feat: major chapter 4

latex/chapters/id/04_results.tex

@@ -1,149 +1,666 @@
 \chapter{Hasil Penelitian dan Pembahasan}
+Bab ini menyajikan hasil dari proses ekstraksi fitur, analisis eksplorasi data,
+pengembangan model klasifikasi, serta evaluasi kinerja model.
+Hasil yang diperoleh selanjutnya dianalisis untuk menilai kemampuan model dengan fitur yang telah diekstraksi
+dalam mendeteksi dan mengklasifikasikan lokasi kerusakan struktur \textit{grid}.
+% \section{Pendahuluan Singkat}
+% Bab ini menyajikan hasil evaluasi model untuk prediksi lokasi kerusakan berbasis fitur domain waktu dan frekuensi yang diekstrak dari STFT. Tujuan utama evaluasi adalah menguji apakah kombinasi fitur waktu--frekuensi dapat meningkatkan kinerja klasifikasi dibandingkan masing-masing domain secara terpisah, serta menilai kelayakan pendekatan sensor terbatas untuk penerapan di lapangan.
 
-\section{Pendahuluan Singkat}
+% Secara ringkas, kami menampilkan: (i) performa utama pada data uji, (ii) analisis per-kelas dan pola kesalahan, (iii) studi ablation dan sensitivitas mencakup fitur, parameter STFT, serta jumlah/posisi sensor, dan (iv) uji robustness serta implikasi implementasi. Detail metodologi eksperimen telah diuraikan pada Bab Metodologi; bagian ini berfokus pada temuan empiris dan interpretasinya.
-Bab ini menyajikan hasil evaluasi model untuk prediksi lokasi kerusakan berbasis fitur domain waktu dan frekuensi yang diekstrak dari STFT. Tujuan utama evaluasi adalah menguji apakah kombinasi fitur waktu--frekuensi dapat meningkatkan kinerja klasifikasi dibandingkan masing-masing domain secara terpisah, serta menilai kelayakan pendekatan sensor terbatas untuk penerapan di lapangan.
 
-Secara ringkas, kami menampilkan: (i) performa utama pada data uji, (ii) analisis per-kelas dan pola kesalahan, (iii) studi ablation dan sensitivitas mencakup fitur, parameter STFT, serta jumlah/posisi sensor, dan (iv) uji robustness serta implikasi implementasi. Detail metodologi eksperimen telah diuraikan pada Bab Metodologi; bagian ini berfokus pada temuan empiris dan interpretasinya.
+\section{Hasil Ekstraksi Fitur STFT}
+Bagian ini menyajikan contoh hasil transformasi STFT yang diterapkan
+pada sinyal percepatan dari sensor atas dan bawah.
+Analisis dilakukan untuk memastikan konsistensi pola spektral
+dan kesetaraan ukuran data antar kelas sebelum proses pelatihan model.
 
-\section{Rancangan Evaluasi}
+Gambar~\ref{fig:stft-undamaged} memperlihatkan hasil STFT gabungan (\textit{aggregated}) untuk seluruh titik join tanpa kerusakan (kelas 0).
-\subsection{Dataset dan Pembagian Data}
-Evaluasi dilakukan pada himpunan data berlabel yang terdiri dari \textit{[N\_total]} sampel dengan \textit{[K]} kelas lokasi kerusakan. Data dibagi menjadi \textit{[N\_train]} sampel pelatihan, \textit{[N\_val]} validasi, dan \textit{[N\_test]} pengujian, atau menggunakan skema \textit{k}-fold dengan \textit{[k]} lipatan (rincian skema dipertahankan konsisten dengan Bab Metodologi). Potensi ketidakseimbangan kelas dicatat dengan rasio maksimum/minimum sekitar \textit{[imbalance\_ratio:1]}.
-
-\subsection{Pra-pemrosesan dan Ekstraksi Fitur}
-Sinyal diproses dengan normalisasi \textit{[jenis normalisasi/standarisasi]}, dan augmentasi \textit{[jenis augmentasi, jika ada]}. Fitur domain waktu dan frekuensi diekstraksi; komponen frekuensi diperoleh dari STFT dengan window Hann, ukuran jendela \textit{[win\_size]} sampel, overlap
-
-\subsection{Model dan Metrik Evaluasi}
-Model utama adalah SVM dengan kernel \textit{[RBF/Linear]} dan pemilihan hyperparameter (\textit{C}, \textit{gamma}) melalui \textit{[grid/random/bayes] search} pada data validasi. Metrik evaluasi meliputi Akurasi, Macro-F1, Macro-Precision, Macro-Recall, Balanced Accuracy, serta Cohen's Kappa. Untuk analisis multi-kelas yang lebih tajam, kami juga melaporkan metrik per-kelas dan Confusion Matrix.
-
-\section{Hasil Utama}
-\begin{table}[htbp]
-    \centering
-    \caption{Hasil utama pada data uji untuk beberapa konfigurasi fitur dan model. Nilai diisi dari eksperimen akhir.}
-    \label{tab:main-results}
-    \begin{tabular}{lccc}
-        \hline
-        Konfigurasi & Akurasi & Macro-F1 & Kappa \\
-        \hline
-        Time-domain + SVM-RBF & -- & -- & -- \\
-        Freq-domain + SVM-RBF & -- & -- & -- \\
-        Kombinasi (Time+Freq) + SVM-RBF & \textbf{--} & \textbf{--} & \textbf{--} \\
-        \hline
-    \end{tabular}
-\end{table}
-
-Konfigurasi terbaik diperoleh pada kombinasi fitur waktu--frekuensi dengan SVM-\textit{[kernel]}, menghasilkan Akurasi sebesar \textit{[acc\_best]}\%, Macro-F1 sebesar \textit{[f1\_best]}\%, dan Kappa sebesar \textit{[kappa\_best]} pada data uji (Tabel~\ref{tab:main-results}). Dibandingkan baseline domain waktu saja, Macro-F1 meningkat sekitar \textit{[delta\_f1\_time]} poin persentase; dibandingkan domain frekuensi saja, peningkatan mencapai \textit{[delta\_f1\_freq]} poin persentase. Hasil ini mengindikasikan bahwa informasi pelengkap antara dinamika temporal dan spektral berkontribusi nyata terhadap separabilitas kelas.
-
-Performa pada metrik Balanced Accuracy dan Macro-Recall juga konsisten, menandakan model tidak terlalu bias pada kelas mayoritas. Nilai Kappa \textit{[kappa\_best]} mengindikasikan tingkat kesepakatan yang \textit{[moderat/tinggi]} melampaui kebetulan.
-
-\section{Analisis Per-Kelas dan Kesalahan}
 \begin{figure}[htbp]
     \centering
-    % \includegraphics[width=0.8\textwidth]{img/confusion_matrix.pdf}
+    \begin{minipage}{0.48\textwidth}
-    \fbox{\begin{minipage}[c][0.30\textheight][c]{0.80\textwidth}\centering
+        \centering
-    Placeholder Confusion Matrix
+        \includesvg[width=\textwidth, pretex=\tiny]{chapters/img/sensor1/stft-undamaged-1}
-    \end{minipage}}
+        % \caption{Caption for the first image.}
-    \caption{Confusion matrix pada data uji. Isikan gambar aktual dari pipeline evaluasi.}
+        % \label{fig:image1}
-    \label{fig:cm}
+    \end{minipage}\hfill
+    \begin{minipage}{0.48\textwidth}
+        \centering
+        \includesvg[width=\textwidth, pretex=\tiny]{chapters/img/sensor2/stft-undamaged-2}
+        % \caption{Caption for the second image.}
+        % \label{fig:image2}
+    \end{minipage}
+    \caption{STFT tanpa kerusakan (undamaged). Sensor A (kiri) dan Sensor B (kanan)}
+    \label{fig:stft-undamaged}
 \end{figure}
 
