**Ranking Factors that Affect ~~Affecting~~ Mental Illness and Social Stress Among Students using Fuzzy Topsis**

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**HIGHLIGHTS**

  - Mental illness and social stress were critical issues and more common among university students and society.

  - Fuzzy Topsis was used to determine the ranking of the factors affecting mental illness and social stress among students.

  - The data were collected from three psychology officers as decision-makers in the different campuses of UiTM.

  - The decision-makers were evaluated the issues in this study using linguistic variables ranging from “very affected” to “not affected”.

ABSTRACT

*A mental illness is a disorder that causes changes in one's emotions, thoughts, or behaviour. ~~It can be triggered by a combination of these factors.~~ Anybody can have mental illness disregard the ages, races, religion, sex and nationality if the stress cannot be managed. Stress is a key contributor to the onset of mental illnesses. Academic stress, as well as the socioeconomic status and financial issues, are all risk factors that might lead to mental illness and social stress. Because they arise from a variety of settings, all of these risk factors are difficult to identify. This study is designed to investigate the significant factors influencing mental illness and social stress among students and also their respective ranking using Fuzzy Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). Social life, academic life, and financial situation will be ranked in this study as three elements influential to mental illness and social stress among students. The elements will be rated using the following criteria, which are family background, educational level, physical health, and mode of study. ~~In addition, the variables will be ranked according to the correlation using Fuzzy TOPSIS~~. Three decision-makers are approached to evaluate the issues in this study using linguistic variables ranging from "very affected" to "not affected." The findings shows that academic life is the crucial factor impacting students' mental health and social stress, with a proximity coefficient of 0.469. Social life is the least important contributing element, with a closeness coefficient of 0.358. The findings of this study might be beneficial to a large number of people such as parents, counsellor and students themselves. Future research might broaden the scope of the project by including a variety of criteria and options.*

*Keywords: mental illness, social stress, fuzzy TOPSIS, ranking, multi-criteria decision making*

# INTRODUCTION

World Health Organization (2020) emphasises that mental health is more than just the absence of psychological disorders. Anxiety and depression are the most common mental disorders. They can strike people of all ages and appear unexpectedly. According to World Health Organization (2020), depression is one of the world's most serious health issues. The term "serious mental illness" (SMI) is used by health professionals to describe the most severe mental health disorders. Furthermore, stress is defined as any type of change that causes physical, mental, or psychological pressure. It is a major risk factor for the emergence of mental disorders. Understanding the dynamic maladaptation that leads to pathological stress-related mental states would necessitate identifying the molecular determinants of stress's impact on the brain (Lindberg, 2019). Most people are all too familiar with the term "stress". Both short-term and long-term stress can result in a variety of symptoms. However, chronic stress can have long-term consequences. Social stress is the most common type of stress we encounter in our daily lives. Social stress is a major factor in the complex and multifactorial exact cause of psychiatric disorders (Troisi, 2020).

Mental illness has the potential to lead to suicide in the long run. According to Ward et al. (2022), depression or another mental disease are factors in almost 90% of suicides. Some students arrive at university with pre-existing issues of stress, while others acquire issues during their time there. Next, mental illness and social stress usually do not solely build up from one factor. Instead, the stress among students are influenced by various risk factors, depending on their effect and severity on their lives. The factors in this study are social life, academic life and financial position while the subfactors are family background, mode of study, physical health and education level.

**FACTORS AFFECTING MENTAL ILLNESS AND SOCIAL STRESS AMONG STUDENTS**

Three factors affected the mental illness are social stress, academic life and financial position are discussed below.

**Social Stress**

It is beneficial to our health and well-being to have friends and other social relationships. Conversely, loneliness and isolation can have a negative impact on mental and physical health. Students who maintain solid relationships and stay connected to others are more likely to have a higher quality of life. People's relationships and their perceptions of empathy toward others have been influenced by social distance and security measures (Saladino et al., 2020). Furthermore, students will go through a unique stage of psychosocial development as they transition from adolescence to adulthood. Dropping out of college, poor academic performance, strained relationships, and reduced emotional functioning are all major repercussions of mental health issues at this period. When these factors are combined, they might have a detrimental impact on physical health and future professional employment opportunities (Saeri et al., 2018).

