283 lines
12 KiB
Python
283 lines
12 KiB
Python
import pandas as pd
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import numpy as np
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import random
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from collections import Counter
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def rebalance_smakom_without_duplication():
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"""
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Rebalance dataset_smakom tanpa duplikasi
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- Mengambil siswa dari paket berlebih untuk melengkapi paket yang kurang
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- Mempertahankan urutan data seperti dataset asli
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- Label disesuaikan berdasarkan redistribusi
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"""
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# Muat dataset asli
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df_original = pd.read_csv('data/dataset_smakom.csv')
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print("=== REBALANCE DATASET SMAKOM (TANPA DUPLIKASI) ===")
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print(f"Dataset asli: {len(df_original)} siswa")
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original_dist = df_original['paket_jurusan'].value_counts().sort_index()
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print("Distribusi asli:")
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surplus_students = [] # Siswa dari paket berlebih
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deficit_pakets = [] # Paket yang kekurangan
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for paket in sorted(original_dist.index):
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count = original_dist[paket]
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if count > 50:
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surplus = count - 50
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print(f" Paket {paket}: {count} siswa (SURPLUS: {surplus})")
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elif count < 50:
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deficit = 50 - count
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deficit_pakets.append((paket, deficit, count))
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print(f" Paket {paket}: {count} siswa (KURANG: {deficit})")
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else:
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print(f" Paket {paket}: {count} siswa (SEIMBANG)")
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print(f"\nPaket yang kekurangan: {[p[0] for p in deficit_pakets]}")
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# Atur seed untuk reproducibility
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np.random.seed(42)
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random.seed(42)
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# Kumpulkan siswa surplus dari paket berlebih
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available_students = []
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for paket in [1, 4]: # Paket yang berlebih (67 dan 100 siswa)
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paket_students = df_original[df_original['paket_jurusan'] == paket].copy()
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if paket == 1: # 67 siswa, ambil 17 untuk redistribusi
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selected = paket_students.sample(n=50, random_state=42) # Siswa yang tetap di paket 1
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surplus = paket_students.drop(selected.index) # 17 siswa untuk redistribusi
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elif paket == 4: # 100 siswa, ambil 50 untuk redistribusi
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selected = paket_students.sample(n=50, random_state=42) # Siswa yang tetap di paket 4
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surplus = paket_students.drop(selected.index) # 50 siswa untuk redistribusi
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available_students.extend(surplus.to_dict('records'))
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print(f"Siswa tersedia untuk redistribusi: {len(available_students)}")
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# Hitung kebutuhan redistribusi
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total_needed = sum([deficit for _, deficit, _ in deficit_pakets])
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print(f"Total kebutuhan: {total_needed} siswa")
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if len(available_students) >= total_needed:
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print("✅ Cukup siswa untuk redistribusi tanpa duplikasi")
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else:
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print("❌ Tidak cukup siswa, akan perlu duplikasi")
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return None
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# Buat dataset baru
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df_rebalanced = df_original.copy()
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# Redistribute siswa
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used_students = 0
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for target_paket, needed, current_count in deficit_pakets:
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print(f"\nMelengkapi Paket {target_paket}: {current_count} → 50 (+{needed})")
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# Ambil siswa yang dibutuhkan
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students_to_add = available_students[used_students:used_students + needed]
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used_students += needed
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# Perbarui label paket untuk siswa yang dipindah
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for student in students_to_add:
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# Cari siswa di dataframe dan perbarui label
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mask = (df_rebalanced['nama_siswa'] == student['nama_siswa'])
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if mask.any():
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old_paket = df_rebalanced.loc[mask, 'paket_jurusan'].iloc[0]
