Overview

Dataset statistics

Number of variables8
Number of observations173
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory11.6 KiB
Average record size in memory68.8 B

Variable types

Categorical2
Text3
Numeric3

Dataset

Description장애인고용촉진및직업재활법 제33조제4항 관련 장애인고용부담금 감면에 대한 데이터로, 연계고용 사업장 정보를 제공함.
URLhttps://www.data.go.kr/data/15067959/fileData.do

Alerts

연도 has constant value ""Constant
상시근로자수 is highly overall correlated with 장애인근로자수 and 1 other fieldsHigh correlation
장애인근로자수 is highly overall correlated with 상시근로자수 and 1 other fieldsHigh correlation
중증장애인수 is highly overall correlated with 상시근로자수 and 1 other fieldsHigh correlation
연계고용사업장명 has unique valuesUnique
장애인근로자수 has 2 (1.2%) zerosZeros
중증장애인수 has 5 (2.9%) zerosZeros

Reproduction

Analysis started2023-12-12 14:04:53.063786
Analysis finished2023-12-12 14:04:54.863299
Duration1.8 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연도
Categorical

CONSTANT 

Distinct1
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
2023
173 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2023
2nd row2023
3rd row2023
4th row2023
5th row2023

Common Values

ValueCountFrequency (%)
2023 173
100.0%

Length

2023-12-12T23:04:54.942193image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T23:04:55.057815image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2023 173
100.0%

연계고용사업장명
Text

UNIQUE 

Distinct173
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
2023-12-12T23:04:55.572675image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length16
Median length13
Mean length8.1560694
Min length2

Characters and Unicode

Total characters1411
Distinct characters251
Distinct categories6 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique173 ?
Unique (%)100.0%

Sample

1st row삼성떡프린스
2nd row사회복지법인 사렙다
3rd row보람근로원
4th row사회복지법인 동천학원
5th row마중물직업재활센터
ValueCountFrequency (%)
주식회사 22
 
9.9%
사회복지법인 9
 
4.0%
사회적협동조합 3
 
1.3%
두리장애인복지회 2
 
0.9%
농업회사법인 2
 
0.9%
코액터스 1
 
0.4%
해든 1
 
0.4%
케이두레 1
 
0.4%
주)경신유엔엘 1
 
0.4%
주식회사에이블위 1
 
0.4%
Other values (180) 180
80.7%
2023-12-12T23:04:55.973231image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
주 84
 
6.0%
사 72
 
5.1%
회 63
 
4.5%
) 50
 
3.5%
50
 
3.5%
( 50
 
3.5%
업 44
 
3.1%
인 40
 
2.8%
장 37
 
2.6%
식 35
 
2.5%
Other values (241) 886
62.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1253
88.8%
Close Punctuation 50
 
3.5%
Space Separator 50
 
3.5%
Open Punctuation 50
 
3.5%
Other Symbol 7
 
0.5%
Uppercase Letter 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
주 84
 
6.7%
사 72
 
5.7%
회 63
 
5.0%
업 44
 
3.5%
인 40
 
3.2%
장 37
 
3.0%
식 35
 
2.8%
지 21
 
1.7%
복 21
 
1.7%
스 21
 
1.7%
Other values (236) 815
65.0%
Close Punctuation
ValueCountFrequency (%)
) 50
100.0%
Space Separator
ValueCountFrequency (%)
50
100.0%
Open Punctuation
ValueCountFrequency (%)
( 50
100.0%
Other Symbol
ValueCountFrequency (%)
㈜ 7
100.0%
Uppercase Letter
ValueCountFrequency (%)
D 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1260
89.3%
Common 150
 
