Tugas 6.2 : Uji Coba Model Logistic Regression#
Pada Tugas 6.2 ini diminta untuk melakukan uji coba model yang telah dibuat dengan algoritma Logistic Regression dari data SVD (Singular Value Decomposition).
Dibuat Oleh:
Nama : Sabil Ahmad Hidayat
NIM : 220411100058
Kelas : PPW A
Link Code : https://colab.research.google.com/drive/1kOIWgzTUapCLbDVpFRFWMu5hCXrKnHxp?usp=sharing
Link Github : meinhere/ppw
Import Library#
!pip install -q Sastrawi
[notice] A new release of pip is available: 23.2.1 -> 24.3.1
[notice] To update, run: python.exe -m pip install --upgrade pip
# library awal untuk perhitungan dan pengolahan teks
import numpy as np
import re
import pandas as pd
# alat untuk crawling
from urllib.request import urlopen
from bs4 import BeautifulSoup
# monitoring
from tqdm import tqdm
# library untuk praproses teks
import nltk
nltk.download('stopwords')
nltk.download('wordnet')
nltk.download('punkt')
from nltk.corpus import stopwords
from Sastrawi.Stemmer.StemmerFactory import StemmerFactory
# library untuk proses modeling
from sklearn import preprocessing
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression
# library untuk evaluasi model
from sklearn.metrics import classification_report, confusion_matrix
# plotting
import matplotlib.pyplot as plt
import seaborn as sns
# save model
import pickle
import requests
preprocessing disini digunakan untuk melakukan proses encoding pada label
train_test_split digunakan untuk membagi dataset menjadi data training dan testing
LogisticRegression digunakan untuk tahap modeling menggunakan library LogisticRegression
classification_report dan confusion_matrix digunakan untuk melihat laporan dan hasil evaluasi setelah proses training data
matplotlib dan seaborn digunakan untuk plotting grafik
pickle digunakan untuk menyimpan model hasil training dan testing
Persiapan Data#
Load Data#
main_df = pd.read_csv('https://raw.githubusercontent.com/meinhere/ppw/master/publish/tugas-2/dataset/data_berita.csv', delimiter=',')
main_df
| No | Judul Berita | Isi Berita | Tanggal Berita | Kategori Berita | |
|---|---|---|---|---|---|
| 0 | 1 | Simak Jadwal dan Lokasi SIM Keliling di Jakart... | JAKARTA, KOMPAS.com - Surat Izin Mengemudi (S... | 07/09/2024 | OTOMOTIF |
| 1 | 2 | [POPULER OTOMOTIF] Diskon Motor Honda Septembe... | JAKARTA, KOMPAS.com - Banyak pembaca yang ingi... | 07/09/2024 | OTOMOTIF |
| 2 | 3 | Cek Saldo Minimal BRI, BNI, BCA, Mandiri, dan BSI | JAKARTA, KOMPAS.com - Penting bagi calon nasab... | 06/09/2024 | MONEY |
| 3 | 4 | KAI Uji Coba Teknologi "Face Recognition Board... | KOMPAS.com - PT Kereta Api Indonesia (KAI) Div... | 06/09/2024 | MONEY |
| 4 | 5 | OJK Blokir 10.890 Entitas Keuangan Ilegal Seja... | JAKARTA, KOMPAS.com - Otoritas Jasa Keuangan (... | 06/09/2024 | MONEY |
| ... | ... | ... | ... | ... | ... |
| 95 | 96 | Waspada Masalah yang Timbul akibat Telat Ganti... | JAKARTA, KOMPAS.com - Oli mesin pada mobil den... | 06/09/2024 | OTOMOTIF |
| 96 | 97 | Sosok Faisal Basri di Mata Para Tokoh, Ekonom ... | JAKARTA, KOMPAS.com - Ekonom senior Faisal Bas... | 06/09/2024 | MONEY |
| 97 | 98 | Pendaftaran CPNS Diperpanjang 4 Hari, Pelamar ... | JAKARTA, KOMPAS.com - Pemerintah telah memperp... | 06/09/2024 | MONEY |
