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feat(latex): refactor tables in chapter 4

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Rifqi D. Panuluh
2025-10-16 03:41:54 +00:00
parent e1376b6d03
commit 59793e83de
10 changed files with 137 additions and 123 deletions
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130
latex/chapters/id/04_results.tex
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@@ -203,42 +203,16 @@ pada data validasi. Proses ini memakan waktu hingga 1 jam 38 detik untuk Sensor
\begin{table}[H]
\centering
\begin{tabular}{rrrrr}
\toprule
$n_{\text{components}}$ & $C (\log_2)$ & $\gamma (\log_2)$ & \gls{not:S_i} & \gls{not:T_i} \\
\midrule
4 & 5 & -5 & 0.80764 & 11.22306 \\
8 & 5 & -5 & 0.97076 & 10.88293 \\
16 & 5 & -5 & 0.99116 & 10.53770 \\
32 & 10 & -10 & 0.99394 & 10.45783 \\
64 & 10 & -10 & 0.99631 & 13.46819 \\
128 & 5 & -10 & 0.99728 & 13.43715 \\
256 & 5 & -10 & 0.99756 & 17.84189 \\
512 & 5 & -10 & 0.99763 & 31.24036 \\
\bottomrule
\end{tabular}
\caption{Hasil ringkasan \textit{coarse grid-search} pada Sensor A ($\mathcal{D}_A$).}
\label{tab:coarse_summary_A}
\input{chapters/id/tables/coarse_summary_A}
\end{table}
\begin{table}[H]
\centering
\begin{tabular}{rrrrr}
\toprule
$n_{\text{components}}$ & $C (\log_2)$ & $\gamma (\log_2)$ & \gls{not:S_i} & \gls{not:T_i} \\
\midrule
4 & 5 & -5 & 0.87845 & 13.77282 \\
8 & 0 & -5 & 0.98051 & 12.51643 \\
16 & 5 & -5 & 0.99443 & 10.90890 \\
32 & 5 & -10 & 0.99596 & 13.42619 \\
64 & 5 & -10 & 0.99735 & 11.40759 \\
128 & 5 & -10 & 0.99728 & 14.54694 \\
256 & 5 & -10 & 0.99777 & 20.27980 \\
512 & 5 & -10 & 0.99791 & 39.63068 \\
\bottomrule
\end{tabular}
\caption{Hasil ringkasan \textit{coarse grid-search} pada Sensor B ($\mathcal{D}_B$).}
\label{tab:coarse_summary_B}
\input{chapters/id/tables/coarse_summary_B}
\end{table}
Tabel~\ref{tab:coarse_summary_A} dan~\ref{tab:coarse_summary_B} menunjukkan hasil ringkasan \textit{coarse grid-search} dengan nilai maksimum \textit{mean test score} untuk setiap konfigurasi $n_{\text{components}}$ pada Sensor A ($\mathcal{D}_A$) dan Sensor B ($\mathcal{D}_B$). Kolom \gls{not:S_i} menunjukkan akurasi tertinggi yang dicapai, sedangkan kolom \gls{not:T_i} mencatat waktu rata-rata (dalam detik) yang dibutuhkan untuk melatih model (\textit{mean fit time}) pada setiap kombinasi parameter.
@@ -333,42 +307,16 @@ model mampu mencapai akurasi tinggi dengan waktu pelatihan yang relatif singkat.
\begin{table}[H]
\centering
\begin{tabular}{rrrrrr}
\toprule
$n_{\text{components}}$ & $C (\log_2)$ & $\gamma (\log_2)$ & \gls{not:S_i} & \gls{not:T_i} & \gls{not:E_i} $(\times10^{-3})$ \\
\midrule
4 & 5 & -5 & 0.80764 & 11.22306 & 71.96291 \\
8 & 5 & -5 & 0.97076 & 10.88293 & 89.20027 \\
16 & 5 & -5 & 0.99116 & 10.53770 & 94.05832 \\
32 & 10 & -10 & 0.99394 & 10.45783 & 95.04296 \\
64 & 10 & -10 & 0.99631 & 13.46819 & 73.97505 \\
128 & 5 & -10 & 0.99728 & 13.43715 & 74.21849 \\
256 & 5 & -10 & 0.99756 & 17.84189 & 55.91131 \\
512 & 5 & -10 & 0.99763 & 31.24036 & 31.93410 \\
\bottomrule
\end{tabular}
\caption{Hasil ringkasan nilai maksimum \textit{mean test score} untuk setiap konfigurasi $n_{\text{components}}$ pada Sensor A ($\mathcal{D}_A$).}
