ANGGORO, SETO PRI (2023) Visualisasi Hasil Klasifikasi Bansos Menggunakan Algoritma kNearest Neighbor & Algoritma Naïve Bayes (Studi Kasus : SMK CBA). S1 thesis, Universitas Mercu Buana Bekasi.
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Abstract
Kemiskinan bukan sekedar statistik atau angka. Karena kondisi kehidupan masyarakat yang sulit, kemiskinan telah menjadi masalah nyata. Salah satu fenomena kemiskinan muncul dari kurangnya kebutuhan dasar masyarakat. Tujuan dari penelitian ini adalah membuat sistem perhitungan untuk penerimaan bantuan sosial dengan mengetahui hasil prediksi dan tingkat akurasi dari algoritma kNearest Neighbor dan Naive Bayes. Dataset yang digunakan adalah dataset siswa yang mengajukan KJP di SMK CBA tahun 2018-2022, sebanyak 1325 data. Atribut data terdiri dari nama, jumlah tanggungan, pendapatan orang tua, nilai aset keseluruhan dan status penerimaan KJP. Adapun atribut data yang di gunakan untuk perhitungan yaitu pendapatan orang tua dan total aset keseluruhan. Dari kedua hasil pengujian menggunakaan 25% data uji, algoritma k-nearest neighbor dengan k=20 menghasilkan nilai akurasi sebesar 95% dan dari hasil pengujian menggunakan algoritma naive bayes menghasilkan akurasi sebesar 91%. Kata kunci : k-nearest neighbor, naïve bayes, prediksi , bantuan sosial. Poverty is not just statistics or numbers. Due to the difficult living conditions of the community, poverty has become a real problem. One of the phenomena of poverty arises from the lack of basic needs of the society. The aim of this research is to create a calculation system for social assistance recipients by determining the prediction results and accuracy level of the k-Nearest Neighbor and Naive Bayes algorithms. The dataset used consists of student data who applied for the KJP program at SMK CBA from 2018 to 2022, totaling 1325 data points. The data attributes include name, number of dependents, parental income, total asset value, and KJP acceptance status. The attributes used for calculation are parental income and total overall assets. From the two testing results using 25% of the test data, knearest neighbor algorithm with k=20 achieved an accuracy of 95%, while the testing using the naïve bayes algorithm resulted in an accuracy of 91%. Keywords : k-nearest neighbor, naïve bayes, prediction, social assistance.
| Item Type: | Thesis (S1) |
|---|---|
| Call Number CD: | FIK/INFO 23 040 |
| NIM/NIDN Creators: | 41519210083 |
| Uncontrolled Keywords: | k-nearest neighbor, naïve bayes, prediksi , bantuan sosial. |
| Subjects: | 000 Computer Science, Information and General Works/Ilmu Komputer, Informasi, dan Karya Umum > 000. Computer Science, Information and General Works/Ilmu Komputer, Informasi, dan Karya Umum > 004 Data Processing, Computer Science/Pemrosesan Data, Ilmu Komputer, Teknik Informatika |
| Divisions: | Fakultas Ilmu Komputer > Informatika |
| Depositing User: | siti maisyaroh |
| Date Deposited: | 29 Sep 2023 03:16 |
| Last Modified: | 29 Sep 2023 03:16 |
| URI: | http://repository.mercubuana.ac.id/id/eprint/81606 |
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