Grafik_Model.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/dayudyah20/Belajar-Coursera/blob/main/Grafik_Model.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Model pada Orang Normal**"
      ],
      "metadata": {
        "id": "Cref6i54lL7l"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "\n",
        "# Definisi fungsi untuk sistem persamaan\n",
        "def f(z, t, alpha, gamma, theta, delta, beta):\n",
        "    G, I = z\n",
        "    dGdt = alpha - gamma * G - theta * G * I  # Penurunan glukosa perlahan\n",
        "    dIdt = delta * G - beta * I * G           # Insulin naik sebagai respons glukosa\n",
        "    return np.array([dGdt, dIdt])\n",
        "\n",
        "\n",
        "# Metode Runge-Kutta orde 4\n",
        "def runge_kutta_4(f, z0, t, params):\n",
        "    n = len(t)\n",
        "    z = np.zeros((n, len(z0)))\n",
        "    z[0] = z0\n",
        "    for i in range(1, n):\n",
        "        h = t[i] - t[i-1]\n",
        "        k1 = f(z[i-1], t[i-1], *params)\n",
        "        k2 = f(z[i-1] + 0.5*h*k1, t[i-1] + 0.5*h, *params)\n",
        "        k3 = f(z[i-1] + 0.5*h*k2, t[i-1] + 0.5*h, *params)\n",
        "        k4 = f(z[i-1] + h*k3, t[i-1] + h, *params)\n",
        "        z[i] = z[i-1] + (h/6) * (k1 + 2*k2 + 2*k3 + k4)\n",
        "    return z\n",
        "\n",
        "\n",
        "# Parameter-parameter\n",
        "alpha = 6    # Penerimaan glukosa\n",
        "gamma = 0.005 # Penggunaan glukosa tanpa insulin\n",
        "theta = 0.001 # Pengaruh insulin pada glukosa\n",
        "delta = 0.013  # Sekresi insulin dari glukosa\n",
        "beta = 0.0003  # Clearance insulin\n",
        "\n",
        "\n",
        "# Waktu simulasi\n",
        "t = np.linspace(0, 200, 1000)\n",
        "\n",
        "\n",
        "# Kondisi awal [Glukosa (G), Insulin (I)]\n",
        "z0 = [90, 10]\n",
        "\n",
        "\n",
        "params = (alpha, gamma, theta, delta, beta)\n",
        "z_adjusted_slightly_higher_glucose = runge_kutta_4(f, z0, t, params)\n",
        "\n",
        "# Ekstrak Glukosa dan Insulin\n",
        "glucose = z_adjusted_slightly_higher_glucose[:, 0]  # Ambil nilai glukosa\n",
        "insulin = z_adjusted_slightly_higher_glucose[:, 1]  # Ambil nilai insulin\n",
        "\n",
        "# Konversi glukosa dari mg/dL ke mg/mL (membagi dengan 100)\n",
        "glucose_converted = glucose / 100  # Mengubah mg/dL ke mg/mL\n",
        "\n",
        "# Plot gabungan dengan dua sumbu y\n",
        "fig, ax1 = plt.subplots(figsize=(10, 6))\n",
        "\n",
        "# Sumbu Y kiri untuk Glukosa\n",
        "ax1.set_xlabel('Waktu (menit)')\n",
        "ax1.set_ylabel('Glukosa (mg/mL)', color='black', labelpad=15)\n",
        "ax1.plot(t, glucose_converted, label='Glukosa (mg/mL)', color='red', linewidth=2)\n",
        "ax1.tick_params(axis='y', labelcolor='black')\n",
        "ax1.set_ylim(0, max(glucose_converted) + 0.1)  # Mengatur batas atas sesuai dengan nilai glukosa\n",
        "\n",
        "# Sumbu Y kanan untuk Insulin\n",
        "ax2 = ax1.twinx()\n",
        "ax2.set_ylabel('Insulin (μU/mL)', color='black', labelpad=15)\n",
        "ax2.plot(t, insulin, label='Insulin (μU/mL)', color='blue', linewidth=2)\n",
        "ax2.tick_params(axis='y', labelcolor='black')\n",
        "ax2.set_ylim(0, max(insulin) + 10)\n",
        "\n",
        "# Tambahkan legenda\n",
        "fig.legend(loc='upper right', fontsize=10)\n",
        "\n",
        "# Judul dan grid\n",
        "fig.suptitle('Dinamika Glukosa dan Insulin Pada Orang Normal', fontsize=14)\n",
        "ax1.grid(alpha=0.3)\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 217
        },
        "id": "lCQZpEcdBGaY",
        "outputId": "7a4585d4-9bd1-4934-b607-777e8e97394c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Menghitung titik kesetimbangan berdasarkan formula yang diberikan\n",
        "G_eq = alpha * beta / (beta * gamma + delta * theta)\n",
        "I_eq = delta / beta\n",
        "\n",
        "# Menghitung nilai eigen berdasarkan formula yang diberikan\n",
        "lambda_1 = (-(beta * gamma) - (theta * delta)) / beta\n",
        "lambda_2 = -(alpha * beta**2) / (beta * gamma + delta * theta)\n",
        "\n",
        "# Menampilkan titik kesetimbangan dan nilai eigen\n",
        "print(f\"Titik Kesetimbangan: G = {G_eq:.2f}, I = {I_eq:.2f}\")\n",
        "print(f\"Nilai Eigen: \\u03bb_1 = {lambda_1:.2f}, \\u03bb_2 = {lambda_2:.2f}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iXAKTcJeBs8m",
        "outputId": "774d7067-bb5a-4289-edfb-102e1a946924"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Titik Kesetimbangan: G = 124.14, I = 43.33\n",
            "Nilai Eigen: λ_1 = -0.05, λ_2 = -0.04\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 217
        },
        "id": "578kjfgJk5og",
        "outputId": "2106738b-1f8f-42bd-8668-cf95cca56cf0"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "\n",
        "\n",
        "\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# Definisi fungsi untuk sistem persamaan\n",
        "def f(z, t, alpha, gamma, theta, delta, beta):\n",
        "    G, I = z\n",
        "    dGdt = alpha - gamma * G - theta * G * I  # Penurunan glukosa perlahan\n",