| 98 | 99 | Harga Emas Terbaru Pegadaian, Jumat 6 Septembe... | JAKARTA, KOMPAS.com - Pegadaian menyediakan be... | 06/09/2024 | MONEY |
| 99 | 100 | Harga Emas Antam Terbaru Jumat 6 September 202... | JAKARTA, KOMPAS.com - Pada Jumat 6 September 2... | 06/09/2024 | MONEY |
100 rows × 5 columns
Membuat Fungsi untuk Persiapan Crawling#
# fungsi untuk mengambil link yang akan dilakukan crawling
def extract_urls(url):
html = urlopen(url).read()
soup = BeautifulSoup(html, 'html.parser')
urls = soup.find_all("a", {"class": "paging__link"})
urls = [url.get('href') for url in urls]
return urls
# fungsi untuk mengambil isi dari berita
def get_content(url):
html = urlopen(url).read()
soup = BeautifulSoup(html, 'html.parser')
div = soup.find("div", {"class": "read__content"})
paragraf = div.find_all("p")
content = ''
for p in paragraf:
content += p.text
return content
# fungsi utama crawling
def crawl(link = "https://indeks.kompas.com", max_money = 1, max_otomotif = 1, allow_category = ["OTOMOTIF", "MONEY"], is_train = True, title_old = []):
# inisialisasi variabel penampung hasil berita
news_data = []
# inisialisasi persiapan untuk crawling berita
last_url = extract_urls(link).pop()
page = last_url.split('=').pop() # jumlah halaman secara otomatis
# page = 1 # jumlah halaman secara manual
# persiapan link yang akan dilakukan crawling
urls = [link + '/?page=' + str(a) for a in range(1, int(page) + 1)]
count_money = 0
count_otomotif = 0
# menelusuri semua link yang telah ditentukan
for idx, url in enumerate(urls):
if (len(news_data) == max_money + max_otomotif) :
break
html = urlopen(url).read()
soup = BeautifulSoup(html, 'html.parser')
# mengambil data yang diperlukan pada struktur html
links = soup.find_all("a", {"class": "article-link"})
titles = soup.find_all("h2", {"class": "articleTitle"})
dates = soup.find_all("div", {"class": "articlePost-date"})
categories = soup.find_all("div", {"class": "articlePost-subtitle"})
news_per_page = len(links) # berita artikel yang ditampilkan
# memasukkan data ke dalam list
for elem in tqdm(range(news_per_page), desc=f"Crawling page {idx+1}"):
news = {}
category = categories[elem].text
title = titles[elem].text
if (category in allow_category):
if (is_train):
cond = (category == "MONEY" and count_money < max_money) or (category == "OTOMOTIF" and count_otomotif < max_otomotif)
else:
cond = (category == "MONEY" and count_money < max_money) or (category == "OTOMOTIF" and count_otomotif < max_otomotif) and title not in title_old
if (cond):
news['No'] = len(news_data) + 1
news['Judul Berita'] = title
news['Isi Berita'] = get_content(links[elem].get("href"))
news['Tanggal Berita'] = dates[elem].text
news['Kategori Berita'] = category
news_data.append(news)
if (category == "MONEY"):
count_money += 1
else:
count_otomotif += 1
print(f"=======> Money: {count_money} | Otomotif: {count_otomotif} | Total: {count_money + count_otomotif}")
return news_data
function extract_urls digunakan untuk melakukan ekstraksi link url yang memiliki pagination pada halaman awal, sehingga didapat beberapa url yang bisa mengarah ke halaman selanjutnya atau sebelumnya.
function get_content digunakan untuk melakukan proses pembuatan isi berita sesuai link berita yang dicari.