\label{tab:efficiency_summary_A}
\input{chapters/id/tables/efficiency_summary_A}
\end{table}
\begin{table}[H]
\centering
\begin{tabular}{rrrrrr}
\toprule
$n_{\text{components}}$ & $C (\log_2)$ & $\gamma (\log_2)$ & \gls{not:S_i} & \gls{not:T_i} & \gls{not:E_i} $(\times10^{-3})$ \\
\midrule
4 & 5 & -5 & 0.87845 & 13.77282 & 63.78107 \\
8 & 0 & -5 & 0.98051 & 12.51643 & 78.33758 \\
16 & 5 & -5 & 0.99443 & 10.90890 & 91.15776 \\
32 & 5 & -10 & 0.99596 & 13.42619 & 74.18057 \\
64 & 5 & -10 & 0.99735 & 11.40759 & 87.42906 \\
128 & 5 & -10 & 0.99728 & 14.54694 & 68.55632 \\
256 & 5 & -10 & 0.99777 & 20.27980 & 49.20029 \\
512 & 5 & -10 & 0.99791 & 39.63068 & 25.18027 \\
\bottomrule
\end{tabular}
\caption{Hasil ringkasan nilai maksimum \textit{mean test score} untuk setiap konfigurasi $n_{\text{components}}$ pada Sensor B ($\mathcal{D}_B$).}
\label{tab:efficiency_summary_B}
\input{chapters/id/tables/efficiency_summary_B}
\end{table}
Hasil pada Tabel~\ref{tab:efficiency_summary_A} dan Tabel~\ref{tab:efficiency_summary_B} menunjukkan bahwa,
@@ -400,44 +348,14 @@ Metrik klasifikasi model \textit{baseline} pada dataset pengujian disajikan pada
\centering
\caption{\textit{Classification report} model \textit{baseline} pada Sensor A}
\label{tab:metrics-baseline_A}
\begin{tabular}{lrrrr}
\toprule
& precision & recall & f1-score & support \\
\midrule
0 & 0.99 & 0.98 & 0.99 & 2565.00 \\
1 & 0.99 & 1.00 & 1.00 & 2565.00 \\
2 & 1.00 & 1.00 & 1.00 & 2565.00 \\
3 & 1.00 & 1.00 & 1.00 & 2565.00 \\
4 & 1.00 & 1.00 & 1.00 & 2565.00 \\
5 & 1.00 & 1.00 & 1.00 & 2565.00 \\
6 & 0.99 & 1.00 & 0.99 & 2565.00 \\
accuracy & 1.00 & 1.00 & 1.00 & 1.00 \\
macro avg & 1.00 & 1.00 & 1.00 & 17955.00 \\
weighted avg & 1.00 & 1.00 & 1.00 & 17955.00 \\
\bottomrule
\end{tabular}
\input{chapters/id/tables/metrics-baseline_A.tex}
\end{table}
\begin{table}[htbp]
\centering
\caption{\textit{Classification report} model \textit{baseline} pada Sensor B}
\label{tab:metrics-baseline_B}
\begin{tabular}{lrrrr}
\toprule
& precision & recall & f1-score & support \\
\midrule
0 & 0.98 & 0.99 & 0.99 & 2565.00 \\
1 & 0.99 & 1.00 & 0.99 & 2565.00 \\
2 & 1.00 & 1.00 & 1.00 & 2565.00 \\
3 & 1.00 & 1.00 & 1.00 & 2565.00 \\
4 & 1.00 & 1.00 & 1.00 & 2565.00 \\
5 & 1.00 & 1.00 & 1.00 & 2565.00 \\
6 & 1.00 & 1.00 & 1.00 & 2565.00 \\
accuracy & 1.00 & 1.00 & 1.00 & 1.00 \\
macro avg & 1.00 & 1.00 & 1.00 & 17955.00 \\
weighted avg & 1.00 & 1.00 & 1.00 & 17955.00 \\
\bottomrule
\end{tabular}
\input{chapters/id/tables/metrics-baseline_B.tex}
\end{table}
Hasil ini menunjukkan bahwa model \textit{baseline} kedua sensor mencapai akurasi 99\%. Nilai \textit{recall} yang relatif tinggi (99.0\%) menunjukkan bahwa model lebih sensitif untuk mendeteksi kelas kerusakan, meskipun nilai \textit{precision} yang sedikit lebih rendah, menunjukkan bahwa ada beberapa \textit{false-positive} yang dihasilkan.
@@ -472,7 +390,7 @@ Optimasi model yang dilakukan yaitu dengan melakukan \textit{fine \gls{grid-sear
\end{align*}. Pada proses ini, \textit{\gls{standard-scaler}} dan \textit{\gls{stratified-k-fold} \gls{cross-validation}} dengan $k=5$ tetap digunakan untuk menjaga konsistensi evaluasi model, sehingga total kombinasi parameter yang diuji adalah \(9\times9 = 81\) kandidat model dengan total 405 kali \textit{fitting}.
