Stiffness is the main parameter of the beam’s resistance to deformation. Based on advanced research, the stiffness of bamboo-reinforced concrete beams (BRC) tends to be lower than the stiffness of steel-reinforced concrete beams (SRC). However, the advantage of bamboo-reinforced concrete beams has enough good ductility according to the fundamental properties of bamboo, which have high tensile strength and high elastic properties. This study aims to predict and validate the stiffness of bamboo-reinforced concrete beams from the experimental results data using artificial neural networks (ANNs). The number of beam test specimens were 25 pieces with a size of 75 mm × 150 mm × 1100 mm. The testing method uses the four-point method with simple support. The results of the analysis showed the similarity between the stiffness of the beam’s experimental results with the artificial neural network (ANN) analysis results. The similarity rate of the two analyses is around 99% and the percentage of errors is not more than 1%, both for bamboo-reinforced concrete beams (BRC) and steel-reinforced concrete beams (SRC).

Truss structures are usually only considered to accept compressive axial forces and tensile axial forces. Structure node point is assumed to be a joint that cannot receive moments. But it's not the case with truss structures from concrete, large self-weight causes moment, so that "Truss" structures from concrete are called "Frame" structures. This study aims to increase the capacity and crack pattern of the precast bridge frame from bamboo reinforced concrete with different reinforcement variants. Two frames are made with the focus of observation on the underside element of the frame. Frame variations consist of one frame with symmetry reinforcement as the joint frame model or "truss model", one frame with flexural reinforcement as the rigid portal model or "frame model". Testing is done with two load points at the knot on the top side of the frame. The test results show that the bamboo reinforced concrete frame with a rigid portal model or "frame model" has stiffness and higher load capacity than the stiffness of the joint frame model or "truss model". Large self-weight will cause the moment can not be zero on each element, and the knot point behaves elastic clasps. However, a reinforced concrete frame does not fully behave as a rigid portal, this is evidenced by the crack pattern similar to the crack pattern on the "truss model", namely cracking perpendicular to the stem element and propagate across the tensile element.

The method of memorizing the Qur'an Tawazun is a method that maximizes the use of the right brain and left brain, allowing a person to memorize, understand, and believe. Each category has several assessment points as a benchmark for the ability of students, which are used to overcome the level of failure of students in each category of learning the method of memorizing the Qur'an tawazun. The results of the evaluation of the learning of the tahfidz Islamic boarding school Daarul Huffadz Indonesia in 2020 were felt to be less than optimal, because the learning process was carried out simultaneously. This can be seen from the difference in scores that are quite different in each category of assessment. Based on the previous problem, it is necessary to group the results of the evaluation of learning the Qur'an memorization method. The goal is that every student gets maximum treatment and provides convenience for the institution, as well as teaching staff to carry out learning. The purpose of this study is to determine the optimum number of clusters as well as members of each cluster by measuring cluster performance using the Davies Bouldin Index (DBI) method and implementing the K-Means algorithm. The K-Means algorithm is a non-hierarchical data clustering method that is able to group large amounts of data, relatively quickly, and efficiently. This study uses 401 observational data and 12 attributes. From the calculation results, the optimal number of clusters lies in 2 clusters, with a Davies-Bouldin Index (DBI) value of 1.439. There are 26 members of cluster 1, and 375 members of cluster 2.