refactor(documentclass): update title handling by using input files for maketitle

Closes #91

code/notebooks/stft.ipynb

@@ -334,8 +334,9 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "len(ready_data1a)\n",
+    "# len(ready_data1a)\n",
-    "# plt.pcolormesh(ready_data1[0])"
+    "# plt.pcolormesh(ready_data1[0])\n",
+    "ready_data1a[0].max().max()"
    ]
   },
   {
@@ -345,7 +346,8 @@
    "outputs": [],
    "source": [
     "for i in range(6):\n",
-    "    plt.pcolormesh(ready_data1a[i])\n",
+    "    plt.pcolormesh(ready_data1a[i], cmap=\"jet\", vmax=0.03, vmin=0.0)\n",
+    "    plt.colorbar() \n",
     "    plt.title(f'STFT Magnitude for case {i} sensor 1')\n",
     "    plt.xlabel(f'Frequency [Hz]')\n",
     "    plt.ylabel(f'Time [sec]')\n",
@@ -535,8 +537,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# len(y_data[0])\n",
+    "len(y_data[0])\n",
-    "y_data"
+    "# y_data"
    ]
   },
   {
@@ -619,137 +621,15 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "accuracies1 = []\n",
+    "def train_and_evaluate_model(model, model_name, sensor_label, x_train, y_train, x_test, y_test):\n",
-    "accuracies2 = []\n",
+    "    model.fit(x_train, y_train)\n",
-    "\n",
+    "    y_pred = model.predict(x_test)\n",
-    "\n",
+    "    accuracy = accuracy_score(y_test, y_pred) * 100\n",
-    "# 1. Random Forest\n",
+    "    return {\n",
-    "rf_model1 = RandomForestClassifier()\n",
+    "        \"model\": model_name,\n",
-    "rf_model1.fit(x_train1, y_train)\n",
+    "        \"sensor\": sensor_label,\n",
-    "rf_pred1 = rf_model1.predict(x_test1)\n",
+    "        \"accuracy\": accuracy\n",
-    "acc1 = accuracy_score(y_test, rf_pred1) * 100\n",
+    "    }"
-    "accuracies1.append(acc1)\n",
-    "# format with color coded if acc1 > 90\n",
-    "acc1 = f\"\\033[92m{acc1:.2f}\\033[00m\" if acc1 > 90 else f\"{acc1:.2f}\"\n",
-    "print(\"Random Forest Accuracy for sensor 1:\", acc1)\n",
-    "rf_model2 = RandomForestClassifier()\n",
-    "rf_model2.fit(x_train2, y_train)\n",
-    "rf_pred2 = rf_model2.predict(x_test2)\n",
-    "acc2 = accuracy_score(y_test, rf_pred2) * 100\n",
-    "accuracies2.append(acc2)\n",
-    "# format with color coded if acc2 > 90\n",
-    "acc2 = f\"\\033[92m{acc2:.2f}\\033[00m\" if acc2 > 90 else f\"{acc2:.2f}\"\n",
-    "print(\"Random Forest Accuracy for sensor 2:\", acc2)\n",
-    "# print(rf_pred)\n",
-    "# print(y_test)\n",
-    "\n",
-    "# 2. Bagged Trees\n",
-    "bagged_model1 = BaggingClassifier(estimator=DecisionTreeClassifier(), n_estimators=10)\n",
-    "bagged_model1.fit(x_train1, y_train)\n",
-    "bagged_pred1 = bagged_model1.predict(x_test1)\n",
-    "acc1 = accuracy_score(y_test, bagged_pred1) * 100\n",
-    "accuracies1.append(acc1)\n",
-    "# format with color coded if acc1 > 90\n",
-    "acc1 = f\"\\033[92m{acc1:.2f}\\033[00m\" if acc1 > 90 else f\"{acc1:.2f}\"\n",
-    "print(\"Bagged Trees Accuracy for sensor 1:\", acc1)\n",
-    "bagged_model2 = BaggingClassifier(estimator=DecisionTreeClassifier(), n_estimators=10)\n",
-    "bagged_model2.fit(x_train2, y_train)\n",
-    "bagged_pred2 = bagged_model2.predict(x_test2)\n",
-    "acc2 = accuracy_score(y_test, bagged_pred2) * 100\n",
-    "accuracies2.append(acc2)\n",
-    "# format with color coded if acc2 > 90\n",
-    "acc2 = f\"\\033[92m{acc2:.2f}\\033[00m\" if acc2 > 90 else f\"{acc2:.2f}\"\n",
