# Micro, Small, and Medium Enterprises’ Business Vulnerability Cluster in Indonesia: An Analysis Using Optimized Fuzzy Geodemographic Clustering

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## Abstract

**:**

## 1. Introduction

## 2. Materials and Methods

#### 2.1. The National Socioeconomic Survey (SUSENAS)

#### 2.2. The National Labour Force Survey (SAKERNAS)

#### 2.3. Nature-Inspired Spatial Clustering: The Naspaclust Package

## 3. Methodology

#### 3.1. Data and Algorithms

#### 3.2. Flower Pollination Algorithm

#### 3.3. Research Workflow

#### 3.4. Evaluation Method

- (1)
- Partition coefficient (PC)The partition coefficient reflects the overlap between the fuzzy subsets and relies on the membership coefficients. Therefore, it lacks the additional consideration of the data and centroid. The partition index is calculated using [20].$$PC=\frac{1}{n}{{\displaystyle \sum}}_{i=1}^{c}{{\displaystyle \sum}}_{j=1}^{n}{\mu}_{ij}^{2}$$
- (2)
- Classification entropy (CE)CE represents the fuzziness between clusters. Based on the equation, CE index value will always range from 0 to ${\mathrm{log}}_{a}c$. Thus, low CE index shows a more optimal cluster. The CE index is calculated as follows [20]$$CE=-\frac{1}{n}{{\displaystyle \sum}}_{i=1}^{c}{{\displaystyle \sum}}_{j=1}^{n}{\mu}_{ij}{\mathrm{log}}_{a}{\mu}_{ij}$$
- (3)
- Separation index (S)The S index is a proportion of the objective function value to the minimum cluster separation. The minimum S index displays a an optimal cluster partition. On the other hand, the sum of the distances between centroids reflects the cluster separation [66].$$S=\frac{{{\displaystyle \sum}}_{j=1}^{c}{{\displaystyle \sum}}_{i=1}^{n}{\mu}_{ij}^{m}\Vert {x}_{i}-{v}_{j}{\Vert}^{2}}{n{\mathrm{min}}_{j,k}\Vert {v}_{k}-{v}_{j}{\Vert}^{2}}$$
- (4)
- Xie and Beni index (XB)Along with the SC index, the XB index shows the variation magnitude between clusters as well as the separation clarity [66].$$XB=\frac{{{\displaystyle \sum}}_{j=1}^{c}{{\displaystyle \sum}}_{i=1}^{n}{\mu}_{ij}^{m}\Vert {x}_{i}-{v}_{j}{\Vert}^{2}}{n{\mathrm{min}}_{i,j}\Vert {x}_{i}-{v}_{j}{\Vert}^{2}}$$
- (5)
- IFV indexThe IFV index is often used to validate spatial clustering due to its robustness and stability [67]. A maximum IFV index value reflects a good spatial cluster separation. The IFV index is measured using the equation:$$IFV=-\frac{1}{c}{{\displaystyle \sum}}_{j=1}^{c}\{\frac{1}{n}{{\displaystyle \sum}}_{i=1}^{n}{\mu}_{ij}^{2}{\left[{\mathrm{log}}_{2}c-\frac{1}{n}{\mathrm{log}}_{2}{\mu}_{ij}\right]}^{2}\}\frac{{\mathrm{max}}_{k,j}\Vert {v}_{k}-{v}_{j}{\Vert}^{2}}{\overline{{\sigma}_{d}}}$$$$\overline{{\sigma}_{d}}=\frac{1}{c}{{\displaystyle \sum}}_{j=1}^{c}(\frac{1}{n}{{\displaystyle \sum}}_{i=1}^{n}\Vert {x}_{i}-{v}_{j}{\Vert}^{2})$$

#### 3.5. Parameter Setups

^{−6}for the error tolerance, which is lower than Nasution et al. [19], to make sure that we obtained the best solution.

