{
"cells": [
{
"cell_type": "markdown",
"id": "60af6d6b-3131-4edf-a4ab-8bb90ed0d71c",
"metadata": {
"user_expressions": []
},
"source": [
"# Tugas 1\n",
"\n",
"Misalkan kita memiliki data jumlah desa di Indonesia berdasarkan sensus [Potensi Desa (PoDes) BPS](https://katalog.data.go.id/dataset/jumlah-desa-menurut-provinsi-dan-topografi-wilayah/resource/15444f70-af43-4cd6-8e15-b4cd709b89ad) dengan format [csv](https://katalog.data.go.id/datastore/dump/15444f70-af43-4cd6-8e15-b4cd709b89ad?bom=True). Untuk mengetahui isi dari data tersebut, kita memerlukan library: `CSV` dan `DataFrames`. Sebelumnya pastikan library tersebut sudah di tambahkan ke dalam Julia dengan perintah: `] add CSV` dan `add DataFrames`.\n",
"\n",
"**Pertanyaan**\n",
"\n",
"Terapkan cari fungsi interpolasi Polynomial dan interpolasi Lagrange dari permasalahan di atas. Code harus memuat **plot data** dan **plot fungsinya**."
]
},
{
"cell_type": "markdown",
"id": "55c3f794-6ef4-4412-bf52-aee4e6cc29e2",
"metadata": {
"user_expressions": []
},
"source": [
"## Load data"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "4577b4d9-a9a4-46d8-b1c3-3336d9931331",
"metadata": {},
"outputs": [],
"source": [
"using CSV\n",
"using DataFrames\n",
"using Plots"
]
},
{
"cell_type": "code",
"execution_count": 22,
"id": "e6d262f8-f096-4145-891a-1329b175e193",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"
1 1 INDONESIA 4749 5168 4845 3745 3630 3187 2 2 ACEH 333 457 427 206 355 327 3 3 SUMATERA UTARA 222 316 300 264 582 371 4 4 SUMATERA BARAT 61 72 60 66 51 86 5 5 RIAU 187 194 222 11 16 4 6 6 JAMBI 173 298 235 39 20 46
"
],
"text/latex": [
"\\begin{tabular}{r|cccccccc}\n",
"\t& \\_id & Provinsi & 2003 & 2005 & 2008 & 2011 & 2014 & 2018\\\\\n",
"\t\\hline\n",
"\t& Int64 & String31 & Int64? & Int64? & Int64? & Int64? & Int64? & Int64?\\\\\n",
"\t\\hline\n",
"\t1 & 1 & INDONESIA & 4749 & 5168 & 4845 & 3745 & 3630 & 3187 \\\\\n",
"\t2 & 2 & ACEH & 333 & 457 & 427 & 206 & 355 & 327 \\\\\n",
"\t3 & 3 & SUMATERA UTARA & 222 & 316 & 300 & 264 & 582 & 371 \\\\\n",
"\t4 & 4 & SUMATERA BARAT & 61 & 72 & 60 & 66 & 51 & 86 \\\\\n",
"\t5 & 5 & RIAU & 187 & 194 & 222 & 11 & 16 & 4 \\\\\n",
"\t6 & 6 & JAMBI & 173 & 298 & 235 & 39 & 20 & 46 \\\\\n",
"\\end{tabular}\n"
],
"text/plain": [
"\u001b[1m6×8 DataFrame\u001b[0m\n",
"\u001b[1m Row \u001b[0m│\u001b[1m _id \u001b[0m\u001b[1m Provinsi \u001b[0m\u001b[1m 2003 \u001b[0m\u001b[1m 2005 \u001b[0m\u001b[1m 2008 \u001b[0m\u001b[1m 2011 \u001b[0m\u001b[1m 2014 \u001b[0m\u001b[1m 2018 \u001b[0m\n",
" │\u001b[90m Int64 \u001b[0m\u001b[90m String31 \u001b[0m\u001b[90m Int64? \u001b[0m\u001b[90m Int64? \u001b[0m\u001b[90m Int64? \u001b[0m\u001b[90m Int64? \u001b[0m\u001b[90m Int64? \u001b[0m\u001b[90m Int64? \u001b[0m\n",
"─────┼───────────────────────────────────────────────────────────────────────\n",
" 1 │ 1 INDONESIA 4749 5168 4845 3745 3630 3187\n",
" 2 │ 2 ACEH 333 457 427 206 355 327\n",
" 3 │ 3 SUMATERA UTARA 222 316 300 264 582 371\n",
" 4 │ 4 SUMATERA BARAT 61 72 60 66 51 86\n",
" 5 │ 5 RIAU 187 194 222 11 16 4\n",
" 6 │ 6 JAMBI 173 298 235 39 20 46"
]
},
"execution_count": 22,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data = DataFrame(CSV.File(\"./pertemuan_4.1/dataset/podes.csv\")) # download data di link yang terdapat di soal\n",
"first(data,6)"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "6a9e9f58-0be2-410d-998b-cce2037475c8",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"1 4749 5168 4845 3745 3630 3187
"
],
"text/latex": [
"\\begin{tabular}{r|cccccc}\n",
"\t& 2003 & 2005 & 2008 & 2011 & 2014 & 2018\\\\\n",