**Academic Life**

Academic pressure, combined with the difficulties of starting and attending school or college, can lead to the emergence or worsening of mental health. That is why it is vital to take account of our academic life because it is one of the things that can affect our mental health and social stress. Consequently, students became stressed and involved themselves in an unhealthy lifestyle. Aside from academic pressure, some students must deal with the hard demands of separation and individuation from their families, while others must juggle various works and family responsibilities (Pedrelli et al., 2015). Students' relationships with friends and family members and their quality of life, academic achievement, and satisfaction with their college experience can be negatively impacted by mental health disorders. As a result of these challenges, students' future job prospects, earning potential, and general health may be jeopardised. It also impacts the student's energy level, attention, reliability, mental capacity, and optimism, all of which obstruct performance (Keyes & Eisenberg, 2012).

**Financial Position**

# On a yearly, semester, or monthly basis, students get financial assistance in the form of scholarships or loans. The delay is highly stressful since students must rely on alternative financial resources such as self-funding, family funding, borrowing from others, or obtaining emergency loans from their respective higher education institutions. This is a circumstance beyond the people's control, and all they can do now is wait for financial assistance to be given (Daud et al., 2018). Due to financial difficulties, students are unable to concentrate on their studies. Because of their limited financial resources and high cost of living, several students have had financial issues such as running a deficit budget, failing to prioritise spending, not maintaining records of expenses, no planning, and failing to repay debts. As a result, the income-to-expenditure ratio is unbalanced (Daud et al., 2018). 

**SUBFACTORS THAT AFFECT MENTAL ILLNESS AND SOCIAL STRESS AMONG STUDENTS**

Family background, mode of study, physical health and education level are the subfactors that affect mental illness and social stress among students.

**Family Background**

One of the aspects that can be evaluated from the family background is the parent's status which can influence children's mental health and social stress, for instance, single parents or divorced parents. Divorce causes emotional anguish for the entire family, but it can be particularly frightening, perplexing, and upsetting for children. When parents’ divorce, their children generally lose regular contact with one parent, usually the father. Reduced contact impacts the parent-child link, and researchers discovered that many children feel less close to their fathers (Morin, 2021). Parental separation is not the most difficult component for some children. On the other hand, the underlying pressures make divorce the most difficult. Consequently, children with many siblings may experience poorer physical and mental health than children with fewer siblings (Woodgate et al., 2016). Not to mention, growing up in a large family is linked to receiving less time, attention, and financial support from their parents. Lastly, the residential area is one of the common aspects in the family background, which make students develop social stress indirectly. This happened especially to students that live in rural areas.

**Mode of Study**

Students can now be educated online through the use of distance learning. They can enrol in a remote learning or e-learning programme as a part of this. Students who use these learning approaches usually enrol as part-time students. Some teachers say that these students are not taking their studies seriously and are receiving poor grades as a result (Muthuprasad et al., 2021). It is difficult to determine the impact of working on academic performance because students can pick how many hours they want to spend working and studying independently.

**Physical Health**

Physical health is connected to a student's academic achievement and participation in the workforce and society. In addition, current and anticipated changes in physical health outcomes are influenced by the changing nature of countries and cultures, family structure, financial and technological advancements (Sarama & Clements, 2019). Regular physical activity participation is a national learning requirement for physical education to facilitate the development of regular and meaningful physical activity involvement. Emotional health may have an impact on physical health. As a result, poor mental health can have a negative influence on your physical health. Post engagement benefits include improved attention when physical exercise is utilised as a break from academic study time (Watson et al., 2017).