        "    dIdt = delta * G - beta * I * G           # Insulin naik sebagai respons glukosa\n",
        "    return np.array([dGdt, dIdt])\n",
        "\n",
        "# Metode Runge-Kutta orde 4\n",
        "def runge_kutta_4(f, z0, t, params):\n",
        "    n = len(t)\n",
        "    z = np.zeros((n, len(z0)))\n",
        "    z[0] = z0\n",
        "    for i in range(1, n):\n",
        "        h = t[i] - t[i-1]\n",
        "        k1 = f(z[i-1], t[i-1], *params)\n",
        "        k2 = f(z[i-1] + 0.5*h*k1, t[i-1] + 0.5*h, *params)\n",
        "        k3 = f(z[i-1] + 0.5*h*k2, t[i-1] + 0.5*h, *params)\n",
        "        k4 = f(z[i-1] + h*k3, t[i-1] + h, *params)\n",
        "        z[i] = z[i-1] + (h/6) * (k1 + 2*k2 + 2*k3 + k4)\n",
        "    return z\n",
        "\n",
        "# Parameter-parameter\n",
        "alpha = 6.5    # Penerimaan glukosa\n",
        "gamma = 0.0055 # Penggunaan glukosa tanpa insulin\n",
        "theta = 0.0012 # Pengaruh insulin pada glukosa\n",
        "delta = 0.013  # Sekresi insulin dari glukosa\n",
        "beta = 0.0003  # Clearance insulin\n",
        "\n",
        "# Waktu simulasi\n",
        "t = np.linspace(0, 500, 1000)\n",
        "\n",
        "# Kondisi awal [Glukosa (G), Insulin (I)]\n",
        "z0 = [90, 10]\n",
        "\n",
        "# Simulasi dengan parameter baru\n",
        "params = (alpha, gamma, theta, delta, beta)\n",
        "z_adjusted_slightly_higher_glucose = runge_kutta_4(f, z0, t, params)\n",
        "\n",
        "# Ekstrak Glukosa dan Insulin\n",
        "glucose = z_adjusted_slightly_higher_glucose[:, 0]  # Ambil nilai glukosa\n",
        "insulin = z_adjusted_slightly_higher_glucose[:, 1]  # Ambil nilai insulin\n",
        "\n",
        "# Konversi glukosa dari mg/dL ke mg/mL (membagi dengan 100)\n",
        "glucose_converted = glucose / 100  # Mengubah mg/dL ke mg/mL\n",
        "\n",
        "# Plot gabungan dengan dua sumbu y\n",
        "fig, ax1 = plt.subplots(figsize=(10, 6))\n",
        "\n",
        "# Sumbu Y kiri untuk Glukosa\n",
        "ax1.set_xlabel('Waktu (menit)')\n",
        "ax1.set_ylabel('Glukosa (mg/mL)', color='black', labelpad=15)\n",
        "ax1.plot(t, glucose_converted, label='Glukosa (mg/mL)', color='red', linewidth=2)\n",
        "ax1.tick_params(axis='y', labelcolor='black')\n",
        "ax1.set_ylim(0, max(glucose_converted) + 0.1)  # Mengatur batas atas sesuai dengan nilai glukosa\n",
        "\n",
        "# Sumbu Y kanan untuk Insulin\n",
        "ax2 = ax1.twinx()\n",
        "ax2.set_ylabel('Insulin (μU/mL)', color='black', labelpad=15)\n",
        "ax2.plot(t, insulin, label='Insulin (μU/mL)', color='blue', linewidth=2)\n",
        "ax2.tick_params(axis='y', labelcolor='black')\n",
        "ax2.set_ylim(0, max(insulin) + 10)\n",
        "\n",
        "# Tambahkan legenda\n",
        "fig.legend(loc='upper right', fontsize=10)\n",
        "\n",
        "# Judul dan grid\n",
        "fig.suptitle('Dinamika Glukosa dan Insulin Pada Orang Normal', fontsize=14)\n",
        "ax1.grid(alpha=0.3)\n",
        "\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Menghitung titik kesetimbangan berdasarkan formula yang diberikan\n",
        "G_eq = alpha * beta / (beta * gamma + delta * theta)\n",
        "I_eq = delta / beta\n",
        "\n",
        "# Menghitung nilai eigen berdasarkan formula yang diberikan\n",
        "lambda_1 = (-(beta * gamma) - (theta * delta)) / beta\n",
        "lambda_2 = -(alpha * beta**2) / (beta * gamma + delta * theta)\n",
        "\n",
        "# Menampilkan titik kesetimbangan dan nilai eigen\n",
        "print(f\"Titik Kesetimbangan: G = {G_eq:.2f}, I = {I_eq:.2f}\")\n",
        "print(f\"Nilai Eigen: \\u03bb_1 = {lambda_1:.2f}, \\u03bb_2 = {lambda_2:.2f}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ksi1TzwJB9hD",
        "outputId": "cbca15bc-fd74-4a11-fd95-642f218d46a2"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Titik Kesetimbangan: G = 113.04, I = 43.33\n",
            "Nilai Eigen: λ_1 = -0.06, λ_2 = -0.03\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **MODEL PADA DM1 TK1 = 0**"
      ],
      "metadata": {
        "id": "u8SgoL4blukm"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# Parameter-parameter\n",
        "alpha = 10.7  # Penerimaan glukosa\n",
        "gamma = 0.069 # Penggunaan glukosa\n",
        "theta = 0.0002  # Pengaruh insulin\n",
        "sigma = 0.0006  # Efektivitas insulin\n",
        "epsilon = 0.001  # Tingkat kejenuhan\n",
        "beta = 0.0001  # Laju clearance insulin\n",
        "\n",
        "# Definisi sistem persamaan glukosa dan insulin\n",
        "def glucose_insulin_system(z, t, alpha, gamma, theta, sigma, beta, epsilon):\n",
        "    G, I = z\n",
        "    # Perubahan kadar glukosa (mg/dL/min)\n",
        "    dGdt = alpha - gamma * G - theta * G * I\n",
        "    # Perubahan kadar insulin (μU/ml/min)\n",
        "    dIdt = sigma * I / (1 + epsilon * I) - beta * I * G\n",
        "    return np.array([dGdt, dIdt])\n",
        "\n",
        "# Fungsi untuk metode Runge-Kutta orde 4\n",
        "def runge_kutta_4(f, z0, t, params):\n",
        "    n = len(t)\n",
        "    z = np.zeros((n, len(z0)))\n",
        "    z[0] = z0\n",
        "    for i in range(1, n):\n",