\subsection{Diagram \textit{Fine Grid-Search Heatmap}}
Gambar~\ref{fig:svm_fine_heatmap} menunjukkan diagram \textit{heatmap} terhadap parameter \textit{fine grid-search} \gls{not:C}dan~\gls{not:gamma} untuk masing-masing sensor. Akurasi tertinggi pada Sensor A diperoleh pada $C= \{\,2^{8}, \,2^{8.5}, \,2^{9}, \,2^{9.5}, \,2^{10}, \,2^{10.5},\,2^{11}, \,2^{11.5}, \,2^{12} \,\}$ dan $\gamma=2^{-9.5}$ dengan akurasi meningkat 0.15\% menjadi 99.54\%, sedangkan pada Sensor B diperoleh pada $C = \{\,2^{5},\,2^{5.5} \,\}$ dan $\gamma= \{\, 2^{-3},\, 2^{-3.5},\, 2^{-4}\,\}$ dengan akurasi meningkat 0.05\% menjadi 99.49\%. Hasil ini menunjukkan bahwa optimasi \textit{hyperparameter} lebih lanjut dapat meningkatkan performa model meskipun peningkatannya relatif kecil dibandingkan dengan model \textit{baseline}.
Gambar~\ref{fig:svm_fine_heatmap} menunjukkan diagram \textit{heatmap} terhadap parameter \textit{fine grid-search} \gls{not:C}dan~\gls{not:gamma} untuk masing-masing sensor. Akurasi tertinggi pada Sensor A diperoleh pada $C= \{\,2^{8}, \,2^{8.5}, \,2^{9}, \,2^{9.5}, \,2^{10}, \,2^{10.5},\,2^{11}, \,2^{11.5}, \,2^{12} \,\}$ dan $\gamma=2^{-8}$ dengan akurasi meningkat 0.15\% menjadi 99.54\%, sedangkan pada Sensor B diperoleh pada $\gamma = \{\,2^{-5},\,2^{-5.5} \,\}$ dan $C= \{\, 2^{3},\, 2^{3.5},\, 2^{4}\,\}$ dengan akurasi meningkat 0.05\% menjadi 99.49\%. Hasil ini menunjukkan bahwa optimasi \textit{hyperparameter} lebih lanjut dapat meningkatkan performa model meskipun peningkatannya relatif kecil dibandingkan dengan model \textit{baseline}.
\begin{figure}[H]
\centering
\subfloat[Sensor A (PCA 32)]{\includegraphics[width=.48\textwidth]{chapters/img/sensor1/grid_fine_pca32.png}}
@@ -491,44 +409,14 @@ Hasil performa model \textit{fine \gls{grid-search}} pada data uji disajikan pad
\centering
\caption{\textit{Classification report} model Sensor A}
\label{tab:metrics-fine-a}
\begin{tabular}{lrrrr}
\toprule
& precision & recall & f1-score & support \\
\midrule
0 & 0.99 & 0.99 & 0.99 & 2565.00 \\
1 & 0.99 & 1.00 & 0.99 & 2565.00 \\
2 & 1.00 & 1.00 & 1.00 & 2565.00 \\
3 & 1.00 & 1.00 & 1.00 & 2565.00 \\
4 & 1.00 & 1.00 & 1.00 & 2565.00 \\
5 & 1.00 & 1.00 & 1.00 & 2565.00 \\
6 & 1.00 & 1.00 & 1.00 & 2565.00 \\
accuracy & 1.00 & 1.00 & 1.00 & 1.00 \\
macro avg & 1.00 & 1.00 & 1.00 & 17955.00 \\
weighted avg & 1.00 & 1.00 & 1.00 & 17955.00 \\
\bottomrule
\end{tabular}
\input{chapters/id/tables/metrics-fine-a.tex}
\end{table}
\begin{table}
\centering
\caption{\textit{Classification report} model Sensor B}
\label{tab:metrics-fine-b}
\begin{tabular}{lrrrr}
\toprule
& precision & recall & f1-score & support \\
\midrule
0 & 0.98 & 0.97 & 0.98 & 2565.00 \\
1 & 0.99 & 1.00 & 1.00 & 2565.00 \\
2 & 1.00 & 1.00 & 1.00 & 2565.00 \\
3 & 1.00 & 1.00 & 1.00 & 2565.00 \\
4 & 0.99 & 1.00 & 1.00 & 2565.00 \\
5 & 1.00 & 1.00 & 1.00 & 2565.00 \\
6 & 0.98 & 0.99 & 0.99 & 2565.00 \\
accuracy & 0.99 & 0.99 & 0.99 & 0.99 \\
macro avg & 0.99 & 0.99 & 0.99 & 17955.00 \\
weighted avg & 0.99 & 0.99 & 0.99 & 17955.00 \\
\bottomrule
\end{tabular}
\input{chapters/id/tables/metrics-fine-b.tex}
\end{table}
\subsection{\textit{Confusion Matrix}}

