sampling considerations. Definition of purposive sampling is a sampling technique used by researchers if researchers have certain considerations in sampling or determining samples
for specific purposes (Doherty, 1994) (Showkat &
Parveen, 2017). the research stages as illustrated in the flow diagram.
Figure 4. Flowchart Diagram
3. RESULTS AND DISCUSSION
ICMST 2019 August, 1 2019 Table 2. Average data expert
(Source: data processed with Microsoft Excel).
3.1.2. Normalize the linkage matrix directly by multiplying the values in each column with the total value of the number of values for each row. This result is obtained after doing an average in the data
expert which then normalizes the matrix, after going through the stages of multiplying the values in each column with the total value of the number of values for each row, as follows.
Table 3. Normalization on the relationship matrix
(Source: data processed with Microsoft Excel) 3.1.3. Obtain a total linkage matrix by subtracting each column of identity matrix values from the
normalized matrix, which is continued by processing it with minverse and mmult.
Table 4. Minverse results matrix
(Source: data processed with Microsoft Excel)
education experience mental effort physical effort inconveniences danger relation tool method supervision
education 0,0 2,3 3,7 2,0 1,0 1,0 2,7 2,3 2,7 4,0 21,7
experience 2,7 0,0 4,0 2,0 1,0 1,3 3,7 2,3 2,0 3,3 22,3
mental effort 2,3 2,3 0,0 1,7 1,0 1,0 2,0 1,0 2,3 3,0 16,6
physical effort 1,0 2,0 1,0 0,0 1,0 1,0 1,0 1,0 1,0 1,0 10,0
inconveniences 1,0 1,0 1,0 1,0 0,0 1,3 1,0 1,0 1,0 1,0 9,3
danger 1,3 1,0 1,7 1,7 2,0 0,0 2,0 2,0 1,0 2,3 15,0
relation 3,0 2,7 2,7 2,0 1,0 1,3 0,0 1,3 2,0 2,0 18,0
tool 1,0 2,0 1,0 1,0 1,0 2,0 1,0 0,0 2,0 2,0 13,0
method 2,3 2,0 1,0 1,0 1,0 1,0 1,7 1,7 0,0 2,3 14,0
supervision 2,7 3,7 2,7 3,0 1,0 2,7 2,7 3,0 3,0 0,0 24,5
17,3 19,0 18,8 15,4 10,0 12,6 17,8 15,6 17,0 20,9 0,0400
0,0476
Knowledge Effort Working Condition Responbilities
Knowledge Effort Working Condition Responbilities
education experience mental effort physical effort inconveniences danger relation tool method supervision
education 0,0000 0,0933 0,1467 0,0800 0,0400 0,0400 0,1067 0,0933 0,1067 0,1600
experience 0,1067 0,0000 0,1600 0,0800 0,0400 0,0533 0,1467 0,0933 0,0800 0,1333
mental effort 0,0933 0,0933 0,0000 0,0667 0,0400 0,0400 0,0800 0,0400 0,0933 0,1200
physical effort 0,0400 0,0800 0,0400 0,0000 0,0400 0,0400 0,0400 0,0400 0,0400 0,0400
inconveniences 0,0400 0,0400 0,0400 0,0400 0,0000 0,0533 0,0400 0,0400 0,0400 0,0400
danger 0,0533 0,0400 0,0667 0,0667 0,0800 0,0000 0,0800 0,0800 0,0400 0,0933
relation 0,1200 0,1067 0,1067 0,0800 0,0400 0,0533 0,0000 0,0533 0,0800 0,0800
tool 0,0400 0,0800 0,0400 0,0400 0,0400 0,0800 0,0400 0,0000 0,0800 0,0800
method 0,0933 0,0800 0,0400 0,0400 0,0400 0,0400 0,0667 0,0667 0,0000 0,0933
supervision 0,1067 0,1467 0,1067 0,1200 0,0667 0,1067 0,1067 0,1200 0,1200 0,0000
Knowledge Effort Working Condition
Responbilities
Knowledge Effort Working Condition Responbilities
education experience mental effort physical effort inconveniences danger relation tool method supervision education 1,2121 0,3175 0,3558 0,2598 0,1625 0,1907 0,3092 0,2727 0,3056 0,3887
experience 0,3147 1,2374 0,3746 0,2647 0,1654 0,2050 0,3487 0,2760 0,2877 0,3734
mental effort 0,2538 0,2683 1,1807 0,2101 0,1368 0,1580 0,2441 0,1887 0,2501 0,3047
physical effort 0,1365 0,1797 0,1450 1,0861 0,0969 0,1088 0,1390 0,1262 0,1339 0,1544
inconveniences 0,1268 0,1345 0,1333 0,1168 1,0541 0,1150 0,1283 0,1181 0,1251 0,1432
danger 0,1888 0,1914 0,2096 0,1859 0,1592 1,1027 0,2130 0,1986 0,1755 0,2469
relation 0,2851 0,2884 0,2888 0,2291 0,1419 0,1748 1,1797 0,2074 0,2473 0,2839
tool 0,1650 0,2104 0,1737 0,1499 0,1153 0,1667 0,1678 1,1147 0,1972 0,2225
method 0,2269 0,2281 0,1927 0,1635 0,1220 0,1407 0,2062 0,1901 1,1395 0,2525
supervision 0,3249 0,3783 0,3422 0,3089 0,1987 0,2611 0,3288 0,3126 0,3314 1,2704
Knowledge Effort Working Condition Responbilities
Knowledge Effort Working Condition Responbilities
The Mmult result matrix value shows the relationship between subcriteria. To find out the relationship between subcriteria, the threshold value must be determined first. The threshold value can be determined in two ways, namely:
a. Take the average value from the Mmult result table value.