-    "print(\"Bagged Trees Accuracy for sensor 2:\", acc2)\n",
-    "\n",
-    "# 3. Decision Tree\n",
-    "dt_model = DecisionTreeClassifier()\n",
-    "dt_model.fit(x_train1, y_train)\n",
-    "dt_pred1 = dt_model.predict(x_test1)\n",
-    "acc1 = accuracy_score(y_test, dt_pred1) * 100\n",
-    "accuracies1.append(acc1)\n",
-    "# format with color coded if acc1 > 90\n",
-    "acc1 = f\"\\033[92m{acc1:.2f}\\033[00m\" if acc1 > 90 else f\"{acc1:.2f}\"\n",
-    "print(\"Decision Tree Accuracy for sensor 1:\", acc1)\n",
-    "dt_model2 = DecisionTreeClassifier()\n",
-    "dt_model2.fit(x_train2, y_train)\n",
-    "dt_pred2 = dt_model2.predict(x_test2)\n",
-    "acc2 = accuracy_score(y_test, dt_pred2) * 100\n",
-    "accuracies2.append(acc2)\n",
-    "# format with color coded if acc2 > 90\n",
-    "acc2 = f\"\\033[92m{acc2:.2f}\\033[00m\" if acc2 > 90 else f\"{acc2:.2f}\"\n",
-    "print(\"Decision Tree Accuracy for sensor 2:\", acc2)\n",
-    "\n",
-    "# 4. KNeighbors\n",
-    "knn_model = KNeighborsClassifier()\n",
-    "knn_model.fit(x_train1, y_train)\n",
-    "knn_pred1 = knn_model.predict(x_test1)\n",
-    "acc1 = accuracy_score(y_test, knn_pred1) * 100\n",
-    "accuracies1.append(acc1)\n",
-    "# format with color coded if acc1 > 90\n",
-    "acc1 = f\"\\033[92m{acc1:.2f}\\033[00m\" if acc1 > 90 else f\"{acc1:.2f}\"\n",
-    "print(\"KNeighbors Accuracy for sensor 1:\", acc1)\n",
-    "knn_model2 = KNeighborsClassifier()\n",
-    "knn_model2.fit(x_train2, y_train)\n",
-    "knn_pred2 = knn_model2.predict(x_test2)\n",
-    "acc2 = accuracy_score(y_test, knn_pred2) * 100\n",
-    "accuracies2.append(acc2)\n",
-    "# format with color coded if acc2 > 90\n",
-    "acc2 = f\"\\033[92m{acc2:.2f}\\033[00m\" if acc2 > 90 else f\"{acc2:.2f}\"\n",
-    "print(\"KNeighbors Accuracy for sensor 2:\", acc2)\n",
-    "\n",
-    "# 5. Linear Discriminant Analysis\n",
-    "lda_model = LinearDiscriminantAnalysis()\n",
-    "lda_model.fit(x_train1, y_train)\n",
-    "lda_pred1 = lda_model.predict(x_test1)\n",
-    "acc1 = accuracy_score(y_test, lda_pred1) * 100\n",
-    "accuracies1.append(acc1)\n",
-    "# format with color coded if acc1 > 90\n",
-    "acc1 = f\"\\033[92m{acc1:.2f}\\033[00m\" if acc1 > 90 else f\"{acc1:.2f}\"\n",
-    "print(\"Linear Discriminant Analysis Accuracy for sensor 1:\", acc1)\n",
-    "lda_model2 = LinearDiscriminantAnalysis()\n",
-    "lda_model2.fit(x_train2, y_train)\n",
-    "lda_pred2 = lda_model2.predict(x_test2)\n",
-    "acc2 = accuracy_score(y_test, lda_pred2) * 100\n",
-    "accuracies2.append(acc2)\n",
-    "# format with color coded if acc2 > 90\n",
-    "acc2 = f\"\\033[92m{acc2:.2f}\\033[00m\" if acc2 > 90 else f\"{acc2:.2f}\"\n",
-    "print(\"Linear Discriminant Analysis Accuracy for sensor 2:\", acc2)\n",
-    "\n",
-    "# 6. Support Vector Machine\n",
-    "svm_model = SVC()\n",
-    "svm_model.fit(x_train1, y_train)\n",
-    "svm_pred1 = svm_model.predict(x_test1)\n",
-    "acc1 = accuracy_score(y_test, svm_pred1) * 100\n",
-    "accuracies1.append(acc1)\n",
-    "# format with color coded if acc1 > 90\n",
-    "acc1 = f\"\\033[92m{acc1:.2f}\\033[00m\" if acc1 > 90 else f\"{acc1:.2f}\"\n",
-    "print(\"Support Vector Machine Accuracy for sensor 1:\", acc1)\n",
-    "svm_model2 = SVC()\n",
-    "svm_model2.fit(x_train2, y_train)\n",
-    "svm_pred2 = svm_model2.predict(x_test2)\n",
-    "acc2 = accuracy_score(y_test, svm_pred2) * 100\n",
