Learning systems built around meaningful questions.

Kampar Karhutla Watch began as a research initiative on hotspot monitoring, weather conditions, remote sensing, and early warning for forest and land fires. The system brings together hotspot data, weather information, air quality, peatland conditions, administrative boundaries, and spatial analysis to identify areas that require priority attention.

In 2026, the project received DIPA research funding from Universitas Riau for further development and testing. The research is led by Assoc. Prof. Dr. Rahmad Kurniawan from the KJFD:MLKD, Department of Computer Science, Faculty of Mathematics and Natural Sciences, Universitas Riau.

The project is conducted in partnership with the Regional Disaster Management Agency of Kampar Regency, or BPBD Kampar. This collaboration supports data exchange, system evaluation, disaster education, and the practical use of research results.

Kampar Karhutla Watch is expected to strengthen preparedness, support field monitoring, and provide clear information for decision-making in forest and land fire mitigation. It also represents the contribution of machine learning and knowledge discovery research to solving real disaster management problems.

Kampar Fuel Intelligence was developed in 2026 in response to recurring vehicle queues at fuel stations in Kampar Regency. The project introduces a traffic-based approach for monitoring queue potential without relying entirely on continuous CCTV observation or manual public reports. The system maps fuel station locations and analyses traffic conditions at several road points around each station. It considers current traffic speed, normal road speed, time patterns, surrounding road conditions, unusual traffic changes, and the availability of supporting data. These indicators are combined to estimate the queue risk for each fuel station. The results are presented through an interactive map, clear station status, priority rankings, and practical recommendations. The system does not claim to measure the exact number of vehicles in a queue. Instead, it uses traffic conditions as a proxy to identify stations with a higher potential for congestion and long queues. During its initial public monitoring, Kampar Fuel Intelligence displayed information from 17 fuel stations in Kampar. The system identified several locations requiring greater attention and provided alternative recommendations to help users avoid stations with higher queue potential. A public report on 3 May 2026 highlighted its monitoring results, including higher queue potential around Air Tiris and Bangkinang. The next research stage focuses on field validation by comparing system estimates with direct observations at selected fuel stations. This validation will assess how accurately the system distinguishes normal conditions from light, moderate, and heavy queues. Kampar Fuel Intelligence demonstrates how traffic data, spatial analysis, and decision intelligence can be applied to address an everyday public problem. The project also provides a foundation for developing wider fuel monitoring and energy crisis response systems in Riau.

Developed through Universitas Riau’s 2024 DIPA research funding in collaboration with MUI Riau, the system identifies nearby social issues, recommends relevant sermon outlines, and supports activity planning based on location and weather. It was publicly launched in December 2024 to help make da’wah more responsive to local community needs

The system processes written feedback using topic modelling and sentiment analysis, then presents the results through a dashboard for MUI Riau. It is designed to complement the Smart Da’wah Map and support more relevant, measurable, and responsive da’wah planning.