Journal of Science and Technology UTHM
The Development of Actual Labour
Productivity Measurement Model for
Medium-Class Housing in
Malang East Java, Indonesia
Tjaturono1*, Maziah Ismail2
Department of Civil Engineering, Institut Teknologi Sepuluh Nopember,
Surabaya, East Java - Indonesia
1
Faculty of Management Technology,
Universiti Tun Hussein Onn Malaysia, Batu Pahat, Malaysia
2
*Corresponding Email: tjaturono@hotmail.com
Abstract
Up to the moment, the method used for measuring labor productivity in housing
construction is the ‘Burgelijke Openbare Werken (BOW) 1921’, its modification, and the
Indonesian National Standard (SNI) 2001. However, the labour productivity measured
based on these conventional methods does not show the actual on-site productivity. This
will make the construction cost estimate inefficient. This research carried out a study
on the actual productivity based on eight internal factors and nine external factors, in
order to obtain a model for actual labour productivity measurement. The data collection
method used was site observation, questionnaires, and interview with the labours in 21
work items in medium-class housing construction in Malang, East Java. Analysis of
the main components and multiple regression were applied on the data to obtain the
determinants of the productivity model. From this research valid models for labour
productivity measurements under the smallest coefficient of determination of 80% were
obtained with eight factors strongly affecting the models. Besides, it is also proved that
there are quite significant differences between the actual productivities and those of
BOW 1921 and SNI 2001.
Key words: model, labor productivity, medium-class house, efficient, internal and
external factors.
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1. INTRODUCTION
Among the production factors (labor, land, capital, material, machine, method, etc), labor
factor has an important role in achieving a certain productivity level [1,2,3]. Based on the
BOW 1921 the labor cost reaches 30-35% of the total construction cost [4,5,6]. Therefore,
in facing the competition challenge in the housing industry in Indonesia nowadays, many
developers use their own experiences as a reference for the estimate of the production
instead of BOW 1921 [7,8,9]. They even revise the legally used government’s standard,
the Indonesian National Standard 2001 [10] which has not shown the labor productivity
as expected. By using this SNI 2001 the labor cost is still high, which is 23% - 28% of the
total construction cost [11]. However, this ‘trial and error’ way of calculation has not given
the real productivity either, so that the efficient actual labor cost has not been achieved as
yet. Therefore, information on the actual on-site productivity rate is really needed by the
developers, since this is one of the key factors to obtain the efficient construction cost of
medium-class houses and to increase their competitive power [12,13,14,15,16].
There are many factors which affect labor productivity, internal factors as well as
external ones. Suternaister [17] stated that 90% of the labor productivity depends on the labor
performance and 10% depends on the technological development and the raw materials.
Researches in construction world show that the factors affecting labor productivity are
age [18,19], experience [5,18,19], raw material location, height of the brickwork, wage,
site condition, labor’s origin [18], building quality [5], education [11,18], supervision,
coordination, work sequence, working group composition, facility, overhours work [2],
motivation, discipline and skill [11].
Weather condition is a factor affecting productivity too [5,18,20,21], also material
supply [5,21,22,23], tools [5,22] and design [2,20].
There are more factors affecting labor productivity according to researchers outside
Indonesia, they are accident, disruptions, rework [24], project uniqueness, technology,
management [14], information, ability to arrange work sequence [23], overwork schedule
[21], work item and construction method [20].
Randolph Thomas stated [25] that there are hardly any researches have been done
in housing construction; let alone the modelling of labor productivity in medium-class
housing construction, either in or outside Indonesia. Sutikno [18] and Rostiyanti [19] did
researches particularly in brickwork, while Kaming et al. [4] did a research on building
construction covering soil, masonry foundation, concrete, brick, timber, floor and painting
works. Ratnayanti [2] did a research on soil, foundation and concrete works. Particularly
in housing construction Tjaturono [11] did a research for 21 work items. As mentioned by
Kaming et al [5] the building quality affects the labor productivity, therefore a research
on the labor productivity particularly in medium-class housing construction needs to be
conducted in order to increase the competitiveness of developers in Indonesia.
Based on the experience and the above researches, particularly in medium-class housing
construction in Indonesia, the factors need to be studied for their effects on productivity
consist of 8 internal factors, namely: age, experience, education, ethnic or labor’s origin,
motivation, discipline, skill and health, and 9 external factors, namely: material location,
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wage, weather, site condition, material supply, tools, coordination & communication,
supervisor’s experience and rework.
This research was conducted in Malang, since this city has quite a number of developers.
There are 34 developers, which is around 25% of the total developers in East Java [26].
The houses in the research are one-story medium-class ones [27,28], while the construction
method is a standard one used to be carried out by developers.
2. OBJECTIVES
The objectives of this research is to obtain:
a. Labor internal and external factors which affect simultaneously and partially the
productivity in medium-class housing construction
b.
Factors which dominantly affect the labor productivity
c.
The difference between the actual productivity and the productivity based on the
conventional methods of BOW 1921 and SNI 2001
d.
Labor productivity model affected by the dominant factors
3. RESEARCHMETHODOLOGY
The research methodology consists of the following steps:
3.1.
Sampling
The research covers six medium-class housing developers [26]. Simple random sampling
was applied to developers who have built more than 200 medium-class houses. Four
developers out of six were selected. This research was done from March, 2004 until August,
2004.
Selection of the labor groups of those four developers was based on simple random
sampling [29] out of 160 houses of type 70. From each developers 10 groups were taken,
so that 40 groups were available for the analysis.
Data sufficiency was tested using the formula given by Groeneveld [30] as follows:
[
]
2
Za/2 . S
n* = –––––––
, therefore n* = [(1,96 * 0,5724) / 0,184]2 = 36,85.
l
It shows that the data needed (n*) is smaller than the data available, which are 40
samples, Therefore the samples are sufficient.
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3.2.
Data collection
3.2.1. Productivity
Productivity can be defined as a ratio between the output (achievement) and the input
(resources used) or the effectivity and the efficiency [15,31,32], so that:
output (achievement)
effectivity
Productivity = ––––––––––––––––––– or ––––––––––.
input (resourcesused)
efficiency
This means that productivity has two dimensions. First, the effectivity, which is towards
the achievement of the maximal performance, that is the achievement related to the quality,
quantity and time. The concept of effectivity is based on the output, not the input. Second,
the efficiency related to the effort to compare the input with the result of the use of less
resources to obtain the same result. So the better the use of the resources is, the higher the
efficiency will be.
