PREDIKSI PENERIMAAN BANTUAN PIP PADA SMKS AL-FURQON BATUBARA DENGAN METODE NAÃVE BAYES - Aini, Nur; Handoko, Wiwin; Nurhaliza, Rizky (LPPM STMIK Royal Kisaran, 2024)
Abstract: The PIP program is assistance for poor students which is provided to students from families who are poor and cannot carry out learning activities at school. At Al-Furqon Private Vocational School, Batubara Regency, there are still problems in the decision-making process to determine which students are entitled to PIP scholarships, so researchers apply the Naïve Bayes method. Naive Bayes is a simple probabilistic forecasting method based on the application of Bayes' theorem (or Bayes' rule) with the assumption of independence (non-independence) in the selection of PIP recipient students with the criteria of Report Card Value, Parent's Dependents, Parent's Income, and KIP Recipients using the above calculations. The report card value is 75 dependent parents, more than 3 people with income below IDR 1,500,000 and those who do not receive PKH so that 74 people receive PIP results and 117 people do not receive results. In calculating the Naive Bayes method using Python tools, the accuracy results were 96%.Keywords: data mining; naïve bayes; scholarship pip Abstrak: Program PIP termasuk beasiswa untuk siswa tidak mampu yang disajikan kepada siswa dari keluarga miskin dan tidak bisa melaksanakan kegiatan pembelajaran di sekolah. Pada SMK Swasta Al-Furqon Kabupaten Batubara masih menghadapi masalah dalam cara mengambil keputusan untuk penentuan peserta didik yang berwenang atas bantuan PIP sehinga peneliti menerapkan pendekatan Naïve Bayes. Naive Bayes suatu metode prediksi probabilistik sederhana yang berlandaskan pada teorema Bayes dengan hipotesis independensi (non-independent) dalam pemilihan peserta didik penerima PIP dengan kriteria Nilai Raport, Tanggungan Ortu, Penghasilan Ortu, dan Penerima PKH dengan perhitungan nilai raport diatas 75, tanggungan ortu lebih dari 3 orang, penghasilan dibawah Rp 1.500.000 dan tidak menerima PKH sehingga mendapatkan hasil yang Diterima PIP sebanyak 74 orang dan yang Tidak Diterima sebanyak 117 orang. Dalam perhitungan metode Naive Bayes dengan tools jupyter notebook dari anaconda mendapatkan hasil akurasi 97%.Kata Kunci: data mining; naïve bayes; beasiswa pip