Qalama: Towards Semantic Autocomplete System for Indonesian Quran

Semantically retrieving Quran verses offers valuable benefits to Muslims, especially when we recall only a portion of the verse and need to find a specific one. This research introduces Qalama, an Islamic lecture note-taking application that employs a Quranic autocomplete system to retrieve referenced verses during note-taking. Developing such applications requires fast retrieval and minimal resource consumption. We present a retrieval scheme and implement various optimization strategies to ensure the system's effectiveness in real-world usage. The proposed method achieves 76.47% accuracy in 2.2s retrieval time with around 384 MB memory consumption.