According to Christof [15] there are two measurments of productivity, first is the labor
productivity based on output/input ratio approach or single/partial factor productivity or a
ratio of output to one of the inputs (labor, capital, etc.);
output (achievement)
Labor productivity = ––––––––––––––––––– .
labor input
Second is the multiple-factor productivity, which is the ratio of the total output to
several inputs (capital and labor)
total output
Total productivity = ––––––––––––––––––––––– .
total input (labor + capital)
This research used the single/partial factor productivity measurement. The productivity
resulted from this research will be compared to that of BOW 1921 and SNI 2001. Since the
BOW 1921 and SNI 2001 express the productivity in term of labor coefficient, which is the
man-day needed to carry out 1 unit of work volume, this coefficient should be tranformed
first into productivity (work volume in 1 man-day) by using the following formula:
1
Productivity = –––––––––––––––– .
labor’s coefficient
The following example on soil excavation work describes the labor productivity
measurement according to BOW 1921 [33]. The labors needed to carry out 1 m3 common
soil excavation are: 0.75 day worker and 0.025 day supervisor. The figures 0.75 and 0.025
are the worker’s and the supervisor’s coefficients respectively. So the productivity of the
worker in soil excavation is 1/0.75 m3 per day.
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Observation on those 40 working groups were carried out daily for 7 days in the
morning, midday and afternoon during the working hours from 7.30 up to 15.30 or 6
effective hours. The measurement of the production is expressed in the production unit/
day/group.
3.2.2. Factors affecting productivity and methods of measurement
Data collection was carried out by observation, direct interview and questionnaire.
On site observation and questionnaire were carried out to obtain the production of each
group for the following qualitative variables:
– Skill (SKL), is measured by accuracy, initiative, quick and precise in doing work.
Skill categorization: G (good) with score 35- 45; F (fair) with score 25-35; and P
(poor) with score 15-25.
–
Discipline (DSP), is measured by on time, rule obedience, and doing instruction well.
DSP categorization: G (good) with score 35- 45; F (fair) with score 25-35; and P (poor)
with score 15-25.
–
Motivation (MTS), is measured by attitude and enthusiasm in doing work
productively.
MTS categorization: G (good) with score 35- 45; F (fair) with score 25-35; and P
(poor) with score 15-25.
Scoring for skill, discipline, and motivation are determined by these following steps:
15 items of questionnaire which set using Likert scale. To score the scale, the response
categories must weighted, with the numeric values 1, 2, and 3 respectively are assigned
to the response categories. Sugiono [34] are answered by each mason. The sum of the
weights of all items would represent the individual’s total score: G (good): 35-45; Fair (2535); and Poor (15-25). The highest score is 45, the middle score is 30, and the lowest is 15,
with range 10 for each category.
– Material supply (MS) is measured by timely in preparing materials
MS categorization is G (good) = often on time, F (fair)= almost on time, P (poor) =
often too late.
–
Health (HTH), is measured by physical appearance, not handicapped, and having good
sense organs.
HTH categorization is G = healthy, not handicapped, good physical appearance F =
somewhat healthy, handicapped but able to work well; P = unhealthy, handicapped and
the productivity is less than average.
–
Communication and Coordination (CC), is measured by information accuracy and
fluency from the manager to the labor.
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CC Categorization: G = fluent and accurate information, F: adequate information; P =
information is almost unavailable.
–
Rework (RW) is measured by the amount of works which are repeated.
RW Categorization: G = no work repeated work, F = work is sometimes repeated
work, P = work is often repeated.
–
Site (St), is measured by the in situ real condition or there is no puddle of water
St Categorization G = dry, F= wet, and P = muddy.
–
Tool (TL) is measured by how complete is the tools brought by a mason.
TL Categorization: G = complete tools, F= incomplete tools, P= shortcomings of
tools.
–
Education (EDU), is measured by level of formal education.
EDU Categorization = Primary School, Junior High School, and Senior High School.
Direct interview and observation to the labors/labor groups are conducted for the
following quantitative variables:
– Experience (EXP), is measured by the length of experience and measured in year.
EXP Categorization: less than 5 years, from 5 to 10 years, and more than 10 years.
–
Age (AGE), is measured by the length of living
AGE Categorization: less than 20 years, from 20 – 30 years, more than 30 years.
–
Material location (MD), is measured by distance between materials and location of
work.
MD Categorization: less than 5 meters, from 5 – 10 meters, and more than
10 meters.
–
Weather Condition (W), is measured by the temperature in the location of work
W Categorization: less than 25º C, from 25- 30º C, and more than 30º C
–
Wage (WG), is measured by the amount of money received by a labor.
WG Categorization: Rp. 30.000; Rp. 27.500, and Rp. 25.000
–
Supervisor experience (SE), is measured by the length of supervising is conducted.
SE Categorization: less than 5 years, from 5 – 10 years, and more than 10 years.
3.3.
Data grouping and their scales
The data obtained from 21 work items are classified based on some characteristics and
measurements, with the following scales:
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–
Rational, for experience (years), age (years), material location (m), weather/ temperature
(C), wage (Rp), and supervisor’s experience (years)
–
Ordinal to interval with Internal Successive Method [35] for education, skill,
discipline, motivation, health, rework, material supply, site condition, communication
and coordination and tools (Table 1).
–
Nominal: for ethnic.
3.4.
Statistical method
The statistical method used consists of descriptive statistics, correlation, factor analysis
and multiple regression analysis. Before carrying out the factor analysis it is necessary
to define the number of factors properly representing the variable groups by using main
component analysis. The number of factors was decided based on the number of Eigen
values which are more than 1 [36].
The productivity model obtained from the analysis was then tested for its validity
by checking whether the assumptions defined as the requirements for the application
of regression analysis method are satisfied or not. The assumptions to be satisfied are
that the residuals of the regression equation are identical (the variances are uniform),
independent (no correlation among the residuals) and normally distributed.
Whether the first assumption was satisfied or not can be shown by the plot between
the residuals and the predicted responses calculated based on the model. If the plot
does not show the behavior as shown by [37], then it can be concluded that the variances
of the residuals are uniform. The satisfaction of the second assumption can be proved
by carrying out a test on whether there is a serial autocorrelation among the residuals
or not. The testing procedure is given by Draper and Smith [37] by comparing the
Durbin-Watson statistics to the limit of the refusal area. While the third assumption test
was carried out by residual normal plotting and Kolmogorov-Smirnov test.
4. DATAANALYSISANDDISCUSSION
In order to show the steps in developing the model, out of 21 work items one is selected
as an example, namely masonry foundation. Discrete data of the masons used for the
calculation and analysis with the new scales are shown in Table 1. From the data in Table
1 a descriptive statistics of all variables was obtained as shown in Table 2. It can be seen in
this table that the average mason productivity in a day is 3.69 m3, the standard deviation is
0.5724 m3, the productivity range is 1.7 m3 with the lowest productivity is 2.8714 m3 and
the highest one 4.5724 m3.