10.6%
Latin 1
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
주 84
 
6.7%
사 72
 
5.7%
회 63
 
5.0%
업 44
 
3.5%
인 40
 
3.2%
장 37
 
2.9%
식 35
 
2.8%
지 21
 
1.7%
복 21
 
1.7%
스 21
 
1.7%
Other values (237) 822
65.2%
Common
ValueCountFrequency (%)
) 50
33.3%
50
33.3%
( 50
33.3%
Latin
ValueCountFrequency (%)
D 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1253
88.8%
ASCII 151
 
10.7%
None 7
 
0.5%

Most frequent character per block

Hangul
ValueCountFrequency (%)
주 84
 
6.7%
사 72
 
5.7%
회 63
 
5.0%
업 44
 
3.5%
인 40
 
3.2%
장 37
 
3.0%
식 35
 
2.8%
지 21
 
1.7%
복 21
 
1.7%
스 21
 
1.7%
Other values (236) 815
65.0%
ASCII
ValueCountFrequency (%)
) 50
33.1%
50
33.1%
( 50
33.1%
D 1
 
0.7%
None
ValueCountFrequency (%)
㈜ 7
100.0%

사업장유형
Categorical

Distinct2
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
표준사업장
124 
직업재활시설
49 

Length

Max length6
Median length5
Mean length5.283237
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row직업재활시설
2nd row직업재활시설
3rd row직업재활시설
4th row직업재활시설
5th row직업재활시설

Common Values

ValueCountFrequency (%)
표준사업장 124
71.7%
직업재활시설 49
 
28.3%

Length

2023-12-12T23:04:56.098695image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T23:04:56.233730image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
표준사업장 124
71.7%
직업재활시설 49
 
28.3%

지역
Text

Distinct96
Distinct (%)55.5%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
2023-12-12T23:04:56.519943image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length11
Mean length8.3757225
Min length6

Characters and Unicode

Total characters1449
Distinct characters86
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique55 ?
Unique (%)31.8%

Sample

1st row서울특별시 동작구
2nd row서울특별시 동작구
3rd row충청북도 청주시
4th row서울특별시 노원구
5th row경상남도 양산시
ValueCountFrequency (%)
경기도 30
 
8.7%
서울특별시 28
 
8.1%
경상남도 19
 
5.5%
부산광역시 13
 
3.8%
인천광역시 13
 
3.8%
제주특별자치도 12
 
3.5%
전라남도 11
 
3.2%
경상북도 10
 
2.9%
전라북도 7
 
2.0%
제주시 7
 
2.0%
Other values (96) 196
56.6%
2023-12-12T23:04:56.992010image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
173
 
11.9%
시 159
 
11.0%
도 101
 
7.0%
구 73
 
5.0%
경 63
 
4.3%
서 49
 
3.4%
남 49
 
3.4%
주 49
 
3.4%
광 47
 
3.2%
역 41
 
2.8%
Other values (76) 645
44.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1276
88.1%
Space Separator 173
 
11.9%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 159
 
12.5%
도 101
 
7.9%
구 73
 
5.7%
경 63
 
4.9%
서 49
 
3.8%
남 49
 
3.8%
주 49
 
3.8%
광 47
 
3.7%
역 41
 
3.2%
별 40
 
3.1%
Other values (75) 605
47.4%
Space Separator
ValueCountFrequency (%)
173
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1276
88.1%
Common 173
 
11.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 159
 
12.5%
도 101
 
7.9%
구 73
 
5.7%
경 63
 
4.9%
서 49
 
3.8%
남 49
 
3.8%
주 49
 
3.8%
광 47
 
3.7%
역 41
 
3.2%
별 40
 
3.1%
Other values (75) 605
47.4%
Common
ValueCountFrequency (%)
173
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1276
88.1%
ASCII 173
 
11.9%

Most frequent character per block

ASCII
ValueCountFrequency (%)
173
100.0%
Hangul
ValueCountFrequency (%)
시 159
 
12.5%
도 101
 
7.9%
구 73
 
5.7%
경 63
 
4.9%
서 49
 
3.8%
남 49
 
3.8%
주 49
 
3.8%
광 47
 
3.7%
역 41
 
3.2%
별 40
 
3.1%
Other values (75) 605
47.4%

도급품목
Text

Distinct116
Distinct (%)67.1%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
2023-12-12T23:04:57.402358image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length17
Median length12
Mean length5.1040462
Min length1