Pengambilan Data Baru#
title_old = main_df["Judul Berita"].tolist()
test_news = crawl(max_money=5, max_otomotif=5, is_train=False, title_old=title_old)
Crawling page 1: 100%|██████████| 15/15 [00:00<00:00, 68.89it/s]
=======> Money: 1 | Otomotif: 0 | Total: 1
Crawling page 2: 100%|██████████| 15/15 [00:00<00:00, 30.83it/s]
=======> Money: 4 | Otomotif: 0 | Total: 4
Crawling page 3: 100%|██████████| 15/15 [00:00<00:00, 12136.30it/s]
=======> Money: 4 | Otomotif: 0 | Total: 4
Crawling page 4: 100%|██████████| 15/15 [00:00<00:00, 47.77it/s]
=======> Money: 5 | Otomotif: 0 | Total: 5
Crawling page 5: 100%|██████████| 15/15 [00:00<00:00, 36074.86it/s]
=======> Money: 5 | Otomotif: 0 | Total: 5
Crawling page 6: 100%|██████████| 15/15 [00:00<00:00, 31.37it/s]
=======> Money: 5 | Otomotif: 1 | Total: 6
Crawling page 7: 100%|██████████| 15/15 [00:00<00:00, 4068.19it/s]
=======> Money: 5 | Otomotif: 1 | Total: 6
Crawling page 8: 100%|██████████| 15/15 [00:00<00:00, 16.06it/s]
=======> Money: 5 | Otomotif: 2 | Total: 7
Crawling page 9: 100%|██████████| 15/15 [00:00<00:00, 34971.96it/s]
=======> Money: 5 | Otomotif: 2 | Total: 7
Crawling page 10: 100%|██████████| 15/15 [00:00<00:00, 23643.20it/s]
=======> Money: 5 | Otomotif: 2 | Total: 7
Crawling page 11: 100%|██████████| 15/15 [00:00<00:00, 46.79it/s]
=======> Money: 5 | Otomotif: 3 | Total: 8
Crawling page 12: 100%|██████████| 15/15 [00:00<00:00, 17327.06it/s]
=======> Money: 5 | Otomotif: 3 | Total: 8
Crawling page 13: 100%|██████████| 15/15 [00:00<00:00, 30.36it/s]
=======> Money: 5 | Otomotif: 4 | Total: 9
Crawling page 14: 100%|██████████| 15/15 [00:00<00:00, 33.93it/s]
=======> Money: 5 | Otomotif: 5 | Total: 10
main_df = pd.DataFrame(test_news)
main_df
| No | Judul Berita | Isi Berita | Tanggal Berita | Kategori Berita | |
|---|---|---|---|---|---|
| 0 | 1 | Harga Bitcoin Kembali Sentuh Rekor Tertinggi, ... | JAKARTA, KOMPAS.com - Harga bitcoin kembali me... | 07/11/2024 | MONEY |
| 1 | 2 | PNM Kembali Buka Unit Mekaar di Wilayah 3T | JAKARTA, KOMPAS.com – PT Permodalan Nasional M... | 07/11/2024 | MONEY |
| 2 | 3 | Djoko Siswanto Dilantik Jadi Kepala SKK Migas ... | JAKARTA, KOMPAS.com - Menteri Energi dan Sumbe... | 07/11/2024 | MONEY |
| 3 | 4 | Apakah Danantara Bisa Berbisnis? Ini Penjelasa... | JAKARTA, KOMPAS.com - Menteri Badan Usaha Mili... | 07/11/2024 | MONEY |
| 4 | 5 | Moratorium Kenaikan Tarif Cukai Penting untuk ... | JAKARTA, KOMPAS.com - Pusat Penelitian Kebijak... | 07/11/2024 | MONEY |
| 5 | 6 | MPV Listrik Maxus Mifa 7 Dijadwalkan Meluncur ... | JAKARTA, KOMPAS.com - Maxus akan resmi berbisn... | 07/11/2024 | OTOMOTIF |
| 6 | 7 | Maxus Mifa 9 yang Meluncur di GJAW 2024 Sudah ... | JAKARTA, KOMPAS.com - Maxus Mifa 9 akan resmi ... | 07/11/2024 | OTOMOTIF |
| 7 | 8 | United E-Motor C2000 Resmi Meluncur, Harga mul... | JAKARTA, KOMPAS.com - United E-Motor resmi me... | 07/11/2024 | OTOMOTIF |
| 8 | 9 | Kata AHM Soal Wacana Sepeda Motor Wajib Pakai ... | JAKARTA, KOMPAS.com – Kementerian Perhubungan ... | 07/11/2024 | OTOMOTIF |
