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\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}
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.
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}
\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}
Gambar~\ref{fig:stft-undamaged} memperlihatkan hasil STFT gabungan (\textit{aggregated}) untuk seluruh titik join tanpa kerusakan (kelas 0).
\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}
\begin{minipage}{0.48\textwidth}
\centering
\includesvg[width=\textwidth, pretex=\tiny]{chapters/img/sensor1/stft-undamaged-1}
% \caption{Caption for the first image.}
% \label{fig:image1}
\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]
\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}
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.
\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}
\includesvg[width=\textwidth, pretex=\tiny, inkscapelatex=true]{chapters/img/sensor1/stft-damaged-multiple-1.svg}
\caption{STFT sensor A dengan kerusakan (damaged $d_1$\textemdash $d_6$).}
\label{fig:stft-damaged-multiple-a}
\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}
\includesvg[width=1\textwidth, pretex=\tiny, inkscapelatex=true]{chapters/img/sensor2/stft-damaged-multiple-2.svg}
\caption{STFT sensor B dengan kerusakan (damaged $d_1$\textemdash $d_6$).}
\label{fig:stft-damaged-multiple-b}
\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]
\section{Analisis Eksplorasi Data}
\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).}
\label{tab:kfold}
\begin{tabular}{lcc}
\hline
Metrik & Rata-rata & Deviasi Standar \\
\hline
Macro-F1 & -- & -- \\
Akurasi & -- & -- \\
\hline
\end{tabular}
\end{table}
\includegraphics[width=.75\textwidth]{chapters/img/sensor1/scree_plot.png}
\caption{Diagram \textit{scree} hasil analisis PCA pada dataset $\mathcal{D}_A$ dan $\mathcal{D}_B$.}
\label{fig:scree_plot}
\end{figure}
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}
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.
Pada penelitian ini, beberapa nilai komponen PCA digunakan \\
($n_\text{components}\in\{512,128,32,8\}$)
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}
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]}.
\begin{figure}[H]
\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 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}
\item $S_i$ = rata-rata skor akurasi hasil 5-\textit{fold cross-validation} (0–1),
\item $T_i$ = rata-rata waktu pelatihan per iterasi (dalam detik),
\end{itemize}
Metrik $E_i$ menggambarkan rasio akurasi terhadap biaya waktu pelatihan.
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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