b. Can be determined through expert opinion.
In this study, the value of the Threshold Value is determined by the average value of the results of the mmult table, which is 0.2112. The column matrix value which is equal to or greater than the threshold value in the mmult matrix is given a yellow color, while the smaller column value is still white. The yellow value indicates that the subcriteria in the row affects the subcriteria in the column. While for white means the subcriteria in the row does not affect the subcriteria in the column.
Table 5. Mmult results matrix with Threshold Value
(Source: data processed with Microsoft Excel) 3.1.4. Calculates dispatcher groups and receiver groups.
The value of D is obtained from the number of values per row while the R value is obtained from the number of values for each column of the mmult matrix. From the values of D and R, the D + R (Prominence) and D-R (Relation) values can be determined, the D + R or Prominence values indicate the importance of these sub-criteria (Lin and
Wu, 2004). The greater the D + R value, the more important the criteria are. While for the D-R or Relation value it means two kinds. If the D-R value is positive, then the sub-criteria that influence (influence) other sub-criteria (dispatchers) or as a cause of the sub-criteria has a negative D-R value.
And if the D-R value is negative, then the subcriteria is the effect (effect) of the subcriteria that is positive or in the term given the name of the receiver.
ICMST 2019 August, 1 2019 Table 6. Dispatcher and Receiver sub-criteria
(Source: data processed with Microsoft Excel)
From the relationship between sub-criteria which is indicated by the yellow column, then the method selection model will be built in evaluating and
determining the level of position with a case study in the position of Commander. Establish threshold values and get impact-diagraph maps.
Figure 5. Map of Impact Diagraph
(Source: data processed with Microsoft Excel)
education 2,7747 2,2346 5,0093 0,5400 dispatcher
experience 2,8476 2,4340 5,2816 0,4136 dispatcher
mental effort 2,1953 2,3964 4,5917 -0,2011 receiver
physical effort 1,3064 1,9749 3,2813 -0,6685 receiver inconveniences 1,1953 1,3527 2,5480 -0,1574 receiver
danger 1,8716 1,6234 3,4950 0,2481 dispatcher
relation 2,3263 2,2648 4,5911 0,0616 dispatcher
tool 1,6830 2,0051 3,6881 -0,3221 receiver
method 1,8622 2,1932 4,0554 -0,3311 receiver
supervision 3,0574 2,6406 5,6980 0,4169 dispatcher
Knowledge Effort Working Condition
Responbilities
D R D+R D-R
Figure 6. Network ANP with the relationship between Innendependence and Outerdependence
After knowing the Innerdependence and Outerdependence relationship between criteria and subcriteria, then a valid model can be prepared from
the method chosen in carrying out job evaluation and determining the level of position.
Figure 7. Criteria and Subcriteria Model in the selection of methods in evaluating and determining the level of position
(Source: data processed with Microsoft Excel)
ICMST 2019 August, 1 2019 3.2 ANP Data Processing
The data obtained from the expert questionnaire is comparative data between sub- criteria, which will then be combined with the geometric mean process into a single comparison data, which is then processed using Superdecision software to be the weight value of each subcriteria.
Next will be described ANP comparison scale data
obtained from three experts based on the criteria and subcriteria used in this study.
3.2.1. Geometric Mean Calculation.
After the results of the questionnaire testing from each expert tested its consistency, the results of the filling are feasible to be put together through the geometric averages of each of these questions.
Table 7. Geometric mean pairwise comparison matrix on Criteria
KNOWLEDGE EFFORT WORKING
CONDITIONS RESPONSIBILITY
KNOWLEDGE 1,00 2,62 2,00 0,44
EFFORT 0,38 1,00 2,29 3,63
WORKING
CONDITIONS 0,50 0,44 1,00 2,62
RESPONSIBILITY 2,29 0,28 0,38 1,00
(Source: data processed with Microsoft Excel)
Each value of comparison between criteria and subcriteria in this study must be tested whether the inconsistency index is worth below 0.1 or not. If the inconsistency index value is below 0.1 or 10%, according to Saaty, the criteria and subcriteria are consistent.
