Optimalisasi dalam Mengidentifikasi Seleksi Mahasiswa Jalur Cepat (Fast-track) Menggunakan Metode K-Nearest Neighbor
DOI:
https://doi.org/10.37034/jsisfotek.v5i2.166Keywords:
Sistem Pendukung Keputusan (SPK), K-Nearest Neighbor (K-NN), Fast-Track, Data Mining, OptimalisasiAbstract
Penerimaan fast-track dilakukan untuk membantu penyeleksian dalam memberikan rekomendasi mahasiswa yang berpotensi bergabung pada program fast-track maka dibutuhkan Sistem Pendukung Keputusan, dikarenakan sistem penyeleksian calon penerima mahasiswa fast-track masih manual, dan banyak sekali kelemahannya. Banyaknya peminat dalam mendaftar fast-track menyebabkan ketua jurusan mengalami kesusahan saat mengolah data yang manual sehingga dibutuhkan perangkat lunak untuk memudahkan pengolahan data tersebut. Tidak semua mahasiswa yang mengajukan permohonan untuk mendapatkan fast-track dapat disetujui, di karenakan mahasiswa yang mengajukan permohonan cukup banyak, maka begitu dibutuhkan sekali agar dibangun suatu SPK dengan metode K-Nearest Neighbor (K-NN) yang dapat membantu memberikan rekomendasi kepada peminat fast-track. Berdasarkan analisis terhadap SPK dengan metode K-NN ini dilakukan dengan cara observasi wawancara dan implementasi sistem. Dalam penilaian penerimaan fast-track dapat dijadikan dasar untuk memudahkan keputusan pada penyeleksian mahasiswa fast-track karena sistem dapat mengolah data dan menghasilkan informasi secara cepat, tepat dan konsisten kepada ketua jurusan terhadap mahasiswa untuk bergabung fast-track yang akan diberikan. Dapat membentuk suatu keputusan yang tepat, efektif dan efisien pada pengelolaan data seleksi penerimaan fast-track yang memang berpotensi diterima fast-track. Metode K-NN dapat digunakan untuk mengidentifikasi seleksi penerimaan mahasiswa fast-track, SPK dalam penilaian penyeleksian mahasiswa fast-track dapat memudahkan keputusan pada mahasiswa secara proporsional dengan berdasarkan hasil proses data mahasiswa meliputi indeks prestasi mahasiswa semester 1-6, jumlah sks sampai semester 6 dengan tepat dan akurat karena sistem dapat meminimalisir kesalahan dalam proses perhitungan normalisasi data.
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