Software development management system is important in application development. A proper software development management system will create a team that can adapt to system requirements and changes during application development. Various software development management systems are developed and widely implemented in software development, one of which is Agile Scrum. This study aims to implement well-documented Scrum for end-to-end application development, including the development of servers and mobile applications that we develop. We developed a bus application called SICITRA, with the main feature of being able to help passengers share their travel information with those closest to them. Scrum is used because it has agility which can make application development faster and more organized, and there is a close relationship between everyone involved in the project. The results of this study are that by using well-documented Scrum, we can make it easier to track progress, become a guide during system development, become history and evaluate Scrum implementation during development.

Signature genuine verifications of offline hand-written signatures are critical for preventing forgery and fraud. With the growth of protecting personal identity and preventing fraud, the demand for an automatic system for signature verification is high. The signature verification system is then studied by many researchers using various methods, especially deep learning-based methods. Hence, deep learning has a problem. Deep learning requires much training time for the data to obtain the best model accuracy result. Therefore, this paper proposed a CNN Batch Normalization, the CNN architectural adaptation model with a normalization batch number added, to obtain a CNN model optimization with high accuracy and less training time for offline hand-written signature verification. We compare CNN with our proposed model in the experiments. The research method in this study is data collection, pre-processing, and testing using our private signature dataset (collected by capturing signature images using a smartphone), which becomes the difficulties of our study because of the different lighting, media, and pen used to sign. Experiment results show that our model ranks first, with a training accuracy of 88.89%, an accuracy validation of 75.93%, and a testing accuracy of 84.84%—also, the result of 2638.63 s for the training time consumed with CPU usage. The model evaluation results show that our model has a smaller EER value; 2.583, with FAR = 0.333 and FRR = 4.833. Although the results of our proposed model are better than basic CNN, it is still low and overfitted. It has to be enhanced by better pre-processing steps using another augmentation method required to improve dataset quality.

Sebagian besar muslim di dunia menganut Mazhab Syafi'i, sehingga pengetahuan mengenai Mazhab Syafi'i, utamanya biografi dan sanad keilmuan Imam Syafi'i dan para ulama bermazhab Syafi'i menjadi hal yang penting untuk dipelajari. Namun, informasi biografi para ulama bermazhab Syafi'i masih berupa media online, buku teks atau kitab yang berbahasa Arab, terpencar-pencar, dan sulit dimengerti. Pada penelitian ini dilakukan pengumpulan data ulama bermazhab Syafi'i dari berbagai sumber dengan suatu pendekatan baru untuk mempelajari biografi ulama bermazhab Syafi'i melalui aplikasi berbasis mobile, dengan metode spatio-temporal. Aplikasi ini menampilkan biografi ulama berdasarkan urutan waktu dan lokasi kelahiran para ulama bermazhab Syafi'i dengan visualisasi peta, sehingga memudahkan pengguna dalam mempelajari biografi ulama. Administrator dapat menambah data ulama melalui website administrator yang terhubung dengan aplikasi secara dinamis. Aplikasi berjalan dengan baik sesuai fungsinya dan pengguna dapat mempelajari biografi para ulama bermazhab Syafi'i dengan visualisasi yang mudah dengan ukuran aplikasi yang ringan dan familiar digunakan oleh pengguna akhir. System Usability Scale (SUS) digunakan sebagai metode usability testing terhadap aplikasi. Skor rata-rata dari 35 responden SUS adalah 77,71 dan termasuk aplikasi yang acceptable dengan kategori "GOOD".