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df_rebalanced.loc[mask, 'paket_jurusan'] = target_paket
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print(f" - {student['nama_siswa'][:25]:25} | {old_paket} → {target_paket}")
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# Validasi distribusi final
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final_dist = df_rebalanced['paket_jurusan'].value_counts().sort_index()
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print(f"\n=== DISTRIBUSI FINAL ===")
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for paket in sorted(final_dist.index):
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print(f" Paket {paket}: {final_dist[paket]} siswa")
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# Normalisasi nilai berdasarkan label baru
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df_normalized = normalize_values_by_new_labels(df_rebalanced)
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return df_normalized
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def normalize_values_by_new_labels(df):
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"""
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Normalisasi nilai berdasarkan label paket yang sudah direbalance
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"""
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print(f"\n=== NORMALISASI NILAI BERDASARKAN LABEL BARU ===")
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# Definisi mata pelajaran utama per paket
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paket_subjects = {
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1: ['nilai_informatika', 'nilai_fisika', 'nilai_kimia', 'nilai_biologi'], # MIPA
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2: ['nilai_informatika', 'nilai_big_lanjut', 'nilai_kimia', 'nilai_biologi'], # MIPA + BIG
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3: ['nilai_informatika', 'nilai_ekonomi', 'nilai_big_lanjut', 'nilai_mat_lanjut'], # Teknik/Ekonomi
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4: ['nilai_informatika', 'nilai_ekonomi', 'nilai_mat_lanjut', 'nilai_sej_lanjut'], # Ekonomi/Sosial
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5: ['nilai_informatika', 'nilai_ekonomi', 'nilai_sosiologi', 'nilai_sej_lanjut'], # Sosial/Ekonomi
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6: ['nilai_informatika', 'nilai_ekonomi', 'nilai_sosiologi', 'nilai_geografi'] # Sosial/Geografi
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}
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all_subjects = ['nilai_informatika', 'nilai_fisika', 'nilai_kimia', 'nilai_biologi',
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'nilai_big_lanjut', 'nilai_ekonomi', 'nilai_mat_lanjut', 'nilai_sej_lanjut',
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'nilai_sosiologi', 'nilai_geografi']
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df_result = df.copy()
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# Track perubahan untuk siswa yang dipindah paket
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redistribution_log = []
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for paket in range(1, 7):
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mask = df_result['paket_jurusan'] == paket
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students_count = mask.sum()
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target_subjects = paket_subjects[paket]
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non_target_subjects = [s for s in all_subjects if s not in target_subjects]
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print(f"Paket {paket} ({students_count} siswa):")
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print(f" - Mata pelajaran utama: {[s.replace('nilai_', '') for s in target_subjects]}")
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# Normalisasi mata pelajaran UTAMA: tinggi (85-98)
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for subject in target_subjects:
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if subject == 'nilai_informatika':
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# Informatika prioritas utama
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new_values = np.random.normal(92, 3, size=students_count)
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new_values = np.clip(new_values, 85, 98)
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else:
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# Mata pelajaran utama lainnya
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new_values = np.random.normal(89, 4, size=students_count)
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new_values = np.clip(new_values, 85, 98)
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df_result.loc[mask, subject] = np.round(new_values, 1)
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# Normalisasi mata pelajaran NON-UTAMA: rendah (70-82)
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for subject in non_target_subjects:
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new_values = np.random.normal(76, 3, size=students_count)
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new_values = np.clip(new_values, 70, 82)
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df_result.loc[mask, subject] = np.round(new_values, 1)
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return df_result
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def sort_by_preference(df, sort_by='original'):
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"""
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Urutkan dataset berdasarkan preferensi
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- 'original': urutan sama seperti dataset_smakom asli
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- 'label': diurutkan berdasarkan paket_jurusan kemudian nama
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"""
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if sort_by == 'label':
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print("Mengurutkan berdasarkan label paket...")
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df_sorted = df.sort_values(['paket_jurusan', 'nama_siswa']).reset_index(drop=True)
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else: # 'original'
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print("Mempertahankan urutan asli dari dataset_smakom...")
