Innovative Time and Attendance System Software Selection for a Private Hospital: Leveraging the Entropy-TOPSIS Method Norazean Nordin1, Eaisya Nurfarhana Samat2, Fairuz Noraainaa Adam3, Nor Faradilah Mahad\[1\]4\* *1,2,3,4* *College of Computing, Informatics and Mathematics, Universiti Teknologi MARA Cawangan Negeri Sembilan, Kampus Seremban, Persiaran Seremban Tiga/1, Seremban 3, 70300 Seremban, Negeri Sembilan*
ARTICLE INFO ABSTRACT

Article history:

Received: 31 January 2024

Revised: 13 February 2024

Accepted: 13 February 2024

Online first: 1 March 2024

Published 1 March 2024

 Automated time and attendance systems offer the capability to track employee attendance, calculate working days, overtime hours, and late arrivals, and generate comprehensive attendance reports, thereby improving workforce productivity. Investing in suitable time and attendance system software is crucial for a company since many businesses are adopting digital time and attendance systems that automatically collect and analyse data to increase productivity and efficiency. This decision-making process considers numerous contradictory criteria. Thus, for this study, the Multi-Criteria Decision Making (MCDM) methods, namely Entropy and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), were used to choose the best time and attendance system software for a private hospital. There were six (6) criteria used to evaluate the time and attendance system software. The criteria were categorised as cost ease of use being compatible with existing HR software and operating system reporting capabilities customer service and scheduling capabilities Meanwhile, the alternatives are labelled as The outcomes showed that the ranking order for the criteria is while the ranking order for the alternative is respectively. In conclusion, the Entropy-TOPSIS can be used to assess and rank the alternatives.

Keywords:

MCDM

Entropy

TOPSIS

Time and attendance software

DOI:

