One of the most commonly used materials in civil engineering is concrete; not only is it cheap and strong, but it is also efficient and convenient. The efficiency of concrete is based on the easiness to place and to compact, which is usually known as workability. However, concrete strength and workability works in different ways; hence it is important to divide concrete into two groups: concrete with low workability and concrete with high workability, in order to achieve a more accurate prediction. Since there is a lot of variations of concrete mix designs, the relationship between each mixture is complex and, thus, requires advanced prediction methods in order to find the most accurate relationships between concrete mix proportion and its compression test result.–Recently, many studies have been conducted on applying multiple artificial intelligence (AI) methods in building different complex and challenging prediction models. Thus, this research employs ensemble machine learning methods to precisely forecast compression strength of concrete mix proportion. The accuracy of the proposed method was calculated using two performance measurements. Subsequently, the study has successfully built the prediction model that can accurately map the relationship between concrete mix proportion and compressive strength.

Employee turnover is the loss of talent in the workforce that can be costly for a company. Uplift modeling is one of the prescriptive methods in machine learning models that not only predict an outcome but also prescribe a solution. Recent studies are focusing on the conventional predictive models to predict employee turnover rather than uplift modeling. In this research, we analyze whether the uplifting model has better performance than the conventional predictive model in solving employee turnover. Performance comparison between the two methods was carried out by experimentation using two synthetic datasets and one real dataset. The results show that despite the conventional predictive model yields an average prediction accuracy of 84%; it only yields a success rate of 50% to target the right employee with a retention program on the three datasets. By contrast, the uplift model only yields an average accuracy of 67% but yields a consistent success rate of 100% in targeting the right employee with a retention program.

In overcoming the problem of poverty, the Office of Community and Village Empowerment of Sampang Regency implements the GEMASAHABAT (Joint Movement Towards Harmonious and Dignified Movement). This program is a program of providing assistance to poor families, but with a large number of poor families, a ranking process is needed to find out poor families that are right on target. The program can be assisted by the existence of a method designed into a decision support system that can include assessment parameters related to ranking poor families, a method deemed appropriate in assisting the ranking process, namely the Simple Multi-Attribute Branch Technique Exploitng Ranks (SMARTER) method, where the method is part of the Multiple Criteria Decision Making method. This method was chosen because it can rank data with multiple criteria. The results of this study are software that was developed based on 100% functionality test and 91% of speed tests stated that high speed and ease of implementation.

Concrete is the most used material in infrastructure development, especially in a developing country. The concrete used in project must not only satisfy the desired concrete strength, but also the workability. Additionally, due to different conditions in construction projects, the requirement for workability varies. Workability can be measured using several methods. Previously, traditional trial-and-error of concrete mix design were used to achieve desired slump and flow test value. However, the experiment is often inexpensive, and the obtained results may not be sufficiently accurate. Recently, the potential of the AI method has been gaining increased attention as the new and promising alternative method to predict slump and flow tests, based on historical data. Thus, this study develops an effective hybrid AI-based method to predict slump and flow tests from the given concrete mixture dataset. A total of 103 historical data are used. At the beginning, the samples are separated into two groups using k-means clustering. Each cluster is modelled using the ensemble of six prediction methods, which are REG, CART, GENLIN, CHAID, ANN and SVM. The obtained results show that our proposed method can build the prediction method with a high accuracy, measured by several performance indicators.