| 9 | 10 | Merasakan Fitur Corolla Cross Hybrid GR Sport,... | JAKARTA, KOMPAS.com - Toyota Corolla Cross Hyb... | 07/11/2024 | OTOMOTIF |
Praproses Teks#
Membuat Fungsi#
# Case Folding
def clean_lower(lwr):
lwr = lwr.lower() # lowercase text
return lwr
# Menghapus tanda baca, angka, dan simbol
def clean_punct(text):
clean_spcl = re.compile('[/(){}\[\]\|@,;_]')
clean_symbol = re.compile('[^0-9a-z]')
clean_number = re.compile('[0-9]')
text = clean_spcl.sub('', text)
text = clean_symbol.sub(' ', text)
text = clean_number.sub('', text)
return text
# Menghaps double atau lebih whitespace
def _normalize_whitespace(text):
corrected = str(text)
corrected = re.sub(r"//t",r"\t", corrected)
corrected = re.sub(r"( )\1+",r"\1", corrected)
corrected = re.sub(r"(\n)\1+",r"\1", corrected)
corrected = re.sub(r"(\r)\1+",r"\1", corrected)
corrected = re.sub(r"(\t)\1+",r"\1", corrected)
return corrected.strip(" ")
# Menghapus stopwords
def clean_stopwords(text):
stopword = set(stopwords.words('indonesian'))
text = ' '.join(word for word in text.split() if word not in stopword) # hapus stopword dari kolom deskripsi
return text
# Stemming with Sastrawi
def sastrawistemmer(text):
factory = StemmerFactory()
st = factory.create_stemmer()
text = ' '.join(st.stem(word) for word in tqdm(text.split()) if word in text)
return text
function clean_lower digunakan untuk merubah semua kata atau huruf menjadi huruf kecil semua
function clean_punct digunakan untuk menghapus karakter, simbol, dan angka
function _normalize_whitespace digunakan untuk menghapus spasi yang double atau lebih dari 2 spasi
function clean_stopwords digunakan untuk menghilangkan kata yang tidak perlu (kata hubung, kata tambahan dll)
function sastrawistemmer digunakan untuk proses stemming (mendapatkan kata dasar dari suatu kata)
Clean Lower#
# Buat kolom tambahan untuk data description yang telah dilakukan proses case folding
main_df['lwr'] = main_df['Isi Berita'].apply(clean_lower)
casefolding=pd.DataFrame(main_df['lwr'])
casefolding
| lwr | |
|---|---|
| 0 | jakarta, kompas.com - harga bitcoin kembali me... |
| 1 | jakarta, kompas.com – pt permodalan nasional m... |
| 2 | jakarta, kompas.com - menteri energi dan sumbe... |
| 3 | jakarta, kompas.com - menteri badan usaha mili... |
| 4 | jakarta, kompas.com - pusat penelitian kebijak... |
| 5 | jakarta, kompas.com - maxus akan resmi berbisn... |
| 6 | jakarta, kompas.com - maxus mifa 9 akan resmi ... |
| 7 | jakarta, kompas.com - united e-motor resmi me... |
| 8 | jakarta, kompas.com – kementerian perhubungan ... |
| 9 | jakarta, kompas.com - toyota corolla cross hyb... |
Clean Punct#
# Buat kolom tambahan untuk data description yang telah dilakukan proses penghapusan tanda baca
main_df['clean_punct'] = main_df['lwr'].apply(clean_punct)
main_df['clean_punct']
| clean_punct | |
|---|---|
| 0 | jakarta kompas com harga bitcoin kembali men... |
| 1 | jakarta kompas com pt permodalan nasional ma... |
| 2 | jakarta kompas com menteri energi dan sumber... |
| 3 | jakarta kompas com menteri badan usaha milik... |
| 4 | jakarta kompas com pusat penelitian kebijaka... |
