Data Alumni

Validasi & Analisis Bobot Algoritma

➕ Input Alumni Baru
@if(session('success'))
{{ session('success') }}
@endif @if($summary)

Total Alumni

{{ $summary['total'] }}

@if($summary['prediction_accuracy'])

Top-1 Accuracy

{{ $summary['prediction_accuracy']['top_1'] }}%

Top-3 Accuracy

{{ $summary['prediction_accuracy']['top_3'] }}%

Top-5 Accuracy

{{ $summary['prediction_accuracy']['top_5'] }}%

@endif
@if($summary['by_major']->isNotEmpty())

Distribusi Alumni per Jurusan

@foreach($summary['by_major'] as $major)
{{ $major->major_masuk }}
{{ $major->count }}
@endforeach
@endif @endif
@forelse($alumni as $a) @empty @endforelse
Nama NIS Kelompok Nilai Rata Major Ranking Success Aksi
{{ $a->nama_alumni }} {{ $a->nis ?? '-' }} {{ $a->kelompok_asal }} {{ $a->nilai_rata_rata ? number_format($a->nilai_rata_rata, 2) : '-' }} {{ $a->major_masuk }} @if($a->ranking_saat_rekomendasi) #{{ $a->ranking_saat_rekomendasi }} @else - @endif @if($a->success_status) @switch($a->success_status) @case('sangat_sukses') ✓ Sangat @break @case('sukses') ✓ Sukses @break @case('cukup') • Cukup @break @case('kurang_sukses') ✗ Kurang @break @endswitch @else - @endif 👁 Lihat ✏ Edit
@csrf @method('DELETE')
Belum ada data alumni. Tambah sekarang
{{ $alumni->links() }}

📊 Data Alumni digunakan untuk:
1. Validasi akurasi algoritma Naive Bayes
2. Analisis faktor-faktor mana yang paling berpengaruh terhadap kesuksesan
3. Re-weighting: menyesuaikan bobot jika data menunjukkan faktor lain lebih penting