-\begin{table}[htbp]
+Gambar~\ref{fig:stft-damaged-multiple-a} dan Gambar~\ref{fig:stft-damaged-multiple-b} memperlihatkan hasil STFT gabungan (\textit{aggregated}) untuk seluruh titik join dengan kerusakan (kelas 1--6). Setiap 513 segmen waktu merepresentasikan kolom sensor yang ditinjau. 
-    \centering
-    \caption{Metrik per-kelas pada data uji. Gunakan bila diperlukan untuk melengkapi Confusion Matrix.}
-    \label{tab:per-class}
-    \begin{tabular}{lccc}
-        \hline
-        Kelas & Precision & Recall & F1 \\
-        \hline
-        A & -- & -- & -- \\
-        B & -- & -- & -- \\
-        C & -- & -- & -- \\
-        % ... tambah baris sesuai jumlah kelas
-        \hline
-    \end{tabular}
-\end{table}
-
-Confusion Matrix pada Gambar~\ref{fig:cm} menunjukkan pola salah klasifikasi yang dominan antara kelas \textit{[kelas\_A]} dan \textit{[kelas\_B]}. Dua kelas ini memiliki respons spektral yang mirip pada rentang \textit{[f\_low--f\_high]} Hz, sehingga kesalahan terutama terjadi ketika amplitudo sinyal rendah atau \textit{signal-to-noise ratio} menurun. Sebaliknya, kelas \textit{[kelas\_C]} memperlihatkan separasi yang baik dengan Recall \textit{[recall\_C]}\% dan F1 \textit{[f1\_C]}\% (Tabel~\ref{tab:per-class}).
-
-Analisis kesalahan kasus-per-kasus menunjukkan bahwa \textit{[proporsi\_\%]}\% prediksi keliru terjadi pada sampel dengan \textit{[ciri sinyal/condisi uji]} dan \textit{[konfigurasi sensor]}. Hal ini menyarankan perlunya \textit{[strategi perbaikan, mis. penambahan fitur bandpass tertentu atau penyeimbangan kelas]}.
-
-\section{Ablasi dan Sensitivitas}
-\subsection{Ablasi Fitur}
 \begin{figure}[htbp]
     \centering
-    \includegraphics[width=0.75\textwidth]{example-image-a}
+    \includesvg[width=\textwidth, pretex=\tiny, inkscapelatex=true]{chapters/img/sensor1/stft-damaged-multiple-1.svg}
-    \fbox{\begin{minipage}[c][0.22\textheight][c]{0.70\textwidth}\centering
+    \caption{STFT sensor A dengan kerusakan (damaged $d_1$\textemdash $d_6$).}
-    Placeholder Bar Chart: Time vs Freq vs Kombinasi
+    \label{fig:stft-damaged-multiple-a}
-    \end{minipage}}
-    \caption{Perbandingan performa berdasarkan jenis fitur.}
-    \label{fig:ablation-features}
 \end{figure}
-
-Studi ablation pada Gambar~\ref{fig:ablation-features} menegaskan bahwa kombinasi fitur memberikan peningkatan \textit{[delta\_ablation]} poin persentase pada Macro-F1 dibandingkan fitur domain waktu saja. Hal ini mengindikasikan bahwa karakteristik harmonik dan komponen frekuensi transien yang ditangkap STFT berkontribusi pada pemisahan kelas yang lebih baik.
-
-\subsection{Parameter STFT dan Windowing}
-\begin{table}[htbp]
-    \centering
-    \caption{Sensitivitas terhadap parameter STFT pada data validasi.}
-    \label{tab:stft-sensitivity}
-    \begin{tabular}{lcccc}
-        \hline
-        Window & n\_fft & Overlap & Akurasi & Macro-F1 \\
-        \hline
-        Hann & -- & -- & -- & -- \\
-        Hann & -- & -- & -- & -- \\
-        (Tanpa window) & -- & -- & -- & -- \\
-        \hline
-    \end{tabular}
-\end{table}
-
-Eksperimen sensitivitas pada Tabel~\ref{tab:stft-sensitivity} memperlihatkan adanya \textit{trade-off} antara resolusi waktu dan frekuensi. Peningkatan \textit{n\_fft} cenderung memperhalus resolusi frekuensi namun mengurangi ketelitian temporal, sedangkan overlap yang lebih besar \textit{[overlap\_\% range]}\% membantu stabilitas estimasi fitur pada sinyal bising. Penggunaan window Hann memberikan kenaikan Macro-F1 sekitar \textit{[delta\_hann]} poin dibanding tanpa window, menegaskan peran pengurangan \textit{spectral leakage}.
-
-\subsection{Pendekatan Sensor Terbatas}
 \begin{figure}[htbp]
     \centering
-    % placeholder
+    \includesvg[width=1\textwidth, pretex=\tiny, inkscapelatex=true]{chapters/img/sensor2/stft-damaged-multiple-2.svg}
-    \includegraphics[width=0.75\textwidth]{example-image-a}
+    \caption{STFT sensor B dengan kerusakan (damaged $d_1$\textemdash $d_6$).}
-    \fbox{\begin{minipage}[c][0.22\textheight][c]{0.70\textwidth}\centering
+    \label{fig:stft-damaged-multiple-b}
-    Placeholder: Performa vs Jumlah/Posisi Sensor
-    \end{minipage}}
-    \caption{Dampak jumlah/konfigurasi sensor terhadap performa.}
-    \label{fig:sensor-limited}
 \end{figure}
 
-Hasil pada Gambar~\ref{fig:sensor-limited} menunjukkan bahwa pengurangan dari \textit{[n\_sensors\_full]} menjadi \textit{[n\_sensors\_min]} sensor hanya menurunkan Macro-F1 sekitar \textit{[delta\_perf\_sensors]} poin, khususnya ketika sensor ditempatkan pada \textit{[posisi sensor terbaik]}. Ini mengindikasikan bahwa pendekatan sensor terbatas tetap layak untuk implementasi dengan biaya perangkat keras yang lebih rendah, selama pemilihan posisi sensor dioptimalkan.
 