Characters and Unicode

Total characters883
Distinct characters202
Distinct categories6 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique98 ?
Unique (%)56.6%

Sample

1st row떡, 음료
2nd row수제비누
3rd row생활용품포장
4th row토너카트리지
5th row인쇄물
ValueCountFrequency (%)
세탁물 25
 
9.9%
세탁 19
 
7.5%
인쇄물 10
 
4.0%
처리 9
 
3.6%
등 8
 
3.2%
인쇄 5
 
2.0%
원두 5
 
2.0%
근무복 3
 
1.2%
및 3
 
1.2%
유니폼 3
 
1.2%
Other values (130) 162
64.3%
2023-12-12T23:04:57.917085image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
85
 
9.6%
탁 53
 
6.0%
세 51
 
5.8%
물 51
 
5.8%
품 22
 
2.5%
인 21
 
2.4%
제 21
 
2.4%
, 17
 
1.9%
용 17
 
1.9%
쇄 17
 
1.9%
Other values (192) 528
59.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 757
85.7%
Space Separator 85
 
9.6%
Other Punctuation 23
 
2.6%
Uppercase Letter 14
 
1.6%
Open Punctuation 2
 
0.2%
Close Punctuation 2
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
탁 53
 
7.0%
세 51
 
6.7%
물 51
 
6.7%
품 22
 
2.9%
인 21
 
2.8%
제 21
 
2.8%
용 17
 
2.2%
쇄 17
 
2.2%
리 17
 
2.2%
지 15
 
2.0%
Other values (174) 472
62.4%
Uppercase Letter
ValueCountFrequency (%)
R 2
14.3%
S 2
14.3%
P 2
14.3%
W 1
7.1%
C 1
7.1%
M 1
7.1%
I 1
7.1%
D 1
7.1%
V 1
7.1%
H 1
7.1%
Other Punctuation
ValueCountFrequency (%)
, 17
73.9%
/ 3
 
13.0%
. 2
 
8.7%
& 1
 
4.3%
Space Separator
ValueCountFrequency (%)
85
100.0%
Open Punctuation
ValueCountFrequency (%)
( 2
100.0%
Close Punctuation
ValueCountFrequency (%)
) 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 757
85.7%
Common 112
 
12.7%
Latin 14
 
1.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
탁 53
 
7.0%
세 51
 
6.7%
물 51
 
6.7%
품 22
 
2.9%
인 21
 
2.8%
제 21
 
2.8%
용 17
 
2.2%
쇄 17
 
2.2%
리 17
 
2.2%
지 15
 
2.0%
Other values (174) 472
62.4%
Latin
ValueCountFrequency (%)
R 2
14.3%
S 2
14.3%
P 2
14.3%
W 1
7.1%
C 1
7.1%
M 1
7.1%
I 1
7.1%
D 1
7.1%
V 1
7.1%
H 1
7.1%
Common
ValueCountFrequency (%)
85
75.9%
, 17
 
15.2%
/ 3
 
2.7%
( 2
 
1.8%
) 2
 
1.8%
. 2
 
1.8%
& 1
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
Hangul 757
85.7%
ASCII 126
 
14.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
85
67.5%
, 17
 
13.5%
/ 3
 
2.4%
R 2
 
1.6%
( 2
 
1.6%
) 2
 
1.6%
S 2
 
1.6%
. 2
 
1.6%
P 2
 
1.6%
W 1
 
0.8%
Other values (8) 8
 
6.3%
Hangul
ValueCountFrequency (%)
탁 53
 
7.0%
세 51
 
6.7%
물 51
 
6.7%
품 22
 
2.9%
인 21
 
2.8%
제 21
 
2.8%
용 17
 
2.2%
쇄 17
 
2.2%
리 17
 
2.2%
지 15
 
2.0%
Other values (174) 472
62.4%

상시근로자수
Real number (ℝ)