-    "accuracies2.append(acc2)\n",
-    "# format with color coded if acc2 > 90\n",
-    "acc2 = f\"\\033[92m{acc2:.2f}\\033[00m\" if acc2 > 90 else f\"{acc2:.2f}\"\n",
-    "print(\"Support Vector Machine Accuracy for sensor 2:\", acc2)\n",
-    "\n",
-    "# 7. XGBoost\n",
-    "xgboost_model = XGBClassifier()\n",
-    "xgboost_model.fit(x_train1, y_train)\n",
-    "xgboost_pred1 = xgboost_model.predict(x_test1)\n",
-    "acc1 = accuracy_score(y_test, xgboost_pred1) * 100\n",
-    "accuracies1.append(acc1)\n",
-    "# format with color coded if acc1 > 90\n",
-    "acc1 = f\"\\033[92m{acc1:.2f}\\033[00m\" if acc1 > 90 else f\"{acc1:.2f}\"\n",
-    "print(\"XGBoost Accuracy:\", acc1)\n",
-    "xgboost_model2 = XGBClassifier()\n",
-    "xgboost_model2.fit(x_train2, y_train)\n",
-    "xgboost_pred2 = xgboost_model2.predict(x_test2)\n",
-    "acc2 = accuracy_score(y_test, xgboost_pred2) * 100\n",
-    "accuracies2.append(acc2)\n",
-    "# format with color coded if acc2 > 90\n",
-    "acc2 = f\"\\033[92m{acc2:.2f}\\033[00m\" if acc2 > 90 else f\"{acc2:.2f}\"\n",
-    "print(\"XGBoost Accuracy:\", acc2)"
    ]
   },
   {
@@ -758,8 +638,59 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "print(accuracies1)\n",
+    "# Define models for sensor1\n",
-    "print(accuracies2)"
+    "models_sensor1 = {\n",
+    "    # \"Random Forest\": RandomForestClassifier(),\n",
+    "    # \"Bagged Trees\": BaggingClassifier(estimator=DecisionTreeClassifier(), n_estimators=10),\n",
+    "    # \"Decision Tree\": DecisionTreeClassifier(),\n",
+    "    # \"KNN\": KNeighborsClassifier(),\n",
+    "    # \"LDA\": LinearDiscriminantAnalysis(),\n",
+    "    \"SVM\": SVC(),\n",
+    "    \"XGBoost\": XGBClassifier()\n",
+    "}\n",
+    "\n",
+    "results_sensor1 = []\n",
+    "for name, model in models_sensor1.items():\n",
+    "    res = train_and_evaluate_model(model, name, \"sensor1\", x_train1, y_train, x_test1, y_test)\n",
+    "    results_sensor1.append(res)\n",
+    "    print(f\"{name} on sensor1: Accuracy = {res['accuracy']:.2f}%\")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "models_sensor2 = {\n",
+    "    # \"Random Forest\": RandomForestClassifier(),\n",
+    "    # \"Bagged Trees\": BaggingClassifier(estimator=DecisionTreeClassifier(), n_estimators=10),\n",
+    "    # \"Decision Tree\": DecisionTreeClassifier(),\n",
+    "    # \"KNN\": KNeighborsClassifier(),\n",
+    "    # \"LDA\": LinearDiscriminantAnalysis(),\n",
+    "    \"SVM\": SVC(),\n",
+    "    \"XGBoost\": XGBClassifier()\n",
+    "}\n",
+    "\n",
+    "results_sensor2 = []\n",
+    "for name, model in models_sensor2.items():\n",
+    "    res = train_and_evaluate_model(model, name, \"sensor2\", x_train2, y_train, x_test2, y_test)\n",
+    "    results_sensor2.append(res)\n",
+    "    print(f\"{name} on sensor2: Accuracy = {res['accuracy']:.2f}%\")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "all_results = {\n",
+    "    \"sensor1\": results_sensor1,\n",
+    "    \"sensor2\": results_sensor2\n",
+    "}\n",
+    "\n",
+    "print(all_results)"
    ]
   },
   {
@@ -771,36 +702,48 @@
     "import numpy as np\n",
     "import matplotlib.pyplot as plt\n",
     "\n",
-    "models = [rf_model, bagged_model, dt_model, knn_model, lda_model, svm_model, xgboost_model]\n",
+    "def prepare_plot_data(results_dict):\n",