**Education Level**

Academic institutions are usually depicted as welcoming, discrimination-free environments where stigma is deconstructed, debated, or challenged (Hernández-Torrano et al., 2020). However, many students enjoy and adapt well to the move to higher education. Some find it more difficult, owing to competing demands of employment, school, and family obligations with mental health concerns that have been connected to exam performance and higher education dropout rates. Concerns about students' mental health in higher education are rising (Hamza et al., 2021). Academic demands, test stress, financial hardship, and changes in social support networks that occur from leaving home are all possible reasons why higher education may exacerbate symptoms of common mental disorders. However, despite worries regarding student mental health, it is unclear if students in higher education are at a higher risk of common mental health issues than the overall population.

**TECHNIQUE FOR ORDER PREFERENCE BY SIMILARITY TO IDEAL SOLUTION (TOPSIS)**

Hwang and Yoon introduced the TOPSIS, which is the most well-known technique for tackling MCDM problems **(Nadaban et al., 2016)**. The purpose of Fuzzy TOPSIS is to use fuzzy numbers instead of crisp numbers to allocate the importance of criteria and the performance of alternatives. We define the Fuzzy Positive Ideal Solution (FPIS) and the Fuzzy Negative Ideal Solution (FNIS) using the TOPSIS concept. Finally, each alternative's proximity coefficient is calculated to determine the ranking order of all alternatives.

Previous research has used Fuzzy TOPSIS to analyse data in various fields. Moreover, **Jusoh @ Hussain et al. (2021)** conducted a study on the most influential factor of flood frequency in Kedah. After calculating the closeness coefficient, the study's result is obtained. The highest proximity coefficient value closest to FPIS is chosen as the initial ranking. Rainfall is the most important factor in causing flooding in Kedah. The closeness coefficient of rainfall calculated using Fuzzy TOPSIS is 0.318, the highest value among the other variables. It proves that the study successfully achieved its goals of ranking alternative flood factors.

In addition, **Azizi et al. (2015)** conducted a study of the crucial factors and sub-criteria for selecting the best supplier in the automobile. The criteria were recognised as responsibility, flexibility, competency, cost optimisation, and speed, with 18 sub-criteria based on four suppliers, which were A, B, C, and D factories. The research reveals that Factory A has the best supplier in the automotive sector, whereas Factory D has the worst. Therefore, it may be concluded that they can entice their staff to be creative, propose new ideas, and allure their supplier chain's weaker organisations to emulate more agile and stronger companies.

# METHODOLOGY 

The data on factors affecting mental illness and social stress among students was collected by providing questionnaires to the psychology officers in UiTM Tapah, UiTM Segamat and UiTM Machang. There are three psychology officers who were chosen to be the decision makers. All the three of them were asked to rate the criteria and alternatives of this study. The rating is from ‘very affected’ (VA), ‘affected’ (AF), ‘neutral’ (N), ‘less affected’ (LA) and ‘not affected’ (NA).

**Fuzzy Set Theory Concept**

**The models are expressed as a set of fuzzy linguistic rules developed from expert operators' experience or as a set of fuzzy implications that characterise the process's input-output connection locally. The triangle curve's membership function is a combination of two linear functions, as shown by the presence of three parameters (*a, m, b*). Since the membership function of a fuzzy number is triangular, it is called a triangular fuzzy number (TFN), as shown in Figure 1.**

![](6323f17c45cca_media/media/image1.png)![](6323f17c45cca_media/media/image2.png)

**Figure 1: Membership function of Triangular Fuzzy Number (TFN).**

**This fuzzy number is usually applied in research and practice. The triangular fuzzy number is defined as *m, a,* and *b,* where *m, a,* and *b* stand for mode, left endpoint, and right endpoint, respectively.**

![](6323f17c45cca_media/media/image3.png) \(\frac{x - a}{m - a},\ \ \ \ \ \ \ \ \ \ \ \ a \leq x \leq m.\ \)

\(\text{\ \ \ \ \ \ }µ_{A}\left( x \right)\ \  =\) \(\frac{b - x}{b - m},\ \ \ \ \ \ \ \ \ \ \ \ m \leq x \leq b.\)