        "        h = t[i] - t[i-1]\n",
        "        k1 = f(z[i-1], t[i-1], *params)\n",
        "        k2 = f(z[i-1] + 0.5*h*k1, t[i-1] + 0.5*h, *params)\n",
        "        k3 = f(z[i-1] + 0.5*h*k2, t[i-1] + 0.5*h, *params)\n",
        "        k4 = f(z[i-1] + h*k3, t[i-1] + h, *params)\n",
        "        z[i] = z[i-1] + (h/6) * (k1 + 2*k2 + 2*k3 + k4)\n",
        "    return z\n",
        "\n",
        "# Kondisi awal\n",
        "z0 = np.array([110.0, 20.0])  # Glukosa mulai dari 110 mg/dL, Insulin mulai dari 20 μU/ml\n",
        "\n",
        "# Rentang waktu simulasi\n",
        "t = np.linspace(0, 300, 3000)  # Hingga 200 menit\n",
        "\n",
        "# Simulasi numerik\n",
        "params = (alpha, gamma, theta, sigma, beta, epsilon)\n",
        "solution = runge_kutta_4(glucose_insulin_system, z0, t, params)\n",
        "\n",
        "# Pisahkan hasil untuk glukosa dan insulin\n",
        "glucose = solution[:, 0]\n",
        "insulin = solution[:, 1]\n",
        "\n",
        "# Konversi glukosa dari mg/dL ke mg/mL (membagi dengan 100)\n",
        "glucose_converted = glucose / 100  # Mengubah mg/dL ke mg/mL\n",
        "\n",
        "# Plot gabungan dengan dua sumbu y\n",
        "fig, ax1 = plt.subplots(figsize=(10, 6))\n",
        "\n",
        "# Sumbu Y kiri untuk Glukosa\n",
        "ax1.set_xlabel('Waktu (menit)')\n",
        "ax1.set_ylabel('Glukosa (mg/mL)', color='black', labelpad=15)\n",
        "ax1.plot(t, glucose_converted, label='Glukosa (mg/mL)', color='red', linewidth=2)\n",
        "ax1.tick_params(axis='y', labelcolor='black')\n",
        "ax1.set_ylim(0, max(glucose_converted) + 0.1)  # Mengatur batas atas sesuai dengan nilai glukosa\n",
        "\n",
        "# Sumbu Y kanan untuk Insulin\n",
        "ax2 = ax1.twinx()\n",
        "ax2.set_ylabel('Insulin (μU/mL)', color='black', labelpad=15)\n",
        "ax2.plot(t, insulin, label='Insulin (μU/mL)', color='blue', linewidth=2)\n",
        "ax2.tick_params(axis='y', labelcolor='black')\n",
        "ax2.set_ylim(0, max(insulin) + 10)\n",
        "\n",
        "\n",
        "# Tambahkan legenda\n",
        "fig.legend(loc='upper right', fontsize=10)\n",
        "\n",
        "# Judul dan grid\n",
        "fig.suptitle('Dinamika Glukosa dan Insulin Pada Penderita DM1 TK 1', fontsize=14)\n",
        "ax1.grid(alpha=0.3)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 217
        },
        "id": "44uHALwcltU-",
        "outputId": "5bbbdd5c-b687-44ce-a493-833f8279941e"
      },
      "execution_count": 88,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Titik Kritis 1\n",
        "G_kritis = alpha / gamma\n",
        "I_kritis = 0  # Insulin pada titik kritis adalah 0\n",
        "\n",
        "# Menampilkan titik kritis pertama\n",
        "print(\"Titik Kritis 1:\")\n",
        "print(\"G (Glukosa) =\", G_kritis)\n",
        "print(\"I (Insulin) =\", I_kritis)\n",
        "\n",
        "# Hitung Nilai Eigen\n",
        "lambda_1 = -gamma\n",
        "lambda_2 = (sigma * gamma - beta * alpha) / gamma\n",
        "\n",
        "print(\"\\nNilai Eigen 1 (λ_1):\", lambda_1)\n",
        "print(\"Nilai Eigen 2 (λ_2):\", lambda_2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gq_3RH9fl4TI",
        "outputId": "3f139a9a-c83d-4a28-fa8b-31d5e5a7ee96"
      },
      "execution_count": 89,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Titik Kritis 1:\n",
            "G (Glukosa) = 155.0724637681159\n",
            "I (Insulin) = 0\n",
            "\n",
            "Nilai Eigen 1 (λ_1): -0.069\n",
            "Nilai Eigen 2 (λ_2): -0.014907246376811593\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "\n",
        "# Parameter-parameter\n",
        "alpha = 10  # Penerimaan glukosa\n",
        "gamma = 0.05  #Penggunaan glukosa tanpa keterlibatan insulin\n",
        "theta = 0.0001  #Penggunaan glukosa tergantung pada insulin\n",
        "sigma = 0.0002  # Efektivitas insulin\n",
        "epsilon = 0.002  # Tingkat kejenuhan\n",
        "beta = 0.0001  # Laju clearance insulin\n",
        "\n",
        "\n",
        "# Definisi sistem persamaan glukosa dan insulin\n",
        "def glucose_insulin_system(z, t, alpha, gamma, theta, sigma, beta, epsilon):\n",
        "    G, I = z\n",
        "    dGdt = alpha - gamma * G - theta * G * I  # Perubahan kadar glukosa\n",
        "    dIdt = sigma * I / (1 + epsilon * I) - beta * I * G  # Perubahan kadar insulin\n",
        "    return np.array([dGdt, dIdt])\n",
        "\n",
        "\n",
        "# Fungsi untuk metode Runge-Kutta orde 4\n",
        "def runge_kutta_4(f, z0, t, params):\n",
        "    n = len(t)\n",
        "    z = np.zeros((n, len(z0)))\n",
        "    z[0] = z0\n",
        "    for i in range(1, n):\n",
        "        h = t[i] - t[i-1]\n",
        "        k1 = f(z[i-1], t[i-1], *params)\n",
        "        k2 = f(z[i-1] + 0.5*h*k1, t[i-1] + 0.5*h, *params)\n",
        "        k3 = f(z[i-1] + 0.5*h*k2, t[i-1] + 0.5*h, *params)\n",
        "        k4 = f(z[i-1] + h*k3, t[i-1] + h, *params)\n",
        "        z[i] = z[i-1] + (h/6) * (k1 + 2*k2 + 2*k3 + k4)\n",
        "    return z\n",
        "\n",
        "\n",
        "# Kondisi awal\n",
        "z0 = np.array([140.0, 20.0])\n",