10.24191/jcrinn.v9i1
# 1.0 introduction An attendance record is used to determine the presence or absence of employees. Recorded information includes employees begin and end hours, as well as their respective departments (Tuş & Adalı, 2019). For all business organisations, employee attendance is a major factor because it has a direct impact on both the individual and the organisation's performance (Ebrahim et al., 2019). Only with adequate and reliable records and monitoring can we uncover the full extent of absenteeism (Tyagi, 2019). Therefore, it is crucial for companies to find an effective way to monitor employees’ attendance. There are so many organisations that are going through technological advancements and process changes (Oloyede et al., 2013). According to Olagunju et al. (2018), an automated time and attendance system can monitor employees' presence, notifying them of their arrival and departure times. Additionally, it can calculate the employees' total working days, overtime hours, and instances of late arrival, and generate attendance reports. With technology, it is possible to increase productivity through time and attendance systems that monitor and compute data automatically (Oloyede et al., 2013). Therefore, to select the time and attendance system software, Multi-Criteria Decision Making (MCDM) methods were chosen in this study. The integrated Entropy-The Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) method is used to select the time and attendance system software problem of a private hospital. A time and attendance system can be either manual or automated. However, manual systems are not effective at keeping employees updated with information and evaluating their performance or working hours (Oo et al., 2018). Traditional methods of staff attendance, such as having employees sign attendance sheets upon entering an organisation, contribute to the low productivity of the organisation, although different market sectors have different impacts on staff absenteeism and staff lateness (Adewole et al., 2014). It is evident that by introducing time and attendance systems software that automatically monitors and analyses data, buddy punching and absenteeism can be reduced, and a company's productivity can be increased. As part of the MCDM method, the Entropy-TOPSIS method is employed for complex decisions that require the consideration of multiple criteria. The TOPSIS method is one of the commonly used methods to solve the MCDM problem. However, this method has a drawback. The major weakness of TOPSIS can be seen since this method does not offer weight elicitation (Roszkowska, 2013). The TOPSIS method requires criterion weights to be known but provides no instructions for calculating them (Dwivedi & Sharma, 2023). Thus, the weighting method is required to solve the TOPSIS method problem, and the weighting method can be defined based on static analysis, and their findings typically only represent the intuition or perception of those making the decisions at the time of analysis (Tzeng et al., 1998). Zhang et al. (2022) assert that TOPSIS and Entropy methods have numerous advantages. An important characteristic of the Entropy method is its ease of calculation. It is an objective weighting method that overcomes the disadvantages of subjective weighting and provides a more accurate assessment of the importance of each MCDM indicator. The concept of Entropy can be applied to the decision-making process effectively because it quantifies the disparities across data sets and clarifies the average intrinsic information that was conveyed to the decision-maker (Hafezalkotob & Hafezalkotob, 2015). As a result, combining both Entropy and TOPSIS would ensure unbiased, precise, and stable results (Goswami et al., 2022). In conclusion, this study integrates the Entropy method with TOPSIS to select the most suitable time and attendance system software based on the multiple criteria that are often in conflict. This paper contains five (5) sections. The introduction is the first section, and the literature review follows. The next section is the methodology that presents the framework of the Entropy-TOPSIS method. Section four contains results and discussions. The conclusion and suggestions are presented in the final section. # 2.0 Literature review A review of the literature and the background theory of the Entropy method and the TOPSIS method are included in this section. ## 2.1 Entropy Method One of the objective fixed-weight methods that uses a significant amount of information to calculate the weight of criteria is the Entropy weight method, which Shannon introduced in 1948 (Li et al., 2011). Shannon (1948) created this approach, which uses a discrete probability distribution to assess the uncertainties associated with the source of information. In information theory, Entropy is a measure of uncertainty, where the smaller the information quantity, the higher the uncertainty, and the lower the Entropy (Zhang et al., 2022). According to Abdullah and Otheman (2013), the higher the Entropy value, the lower the Entropy weight, and the smaller the different alternatives in this specific attribute, the less information the specific attribute provides and the less important the specific attribute becomes. In a straightforward and simple manner, the Entropy approach is able to measure the sources of information about the importance of each criterion and determine the relative weights (Srdjevic et al., 2004). The weights of criteria are vital in addressing MCDM issues, and sometimes applying unreasonable weights will lead to impractical outcomes (Luo et al., 2019).  