ARTÍCULO
TITULO

Implementation of Naive Bayes Classification Algorithm in Determining Appropriate Help Targets of Unlimited Houses (RTLH) in Bojonegoro District DOI : 10.24114/cess.v8i2.46295 | Abstract views : 0 times

Anissa Nurul Farida Tussholikhah    
Nurissaidah Ulinnuha    
Wika Dianita Utami    

Resumen

Rumah merupakan salah satu kebutuhan primer bagi setiap individu, dan termasuk kedalam aset terpenting yang harus dimiliki. Kelayakan rumah yang layak huni dan tidak layak huni harus dipertimbangkan. Rumah yang tidak memenuhi kecukupan minimum dari segi ruang dan luas ruangan dianggap sebagai Rumah tidak layak huni (RTLH). Untuk mengatasi terjadinya peningkatan RTLH maka pemerintah menanggulanginya dengan memberikan bantuan kepada masyarakat yang layak menerima dengan tepat sasaran. Penelitian ini bertujuan untuk menerapkan metode Naïve Bayes dalam menentukan bantuan tepat sasaran menggunakan dua kelas penelitian, yakni layak menerima bantuan RTLH dan tidak layak menerima bantuan RTLH. Dari analisis klasifikasi menggunakan confusion matrix didapatkan hasil akurasi sebesar 63%, recall 100% dan presisi 25% untuk 400 data training dan 100 data testing dari total 500 data dengan 10 atribut pengujian.The house is one of the primary needs for every individual, and is included in the most important asset that must be owned. The livability of the livable and uninhabitable houses should be considered. A house that does not meet the minimum adequacy in terms of space and room area is considered an uninhabitable house (RTLH). To overcome the increase in RTLH, the government overcomes it by providing assistance to people who deserve to receive it on target. This study aims to apply the Naïve Bayes method in determining targeted assistance using two research classes, namely eligible to receive RTLH assistance and not eligible to receive RTLH assistance. From the classification analysis using the confusion matrix, the results obtained are 63% accuracy, 100% recall and 25% precision for 400 training data and 100 testing data from a total of 500 data with 10 test attributes.

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Revista: Applied Sciences