        "\n",
        "\n",
        "# Rentang waktu simulasi\n",
        "t = np.linspace(0, 200, 2000)\n",
        "\n",
        "\n",
        "# Simulasi numerik\n",
        "params = (alpha, gamma, theta, sigma, beta, epsilon)\n",
        "solution = runge_kutta_4(glucose_insulin_system, z0, t, params)\n",
        "\n",
        "\n",
        "# Pisahkan hasil untuk glukosa dan insulin\n",
        "glucose = solution[:, 0]\n",
        "insulin = solution[:, 1]\n",
        "\n",
        "\n",
        "# Plot gabungan dengan dua sumbu y (Glukosa di kiri, Insulin di kanan)\n",
        "fig, ax1 = plt.subplots(figsize=(10, 6))\n",
        "\n",
        "\n",
        "# Sumbu Y kiri untuk Glukosa\n",
        "ax1.set_xlabel('Waktu (menit)')\n",
        "ax1.set_ylabel('Glukosa (mg/dL)', color='black', labelpad=15)\n",
        "ax1.plot(t, glucose, label='Glukosa (mg/dL)', color='red', linewidth=2)\n",
        "ax1.tick_params(axis='y', labelcolor='black')\n",
        "ax1.set_ylim(0, max(glucose) + 10)\n",
        "\n",
        "\n",
        "# Sumbu Y kanan untuk Insulin\n",
        "ax2 = ax1.twinx()\n",
        "ax2.set_ylabel('Insulin (μU/mL)', color='black', labelpad=15)\n",
        "ax2.plot(t, insulin, label='Insulin (μU/mL)', color='blue', linewidth=2)\n",
        "ax2.tick_params(axis='y', labelcolor='black')\n",
        "ax2.set_ylim(0, 30)\n",
        "ax2.set_yticks(range(0, 31, 5))\n",
        "\n",
        "\n",
        "# Tambahkan garis vertikal di sumbu Y kanan\n",
        "ax2.spines['right'].set_visible(True)\n",
        "ax2.spines['right'].set_color('black')\n",
        "\n",
        "\n",
        "# Tambahkan legenda\n",
        "fig.legend(loc=\"upper right\", bbox_to_anchor=(0.83, 0.83))\n",
        "\n",
        "\n",
        "# Tambahkan judul\n",
        "fig.suptitle('Dinamika Glukosa dan Insulin Pada Penderita DM1 Saat TK1', fontsize=14)\n",
        "\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 221
        },
        "id": "jqKhrTM0by0A",
        "outputId": "a3f1b873-669d-41d7-dbb0-70761641e827"
      },
      "execution_count": 100,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Titik Kritis 1\n",
        "G_kritis = alpha / gamma\n",
        "I_kritis = 0  # Insulin pada titik kritis adalah 0\n",
        "\n",
        "# Menampilkan titik kritis pertama\n",
        "print(\"Titik Kritis 1:\")\n",
        "print(\"G (Glukosa) =\", G_kritis)\n",
        "print(\"I (Insulin) =\", I_kritis)\n",
        "\n",
        "# Hitung Nilai Eigen\n",
        "lambda_1 = -gamma\n",
        "lambda_2 = (sigma * gamma - beta * alpha) / gamma\n",
        "\n",
        "print(\"\\nNilai Eigen 1 (λ_1):\", lambda_1)\n",
        "print(\"Nilai Eigen 2 (λ_2):\", lambda_2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bvy_xMGxb9GD",
        "outputId": "f3aa2a86-f7a8-4c83-8a1d-c47382981457"
      },
      "execution_count": 101,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Titik Kritis 1:\n",
            "G (Glukosa) = 200.0\n",
            "I (Insulin) = 0\n",
            "\n",
            "Nilai Eigen 1 (λ_1): -0.05\n",
            "Nilai Eigen 2 (λ_2): -0.019799999999999998\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "MODEL DM1 INJEKSI INSULIN"
      ],
      "metadata": {
        "id": "FnIbEayzmrGu"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# Definisi sistem persamaan glukosa dan insulin\n",
        "def glucose_insulin_system(z, t, alpha, gamma, theta, sigma, beta, epsilon):\n",
        "    G, I = z\n",
        "    dGdt = alpha - gamma * G - theta * G * I  # Perubahan kadar glukosa (mg/dL/min)\n",
        "    dIdt = sigma * I / (1 + epsilon * I) - beta * I * G  # Perubahan kadar insulin (μU/mL/min)\n",
        "    return np.array([dGdt, dIdt])\n",
        "\n",
        "# Metode Runge-Kutta orde 4 untuk menyelesaikan sistem persamaan diferensial\n",
        "def runge_kutta_4(f, z0, t, params):\n",
        "    n = len(t)\n",
        "    z = np.zeros((n, len(z0)))\n",
        "    z[0] = z0\n",
        "    for i in range(1, n):\n",
        "        h = t[i] - t[i-1]\n",
        "        k1 = f(z[i-1], t[i-1], *params)\n",
        "        k2 = f(z[i-1] + 0.5*h*k1, t[i-1] + 0.5*h, *params)\n",
        "        k3 = f(z[i-1] + 0.5*h*k2, t[i-1] + 0.5*h, *params)\n",
        "        k4 = f(z[i-1] + h*k3, t[i-1] + h, *params)\n",
        "        z[i] = z[i-1] + (h/6) * (k1 + 2*k2 + 2*k3 + k4)\n",
        "    return z\n",
        "\n",
        "# Parameter-Parameter\n",
        "alpha = 13 # Penerimaan glukosa\n",
        "gamma = 0.075    # Penggunaan glukosa tanpa keterlibatan insulin\n",
        "theta = 0.0005 # Penggunaan glukosa tergantung pada insulin\n",
        "sigma = 1.85       # Efektivitas atau efisiensi dari insulin yang diberikan melalui injeksi insulin\n",
        "beta = 0.0005   # Laju clearance insulin\n",
        "epsilon = 0.7   # Tingkat kejenuhan dalam respon insulin terhadap glukosa\n",
        "\n",
        "# Rentang waktu simulasi\n",
        "t = np.linspace(0, 200, 3000)\n",
        "\n",
        "# Kondisi awal\n",
        "z0 = np.array([190.0, 20.0])  # Glukosa (mg/dL) dan Insulin (μU/mL)\n",
        "\n",
        "# Simulasi\n",
        "params = (alpha, gamma, theta, sigma, beta, epsilon)\n",