-\section{Robustness dan Generalisasi}
+\section{Analisis Eksplorasi Data}
-\begin{table}[htbp]
+\label{sec:eda}
+
+Sebelum tahap pelatihan model dilakukan, diperlukan analisis eksplorasi
+untuk memahami distribusi dan karakteristik data fitur hasil ekstraksi
+STFT pada himpunan $\mathcal{D}_A$ dan $\mathcal{D}_B$.
+Analisis ini bertujuan untuk menilai sejauh mana fitur yang diperoleh
+mampu merepresentasikan perbedaan kondisi struktur
+serta menentukan parameter reduksi dimensi yang sesuai
+pada tahap pemodelan berikutnya.
+
+\subsection{Analisis Komponen Utama (PCA)}
+Transformasi \gls{pca} diterapkan terhadap data fitur berdimensi
+$513$ untuk mengevaluasi proporsi variansi yang dapat dijelaskan
+oleh setiap komponen utama. 
+Dengan menghitung \textit{explained variance ratio}, diperoleh
+diagram \textit{scree} seperti pada Gambar~\ref{fig:scree_plot},
+yang menunjukkan kontribusi masing-masing komponen terhadap
+total variansi data.
+
+\begin{figure}[H]
     \centering
-    \caption{Ringkasan kinerja antar-fold (jika menggunakan k-fold).}
+    \includegraphics[width=.75\textwidth]{chapters/img/sensor1/scree_plot.png}
-    \label{tab:kfold}
+    \caption{Diagram \textit{scree} hasil analisis PCA pada dataset $\mathcal{D}_A$ dan $\mathcal{D}_B$.}
-    \begin{tabular}{lcc}
+    \label{fig:scree_plot}
-        \hline
+\end{figure}
-        Metrik & Rata-rata & Deviasi Standar \\
-        \hline
-        Macro-F1 & -- & -- \\
-        Akurasi & -- & -- \\
-        \hline
-    \end{tabular}
-\end{table}
 
-Pada skema validasi silang \textit{k}-fold, variasi performa relatif rendah dengan simpangan baku Macro-F1 sebesar \textit{[std\_f1]} (Tabel~\ref{tab:kfold}), menandakan stabilitas model terhadap variasi subset data. Penambahan noise sintetis pada tingkat SNR \textit{[snr levels]} menunjukkan penurunan performa yang \textit{[ringan/sedang/bermakna]} sekitar \textit{[delta\_snr]} poin; augmentasi \textit{[jenis augmentasi]} membantu mengkompensasi sebagian penurunan tersebut.
+Dari Gambar~\ref{fig:scree_plot} terlihat bahwa \textit{explained ratio cumulative} 0.95 dicapai pada sekitar 300 komponen utama,
+% Sebagai contoh, sepuluh komponen pertama menjelaskan sekitar
+% $\alpha\%$ variansi kumulatif pada kanal sensor~A
+% dan $\beta\%$ pada kanal sensor~B.
+% Hasil ini menunjukkan bahwa terdapat redundansi di antara fitur-fitur
+% frekuensi yang diekstraksi, sehingga reduksi dimensi
+% dapat dilakukan tanpa kehilangan informasi signifikan.
 
-Pada skenario \textit{domain shift} \textit{[nama skenario]}, model mempertahankan Macro-F1 sebesar \textit{[f1\_shift]}\%, yang menunjukkan \textit{[derajat generalisasi]} terhadap kondisi yang berbeda dari data pelatihan.
+\subsection{Reduksi Dimensi Sebelum Visualisasi}
+Sebelum diterapkan metode reduksi dimensi non-linear seperti \gls{tsne}
+dan \gls{pacmap}, terlebih dahulu dilakukan reduksi dimensi linear
+menggunakan \gls{pca} untuk menghilangkan derau dan mengurangi kompleksitas
+fitur STFT yang berukuran tinggi ($513$ dimensi).
+Langkah ini umum digunakan untuk meningkatkan stabilitas dan efisiensi
+proses embedding \parencite{JMLR:v9:vandermaaten08a}.
 
-\section{Perbandingan dengan Pustaka/Baseline}
+Pada penelitian ini, beberapa nilai komponen PCA digunakan \\
-Temuan kami selaras dengan tren yang dilaporkan oleh \textcite{abdeljaber2017}, khususnya mengenai pentingnya informasi frekuensi untuk mendeteksi lokasi kerusakan. Meskipun demikian, perbedaan \textit{setup} eksperimen (\textit{[jenis struktur/skenario uji]}, konfigurasi sensor, dan definisi kelas) membuat angka metrik tidak dapat dibandingkan secara langsung. Oleh karena itu, perbandingan difokuskan pada pola dan arah peningkatan, bukan nilai absolut.
+($n_\text{components}\in\{512,128,32,8\}$)
+untuk menilai pengaruh tingkat reduksi terhadap hasil proyeksi t-SNE
+dan PaCMAP. 
+Gambar~\ref{fig:pca_tsne_pacmap} memperlihatkan contoh visualisasi 
+dua dimensi hasil reduksi berurutan PCA $\rightarrow$ t-SNE dan 
+PCA $\rightarrow$ PaCMAP pada dataset $\mathcal{D}_A$.
 
-\section{Kompleksitas dan Implementasi}
+\begin{figure}[H]
-Model SVM dengan fitur \textit{[jenis fitur terbaik]} menawarkan waktu inferensi sekitar \textit{[t\_infer\_ms]} ms per sampel pada \textit{[perangkat/CPU/GPU]}. Tahap ekstraksi STFT memerlukan \textit{[t\_stft\_ms]} ms per segmen dengan parameter \textit{[n\_fft]}, overlap \textit{[overlap\_\%]}\%, dan window Hann. Secara keseluruhan, latensi ujung-ke-ujung diperkirakan \textit{[t\_end2end\_ms]} ms, yang \textit{[memadai/belum memadai]} untuk aplikasi \textit{[real-time/near real-time]}.
+    \centering
+    \subfloat[PCA=512]{\includegraphics[width=.24\textwidth]{chapters/img/sensor1/tsne_original.png}}
+    \subfloat[PCA=16]{\includegraphics[width=.24\textwidth]{chapters/img/sensor1/tsne_pca16.png}}
+    \subfloat[PCA=8]{\includegraphics[width=.24\textwidth]{chapters/img/sensor1/tsne_pca8.png}}
+    \subfloat[PCA=4]{\includegraphics[width=.24\textwidth]{chapters/img/sensor1/tsne_pca4.png}} \\[1ex]
+    \subfloat[PCA=512]{\includegraphics[width=.24\textwidth]{chapters/img/sensor1/pacmap_original.png}}
+    \subfloat[PCA=16]{\includegraphics[width=.24\textwidth]{chapters/img/sensor1/pacmap_pca16.png}}
+    \subfloat[PCA=8]{\includegraphics[width=.24\textwidth]{chapters/img/sensor1/pacmap_pca8.png}}
+    \subfloat[PCA=4]{\includegraphics[width=.24\textwidth]{chapters/img/sensor1/pacmap_pca4.png}}
+    \caption{Visualisasi hasil reduksi bertahap pada $\mathcal{D}_A$ dengan PCA $\rightarrow$ t-SNE (baris atas) 
+    dan PCA $\rightarrow$ PaCMAP (baris bawah).}
+    \label{fig:pca_tsne_pacmap_A}
+\end{figure}
+\begin{figure}[H]
+    \centering
+    \subfloat[PCA=512]{\includegraphics[width=.24\textwidth]{chapters/img/sensor2/tsne_original.png}}
+    \subfloat[PCA=16]{\includegraphics[width=.24\textwidth]{chapters/img/sensor2/tsne_pca16.png}}
+    \subfloat[PCA=8]{\includegraphics[width=.24\textwidth]{chapters/img/sensor2/tsne_pca8.png}}
+    \subfloat[PCA=4]{\includegraphics[width=.24\textwidth]{chapters/img/sensor2/tsne_pca4.png}} \\[1ex]
+    \subfloat[PCA=512]{\includegraphics[width=.24\textwidth]{chapters/img/sensor2/pacmap_original.png}}
+    \subfloat[PCA=16]{\includegraphics[width=.24\textwidth]{chapters/img/sensor2/pacmap_pca16.png}}
+    \subfloat[PCA=8]{\includegraphics[width=.24\textwidth]{chapters/img/sensor2/pacmap_pca8.png}}
+    \subfloat[PCA=4]{\includegraphics[width=.24\textwidth]{chapters/img/sensor2/pacmap_pca4.png}}
+    \caption{Visualisasi hasil reduksi bertahap pada $\mathcal{D}_B$ dengan PCA $\rightarrow$ t-SNE (baris atas) 
+    dan PCA $\rightarrow$ PaCMAP (baris bawah).}
+    \label{fig:pca_tsne_pacmap_B}
+\end{figure}
 
