Implementasi Metodologi Feature-Driven Development (FDD) pada Aplikasi Monitoring dan Analisis Performa Akademik Siswa Berbasis Web (Studi Kasus: TPA Al-Falah Ponorogo)

Implementation of Feature-Driven Development (FDD) Methodology in a Web-Based Student Academic Performance Monitoring and Analysis Application (Case Study: TPA Al-Falah Ponorogo)

Pembelajaran Al-Qur'an di TPA Al-Falah Ponorogo masih menggunakan pencatatan manual sehingga data rawan hilang, guru kesulitan memantau perkembangan siswa, dan orang tua tidak dapat mengakses informasi akademik anak secara real-time. Penelitian ini bertujuan mengembangkan aplikasi monitoring dan analisis performa akademik siswa berbasis web menggunakan metodologi Feature-Driven Development (FDD) dengan metode Simple Additive Weighting (SAW) untuk menganalisis performa serta menghasilkan rekomendasi kelayakan ujian kenaikan jilid. Aplikasi dikembangkan dalam tiga modul: Admin, Guru, dan Orang Tua. Pengujian Blackbox Testing menunjukkan validitas 100% dan pengujian System Usability Scale (SUS) menghasilkan skor rata-rata 73,75 kategori Good. Hasil penelitian menunjukkan bahwa aplikasi berhasil mengatasi permasalahan pencatatan manual, mendukung monitoring data akademik secara real-time, serta mendukung guru dalam pengambilan keputusan kelayakan ujian kenaikan jilid secara objektif berbasis data.

Qur'an learning at TPA Al-Falah Ponorogo still relies on manual recording, causing data loss risks, difficulty for teachers in tracking student progress, and limited parental access to academic information in real-time. This study aims to develop a web-based student academic performance monitoring and analysis application using the Feature-Driven Development (FDD) methodology with the Simple Additive Weighting (SAW) method to analyze performance and generate eligibility recommendations for the jilid promotion exam. The application was developed across three modules: Admin, Teacher, and Parent. Blackbox Testing showed 100% validity and System Usability Scale (SUS) testing produced an average score of 73.75, categorized as Good. The results demonstrate that the application successfully addresses manual recording issues, improves academic data management efficiency, and supports teachers in objectively determining students promotion exam eligibility based on data.