        "solution = runge_kutta_4(glucose_insulin_system, z0, t, params)\n",
        "\n",
        "# Pisahkan hasil\n",
        "glucose = solution[:, 0]\n",
        "insulin = solution[:, 1]\n",
        "\n",
        "# Konversi glukosa dari mg/dL ke mg/mL\n",
        "glucose_converted = glucose / 100\n",
        "\n",
        "# Plot gabungan dengan dua sumbu y\n",
        "fig, ax1 = plt.subplots(figsize=(10, 6))\n",
        "\n",
        "# Sumbu Y kiri untuk Glukosa\n",
        "ax1.set_xlabel('Waktu (menit)')\n",
        "ax1.set_ylabel('Glukosa (mg/mL)', color='black', labelpad=15)\n",
        "ax1.plot(t, glucose_converted, label='Glukosa (mg/mL)', color='red', linewidth=2)\n",
        "ax1.tick_params(axis='y', labelcolor='black')\n",
        "ax1.set_ylim(0, max(glucose_converted) + 0.1)\n",
        "\n",
        "# Tambahkan grid\n",
        "ax1.grid(True, linestyle='--', alpha=0.7)\n",
        "\n",
        "# Sumbu Y kanan untuk Insulin\n",
        "ax2 = ax1.twinx()\n",
        "ax2.set_ylabel('Insulin (μU/mL)', color='black', labelpad=15)\n",
        "ax2.plot(t, insulin, label='Insulin (μU/mL)', color='blue', linewidth=2)\n",
        "ax2.tick_params(axis='y', labelcolor='black')\n",
        "ax2.set_ylim(0, max(insulin) + 10)\n",
        "\n",
        "# Tambahkan garis vertikal di sumbu Y kanan\n",
        "ax2.spines['right'].set_visible(True)\n",
        "ax2.spines['right'].set_color('black')\n",
        "\n",
        "# Tambahkan legenda\n",
        "fig.legend(loc=\"upper right\", bbox_to_anchor=(0.85, 0.85))\n",
        "\n",
        "# Tambahkan judul\n",
        "fig.suptitle('Dinamika Glukosa dan Insulin Penderita DM1 Pada Saat Diinjeksi Insulin', fontsize=14)\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 220
        },
        "id": "lCxaZxFePy7W",
        "outputId": "1e4e3a3f-5c7f-4060-c936-2d8b80d8d537"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import math\n",
        "\n",
        "G_kritis_2 = (alpha * beta * epsilon - theta * sigma) / (beta * (gamma * epsilon - theta))\n",
        "I_kritis_2 = (-alpha * beta + gamma * sigma) / (alpha * beta * epsilon - theta * sigma)\n",
        "\n",
        "# Menampilkan Titik Kritis Kedua\n",
        "print(\"Titik Kritis 2:\")\n",
        "print(\"G (Glukosa) =\", G_kritis_2)\n",
        "print(\"I (Insulin) =\", I_kritis_2)\n",
        "\n",
        "# Rumus bagian dalam akar kuadrat\n",
        "term_inside_sqrt = (gamma * sigma * (alpha * beta * epsilon - theta * sigma) * (epsilon * gamma - theta) ** 2 +\n",
        "                    theta * sigma * (-alpha * beta + gamma * sigma) * (epsilon * gamma - theta) ** 2 -\n",
        "                    (alpha * beta * epsilon - theta * sigma) ** 3 +\n",
        "                    sigma * (alpha * beta * epsilon - theta * sigma) ** 2 * (epsilon * gamma - theta))\n",
        "\n",
        "# Menghitung λ₁\n",
        "lambda_1 = -((gamma * sigma * (alpha * beta * epsilon - theta * sigma) * (epsilon * gamma - theta) ** 2 +\n",
        "              theta * sigma * (-alpha * beta + gamma * sigma) * (epsilon * gamma - theta) ** 2 -\n",
        "              (alpha * beta * epsilon - theta * sigma) ** 3 +\n",
        "              sigma * (alpha * beta * epsilon - theta * sigma) ** 2 * (epsilon * gamma - theta)) /\n",
        "             (2 * sigma * (epsilon * gamma - theta) ** 2 * (alpha * beta * epsilon - sigma * theta))) + \\\n",
        "           math.sqrt((term_inside_sqrt) ** 2 -\n",
        "                     4 * sigma * (alpha * beta * epsilon - theta * sigma) ** 3 * (epsilon * gamma - theta) ** 3 * (-alpha * beta + gamma * sigma))\n",
        "\n",
        "# Menghitung λ₂\n",
        "lambda_2 = -((gamma * sigma * (alpha * beta * epsilon - theta * sigma) * (epsilon * gamma - theta) ** 2 +\n",
        "              theta * sigma * (-alpha * beta + gamma * sigma) * (epsilon * gamma - theta) ** 2 -\n",
        "              (alpha * beta * epsilon - theta * sigma) ** 3 +\n",
        "              sigma * (alpha * beta * epsilon - theta * sigma) ** 2 * (epsilon * gamma - theta)) /\n",
        "             (2 * sigma * (epsilon * gamma - theta) ** 2 * (alpha * beta * epsilon - sigma * theta))) - \\\n",
        "           math.sqrt((term_inside_sqrt) ** 2 -\n",
        "                     4 * sigma * (alpha * beta * epsilon - theta * sigma) ** 3 * (epsilon * gamma - theta) ** 3 * (-alpha * beta + gamma * sigma))\n",
        "\n",
        "# Menampilkan hasil\n",
        "print(\"Nilai Lambda 1 (λ₁):\", lambda_1)\n",
        "print(\"Nilai Lambda 2 (λ₂):\", lambda_2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jNqeCgEkQFtK",
        "outputId": "c1ce420f-4e5c-42a8-b251-bab537686acc"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Titik Kritis 2:\n",
            "G (Glukosa) = 139.42307692307693\n",
            "I (Insulin) = 36.48275862068966\n",
            "Nilai Lambda 1 (λ₁): -0.08016164930317198\n",
            "Nilai Lambda 2 (λ₂): -0.08016440436348186\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# Definisi sistem persamaan glukosa dan insulin\n",
        "def glucose_insulin_system(z, t, alpha, gamma, theta, sigma, beta, epsilon):\n",
        "    G, I = z\n",