15
latex/chapters/id/tables/coarse_summary_A.tex Normal file
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@@ -0,0 +1,15 @@
\centering
\begin{tabular}{rrrrr}
\toprule
$n_{\text{components}}$ & $C (\log_2)$ & $\gamma (\log_2)$ & \gls{not:S_i} & \gls{not:T_i} \\
\midrule
4 & 5 & -5 & 0.80764 & 11.22306 \\
8 & 5 & -5 & 0.97076 & 10.88293 \\
16 & 5 & -5 & 0.99116 & 10.53770 \\
32 & 10 & -10 & 0.99394 & 10.45783 \\
64 & 10 & -10 & 0.99631 & 13.46819 \\
128 & 5 & -10 & 0.99728 & 13.43715 \\
256 & 5 & -10 & 0.99756 & 17.84189 \\
512 & 5 & -10 & 0.99763 & 31.24036 \\
\bottomrule
\end{tabular}

15
latex/chapters/id/tables/coarse_summary_B.tex Normal file
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@@ -0,0 +1,15 @@
\centering
\begin{tabular}{rrrrr}
\toprule
$n_{\text{components}}$ & $C (\log_2)$ & $\gamma (\log_2)$ & \gls{not:S_i} & \gls{not:T_i} \\
\midrule
4 & 5 & -5 & 0.87845 & 13.77282 \\
8 & 0 & -5 & 0.98051 & 12.51643 \\
16 & 5 & -5 & 0.99443 & 10.90890 \\
32 & 5 & -10 & 0.99596 & 13.42619 \\
64 & 5 & -10 & 0.99735 & 11.40759 \\
128 & 5 & -10 & 0.99728 & 14.54694 \\
256 & 5 & -10 & 0.99777 & 20.27980 \\
512 & 5 & -10 & 0.99791 & 39.63068 \\
\bottomrule
\end{tabular}

15
latex/chapters/id/tables/efficiency_summary_A.tex Normal file
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@@ -0,0 +1,15 @@
\centering
\begin{tabular}{rrrrrr}
\toprule
$n_{\text{components}}$ & $C (\log_2)$ & $\gamma (\log_2)$ & \gls{not:S_i} & \gls{not:T_i} & \gls{not:E_i} $(\times10^{-3})$ \\
\midrule
4 & 5 & -5 & 0.80764 & 11.22306 & 71.96291 \\
8 & 5 & -5 & 0.97076 & 10.88293 & 89.20027 \\
16 & 5 & -5 & 0.99116 & 10.53770 & 94.05832 \\
32 & 10 & -10 & 0.99394 & 10.45783 & 95.04296 \\
64 & 10 & -10 & 0.99631 & 13.46819 & 73.97505 \\
128 & 5 & -10 & 0.99728 & 13.43715 & 74.21849 \\
256 & 5 & -10 & 0.99756 & 17.84189 & 55.91131 \\
512 & 5 & -10 & 0.99763 & 31.24036 & 31.93410 \\
\bottomrule
\end{tabular}

15
latex/chapters/id/tables/efficiency_summary_B.tex Normal file
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@@ -0,0 +1,15 @@
\centering
\begin{tabular}{rrrrrr}
\toprule
$n_{\text{components}}$ & $C (\log_2)$ & $\gamma (\log_2)$ & \gls{not:S_i} & \gls{not:T_i} & \gls{not:E_i} $(\times10^{-3})$ \\
\midrule
4 & 5 & -5 & 0.87845 & 13.77282 & 63.78107 \\
8 & 0 & -5 & 0.98051 & 12.51643 & 78.33758 \\
16 & 5 & -5 & 0.99443 & 10.90890 & 91.15776 \\
32 & 5 & -10 & 0.99596 & 13.42619 & 74.18057 \\
64 & 5 & -10 & 0.99735 & 11.40759 & 87.42906 \\
128 & 5 & -10 & 0.99728 & 14.54694 & 68.55632 \\
256 & 5 & -10 & 0.99777 & 20.27980 & 49.20029 \\
512 & 5 & -10 & 0.99791 & 39.63068 & 25.18027 \\
\bottomrule
\end{tabular}