## 4. Results

#### 4.1. Performance Evaluation

#### 4.2. Clustering Results

## 5. Discussion

## Author Contributions

## Funding

## Informed Consent Statement

## Data Availability Statement

## Conflicts of Interest

## Appendix A. (Pseudocode of Fuzzy Geographically Weighted Clustering with Flower Pollination Algorithm)

Algorithm A1 |

Input: Data $x$, spatial distance matrix $d$, population matrix $p$, number of clusters $c$, fuzzifier $m$, ratio original membership $\alpha $, population effect $b$, spatial distance effect $a$, error tolerance $\epsilon $, maximum iteration $ma{x}_{iter}$, number of flowers ${n}_{f}$, switch probability $p$, Levy step-size factor $\gamma ,$ Levy index $\mathsf{\lambda}$, Levy flights shift $\mathsf{\delta}$ |

Output: cluster of data, optimum centroid $v$, optimum objective function ${f}_{g}$, validation indices $\left(\mathrm{PC},\mathrm{CE},\text{}\mathrm{SC},\mathrm{S},\mathrm{XB},\mathrm{IFV}\right)$ |

$\beta =1-\alpha $ |

Initialize flower from centroid matrix ${v}_{1},{v}_{2},\dots ,{v}_{{n}_{f}}$ |

calculate fitness ${f}_{1},{f}_{2},\dots ,{f}_{{n}_{f}}$ using the ${J}_{m}\left(v;X\right)={{\displaystyle \sum}}_{i=1}^{N}{{\displaystyle \sum}}_{j=1}^{c}\frac{{d}^{2}\left({v}_{j},{x}_{i}\right)}{{\left({{\displaystyle \sum}}_{k=1}^{c}{\left(\frac{d\left({v}_{j},{x}_{i}\right)}{d\left({v}_{k},{x}_{i}\right)}\right)}^{\frac{2}{m-1}}\right)}^{m}}$ |

obtain the current global best $g$ based on $\mathrm{max}\left({f}_{1},{f}_{2},\dots ,{f}_{{n}_{f}}\right)$ |

$t=0$ |

while ${f}_{{c}_{t}}-{f}_{{c}_{t-1}}\ge \epsilon $ or $t\le ma{x}_{iter}$ do |

$t\leftarrow t+1$ |

for $k=1\to {n}_{f}$ do |

generate random number rand = [0,1] |

if rand<p |

generate step vector $L\left(\mathsf{\lambda}\right)$ from levy distribution |

update centroid using global pollination ${v}_{k}^{t+1}={v}_{k}^{t+1}+\mathsf{\gamma}L\left(\mathsf{\lambda}\right)\left({v}_{g}-{x}_{i}^{t}\right)$ |

Else |

draw $\u03f5$ from selected distribution |

select random centroid ${v}_{r}^{t}$ |

update centroid using local pollination ${v}_{k}^{t+1}={v}_{k}^{t+1}+\u03f5L\left(\mathsf{\lambda}\right)\left({v}_{r}^{t}-{v}_{k}^{t}\right)$ |

calculate distance between data and centroid using Euclidean distance |

calculate membership using ${\mu}_{ij}=\frac{1}{{{\displaystyle \sum}}_{k}^{c}{\left(\frac{d\left({x}_{i},{\upsilon}_{j}\right)}{d\left({x}_{i},{\upsilon}_{k}\right)}\right)}^{2/m-1}}$ |

use geographical modification to update based on ${\mathsf{\mu}}_{\mathrm{i}}^{\prime}=\alpha {\mu}_{i}+\beta \frac{1}{A}{{\displaystyle \sum}}_{j=1}^{n}{w}_{ij}{\mu}_{j}$ |

recalculate centroid using ${v}_{j}=\frac{{{\displaystyle \sum}}_{i=1}^{n}{\left({\mu}_{ij}\right)}^{m}{x}_{i}}{{{\displaystyle \sum}}_{i=1}^{n}{\left({\mu}_{ij}\right)}^{m}}$ |

