@foreach ($kodes as $k) @endforeach @foreach ($kodes as $i) @foreach ($kodes as $j) @php $val = $crispMatrix[$i][$j]; $cetak = ($val == 1) ? "1" : (($val < 1 && $val > 0) ? "1/" . round(1/$val) : number_format($val, 2)); @endphp @endforeach @endforeach

Tahap 1: Matriks Konsensus Multi-Pakar (Geometric Mean)
Kode	{{ $k }}
{{ $i }}	{{ $cetak }}

@foreach ($kodes as $k) @endforeach

Tahap Validasi: Uji Konsistensi (AHP Klasik)
Sub-Kode	Total Kolom Matriks	Nilai Eigen (Priority Vector)
{{ $k }}	{{ number_format($colSums[$k], 4) }}	{{ number_format($priorityVector[$k], 4) }}
Consistency Ratio (CR)		{{ number_format($cr * 100, 2) }}% (VALID)

@foreach ($kodes as $k) @endforeach @foreach ($kodes as $i) @foreach ($kodes as $j) @php $v = $matrix[$i][$j]; @endphp @endforeach @endforeach

Tahap 2: Matriks Perbandingan Berpasangan Fuzzy (TFN)
Kode	{{ $k }}
{{ $i }}	({{ number_format($v[0],3) }}, {{ number_format($v[1],3) }}, {{ number_format($v[2],3) }})

@foreach ($si as $kode => $v) @php $namaSub = $subs->where('kode', $kode)->first()->nama_sub ?? '-'; @endphp @endforeach

Tahap 3: Nilai Sintesis Fuzzy (Si)
Sub-Kriteria	Vektor Si (l, m, u)
{{ $kode }} - {{ $namaSub }}	({{ number_format($v[0],4) }}, {{ number_format($v[1],4) }}, {{ number_format($v[2],4) }})

@foreach ($kodes as $k) @endforeach @foreach ($vMatrix as $i => $rows) @foreach ($rows as $val) @endforeach @endforeach

Tahap 4: Derajat Kemungkinan (V Matrix)
V(Si ≥ Sk)	{{ $k }}
{{ $i }}	{{ number_format($val, 3) }}

@php $sumDPrime = array_sum($dPrime); @endphp @foreach ($dPrime as $kode => $minV) @php $w = ($sumDPrime > 0) ? ($minV / $sumDPrime) : 0; $namaSub = $subs->where('kode', $kode)->first()->nama_sub ?? '-'; @endphp @endforeach

Tahap Akhir: Bobot Lokal (W) & Skala 1000
Sub-Kriteria	Min Degree (d')	Bobot Lokal (W)	Bobot Skala 1000
{{ $kode }} - {{ $namaSub }}	{{ number_format($minV, 4) }}	{{ number_format($w, 4) }}	{{ $bobot1000[$kode] ?? '-' }}
TOTAL AKUMULASI	{{ number_format($sumDPrime, 4) }}	1.0000	{{ array_sum($bobot1000) }}