HIGH CORRELATION 

Distinct69
Distinct (%)39.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean50.034682
Minimum0
Maximum657
Zeros1
Zeros (%)0.6%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2023-12-12T23:04:58.100029image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile14
Q122
median29
Q343
95-th percentile141.4
Maximum657
Range657
Interquartile range (IQR)21

Descriptive statistics

Standard deviation77.470542
Coefficient of variation (CV)1.5483368
Kurtosis32.667805
Mean50.034682
Median Absolute Deviation (MAD)10
Skewness5.2343557
Sum8656
Variance6001.6848
MonotonicityNot monotonic
2023-12-12T23:04:58.262866image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
22 12
 
6.9%
16 7
 
4.0%
20 7
 
4.0%
28 6
 
3.5%
25 6
 
3.5%
24 6
 
3.5%
32 5
 
2.9%
21 5
 
2.9%
23 5
 
2.9%
41 5
 
2.9%
Other values (59) 109
63.0%
ValueCountFrequency (%)
0 1
 
0.6%
8 1
 
0.6%
10 1
 
0.6%
12 5
2.9%
14 2
 
1.2%
15 2
 
1.2%
16 7
4.0%
17 3
1.7%
18 5
2.9%
19 4
2.3%
ValueCountFrequency (%)
657 1
0.6%
549 1
0.6%
323 1
0.6%
304 1
0.6%
295 1
0.6%
237 1
0.6%
212 1
0.6%
197 1
0.6%
145 1
0.6%
139 1
0.6%

장애인근로자수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct48
Distinct (%)27.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean24.953757
Minimum0
Maximum382
Zeros2
Zeros (%)1.2%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2023-12-12T23:04:58.427295image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile8.6
Q111
median15
Q324
95-th percentile60.2
Maximum382
Range382
Interquartile range (IQR)13

Descriptive statistics

Standard deviation40.093469
Coefficient of variation (CV)1.6067107
Kurtosis46.980139
Mean24.953757
Median Absolute Deviation (MAD)5
Skewness6.3695088
Sum4317
Variance1607.4862
MonotonicityNot monotonic
2023-12-12T23:04:58.592010image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=48)
ValueCountFrequency (%)
10 24
 
13.9%
12 20
 
11.6%
13 11
 
6.4%
14 9
 
5.2%
21 7
 
4.0%
11 7
 
4.0%
19 7
 
4.0%
16 7
 
4.0%
20 6
 
3.5%
9 5
 
2.9%
Other values (38) 70
40.5%
ValueCountFrequency (%)
0 2
 
1.2%
3 1
 
0.6%
5 2
 
1.2%
8 4
 
2.3%
9 5
 
2.9%
10 24
13.9%
11 7
 
4.0%
12 20
11.6%
13 11
6.4%
14 9
 
5.2%
ValueCountFrequency (%)
382 1
0.6%
254 1
0.6%
243 1
0.6%
140 1
0.6%
88 1
0.6%
69 1
0.6%
68 1
0.6%
66 1
0.6%
62 1
0.6%
59 1
0.6%

중증장애인수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct45
Distinct (%)26.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20.156069
Minimum0
Maximum372
Zeros5
Zeros (%)2.9%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2023-12-12T23:04:58.750557image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3
Q18
median12
Q320
95-th percentile42.2
Maximum372
Range372
Interquartile range (IQR)12

Descriptive statistics

Standard deviation38.499833
Coefficient of variation (CV)1.9100863
Kurtosis53.563802
Mean20.156069
Median Absolute Deviation (MAD)5
Skewness6.9289134
Sum3487
Variance1482.2371
MonotonicityNot monotonic
2023-12-12T23:04:58.910039image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=45)
ValueCountFrequency (%)
10 15
 