        "    dGdt = alpha - gamma * G - theta * G * I  # Perubahan kadar glukosa (mg/dL/min)\n",
        "    dIdt = sigma * I / (1 + epsilon * I) - beta * I * G  # Perubahan kadar insulin (μU/mL/min)\n",
        "    return np.array([dGdt, dIdt])\n",
        "\n",
        "# Metode Runge-Kutta orde 4 untuk menyelesaikan sistem persamaan diferensial\n",
        "def runge_kutta_4(f, z0, t, params):\n",
        "    n = len(t)\n",
        "    z = np.zeros((n, len(z0)))\n",
        "    z[0] = z0\n",
        "    for i in range(1, n):\n",
        "        h = t[i] - t[i-1]\n",
        "        k1 = f(z[i-1], t[i-1], *params)\n",
        "        k2 = f(z[i-1] + 0.5*h*k1, t[i-1] + 0.5*h, *params)\n",
        "        k3 = f(z[i-1] + 0.5*h*k2, t[i-1] + 0.5*h, *params)\n",
        "        k4 = f(z[i-1] + h*k3, t[i-1] + h, *params)\n",
        "        z[i] = z[i-1] + (h/6) * (k1 + 2*k2 + 2*k3 + k4)\n",
        "    return z\n",
        "\n",
        "# Parameter-Parameter\n",
        "alpha = 13      #Penerimaan glukosa\n",
        "gamma = 0.075   #Penggunaan glukosa tanpa keterlibatan insulin\n",
        "theta = 0.0005  #Penggunaan glukosa tergantung pada insulin\n",
        "sigma = 1.6     #Efektivitas atau efisiensi dari insulin yang diberikan melalui injeksi insulin\n",
        "beta = 0.0004   #Laju clearance insulin\n",
        "epsilon = 0.9   #Tingkat kejenuhan dalam respon insulin terhadap glukosa\n",
        "\n",
        "# Rentang waktu simulasi\n",
        "t = np.linspace(0, 200, 3000)\n",
        "\n",
        "# Kondisi awal\n",
        "z0 = np.array([190.0, 20.0])  # Glukosa (mg/dL) dan Insulin (μU/mL)\n",
        "\n",
        "# Simulasi\n",
        "params = (alpha, gamma, theta, sigma, beta, epsilon)\n",
        "solution = runge_kutta_4(glucose_insulin_system, z0, t, params)\n",
        "\n",
        "# Pisahkan hasil\n",
        "glucose = solution[:, 0]\n",
        "insulin = solution[:, 1]\n",
        "\n",
        "# Konversi glukosa dari mg/dL ke mg/mL (membagi dengan 100)\n",
        "glucose_converted = glucose / 100  # Mengubah mg/dL ke mg/mL\n",
        "\n",
        "# Plot gabungan dengan dua sumbu y\n",
        "fig, ax1 = plt.subplots(figsize=(10, 6))\n",
        "\n",
        "# Sumbu Y kiri untuk Glukosa\n",
        "ax1.set_xlabel('Waktu (menit)')\n",
        "ax1.set_ylabel('Glukosa (mg/mL)', color='black', labelpad=15)\n",
        "ax1.plot(t, glucose_converted, label='Glukosa (mg/mL)', color='red', linewidth=2)\n",
        "ax1.tick_params(axis='y', labelcolor='black')\n",
        "ax1.set_ylim(0, max(glucose_converted) + 0.1)  # Mengatur batas atas sesuai dengan nilai glukosa\n",
        "\n",
        "# Sumbu Y kanan untuk Insulin\n",
        "ax2 = ax1.twinx()\n",
        "ax2.set_ylabel('Insulin (μU/mL)', color='black', labelpad=15)\n",
        "ax2.plot(t, insulin, label='Insulin (μU/mL)', color='blue', linewidth=2)\n",
        "ax2.tick_params(axis='y', labelcolor='black')\n",
        "ax2.set_ylim(0, max(insulin) + 10)\n",
        "\n",
        "# Tambahkan garis vertikal di sumbu Y kanan\n",
        "ax2.spines['right'].set_visible(True)\n",
        "ax2.spines['right'].set_color('black')\n",
        "\n",
        "# Tambahkan legenda\n",
        "fig.legend(loc=\"upper right\", bbox_to_anchor=(0.85, 0.85))\n",
        "\n",
        "# Tambahkan judul\n",
        "fig.suptitle('Dinamika Glukosa dan Insulin Penderita DM1 Pada Saat Diinjeksi Insulin', fontsize=14)\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 602
        },
        "id": "92ZhbKEhnBVa",
        "outputId": "d8898210-264d-476d-fa70-0cab45845cdf"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import math\n",
        "\n",
        "G_kritis_2 = (alpha * beta * epsilon - theta * sigma) / (beta * (gamma * epsilon - theta))\n",
        "I_kritis_2 = (-alpha * beta + gamma * sigma) / (alpha * beta * epsilon - theta * sigma)\n",
        "\n",
        "# Menampilkan Titik Kritis Kedua\n",
        "print(\"Titik Kritis 2:\")\n",
        "print(\"G (Glukosa) =\", G_kritis_2)\n",
        "print(\"I (Insulin) =\", I_kritis_2)\n",
        "\n",
        "# Rumus bagian dalam akar kuadrat\n",
        "term_inside_sqrt = (gamma * sigma * (alpha * beta * epsilon - theta * sigma) * (epsilon * gamma - theta) ** 2 +\n",
        "                    theta * sigma * (-alpha * beta + gamma * sigma) * (epsilon * gamma - theta) ** 2 -\n",
        "                    (alpha * beta * epsilon - theta * sigma) ** 3 +\n",
        "                    sigma * (alpha * beta * epsilon - theta * sigma) ** 2 * (epsilon * gamma - theta))\n",
        "\n",
        "# Menghitung λ₁\n",
        "lambda_1 = -((gamma * sigma * (alpha * beta * epsilon - theta * sigma) * (epsilon * gamma - theta) ** 2 +\n",
        "              theta * sigma * (-alpha * beta + gamma * sigma) * (epsilon * gamma - theta) ** 2 -\n",
        "              (alpha * beta * epsilon - theta * sigma) ** 3 +\n",
        "              sigma * (alpha * beta * epsilon - theta * sigma) ** 2 * (epsilon * gamma - theta)) /\n",
        "             (2 * sigma * (epsilon * gamma - theta) ** 2 * (alpha * beta * epsilon - sigma * theta))) + \\\n",
        "           math.sqrt((term_inside_sqrt) ** 2 -\n",