update fitness of each flower using FGWC-V objective function in step 2 |

set $max\left({f}_{1},{f}_{2},\dots ,{f}_{{n}_{f}}\right)$ as ${f}_{{c}_{t}}$ and $max\left({v}_{1},{v}_{2},\dots ,{v}_{{n}_{f}}\right)$ as ${v}_{{c}_{t}}$ |

if ${f}_{g}<{f}_{{c}_{t}}$ |

update ${f}_{g}$ and ${v}_{g}$ |

Calculate validation index of the cluster formation $\left(\mathrm{PC},\mathrm{CE},\text{}\mathrm{SC},\mathrm{S},\mathrm{XB},\mathrm{IFV}\right)$ |

Obtain the cluster of each data using the best membership |

## Appendix B. (Variable Name)

Variable | Description |
---|---|

use_cellph | % of population who use cell phones |

have_cellph | % of population who own a cell phone |

use_pc | % of population who use a PC |

acc_int | % of population who access the internet |

saving | % of population who have savings |

credit_A | % of households that make credit to KUR |

credit_B | % of households that make credit to commercial banks |

credit_C | % of households that make credit to the BPR |

credit_D | % of households that make credit to cooperatives |

sour_money | % of households whose source of income comes from working household members |

edu | % of population who have senior high and above education |

no_empl_14 | % of population who own a micro business |

no_empl_519 | % of population who have small businesses |

course | % of working population who attended training |

job_dur | the average duration of work of the population |

digitech1 | % of population who use computers |

digitech2 | % of population who use smartphones |

digitech3 | % of population who use other digital technologies |

job_int | % of population who use the internet |

jobint_use1 | % of population who use the internet for communication |

jobint_use2 | % of population use the internet for promotion |

jobint_use3 | % of population who use the internet to sell via email/social media |

jobint_use4 | % of population who use the internet to sell via e-commerce |

financebook | % of population who have financial accounting |

work_org | % of population who work with individual/household businesses |

work_loc | % of population who work in their own homes |

prev_work | % of population who have previous work experience |

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**Figure 1.**Kruskal-Wallis results for each evaluation method and number of clusters combination. The degrees of freedom used in this study is 5, which comes from the number of algorithms subtracted by 1 [64].

**Figure 2.**FPAFGWC clustering results summary based on: (

**a**) Objective function, (

**b**) PC Index, (

**c**) CE Index, (

**d**) SC Index, (

**e**) XB index, and (

**f**) IFV Index.

Information, technology, and communication | [R701] Do you use a cell phone? |

[R702] Do you have a cell phone? | |

[R703] Do you use a computer (pc/desktop, laptop/notebook/tablet? | |

[R704] Have you ever accessed the internet (including Facebook, Twitter, BBM, and WhatsApp)? | |

Access to financial services | [R2101] How many adult household members (15 years and over) have savings in formal financial institutions (banks, cooperatives, etc.)? |

Description of sources of income household | [R2301A] What is the main source of financing? |

General characteristics | What is the highest certificate/STTB owned rank: Do not have SD certificate, Package A, SDLB, SD/MI, Package B, SMPLB, SMP/MTS, Package C |

Has (NAME) ever received any training/courses/training and obtained a certificate? | |

Is (NAME) currently attending any training/courses/training (does not have to be certified)? | |

The main job | What was the main business field/line of work of the place (NAME) worked during the past week? |

What is the type of occupation/position of the main job (NAME) during the past week? | |

How long has (NAME) been looking for a job/preparing for a business in the main job? | |

Is there a certain party (individual/business/company) that regulates/coordinates the business/work (NAME)? | |

