Created
December 21, 2025 06:05
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| { | |
| "cells": [ | |
| { | |
| "cell_type": "code", | |
| "execution_count": 1, | |
| "id": "807b4366-24e6-48cd-8cd8-5595d9352ddf", | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "image/png": 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", | |
| "text/plain": [ | |
| "<Figure size 640x480 with 1 Axes>" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "output_type": "display_data" | |
| } | |
| ], | |
| "source": [ | |
| "# soal no 4\n", | |
| "\n", | |
| "import numpy as np\n", | |
| "import matplotlib.pyplot as plt\n", | |
| "\n", | |
| "t = (0, 0.5, 1, 1.5, 2, 2.5, 3, 3.5, 4, 4.5, 5, 5.5, 6, 6.5, 7, 7.5, 8, 8.5, 9, 9.5, 10)\n", | |
| "x = (30, 31.5, 33, 34.5, 36, 37.5, 39, 40.5, 42, 43.5, 45, 46.5, 48, 49.5, 51, 52.5, 54, 55.5, 57, 58.5, 60)\n", | |
| "y = (10, 9.25, 6, 0.25, -8, -18.75, -32, -47.75, -66, -86.75, -110, -135.75, -164, -194.75, -228, -263.75, -302, -342.75, -386, -431.75, -480)\n", | |
| "\n", | |
| "plt.figure()\n", | |
| "plt.plot(x, y, 'r-', label='Lintasan gerak')\n", | |
| "plt.scatter(x, y, label='Titik posisi')\n", | |
| "\n", | |
| "plt.xlabel('x (m)')\n", | |
| "plt.ylabel('y (m)')\n", | |
| "plt.title('Simulasi Gerak Parabola')\n", | |
| "plt.grid(True)\n", | |
| "plt.legend()\n", | |
| "plt.show()\n", | |
| " " | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 2, | |
| "id": "fd1ea5dc-a767-413e-9bc0-73c4de773f2a", | |
| "metadata": {}, | |
| "outputs": [], | |
| "source": [ | |
| "def hitung_parameter_quadratic(t, x, y):\n", | |
| " import numpy as np\n", | |
| "\n", | |
| " # 1. Posisi awal\n", | |
| " x0 = x[0]\n", | |
| " y0 = y[0]\n", | |
| "\n", | |
| " # 2. Kecepatan awal arah x (linier)\n", | |
| " v0x = np.polyfit(t, x, 1)[0]\n", | |
| "\n", | |
| " # 3. Quadratic fit untuk y(t): y = a t^2 + b t + c\n", | |
| " a, b, c = np.polyfit(t, y, 2)\n", | |
| "\n", | |
| " # Parameter fisika\n", | |
| " g_hitung = -2 * a\n", | |
| " v0y = b\n", | |
| "\n", | |
| " # 4. Sudut lemparan\n", | |
| " theta_rad = np.arctan(v0y / v0x)\n", | |
| " theta_deg = np.degrees(theta_rad)\n", | |
| "\n", | |
| " return x0, y0, v0x, v0y, g_hitung, theta_deg\n" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 3, | |
| "id": "bc8afe68-0b52-4e35-a161-1d0c082e5123", | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| "x0 = 30.00 m\n", | |
| "y0 = 10.00 m\n", | |
| "v0x = 3.00 m/s\n", | |
| "v0y = 1.00 m/s\n", | |
| "g = 10.00 m/s^2\n", | |
| "theta = 18.43 derajat\n" | |
| ] | |
| } | |
| ], | |
| "source": [ | |
| "x0, y0, v0x, v0y, g_hitung, theta = hitung_parameter_quadratic(t, x, y)\n", | |
| "\n", | |
| "print(f\"x0 = {x0:.2f} m\")\n", | |
| "print(f\"y0 = {y0:.2f} m\")\n", | |
| "print(f\"v0x = {v0x:.2f} m/s\")\n", | |
| "print(f\"v0y = {v0y:.2f} m/s\")\n", | |
| "print(f\"g = {g_hitung:.2f} m/s^2\")\n", | |
| "print(f\"theta = {theta:.2f} derajat\")\n" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 2, | |
| "id": "24f9773f-3827-4190-b822-c6607d058d36", | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| "t_max = 0.1000 s\n", | |
| "x_max = 30.3000 m\n", | |
| "y_max = 10.0500 m\n" | |
| ] | |
| } | |
| ], | |
| "source": [ | |
| "# no 6 mencari ketinggian maksimum (y_maks) dan waktu saat ketinggian maksimal (t_maks)\n", | |
| "\n", | |
| "# Parameter yang sudah diperoleh\n", | |
| "x0 = 30.0 # m\n", | |
| "y0 = 10.0 # m\n", | |
| "v0x = 3.0 # m/s\n", | |
| "v0y = 1.0 # m/s\n", | |
| "g = 10 # m/s^2\n", | |
| "\n", | |
| "# Waktu saat tinggi maksimum\n", | |
| "t_max = v0y / g\n", | |
| "\n", | |
| "# Posisi saat tinggi maksimum\n", | |
| "x_max = x0 + v0x * t_max\n", | |
| "y_max = y0 + v0y * t_max - 0.5 * g * t_max**2\n", | |
| "\n", | |
| "print(f\"t_max = {t_max:.4f} s\")\n", | |
| "print(f\"x_max = {x_max:.4f} m\")\n", | |
| "print(f\"y_max = {y_max:.4f} m\")" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "id": "b8140436-3541-42c4-9b77-6c082582d4d2", | |
| "metadata": {}, | |
| "source": [ | |
| "Benda mencapai tinggi maksimum ketika kecepatan vertikalnya sama dengan nol. Pada saat ini, gerak ke atas berhenti dan gerak ke bawah mulai terjadi. Waktu saat tinggi maksimum diperoleh dari persamaan : \n", | |
| " vy_max = v0y - gt = 0\n", | |
| " t_max = v0y / g \n", | |
| "dan posisi benda ditentukan dengan mensubstitusikan waktu tersebut ke dalam persamaan gerak." | |
| ] | |
| } | |
| ], | |
| "metadata": { | |
| "kernelspec": { | |
| "display_name": "Python 3 (ipykernel)", | |
| "language": "python", | |
| "name": "python3" | |
| }, | |
| "language_info": { | |
| "codemirror_mode": { | |
| "name": "ipython", | |
| "version": 3 | |
| }, | |
| "file_extension": ".py", | |
| "mimetype": "text/x-python", | |
| "name": "python", | |
| "nbconvert_exporter": "python", | |
| "pygments_lexer": "ipython3", | |
| "version": "3.13.9" | |
| } | |
| }, | |
| "nbformat": 4, | |
| "nbformat_minor": 5 | |
| } |
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