| 5 | jakarta kompas com maxus akan resmi berbisni... |
| 6 | jakarta kompas com maxus mifa akan resmi di... |
| 7 | jakarta kompas com united e motor resmi men... |
| 8 | jakarta kompas com kementerian perhubungan b... |
| 9 | jakarta kompas com toyota corolla cross hybr... |
Normalize Whitespace#
main_df['clean_double_ws'] = main_df['clean_punct'].apply(_normalize_whitespace)
main_df['clean_double_ws']
| clean_double_ws | |
|---|---|
| 0 | jakarta kompas com harga bitcoin kembali menca... |
| 1 | jakarta kompas com pt permodalan nasional mada... |
| 2 | jakarta kompas com menteri energi dan sumber d... |
| 3 | jakarta kompas com menteri badan usaha milik n... |
| 4 | jakarta kompas com pusat penelitian kebijakan ... |
| 5 | jakarta kompas com maxus akan resmi berbisnis ... |
| 6 | jakarta kompas com maxus mifa akan resmi dijua... |
| 7 | jakarta kompas com united e motor resmi menjua... |
| 8 | jakarta kompas com kementerian perhubungan ber... |
| 9 | jakarta kompas com toyota corolla cross hybrid... |
Clean Stopwords#
# Buat kolom tambahan untuk data description yang telah dilakukan proses penghapusan stopwords
main_df['clean_sw'] = main_df['clean_double_ws'].apply(clean_stopwords)
main_df['clean_sw']
| clean_sw | |
|---|---|
| 0 | jakarta kompas com harga bitcoin mencapai reko... |
| 1 | jakarta kompas com pt permodalan nasional mada... |
| 2 | jakarta kompas com menteri energi sumber daya ... |
| 3 | jakarta kompas com menteri badan usaha milik n... |
| 4 | jakarta kompas com pusat penelitian kebijakan ... |
| 5 | jakarta kompas com maxus resmi berbisnis indon... |
| 6 | jakarta kompas com maxus mifa resmi dijual pam... |
| 7 | jakarta kompas com united e motor resmi menjua... |
| 8 | jakarta kompas com kementerian perhubungan ber... |
| 9 | jakarta kompas com toyota corolla cross hybrid... |
Stemming dengan Sastrawi#
# Buat kolom tambahan untuk data description yang telah dilemmatization
main_df['desc_clean_stem'] = main_df['clean_sw'].apply(sastrawistemmer)
main_df['desc_clean_stem']
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100%|██████████| 207/207 [00:07<00:00, 27.65it/s]
100%|██████████| 261/261 [00:11<00:00, 22.67it/s]
| desc_clean_stem | |
|---|---|
| 0 | jakarta kompas com harga bitcoin capai rekor t... |
| 1 | jakarta kompas com pt modal nasional madani pn... |
| 2 | jakarta kompas com menteri energi sumber daya ... |
| 3 | jakarta kompas com menteri badan usaha milik n... |
| 4 | jakarta kompas com pusat teliti bijak ekonomi ... |
| 5 | jakarta kompas com maxus resmi bisnis indonesi... |
| 6 | jakarta kompas com maxus mifa resmi jual pamer... |
| 7 | jakarta kompas com united e motor resmi jual s... |
| 8 | jakarta kompas com menteri hubung wacana rem a... |
| 9 | jakarta kompas com toyota corolla cross hybrid... |
Pembuatan VSM#
# Load the saved model from file
github_raw_url = "https://raw.githubusercontent.com/meinhere/ppw/master/publish/tugas-6/model/tfidf_vectorizer.sav"
response = requests.get(github_raw_url)
response.raise_for_status()
vectorizer = pickle.loads(response.content)