        "                     4 * sigma * (alpha * beta * epsilon - theta * sigma) ** 3 * (epsilon * gamma - theta) ** 3 * (-alpha * beta + gamma * sigma))\n",
        "\n",
        "# Menghitung λ₂\n",
        "lambda_2 = -((gamma * sigma * (alpha * beta * epsilon - theta * sigma) * (epsilon * gamma - theta) ** 2 +\n",
        "              theta * sigma * (-alpha * beta + gamma * sigma) * (epsilon * gamma - theta) ** 2 -\n",
        "              (alpha * beta * epsilon - theta * sigma) ** 3 +\n",
        "              sigma * (alpha * beta * epsilon - theta * sigma) ** 2 * (epsilon * gamma - theta)) /\n",
        "             (2 * sigma * (epsilon * gamma - theta) ** 2 * (alpha * beta * epsilon - sigma * theta))) - \\\n",
        "           math.sqrt((term_inside_sqrt) ** 2 -\n",
        "                     4 * sigma * (alpha * beta * epsilon - theta * sigma) ** 3 * (epsilon * gamma - theta) ** 3 * (-alpha * beta + gamma * sigma))\n",
        "\n",
        "# Menampilkan hasil\n",
        "print(\"Nilai Lambda 1 (λ₁):\", lambda_1)\n",
        "print(\"Nilai Lambda 2 (λ₂):\", lambda_2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "poXEjgFwl-h1",
        "outputId": "9186930c-de9e-4fea-978d-141f357a8a3e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Titik Kritis 2:\n",
            "G (Glukosa) = 144.7761194029851\n",
            "I (Insulin) = 29.587628865979372\n",
            "Nilai Lambda 1 (λ₁): -0.07280223859140554\n",
            "Nilai Lambda 2 (λ₂): -0.07280601112783945\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "MODEL MENGGGUNAKAN NILAI DARI **JURNAL**"
      ],
      "metadata": {
        "id": "LNPn34KEpreO"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "ORANG NORMAL"
      ],
      "metadata": {
        "id": "J8z82PlMqcUC"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# Definisi fungsi untuk sistem persamaan\n",
        "def f(z, t, alpha, gamma, theta, delta, beta):\n",
        "    G, I = z\n",
        "    dGdt = alpha - gamma * G - theta * G * I  # Penurunan glukosa perlahan\n",
        "    dIdt = delta * G - beta * I * G           # Insulin naik sebagai respons glukosa\n",
        "    return np.array([dGdt, dIdt])\n",
        "\n",
        "# Metode Runge-Kutta orde 4\n",
        "def runge_kutta_4(f, z0, t, params):\n",
        "    n = len(t)\n",
        "    z = np.zeros((n, len(z0)))\n",
        "    z[0] = z0\n",
        "    for i in range(1, n):\n",
        "        h = t[i] - t[i-1]\n",
        "        k1 = f(z[i-1], t[i-1], *params)\n",
        "        k2 = f(z[i-1] + 0.5*h*k1, t[i-1] + 0.5*h, *params)\n",
        "        k3 = f(z[i-1] + 0.5*h*k2, t[i-1] + 0.5*h, *params)\n",
        "        k4 = f(z[i-1] + h*k3, t[i-1] + h, *params)\n",
        "        z[i] = z[i-1] + (h/6) * (k1 + 2*k2 + 2*k3 + k4)\n",
        "    return z\n",
        "\n",
        "# Parameter-parameter\n",
        "alpha = 4.5    # Penerimaan glukosa\n",
        "gamma = 0.0002 # Penggunaan glukosa tanpa insulin\n",
        "theta = 0.00075919 # Pengaruh insulin pada glukosa\n",
        "delta = 0.2298 # Sekresi insulin dari glukosa\n",
        "beta = 1500  # Clearance insulin\n",
        "\n",
        "# Waktu simulasi\n",
        "t = np.linspace(0, 500, 1000)\n",
        "\n",
        "# Kondisi awal [Glukosa (G), Insulin (I)]\n",
        "z0 = [100, 10]\n",
        "\n",
        "# Simulasi dengan parameter baru\n",
        "params = (alpha, gamma, theta, delta, beta)\n",
        "z_adjusted_slightly_higher_glucose = runge_kutta_4(f, z0, t, params)\n",
        "\n",
        "# Ekstrak Glukosa dan Insulin\n",
        "glucose = z_adjusted_slightly_higher_glucose[:, 0]  # Ambil nilai glukosa\n",
        "insulin = z_adjusted_slightly_higher_glucose[:, 1]  # Ambil nilai insulin\n",
        "\n",
        "# Konversi glukosa dari mg/dL ke mg/mL (membagi dengan 100)\n",
        "glucose_converted = glucose / 100  # Mengubah mg/dL ke mg/mL\n",
        "\n",
        "# Plot gabungan dengan dua sumbu y\n",
        "fig, ax1 = plt.subplots(figsize=(10, 6))\n",
        "\n",
        "# Sumbu Y kiri untuk Glukosa\n",
        "ax1.set_xlabel('Waktu (menit)')\n",
        "ax1.set_ylabel('Glukosa (mg/mL)', color='black', labelpad=15)\n",
        "ax1.plot(t, glucose_converted, label='Glukosa (mg/mL)', color='red', linewidth=2)\n",
        "ax1.tick_params(axis='y', labelcolor='black')\n",
        "ax1.set_ylim(0, max(glucose_converted) + 0.1)  # Mengatur batas atas sesuai dengan nilai glukosa\n",
        "\n",
        "# Sumbu Y kanan untuk Insulin\n",
        "ax2 = ax1.twinx()\n",
        "ax2.set_ylabel('Insulin (μU/mL)', color='black', labelpad=15)\n",
        "ax2.plot(t, insulin, label='Insulin (μU/mL)', color='blue', linewidth=2)\n",
        "ax2.tick_params(axis='y', labelcolor='black')\n",
        "ax2.set_ylim(0, max(insulin) + 10)\n",
        "\n",
        "# Tambahkan legenda\n",
        "fig.legend(loc='upper right', fontsize=10)\n",
        "\n",
        "# Judul dan grid\n",
        "fig.suptitle('Dinamika Glukosa dan Insulin Pada Orang Normal', fontsize=14)\n",
        "ax1.grid(alpha=0.3)\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 711
        },
        "id": "ItA9Dm2OnC0X",
