Real-Time, Machine Learning-Based Personalized Notifications in the Al-Qur’an Tahsin and Tahfiz Mentoring System - Fendri Martadinata; Ridwan Hakim; M. Ridho Tahsinul Amal (ISB Atma Luhur, 2026)
Mentoring for tahsin and tahfiz of the Qur’an is a crucial activity in enhancing the ability to read and memorize the Qur’an correctly and consistently. However, its implementation still faces various challenges, including irregular participant attendance, ineffective schedule coordination, and limitations in the communication system that hinder optimal fulfillment of individual needs. The notification systems currently in use tend to be generic, making them less effective in boosting participant engagement. Furthermore, previous research has generally not integrated machine learning-based predictive approaches with adaptive notification systems in the context of Qur’anic recitation and memorization mentoring, resulting in a gap in efforts to proactively increase participant participation. This study aims to develop an adaptive and personalized real-time notification system using the Random Forest algorithm and WhatsApp Gateway. Random Forest was chosen because it can handle highly complex data, reduce overfitting, and provide stable classification performance. The model is used to analyze attendance patterns, predict potential absences, and determine the appropriate timing and content of messages for each participant. The dataset consists of 5,664 mentoring activity records collected from a campus environment over a specific period. Each record represents a single attendance activity at one session, with a total of 16 sessions per participant. It includes attendance history, activity time, and participant engagement levels. The testing phase indicates that the model achieves an accuracy of 94.17%, with precision, recall, and F1-score of 91.67%, 97.17%, and 94.34%, respectively. These results correspond to a binary classification task (Present and Absent), where a probability threshold of ≥0.5 is applied for triggering notifications. This study offers novelty through the integration of a predictive model with a real-time WhatsApp-based notification system capable of enhancing communication personalization. Its contribution lies in improving the effectiveness of participant engagement through a data-driven adaptive notification approach.