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All Journal TIN: TERAPAN INFORMATIKA NUSANTARA
RR. Hajar Puji Sejati
Universitas Teknologi Yogyakarta, Yogyakarta
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Sistem Informasi Akademik Mobile Berbasis QR Code dan Geolocation untuk Monitoring Kehadiran Siswa Nissan Abdul Agung; Rr. Hajar Puji Sejati
TIN: Terapan Informatika Nusantara Vol 7 No 1 (2026): June 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/tin.v7i1.10074

Abstract

Digital transformation in the education sector encourages schools to adopt information systems that improve the efficiency and accuracy of academic data management. This study aims to develop and evaluate a mobile-based academic information system integrated with student attendance validation at SMAN 1 Sliyeg. The main problem identified is the inefficiency of manual attendance recording, which leads to delays in reporting and a high risk of data errors. The system is developed using a client-server architecture that integrates web and mobile platforms. The mobile application is built using Flutter, while the backend uses PHP and MySQL. Attendance validation is implemented through QR Code scanning combined with geolocation to ensure accurate user location verification. The system testing method used in this study is black-box testing to evaluate system functionality and User Acceptance Testing (UAT) to measure user acceptance of the developed system. The test results show that all system features function properly with a success rate of ≥ 90% in functional testing scenarios. In addition, the UAT results indicate that user satisfaction is categorized as good to very good. The results show that the developed system improves the effectiveness of academic data management, reduces recording errors, and accelerates the reporting process. Furthermore, the system enhances transparency and increases parents’ involvement in monitoring students’ academic progress. The main contribution of this study is the development of a mobile-based academic information system that integrates QR Code and geolocation technologies with ±100 meter radius for attendance validation, providing a more accurate, secure, and tamper-resistant attendance mechanism. Furthermore, this study offers a practical model for implementing digital transformation in secondary schools, which can serve as a reference for other educational institutions seeking to develop integrated and mobile-based academic information systems.
Klasifikasi Motif Batik Solo Menggunakan Convolutional Neural Network dengan Transfer Learning VGG16 Daffa Ferdinan Aditama; RR. Hajar Puji Sejati; Fadil Indra Sanjaya
TIN: Terapan Informatika Nusantara Vol 6 No 7 (2025): December 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/tin.v6i7.8940

Abstract

Batik Solo has a rich variety of motifs with high philosophical value, but the process of identifying motifs is still largely done manually, making it subjective and prone to error. This study aims to develop an automatic classification system to distinguish four Batik Solo motifs, namely Parang, Kawung, Truntum, and Sekar Jagad, using the Convolutional Neural Network (CNN) method based on the VGG16 architecture with a transfer learning approach. The dataset used consists of 280 batik images divided evenly into four classes (70 images per class), where data limitations are overcome using ImageNet pre-trained weights, freezing all convolution layers, and applying data augmentation to reduce the risk of overfitting. The selection of VGG16 was based on the consideration that this study focused on evaluating feature extraction capabilities and analyzing the classification performance of Batik Solo visual patterns in depth, so VGG16 was used as a stable and interpretative baseline model, not for the purposes of computational efficiency or mobile implementation. The training process was carried out for 50 epochs with a data division of 60% training data, 20% validation data, and 20% test data, and the test results showed an accuracy of 85.71% with average precision, recall, and F1-score values of 0.88; 0.86; and 0.86, respectively, where the Sekar Jagad motif performed the best, while the Truntum motif was the most challenging class due to its smooth and repetitive texture characteristics.
Co-Authors Daffa Ferdinan Aditama Fadil Indra Sanjaya Nissan Abdul Agung