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ICIEVE 2017 IOP Publishing

IOP Conf. Series: Materials Science and Engineering1234567890 306 (2018)‘’“” 012027 doi:10.1088/1757-899X/306/1/012027

Priority of VHS Development Based in Potential Area using Principal Component Analysis

D Meirawan, A Ana, and S Saripudin Universitas Pendidikan Indonesia, Bandung, Indonesia

*[email protected]

Abstract. The current condition of VHS is still inadequate in quality, quantity and relevance. The purpose of this research is to analyse the development of VHS based on the development of regional potential by using principal component analysis (PCA) in Bandung, Indonesia. This study used descriptive qualitative data analysis using the principle of secondary data reduction component. The method used is Principal Component Analysis (PCA) analysis with Minitab Statistics Software tool. The results of this study indicate the value of the lowest requirement is a priority of the construction of development VHS with a program of majors in accordance with the development of regional potential. Based on the PCA score found that the main priority in the development of VHS in Bandung is in Saguling, which has the lowest PCA value of 416.92 in area 1, Cihampelas with the lowest PCA value in region 2 and Padalarang with the lowest PCA value.

1. Introduction The quality of VHS (vocational school) graduates in Indonesia encountered several problems. In Indonesia, the problem is the lack of trust from the industrial world to VHS which includes aspect of knowledge, skills and attitudes possessed by VHS graduates which is seen to be inadequate to enter the industrial world. According to data analyzed by West Java BPS in 2017, the number of unemployement in West Java is approximately 1.9 million people. Of that number, about 38.11% are graduates of SMA / VHS with age range between 20-24 years. The high unemployment rate of VHS graduates is due to the incompatible competencies between the VHS graduates and the required by the industry [1]. Government programs to expand the education provision is by increasing the number of schools such as VHS. The problem is how the Office of Education (DISDIK) can accurately locate the school in line with the region potential so that the location of VHS is not centered on a single point. The school location should not be far from the activities of educators, settlements, and transportation access to enable them easily accessed. The location of schools should also be supported with strategic conditions in the hope of supporting the learning so as to generate qualified graduates. Essentially, education planning is very influential on the quality of education [2]. VHS graduates are expected to work within the city/regency by developing the potential of the respective region. Regional potential can be more economic value if there are human resources who are capable to manage it well. The opening of VHS and competency of expertise is conducted based on the interest of the society instead of the needs [3]. Many vocational graduates are unemployed due to the little availability of

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ICIEVE 2017 IOP Publishing

IOP Conf. Series: Materials Science and Engineering1234567890 306 (2018)‘’“” 012027 doi:10.1088/1757-899X/306/1/012027

jobs and relevance to their competence or expertise. In addition, VHS graduates tend to seek for jobs in other areas.The slow growth of the local economy is due to the lack of skilled relevant workers to the needs of the community besides the VHS graduates tend to work in other areas [4]. The increasing number of students, educators and schools at vocational school level as well as the school potential have not been properly recorded. The data recording are done manually and has not been well integrated so the fast and accurate information need and reporting is still difficult to do. School mapping is an important part of educational planning process because it is dynamic, meaning that it follows the ongoing development of education.

2. Methods The research approach used is descriptive qualitative analysis, using the principle of this component of analysis is the reduction of secondary data of education. The method used for analysis of potential development area is by using Principal Component Analysis (PCA) analysis. PCA is one form of multivariate analysis with high degree of difficulty but it allows to generate accurate data compared to other methods [5] [6]. PCA in this study is used to measure the variables of educational indicators, i.e the number of population aged 16-18 years, the ratio of school students, the ratio of students per class, the ratio of students per teacher, the ratio of students per new student, APK, ratio of SMA / VHS. Meanwhile, the needs of vocational school is done with PCA analysis with the help of Minitab Release Statistics software that is by considering the educational indicators used. The subject of this research is all the potential of regional and industrial world in West Bandung regency, West Java. The sample of research used is VHS and potential industrial area in West Bandung Regency (KBB). Data collection tool used in this research is interview, observation and sheet for data grouping or recapitulation table. This data collection tool is prepared by researchers by adopting the standards used by the Central Bureau of Statistics (BPS). The research designs developed are as follows. See Figure 1:

Figure 1. Research Design

3. Results and Discussion The priority development of West Bandung Regency has 16 Districts with 165 Villages. West Bandung regency consists of 16 districts, namely: Rongga District, Gununghalu District, Sindangkerta

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IOP Conf. Series: Materials Science and Engineering1234567890 306 (2018)‘’“” 012027 doi:10.1088/1757-899X/306/1/012027

District, Cililin District, Cihampelas District, Cipongkor District, Batujajar District, Saguling District, Cipatat District, Padalarang District, Ngamprah District, Parongpong District, Lembang District, Cisarua District, Cikalongwetan District and Cipeundeuy District. West Bandung Regency is 1,305.77 km2 wide. Figure 2 shows the widest district in West Bandung Regency is Gununghalu District and the smallest one is Batujajar District. District of Padalarang, Lembang, Cililin, and Batujajar is the region growth point. District of Ngamprah, Cikalongwetan, and Cipatat serve as the main service center. District of Parongpong, Cisarua, Sindangkerta, and Cihampelas serve as local service centers. Meanwhile, District of Cipeundeuy, Cipongkor, Gununghalu, and Rongga serve as the smallest service centers.