-    "model_names = [\"Random Forest\", \"Bagged Trees\", \"Decision Tree\", \"KNN\", \"LDA\", \"SVM\", \"XGBoost\"]\n",
+    "    # Gather unique model names\n",
+    "    models_set = {entry['model'] for sensor in results_dict.values() for entry in sensor}\n",
+    "    models = sorted(list(models_set))\n",
     "    \n",
-    "bar_width = 0.35  # Width of each bar\n",
+    "    # Create dictionaries mapping sensor -> accuracy list ordered by model name\n",
-    "index = np.arange(len(model_names))  # Index for the bars\n",
+    "    sensor_accuracies = {}\n",
+    "    for sensor, entries in results_dict.items():\n",
+    "        # Build a mapping: model -> accuracy for the given sensor\n",
+    "        mapping = {entry['model']: entry['accuracy'] for entry in entries}\n",
+    "        # Order the accuracies consistent with the sorted model names\n",
+    "        sensor_accuracies[sensor] = [mapping.get(model, 0) for model in models]\n",
     "    \n",
-    "# Plotting the bar graph\n",
+    "    return models, sensor_accuracies\n",
-    "plt.figure(figsize=(14, 8))\n",
     "\n",
-    "# Bar plot for Sensor 1\n",
+    "def plot_accuracies(models, sensor_accuracies):\n",
-    "plt.bar(index, accuracies1, width=bar_width, color='blue', label='Sensor 1')\n",
+    "    bar_width = 0.35\n",
+    "    x = np.arange(len(models))\n",
+    "    sensors = list(sensor_accuracies.keys())\n",
     "    \n",
-    "# Bar plot for Sensor 2\n",
+    "    plt.figure(figsize=(10, 6))\n",
-    "plt.bar(index + bar_width, accuracies2, width=bar_width, color='orange', label='Sensor 2')\n",
+    "    # Assume two sensors for plotting grouped bars\n",
+    "    plt.bar(x - bar_width/2, sensor_accuracies[sensors[0]], width=bar_width, color='blue', label=sensors[0])\n",
+    "    plt.bar(x + bar_width/2, sensor_accuracies[sensors[1]], width=bar_width, color='orange', label=sensors[1])\n",
     "    \n",
-    "# Add values on top of each bar\n",
+    "    # Add text labels on top of bars\n",
-    "for i, acc1, acc2 in zip(index, accuracies1, accuracies2):\n",
+    "    for i, (a1, a2) in enumerate(zip(sensor_accuracies[sensors[0]], sensor_accuracies[sensors[1]])):\n",
-    "    plt.text(i, acc1 + .1, f'{acc1:.2f}%', ha='center', va='bottom', color='black')\n",
+    "        plt.text(x[i] - bar_width/2, a1 + 0.1, f\"{a1:.2f}%\", ha='center', va='bottom', color='black')\n",
-    "    plt.text(i + bar_width, acc2 + 1, f'{acc2:.2f}%', ha='center', va='bottom', color='black')\n",
+    "        plt.text(x[i] + bar_width/2, a2 + 0.1, f\"{a2:.2f}%\", ha='center', va='bottom', color='black')\n",
     "    \n",
-    "# Customize the plot\n",
+    "    plt.xlabel('Model Name')\n",
-    "plt.xlabel('Model Name →')\n",
+    "    plt.ylabel('Accuracy (%)')\n",
-    "plt.ylabel('Accuracy →')\n",
+    "    plt.title('Accuracy of Classifiers for Each Sensor')\n",
-    "plt.title('Accuracy of classifiers for Sensors 1 and 2 with 513 features')\n",
+    "    plt.xticks(x, models)\n",
-    "plt.xticks(index + bar_width / 2, model_names)  # Set x-tick positions\n",
     "    plt.legend()\n",
-    "plt.ylim(0, 100)\n",
+    "    plt.ylim(0, 105)\n",
+    "    plt.tight_layout()\n",
+    "    plt.show()\n",
     "\n",
-    "# Show the plot\n",
+    "# Use the functions\n",
-    "plt.show()\n"
+    "models, sensor_accuracies = prepare_plot_data(all_results)\n",
+    "plot_accuracies(models, sensor_accuracies)\n"
    ]
   },
   {

