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An effective source of financing plays a role among micro enterprises because it can help micro-enterprises survive, function, and grow in their business. This shows that the type of financing resources needed to drive the productivity growth of micro-enterprises to build and promote the rural financial system is needed to drive the overall growth of the economy in Malaysia. Microenterprises suffer from a lack of resources because small firms and involved in the start-up stage, and lenders are not interested in providing financing for this group. The problem of this study is that access to financing sources is one of the main obstacles to the development of micro-enterprises to build a successful business. The main objective of this study is to examine the probability between financing problems and entrepreneurial skills that have the highest influence on funding sources. The study respondents consisted of managers of micro-enterprises who do business in the Batu Pahat District area. The research conducted is quantitative by using questionnaire instruments for the purpose of data collection. A total of 118 people were made as respondents and the data obtained will be analyzed using the Statistical Package for Social Science (SPSS) version 23.0 software. The collected data were analyzed using descriptive analysis, correlation analysis, and binary logistic regression. The results of the study found that financing problems and entrepreneurial skills affect funding sources with a moderate degree of tendency. In addition, the results of this study also found that the probability ratio of micro-enterprises with entrepreneurial skills will have a better advantage in influencing effective funding sources.

Lung cancer is the most prevalent cancer in the world, accounting for 12.2% of all newly diagnosed cases in 2020 and has the highest mortality rate due to its late diagnosis and poor symptom detection. Currently, there are 4,319 lung cancer deaths in Malaysia, representing 2.57 percent of all mortality in 2020. The late diagnosis of lung cancer is common, which makes survival more difficult. In Malaysia, however, most cases are detected when the tumors have become too large, or cancer has spread to other body areas that cannot be removed surgically. This is a frequent situation due to the lack of public awareness among Malaysians regarding cancer-related symptoms. Malaysians must be acknowledged the highrisk symptoms of lung cancer to enhance the survival rate and reduce the mortality rate. This study aims to use a fuzzy linear regression model with heights of triangular fuzzy by Tanaka (1982), H-value ranging from 0.0 to 1.0, to predict high-risk symptoms of lung cancer in Malaysia. The secondary data is analyzed using the fuzzy linear regression model by collecting data from patients with lung cancer at Al-Sultan Abdullah Hospital (UiTM Hospital), Selangor. The results found that haemoptysis and chest pain has been proven to be the highest risk, among other symptoms obtained from the data analysis. It has been discovered that the H-value of 0.0 has the least measurement error, with mean square error (MSE) and root mean square error (RMSE) values of 1.455 and 1.206, respectively.

Lung cancer has been recorded as the most common cancer globally, contributing 12.2% of all new cases diagnosed in 2020, with the greatest mortality rate due to its late diagnosis and poor symptom detection. Nowadays, Malaysia has reached 4,319 lung cancer deaths, accounting for 2.57 per cent of all deaths in 2020. Late diagnosis is the norm for lung cancer, which makes survival challenging and the likelihood of recovery low. Nevertheless, in Malaysia, most cases are discovered late, when the tumors have grown too far, or the disease has spread to other body parts that cannot be removed through surgery. This situation frequently occurs due to the lack of public knowledge among Malaysians regarding cancer-related signs and symptoms. Therefore, Malaysians must be aware of the high-risk symptoms of lung cancer to increase the survival rate and decrease the mortality rate. This study aims to compare multiple linear regression and fuzzy linear regression model using a triangular fuzzy number proposed by Tanaka. The H-value from 0.0 to 1.0 is adjusted to find the optimal value of an objective function to predict high-risk lung cancer symptoms in Malaysia. The secondary data is analyzed using the fuzzy linear regression model, which can reduce the interference of irrelevant information and improve the precision of the results. This research data was collected from patients with lung cancer at Al-Sultan Abdullah Hospital (UiTM Hospital), Selangor. The data of 124 lung cancer patients were analyzed using Microsoft Excel and MATLAB. The study implemented measurement error of cross-validation technique, which is mean square error (MSE) and root mean square error (RMSE), to enhance data accuracy. The results show that haemoptysis and chest pain has been proven to be the highest risk, among other symptoms acquired from the data analysis. It has been determined that H-value of 0.0 has the smallest measurement error, with MSE of 1.455 and RMSE of 1.206 as the multiple linear regre