Prediksi Keterserapan Siswa SMK Pada Dunia Industri Dengan Pendekatan Educational Data Mining
DOI:
https://doi.org/10.22441/jte.2024.v15i1.011Kata Kunci:
Data mining, MAE, Prediksi keterserapan, RegresiAbstrak
Tingginya tingkat penyerapan tenaga kerja siswa kejuruan sangat menentukan kualitas suatu Sekolah Menengah Kejuruan (SMK). Semakin banyak siswa yang terserap ke dunia kerja dan semakin cepat bekerja setelah lulus maka semakin baik bagi SMK tersebut. Data mining adalah solusi yang berguna untuk mengidentifikasi pola tersembunyi dan memberikan saran untuk meningkatkan kinerja siswa. Penelitian ini menggunakan data rapor dari 167 siswa jurusan Teknik Jaringan Komputer SMK Negeri 26 Jakarta selama enam semester, dari tiga angkatan siswa yang lulus tahun 2015 hingga 2017. Penelitian ini menggunakan model SVR dan ANN dan metode Mean Absolute Error (MAE). Hasil penelitian menunjukkan bahwa ANN dengan data seluruh fitur yang digunakan, dengan model normalisasi Standard Scaler, dan algoritma aktifasi Relu, jumlah Neuron sebanyak 128 dan Iter Max 150 menunjukkan performa terbaik, yaitu MAE sebesar 2,2 bulan. Heatmap korelasi Pearson mengungkapkan bahwa semua mata pelajaran yang sangat erat hubungannya dan mempengaruhi jumlah serapan mahasiswa di dunia kerja adalah mata pelajaran produktif (vokasi) pada semester 1 & 2 pada aspek penilaian keterampilan (praktik). Untuk meningkatkan angka penyerapan tenaga kerja, mahasiswa harus mempertajam dan memperdalam kompetensi mata pelajaran praktik vokasi pada awal semester. Hasil penelitian ini dapat dijadikan acuan untuk memprediksi penyerapan lulusan SMK di dunia kerja dan sebagai langkah antisipatif untuk meningkatkan nilai kompetensi sebelum memasuki dunia kerja.
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