How many workers/employees/employees are paid? | |

Did (NAME) use digital technology in their main job during the past week? | |

Did (NAME) use the internet in their main job during the past week? | |

Is the internet used for: 1. Communication 2. Promotion 3. Carrying out the process of selling goods/services via email/social media (Instagram, Facebook, Twitter, etc.)/instant messaging services (LINE, Whatsapp, Telegram, etc.) 4. Carrying out the process of selling goods/services through the website/marketplace application (Tokopedia, Bukalapak, OLX, etc.) 5. Others, please explain…………………… | |

How does the agency/institution/company/business where (NAME) works do financial accounting? | |

Are the goods/services produced from work a week ago prioritized for their own use? | |

Number of working days, income and wages/salary. | |

What is the type of agency/institution from the workplace/business of (NAME)? | |

What is the main location of the workplace/business (NAME) at home? | |

Work experience | Has (NAME) ever had a previous occupation/main business? |

Has (NAME) stopped working from the main job/business in the past year? | |

What was the main reason (NAME) stopped working at the main job/business during the past year? Layoffs 1 Business closes/goes bankrupt 2 Income is not satisfactory 3 Not suitable for the work environment 4 Out of work period/contract 5 Not in accordance with skills/skills gained 6 Pregnant/giving birth/childbirth 7 Taking care of the household 8 Cannot be classified into codes 1–8, write:……………………… 9 | |

What was the status/position of (NAME) before resigning from the last main job/business? | |

Doing business alone (1) Doing business assisted by temporary workers/workers (2) |

Algorithm | Parameters |
---|---|

ABC | ${n}_{onlooker}=5,limit=5$ |

FPA | $\gamma =1.2,\mathsf{\lambda}=1.5,\mathrm{p}=0.7$ |

GSA | $G=1,{v}_{max}=0.7$ |

IFA | $\mathsf{\gamma}=1,\mathsf{\beta}=1.5,{\mathsf{\alpha}}_{\mathrm{k}}=1$ |

PSO | ${v}_{max}=0.7,{c}_{1}=0.7,{c}_{2}=0.6,{w}_{min}=0.2,{w}_{max}=0.9$ |

Performance Measurement | C | FGWC Algorithms | |||||
---|---|---|---|---|---|---|---|

CLASSIC | ABC | FPA | GSA | IFA | PSO | ||

Objective function | 2 | 1.3546 × 10^{6} | 1.3538 × 10^{6} | 1.3497 × 10^{6} | 1.3544 × 10^{6} | 1.3545 × 10^{6} | 1.3544 × 10^{6} |

3 | 9.0303 × 10^{5} | 9.0241 × 10^{5} | 8.9936 × 10^{5} | 9.0288 × 10^{5} | 9.0295 × 10^{5} | 9.0288 × 10^{5} | |

4 | 6.7728 × 10^{5} | 6.7696 × 10^{5} | 6.7426 × 10^{5} | 6.7719 × 10^{5} | 6.7722 × 10^{5} | 6.7719 × 10^{5} | |

5 | 5.4182 × 10^{5} | 5.4162 × 10^{5} | 5.3957 × 10^{5} | 5.4176 × 10^{5} | 5.4178 × 10^{5} | 5.4175 × 10^{5} | |

6 | 4.5152 × 10^{5} | 4.5136 × 10^{5} | 4.4970 × 10^{5} | 4.5147 × 10^{5} | 4.5149 × 10^{5} | 4.5146 × 10^{5} | |

7 | 3.8701 × 10^{5} | 3.8689 × 10^{5} | 3.8554 × 10^{5} | 3.8698 × 10^{5} | 3.8699 × 10^{5} | 3.8698 × 10^{5} | |

8 | 3.3864 × 10^{5} | 3.3856 × 10^{5} | 3.3740 × 10^{5} | 3.3861 × 10^{5} | 3.3862 × 10^{5} | 3.3861 × 10^{5} | |

9 | 3.0101 × 10^{5} | 3.0090 × 10^{5} | 3.0002 × 10^{5} | 3.0099 × 10^{5} | 3.0100 × 10^{5} | 3.0099 × 10^{5} | |

