This study aimed to analyze the level of students' metacognition skills and creative thinking in the generalization of a two-dimensional arithmetic sequence. A qualitative descriptive is a scientific approach used in this study. Students' of the Mathematics Education Study Program in Tarbiyah Faculty of Ibrahimy University are subjects in the study. Through this article, the author will describe the results of the research in the combinatorics course. The initial data was collected by assigning open problem-solving assignments to students and conducting documentation studies on students in generating arithmetic generalization patterns based on function formulae. Then, students are assigned to complete the second task, which is to compile a two-dimensional arithmetic sequence based on the multilevel function formula of arranged arithmetic. The analysis model of Miles and Huberman is the analytical methodology used in this study. The collected data indicated that the level of students’ creative thinking skills in combinatorics could be in the category of creative enough (16.67%), creative (50%), and very creative (33.33%). While the other analyzed data showed that the student’s level of metacognitive on level 3 (77.78%) and the remainder on level 4 (22.22%). These analysis results are influenced by several factors such as accuracy in compiling numbers and expanding data, conceptual mastery of arithmetic progression permutation concept, and its application, the tendency of students’ to rely on memorization and imitation of the examples.

Anemia, vaginal discharge, menstrual disorders, dysmenorrhea are some of the reproductive health problems experienced by women both on a mild to severe scale. And some can be fatal to death. However, reproductive health problems are not balanced with the level of knowledge by women about the problems experienced. So it is not uncommon, the women only realize after the situation is getting worse. Therefore an information system is needed that can help women early detect reproductive health problems experienced by women, one of them with an expert system. The purpose of this study was to create a Screening of Reproductive Health (SHE) application in an effort to improve Women's Reproductive Health. The research method used the expert system design method by coding and decision tables, making decision trees and flowcharts. conduct stages of data collection in the form of problem domain discussions with experts, literature studies, decision tree discussions with experts and trials to 30 respondents. The results showed that the expert system had been made according to the design and had produced the solutions needed in reproducing reproductive health along with suggestions and recommendations from the diagnosis results.

The problem found was ineffective service because the information was not appropriately conveyed to the parents of toddlers because the cadres did the recording and counting manually. Age calculations were carried out every month by cadres using a specific formula. In addition, determining nutritional status was also done manually by matching the examination data with the Z-score table according to the sex, weight, and height of each toddler. Due to limited human resources, this was often not done, so the nutritional status of toddlers was not filled, and parents did not know for sure the nutritional status of their toddlers. Solution provided by implementing an information system referred to as e-posyandu. E-posyandu can display a recapitulation of toddler examinations in real-time, which can be monitored directly by the responsible local health center midwife. The implementation method in this study is divided into several stages: problem identification, system design, information system development, and implementation. The results obtained are that e-posyandu can assist Posyandu Delima cadres in recording development and growth digitally, thus facilitating and improving the quality of cadre services in carrying out posyandu checks can also help parents to monitor the results of the toddlers' development and growth

adalah memenuhi target pertolongan persalinan, baik fisiologis ataupun patologis. Dalam memenuhi target itu, mahasiswa didistribusikan untuk melakukan praktik klinik kebidanan salah satunya di Bidan Praktik Swasta (BPS) di wilayah Banyuputih. Pada pelaksanaannya, jumlah pertolongan persalinan dalam tiap BPS tidak sama, bahkan ada beberapa BPS yang tidak mendapat pasien ketika mahasiswa sedang melakukan praktik klinik. Dari semua BPS yang ada di Kecamatan Banyuputih, hanya beberapa BPS yang digunakan sebagai lahan praktik klinik kebidanan oleh Universitas Ibrahimy. Karena hal itu, pasien bersalin di BPS lain yang bukan lahan praktik tidak dapat digunakan sebagai pemenuhan target pertolongan persalinan mahasiswa.

Technology advancements in the world of information have made it easier for many people to process data. Data mining is a process of mining more valuable information from large data sets. The research aims to determine the difference between the C.45 and random forest algorithms in data mining to predict the childbirth process of pregnant women. It compares the accuracy of the performance results of the C4.5 and random forest algorithms to predict the delivery process for pregnant women. Then, experimental research is conducted to classify the childbirth process in Situbondo, Indonesia, by applying the C.45 and the random forest algorithm in the data mining. The decision tree J48 algorithm is used for the C4.5 algorithm in the research. Both algorithms are compared for their error classification and accuracy level. The research uses 1,000 data for training and 200 data for testing. The results show the accuracy of implementing the C4.5 and random forest algorithms with data mining using 10-fold cross-validation, generating 96% and 95% as correctly classified data. Then, the Relative Absolute Error for both algorithms has the same result. It is 15%. The C4.5 algorithm has a better result than the random forest algorithm by comparing the performance results. Further research can add more data to improve the accuracy of the analysis results by using another algorithm.