After all comparison data between clusters and nodes of the models in the superdecision
program have been filled in, then we can find out unweighted supermatrix, supermatrix weighted, matrix limit, and priorities. Single comparison data which is the result of the Geometric Mean process for the three data from the experts which is then carried out rounding so that the value can be used in the Superdecision software.
Table 8. Rounding Geometric mean comparison matrices on criteria.
Knowledge Effort Working Conditions Responsibility
Knowledge 1 3 2 0
Effort 0 1 2 4
Working Conditions 1 0 1 3
Responsibility 2 0 0 1
(Source: data processed with Microsoft Excel)
In table 8 above, number 3 belongs to the criteria of knowledge (lines) with the effort criteria (columns) which become input data in superdecision software.
After the Mean Geometric process is carried out, then the next step is processing data values of
comparison between criteria and subcriteria using Superdecision software. This process begins by entering the comparison value of the results of the Geometric Mean process.
Figure 8. Input the mean Geometric value on the cluster criteria into the superdecision program.
(Source: data processed with Superdecision)
Figure 9. consistency index above is 0.05770
(Source: data processed with Superdecision) Each value of comparison between criteria
and subcriteria in this study must be tested whether the inconsistency index is worth below 0.1 or not. If the inconsistency index value is below 0.1 or 10%, according to Saaty, the criteria and subcriteria are
consistent. After all comparison data between clusters and nodes of the models in the superdecision program have been filled in, then we can find out unweighted supermatrix, supermatrix weighted, matrix limit, and priorities.
ICMST 2019 August, 1 2019 Figure 10. Priorities cluster sub-criteria
(Source: data processed with Superdecision) From the results of fig.10. Priorities it is known that the five sub-criteria that have the greatest weight are Supervision (0.339194), Experience (0.163616), Education (0.132956), Relations (0.081271) and Physical Effort (0.076682). After knowing the weight of each subcriteria, the weights of each criterion can also be known. The way to find out the weight of the criteria is to add the weight of the subcriteria to each criterion respectively.
Based on the results of figure 4.5 about the weight of the criteria, the results obtained that the criteria that have the highest weight are the criteria for Responsibility (0.557309). The next rating is
Knowledge (0.296572), Effort (0.135989) and Working Conditions (0.010131).
3.2.2. Analysis of Consistency Ratio on Criteria and sub-criteria
From the results of processing the data in the form of questionnaires, it can be obtained Inconsistency Index (inconsistent numbers), where all inconsistency index values are at 0.01354, which is below 10% (0.1) so that according to Saaty (1990) then the system this assessment can be called
"consistent".
Table 9. List of Criteria and Subcriteria Inconsistency Index
No Comparative Matrix Index Consistency
1 Between criteria 0,01354
2 Ybd node GOAL in the cluster criteria 0,00000
3 Ybd node Education in the Knowledge cluster 0,00000
4 Ybd Mental Business node in the Knowledge cluster 0,00000
5 Ybd node Relations in the knowledge cluster 0,00000
6 Ybd Supervision node in the knowledge cluster 0,00000
(source: data obtained from superdecision)
3.2.3. Subcriteria Priority Analysis
In addition to alternative priorities, the results of data processing using Superdecisions software also contain priority subcategories that can be
identified by looking at the weight values of each subcriteria. Subcriteria with the highest weight is Supervision (0.281658), according to the table below:
Table 10. Sub-criteria Priority Sequence No Sub-
criteria
Weight value 1 Supervision 0.339194 2 Experience 0.163616 3 Education 0.132956 4 Relation 0.081271 5 Physical
effort
0.076682 (source: data obtained from superdecision)
3.2.4. Criteria Priority Analysis
Priority criteria can be obtained when the priority of all subcriteria is known. The sum of the weights of each sub-criteria in one criterion will be the weight value of the criteria. The order of priority criteria is based on the amount of weight value of each alternative as follows:
Table 11. Priority Sequence Criteria No Criteria Weight
value 1 Responsibility 0,557309 2 Knowledge 0,296572
3 Effort 0,135989
4 Kondisi Kerja 0,010131 (source: data obtained from superdecision)
4. CONCLUSION
In conclusion, this study provides a new systematic approach to job evaluation analysis in organizations through the concept of strategic planning. The combination of DEMATEL and ANP in the effectiveness of the evaluation matrix and in qualitative and quantitative approaches one way integrated through criteria analysis is the most important part of this paper. Along with the development of organizational dynamics in the field of service, new positions will emerge in the organizational structure so that it needs organizational restructuring by adjusting the position level. So it is necessary to compile a model for determining the level of position and description and standardized specifications in helping carry out job evaluation.
Where it relates to a complete position class from the lowest position level to the highest position level so that between performance appraisal, workload and performance allowance can run in harmony and increasingly spur personnel performance. Furthermore, it can be standardized and applied in the Personnel Composition List by including the description, specifications, level, rank, class and number of performance allowances in the office clearly and in detail.
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