10 | 2.7091 × 10^{5} | 2.7084 × 10^{5} | 2.7009 × 10^{5} | 2.7089 × 10^{5} | 2.7090 × 10^{5} | 2.7089 × 10^{5} | |

PC Index | 2 | 5.0000 × 10^{−1} | 5.0722 × 10^{−1} | 5.2241 × 10^{−1} | 5.1076 × 10^{−1} | 5.0093 × 10^{−1} | 5.0354 × 10^{−1} |

3 | 3.3333 × 10^{−1} | 3.4143 × 10^{−1} | 3.5743 × 10^{−1} | 3.4124 × 10^{−1} | 3.3418 × 10^{−1} | 3.3974 × 10^{−1} | |

4 | 2.5000 × 10^{−1} | 2.5882 × 10^{−1} | 2.7072 × 10^{−1} | 2.5596 × 10^{−1} | 2.5080 × 10^{−1} | 2.5246 × 10^{−1} | |

5 | 2.0000 × 10^{−1} | 2.0537 × 10^{−1} | 2.1398 × 10^{−1} | 2.0492 × 10^{−1} | 2.0053 × 10^{−1} | 2.0380 × 10^{−1} | |

6 | 1.6667 × 10^{−1} | 1.7195 × 10^{−1} | 1.7687 × 10^{−1} | 1.7186 × 10^{−1} | 1.6712 × 10^{−1} | 1.6815 × 10^{−1} | |

7 | 1.4286 × 10^{−1} | 1.4687 × 10^{−1} | 1.5209 × 10^{−1} | 1.4701 × 10^{−1} | 1.4324 × 10^{−1} | 1.4289 × 10^{−1} | |

8 | 1.2500 × 10^{−1} | 1.2891 × 10^{−1} | 1.3141 × 10^{−1} | 1.2884 × 10^{−1} | 1.2525 × 10^{−1} | 1.2503 × 10^{−1} | |

9 | 1.1111 × 10^{−1} | 1.1425 × 10^{−1} | 1.1630 × 10^{−1} | 1.1459 × 10^{−1} | 1.1134 × 10^{−1} | 1.1113 × 10^{−1} | |

10 | 1.0000 × 10^{−1} | 1.0201 × 10^{−1} | 1.0412 × 10^{−1} | 1.0257 × 10^{−1} | 1.0014 × 10^{−1} | 1.0002 × 10^{−1} | |

CE Index | 2 | 6.9315 × 10^{−1} | 6.8589 × 10^{−1} | 6.7032 × 10^{−1} | 6.8229 × 10^{−1} | 6.9221 × 10^{−1} | 6.8958 × 10^{−1} |

3 | 1.0986 × 10^{0} | 1.0866 × 10^{0} | 1.0603 × 10^{0} | 1.0868 × 10^{0} | 1.0973 × 10^{0} | 1.0890 × 10^{0} | |

4 | 1.3863 × 10^{0} | 1.3688 × 10^{0} | 1.3390 × 10^{0} | 1.3744 × 10^{0} | 1.3847 × 10^{0} | 1.3814 × 10^{0} | |

5 | 1.6094 × 10^{0} | 1.5961 × 10^{0} | 1.5680 × 10^{0} | 1.5972 × 10^{0} | 1.6081 × 10^{0} | 1.5999 × 10^{0} | |

6 | 1.7918 × 10^{0} | 1.7761 × 10^{0} | 1.7534 × 10^{0} | 1.7762 × 10^{0} | 1.7904 × 10^{0} | 1.7873 × 10^{0} | |

7 | 1.9459 × 10^{0} | 1.9322 × 10^{0} | 1.9051 × 10^{0} | 1.9314 × 10^{0} | 1.9446 × 10^{0} | 1.9458 × 10^{0} | |