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Table 1. Productivity data of masons in masonry foundation with new scales
(rational and interval)
NO
chap 6.indd 78
NAME
MtS
MD W
St
Wg
SE
CC
RW
TL
EXP AGE EDU DSP MTV ETC HTH SKL
AVE
PRDCTV
1
Sutrisno
2.290
10 25 3.944
27.5
8 2.133
3.795
2.264
10
32 1.000 2.392 2.297 Jw 2.624 2.129
3.6929
2
Maun
2.290
10 30 2.476
25
8 2.133
2.375
1.000
14
36 1.000 2.392 2.297
M 2.624 2.129
3.3714
3
Tomy
2.290
5
25 2.476
30
8 2.133
3.795
3.572
7
26 2.822 3.802 3.624 Jw 2.624 3.359
4.4714
4
Sanapi
2.290
8
28 2.476
25
8 2.133
2.375
2.264
10
35 1.000 2.392 2.297
M 1.000 2.129
3.4714
5
Martoyo
3.666
8
28 2.476
27.5
4 2.133
3.795
2.264
15
40 1.000 2.392 2.297 Jw 1.000 2.129
3.6214
6
Buasim
2.290
10 32 2.476
25
4 1.000
2.375
2.264
4
26 1.000 2.392 1.000
M 2.624 1.000
3.0500
7
Yasin
2.290
10 32 1.000
25
4 1.000
1.000
1.000
2
23 1.000 2.392 1.000
M 1.000 1.000
2.8786
8
Nurali
1.000
14 28 2.476
25
4 1.000
2.375
2.264
8
28 1.000 2.392 2.297
M 1.000 2.129
3.1429
9
Husen
1.000
8
30 1.000
27.5
8 2.133
2.375
1.000
2
26 1.000 1.000 2.297
M 1.000 1.000
2.9286
10
Suwono
2.290
14 25 2.476
27.5
8 2.133
2.375
2.264
7
30 1.000 2.392 2.297 Jw 2.624 2.129
3.2071
11
Sugeng A
3.666
10 24 3.944
30
8 2.133
3.795
3.572
6
26 1.000 2.392 2.297 Jw 2.624 3.359
4.0571
12
Samaun
2.290
10 24 3.944
30
8 3.388
2.375
3.572
10
30 1.000 2.392 2.297 Jw 1.000 3.359
4.0000
13
Tukimin
3.666
8
23 3.944
30
12 3.388
3.795
3.572
10
32 2.822 3.802 3.624 Jw 2.624 3.359
4.2429
14
Sugeng B
3.666
8
23 2.476
30
12 3.388
2.375
3.572
10
28 2.822 3.802 3.624 Jw 1.000 3.359
4.2143
15
Mat Supi'I
3.666
10 30 2.476
27.5
12 3.388
2.375
2.264
6
24 1.000 2.392 2.297
M 1.000 2.129
3.6143
16
Sapit
3.666
14 24 2.476
30
12 3.388
3.795
3.572
12
30 1.000 3.802 3.624 Jw 1.000 3.359
4.2429
17
Suroso
2.290
14 23 2.476
30
12 3.388
3.795
2.264
10
30 1.000 3.802 3.624 Jw 2.624 3.359
4.3000
18
Martono
2.290
5
28 2.476
25
12 3.388
2.375
1.000
8
26 1.000 1.000 1.000 Jw 2.624 1.000
2.9714
19
Husin
3.666
8
28 3.944
30
12 3.388
3.795
2.264
8
28 1.000 3.802 2.297
M 2.624 3.359
4.3286
20
Mulyono
3.666
8
28 2.476
27.5
12 3.388
3.795
2.264
5
26 1.000 2.392 2.297 Jw 2.624 2.129
3.4714
21
Rewok
3.666
7
25 2.476
30
12 3.388
3.795
3.572
8
28 1.000 3.802 3.624 Jw 2.624 3.359
4.4286
22
Basori
3.666
7
25 2.476
30
12 3.388
2.375
2.264
6
27 2.822 3.802 2.297 Jw 1.000 3.359
4.2286
23
Bakir
2.290
4
24 2.476
27.5
4 2.133
2.375
2.264
6
26 1.000 2.392 2.297
M 2.624 2.129
3.5143
24
Hartono
2.290
4
24 2.476
25
4 2.133
1.000
1.000
7
30 1.000 1.000 1.000 Jw 1.000 1.000
2.9143
25
Buasin
3.666
14 26 2.476
30
4 2.133
3.795
2.264
14
38 1.000 3.802 3.624
M 1.000 3.359
4.2714
26
Sugeng
2.290
8
25
4 2.133
2.375
2.264
2
20 1.000 2.392 1.000 Jw 2.624 1.000
2.9143
27
Basuki
2.290
10 26 1.000
27.5
8 3.388
2.375
3.572
12
35 1.000 2.392 2.297 Jw 2.624 2.129
3.5000
28
Saturi
2.290
6
24 2.476
30
8 3.388
3.795
3.572
15
38 1.000 3.802 3.624 Jw 2.624 3.359
4.4286
29
Nurali
3.666
6
24 2.476
30
8 3.388
3.795
3.572
10
33 2.822 3.802 3.624 Jw 1.000 3.359
4.5000
30
Suwono
3.666
10 26 2.476
25
8 3.388
2.375
2.264
8
28 1.000 2.392 2.297 Jw 2.624 2.129
3.4429
31
Rodat
3.666
10 28 3.944
27.5
10 2.133
2.375
2.264
5
30 1.000 2.392 2.297
M 2.624 2.129
3.5286
32
Manu
3.666
8
28 2.476
27.5
10 2.133
2.375
1.000
4
25 1.000 2.392 2.297 Jw 2.624 2.129
3.5429
33
Nasim
2.290
5
24 3.944
30
10 2.133
3.795
2.264
20
45 2.822 3.802 3.624 Jw 2.624 3.359
4.5714
34
Warsim
2.290
5
24 3.944
30
10 2.133
3.795
3.572
20
47 1.000 3.802 3.624 Jw 1.000 3.359
4.5286
35
Sukaeri
3.666
8
28 2.476
27.5
4 1.000
2.375
2.264
6
30 1.000 2.392 2.297 Jw 2.624 2.129
3.5571
36
Huda
3.666
8
28 2.476
25
4 1.000
1.000
2.264
5
29 1.000 2.392 1.000 Jw 2.624 1.000
2.9000
37
Hadi
1.000
14 28 1.000
27.5
4 1.000
2.375
1.000
10
30 1.000 2.392 2.297 Jw 2.624 2.129
3.2143
38
Gimin
1.000
10 30 1.000
25
4 1.000
2.375
2.264
5
26 1.000 1.000 1.000 Jw 2.624 1.000
2.8714
39
P. Nur
2.290
10 30 2.476
27.5
12 3.388
3.795
3.572
7
32 1.000 2.392 2.297
M 2.624 2.129
3.3286
40
Turiman
2.290
4
30
12 3.388
3.795
3.572
8
30 1.000 3.802 3.624 Jw 2.624 3.359
4.3714
30 1.000
26 2.476
78
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26
Sugeng
2.290
8
27
Basuki
2.290