latex/frontmatter/glossaries.tex

@@ -0,0 +1,78 @@
+% % A new command that enables us to enter bi-lingual (Slovene and English) terms
+% % syntax: \addterm[options]{label}{Slovene}{Slovene first use}{English}{Slovene
+% % description}
+% \newcommand{\addterm}[6][]{
+%   \newglossaryentry{#2}{
+%     name={#3 (angl.\ #5)},
+%     first={#4 (\emph{#5})},
+%     text={#3},
+%     sort={#3},
+%     description={#6},
+%     #1 % pass additional options to \newglossaryentry
+%   }
+% }
+
+% % A new command that enables us to enter (English) acronyms with bi-lingual
+% % (Slovene and English) long versions
+% % syntax: \addacronym[options]{label}{abbreviation}{Slovene long}{Slovene first
+% % use long}{English long}{Slovene description}
+% \newcommand{\addacronym}[7][]{
+%   % Create the main glossary entry with \newacronym
+%   % \newacronym[key-val list]{label}{abbrv}{long}
+%   \newacronym[
+%     name={#4 (angl.\ #6,\ #3)},
+%     first={\emph{#5} (angl.\ \emph{#6},\ \emph{#3})},
+%     sort={#4},
+%     description={#7},
+%     #1 % pass additional options to \newglossaryentry
+%     ]
+%     {#2}{#3}{#4}
+%   % Create a cross-reference from the abbreviation to the main glossary entry by
+%   % creating an auxiliary glossary entry (note: we set the label of this entry
+%   % to '<original label>_auxiliary' to avoid clashes)
+%   \newglossaryentry{#2_auxiliary}{
+%     name={#3},
+%     sort={#3},
+%     description={\makefirstuc{#6}},
+%     see=[See:]{#2}
+%   }
+% }
+
+% % Change the text of the cross-reference links to the Slovene long version.
+% \renewcommand*{\glsseeitemformat}[1]{\emph{\acrlong{#1}}.}
+
+% Define the Indonesian term and link it to the English term
+\newglossaryentry{jaringansaraf}{
+  name=Jaringan Saraf,
+  description={The Indonesian term for \gls{nn}}
+}
+% \newglossaryentry{pemelajaranmesin}{
+%   name=Pemelajaran Mesin,
+%   description={Lihat \gls{machinelearning}}
+% }
+
+% Define the English term and link it to its acronym
+\newglossaryentry{neuralnetwork}{
+  name=Neural Network,
+  description={A computational model inspired by the human brain, see \gls{nn}}
+}
+
+% \newglossaryentry{machinelearning}{
+%   name=Machine Learning,
+%   description={A program or system that trains a model from input data. The trained model can make useful predictions from new (never-before-seen) data drawn from the same distribution as the one used to train the model.}}
+% \newglossaryentry{pemelajaranmesin}{
+%     name={pemelajaran mesin (angl.\ #5)},
+%     first={pemelajaran mesin (\emph{machine learning})},
+%     text={pemelajaran mesin},
+%     sort={ },
+%     description={#6},
+%     #1 % pass additional options to \newglossaryentry
+% }
+\longnewglossaryentry{machinelearning}{name={machine learning}}
+{A program or system that trains a model from input data. The trained model can make useful predictions from new (never-before-seen) data drawn from the same distribution as the one used to train the model.}
+\newterm[see={machinelearning}]{pemelajaranmesin}
+% \newglossaryentry{pemelajaran mesin}{}
+% \addterm{machinelearning}{pemelajaran mesin}{pemelajaran mesin}{machine learning}{A program or system that trains a model from input data. The trained model can make useful predictions from new (never-before-seen) data drawn from the same distribution as the one used to train the model.}
+\newacronym
+ [description={statistical pattern recognition technique}]
+ {svm}{SVM}{support vector machine}