-Dengan \textit{[n\_sensors\_min]} sensor, kebutuhan komputasi dan bandwidth data berkurang \textit{[proporsi pengurangan]} dibanding konfigurasi penuh, yang memperbaiki kelayakan implementasi lapangan tanpa mengorbankan akurasi secara signifikan.
+Hasil pada Gambar~\ref{fig:pca_tsne_pacmap} menunjukkan bahwa
+pengurangan jumlah komponen PCA hingga 8 dimensi
+masih mempertahankan pemisahan antar kelas secara visual,
+sedangkan reduksi lebih jauh (misalnya $n_\text{components}=4$)
+menyebabkan beberapa klaster saling tumpang tindih (\textit{overlap}).
+Temuan ini mendukung pemilihan nilai $n_\text{components}$
+sebagai salah satu parameter penting yang diuji dalam
+pencarian \textit{grid} pada tahap optimasi model untuk mengurangi kompleksitas model dan efisiensi komputasi.
 
-\section{Ringkasan Bab}
+% \subsection{Visualisasi Ruang Fitur Non-Linear}
+% Selain PCA, digunakan dua metode reduksi dimensi non-linear,
+% yaitu \gls{tsne} dan \gls{pacmap},
+% untuk memvisualisasikan struktur data dalam ruang dua dimensi.
+% Kedua metode ini memproyeksikan vektor fitur berukuran $513$
+% ke bidang dua dimensi dengan mempertahankan hubungan jarak
+% antar sampel secara lokal.
+
+% \begin{figure}[H]
+%     \centering
+%     % \subfloat[t-SNE pada $\mathcal{D}_A$]{%
+%     %     \includegraphics[width=.48\textwidth]{chapters/img/sensor1/tsne_A.png}
+%     % }\hfill
+%     \subfloat[t-SNE pada $\mathcal{D}_B$]{%
+%         \includegraphics[width=.48\textwidth]{chapters/img/sensor1/tsne_B.png}
+%     }\\[1ex]
+%     \subfloat[PaCMAP pada $\mathcal{D}_A$]{%
+%         \includegraphics[width=.48\textwidth]{chapters/img/sensor1/pacmap_A.png}
+%     }\hfill
+%     \subfloat[PaCMAP pada $\mathcal{D}_B$]{%
+%         \includegraphics[width=.48\textwidth]{chapters/img/sensor1/pacmap_B.png}
+%     }
+%     \caption{Visualisasi dua dimensi hasil reduksi dimensi non-linear
+%     menggunakan t-SNE dan PaCMAP pada fitur STFT sensor A dan B.
+% .}
+%     \label{fig:tsne_pacmap}
+% \end{figure}
+
+% Pada Gambar~\ref{fig:tsne_pacmap} tampak bahwa setiap kelas
+% ($d_0$--$d_6$) membentuk klaster yang relatif terpisah,
+% menandakan bahwa fitur hasil STFT memiliki kemampuan diskriminatif
+% terhadap kondisi struktur. 
+% Beberapa tumpang tindih antar klaster (khususnya antara $d_i$ yang berdekatan)
+% masih muncul akibat kemiripan respons getaran pada lokasi
+% yang berdekatan, namun pola pemisahan antar kelompok
+% masih terlihat jelas.
+
+\subsection{Interpretasi dan Implikasi}
+Hasil eksplorasi ini menunjukkan bahwa:
+\begin{enumerate}
+    \item Variansi utama data dapat dijelaskan oleh sejumlah kecil komponen PCA,
+    sehingga reduksi dimensi berpotensi meningkatkan efisiensi komputasi
+    tanpa kehilangan informasi penting.
+    \item Visualisasi t-SNE dan PaCMAP memperlihatkan bahwa fitur STFT
+    mampu mengelompokkan kondisi struktur sesuai label kerusakan,
+    mendukung validitas pemilihan STFT sebagai metode ekstraksi fitur.
+    \item Perbedaan antara kanal sensor~A ($\mathcal{D}_A$) dan sensor~B ($\mathcal{D}_B$) tidak signifikan,
+    sehingga keduanya dapat diperlakukan sebagai dua sumber informasi
+    komplementer pada tahap pelatihan model.
+\end{enumerate}
+
+Temuan ini menjadi dasar untuk menentukan jumlah komponen PCA
+yang akan digunakan pada \textit{grid search} saat optimasi \textit{hyperparameter} model SVM.
+
+\section{Hasil \textit{Coarse Grid-Search}}
+\label{sec:grid-results}
+
+Setelah proses ekstraksi fitur dan pembentukan dataset berlabel,
+tahap berikutnya adalah melakukan pencarian \textit{grid}
+untuk mengoptimalkan parameter model \gls{svm} 
+dengan kernel \gls{rbf}.
+Tiga parameter yang dioptimalkan adalah:
+\begin{enumerate}
+  \item jumlah komponen utama \(\,n_{\text{components}}\,\) pada reduksi dimensi \gls{pca},
+  \item parameter regulasi \(C\),
+  \item parameter kernel \(\gamma\).
+\end{enumerate}
+
+Total kombinasi parameter yang diuji berjumlah \(5\times5\times8 = 200\) kandidat model
+dengan skema \textit{stratified 5-fold cross-validation} menghasilkan total 1000 kali \textit{fitting}.
+Setiap kombinasi dievaluasi menggunakan metrik akurasi rata-rata
+pada data validasi.
+
+\subsection{Evaluasi Keseluruhan}
+Distribusi akurasi seluruh kandidat model ditunjukkan pada
+Gambar~\ref{fig:grid_hist}.
+Sebagian besar kombinasi menghasilkan akurasi di atas~95\%,
+menunjukkan bahwa fitur STFT memiliki daya klasifikasi yang kuat
+terhadap kondisi struktur.
+
+\begin{figure}[H]
+    \centering
+    % \includegraphics[width=.65\textwidth]{figures/grid_hist.pdf}
+    \caption{Distribusi akurasi validasi silang dari 225 kombinasi parameter $(C,\gamma,n_{\text{components}})$.}
+    \label{fig:grid_hist}
+\end{figure}
+
+\subsection{Pengaruh Jumlah Komponen PCA}
+Rata-rata akurasi tertinggi untuk setiap nilai $n_{\text{components}}$
+ditampilkan pada Gambar~\ref{fig:pca_acc_overall}.
+Terlihat bahwa akurasi meningkat hingga mencapai puncak pada rentang
+$n_{\text{components}} = 64$--$128$, kemudian menurun ketika jumlah komponen
+dikurangi secara agresif.
+Hal ini menunjukkan bahwa sekitar 10–25\% komponen utama sudah cukup
+merepresentasikan informasi penting dari fitur STFT.
+
+\begin{figure}[H]
+    \centering
+    % \includegraphics[width=.7\textwidth]{figures/pca_acc_overall.pdf}
+    \caption{Rata-rata akurasi terhadap jumlah komponen PCA berdasarkan hasil pencarian \textit{grid}.}
+    \label{fig:pca_acc_overall}
+\end{figure}
+
+\subsection{Peta Akurasi terhadap Parameter SVM}
+Untuk setiap kanal sensor, peta akurasi terhadap parameter $C$ dan~$\gamma$
+pada konfigurasi PCA terbaik ($n_{\text{components}}=128$)