vectorizer
TfidfVectorizer()In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
TfidfVectorizer()
corpus = main_df['desc_clean_stem']
tfidf = vectorizer.transform(corpus)
tfidf.shape
(10, 3106)
vocabulary = vectorizer.get_feature_names_out().tolist()
tfidf_df = pd.DataFrame(tfidf.toarray(), columns=vocabulary)
tfidf_df['label'] = main_df['Kategori Berita'].tolist()
tfidf_df
| aaion | aali | abadi | abai | abenkh | abnormal | absurd | ac | acara | access | ... | yzr | za | zad | zaman | zarco | zenix | zero | zigzag | zona | label | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.00000 | 0.0 | 0.0 | ... | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | MONEY |
| 1 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.00000 | 0.0 | 0.0 | ... | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | MONEY |
| 2 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.00000 | 0.0 | 0.0 | ... | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | MONEY |
| 3 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.00000 | 0.0 | 0.0 | ... | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | MONEY |
| 4 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.00000 | 0.0 | 0.0 | ... | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | MONEY |
| 5 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.00000 | 0.0 | 0.0 | ... | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | OTOMOTIF |
| 6 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.00000 | 0.0 | 0.0 | ... | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | OTOMOTIF |
| 7 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.00000 | 0.0 | 0.0 | ... | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | OTOMOTIF |
| 8 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.00000 | 0.0 | 0.0 | ... | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | OTOMOTIF |
| 9 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.04023 | 0.0 | 0.0 | ... | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | OTOMOTIF |
10 rows × 3107 columns
Konversi ke SVD#
# Load the saved model from file
github_raw_url = "https://raw.githubusercontent.com/meinhere/ppw/master/publish/tugas-6/model/svd_model.pkl"
response = requests.get(github_raw_url)
response.raise_for_status()
svd = pickle.loads(response.content)
svd
TruncatedSVD(n_components=80)In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
TruncatedSVD(n_components=80)
svd_df = tfidf_df.copy()
svd_df.drop(columns=['label'], inplace=True)
svd_df
| aaion | aali | abadi | abai | abenkh | abnormal | absurd | ac | acara | access | ... | yusdistira | yzr | za | zad | zaman | zarco | zenix | zero | zigzag | zona | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.00000 | 0.0 | 0.0 | ... | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 1 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.00000 | 0.0 | 0.0 | ... | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
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svd.transform(svd_df)
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2.59723073e-02, -3.63049817e-03, 3.30141593e-02,