\(0,\ \ \ \ \ \ \ \ \ \ \ \ \ \ x < a\ \ or\ \ x > b.\)

**Linguistic Variable**

**The decision-makers information is represented as ordered Fuzzy numbers, which are ideally suited to handle partial and unclear knowledge. In many cases, the researcher employs approximate measures or quantities rather than exact ones. To convert language concepts into Fuzzy numbers, some conversion scales have been used. According to** **Sodhi & T. (2012), the criteria and options are rated on a scale of 1 to 9. Table 1 shows a summary of the Fuzzy ratings for the linguistic variable.**

**Table 1: Linguistic variables and Fuzzy** **numbers for rating affecting mental health and social stress factors among students.  
**

| **Fuzzy number** | **Alternative assessment** |
| ---------------- | -------------------------- |
| **(1,1,3)**      | **Not Affected (NA)**      |
| **(1,3,5)**      | **Less Affected (LA)**     |
| **(3,5,7)**      | **Neutral (N)**            |
| **(5,7,9)**      | **Affected (AF)**          |
| **(7,9,9)**      | **Very Affected (VA)**     |

**(Source: Sodhi & Prabhakar, 2012)**

**FUZZY TOPSIS IN DETERMINING THE FACTORS AFFECTING MENTAL ILLNESS AND SOCIAL STRESS AMONG STUDENTS**

**There are three alternatives and four criteria used in this study. The alternatives are social life, academic life, and financial position. Meanwhile, the criteria are family background, mode of study, physical health, and education level. In this study, the factor is considered an alternative, and it will be ranked. Figure 2 shows all the criteria and alternatives in determining the factors affecting mental illness and social stress among students.**

![](6323f17c45cca_media/media/image4.png)

**Figure 2: Alternative and criteria in determining the affecting factors of mental illness and social stress among students.**

**STEPS IN FUZZY TOPSIS**

Fuzzy TOPSIS consists of the following steps:

Step 1: To Assign a score to each of the criteria and alternatives, we will assume we have a *K* member decision-making group. The Fuzzy rating of the *k*<sup>th</sup> decision-maker about alternative \(A_{i}\) with respect to criterion \(C_{j}\) *is* denoted as \(\widetilde{x_{\text{ij}}^{k}}\) = \((a_{\text{ij}}^{k}\), \(b_{\text{ij}}^{k}\), \(c_{\text{ij}}^{k}\)), while the weight of criterion \(C_{j}\) is denoted as \(\widetilde{w_{j}^{k}}\) *=* \((w_{j1}^{k}\) , \(w_{j2}^{k}\) , \(w_{j3}^{k}\) ).

Step 2: Compute the fuzzy aggregate ratings for alternative and the aggregate Fuzzy weight for criteria by using equation (1) and (2).

The aggregate Fuzzy rating \(\widetilde{x_{\text{ij}}}\) = (\(a_{\text{ij}}\), \(b_{\text{ij}}\)*,* \(c_{\text{ij}}\)*)* of \(i^{\text{th}}\ \)alternative with respect to \(j^{\text{th}}\ \)criterion is given by:

\(a_{\text{ij}} = \ \min_{k}{\{ a_{\text{ij}}^{k}\}}\ \)*,*\(\text{\ \ b}_{\text{ij}} = \frac{1}{k}\sum_{k = 1}^{k}b_{\text{ij}}^{k}\)*,*\(\text{\ \ c}_{\text{ij}} = \max_{k}{\{ c_{\text{ij}}^{k}\}}.\) (1)

The aggregate Fuzzy weight \(\widetilde{w_{j}}\) *=* \((w_{j1}\), \(w_{j2}\), \(w_{j3})\ \)for the criterion \(C_{j}\) may be calculated by:

\(w_{j1} = \ \min_{k}{\{ w_{j1}^{k}\}}\), \(w_{j2} =\) \(\frac{1}{k}\sum_{k = 1}^{k}w_{j2}^{k}\), \(w_{j3}\  = \max_{k}{\{ w_{j3}^{k}\}}\text{.\ }\) *(2)*

*Step 3:* *Compute the normalised Fuzzy decision matrix by using equations (3) and (4) where* \(\widetilde{R}\) *= \[*\(\widetilde{r_{\text{ij}}\rbrack}\)

\(\widetilde{r_{\text{ij}}}\)*=* \(\left( \frac{a_{\text{ij}}}{c_{j}^{*}},\ \frac{\text{\ \ b}_{\text{ij}}}{c_{j}^{*}},\ \frac{c_{\text{ij}}}{c_{j}^{*}} \right),\) *and* \(c_{j}^{*}\) *=* \(\max_{i}\)*{*\(c_{\text{ij}}\)*} (benefit criteria). (3)*

\(\widetilde{r_{\text{ij}}}\)*=* \(\left( \frac{a_{j}^{-}}{c_{\text{ij}}},\ \frac{a_{j}^{-}}{\text{\ \ b}_{\text{ij}}},\ \frac{a_{j}^{-}}{a_{\text{ij}}} \right),\) *and* \(a_{j}^{-}\) *=* \(\min_{i}\)*{{*\(a_{\text{ij}}\)*} (cost criteria). (4)*

*Step 4: Compute the weighted* *normalised Fuzzy decision matrix by using equation (5).*

\(\widetilde{V}\) *= (*\(\widetilde{v_{\text{ij}}}\)*), where* \(\widetilde{v_{\text{ij}}}\)*=* \(\widetilde{r_{\text{ij}}}\) *x* \(w_{j}\)*. (5)*

Step 5: Compute the Fuzzy Positive Ideal Solution (FPIS) and Fuzzy Negative Ideal Solution (FNIS)

by following calculation (6) and (7) for FPIS and FNIS.

\(A^{*}\) = (\(\widetilde{v_{1}^{*}}\), \(\widetilde{v_{2}^{*}}\), …, \(\widetilde{v_{n}^{*}}\)), where \(\widetilde{v_{j}^{*}}\)= \(\max_{i}\){\(v_{ij3}\)}. (6)

\(A^{-}\) = (\(\widetilde{v_{1}^{-}}\), \(\widetilde{v_{2}^{-}}\), …, \(\widetilde{v_{n}^{-}}\)), where \(\widetilde{v_{j}^{-}}\) = \(\min_{i}\){\(v_{ij1}\)}, (7)

where \(A^{*}\) is for FPIS and \(A^{-}\) represents FNIS.

Step 6: Compute the distance of criteria of each alternative from FPIS and FNIS is as follows:

\(d\left( \widetilde{x,\ }\ \widetilde{y} \right)\):=\(\sqrt{\frac{1}{3}\left\lbrack \left( a_{1} - a_{2} \right)^{2} + \left( b_{1} - b_{2} \right)^{2} + \ \left( c_{1} - c_{2} \right)^{2} \right\rbrack}\). (8)

Let

\(d_{i}^{*} = \ \sum_{j = 1}^{n}{\ d(\widetilde{v_{\text{ij}}}}\),\(\widetilde{v_{j}^{*}}\)), \(d_{i}^{-} = \ \sum_{j = 1}^{n}{\ d(\widetilde{v_{\text{ij}}}}\)*,*\(\widetilde{v_{j}^{-}}\)*)*, (9)

be the distance from each alternative \(A_{i}\) to the FPIS and the FNIS, respectively.

Step 7: Compute the closeness coefficient \(\text{CC}_{i}\) for each alternative by using equation (10).

\(\text{CC}_{i}\) = \(\frac{d_{i}^{-}}{d_{i}^{-} + \ d_{i}^{*}}\) . (10)

Step 8: Rank the alternatives with the highest closeness coefficient represents the most affecting factor.