+ditunjukkan pada Gambar~\ref{fig:svm_heatmap}. Terlihat bahwa area akurasi tinggi terbentuk pada 
+nilai \(C\) menengah dan \(\gamma\) kecil,
+yang menandakan keseimbangan antara margin yang cukup lebar
+dan kompleksitas model yang moderat.
+
+\begin{figure}
+    \centering
+    \subfloat[Baseline]{\includegraphics[width=.48\textwidth]{chapters/img/sensor1/grid_original.png}}\hfill
+    \subfloat[PCA=256]{\includegraphics[width=.48\textwidth]{chapters/img/sensor1/grid_pca256.png}}\hfill \\[1ex]
+    \subfloat[PCA=128]{\includegraphics[width=.48\textwidth]{chapters/img/sensor1/grid_pca128.png}}\hfill
+    \subfloat[PCA=64]{\includegraphics[width=.48\textwidth]{chapters/img/sensor1/grid_pca64.png}}\hfill \\[1ex]
+    \subfloat[PCA=32]{\includegraphics[width=.48\textwidth]{chapters/img/sensor1/grid_pca32.png}}\hfill
+    \subfloat[PCA=16]{\includegraphics[width=.48\textwidth]{chapters/img/sensor1/grid_pca16.png}}\hfill \\[1ex]
+    \subfloat[PCA=8]{\includegraphics[width=.48\textwidth]{chapters/img/sensor1/grid_pca8.png}}\hfill
+    \subfloat[PCA=4]{\includegraphics[width=.48\textwidth]{chapters/img/sensor1/grid_pca4.png}}\hfill
+    \caption{\textit{Heatmap mean test score} terhadap parameter $C$ dan~$\gamma$ untuk setiap komponen utama PCA pada Sensor A ($\mathcal{D}_A$).}
+    \label{fig:svm_heatmap_A}
+\end{figure}
+
+\begin{figure}
+    \centering
+    \subfloat[Baseline]{\includegraphics[width=.48\textwidth]{chapters/img/sensor2/grid_original.png}}\hfill
+    \subfloat[PCA=256]{\includegraphics[width=.48\textwidth]{chapters/img/sensor2/grid_pca256.png}}\hfill \\[1ex]
+    \subfloat[PCA=128]{\includegraphics[width=.48\textwidth]{chapters/img/sensor2/grid_pca128.png}}\hfill
+    \subfloat[PCA=64]{\includegraphics[width=.48\textwidth]{chapters/img/sensor2/grid_pca64.png}}\hfill \\[1ex]
+    \subfloat[PCA=32]{\includegraphics[width=.48\textwidth]{chapters/img/sensor2/grid_pca32.png}}\hfill
+    \subfloat[PCA=16]{\includegraphics[width=.48\textwidth]{chapters/img/sensor2/grid_pca16.png}}\hfill \\[1ex]
+    \subfloat[PCA=8]{\includegraphics[width=.48\textwidth]{chapters/img/sensor2/grid_pca8.png}}\hfill
+    \subfloat[PCA=4]{\includegraphics[width=.48\textwidth]{chapters/img/sensor2/grid_pca4.png}}\hfill
+    \caption{\textit{Heatmap mean test score} terhadap parameter $C$ dan~$\gamma$ untuk setiap komponen utama PCA pada Sensor B ($\mathcal{D}_B$).}
+    \label{fig:svm_heatmap_B}
+\end{figure}
+
+\subsection{Analisis Efisiensi Model pada \textit{Coarse Grid-Search}}
+\label{sec:efficiency_analysis}
+Selain mempertimbangkan akurasi rata-rata (\textit{mean test score}) 
+sebagai satu-satunya metrik evaluasi, penelitian ini juga memperhitungkan
+waktu pelatihan rata-rata (\textit{mean fit time}) untuk menilai efisiensi komputasi.
+Hal ini penting karena peningkatan akurasi sering kali diikuti dengan 
+kenaikan waktu pelatihan yang tidak proporsional, sehingga diperlukan
+kompromi antara performa dan kompleksitas.
+
+Untuk mengukur keseimbangan tersebut, didefinisikan metrik efisiensi:
+\begin{equation}
+E_i = \frac{S_i}{T_i^{\alpha}},
+\label{eq:efficiency_metric}
+\end{equation}
+dengan:
 \begin{itemize}
-    \item Konfigurasi terbaik (\textit{[konfigurasi terbaik]}) mencapai Akurasi \textit{[acc\_best]}\%, Macro-F1 \textit{[f1\_best]}\%, dan Kappa \textit{[kappa\_best]} pada data uji.
+    \item $S_i$ = rata-rata skor akurasi hasil 5-\textit{fold cross-validation} (0–1),
-    \item Kesalahan dominan terjadi antara kelas \textit{[kelas\_A]} dan \textit{[kelas\_B]} karena kemiripan respons pada \textit{[f\_low--f\_high]} Hz; strategi \textit{[strategi perbaikan]} direkomendasikan.
+    \item $T_i$ = rata-rata waktu pelatihan per iterasi (dalam detik),
-    \item Ablasi menegaskan manfaat kombinasi fitur; window Hann dan parameter STFT \textit{[n\_fft, overlap]} memberi keseimbangan resolusi yang baik.
+\end{itemize}
-    \item Pendekatan sensor terbatas dengan \textit{[n\_sensors\_min]} sensor tetap layak dengan penurunan performa \textit{[delta\_perf\_sensors]} poin.
+
-    \item Model menunjukkan stabilitas antar-fold (\textit{[std\_f1]}) dan ketahanan \textit{[terhadap noise/domain shift]} dengan penyesuaian \textit{[augmentasi/penalaan]}. 
+Metrik $E_i$ menggambarkan rasio akurasi terhadap biaya waktu pelatihan.
-\end{itemize}
+Semakin besar nilai $E_i$, semakin efisien model tersebut atau
+model mampu mencapai akurasi tinggi dengan waktu pelatihan yang relatif singkat.
+
+% \begin{figure}[H]
+%     \centering
+%     % \includegraphics[width=.7\textwidth]{figures/efficiency_score.pdf}
+%     \caption{Perbandingan metrik efisiensi ($E_i$) dan akurasi rata-rata ($S_i$)
+%     terhadap jumlah komponen PCA.}
+%     \label{fig:efficiency_score}
+% \end{figure}
+
+\begin{table}[H]
+\centering
+\begin{tabular}{rrrrrr}
+\toprule
+$n_{\text{components}}$ & $C (\log{2})$ & $\gamma (\log{2})$ & $S_i$ & $T_i$ & $E_i (\times10^{-3})$ \\
+\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}
+\end{table}
+
+\begin{table}[H]
+\centering
+\begin{tabular}{rrrrrr}
+\toprule
+$n_{\text{components}}$ & $C (\log{2})$ & $\gamma (\log{2})$ & $S_i$ & $T_i$ & $E_i (\times10^{-3})$ \\
+\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}
+\end{table}
+
+Hasil pada Tabel~\ref{tab:efficiency_summary_A} dan Tabel~\ref{tab:efficiency_summary_B} menunjukkan bahwa,
+meskipun nilai akurasi tertinggi dicapai pada 
+$n_{\text{components}} = 512$ untuk kedua kanal sensor,
+puncak nilai metrik efisiensi dicapai pada 
+$n_{\text{components}} = 32$ dengan $E = 0.9504$ untuk Sensor A ($\mathcal{D}_A$) dan $n_{\text{components}} = 16$ dengan $E = 0.9116$ untuk Sensor B ($\mathcal{D}_B$).