The Entropy technique is used to calculate the criteria weights because it provides a data-driven and objective method for determining the relative importance of criteria, especially when explicit information or subjectivity is lacking in the decision-making process (Alamri et al., 2024). This approach is established as adequately consistent in identifying together the contrast, intensity, and divergence of responses in addition to figuring their weights suitably (Kumar et al., 2021). However, the Entropy method does not give scope to designers' preferences, and weights based solely on Entropy values would not appear sufficient without expert judgment (Zardari et al., 2015). A review of previous studies using the Entropy approach to solve different MCDM methods is presented in Table 1. Goswami and Behera (2020) used Entropy – Additive Ratio Assessment (ARAS) method to choose the best material among seven alternatives for engineering applications based on six criteria: bending fatigue limit, core hardness, cost, surface hardness, ultimate tensile strength, and surface fatigue limit. Meanwhile, Fajdek-Bieda (2021) used Entropy – VIKOR method in chemical process optimisation. In this study, three parameters were tested, which were process temperature, catalyst concentration, and reaction time. On the other hand, Haq et al. (2023) proposed an innovative framework to handle objective and subjective criteria simultaneously with crisp inputs by combining Interval-Valued Neutrosophic Sets (IVNSs) and the Entropy – MAIRCA framework to select the best sustainable material. Table 1. The application of the Entropy method | | | | | ------------------------- | -------------------------------------- | ---------------------------------------- | | Author(s) | Methodologies | Application | | Goswami and Behera (2020) | Entropy – Aras | Selection of best engineering materials. | | Fajdek-Bieda (2021) | Entropy – VIKOR | Chemical processes optimization. | | Haq et al. (2023) | Entropy – MAIRCA integrated with IVNSs | Sustainable material selection. | ## 2.2 TOPSIS Method The TOPSIS method was discovered by Hwang and Yoon (1981). This method will choose the alternative that has the shortest distance from the positive ideal solution and the farthest distance from the negative ideal solution. According to Wang and Lee (2007), the positive ideal solution maximises benefit criteria and minimises cost criteria. In contrast, the negative ideal solution is the solution that maximises the cost criteria and minimises the benefit criteria. From here on, it is found that TOPSIS is an effective method for decision-making because of its efficiency and capacity to rank alternative priorities using ordinary mathematical logic, which has no requirement for pairwise comparisons or consistency tests (Elsayed et al., 2017). In fact, the TOPSIS method is used due to its straightforward, easy-to-understand concept and ease of application (Zeleny, 2012). TOPSIS method is rational and relatively simple since the decision matrix is evaluated in several steps, starting with normalising columns, then multiplying values by the weighted criteria in each column (Triantaphyllou & Lin, 1996). In addition, it is important to note that this method is capable of rapidly identifying which alternative is the best among the available alternatives, is computationally efficient, and can measure the relative performance of each alternative in a simple mathematical form (Thakkar, 2021). Moreover, the TOPSIS method can assist decision-makers in organising issues to be solved and doing analysis, comparisons, and alternative ranking (Shih et al., 2007). As a result, an appropriate alternative(s) will be chosen that fits the goals and objectives. Table 2 depicts a summary of the past research on the TOPSIS method in literature. These studies integrate TOPSIS with other methods. For instance, Wang and Chang (2007) used the fuzzy TOPSIS method to rank the optimal initial training aircraft. Meanwhile, Ramezani et al. (2011) applied the hybridisation method of Goal Programming-TOPSIS to optimise the concepts of non-dominated solutions and prediction intervals. Furthermore, Lan et al. (2023) proposed employing the Analytic Network Process (ANP)-TOPSIS model to address tourist destination selection issues in a temporal neutrosophic setting. The paper proposes the Temporal Complex Neutrosophic Set (TCNS)-ANP-TOPSIS model. The ANP method is used to determine the weights of the criteria based on the relationship between the requirements and the criteria group in the temporal complex neutrosophic environment and TOPSIS to rank alternatives collected from different time intervals. In addition, Yadav et al. (2023) employed the improved MEREC-TOPSIS method to choose the best network in 5G heterogeneous networks for the Internet of Things (IoT). The improved MEREC figures out the priority weight of handover decision attributes by removing the effect that each