Figure 2. Regency of Bandung Barat Source: BPS Regency of Bandung Barat 2016

The number of VHS in West Bandung regency is 91 schools with 201 skill packages. The number of Public VHS in KBB is 8 and Private VHS is 83. Education Office of KBB divides them into 6 clusters as follows: Cluster 1: Lembang, Cisarua, dan Parompong Cluster 2: Ngamprah, Padalarang dan Cipatat Cluster 3: Batujajar, Cihampelas, dan Cililin Cluster 4: Cipendeuy dan Cikalong Wetan Cluster 5: Saguling dan Sindangkerta Cluster 6 : Cipongkor, Rongga dan Gunung Halu

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IOP Conf. Series: Materials Science and Engineering1234567890 306 (2018)‘’“” 012027 doi:10.1088/1757-899X/306/1/012027

(a) Based on survey results and data from NIEP (National Indicators for Education Planning) in 2016 and referring to VHS 2016 spectrum, VHS in KBB includes 6 areas of expertise, 15 Expertise Programs with 201 skill competencies. The technological and engineering expertise is 29.85% (60 skill competencies). The technology and information expertise is 27.36% (55 skill expertise). The health expertise 10.45% (21 skill competencies). The agribusiness and agrotechnology expertise is 8.96% (6 skill competencies). The business management expertise is 20.40% (41 skill competencies) and the tourism expertise is 8.96% (18 skill competencies). (b) The PCA method is used to analyse the VHS development prioritizing by considering the educational indicators. Indicator of VHS education based on SWP in KBB can be seen in table 1 below:

Table 1. Indicator of Education Equality of SMA/MA and VHS According to SWP in Bandung Barat Regency Year 2017

Population Student Ratio Distribution Number of Age of Teacher APK (X5) Area Student School (X2) Class (X3) 16-18 (XI) (X4) Area I 3335 15722 1693,25 282,98 97,62 134,37 Area II 8233 27098 1372,17 191,45 95,63 158,26 Area III 11703 24356 1567,45 250,55 114,05 236,51

The analysis of the need level of vocational school is done using PCA calculation method. The educational indicators used as the basis for PCA calculation are Age Population 16-18 (X1), Ratio of Students Per School (X2), Ratio of Students Per Class (X3), Student Ratio Per Teacher (X4), and Participation Rate (APK / X5) which can be seen in table 2:

Table 2. Indicator of Education Equality Based on District in Bandung Barat Regency Year 2017

Ratio of Number Population Student Ratio Per Name of APK SMA/VHS SWP of Age of 16-18 Distric School Class Teacher (X5) Student (XI) SMA VHS (X2) (X3) (X4) 1 Rongga 582 1797 582 34,24 17,64 32,39 2 1 2 Gununghalu 1224 4042 153 28,47 15,9 30,28 1 8 3 Sindangkerta 364 2241 121,33 91 13,48 16,24 4 3 4 Cililin 1173 5042 195,5 33,51 20,22 23,26 7 6 5 Cihampelas 2631 4245 438,5 38,69 20,72 61,98 1 6 6 Cipongkor 1006 4594 167,67 34,69 17,96 21,90 4 6 7 Batujajar 2624 2856 291,56 82 24,75 91,88 3 9 8 Cipatat 2748 7076 249,82 56,08 27,76 38,84 2 11 9 Padalarang 383 2633 348,18 42,56 22,93 14,55 6 11 10 Ngamprah 2111 7572 351,83 37,7 21,11 27,88 3 6 11 Parongpong 611 4029 152,75 47 17,97 15,17 3 4 12 Lembang 4724 7749 524,89 41,44 22,71 60,96 10 9 13 Cisarua 320 2066 160 40 13,91 15,49 2 2 Cikalong 14 1312 5645 218,67 46,86 15,62 23,24 2 6 Wetan 15 Cipeundeuy 1147 4547 573,5 31,86 20,48 25,23 4 2 16 Saguling 311 1042 103,67 38,88 14,14 29,85 2 3 Total 23271,00 67176,00 4632,87 724,98 307,30 529,12 56 93

Based on indicators of educational equality, the lowest percentage of APK for the secondary education is located at District of Padalarang (14.55%), and District of Parongpong 15.17%.

