In order to improve academic quality in higher education, students’ performance evaluation is becoming important. To prevent increasing failure rate in the course, we need a system that is capable of predicting student’s performance in the end of the course. The research used several factors that are considered to affect students' performance on Problem Based Learning (PBL), such as students’ demography, students’ prior knowledge and group heterogeneity.  The method used in the study was Artificial Neural Network (ANN) with backpropagation training algorithm. Total 8 neurons were used as inputs for ANN which were obtained from gender variable (2 neurons), age variable (1 neuron), students’ average knowledge variable (1 neuron), students’ average skill variable (1 neuron) and group heterogeneity variable (3 neurons). Several different ANN architecture were tested in the study using 2, 7 and 12 hidden neurons respectively. Each architecture was trained using various different training parameters in order to find the best ANN architecture. Dataset used  in the research were obtained from Academic Information System in Faculty of Dentistry Unissula which contained Adult and Elderly Diseases Course’s participants from year 2009 to 2013. The ANN output were numeric values which represented students’ performance in Adult and Elderly Diseases Course. The output of this study is a system that is able to predict the student performance in block course. The result shows that using 7 hidden neurons in the network combining with 0.5 ,0.1 and  9000 for learning rate, momentum and epoch respectively, were the best ANN architechture and parameters in the study. The MSE obtained from validation test was 0,011926 with correlation coefficient (R) 0,796879. The prediction system are expected to help faculty and academic evaluation team to conduct actions to improve student’s academic performance and prevent them from failure in the course.

This study used Corona Virus Disease-19 (Covid-19) data in Indonesia from June to August 2021, consisting of data on people who were infected or positive Covid-19, recovered from Covid-19, and passed away from Covid-19. The data were processed using the adaptive LMS algorithm directly without pre-processing cause calculation errors, because covid-19 data was not balanced. Z-score and min-max normalization were chosen as pre-processing methods. After that, the prediction process can be carried out using the LMS adaptive method. The analysis was done by observing the error prediction that occurred every month per case. The results showed that data pre-processing using min-max normalization was better than with Z-score normalization because the error prediction for pre-processing using min-max and z-score were 18% and 47%, respectively.

Sistem Informasi Surat Online Realtime pada Organisasi Badan Wakaf solusi teknologi informasi untuk mempercepat proses pengelolaan surat dan tugas secara efektif dan efisien. Sistem memungkinkan pengguna untuk menerima surat dari setiap unit secara real-time. Sistem dirancang untuk memudahkan proses pengelolaan tugas dari setiap unit, untuk dapat meminimalkan kesalahan, meningkatkan produktivitas dan mengurangi biaya operasional pengiriman surat fisik. Notifikasi otomatis, arsip digital, dan sebagai tugas pada bagian atau unit merupakan fitur sistem ini. pengguna dapat memantau dan melacak status surat secara real-time, proses tindak lanjut surat. Implementasi sistem pada badan wakaf melibatkan beberapa tahap, analisis kebutuhan, perancangan sistem, pengembangan sistem, uji coba dan peluncuran sistem. Tahap analisis kebutuhan mencakup identifikasi masalah dan kebutuhan pengguna, sedangkan tahap perancangan sistem melibatkan desain dan pemodelan sistem. Tahap pengembangan sistem melibatkan pengkodean dan pengujian sistem, sedangkan tahap uji coba dilakukan untuk memastikan sistem berjalan dengan baik sebelum peluncuran. Diperlukan pelatihan untuk pengguna agar dapat menggunakan sistem dengan benar dan efektif. Dalam implementasi sistem informasi perlu memperhatikan faktor-faktor yang mempengaruhi penerimaan dan penggunaan sistem oleh pengguna. Faktor-faktor ini termasuk keamanan dan privasi, kemudahan penggunaan, dan dukungan dari manajemen. Harapannya untuk efisiensi pekerjaan dan memberikan manfaat yang signifikan proses permintaan dari unit di bawah yayasan badan wakaf.

The golden period is a term used to describe the importance of the first 1000 days of human life. Nutritional intake in children at this time becomes very important because malnutrition in this period will cause disturbances in child development. To prevent this risk, the intake of breast milk is necessary for babies at least in the first 6 months of life. However, there are many internal and external factors that can affect a baby not being able to receive it. One solution that commonly used to share breast milk amongst mothers. A mother can share breast milk directly through personal relations or through human milk bank agencies. The implication of the problem of sharing human milk in Muslim societies is the occurrence of kinship relationship between the child and his milk mother that change the status of mahram and the prohibition of marriage between breast milk children and biological children of the donor because the two children's status changed to breast milk siblings. Referring to these conditions, we designed an integrated information system prototype by integrating breast milk donor data obtained from human milk banks throughout Indonesia. Interoperability problems during data integration process are overcome by implementing a RESTful API as a web service. The output of this information system is the issuance of milk-kinship certificates given to donors and recipients as evidence of donors as well as become a token that there is a mahram bond between donors and recipients. A milk-kinship certificate can prevent marriage between milk-kinship siblings, especially in Muslim communities.

Selection of methods will greatly impact in learning process. One of the methods commonly applied are Cooperative learning. Cooperative learning is one of many learning techniques to improve the performance of students in the academic literature. Moreover, the heterogeneity in study group's academics can improve performance, but only partially implementing cooperative learning in a group of heterogeneous formations. The problem faced in this type is the process of forming group of students into a heterogeneous group and inter-group quality is relatively equal or balanced. In this study, the authors aimed to provide intelligent solutions in the distribution group based on the value (The value of achievement on related subjects) and personality traits of each student in the determination of the performance of students are using the algorithm clustering Partitioning Around Medoids (PAM) in consideration of the value of measurement Euclidean Distance (ED) and the equitable distribution to form heterogeneous groups based on their level of heterogeneity in Measured with Goodness of Heterogeneity in Group (GH) and the rate of coefficient variation (CV) in same group or between groups with groups and equitable distributions on college campuses.