8 | 2.0794 × 10^{0} | 2.0641 × 10^{0} | 2.0463 × 10^{0} | 2.0641 × 10^{0} | 2.0784 × 10^{0} | 2.0793 × 10^{0} | |

9 | 2.1972 × 10^{0} | 2.1834 × 10^{0} | 2.1672 × 10^{0} | 2.1816 × 10^{0} | 2.1962 × 10^{0} | 2.1971 × 10^{0} | |

10 | 2.3026 × 10^{0} | 2.2927 × 10^{0} | 2.2764 × 10^{0} | 2.2897 × 10^{0} | 2.3019 × 10^{0} | 2.3025 × 10^{0} | |

S Index | 2 | 4.6807 × 10^{7} | 1.0339 × 10^{2} | 1.5034 × 10^{1} | 3.1627 × 10^{2} | 1.0507 × 10^{3} | 4.9634 × 10^{2} |

3 | 6.8092 × 10^{10} | 1.2966 × 10^{3} | 2.5468 × 10^{3} | 1.2263 × 10^{3} | 9.5055 × 10^{3} | 4.3475 × 10^{3} | |

4 | 9.6128 × 10^{11} | 1.1329 × 10^{3} | 1.3652 × 10^{4} | 2.8750 × 10^{3} | 1.3684 × 10^{4} | 8.5458 × 10^{3} | |

5 | 2.4582 × 10^{12} | 2.7337 × 10^{3} | 2.4891 × 10^{4} | 1.2615 × 10^{4} | 2.6536 × 10^{4} | 1.1127 × 10^{4} | |

6 | 3.6300 × 10^{13} | 1.8326 × 10^{3} | 8.0931 × 10^{4} | 5.1090 × 10^{2} | 2.1415 × 10^{4} | 9.7044 × 10^{3} | |

7 | 4.1404 × 10^{13} | 6.9357 × 10^{3} | 5.1912 × 10^{4} | 2.6892 × 10^{3} | 6.6719 × 10^{4} | 1.7776 × 10^{4} | |

8 | 2.5955 × 10^{13} | 7.8481 × 10^{3} | 9.2450 × 10^{4} | 1.6523 × 10^{3} | 5.4829 × 10^{4} | 3.0956 × 10^{4} | |

9 | 2.3469 × 10^{13} | 7.0561 × 10^{3} | 1.0135 × 10^{5} | 1.4089 × 10^{3} | 3.3104 × 10^{4} | 2.4702 × 10^{4} | |

10 | 2.5988 × 10^{15} | 1.0877 × 10^{4} | 1.9946 × 10^{5} | 1.7557 × 10^{4} | 4.1839 × 10^{4} | 4.1954 × 10^{4} | |

XB Index | 2 | 4.0829 × 10^{0} | 4.2544 × 10^{0} | 4.1573 × 10^{0} | 4.2736 × 10^{0} | 4.1352 × 10^{0} | 4.1909 × 10^{0} |

3 | 2.7219 × 10^{0} | 2.8401 × 10^{0} | 2.8319 × 10^{0} | 2.8610 × 10^{0} | 2.7720 × 10^{0} | 2.8337 × 10^{0} | |

4 | 2.0414 × 10^{0} | 2.1693 × 10^{0} | 2.1693 × 10^{0} | 2.1557 × 10^{0} | 2.0831 × 10^{0} | 2.1092 × 10^{0} | |

5 | 1.6331 × 10^{0} | 1.7233 × 10^{0} | 1.7308 × 10^{0} | 1.7263 × 10^{0} | 1.6655 × 10^{0} | 1.7135 × 10^{0} | |

6 | 1.3609 × 10^{0} | 1.4438 × 10^{0} | 1.4379 × 10^{0} | 1.4574 × 10^{0} | 1.3914 × 10^{0} | 1.4078 × 10^{0} | |

7 | 1.1665 × 10^{0} | 1.2400 × 10^{0} | 1.2446 × 10^{0} | 1.2472 × 10^{0} | 1.1928 × 10^{0} | 1.1910 × 10^{0} | |