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IOP Conf. Series: Materials Science and Engineering1234567890 306 (2018)‘’“” 012027 doi:10.1088/1757-899X/306/1/012027

Calculation through PCA method can be done by finding the value of PCA 1 first, as can be seen in table 3:

Table 3. Score of PCA 1 for Each District

Popula- Student Ratio Per Number tion Name of APK Score SWP of Age of School Class Teacher Label District (X5) PCA 1 Student 16-18 (X2) (X3) (X4) (XI) I Rongga 582 1797 582 34,24 17,64 32,39 620,22 Priority II Cisarua 320 2066 160 40 13,91 15,49 857,23 Cipeundeuy 1147 4547 573,5 31,86 20,48 25,23 1839,77 Sindangkerta 364 2241 121,33 91 13,48 16,24 936,06 Saguling 311 1042 103,67 38,88 14,14 29,85 416,92 Priority I Parongpong 611 4029 152,75 47 17,97 15,17 1725,21 II Cililin 1173 5042 195,5 33,51 20,22 23,26 2161,66 Cihampelas 2631 4245 438,5 38,69 20,72 61,98 1733,80 Priority I Cipongkor 1006 4594 167,67 34,69 17,96 21,90 1971,62 Priority II Ngamprah 2111 7572 351,83 37,7 21,11 27,88 3235,68 Cikalong 1312 5645 218,67 46,86 15,62 23,24 2420,30 Wetan III Gununghalu 1224 4042 153 28,47 15,9 30,28 1731,18 Batujajar 2624 2856 291,56 82 24,75 91,88 1145,02 Priority II Lembang 4724 7749 524,89 41,44 22,71 60,96 3258,74 Cipatat 2748 7076 249,82 56,08 27,76 38,84 3037,54 Padalarang 383 2633 348,18 42,56 22,93 14,55 1055,02 Priority I Total 23271 67176 4632,87 724,98 307,3 529,12

Based on educational indicators, the lowest PCA 1 score for SWP I was found in Saguling District (416.92). This means for SWP I, the priority location for vocational school development is Saguling District. Next is followed by Rongga District which became the priority location for vocational school development. For SWP II, the lowest PCA 1 score was Cihampelas District (1733.80). Therefore, Cihampelas District is a priority location for the development of vocational schools. Then, followed by Cipongkor District (1971.62) which became the second priority of vocational school development. As for SWP III, the vocational priority point for the development of vocational schools is Padalarang District which has the lowest PCA 1 score (1055.02). Then, the next priority is Batujajar District (1145.02). Table 4. Eigenvalue (Output MINITAB)

Eigenanalysis of the Correlation Matrix Eigenvalue 2,1705 1,3346 0,8196 0,3786 0,2968 Proportion 0,434 0,267 0,164 0,076 0,059 Cumulative 0,434 0,701 0,865 0,941 1,000

The first point to note in table 4 above is the eigenvalue value of the five components. The factoring process is terminated in a component with an eigenvalue value below 1. Therefore, the factoring process will stop at the second component where its eigenvalue value is below 1 (0.8196).

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IOP Conf. Series: Materials Science and Engineering1234567890 306 (2018)‘’“” 012027 doi:10.1088/1757-899X/306/1/012027

Eigenvalue is the value of the main component variant (principal component, PC). The output shows the eigenvalue for the first main component (PC1) and the second (PC2) is 2.1705 and 1.3346, respectively. Eigenvalue of the two main components represent 43.6% and 26.7% of all variability. When accumulated, the two main components represent 70.1% of the total variability. This means that if the five variables are reduced to 2 variables, then the two new variables can account for 70.1% of the total variability. The following table 5 is the component matrix table of each variable:

Table 5. Component Matrix

Eigenvectors Variable PC1 PC2 PC3 PC4 PC5 The population aged 16-18 (X1) 0,442 -0,291 -0,690 0,006 -0,493 School (X2) 0,467 -0,272 0,614 -0,499 -0,286 Class (X3) -0,006 0,800 -0,176 -0,522 -0,238 Teacher (X4) 0,600 0,085 -0,193 -0,155 0,756 APK (X5) 0,476 0,440 0,280 0,674 -0,217

Scores for established components can be calculated by looking at the coefficient values for each variable. For PC1 components, the score can be calculated as follows:

PC1 = 0,442 population age of 16-18 years old + 0.467 schools - 0.006 class + 0.600 teacher + 0.476 APK

The second component (PC2) has an eigenvalue of 1.3346 and can account for 26.7 percent of the diversity. Together with the first component (PC1), both represent 70.1 percent of total diversity. Scores for PC2 are calculated as follows:

PC2 = -0.291 population age of 16-18 years old - 0.272 schools + 0.800 class + 0.085 teacher + 0.440 APK

Determination of the number of components to be used is very subjective. In this case, both PC1 and PC2 components which represen 70.1 percent of total diversity can be judged to have adequately captured the data structure. Or, using the first component is adequate to capture the data structure, when viewed with the eigenvalue value criterion greater than 1. Other components have a small proportion of diversity can be considered unimportant.

Figure 3. Scree Plot

From the scree plots of Figures 3 and 4 it can be seen that the eigenvalue above 1 is only 2. This proves that there are only two factors can be established.

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IOP Conf. Series: Materials Science and Engineering1234567890 306 (2018)‘’“” 012027 doi:10.1088/1757-899X/306/1/012027

Figure 4. Score Plot

4. Conclusions The regional potential of Bandung Barat Regency (KBB) is largely agriculture, the prominent potential of a prominent is Agribusiness and Agrotechnology, Livestock, Plantation, Creative Industries, Food and Beverage and Tourism. The main finding of this research is that there is a disparity between the vocational skills program in KBB that is not in accordance with the industry availability in every district.

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