attribute has on the performance of the candidate network. TOPSIS then figures out the ranking of the available networks. Table 2 contains multiple studies in which the TOPSIS methodology is used with various weighting methods to address MCDM issues. Thus, in this paper, TOPSIS is integrated with another weighting method, the Entropy method, to be used in the TOPSIS framework. Table 2. Application of TOPSIS method | | | | | ---------------------- | --------------------------- | ------------------------------------------------------------------ | | Author(s) | Methodologies | Application | | Wang and Chang (2007) | Fuzzy TOPSIS | Selection of best engineering materials. | | Ramezani et al. (2011) | A goal programming – TOPSIS | Sustainable material selection. | | Lan et al. (2023) | TCNS – ANP – TOPSIS | Tourist destination | | Yadav et al. (2023) | Improved MEREC – TOPSIS | Selecting the optimal network in 5G heterogeneous network for IoT. | # 3.0 methodology For this study, the weights of the criteria are calculated using the Entropy approach. As an objective weighting method, Entropy has higher reliability and accuracy than the subjective weighting method (Zhang et al., 2022). The following are the steps involved in applying the Entropy method (Huang, 2008): *Step 1*: Set up a decision matrix, as follows: | | | | | | | --- | | | | (1) | *Step 2*: Normalise the decision matrix by using Eq.(2) for both cost and benefit criteria. | | | | | | | --- | | | | (2) | Thus, a normalised decision matrix representing the relative performance of the alternatives is formed, as shown as follows: | | | | | | | --- | | | | (3) | *Step 3*: Calculate the Entropy value for the criterion by using: | | | | | | | --- | | | | (4) | where is the number of alternatives. *Step 4*: Calculate the Entropy weight of the criterion by using: | | | | | | | --- | | | | (5) | where refers to the degree of diversity of the information contained by each criterion. The weights determined by the Entropy method were further used as the input in the framework of TOPSIS in order to choose the best alternatives. The TOPSIS method can be implemented as follows (Jahanshahloo et al., 2006): *Step 1*: Consider the decision matrix, as shown in Eq. (1). *Step 2*: Normalise the decision matrix by using Eq. (6) for both cost and benefit criteria. | | | | | | | --- | | | | (6) | Thus, a normalised decision matrix, | | | | | | | --- | | | | (7) | is formed. *Step 3*: The weighted normalised decision matrix, is constructed as follows: | | | | | | | --- | | | | (8) | where is the weight of the criterion and *Step 4*: Determine the positive ideal solution (PIS) and negative ideal solution (NIS), respectively, as follows: | | | | | | | ---- | | | | (9) | | | | (10) | where is associated with benefit criteria and is associated with cost criteria. *Step 5*: Calculate the separation measures using the Euclidean distance. The separation of each alternative from the PIS is given as Eq. (11), meanwhile the separation of each alternative from the NIS is given as eq. (12), respectively. | | | | | | | ---- | | | | (11) | | | | (12) | *Step 6*: Calculate the relative closeness coefficient for each alternative using: | | | | | | | ---- | | | | (13) | *Step 7*: Rank the alternatives. In this study, the data about the selection of the suitable time and attendance system software for a private hospital in Türkiye (Tuş & Adalı, 2019) is used to implement the Entropy-TOPSIS method. A committee consisting of two employees from the Human Resources (HR) department and two employees from the information technology department was established to evaluate the alternative based on the criteria. The committee listed out all five (5) different time and attendance software, which were . Then, there were six (6) criteria that were evaluated to select the best time and attendance system software. The criteria considered were: cost ease of use being compatible with existing HR software and operating system reporting capabilities customer service and scheduling capabilities The criteria were separated into costs (inefficiency) and benefits (efficiency), so that was classified as cost criterion, while others were classified as benefit criteria. Since the last five criteria are qualitative, each alternative was evaluated using a 5-point scale (5: Excellent, 4: Very good, 3: Good, 2: Fair, 1: Poor) based on these criteria as the values can be seen in Table 3. Moreover, Microsoft Excel was used to analyse the data. Table 3. The decision matrix | Weightage | 0.56239 | 0.07041 | 0.03539 | 0.13237 | 0.05521 | 0.14424 | | ------------ | ------- | ------- | ------- | ------- | ------- | ------- | | Alternatives | | | | | | | | | 5000 | 3 | 3 | 4 | 3 | 2 | | | 680 | 5 | 3 | 2 | 2 | 1 | | | 2000 | 3 | 2 | 3 | 4 | 3 | | | 600 | 4 | 3 | 1 | 2 | 2 | | | 800 | 2 | 4 | 3 | 3 | 4 | # 4.0 RESULTS AND DISCUSSION This section includes the results and discussion of the weight and rank of the criteria and the rank of the alternatives. ## 4.1 Weightage and rank of criteria Table 4. Weight and ranking for criteria by using the Entropy and CRITIC methods | Criteria | Entropy Weight, | CRITIC Weight, | | | | | -------- | --------------------------------------------------------------- | -------------- | ------ | ----- | - | | | Weight | Rank | Weight | Rank | | | | Cost | 0.56239 | 1 | 0.157 | 3 | | | Ease of use | 0.07041 | 4 | 0.249 | 1 | | | Being compatible with existing HR software and operating system | 0.03539 | 6 | 0.168 | 2 | | | Reporting capabilities | 0.13237 | 3 | 0.121 | 6 | | | Customer services | 0.05521 | 5 | 0.154 | 4 | | | Scheduling capabilities | 0.14424 | 2 | 0.151 | 5 | | Sum | | 1 | | 1 | | In this study, the Entropy method was applied to compute the weights of the criterion. The data used was about the time and attendance system software selection problem for a private hospital in Türkiye (Tuş & Adalı, 2019). This data has six (6) criteria considered in selecting the time and attendance system software, and all the weights of the criteria were determined by the authors using the CRITIC method. Table 4 shows the weight of each criterion by using both the Entropy and CRITIC methods. Following the use of the Entropy approach to establish the weight of the criterion, the ranking order for criteria was It can be observed that the cost criterion has been chosen as the best criterion, and being compatible with existing HR software and operating system criterion has the least weightage in the case of the Entropy method. Using the CRITIC method, the criteria were ranked in the order Tuş and Adalı (2019) used the CRITIC method to compute the weight of each criterion. Notably, the best criteria selected for the CRITIC method is the ease of use criterion and reporting capabilities criterion is chosen as the least preferred criteria. Both techniques employed distinct mathematical models, therefore it is understandable that they will generate different results. The cost criterion is the most preferred weight for the Entropy method and the ease of use criterion is the most preferred weight for the CRITIC method. ## 4.2 The ranking of alternatives Table 5 shows the ranking of alternatives for the Entropy-TOPSIS method and the CRITIC-WASPAS method. The ranking order of Entropy-TOPSIS method is . However, in the case of the CRITIC-WASPAS method, there have been some slight changes in the ranking order where the positions of and have swapped with each other. Thus, the ranking order of the CRITIC-WASPAS method is . The relative value of the alternatives for the CRITIC-WASPAS method depends on the value of the coefficient, when . It can be noted that the value of the coefficient does not affect the change in the rank of the alternative and retains the ranking. Moreover, in both methods, and seems to be the least preferred alternatives. Table 5. Ranking of alternatives by using the Entropy – TOPSIS method and the CRITIC – WASPAS method | | Entropy-TOPSIS | CRITIC-WASPAS (Tuş & Adalı, 2019) | | | --- | -------------- | --------------------------------- | ---- | | | | Rank | Rank | | | 0.13343 | 5 | 5 | | | 0.83432 | 3 | 2 | | | 0.67941 | 4 | 4 | | | 0.84413 | 2 | 3 | | | 0.91500 | 1 | 1 | | Sum | 3.40628 | | | This finding may be affected by the high cost of and specifically $5000 and $2000, respectively. Both methods have agreed that is the best software for the private hospital. This result might be a result of the alternative receiving a high score when compared to compatibility with existing HR software and operating systems criterion and scheduling capabilities criterion. Also, cost criterion offered by makes it the third-lowest price among other alternatives. Therefore, by using the Entropy-TOPSIS method and the CRITIC-WASPAS method, was selected as the best time and attendance system software in a private hospital. # 5.0 CONCLUSION AND RECOMMENDATIONS This study used the Entropy-TOPSIS hybridisation method to solve a time and attendance system software selection problem for a private hospital in Türkiye. The Entropy method determined criteria weight, while TOPSIS ranked the software. Multiple alternatives were evaluated using qualitative and quantitative methodologies. Microsoft Excel was used for Entropy weight determination and TOPSIS evaluation. The combined method was found to be feasible and effective. From the results obtained, was the most favourable time and attendance system software and was the least favourable. This research proposes a hybrid decision-making model, the Entropy-TOPSIS method, to address complex time and attendance system software selection problems in a private hospital in Türkiye. The study emphasises the importance of effective evaluation methods in achieving quality decision-making, demonstrating the effectiveness of the Entropy-TOPSIS method. For future studies, it is recommended to adopt any weighting methods, such as the Analytic Hierarchy Process (AHP) for time and attendance system software selection. AHP uses paired comparisons for both discrete and continuous scales (Saaty, 1987), breaking the general method into easier-to-evaluate sub-problems (Alonso & Lamata, 2006). Consistency tests are conducted to ensure expert inputs are consistent. It is also recommended to assess the issues in a fuzzy environment. Fuzzy TOPSIS, developed upon TOPSIS, addresses MCDM problems in a fuzzy environment and balances uncertainty in human judgement data (Chen, 2000). It is simple and straightforward to use in solving MCDM problems with imprecise data (Rajak & Shaw, 2019). 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