10 26 1.000
30 1.000
25
4 2.133
2.375
2.264
2
20 1.000 2.392 1.000 Jw 2.624 1.000
2.9143
27.5
8 3.388
2.375
3.572
12
35 1.000 2.392 2.297 Jw 2.624 2.129
3.5000
28
Saturi
2.290
6
24 2.476
30
8 3.388
3.795
3.572
15
38 1.000 3.802 3.624 Jw 2.624 3.359
4.4286
29
Nurali
3.666
6
30
Suwono
3.666
24 2.476
30
8 3.388
3.795
3.572
10
33 2.822 3.802 3.624 Jw 1.000 3.359
4.5000
10 26 2.476
25
8 3.388
2.375
2.264
8
28 1.000 2.392 2.297 Jw 2.624 2.129
3.4429
31
Rodat
3.666
10 28 3.944
27.5
10 2.133
2.375
2.264
5
30 1.000 2.392 2.297
M 2.624 2.129
3.5286
32
Manu
33
Nasim
3.666
8
28 2.476
27.5
10 2.133
2.375
1.000
4
25 1.000 2.392 2.297 Jw 2.624 2.129
3.5429
2.290
5
24 3.944
30
10 2.133
3.795
2.264
20
45 2.822 3.802 3.624 Jw 2.624 3.359
4.5714
34
Warsim
2.290
5
24 3.944
Journal
Science
and Technology
30 10 of
2.133
3.795
3.572
20 UTHM
47 1.000 3.802 3.624 Jw 1.000 3.359 4.5286
35
Sukaeri
3.666
8
28 2.476
27.5
4 1.000
2.375
2.264
36
Huda
3.666
8
28 2.476
25
4 1.000
1.000
37
Hadi
1.000
14 28 1.000
27.5
4 1.000
2.375
38
Gimin
1.000
10 30 1.000
25
4 1.000
39
P. Nur
2.290
10 30 2.476
27.5
40
Turiman
2.290
4
30
26 2.476
6
30 1.000 2.392 2.297 Jw 2.624 2.129
3.5571
2.264
5
29 1.000 2.392 1.000 Jw 2.624 1.000
2.9000
1.000
10
30 1.000 2.392 2.297 Jw 2.624 2.129
3.2143
2.375
2.264
5
26 1.000 1.000 1.000 Jw 2.624 1.000
2.8714
12 3.388
3.795
3.572
7
32 1.000 2.392 2.297
M 2.624 2.129
3.3286
12 3.388
3.795
3.572
8
30 1.000 3.802 3.624 Jw 2.624 3.359
4.3714
Table 2. Descriptive Statistics of Masons in Masonry Foundation Work
Variable
MtS
MD
W
St
Wg
SE
CC
RW
TL
EXP
AGE
EDU
DSP
MTV
HTH
SKL
AVE PR
N
Mean
Median
StDev
Minimum
Maximum
40 2.746 2.2900.885 1.000
3.666
40 8.750
8.000
2.933
4.000 14.000
40 26.650
26.000
2.597 23.000 32.000
40
2.548
2.476
0.878
1.000
3.944
40
27.813
27.500
2.056 25.000 30.000
40
8.200
8.0003.196
4.000
12.000
40
2.468
2.133
0.895
1.000
3.388
40
2.875
2.375
0.876
1.000
3.795
40 2.468
2.264
0.903 1.000
3.572
40 8.5508.000 4.266
2.000 20.000
40 30.225
30.0005.582
20.000
47.000
40 1.273
1.000 0.659
1.000
2.822
40
2.746
2.3920.885
1.000 3.802
40 2.468
2.2970.902
1.000
3.624
40 2.056
2.6240.785
1.000
2.624
40
2.395
2.129 0.900
1.000
3.359
40
3.6959
3.5500
0.5724
2.8714
4.5714
In order to check whether there is no autocorrelation nor multicollinearity among
the independent variables, it is necessary to identify the relations among these
variables by checking the correlation between pairs of all the variables as seen in
Table 3.
From Table 3 it is seen that there are strong autocorrelations among some of the
variables, which is shown by p-value less than 0.05. This shows an interdependence
among the variables so that the model development between the variable productivity
and the variables which were presumed as affecting can not be carried out directly.
Whilst, the correlations between the productivity and almost all of the independent
variables are quite significant except the variables: material distance (MD) and health
(HTH). However, the variable MD can still be included in the model, since it has a valid
correlation with the variable education. The model was then formulated by grouping the
independent variables having strong correlations to form certain factors.
Based on the main component analysis the ideal number of factors was obtained as
4 factors as shown in Table 4 with their respective Eigen value 7.2176, 1.8660, 1.2316
and 1.1491. Then factor analysis using maximum likelihood method and varimax
rotation, as shown in Table 5, shows that factor 1 was formed by the variables: material
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supply (loading factor 0.565), weather (-0.578), wage (0.830), supervisor’s experience
(0.770), communication and coordination (0.776), rework (0.674), tools (0.684), discipline
(0.756), motivation (0.744) and skill (0.834). Factor 2 was formed by the variables:
experience (-0.918) and age (-0.937), while factor 3 by the variable material distance
(-0.900), and factor 4 by the variable health (0.830). Loading factor for the variable skill is
0.834 which means that this variable has a strong correlation with Factor 1. The variables
which form the respective factors can also bee seen in Fig. 1.