latex/main.tex

@@ -16,22 +16,19 @@
 \input{preamble/macros}
 
 \begin{document}
-
+\input{frontmatter/maketitle}
-\maketitle
+\input{frontmatter/maketitle_secondary}
 \frontmatter
-\input{frontmatter/approval}\clearpage
+% \input{frontmatter/approval}\clearpage
-\input{frontmatter/originality}\clearpage
+% \input{frontmatter/originality}\clearpage
-\input{frontmatter/acknowledgement}\clearpage
+% \input{frontmatter/acknowledgement}\clearpage
 \tableofcontents
 \clearpage
 \mainmatter
 \pagestyle{fancyplain}
-% Include content
-\include{content/abstract}
-\include{content/introduction}
 \include{chapters/01_introduction}
-\include{content/chapter2}
+\include{chapters/id/02_literature_review/index}
-\include{content/conclusion}
+\include{chapters/id/03_methodology/index}
 
 % Bibliography
 % \bibliographystyle{IEEEtran}

latex/thesis.cls

@@ -24,15 +24,14 @@
 \RequirePackage{svg}           % Allows including SVG images directly
 \RequirePackage{indentfirst}   % Makes first paragraph after headings indented
 \RequirePackage{float}         % Provides [H] option to force figure/table placement
-
+\RequirePackage[style=apa, backend=biber, language=indonesian]{biblatex}
 % Polyglossia set language
-+ \setdefaultlanguage[variant=indonesian]{malay}  % Proper Indonesian language setup
+\setdefaultlanguage[variant=indonesian]{malay}  % Proper Indonesian language setup
-+ \setotherlanguage{english}             % Enables English as secondary language
+\setotherlanguage{english}             % Enables English as secondary language
-
+\DefineBibliographyStrings{english}{%  % Customizes bibliography text
-+ \DefineBibliographyStrings{english}{%  % Customizes bibliography text
+  andothers={dkk\adddot},              % Changes "et al." to "dkk."
-+   andothers={dkk\adddot},              % Changes "et al." to "dkk."
+  pages={hlm\adddot},                  % Changes "pp." to "hlm."
-+   pages={hlm\adddot},                  % Changes "pp." to "hlm."
+}
-+ }
 
 % Conditionally load the watermark package and settings
 \if@draftmark
@@ -56,8 +55,6 @@
 \setsansfont{Arial}
 \setmonofont{Courier New}
 
-% Metadata commands
-\input{metadata}
 
 \newcommand{\setthesisinfo}[7]{%
   \renewcommand{\thesistitle}{#1}%
@@ -112,9 +109,6 @@
 
 % \titlespacing*{\chapter}{0pt}{-10pt}{20pt}
 
-% Redefine \maketitle
-\renewcommand{\maketitle}{\input{frontmatter/maketitle}}
-
 % Chapter & Section format
 \renewcommand{\cftchapfont}{\normalsize\MakeUppercase}
 % \renewcommand{\cftsecfont}{}