8 | 1.0207 × 10^{0} | 1.0864 × 10^{0} | 1.0690 × 10^{0} | 1.0923 × 10^{0} | 1.0418 × 10^{0} | 1.0399 × 10^{0} | |

9 | 9.0729 × 10^{−1} | 9.5956 × 10^{−1} | 9.5982 × 10^{−1} | 9.7005 × 10^{−1} | 9.2428 × 10^{−1} | 9.2411 × 10^{−1} | |

10 | 8.1656 × 10^{−1} | 8.5981 × 10^{−1} | 8.5519 × 10^{−1} | 8.6651 × 10^{−1} | 8.3250 × 10^{−1} | 8.3358 × 10^{−1} | |

IFV Index | 2 | 2.3289 × 10^{−8} | 1.1365 × 10^{−2} | 8.2913 × 10^{−2} | 4.1144 × 10^{−3} | 1.2061 × 10^{−3} | 2.6313 × 10^{−3} |

3 | 4.0231 × 10^{−8} | 3.2324 × 10^{−2} | 2.0135 × 10^{−1} | 1.4923 × 10^{−2} | 4.7535 × 10^{−3} | 1.3659 × 10^{−2} | |

4 | 4.9924 × 10^{−8} | 3.7302 × 10^{−2} | 3.2216 × 10^{−1} | 1.7016 × 10^{−2} | 6.0455 × 10^{−3} | 1.2509 × 10^{−2} | |

5 | 4.0018 × 10^{−8} | 3.6548 × 10^{−2} | 3.7416 × 10^{−1} | 2.0551 × 10^{−2} | 7.0764 × 10^{−3} | 1.8697 × 10^{−2} | |

6 | 2.9146 × 10^{−8} | 4.2314 × 10^{−2} | 4.1691 × 10^{−1} | 2.4747 × 10^{−2} | 8.3875 × 10^{−3} | 1.5328 × 10^{−2} | |

7 | 2.4576 × 10^{−8} | 4.1615 × 10^{−2} | 4.5595 × 10^{−1} | 2.2740 × 10^{−2} | 7.8361 × 10^{−3} | 9.4770 × 10^{−3} | |

8 | 2.2690 × 10^{−8} | 3.4981 × 10^{−2} | 4.6065 × 10^{−1} | 2.3783 × 10^{−2} | 7.6422 × 10^{−3} | 9.2776 × 10^{−3} | |

9 | 2.2372 × 10^{−8} | 5.4328 × 10^{−2} | 4.8662 × 10^{−1} | 2.3515 × 10^{−2} | 7.4486 × 10^{−3} | 9.1279 × 10^{−3} | |

10 | 2.0450 × 10^{−8} | 3.7936 × 10^{−2} | 4.3299 × 10^{−1} | 2.1008 × 10^{−2} | 7.1109 × 10^{−3} | 8.5118 × 10^{−3} | |

Computational time (seconds) | 2 | 0.561 | 108.347 | 32.218 | 13.996 | 86.177 | 16.162 |

