan ORC terbukti meningkatkan performa kinerja pada machine learning. 
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Keywords: klasifikasi; prapemrosesan; RoS; OCR; mutu air.

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Section: Original Research Articles
Language : EN
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