Saturday, April 20, 2024

Menulis Source Code Latex

 \usepackage{verbatim} %menulis sourcecode
\usepackage{listings} %menulis sourcecode
\usepackage{xcolor}%menulis sourcecode
%---
\definecolor{codegreen}{rgb}{0,0.6,0}
\definecolor{codegray}{rgb}{0.5,0.5,0.5}
\definecolor{codepurple}{rgb}{0.58,0,0.82}
\definecolor{backcolour}{rgb}{0.95,0.95,0.92}
%===
\lstdefinestyle{mystyle}{
    backgroundcolor=\color{backcolour},   
    commentstyle=\color{codegreen},
    keywordstyle=\color{magenta},
    numberstyle=\tiny\color{codegray},
    stringstyle=\color{codepurple},
    basicstyle=\ttfamily\footnotesize,
    breakatwhitespace=false,         
    breaklines=true,                 
    captionpos=b,                    
    keepspaces=true,                 
    numbers=left,                    
    numbersep=5pt,                  
    showspaces=false,                
    showstringspaces=false,
    showtabs=false,                  
    tabsize=2
}
\lstset{style=mystyle}
%---

\begin{lstlisting}[language=python, caption=Regresi Linier HP dan Kamera]
import numpy as np
import pandas as pd
from matplotlib import pyplot as plt
from sklearn.linear_model import LinearRegression

df = pd.DataFrame([[8,7],[2,3],[6,7],[4,2],[7,8],[3,3]])
df.columns = ['x', 'y']

x_train = df['x'].values[:,np.newaxis]
y_train = df['y'].values

lm = LinearRegression()
lm.fit(x_train,y_train) # fase training

print('Coefficient : ' + str(lm.coef_))
print('Intercept : ' + str(lm.intercept_))

x_test = [[170],[171]] # data yang akan diprediksi
p = lm.predict(x_test) # fase prediksi
print(p) # hasil prediksi

# prepare plot
pb = lm.predict(x_train)
dfc = pd.DataFrame({'x': df['x'],'y':pb})

plt.scatter(df['x'],df['y'])
plt.plot(dfc['x'],dfc['y'],color='red',linewidth=1)
plt.xlabel('kamera')
plt.ylabel('harga')
plt.show()
\end{lstlisting}