3.57035328e-02, 3.22515398e-02, 3.13770329e-02,
1.24432607e-02, 7.89013203e-03, -5.17578232e-02,
-1.53653290e-02, 1.55419481e-02, -2.51019729e-02,
-4.42811330e-02, -1.26164002e-02, 3.41494143e-02,
-3.37932135e-02, 1.43404438e-02, -3.45523650e-02,
2.35658892e-02, -1.05119204e-02, -2.47855161e-02,
5.56079501e-03, -2.13162057e-02, -1.20495866e-02,
-1.45556339e-02, 3.42545515e-02, 1.74862770e-02,
-1.98003998e-02, 7.69283825e-02, 2.94506708e-02,
1.49548388e-03, -3.81724871e-02],
[ 2.86494046e-01, 4.69921795e-02, -9.82816821e-02,
9.26521298e-02, -5.99163206e-02, 2.02248676e-02,
-9.98406975e-02, 6.46518912e-02, -3.08878467e-02,
2.41681688e-02, -1.17132975e-02, -3.51487583e-02,
-1.64530060e-03, 2.64759853e-02, -3.18107879e-02,
4.50980755e-02, -7.82456644e-02, 2.31130541e-02,
3.60612371e-02, -4.66096143e-02, -3.41793403e-02,
3.58492153e-02, 3.37194599e-02, 8.23987755e-03,
-9.12598536e-05, 1.79176625e-04, -7.15572819e-04,
4.83028464e-03, -1.62819908e-03, 6.98231343e-03,
1.30882052e-02, 5.11241370e-03, -8.91062616e-03,
2.47885667e-02, 2.03528699e-02, -3.71819858e-02,
-3.11529310e-02, -2.08953857e-03, 1.30322204e-04,
3.77220530e-03, -9.65121609e-03, -2.39218330e-02,
-2.06416361e-02, 4.01681984e-02, -4.40942617e-02,
-4.58721553e-02, 2.41026360e-02, 2.60194941e-03,
2.56907991e-02, 1.18043978e-02, 8.18365084e-03,
-2.41933480e-02, -5.32216344e-03, -3.35099192e-02,
3.67207041e-02, -1.11233495e-02, -1.31378996e-02,
-3.84650229e-02, 2.65457168e-02, -2.88034759e-02,
5.25929488e-02, -1.28495632e-01, -1.19212261e-02,
1.63404795e-02, -4.17211017e-03, 4.46336455e-03,
1.76664732e-02, 7.19033503e-03, -1.20039044e-03,
2.02557206e-03, 6.32996273e-03, 3.00726156e-02,
-2.54942685e-03, 1.70656829e-02, -4.15314302e-03,
-3.04680917e-02, 1.57448830e-02, 2.37188372e-02,
-1.26977796e-03, -4.29130134e-02]])
# menggunakan label_encoder untuk merubah kata menjadi angka
label_encoder = preprocessing.LabelEncoder()
svd_df = pd.DataFrame(svd.transform(svd_df), columns=[f"fitur_{i}" for i in range(n)])
svd_df['label'] = label_encoder.fit_transform(tfidf_df['label'])
svd_df
| fitur_0 | fitur_1 | fitur_2 | fitur_3 | fitur_4 | fitur_5 | fitur_6 | fitur_7 | fitur_8 | fitur_9 | ... | fitur_71 | fitur_72 | fitur_73 | fitur_74 | fitur_75 | fitur_76 | fitur_77 | fitur_78 | fitur_79 | label | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 0.185530 | -0.127697 | -0.038092 | -0.079755 | 0.069996 | -0.131090 | 0.012985 | -0.108913 | -0.097500 | -0.050160 | ... | 0.000740 | 0.023267 | 0.009781 | -0.048445 | 0.017418 | -0.016001 | -0.008644 | -0.000185 | -0.007935 | 0 |
| 1 | 0.181556 | -0.132466 | 0.011033 | -0.062866 | 0.045804 | -0.023010 | -0.045333 | -0.018734 | 0.026452 | 0.019286 | ... | -0.007485 | 0.022607 | 0.010349 | 0.003262 | 0.005408 | -0.017759 | -0.005255 | -0.004539 | 0.020752 | 0 |
| 2 | 0.108025 | -0.071694 | 0.000226 | -0.036944 | 0.040162 | -0.015771 | 0.006184 | -0.009881 | -0.000725 | -0.003224 | ... | -0.008708 | -0.007285 | 0.003685 | -0.002656 | -0.021014 | 0.001632 | 0.002715 | 0.014760 | 0.025047 | 0 |