+Artinya, pengurangan dimensi hingga 32 komponen untuk Sensor A dan 16 komponen untuk Sensor B
+menghasilkan model yang hampir seakurat konfigurasi berdimensi penuh,
+namun dengan waktu pelatihan yang berkurang lebih dari 75\%.
+% Kompromi ini dianggap sebagai titik optimum antara performa dan efisiensi.
+
+Berdasarkan kombinasi akurasi, waktu pelatihan, dan metrik efisiensi,
+konfigurasi dengan $n_{\text{components}}=32$ untuk Sensor A dan $n_{\text{components}}=16$ untuk Sensor B dipilih sebagai
+\textit{baseline} optimal untuk model akhir.
+Model \textit{baseline} ini akan digunakan sebagai acuan pada tahap evaluasi model dan pencarian \textit{hyperparameter} lanjutan (\textit{fine grid-search})
+yang dibahas pada subab berikutnya.
+
+
+\section{Evaluasi Model \textit{Baseline}}
+\label{sec:baseline_performance}
+Model \textit{baseline} yang digunakan diperoleh dari \textit{coarse grid-search} pada subab \ref{sec:efficiency_analysis} adalah SVM dengan kernel RBF, 32 komponen PCA, dan parameter $C=2^{10}$, $\gamma=2^{-10}$ untuk Sensor A, sedangkan untuk Sensor B adalah SVM dengan kernel RBF, 16 komponen PCA, dan parameter $C=2^{5}$, $\gamma=2^{-5}$. Pada bagian ini, dilakukan evaluasi performa model \textit{baseline} dengan data uji yang berbeda (\textit{Dataset} B).
+
+
+\subsection{Metrik Klasifikasi}
+Metrik klasifikasi model \textit{baseline} pada dataset pengujian disajikan pada Tabel~\ref{tab:metrics-baseline_A} dan~\ref{tab:matrics-baseline_B}.
+
+\begin{table}[htbp]
+    \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}
+\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}
+\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.
+
+\subsection{\textit{Confusion Matrix}}
+\begin{figure}[H]
+    \centering
+    \includegraphics[width=0.8\textwidth]{chapters/img/sensor1/cm_baseline_s1a_eval.png}
+    \caption{\textit{Confusion matrix} model \textit{baseline} SVM (RBF) pada Sensor A}
+    \label{fig:confusion-matrix-baseline_A}
+\end{figure}
+
+\begin{figure}[H]
+    \centering
+    \includegraphics[width=0.8\textwidth]{chapters/img/sensor2/cm_baseline_s2a_eval.png}
+    \caption{\textit{Confusion matrix} model \textit{baseline} SVM (RBF) pada Sensor B}
+    \label{fig:confusion-matrix-baseline_B}
+\end{figure}
+
+Dari Gambar~\ref{fig:confusion-matrix-baseline_A} dan~\ref{fig:confusion-matrix-baseline_B}, terlihat bahwa kedua model minim kesalahan klasifikasi, dengan sebagian besar prediksi berada di diagonal utama. Beberapa kesalahan klasifikasi minor terjadi paling banyak antara kelas 0 dengan kelas 1 dan kelas 6.
+
+
+\section{\textit{Fine Grid-Search}}
+Optimasi model yang dilakukan yaitu dengan melakukan \textit{fine grid-search} pada rentang \textit{hyperparameter} model \textit{baseline} yang digunakan pada Subab~\ref{sec:baseline_performance}. Untuk Sensor A dengan  $n_{\text{components}} = 32$ rentang parameter yang dicari adalah
+\begin{align*}
+    C &= \{\, 2^8,\, 2^{8.5},\, \ldots,\, 2^{12} \,\} \\
+    \gamma &= \{\, 2^{-12},\, 2^{-11.5},\, \ldots ,\, 2^{-8} \,\},
+\end{align*}sedangkan Sensor B dengan $n_{\text{components}} = 16$ rentang parameter yang dicari adalah
+\begin{align*}
+    C &= \{\, 2^3,\, 2^{3.5},\, \ldots,\, 2^{7} \,\} \\
+    \gamma &= \{\, 2^{-7},\, 2^{-6.5},\, \ldots ,\, 2^{-3} \,\}.
+\end{align*}. Pada proses ini, \textit{standard scaler} dan \textit{stratified k-fold 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} $C$ dan~$\gamma$ 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}.
+\begin{figure}
+    \centering
+    \subfloat[Sensor A (PCA 32)]{\includegraphics[width=.48\textwidth]{chapters/img/sensor1/grid_fine_pca32.png}}
+    \centering
+    \subfloat[Sensor B (PCA 16)]{\includegraphics[width=.48\textwidth]{chapters/img/sensor2/grid_fine_pca16.png}}
+    \caption{\textit{Heatmap mean test score} terhadap \textit{fine grid-search parameter} $C$ dan~$\gamma$}
+    \label{fig:svm_fine_heatmap}
+\end{figure}
+
+\section{Evaluasi Model \textit{Fine Grid-Search}}
+Model \textit{fine grid-search} dilatih pada \textit{dataset} A dan perlu dievaluasi performanya dengan data uji yang berbeda (\textit{dataset} B) untuk mengukur peningkatan performa dibandingkan model \textit{baseline}.
+\subsection{Metrik Klasifikasi}
+Hasil performa model \textit{fine grid-search} pada data uji disajikan pada Tabel~\ref{tab:metrics-fine-a} dan~\ref{tab:metrics-fine-b}.
+
+\begin{table}
+\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}
+\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}
+\end{table}
+
+\subsection{\textit{Confusion Matrix}}
+\begin{figure}[H]
+    \centering
+    \includegraphics[width=.8\textwidth]{chapters/img/sensor1/cm_fine_s1a_eval.png}
+    \caption{\textit{Confusion matrix} model \textit{fine grid-search} pada Sensor A}
+    \label{fig:cm_fine_s1a_eval}
+\end{figure}
+\begin{figure}[H]
+    \centering
+    \includegraphics[width=.8\textwidth]{chapters/img/sensor2/cm_fine_s2a_eval.png}
+    \caption{\textit{Confusion matrix} model \textit{fine grid-search} pada Sensor B}
+    \label{fig:cm_fine_s2a_eval}
+\end{figure}
+
+\section{Model \textit{Inference} dan Visualisasi Prediksi}
+Setelah model \textit{fine grid-search} dievaluasi, dilakukan proses \textit{inference} pada data uji untuk memvisualisasikan prediksi model. Gambar~\ref{fig:inference_s1a} dan~\ref{fig:inference_s2a} menunjukkan hasil prediksi model pada Sensor A dan Sensor B dalam \textit{heatmap} dan grafik probabilitasnya.
+% \section{Efisiensi Komputasi}
+
+% \subsection{Perbandingan Waktu Latih}