16
latex/chapters/id/tables/metrics-baseline_A.tex Normal file
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@@ -0,0 +1,16 @@
\begin{tabular}{lrrrr}
\toprule
& precision & recall & f1-score & support \\
\midrule
0 & 0.99 & 0.98 & 0.99 & 2565.00 \\
1 & 0.99 & 1.00 & 1.00 & 2565.00 \\
2 & 1.00 & 1.00 & 1.00 & 2565.00 \\
3 & 1.00 & 1.00 & 1.00 & 2565.00 \\
4 & 1.00 & 1.00 & 1.00 & 2565.00 \\
5 & 1.00 & 1.00 & 1.00 & 2565.00 \\
6 & 0.99 & 1.00 & 0.99 & 2565.00 \\
accuracy & 1.00 & 1.00 & 1.00 & 1.00 \\
macro avg & 1.00 & 1.00 & 1.00 & 17955.00 \\
weighted avg & 1.00 & 1.00 & 1.00 & 17955.00 \\
\bottomrule
\end{tabular}

17
latex/chapters/id/tables/metrics-baseline_B.tex Normal file
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@@ -0,0 +1,17 @@
\centering
\begin{tabular}{lrrrr}
\toprule
& precision & recall & f1-score & support \\
\midrule
0 & 0.99 & 0.98 & 0.99 & 2565.00 \\
1 & 0.99 & 1.00 & 1.00 & 2565.00 \\
2 & 1.00 & 1.00 & 1.00 & 2565.00 \\
3 & 1.00 & 1.00 & 1.00 & 2565.00 \\
4 & 1.00 & 1.00 & 1.00 & 2565.00 \\
5 & 1.00 & 1.00 & 1.00 & 2565.00 \\
6 & 0.99 & 1.00 & 0.99 & 2565.00 \\
accuracy & 1.00 & 1.00 & 1.00 & 1.00 \\
macro avg & 1.00 & 1.00 & 1.00 & 17955.00 \\
weighted avg & 1.00 & 1.00 & 1.00 & 17955.00 \\
\bottomrule
\end{tabular}

16
latex/chapters/id/tables/metrics-fine-a.tex Normal file
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@@ -0,0 +1,16 @@
\begin{tabular}{lrrrr}
\toprule
& precision & recall & f1-score & support \\
\midrule
0 & 0.99 & 0.99 & 0.99 & 2565.00 \\
1 & 0.99 & 1.00 & 0.99 & 2565.00 \\
2 & 1.00 & 1.00 & 1.00 & 2565.00 \\
3 & 1.00 & 1.00 & 1.00 & 2565.00 \\
4 & 1.00 & 1.00 & 1.00 & 2565.00 \\
5 & 1.00 & 1.00 & 1.00 & 2565.00 \\
6 & 1.00 & 1.00 & 1.00 & 2565.00 \\
accuracy & 1.00 & 1.00 & 1.00 & 1.00 \\
macro avg & 1.00 & 1.00 & 1.00 & 17955.00 \\
weighted avg & 1.00 & 1.00 & 1.00 & 17955.00 \\
\bottomrule
\end{tabular}

17
latex/chapters/id/tables/metrics-fine-b.tex Normal file
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@@ -0,0 +1,17 @@
\centering
\begin{tabular}{lrrrr}
\toprule
& precision & recall & f1-score & support \\
\midrule
0 & 0.99 & 0.99 & 0.99 & 2565.00 \\
1 & 0.99 & 1.00 & 0.99 & 2565.00 \\
2 & 1.00 & 1.00 & 1.00 & 2565.00 \\
3 & 1.00 & 1.00 & 1.00 & 2565.00 \\
4 & 1.00 & 1.00 & 1.00 & 2565.00 \\
5 & 1.00 & 1.00 & 1.00 & 2565.00 \\
6 & 1.00 & 1.00 & 1.00 & 2565.00 \\
accuracy & 1.00 & 1.00 & 1.00 & 1.00 \\
macro avg & 1.00 & 1.00 & 1.00 & 17955.00 \\
weighted avg & 1.00 & 1.00 & 1.00 & 17955.00 \\
\bottomrule
\end{tabular}

4
latex/chapters/img/sensor2/grid_fine_pca16.png
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@@ -1,3 +1,3 @@
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