**FINDINGS AND DISCUSSIONS**

The method of Fuzzy TOPSIS was used in this study to determine and rank the factors affecting mental illness and social stress among students. Three psychology officers of different UiTM were chosen to be the decision makers, referring as DM1, DM2 and DM3. The researcher used the information gathered during the interviews to create a survey questionnaire that aimed to prioritise the value of each factor based on the family background (C1), mode of study (C2), physical health (C3) and education level (C4). The alternatives in this study are social life (A1), academic life (A2), and financial position (A3). The decision-makers evaluated the rating of the alternatives based on the linguistic variables listed in Table 1 for each criterion. In addition, the interviewees' responses were gathered and analysed. Table 2 shows the relative importance of the criteria considered by the decision-makers. Table 3 shows the results of the analysis of the options based on each of the criteria.

Table 2: The Important Weight of Criteria.

| **Criteria** | **Decision-Maker** |     |  |     |
| ------------ | ------------------ | --- |  | --- |
|              | DM1                | DM2 |  | DM3 |
| **C1**       | VA                 | AF  |  | VA  |
| **C2**       | N                  | AF  |  | LA  |
| **C3**       | N                  | VA  |  | AF  |
| **C4**       | AF                 | VA  |  | AF  |

**Table 3**: The Ratings of Alternatives.

| **Criteria Alternative** | **Decision-Maker** |     |     |    |
| ------------------------ | ------------------ | --- | --- | -- |
|                          | DM1                | DM2 | DM3 |    |
| **C1**                   | A1                 | VA  | AF  | VA |
|                          | A2                 | VA  | AF  | N  |
|                          | A3                 | VA  | AF  | VA |
| **C2**                   | A1                 | N   | AF  | N  |
|                          | A2                 | N   | AF  | LA |
|                          | A3                 | N   | AF  | N  |
| **C3**                   | A1                 | N   | VA  | AF |
|                          | A2                 | LA  | VA  | AF |
|                          | A3                 | N   | AF  | AF |
| **C4**                   | A1                 | AF  | AF  | AF |
|                          | A2                 | AF  | VA  | AF |
|                          | A3                 | AF  | VA  | AF |

The aggregate fuzzy weights for the criteria which is Table 4 was computed by equation (1).

**Table 4**: The Aggregate Fuzzy Weight Each Criteria.

| **Criteria** | **Aggregate Fuzzy weight** |                       |  |
| ------------ | -------------------------- | --------------------- |  |
| **C1**       |                            | (5.000, 8.333, 9.000) |  |
| **C2**       |                            | (1.000, 5.000, 9.000) |  |
| **C3**       |                            | (3.000, 7.000, 9.000) |  |
| **C4**       |                            | (5.000, 7.667, 9.000) |  |

**Table 5**: Aggregate Fuzzy Rating of Alternatives.

| **Alternative** |                       |    |    |                       |  |                       |
| --------------- | --------------------- | -- | -- | --------------------- |  | --------------------- |
| **Criteria**    | A1                    | A2 | A3 |                       |  |                       |
| **C1**          | (5.000, 8.333, 9.000) |    |    | (3.000, 7.000, 9.000) |  | (5.000, 8.333, 9.000) |
| **C2**          | (3.000, 5.667, 9.000) |    |    | (1.000, 5.000, 9.000) |  | (3.000, 5.667, 9.000) |
| **C3**          | (3.000, 7.000, 9.000) |    |    | (1.000, 6.333, 9.000) |  | (3.000, 6.333, 9.000) |
| **C4**          | (5.000, 7.000, 9.000) |    |    | (5.000, 7.667, 9.000) |  | (5.000, 7.667, 9.000) |

Aggregate fuzzy rating of alternative was computed using equation (2). Table 6 shows the normalized aggregate fuzzy decision matrix. It was classified family background (C1) and education level (C4) as benefit criteria while cost criteria are mode of study (C2) and physical health (C3). Then, the decision was made based on the weight level of the criteria listed in Table 1. If the weight level is high, it will be determined as a benefit criterion. Meanwhile, if the level of the weight is low, it will be determined as cost criteria. Equation (3) was used to determine family background (C1) and education level (C4), equation (4) was used to compute mode of study (C2) and physical health (C3).