Table 3. Matrix of Correlation between Independent and Dependent Variables
MtS
MD
W
S
Wg
SE
CC
MD -0.096
0.555
W -0.244 0.180
0.130 0.266
St 0.412 -0.169 -0.485
0.008 0.298 0.002
Wg 0.350 -0.104 -0.687
0.457
0.027 0.524 0.000 0.003
SE 0.357 -0.137-0.325 0.398
0.537
0.024 0.400 0.041 0.011 0.000
CC
0.402 -0.187 -0.465 0.252 0.532 0.800
0.010 0.248 0.003 0.1170.0000.000
RW
0.217 -0.037-0.416
0.436
0.715
0.478
0.465
0.178 0.819 0.008 0.005 0.000 0.002 0.002
TL
0.276 -0.109-0.536 0.348 0.637 0.355 0.475
0.084 0.504 0.000 0.028 0.000 0.024 0.002
EXP 0.012 -0.052 -0.496
0.392
0.426 0.161 0.215
0.942 0.749 0.001 0.012 0.006 0.321 0.182
AGE -0.011 -0.106 -0.362
0.381 0.323 0.0460.057
0.944 0.515 0.022 0.015 0.042 0.777 0.727
EDU
0.222 -0.326 -0.434 0.202 0.453 0.284 0.238
0.169 0.040 0.005 0.211 0.003 0.076 0.139
DSP
0.414 -0.050 -0.541 0.375
0.780
0.434
0.413
0.008 0.758 0.000 0.017 0.000 0.005 0.008
MTV 0.248 -0.037 -0.666
0.359
0.835
0.502
0.485
0.123 0.820 0.000 0.023 0.000 0.001 0.002
HTH-0.040 -0.063 0.063 0.062 -0.081 0.080 -0.034
0.805 0.698 0.698 0.704 0.621 0.625 0.835
SKL 0.378 -0.031 -0.722
0.543
0.910
0.533
0.545
0.016 0.851 0.000 0.0000.000 0.000 0.000
0.583
0.000
0.496
0.001
0.414
0.008
0.217
0.179
0.623
0.000
0.735
0.000
0.161
0.321
0.717
0.000
AVEPRDTV
0.406 -0.203 -0.702
0.009 0.210 0.000
0.751
0.000
0.544
0.000
0.914
0.537
0.542
0.000 0.000 0.000
RW
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TL
EXP
AGE
EDU
DSP
MTV
HTH
EXP
0.316
0.047
AGE 0.247 0.920
0.124 0.000
EDU 0.315 0.194 0.123
0.048 0.2290.451
DSP
0.609
0.482
0.363
0.507
0.000 0.002 0.021 0.001
MTV 0.603
0.598
0.494
0.441
0.817
0.000 0.0000.001 0.004 0.000
HTH 0.006 -0.153-0.151 -0.132 -0.043-0.097
0.9700.3460.353 0.416 0.792 0.552
SKL 0.671
0.558
0.410
0.456
0.863
0.885 -0.088
0.0000.0000.0090.003 0.000 0.000 0.588
AVEPRDTV0.661
0.000
0.582
0.000
0.453
0.003
0.502
0.001
0.888
0.000
0.888 -0.076
0.000 0.643
SKL
0.961
0.000
CellContents:Pearsoncorrelation
P-Value
Loading Plot of P.Mt-KTRP
0.5
W
MtS
MD HTH
CC
Second Factor
0.0
EDU
TL
RW
ST
-0.5
DSP
MTV
AGE
-1.0
-0.5
0.0
WG
SKL
EXP
0.5
First Factor
Figure 1. Loading Plot of variable MtS – SKL
Table 4. Main Component Analysis for Eigen Value Calculation from the Correlation Matrix
Eigenanalysis of the Correlation Matrix
Eigenvalue
7.2176
1.8660
1.2316
1.1491
0.9683
0.8797
Proportion
0.451
0.117
0.077
0.072
0.061
0.055
Cumulative
0.451
0.568
0.645
0.717
0.777
0.832
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Table 5. Factor Analysis on Independent Variables
Principal Component Factor Analysis of the Correlation Matrix
Rotated Factor Loadings and Communalities
Varimax Rotation
Variable
Factor1
Factor2
Factor3
Factor4 Communality
MtS 0.5650.165
0.231-0.009
0.399
MD -0.0190.062-0.900-0.056
0.817
W -0.5780.435
-0.2440.174
0.614
St 0.422 -0.415
0.3260.263
0.526
Wg 0.830-0.357
0.027-0.146
0.838
SE 0.7700.067
0.1540.217
0.669
CC
0.7760.064
0.1600.087
0.639
RW 0.674-0.478
-0.1190.251
0.759
TL 0.684 -0.2700.014-0.035
0.542
EXP 0.182 -0.918
0.027-0.079
0.882
AGE 0.023 -0.937
0.078-0.044
0.886
EDU 0.425-0.093
0.490
-0.464 0.644
DSP 0.756-0.381
-0.009-0.207
0.760
MTV 0.744-0.5240.066-0.180
0.864
HTH 0.0350.060
0.048 0.830
0.696
SKL 0.834-0.458
-0.010-0.146
0.927
Variance 5.67193.1925
1.34761.2523
11.4642
% Var 0.3540.200
0.0840.078
0.717
Table 6. Factor Score Coefficients
FactorScoreCoefficients
Variable
Factor1
Factor2
Factor3
Factor4
MtS 0.155 0.1770.121 -0.012
MD 0.107 0.039-0.725 -0.070
W -0.046 0.080-0.133 0.096
St 0.003
-0.1380.225 0.265
Wg 0.152 0.002-0.071 -0.081
SE 0.203 0.1510.041 0.185
CC 0.202 0.1620.042 0.078
RW 0.109 -0.115-0.162 0.254
TL 0.131 0.005-0.064 0.002
EXP -0.123 -0.378 0.010 0.017
AGE -0.174 -0.4190.069 0.047
EDU 0.036 0.0700.338 -0.362
DSP 0.131 -0.017-0.093 -0.131
MTV 0.109 -0.086-0.135 -0.098
HTH 0.023 -0.0390.051 0.684
SKL 0.139 -0.043-0.101 -0.073
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From the factor analysis it is also obtained the factor scores for the four factors which
will replace the original independent variables as new variables in the regression analysis.
By using the values of factor score coefficients as shown in Table 6, the factor scores were
obtained from the respective factor as shown in Equation 1.
Factor1=0.155MtS+0.107MD-0.046W0.003St+0.152Wg+0.203SE
+0.202CC+0.109RW+0.131TL-0.123EXP-0.174AGE
+0.036EDU+0.131DSP+0.109MTV+0.023HTH+0.139
SKL
Factor2=0.177MtS+0.039MD+0.08W+0.138St+0.002Wg+
0.151SE+0.162CC-0.115RW+0.005TL-0.378EXP
-0.419AGE+0.07EDU-0.017DSP-0.086MTV-0.039HTH
-0.043SKL
Factor3=0.121MtS-0.725MD-0.133W+0.225St-0.071Wg+
0.041 SE + 0.042 CC -0.162 RW -0.064 TL + 0.01 EXP
0.069AGE+0.338EDU-0.093DSP-0.135MTV+0.051
HTH-0.101SKL
Factor4=-0.012MtS-0.07MD+0.096C+0.265St-0.081Up+
0.185PP+0.078CC+0.254+U+0.002TL+0.017EXP
+0.047AGE-0.362EDU-0.131DSP-0.098MTV+0.684
HTH-0.073+SKL
(E.1)
(E.1)
Then Factor 1, Factor 2, Factor 3 and Factor 4 together with the variable ethnic (ETC)
become independent variables in the regression analysis. Variable ETC is not included as
one of the forming factors since it has a nominal scale and in the regression analysis this
variable is a dummy one which has the value of 0 (Maduranese) or 1 (Javanese). In the
regression model development between the variable productivity and the variables ETC,
Factor 1, Factor 2, Factor 3 and Factor 4 it is first selected which independent variables
should be included in the model. This variable selection was carried out by using stepwise
method [37] with the a value of 5% for the independent variables to be in and out
of the model. Based on the stepwise method the independent variables or factors that
should be included in the model are Factor 1, Factor 2, Factor 3 and Factor 4 with the
variable/factor sequence priority as seen in Table 7. While the variable ETC does not
satisfy the criterium to become one of the independent variables in defining the regression
model of mason.
