REKAYASA PRIORITY NOTIFICATION SYSTEM MENGGUNAKAN MODEL BERBASIS ATURAN UNTUK MENDUKUNG MANAJEMEN WAKTU MAHASISWA AKTIF

Beban akademik mahasiswa aktif kerap memicu manajemen waktu yang buruk dan prokrastinasi akademik, diperparah oleh notification fatigue akibat sistem notifikasi yang tidak membedakan urgensi. Penelitian ini merekayasa Priority Notification System menggunakan model berbasis aturan yang diimplementasikan pada aplikasi penjadwalan tugas berbasis Android bernama SchedU. Landasan teori meliputi Self-Regulated Learning, Eisenhower Matrix, dan rule-based system, dengan pendekatan Research and Development (R&D) menggunakan metodologi Agile. Data dikumpulkan melalui kuesioner analisis kebutuhan, kuesioner evaluasi efektivitas, pengujian Black Box, dan pencatatan data log aplikasi. Model berbasis aturan mengklasifikasikan tugas ke dalam empat kuadran prioritas berdasarkan parameter kepentingan dan urgensi yang diadaptasi dari Eisenhower Matrix, dengan Priority Notification System memberikan perlakuan notifikasi berbeda termasuk notifikasi persisten untuk tugas berprioritas tinggi. Pengujian Black Box menunjukkan seluruh fitur utama berjalan sesuai rancangan. Evaluasi usability menghasilkan skor SUS rata-rata 81,83 dengan kategori Excellent dan berada pada rentang Acceptable. Analisis log dari 50 pengguna menghasilkan rata-rata On-Time Completion Rate sebesar 84,80%, menunjukkan efektivitas SchedU dalam mendukung manajemen waktu akademik yang lebih terstruktur.

Active university students frequently struggle with time management and academic procrastination, worsened by notification fatigue from undifferentiated task reminder systems. This study engineers a Priority Notification System using a rule-based model, implemented in an Android task scheduling application called SchedU. The theoretical framework draws on Self-Regulated Learning, the Eisenhower Matrix, and rule-based systems, with a Research and Development (R&D) approach using the Agile methodology. Data were collected through needs analysis questionnaires, effectiveness evaluation questionnaires, Black Box Testing, and application log recording. The rule-based model classifies tasks into four priority quadrants based on importance and urgency parameters adapted from the Eisenhower Matrix, with the Priority Notification System applying differentiated behaviors including persistent notifications for high-priority tasks. Black Box Testing confirmed all core features functioned as designed. The usability evaluation resulted in an average SUS score of 81,83 with an Excellent category and was in the Acceptable range Log analysis of 50 users showed an average On-Time Completion Rate of 84,80%, demonstrating SchedU's effectiveness in supporting structured academic time management.