3 | 0.799 | 100.495 | 30.331 | 12.932 | 77.502 | 13.480 | |

4 | 0.734 | 112.403 | 36.463 | 15.897 | 94.718 | 17.125 | |

5 | 0.910 | 83.480 | 31.878 | 14.340 | 85.477 | 13.678 | |

6 | 0.906 | 103.444 | 38.463 | 17.064 | 105.026 | 21.565 | |

7 | 0.974 | 124.028 | 41.173 | 18.286 | 108.363 | 19.510 | |

8 | 1.013 | 94.965 | 33.810 | 16.148 | 82.565 | 15.617 | |

9 | 1.111 | 118.785 | 40.832 | 20.101 | 118.395 | 21.933 | |

10 | 1.118 | 104.519 | 38.686 | 17.916 | 81.331 | 11.986 | |

Number of iterations | 2 | 11.440 | 32.380 | 36.940 | 15.080 | 17.840 | 18.140 |

3 | 12.420 | 34.420 | 38.280 | 15.440 | 17.840 | 16.740 | |

4 | 12.780 | 30.420 | 38.100 | 15.440 | 17.800 | 17.300 | |

5 | 13.320 | 26.480 | 36.340 | 15.440 | 17.840 | 17.260 | |

6 | 13.640 | 25.300 | 36.200 | 15.000 | 17.840 | 19.820 | |

7 | 13.780 | 29.380 | 36.960 | 15.160 | 17.840 | 19.820 | |

8 | 13.860 | 26.880 | 34.140 | 15.040 | 17.880 | 20.460 | |

9 | 13.900 | 25.520 | 33.340 | 15.040 | 17.880 | 20.360 | |

10 | 13.960 | 27.260 | 35.720 | 15.400 | 17.920 | 19.020 |

Cluster | Use_Cellph | Have_Cellph | Use_Pc | Acc_Int | Saving | Credit_A | Credit_B | Credit_C | Credit_D |

1 | 88.25552 | 80.55931 | 15.37845 | 27.27296 | 34.50903 | 9.506159 | 10.82205 | 2.227692 | 4.093793 |

2 | 89.42493 | 85.69444 | 27.77035 | 47.54352 | 45.87103 | 11.127022 | 12.41292 | 3.176292 | 5.00927 |

sour_money | edu | course | job_dur | no_empl_14 | no_empl_519 | digitech1 | digitech2 | digitech3 | |

1 | 96.96485 | 46.61807 | 88.37956 | 44.57606 | 84.07763 | 14.43613 | 10.33827 | 39.21705 | 24.71888 |

2 | 96.38307 | 63.90895 | 83.51627 | 47.63658 | 80.92 | 16.39125 | 24.34142 | 68.87062 | 40.24244 |

job_int | jobint_use1 | jobint_use2 | jobint_use3 | jobint_use4 | financebook | work_org | work_loc | prev_work | |

1 | 25.97092 | 25.30045 | 11.53989 | 10.93522 | 2.296787 | 55.59538 | 94.41761 | 30.41872 | 49.09258 |

2 | 61.53131 | 60.9503 | 33.52371 | 34.04755 | 9.251676 | 71.5543 | 92.99104 | 38.11023 | 59.03073 |

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## Share and Cite

**MDPI and ACS Style**

Caraka, R.E.; Kurniawan, R.; Nasution, B.I.; Jamilatuzzahro, J.; Gio, P.U.; Basyuni, M.; Pardamean, B.
Micro, Small, and Medium Enterprises’ Business Vulnerability Cluster in Indonesia: An Analysis Using Optimized Fuzzy Geodemographic Clustering. *Sustainability* **2021**, *13*, 7807.
https://doi.org/10.3390/su13147807

**AMA Style**

Caraka RE, Kurniawan R, Nasution BI, Jamilatuzzahro J, Gio PU, Basyuni M, Pardamean B.
Micro, Small, and Medium Enterprises’ Business Vulnerability Cluster in Indonesia: An Analysis Using Optimized Fuzzy Geodemographic Clustering. *Sustainability*. 2021; 13(14):7807.
https://doi.org/10.3390/su13147807

**Chicago/Turabian Style**

Caraka, Rezzy Eko, Robert Kurniawan, Bahrul Ilmi Nasution, Jamilatuzzahro Jamilatuzzahro, Prana Ugiana Gio, Mohammad Basyuni, and Bens Pardamean.
2021. "Micro, Small, and Medium Enterprises’ Business Vulnerability Cluster in Indonesia: An Analysis Using Optimized Fuzzy Geodemographic Clustering" *Sustainability* 13, no. 14: 7807.
https://doi.org/10.3390/su13147807