By using regression analysis, the calculation result for obtaining the relationship
between the variable productivity and Factor 1, Factor 2, Factor 3 and Factor 4 can be
seen in Table 8. The regression equation expressing the productivity of mason is as
follows:
Productivity=3.70+0.465FACTOR1-0.278FACTOR2 (E.2)
+0.0751FACTOR3-0.0743FACTOR4
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Table 7. Result of the Variable Selection by Stepwise Method
Alpha-to-Enter: 0.05 Alpha-to-Remove: 0.05
Response is AVE PR on 5 predictors, with N = 40
Step 1
Constant 3.696
2
3.696
FACTOR1 0.465
0.465
T-Value 8.57
15.28
P-Value 0.000
0.000
FACTOR2
-0.278
T-Value
-9.16
P-Value
0.000
FACTOR3
T-Value
P-Value
FACTOR4
T-Value
P-Value
S 0.339
0.190
R-Sq 65.91
89.56
R-Sq(adj) 65.01
89.00
C-p 130.5
17.0
3
3.696
4
3.696
0.465
0.465
16.50
18.11
0.000
0.000
-0.278
-0.278
-9.88
-10.85
0.000
0.000
0.075
0.075
2.67
2.93
0.011
0.006
-0.074
-2.90
0.006
0.176
0.160
91.28
92.97
90.56
92.16
10.6
4.4
Tests on the coefficients show that each coefficient of the four factors is significant
(p-value << 0,05). The significant results are also expressed by the regression equation as
shown by the variance analysis in Table 9. The four factors can describe the variation in
the productivity as much as 92.97% (R2). While the percentage of variation which can be
described by the regression equation obtained after the calculation adjustment related to
the free degree between the square sum of the residuals and the square sum of the corrected
total [37] is 92.16% (R2 adj).
Table 8. Result of Regression Analysis between the Variable Productivity and
Factor 1 – Factor 4.
Theregressionequationis
AVEPRDCTV=3.70+0.465FACTOR1-0.278FACTOR2+0.0751FACTOR3-
0.0743FACTOR4
PredictorCoef
SECoefT P VIF
Constant3.69589
0.02533145.89
0.000
FACTOR10.46471
0.0256618.11 0.000 1.0
FACTOR2-0.27836
0.02566-10.85 0.000 1.0
FACTOR30.07510
0.025662.93 0.006 1.0
FACTOR4-0.07433
0.02566-2.90 0.006 1.0
S=0.1602 R-Sq=93.0%
R-Sq(adj)=92.2%
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Table 9. Variance Analysis of Regression Equation
AnalysisofVariance
Source DF
SS
MS F P
Regression 4
11.8794
2.9699 115.68
0.000
ResidualError 35
0.8985
0.0257
Total 39
12.7779
For model validation, whether the assumption that the residuals of the regression
equation is identical (the variances are uniform), the test result shows that the residuals
tend to be constant (Figure 3), which means that the assumption is satisfied.
Plot Residual Vs Response Predicted
S ta n d a r d i z e d R e s i d u a l
0, 3
0, 2
0, 1
0
0
5
10
15
20
25
30
35
40
-0, 1
45
F IT S1
-0, 2
-0, 3
-0, 4
Response Predicted
Figure 3. Plot of Standardized Residual Vs Predicted Response
In testing whether there is a serial correlation among the residuals, the calculation
result shows that the Durbin-Watson statistics is 2.49, while the table value of a = 5%
with 4 independent variables on two-side test is dl = 1,29. Because d = 2,49 > dl or
4 – d > dl then it can be concluded that there is no serial autocorrelation among the
residuals. The third assumption can be satisfied by the assumed model using KolmogorovSmirnov normality test where p-value > 0.15 which means H0 is accepted or it is proved
that the residuals follow the normal distribution. This can also be seen on the plot of the
residuals on the normal paper in Fig. 4 as follows.
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Normal Probability Plot
.9 9 9
.9 9
Probability
.9 5
.8 0
.5 0
.2 0
.0 5
.0 1
.0 0 1
-0.3
A verage: -0.0000000
S tDev: 0.151787
N: 40
-0.2
-0.1
0.0
0.1
0.2
K olm ogorov-S m irnov Norm ality Tes t
D+ : 0.059 D-: 0.074 D : 0.074
A pprox im ate P -V alue > 0.15
Resi 1
Figure 4. Plot of Residual Normal Probability
For the interpretation the equation (e.2) has to be returned to the original variable
so that the role of each variable forming the factors will be known. By replacing Factor
1, Factor 2, Factor 3 and Factor 4 in equation (e.2) with equations (e.1), we will obtain
equation (e.3) which states the direct relations between the internal and external variables
and the variable productivity for mason.
Productivity=3.70+0.0327MtS-0.0102MD-0.0605W
+ 0.0369 St + 0.07085 Wg + 0.04161 SE
+0.04615CC+0.05146RW+0.05473TL
+0.04747EXP+0.03743AGE+0.04962EDU
+0.06856 DSP+ 0.07153 MTV -0.02533 HTH
+0.07442SKL
(E.3)
Based on the equation (e.3) the average productivity of mason can be predicted
as
much as 3.70 m3 per day. The variables having positive contribution to the productivity
increase is material supply (MtS), site (St), wage (Wg), supervisor’s experience (SE),
communication and coordination (CC), rework (RW), tools (Tl), experience (EXP), age
(AGE), discipline (DSP), motivation (MTV) and skill (SKL). The biggest contribution is
given by the variables: skill (0,07442), motivation (0,07153), wage (0,07085) and dicipline
(0,06856). While the variables having negative contribution are: material distance (MD),
weather (W) and health (HTH). The biggest negative contribution is given by the variable
weather for rock foundation work item.
This procedure can be repeated to develop the models of the other 20 work items.
Table 10 shows the whole predicted model equations for 21 work items.
In general for 21 work items, the variables such as material supply (MtS), site condition
(St), wage (Wg), supervisor’s experience (SE), communication and
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chap 6.indd 87
Work item
IntSep
Const.