+% \subsection{Perbandingan Waktu \textit{Inference}}
+% \begin{table}[htbp]
+%     \centering
+%     \caption{Perbandingan waktu inference model \textit{baseline} dan \textit{preprocessing pipeline}}
+%     \label{tab:training-time}
+%     \begin{tabular}{lrr}
+%         \hline
+%         Iterasi & \textit{Baseline} (detik) & \textit{preprocessing pipeline} (detik)\\
+%         1 & & 6.53 \\
+%         2 & & 6.08 \\
+%         3 & & 6.08 \\
+%         4 & & 6.10 \\
+%         5 & & 6.20 \\
+%         Rata-rata & & 6.20 \\
+%         \hline
+%     \end{tabular}
+% \end{table}
+
+% \section{}
+% Model \textit{baseline} yang dilakukan dengan \textit{preprocessing pipeline} mengurangi waktu latih sekitar x \%, sedangkan waktu \textit{inference} tiap sampel berkurang sekitar x \%. Hal ini menunjukkan keefektifan PCA dalam mereduksi dimensi fitur dan \textit{standard scaler}, dengan begitu dapat mengurangi beban komputasi tanpa mengorbankan akurasi.
+
+% Konfigurasi terbaik diperoleh pada kombinasi fitur waktu--frekuensi dengan SVM-\textit{[kernel]}, menghasilkan Akurasi sebesar \textit{[acc\_best]}\%, Macro-F1 sebesar \textit{[f1\_best]}\%, dan Kappa sebesar \textit{[kappa\_best]} pada data uji (Tabel~\ref{tab:main-results}). Dibandingkan baseline domain waktu saja, Macro-F1 meningkat sekitar \textit{[delta\_f1\_time]} poin persentase; dibandingkan domain frekuensi saja, peningkatan mencapai \textit{[delta\_f1\_freq]} poin persentase. Hasil ini mengindikasikan bahwa informasi pelengkap antara dinamika temporal dan spektral berkontribusi nyata terhadap separabilitas kelas.
+
+% Performa pada metrik Balanced Accuracy dan Macro-Recall juga konsisten, menandakan model tidak terlalu bias pada kelas mayoritas. Nilai Kappa \textit{[kappa\_best]} mengindikasikan tingkat kesepakatan yang \textit{[moderat/tinggi]} melampaui kebetulan.
+
+% \section{Analisis Per-Kelas dan Kesalahan}
+% \begin{figure}[htbp]
+%     \centering
+%     % \includegraphics[width=0.8\textwidth]{img/confusion_matrix.pdf}
+%     \fbox{\begin{minipage}[c][0.30\textheight][c]{0.80\textwidth}\centering
+%     Placeholder Confusion Matrix
+%     \end{minipage}}
+%     \caption{Confusion matrix pada data uji. Isikan gambar aktual dari pipeline evaluasi.}
+%     \label{fig:cm}
+% \end{figure}
+
+% \begin{table}[htbp]
+%     \centering
+%     \caption{Metrik per-kelas pada data uji. Gunakan bila diperlukan untuk melengkapi Confusion Matrix.}
+%     \label{tab:per-class}
+%     \begin{tabular}{lccc}
+%         \hline
+%         Kelas & Precision & Recall & F1 \\
+%         \hline
+%         A & -- & -- & -- \\
+%         B & -- & -- & -- \\
+%         C & -- & -- & -- \\
+%         % ... tambah baris sesuai jumlah kelas
+%         \hline
+%     \end{tabular}
+% \end{table}
+
+% Confusion Matrix pada Gambar~\ref{fig:cm} menunjukkan pola salah klasifikasi yang dominan antara kelas \textit{[kelas\_A]} dan \textit{[kelas\_B]}. Dua kelas ini memiliki respons spektral yang mirip pada rentang \textit{[f\_low--f\_high]} Hz, sehingga kesalahan terutama terjadi ketika amplitudo sinyal rendah atau \textit{signal-to-noise ratio} menurun. Sebaliknya, kelas \textit{[kelas\_C]} memperlihatkan separasi yang baik dengan Recall \textit{[recall\_C]}\% dan F1 \textit{[f1\_C]}\% (Tabel~\ref{tab:per-class}).
+
+% Analisis kesalahan kasus-per-kasus menunjukkan bahwa \textit{[proporsi\_\%]}\% prediksi keliru terjadi pada sampel dengan \textit{[ciri sinyal/condisi uji]} dan \textit{[konfigurasi sensor]}. Hal ini menyarankan perlunya \textit{[strategi perbaikan, mis. penambahan fitur bandpass tertentu atau penyeimbangan kelas]}.
+
+% \section{Ablasi dan Sensitivitas}
+% \subsection{Ablasi Fitur}
+% \begin{figure}[htbp]
+%     \centering
+%     \includegraphics[width=0.75\textwidth]{example-image-a}
+%     \fbox{\begin{minipage}[c][0.22\textheight][c]{0.70\textwidth}\centering
+%     Placeholder Bar Chart: Time vs Freq vs Kombinasi
+%     \end{minipage}}
+%     \caption{Perbandingan performa berdasarkan jenis fitur.}
+%     \label{fig:ablation-features}
+% \end{figure}
+
+% Studi ablation pada Gambar~\ref{fig:ablation-features} menegaskan bahwa kombinasi fitur memberikan peningkatan \textit{[delta\_ablation]} poin persentase pada Macro-F1 dibandingkan fitur domain waktu saja. Hal ini mengindikasikan bahwa karakteristik harmonik dan komponen frekuensi transien yang ditangkap STFT berkontribusi pada pemisahan kelas yang lebih baik.
+
+% \subsection{Parameter STFT dan Windowing}
+% \begin{table}[htbp]
+%     \centering
+%     \caption{Sensitivitas terhadap parameter STFT pada data validasi.}
+%     \label{tab:stft-sensitivity}
+%     \begin{tabular}{lcccc}
+%         \hline
+%         Window & n\_fft & Overlap & Akurasi & Macro-F1 \\
+%         \hline
+%         Hann & -- & -- & -- & -- \\
+%         Hann & -- & -- & -- & -- \\
+%         (Tanpa window) & -- & -- & -- & -- \\
+%         \hline
+%     \end{tabular}
+% \end{table}
+
+% Eksperimen sensitivitas pada Tabel~\ref{tab:stft-sensitivity} memperlihatkan adanya \textit{trade-off} antara resolusi waktu dan frekuensi. Peningkatan \textit{n\_fft} cenderung memperhalus resolusi frekuensi namun mengurangi ketelitian temporal, sedangkan overlap yang lebih besar \textit{[overlap\_\% range]}\% membantu stabilitas estimasi fitur pada sinyal bising. Penggunaan window Hann memberikan kenaikan Macro-F1 sekitar \textit{[delta\_hann]} poin dibanding tanpa window, menegaskan peran pengurangan \textit{spectral leakage}.
+
+% \subsection{Pendekatan Sensor Terbatas}
+% \begin{figure}[htbp]
+%     \centering
+%     % placeholder
