Fiscal spending for road construction to link Kalabakan, Sabah, Malaysia with North Kalimantan, Indonesia is an idea that have been proposed for over 20 years. The announcement for the relocation of Indonesia’s capital city from Jakarta to East Kalimantan give a strong justification for the construction of the Serudong-Simanggaris road. The fact that population size is big in Kalimantan and strong purchasing power is estimated in North and East Kaliamantan provide a strong argument for the need to have a road link. Having said that, the effect of road construction on output growth is not clear. The purpose of this study is to estimate the impact of road construction and the business activities across two sectors being assumed on output Sabah’s output growth. Based on the input-output analysis conducted using the output multiplier, the one-off road construction would lead to 1.8% growth in Sabah’s overall output.

Among contemporary computational techniques, Artificial Neural Network (ANN) and Adaptive Neuro-Fuzzy Inference System (ANFIS) are favoured because of their capacity to tackle non-linear modelling and complex stochastic datasets. Nondeterministic models involve some computational intricacies when deciphering real-life problems but always yield better outcomes. For the first time, this study utilized the ANN and ANFIS models for modelling power generation/electric power output (EPO) from databases generated in a combined cycle power plant (CCPP). The study presents a comparative study between ANNs and ANFIS to estimate the power output generation of a combined cycle power plant in Turkey. The inputs of the ANN and ANFIS models are ambient temperature (AT), ambient pressure (AP), relative humidity (RH), and exhaust vacuum (V), correlated with electric power output. Several models were developed to achieve the best architecture as the number of hidden neurons varied for the ANNs, while the training process was conducted for the ANFIS model. A comparison of the developed hybrid models was completed using statistical criteria such as the coefficient of determination (R²), mean average error (MAE), and average absolute deviation (AAD). The R² of 0.945, MAE of 3.001%, and AAD of 3.722% for the ANN model were compared to those of R² of 0.9499, MAE of 2.843% and AAD of 2.842% for the ANFIS model. Even though both ANN and ANFIS are relevant in estimating and predicting power production, the ANFIS model exhibits higher superiority compared to the ANN model in accurately estimating the EPO of the CCPP located in Turkey and its environment.