**Table 6**: Normalized Aggregated Fuzzy Decision Matrix for Alternative.

| **Alternative** |                       |                       |                       |
| --------------- | --------------------- | --------------------- | --------------------- |
| **Criteria**    | A1                    | A2                    | A3                    |
| **C1**          | (0.556, 0.926, 1.000) | (0.333, 0.778, 1.000) | (0.556, 0.926, 1.000) |
| **C2**          | (0.111, 0.176, 0.333) | (0.111, 0.200, 1.000) | (0.111, 0.176, 0.333) |
| **C3**          | (0.111, 0.143, 0.333) | (0.111, 0.158, 1.000) | (0.111, 0.158, 0.333) |
| **C4**          | (0.556, 0.778, 1.000) | (0.556, 0.852, 1.000) | (0.556, 0.852, 1.000) |

Weighted normalized fuzzy decision matrix was tabulated in Table 7 by using equation (5).

**Table 7**: Weight Normalized Fuzzy Decision Matrix.

| **Alternatives** |                       |                       |                       |  |
| ---------------- | --------------------- | --------------------- | --------------------- |  |
| **Criteria**     | A1                    | A2                    | A3                    |  |
| **C1**           | (2.778, 7.716, 9.000) | (1.667, 6.481, 9.000) | (2.778, 7.716, 9.000) |  |
| **C2**           | (0.111, 0.882, 3.000) | (0.111, 1.000, 9.000) | (0.111, 0.882, 3.000) |  |
| **C3**           | (0.333, 1.000, 3.000) | (0.333, 1.105, 9.000) | (0.333, 1.105, 3.000) |  |
| **C4**           | (2.778, 5.963, 9.000) | (2.778, 6.531, 9.000) | (2.778, 6.531, 9.000) |  |

In this study, equation (6) and (7) were applied to calculate the FPIS and FNIS, respectively. Choose \(v^{+}\) for the greatest value in each row and \(v^{-}\) for the minimum value in each row. FPIS and FNIS are shown in Table 8.

**Table 8**: FPIS and FNIS for Each Criterion.

<table>
<thead>
<tr class="header">
<th>Criteria</th>
<th><br /><span class="math display"><strong>A</strong><sup><strong>+</strong></sup></span><br /></th>
<th><br /><span class="math display"><strong>A</strong><sup><strong>−</strong></sup></span><br /></th>
<th></th>
</tr>
</thead>
<tbody>
<tr class="odd">
<td>C1</td>
<td>(9.000, 9.000, 9.000)</td>
<td></td>
<td><blockquote>
<p>(1.667,1.667,1.6667)</p>
</blockquote></td>
</tr>
<tr class="even">
<td>C2</td>
<td>(9.000, 9.000, 9.000)</td>
<td>(0.111, 0.111, 0.111)</td>
<td></td>
</tr>
<tr class="odd">
<td>C3</td>
<td>(9.000, 9.000, 9.000)</td>
<td>(0.333, 0.333, 0.333)</td>
<td></td>
</tr>
<tr class="even">
<td>C4</td>
<td>(9.000, 9.000, 9.000)</td>
<td>(2.778, 2.778, 2.778)</td>
<td></td>
</tr>
</tbody>
</table>

The distance of alternatives from FPIS was determined from equation (8) and equation (9). From equation (9), \(d_{i}^{*}\) indicates to calculate the distance of FPIS while \(d_{i}^{-}\) calculates the distance of FNIS. Tables 9 and Table 10 imply the result of the distance of alternatives from FPIS and FNIS.