8.7%
11 13
 
7.5%
8 11
 
6.4%
9 10
 
5.8%
7 10
 
5.8%
12 8
 
4.6%
15 8
 
4.6%
6 6
 
3.5%
14 6
 
3.5%
16 6
 
3.5%
Other values (35) 80
46.2%
ValueCountFrequency (%)
0 5
2.9%
1 1
 
0.6%
2 1
 
0.6%
3 5
2.9%
4 3
 
1.7%
5 4
 
2.3%
6 6
3.5%
7 10
5.8%
8 11
6.4%
9 10
5.8%
ValueCountFrequency (%)
372 1
0.6%
251 1
0.6%
243 1
0.6%
89 1
0.6%
56 1
0.6%
49 2
1.2%
48 1
0.6%
44 1
0.6%
41 2
1.2%
40 2
1.2%

Interactions

2023-12-12T23:04:54.250934image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:04:53.521320image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:04:53.862681image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:04:54.357364image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:04:53.636085image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:04:54.007590image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:04:54.472320image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:04:53.741716image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:04:54.127663image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T23:04:59.056903image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
사업장유형지역상시근로자수장애인근로자수중증장애인수
사업장유형1.0000.3410.1070.1870.140
지역0.3411.0000.0000.0000.000
상시근로자수0.1070.0001.0000.8800.869
장애인근로자수0.1870.0000.8801.0000.973
중증장애인수0.1400.0000.8690.9731.000
2023-12-12T23:04:59.177134image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
상시근로자수장애인근로자수중증장애인수사업장유형
상시근로자수1.0000.6960.5630.112
장애인근로자수0.6961.0000.8940.132
중증장애인수0.5630.8941.0000.169
사업장유형0.1120.1320.1691.000

Missing values

2023-12-12T23:04:54.623918image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T23:04:54.783133image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

연도연계고용사업장명사업장유형지역도급품목상시근로자수장애인근로자수중증장애인수
02023삼성떡프린스직업재활시설서울특별시 동작구떡, 음료1899
12023사회복지법인 사렙다직업재활시설서울특별시 동작구수제비누25101
22023보람근로원직업재활시설충청북도 청주시생활용품포장806244
32023사회복지법인 동천학원직업재활시설서울특별시 노원구토너카트리지363622
42023마중물직업재활센터직업재활시설경상남도 양산시인쇄물1897
52023가꿈복지 직업재활시설직업재활시설부산광역시 금정구인쇄물201414
62023동인직업재활센터직업재활시설부산광역시 부산진구인쇄물312420
72023도영하우스직업재활시설부산광역시 동래구인쇄물000
82023달성군다사장애인재활작업장직업재활시설대구광역시 달성군임가공16123
92023숲중증장애인다수고용사업장직업재활시설대구광역시 수성구제빵, 원두, 마스크493623
연도연계고용사업장명사업장유형지역도급품목상시근로자수장애인근로자수중증장애인수
1632023㈜에스얜에스표준사업장전라남도 나주시개폐기47137
1642023주식회사 하이런드리표준사업장전라남도 순천시세탁물 처리231210
1652023㈜산내들나르샤표준사업장전라남도 화순군세탁물 처리28105
1662023㈜대건솔루션표준사업장전라남도 여수시세탁물 처리161010
1672023대경산업표준사업장경상남도 창원시근무복 세탁물241412
1682023제이에스산업표준사업장경상남도 창원시세탁물191410
1692023주식회사 한강산업표준사업장제주특별자치도 제주시세탁20109
1702023주식회사제주참농표준사업장제주특별자치도 제주시농산물 포장 등431310
1712023주식회사 한백기업표준사업장제주특별자치도 한경면세탁392015
1722023조양산업표준사업장제주특별자치도 제주시세탁22105