        "outputId": "c727773c-7139-4de8-a430-cc07d67595eb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-13-b36fc30154e9>:7: RuntimeWarning: overflow encountered in scalar multiply\n",
            "  dGdt = alpha - gamma * G - theta * G * I  # Penurunan glukosa perlahan\n",
            "<ipython-input-13-b36fc30154e9>:8: RuntimeWarning: overflow encountered in scalar multiply\n",
            "  dIdt = delta * G - beta * I * G           # Insulin naik sebagai respons glukosa\n",
            "<ipython-input-13-b36fc30154e9>:7: RuntimeWarning: invalid value encountered in scalar subtract\n",
            "  dGdt = alpha - gamma * G - theta * G * I  # Penurunan glukosa perlahan\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "DM1"
      ],
      "metadata": {
        "id": "5V2b99LFqfpN"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "\n",
        "\n",
        "# Definisi sistem persamaan glukosa dan insulin\n",
        "def glucose_insulin_system(z, t, alpha, gamma, theta, sigma, beta, epsilon):\n",
        "    G, I = z\n",
        "    # Perubahan kadar glukosa (mg/dL/min)\n",
        "    dGdt = alpha - gamma * G - theta * G * I\n",
        "    # Perubahan kadar insulin (μU/ml/min)\n",
        "    dIdt = (sigma * I / (1 + epsilon * I)) - beta * I * G\n",
        "    return np.array([dGdt, dIdt])\n",
        "\n",
        "# Fungsi untuk metode Runge-Kutta orde 4\n",
        "def runge_kutta_4(f, z0, t, params):\n",
        "    n = len(t)\n",
        "    z = np.zeros((n, len(z0)))\n",
        "    z[0] = z0\n",
        "    for i in range(1, n):\n",
        "        h = t[i] - t[i-1]\n",
        "        k1 = f(z[i-1], t[i-1], *params)\n",
        "        k2 = f(z[i-1] + 0.5*h*k1, t[i-1] + 0.5*h, *params)\n",
        "        k3 = f(z[i-1] + 0.5*h*k2, t[i-1] + 0.5*h, *params)\n",
        "        k4 = f(z[i-1] + h*k3, t[i-1] + h, *params)\n",
        "        z[i] = z[i-1] + (h/6) * (k1 + 2*k2 + 2*k3 + k4)\n",
        "    return z\n",
        "\n",
        "# Parameter-parameter\n",
        "alpha = 4.5  # Penerimaan glukosa\n",
        "gamma = 0.0002  # Penggunaan glukosa\n",
        "theta = 0.00075919 # Pengaruh insulin\n",
        "sigma = 0.2298  # Efektivitas insulin\n",
        "epsilon = 2300 # Tingkat kejenuhan\n",
        "beta = 1500  # Laju clearance insulin\n",
        "# Kondisi awal\n",
        "z0 = np.array([300.0, 20.0])  # Glukosa mulai dari 110 mg/dL, Insulin mulai dari 20 μU/ml\n",
        "\n",
        "# Rentang waktu simulasi\n",
        "t = np.linspace(0, 300, 3000)  # Hingga 200 menit\n",
        "\n",
        "# Simulasi numerik\n",
        "params = (alpha, gamma, theta, sigma, beta, epsilon)\n",
        "solution = runge_kutta_4(glucose_insulin_system, z0, t, params)\n",
        "\n",
        "# Pisahkan hasil untuk glukosa dan insulin\n",
        "glucose = solution[:, 0]\n",
        "insulin = solution[:, 1]\n",
        "\n",
        "# Konversi glukosa dari mg/dL ke mg/mL (membagi dengan 100)\n",
        "glucose_converted = glucose / 100  # Mengubah mg/dL ke mg/mL\n",
        "\n",
        "# Plot gabungan dengan dua sumbu y\n",
        "fig, ax1 = plt.subplots(figsize=(10, 6))\n",
        "\n",
        "# Sumbu Y kiri untuk Glukosa\n",
        "ax1.set_xlabel('Waktu (menit)')\n",
        "ax1.set_ylabel('Glukosa (mg/mL)', color='black', labelpad=15)\n",
        "ax1.plot(t, glucose_converted, label='Glukosa (mg/mL)', color='red', linewidth=2)\n",
        "ax1.tick_params(axis='y', labelcolor='black')\n",
        "ax1.set_ylim(0, max(glucose_converted) + 0.1)  # Mengatur batas atas sesuai dengan nilai glukosa\n",
        "\n",
        "# Sumbu Y kanan untuk Insulin\n",
        "ax2 = ax1.twinx()\n",
        "ax2.set_ylabel('Insulin (μU/mL)', color='black', labelpad=15)\n",
        "ax2.plot(t, insulin, label='Insulin (μU/mL)', color='blue', linewidth=2)\n",
        "ax2.tick_params(axis='y', labelcolor='black')\n",
        "ax2.set_ylim(0, max(insulin) + 10)\n",
        "\n",
        "\n",
        "# Tambahkan legenda\n",
        "fig.legend(loc='upper right', fontsize=10)\n",
        "\n",
        "# Judul dan grid\n",
        "fig.suptitle('Dinamika Glukosa dan Insulin Pada Penderita DM1 Injeksi Insulin', fontsize=14)\n",
        "ax1.grid(alpha=0.3)\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 746
        },
        "id": "U3jYo-KcqNUM",
        "outputId": "03d986bf-732e-4df9-9c72-baa8d6bf96be"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-17-cad17266e1d5>:10: RuntimeWarning: overflow encountered in scalar multiply\n",
            "  dGdt = alpha - gamma * G - theta * G * I\n",
            "<ipython-input-17-cad17266e1d5>:12: RuntimeWarning: overflow encountered in scalar multiply\n",
            "  dIdt = (sigma * I / (1 + epsilon * I)) - beta * I * G\n",
            "<ipython-input-17-cad17266e1d5>:10: RuntimeWarning: invalid value encountered in scalar subtract\n",
            "  dGdt = alpha - gamma * G - theta * G * I\n",
            "<ipython-input-17-cad17266e1d5>:12: RuntimeWarning: invalid value encountered in scalar divide\n",
            "  dIdt = (sigma * I / (1 + epsilon * I)) - beta * I * G\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "KGjuhD5pqTeg"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}