MtS
MD
W
St
Wg
SE
CC
RW
TL
EXP
AGE
EDU
DSP
MTV
HTH
SKL
R2 (%)
3.7000 0.0327 -0.0102 -0.0605 0.0369 0.0709 0.0416 0.0461 0.0515 0.0547 0.0475 0.0374 0.0496 0.0686 0.0715 -0.0253 0.0744 93.0
7 Masonry
84.79
85.1
84.4
91.1
90.6
21 Wall painting 18.3821 0.3022 -0.0173 0.0596 0.1342 0.3431 0.0778 0.3624 0.3597 0.2961 0.1694 0.1325 0.1139 0.3797 0.3861 0.0861 0.3991 92.0
91.4
88.2
79.7
90.5
Floor tile
13.7286 0.1278 0.0229 -0.0285 0.0960 0.1365 0.1067 0.1446 0.1466 0.1317 0.0196 0.0264 0.1072 0.1550 0.1582 0.0597 0.1429 91.4
installation
Door & window
19 leaf
3.0979 0.0422 -0.0185 -0.0132 -0.0039 0.0302 0.0012 0.0370 0.0444 0.0447 -0.0061 -0.0103 -0.0194 0.0429 0.0335 0.0067 0.0357 80.2
installation
Timber
20
5.7386 0.0042 0.0397 0.0279 0.0384 0.0865 -0.0529 0.0687 0.1023 0.1009 -0.0114 -0.0177 0.0796 0.1165 0.1205 -0.0183 0.1189 88.8
painting
18
89.3
17 Ceiling work 11.7643 0.2561 -0.0290 -0.0118 -0.0317 0.1751 -0.0536 0.2660 0.2787 0.2857 0.0534 0.0238 0.0377 0.2723 0.2588 0.1694 0.2260 91.5
15.5000 0.0707 -0.0690 0.0493 0.0469 0.1376 0.0549 0.1650 0.1515 0.2044 0.0781 0.0590 0.0600 0.1806 0.1936 0.0318 0.1948 90.4
21.2179 0.1631 -0.0138 -0.1172 0.1027 0.3989 0.1118 0.3465 0.3288 0.3828 0.1628 0.0885 0.2004 0.3609 0.4419 0.0845 0.4484 87.88 87.23
27.6679 0.4993 -0.1530 0.1920 -0.0392 0.4697 0.2533 0.3723 0.3781 0.5121 0.0507 0.0714 0.2312 0.5028 0.4728 0.4528 0.4700 86.6
0.2080 0.0029 -0.0027 0.0018 0.0003 0.0030 0.0009 0.0060 0.0058 0.0058 0.0001 -0.0005 -0.0007 0.0068 0.0059 0.0034 0.0047
88.2
87.1
Rafter and
batten work
Roof tile
installation
Wall
rendering
Molding
plaster
Electrical &
plumbing
1.3757 0.0095 -0.0014 0.0142 0.0056 0.0194 0.0137 0.0184 0.0199 0.0180 0.0066 0.0051 0.0110 0.0167 0.0189 0.0086 0.0197 92.3
85.8
93.0
92.2
88.0
94.0
85.8
84.9
94.1
88.3
R2 adj
(%)
5.0750 0.0793 -0.0255 0.0527 0.0530 0.0795 0.0441 0.0760 0.0719 0.0961 0.0211 0.0246 0.1121 0.1096 0.1045 -0.0210 0.0917 88.1
16
15
14
13
12
11 Brick work
Concrete
8
1.1661 0.0063 -0.0024 -0.0006 0.0100 0.0189 0.0150 0.0198 0.0190 0.0222 -0.0021 -0.0024 0.0158 0.0210 0.0218 0.0085 0.0241 93.9
pouring
Timber frame
9
0.4121 0.0063 0.0049 0.0055 -0.0023 0.0069 0.0034 0.0039 0.0093 0.0103 0.0011 0.0006 0.0027 0.0119 0.0111 -0.0009 0.0094 86.9
installation
Roof truss &
10
0.2694 0.0033 -0.0027 -0.0009 0.0000 0.0034 -0.0016 0.0034 0.0042 0.0032 0.0011 0.0007 0.0002 0.0041 0.0031 0.0012 0.0039
purlin work
89.7
48.4670 0.3655 -0.1874 0.1009 0.0538 0.6147 0.6250 0.7383 0.8603 0.9038 0.1732 0.1528 0.3838 0.8109 0.8472 0.5585 0.9215 89.2
6 Form work
Timber frame 0.1158 0.0014 0.0009 -0.0012 0.0012 0.0019 0.0003 0.0012 0.0011 0.0013 0.0005 0.0003 0.0017 0.00170.0017 0.0007 0.0022 86.5
Door & window
4
6.0979 -0.0678 0.0929 -0.0087 -0.0104 0.1244 -0.0002 0.1003 0.1150 0.1089 -0.0069 -0.0264 0.0468 0.1284 0.1389 0.0774 0.1480 86.9
assembling
Steel
5 reinforcement 64.9821 1.6610 -0.7335 0.0820 1.0915 -0.1616 -0.3539 1.1168 1.4406 1.1778 0.4393 0.3897 -0.2367 1.0419 1.4606 0.8113 1.6392 94.6
work
3
1 Excavation
3.8700
0.0599 0.0617 0.1019 -0.0047 0.0807 0.0597 0.0939 0.0076 -0.0034 0.0010 0.10430.1142 0.0254 0.1153 88.9
Border
2 sheeting
35.9491 -0.1677 -0.4216 0.1072 0.4442 0.8153 0.5894 0.7351 0.6177 0.7439 -0.1087 -0.2396 0.7310 1.1125 1.1190 -0.0879 1.1983 94.7
installation
No.
Table 10. Predicted Coefficients of Regression Equation Model for 21 work items in medium-class housing construction in Malang
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coordination (CC), rework (RW), tools (TL), experience (EXP), education (EDU),
discipline (DSP), motivation (MTV), and skill (SKL) have a positive contribution to the
productivity increase. On the contrary the variables: material distance (MD), weather (W),
age (AGE) and health (HTH) give negative contribution. Variable ethnic does not have any
significant effect on the productivity.
The regression equations obtained for all work items have high coefficients of
determination, which is more than 80%. This shows that in general the developed models
are quite good to represent the relations between the variable productivity and the other
affecting variables. The highest coefficient of determination is in the regrssion equation for
border sheeting installation, which is 94.7%, while the lowest is in timber door and window
installation, which is 80.2%.
By inserting the data from each work item into the respective equation as concluded in
Table 10, we can obtain the prediction value of the labor productivity in each work item. To
compare the productivity to the one according to BOW 1921 and SNI 2001 it is necessary
to equalize the effective working hours by changing the effective working hours used in
SNI 2001 from 5 hours to 6 hours as in this research. The results can be seen in Table 11.
Table 11 shows that compared to the conventional productivity of BOW 1921 and SNI
2001, there is a real difference with the productivity in 21 work items resulting from this
research. The biggest difference is in the timber door and window assembling as much as
408% of SNI 2001, and in the timber formwork as much as 869% of BOW 1921, while the
smallest one is in the productivity of steel reinforced work which is 8% of SNI 2001 and in
concrete pouring 17% of BOW 1921.
This research result has a similarity as well as a difference with the research of [11]. The
similarity is on the internal factors: skill, motivation and discipline which are dominantly
affecting the labor productivity. The difference is that the labor productivity model in this
resaerch is more valid, shown by the smallest coefficient of determination of 80.2%, which
in [11] was 68.47%.