"\t\\hline\n",
"\t& Int64? & Int64? & Int64? & Int64? & Int64? & Int64?\\\\\n",
"\t\\hline\n",
"\t1 & 4749 & 5168 & 4845 & 3745 & 3630 & 3187 \\\\\n",
"\\end{tabular}\n"
],
"text/plain": [
"\u001b[1mDataFrameRow\u001b[0m\n",
"\u001b[1m Row \u001b[0m│\u001b[1m 2003 \u001b[0m\u001b[1m 2005 \u001b[0m\u001b[1m 2008 \u001b[0m\u001b[1m 2011 \u001b[0m\u001b[1m 2014 \u001b[0m\u001b[1m 2018 \u001b[0m\n",
" │\u001b[90m Int64? \u001b[0m\u001b[90m Int64? \u001b[0m\u001b[90m Int64? \u001b[0m\u001b[90m Int64? \u001b[0m\u001b[90m Int64? \u001b[0m\u001b[90m Int64? \u001b[0m\n",
"─────┼────────────────────────────────────────────────\n",
" 1 │ 4749 5168 4845 3745 3630 3187"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data[1, 3:end]"
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "3006e400-cb68-4843-ad67-e1ac20ce0e8e",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"6-element Vector{Float64}:\n",
" 4749.0\n",
" 5168.0\n",
" 4845.0\n",
" 3745.0\n",
" 3630.0\n",
" 3187.0"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"x = Array{Float64}(data[1, 3:end])"
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "78bc190b-70ca-403d-9a43-f47675dc7ef6",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"6-element Vector{String}:\n",
" \"2003\"\n",
" \"2005\"\n",
" \"2008\"\n",
" \"2011\"\n",
" \"2014\"\n",
" \"2018\""
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"cols = names(data)\n",
"cols = cols[3:end]"
]
},
{
"cell_type": "code",
"execution_count": 13,
"id": "d1e70567-0e10-4f72-a3ac-89537a136eae",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"6-element Vector{Float64}:\n",
" 2003.0\n",
" 2005.0\n",
" 2008.0\n",
" 2011.0\n",
" 2014.0\n",
" 2018.0"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"y = [parse(Float64, cols[i]) for i=1:6]"
]
},
{
"cell_type": "markdown",
"id": "d09dcc65-bd81-48b6-8faf-1e396a75edb8",
"metadata": {
"user_expressions": []
},
"source": [
"## Plot"
]
},
{
"cell_type": "code",
"execution_count": 16,
"id": "d47693c6-6321-4ae1-abdc-12ee2c1236d4",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"6-element Vector{Float64}:\n",
" 0.0\n",
" 2.0\n",
" 5.0\n",
" 8.0\n",
" 11.0\n",
" 15.0"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"t = y .- 2003"
]
},
{
"cell_type": "code",
"execution_count": 17,
"id": "001e0b1f-8f30-46ef-89eb-246b7b6d74b1",
"metadata": {},
"outputs": [
{
"data": {
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},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"scatter(t, x, label=\"aktual\",\n",
" xlabel=\"sejak tahun 2003\",\n",
" ylabel=\"jumlah desa (ribu)\",\n",
" title=\"Jumlah desa di Indonesia\"\n",
")"
]
},
{
"cell_type": "markdown",
"id": "c8fc7b60-5cd6-43aa-8aef-eb4997c71e9e",
"metadata": {
"user_expressions": []
},
"source": [
"## Code untuk interpolasi polynomial"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "c9631bc9-0974-40bf-8799-dedd20286bb9",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"id": "a9a45356-c7f8-4a02-acfd-a4343683d2b3",
"metadata": {
"user_expressions": []
},
"source": [
"## Code untuk interpolasi Lagrange"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "a23f779f-be62-4490-918f-029f9fb08b2c",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"id": "0cbd4fc5-77f6-4800-818f-8d39f11ee265",
"metadata": {
"user_expressions": []
},
"source": [
"## Plot hasil interpolasi polynomial dan Lagrange"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "1449712a-bd29-45fe-9994-31352e951ebb",
"metadata": {},
"outputs": [],
"source": []
}
],
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