+%     \includegraphics[width=0.75\textwidth]{example-image-a}
+%     \fbox{\begin{minipage}[c][0.22\textheight][c]{0.70\textwidth}\centering
+%     Placeholder: Performa vs Jumlah/Posisi Sensor
+%     \end{minipage}}
+%     \caption{Dampak jumlah/konfigurasi sensor terhadap performa.}
+%     \label{fig:sensor-limited}
+% \end{figure}
+
+% Hasil pada Gambar~\ref{fig:sensor-limited} menunjukkan bahwa pengurangan dari \textit{[n\_sensors\_full]} menjadi \textit{[n\_sensors\_min]} sensor hanya menurunkan Macro-F1 sekitar \textit{[delta\_perf\_sensors]} poin, khususnya ketika sensor ditempatkan pada \textit{[posisi sensor terbaik]}. Ini mengindikasikan bahwa pendekatan sensor terbatas tetap layak untuk implementasi dengan biaya perangkat keras yang lebih rendah, selama pemilihan posisi sensor dioptimalkan.
+
+% \section{Robustness dan Generalisasi}
+% \begin{table}[htbp]
+%     \centering
+%     \caption{Ringkasan kinerja antar-fold (jika menggunakan k-fold).}
+%     \label{tab:kfold}
+%     \begin{tabular}{lcc}
+%         \hline
+%         Metrik & Rata-rata & Deviasi Standar \\
+%         \hline
+%         Macro-F1 & -- & -- \\
+%         Akurasi & -- & -- \\
+%         \hline
+%     \end{tabular}
+% \end{table}
+
+% Pada skema validasi silang \textit{k}-fold, variasi performa relatif rendah dengan simpangan baku Macro-F1 sebesar \textit{[std\_f1]} (Tabel~\ref{tab:kfold}), menandakan stabilitas model terhadap variasi subset data. Penambahan noise sintetis pada tingkat SNR \textit{[snr levels]} menunjukkan penurunan performa yang \textit{[ringan/sedang/bermakna]} sekitar \textit{[delta\_snr]} poin; augmentasi \textit{[jenis augmentasi]} membantu mengkompensasi sebagian penurunan tersebut.
+
+% Pada skenario \textit{domain shift} \textit{[nama skenario]}, model mempertahankan Macro-F1 sebesar \textit{[f1\_shift]}\%, yang menunjukkan \textit{[derajat generalisasi]} terhadap kondisi yang berbeda dari data pelatihan.
+
+% \section{Perbandingan dengan Pustaka/Baseline}
+% Temuan kami selaras dengan tren yang dilaporkan oleh \textcite{abdeljaber2017}, khususnya mengenai pentingnya informasi frekuensi untuk mendeteksi lokasi kerusakan. Meskipun demikian, perbedaan \textit{setup} eksperimen (\textit{[jenis struktur/skenario uji]}, konfigurasi sensor, dan definisi kelas) membuat angka metrik tidak dapat dibandingkan secara langsung. Oleh karena itu, perbandingan difokuskan pada pola dan arah peningkatan, bukan nilai absolut.
+
+% \section{Kompleksitas dan Implementasi}
+% Model SVM dengan fitur \textit{[jenis fitur terbaik]} menawarkan waktu inferensi sekitar \textit{[t\_infer\_ms]} ms per sampel pada \textit{[perangkat/CPU/GPU]}. Tahap ekstraksi STFT memerlukan \textit{[t\_stft\_ms]} ms per segmen dengan parameter \textit{[n\_fft]}, overlap \textit{[overlap\_\%]}\%, dan window Hann. Secara keseluruhan, latensi ujung-ke-ujung diperkirakan \textit{[t\_end2end\_ms]} ms, yang \textit{[memadai/belum memadai]} untuk aplikasi \textit{[real-time/near real-time]}.
+
+% Dengan \textit{[n\_sensors\_min]} sensor, kebutuhan komputasi dan bandwidth data berkurang \textit{[proporsi pengurangan]} dibanding konfigurasi penuh, yang memperbaiki kelayakan implementasi lapangan tanpa mengorbankan akurasi secara signifikan.
+
+% \section{Ringkasan Bab}
+% \begin{itemize}
+%     \item Konfigurasi terbaik (\textit{[konfigurasi terbaik]}) mencapai Akurasi \textit{[acc\_best]}\%, Macro-F1 \textit{[f1\_best]}\%, dan Kappa \textit{[kappa\_best]} pada data uji.
+%     \item Kesalahan dominan terjadi antara kelas \textit{[kelas\_A]} dan \textit{[kelas\_B]} karena kemiripan respons pada \textit{[f\_low--f\_high]} Hz; strategi \textit{[strategi perbaikan]} direkomendasikan.
+%     \item Ablasi menegaskan manfaat kombinasi fitur; window Hann dan parameter STFT \textit{[n\_fft, overlap]} memberi keseimbangan resolusi yang baik.
+%     \item Pendekatan sensor terbatas dengan \textit{[n\_sensors\_min]} sensor tetap layak dengan penurunan performa \textit{[delta\_perf\_sensors]} poin.
+%     \item Model menunjukkan stabilitas antar-fold (\textit{[std\_f1]}) dan ketahanan \textit{[terhadap noise/domain shift]} dengan penyesuaian \textit{[augmentasi/penalaan]}. 
+% \end{itemize}

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+%% PDF/EPS/PS + LaTeX output extension by Johan Engelen, 2010
+%% Accompanies image file 'stft-undamaged-1_svg-tex.pdf' (pdf, eps, ps)
+%%
+%% To include the image in your LaTeX document, write
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+%%  instead of
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+%% To scale the image, write
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+%%  instead of
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+%%
+%% Images with a different path to the parent latex file can
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+%% in the preamble, and then including the image with
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+%% Alternatively, one can specify
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+%% Creator: Inkscape 1.2.2 (b0a8486541, 2022-12-01), www.inkscape.org
+%% PDF/EPS/PS + LaTeX output extension by Johan Engelen, 2010
+%% Accompanies image file 'stft-undamaged-2_svg-tex.pdf' (pdf, eps, ps)
+%%
+%% To include the image in your LaTeX document, write
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+%%
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+%% Alternatively, one can specify
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+%% For more information, please see info/svg-inkscape on CTAN:
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