5. CONCLUSION
a.
There are 16 out of 17 independent variables which affect the labor productivity
in medium-class housing construction, either partially or simultaneously. The only
variable which does not affect is the variable ethnic.
b.
The coefficients of the regression equation obtained for each work item show the
contribution of the independent variables to the productivity, particularly the variables:
skill, motivation, discipline, wage, communication and coordination, tools, rework and
material supply have a strong effect. The variables: material distance, weather, site,
experience, age and supervisor’s experience have a moderate effect, and the variables
education and health have relatively weak effect.
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c.
From the predicted model of productivity there is a quite significant difference between
the conventional productivity (BOW 1921 and SNI 2001) and the actual one on site.
The biggest differences are in the ceiling work and the door and window work, which
is 840% to BOW 1921 and 428% to SNI 2001 respectively. The smallest differences
are in the steel reinforced work and concrete pouring work, which is only 8% to SNI
2001 and 17% to BOW 1921 respectively as seen in Table 11.
d.
The models have a high precision and accuration (valid) as seen in Table 10. This can
be seen in the value of R2, which is minimum 80.2% and maximum 94.7% as well as
satisfying the validation of identical, independency and normally distribution tests.
Table 11. Differences between the Productivity from the Research Result and the
BOW 1921 & SNI 2001
NO
Work Item
1
Soil excavation
2
Border sheeting installation
3
4
5
6
7
8
9
10
11
12
13
14
m3
(SNI 2001)*
Productivity
(Prediction)
3,87
( 1 SL )
to
BOW ‘21
189
To
SNI 2001
30
259
200
231
38
3
( 1 SL )
0,035
0,084
0,116
m3
( 1SL + 1/3UL )
( 1SL + 1/3UL )
( 1 SL )
0,84
( 1SL + 1/3UL )
1,20
( 1SL + 1/3UL )
6,09
( 1 SL )
625
408
m3
16,4
60
65
296
8
kg
( 1 SL + 1 UL )
5
( 1 SL )
12
( 1 SL )
48,47
869
304
m2
( 1SL + 0,4UL )
( 1 SL + 1,2 UL )
( 1 SL + 1 UL )
0,54
2
3,7
585
85
m3
( 1SL+3UL )
( 1 SL + 2,5 UL )
( 1 SL + 2 UL )
17
113
194
25
m3
1
0,55
1,166
( 1SL+6UL )
( 1 SL + 1 UL )
( 1 SL + 1 UL )
0,14
0,33
0,412
m3
( 1SL + 1/3UL )
( 1SL + 1/3UL )
( 1 SL + 1 UL )
0,042
0,10
0,269
540
169
m3
( 1SL + 1/3UL )
0,57
( 1SL + 1/3UL )
1,26
( 1 SL )
0,376
142
9,5
m3
( 1SL+3UL )
( 1 SL + 3,2 UL )
( 1 SL + 2 UL )
0,11
0,12
0,208
89
73
m3
( 1 SL + 1 UL )
10
( 1 SL + 1 UL )
15
( 1 SL )
27,668
176
84,5
m2
( 1SL + 1,5UL )
( 1 SL + 2,5 UL )
( 1 SL + 2 UL )
8,04
21,48
216
164
( 1SL+3UL )
-
-
-
Roof truss and purlin work
Rafter and batten work
Wall rendering
6,7
Molding plaster
16
Electrical & plumbing
titik
17
Ceiling work
1,25
( 1 SL + 1,34 UL ) ( 1SL + 1,5UL )
15,5
( 1 SL )
2,4
5,07
( 1 SL + 0,6 UL ) ( 1 SL + 1 UL )
3,75
11,76
89
( 1 SL + 0,6 UL )
-
111
840
241
m2
( 1 SL + 1 UL )
3,6
3,60
13,73
281
281
m2
( 1 SL + 2 UL )
0,6
( 1 SL + 0,6 UL )
1,20
( 1 SL + 1 UL )
3,10
416
158
m2
( 1SL + 1/3UL )
( 1SL + 1/3UL )
( 1 SL )
Floor tile installation
installation
Compared
Productivity
(BOW)
1,34
( 1 SL )
35,95
Timber frame installation
Door & window leaf
Compared
( 1 SL + 1 UL )
15
19
Result
12
Masonry foundation
Roof tile installation
% Difference
Productivity
( 1 SL + 1 UL )
Steel reinforcement work
Brick work
% Difference
Conventional
10
Door & window assembling
Concrete pouring
Analysis
( 1 SL + 1 UL )
Timber frame
Form work
Average
BOW Revision
m3
m2
m'
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Unit
Average
Average
( 1 SL )
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9
10
11
12
13
14
Timber frame installation
0,14
0,33
0,412
m3
( 1SL + 1/3UL )
( 1SL + 1/3UL )
( 1 SL + 1 UL )
0,042
0,10
m3
( 1SL + 1/3UL )
0,57
( 1SL + 1/3UL )
1,26
m3
( 1SL+3UL )
( 1 SL + 3,2 UL )
( 1 SL + 2 UL )
0,11
0,12
m3
( 1 SL + 1 UL )
10
( 1 SL + 1 UL )
15
m2
( 1SL + 1,5UL )
( 1 SL + 2,5 UL )
( 1 SL + 2 UL )
8,04
21,48
Roof truss and purlin work
Brick work
Rafter and batten work
Roof tile installation
Wall rendering
6,7
194
25
0,269
540
169
( 1 SL )
0,376
142
9,5
0,208
89
73
( 1 SL )
27,668
176
84,5
216
164
-
-
-
111
840
241
Journal of Science and Technology UTHM
15
Molding plaster
m2
m'
16
Electrical & plumbing
titik
-
17
Ceiling work
m2
( 1 SL + 1 UL )
3,6
3,60
13,73
281
281
m2
( 1 SL + 2 UL )
0,6
( 1 SL + 0,6 UL )
1,20
( 1 SL + 1 UL )
3,10
416
158
m2
( 1SL + 1/3UL )
( 1SL + 1/3UL )
( 1 SL )
2,2
4,96
5,74
160
16
m2
( 1SL )
3,34
( 1 SL )
15,2
( 1 SL )
18,38
450
21
m2
( 1SL )
( 1 SL )
( 1 SL )
18
19
installation
20
21
1,25
Floor tile installation
Door & window leaf
Timber painting
Wall painting
( 1SL+3UL )
-
( 1 SL + 1,34 UL ) ( 1SL + 1,5UL )
15,5
( 1 SL )
2,4
5,07
( 1 SL + 0,6 UL ) ( 1 SL + 1 UL )
3,75
11,76
( 1 SL + 0,6 UL )
( 1 SL )
Note: SL Skilled Labor , UL: Unskilled Labor
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