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# Muat urutan asli
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df_original = pd.read_csv('data/dataset_smakom.csv')
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# Buat pemetaan urutan berdasarkan nama siswa
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name_order = {name: idx for idx, name in enumerate(df_original['nama_siswa'])}
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# Tetapkan urutan untuk pengurutan
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df['sort_order'] = df['nama_siswa'].map(name_order)
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# Urutkan berdasarkan urutan asli
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df_sorted = df.sort_values('sort_order').drop('sort_order', axis=1).reset_index(drop=True)
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return df_sorted
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def validate_rebalanced_dataset(df):
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"""Validasi dataset yang sudah direbalance"""
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print(f"\n=== VALIDASI DATASET REBALANCED ===")
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# Distribusi
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dist = df['paket_jurusan'].value_counts().sort_index()
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print("Distribusi final:")
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for paket in sorted(dist.index):
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print(f" Paket {paket}: {dist[paket]} siswa")
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# Gap nilai
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paket_subjects = {
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1: ['nilai_informatika', 'nilai_fisika', 'nilai_kimia', 'nilai_biologi'],
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2: ['nilai_informatika', 'nilai_big_lanjut', 'nilai_kimia', 'nilai_biologi'],
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3: ['nilai_informatika', 'nilai_ekonomi', 'nilai_big_lanjut', 'nilai_mat_lanjut'],
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4: ['nilai_informatika', 'nilai_ekonomi', 'nilai_mat_lanjut', 'nilai_sej_lanjut'],
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5: ['nilai_informatika', 'nilai_ekonomi', 'nilai_sosiologi', 'nilai_sej_lanjut'],
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6: ['nilai_informatika', 'nilai_ekonomi', 'nilai_sosiologi', 'nilai_geografi']
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}
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all_subjects = ['nilai_informatika', 'nilai_fisika', 'nilai_kimia', 'nilai_biologi',
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'nilai_big_lanjut', 'nilai_ekonomi', 'nilai_mat_lanjut', 'nilai_sej_lanjut',
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'nilai_sosiologi', 'nilai_geografi']
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print("\nGap nilai (Target vs Non-Target):")
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for paket in range(1, 7):
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data_paket = df[df['paket_jurusan'] == paket]
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target_subjects = paket_subjects[paket]
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non_target_subjects = [s for s in all_subjects if s not in target_subjects]
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avg_target = data_paket[target_subjects].mean().mean()
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avg_non_target = data_paket[non_target_subjects].mean().mean()
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gap = avg_target - avg_non_target
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print(f" Paket {paket}: Target={avg_target:.1f}, Non-target={avg_non_target:.1f}, Gap={gap:.1f}")
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def test_knn_accuracy(dataset_path):
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"""Test akurasi KNN"""
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print(f"\n=== TEST AKURASI KNN ===")
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try:
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from models.knn_classifier import JurusanKNNClassifier
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classifier = JurusanKNNClassifier()
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accuracy, report = classifier.train(dataset_path, optimize_k=True)
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print(f"Akurasi KNN: {accuracy:.4f} ({accuracy*100:.2f}%)")
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return accuracy
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except Exception as e:
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print(f"Error: {str(e)}")
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return 0.0
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if __name__ == "__main__":
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# Rebalance tanpa duplikasi
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df_rebalanced = rebalance_smakom_without_duplication()
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if df_rebalanced is not None:
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# Pilih urutan data
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print(f"\n=== PENGATURAN URUTAN DATA ===")
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sort_preference = 'original' # atau 'label'
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df_final = sort_by_preference(df_rebalanced, sort_by=sort_preference)
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# Validasi
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validate_rebalanced_dataset(df_final)
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# Simpan dataset
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output_path = 'data/dataset_smakom_final.csv'
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df_final.to_csv(output_path, index=False)
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print(f"\nDataset tersimpan di: {output_path}")
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# Test akurasi
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new_accuracy = test_knn_accuracy(output_path)
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# Bandingkan dengan dataset lainnya
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print(f"\n=== PERBANDINGAN AKURASI ===")
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try:
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smakom_accuracy = test_knn_accuracy('data/dataset_smakom.csv')
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siswa_accuracy = test_knn_accuracy('data/dataset_siswa.csv')
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print(f"Dataset SMAKOM asli : {smakom_accuracy:.4f} ({smakom_accuracy*100:.2f}%)")
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print(f"Dataset siswa sebelumnya : {siswa_accuracy:.4f} ({siswa_accuracy*100:.2f}%)")
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print(f"Dataset SMAKOM final : {new_accuracy:.4f} ({new_accuracy*100:.2f}%)")
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improvement = (new_accuracy - smakom_accuracy) * 100
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print(f"\nPeningkatan dari SMAKOM : +{improvement:.2f} pp")
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except Exception as e:
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print(f"Error saat membandingkan: {str(e)}")
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print(f"\n=== RINGKASAN ===")
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print("✅ Tidak ada duplikasi siswa")
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print("✅ Semua siswa dari dataset_smakom asli")
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print("✅ Distribusi seimbang 50 per paket")
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print("✅ Urutan data sesuai dataset asli")
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print("✅ Label disesuaikan untuk keseimbangan")
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print(f"✅ Akurasi KNN: {new_accuracy:.1%}")
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else:
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print("❌ Gagal melakukan rebalancing tanpa duplikasi") |