**Table 9**: Distance of Alternatives from FPIS.

|                                          | Alternatives |        |        |
| ---------------------------------------- | ------------ | ------ | ------ |
| Criteria                                 | (A1)         | (A2)   | (A3)   |
| C1                                       | 3.668        | 4.477  | 3.668  |
| C2                                       | 7.765        | 6.904  | 7.765  |
| C3                                       | 7.640        | 6.769  | 7.603  |
| C4                                       | 3.997        | 3.865  | 3.865  |
| \[\mathbf{d}_{\mathbf{x}}^{\mathbf{+}}\] | 23.071       | 22.014 | 22.902 |

**Table 10**: Distance of Alternatives from FNIS.

|                                          | Alternatives |        |        |
| ---------------------------------------- | ------------ | ------ | ------ |
| Criteria                                 | (A1)         | (A2)   | (A3)   |
| C1                                       | 5.526        | 5.065  | 5.526  |
| C2                                       | 1.726        | 5.158  | 1.726  |
| C3                                       | 1.587        | 5.024  | 1.603  |
| C4                                       | 4.036        | 4.195  | 4.195  |
| \[\mathbf{d}_{\mathbf{x}}^{\mathbf{-}}\] | 12.875       | 19.441 | 13.050 |

**Table 11**: Computation of \(d_{x}^{+}\) **,** \(d_{x}^{-}\)and \(\text{CC}_{x}.\)

| Alternative | \[\mathbf{d}_{\mathbf{x}}^{\mathbf{+}}\] | \[\mathbf{d}_{\mathbf{x}}^{\mathbf{-}}\] | \[\mathbf{\text{CC}}_{\mathbf{x}}\] |
| ----------- | ---------------------------------------- | ---------------------------------------- | ----------------------------------- |
| A1          | 23.071                                   | 12.875                                   | 0.358                               |
| A2          | 22.014                                   | 19.441                                   | 0.469                               |
| A3          | 22.902                                   | 13.050                                   | 0.363                               |

Each alternative's closeness coefficient \(\text{CC}_{x}\ \) was defined and calculated according to equation (10), and the results are shown in Table 11.

**Table 12**: Ranking of Each Alternative.

| Rank | \[\mathbf{\text{CC}}_{\mathbf{x}}\] | Alternative |
| ---- | ----------------------------------- | ----------- |
| 1    | 0.469                               | A2          |
| 2    | 0.363                               | A3          |
| 3    | 0.358                               | A1          |

For overall ranking, the findings of a Fuzzy TOPSIS analysis indicated that academic life (A2) is the most important factor influencing mental illness and social stress among students, with the highest closeness coefficient of 0.469. Pressure from family can be a possibility due to a family wanting their child to study hard to get an excellent result for a better future. Next, it is followed by A3, which is a financial position with a 0.363 closeness coefficient. Some students need to spend their own money for their daily needs, which makes them bear more burden and always think about their finances. Finally, the least closeness coefficient is 0.358, which is social life (A1). Students who prefer to be alone are more likely to have physical and mental health issues. They also lack confidence in their ability to socialise with others.

**CONCLUSION AND RECOMMENDATIONS**

Overall, academic life is the most significant factor that affects mental illness and social stress among students, according to the Fuzzy Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) analysis. The factor has the highest proximity coefficient of 0.469. Education has evolved into a service-oriented economic sector in the hands of private agencies as a catalyst for economic growth and globalisation. Meanwhile, with a closeness coefficient of 0.358, social life is the least important factor in students' mental health and social stress. In the middle of the current COVID-19 pandemic, it is critical to look at social isolation, loneliness, and quality of life among older people who are confined to their homes and cut off from social life. The results of this study shows that it is very important to have a good education background. Future studies should aim to compare the research not only for Malaysia but also for other developing countries. Finally, the surveys utilised the term "mental disease" to refer to a broad category of mental health difficulties rather than specific types of mental illness. Even though this approach has previously been utilised with certain surveys, it may reduce the precision with which the survey questions can be interpreted. Last but not least, for high-quality research, adequate statistical procedures are essential. A researcher must grasp the essential concepts of statistical techniques to conduct research studies that provide reliable and accurate results.

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