| 3 | 0.118054 | -0.102631 | 0.029150 | -0.045567 | 0.019270 | 0.009511 | -0.014705 | -0.005457 | 0.024549 | -0.026771 | ... | -0.004238 | 0.008641 | -0.006335 | -0.002854 | 0.007896 | -0.014184 | -0.002531 | 0.023301 | 0.003241 | 0 |
| 4 | 0.104933 | -0.069962 | 0.002935 | -0.038352 | 0.026541 | -0.037930 | 0.003384 | -0.018721 | 0.006774 | 0.004636 | ... | -0.005648 | -0.017398 | -0.009982 | -0.012016 | 0.016895 | -0.047842 | 0.012792 | 0.003684 | -0.002408 | 0 |
| 5 | 0.241873 | -0.058057 | -0.037700 | 0.028319 | -0.060636 | 0.007286 | 0.032534 | 0.005289 | -0.003297 | 0.092456 | ... | 0.010115 | 0.001908 | -0.003957 | 0.022982 | 0.007205 | 0.043741 | -0.020273 | -0.021545 | -0.011688 | 1 |
| 6 | 0.209488 | -0.005463 | -0.053148 | 0.009190 | -0.031207 | -0.019766 | 0.015985 | 0.015258 | 0.019682 | 0.052729 | ... | 0.006767 | -0.026174 | -0.017603 | 0.023483 | 0.010571 | 0.014389 | -0.010716 | -0.008709 | 0.013407 | 1 |
| 7 | 0.236427 | -0.008596 | -0.050693 | -0.007234 | 0.048723 | 0.094148 | 0.013897 | -0.077628 | 0.019817 | 0.135887 | ... | 0.036597 | -0.025167 | -0.014243 | 0.084270 | 0.010503 | -0.014840 | 0.014028 | 0.003245 | -0.010550 | 1 |
| 8 | 0.282602 | -0.019730 | 0.072604 | 0.068908 | -0.006561 | 0.115047 | -0.040998 | -0.030941 | 0.001201 | 0.075102 | ... | -0.012050 | -0.014556 | 0.034255 | 0.017486 | -0.019800 | 0.076928 | 0.029451 | 0.001495 | -0.038172 | 1 |
| 9 | 0.286494 | 0.046992 | -0.098282 | 0.092652 | -0.059916 | 0.020225 | -0.099841 | 0.064652 | -0.030888 | 0.024168 | ... | 0.030073 | -0.002549 | 0.017066 | -0.004153 | -0.030468 | 0.015745 | 0.023719 | -0.001270 | -0.042913 | 1 |
10 rows × 81 columns
Testing Data#
# Load the saved model from file
github_raw_url = "https://raw.githubusercontent.com/meinhere/ppw/master/publish/tugas-6/model/lr_model.sav"
response = requests.get(github_raw_url)
response.raise_for_status()
lr_model = pickle.loads(response.content)
lr_model
LogisticRegression()In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
LogisticRegression()
y_test = svd_df['label']
x_test = svd_df.drop(['label'], axis=1)
y_pred = lr_model.predict(x_test)
print(y_pred)
[0 0 0 0 0 1 1 1 1 1]
# melihat nilai actual dan predicted
a = pd.DataFrame({'Actual value': y_test, 'Predicted value':y_pred})
a
| Actual value | Predicted value | |
|---|---|---|
| 0 | 0 | 0 |
| 1 | 0 | 0 |
| 2 | 0 | 0 |
| 3 | 0 | 0 |
| 4 | 0 | 0 |
| 5 | 1 | 1 |
| 6 | 1 | 1 |
| 7 | 1 | 1 |
| 8 | 1 | 1 |
| 9 | 1 | 1 |
# Evaluation metrics
print(classification_report(y_test, y_pred))
# Confusion matrix
cm = confusion_matrix(y_test, y_pred)
print("Confusion Matrix:")
print(cm)
# Plotting the confusion matrix (optional)
plt.figure(figsize=(8, 6))
sns.heatmap(cm, annot=True, fmt='d', cmap='Blues',
xticklabels=label_encoder.classes_,
yticklabels=label_encoder.classes_)
plt.xlabel('Predicted')
plt.ylabel('Actual')
plt.title('Confusion Matrix')
plt.show()
precision recall f1-score support
0 1.00 1.00 1.00 5
1 1.00 1.00 1.00 5
accuracy 1.00 10
macro avg 1.00 1.00 1.00 10
weighted avg 1.00 1.00 1.00 10
Confusion Matrix:
[[5 0]
[0 5]]
