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All Journal MANAJEMEN HUTAN TROPIKA Journal of Tropical Forest Management Dinamika Informatika Jurnal Pengelolaan Sumberdaya Alam dan Lingkungan (Journal of Natural Resources and Environmental Management) InfoTekJar : Jurnal Nasional Informatika dan Teknologi Jaringan Format : Jurnal Imiah Teknik Informatika Jurnal Pengabdian UntukMu NegeRI RABIT: Jurnal Teknologi dan Sistem Informasi Univrab Dinamisia: Jurnal Pengabdian Kepada Masyarakat JOISIE (Journal Of Information Systems And Informatics Engineering) Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer) Jurnal Computer Science and Information Technology (CoSciTech) International Journal of Electronics and Communications Systems Jurnal Dinamika Informatika (JDI) Journal of Software Engineering and Information System (SEIS) Jurnal Pengabdian Kepada Masyarakat Ekonomi, Pendidikan, dan Sosial Humaniora JIM ( Journal International Multidiciplinary)
Unik, Mitra
Universitas Muhammadiyah Riau
Religion Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Social Sciences Other

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Knowledge Management System For Forest and Land Fire Mitigation in Indonesia: A Web-Based Application Development Unik, Mitra; Rizki, Yoze; Sukaesih Sitanggang, Imas; Syaufina, Lailan
Jurnal Manajemen Hutan Tropika Vol. 30 No. 1 (2024)
Publisher : Institut Pertanian Bogor (IPB University)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.7226/jtfm.30.1.12

Abstract

Forest and land fires in Indonesia have serious impacts on many aspects, including the environment, health, economy, politics, and international relations. They cause haze pollution that extends to neighboring countries and peatland degradation. Despite extensive research and mitigation efforts, forest and land fires continue to occur and cost lives. Therefore, effective management and mitigation strategies are required. This research developed a web-based knowledge management system (KMS) using the Laravel framework as an effective forest and land fire mitigation platform. The KMS aims to support decision-making, facilitate knowledge exchange, improve coordination between stakeholders, and expand access to relevant information, while maintaining the sustainability of forest and land resources in Indonesia. The KMS evaluation results cover two important aspects: blackbox evaluation and performance evaluation. The blackbox evaluation showed that KMS provides knowledge retrieval features based on expert knowledge. The performance evaluation revealed that the KMS provides easy and quick access to information on forest and land fire prevention and management. Thus, this research has great potential to help overcome the problem of forest and land fires in Indonesia and protect the environment and society from their adverse effects.
INOVASI EDUKASI: AUGMENTED REALITY UNTUK TATALAKSANA JENAZAH DALAM KONTEKS ISLAM Resiasa, Maskuri; Unik, Mitra
Jurnal Rekayasa Perangkat Lunak dan Sistem Informasi Vol. 4 No. 1 (2024)
Publisher : Department of Information System Muhammadiyah University of Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37859/seis.v4i1.6641

Abstract

This research successfully addresses the complexity of handling corpses within an Islamic framework through the application of educational innovations using Augmented reality (AR) technology. The AR Jenazah application was developed with a focus on increasing understanding and skills in corpse handling procedures, which are in accordance with religious teachings and ethical norms. The results of black box testing showed the reliability of the application's functionality and presentation, while beta testing with the participation of 20 general public respondents gave positive conclusions on user interest, teaching, ease of use, and application performance. This research makes an important contribution to the development of a modern educational approach in understanding and implementing corpse handling procedures in an Islamic context. The AR Jenazah application was not only technically successful, but also achieved positive user acceptance, signalling its potential positive influence in improving the quality of learning and practice of corpse handling. Suggestions for development involve expansion of educational content, expert and user engagement, adoption to other platforms, evaluation of long-term impact, development of interactive features, multi-language versions, and collaboration with educational and religious institutions. These improvements are expected to strengthen the educational value of the app and expand its positive impact in the wider community.
Pengembangan Sistem Internet Of Things (IOT) dengan LoRa (Long Range) dan Energi Surya untuk Deteksi Otomatis Kebakaran Hutan dan Lahan ijra, Zul Ijra Saryendy; Unik, Mitra
Computer Science and Information Technology Vol 5 No 2 (2024): Jurnal Computer Science and Information Technology (CoSciTech)
Publisher : Universitas Muhammadiyah Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37859/coscitech.v5i2.7360

Abstract

Forest and land fires in Riau Province are a serious problem and can cause losses to humans and nature. It is necessary to design an Internet of Things (IoT)-based land and forest fire detection device using an independent power supply energy source. This device is equipped with DHT11 temperature sensor, MQ2 smoke sensor, and flame sensor, as well as LoRa SX276 module as data transmission media. The system utilizes IoT technology to monitor real-time environmental conditions and provide a quick response to potential fires. A standalone power supply energy source is implemented to increase the independence of the device, so that it can operate effectively in emergency situations. The LoRa SX276 module is used as a data transmission medium to enable remote communication and ensure device connectivity with the control center. Device testing was conducted through simulated fire scenarios to evaluate its performance in detecting high temperature, smoke, and fire, as well as the reliability of data transmission over the LoRa network. The results show that the device successfully detects fires with a high level of reliability and is able to transmit data efficiently over the LoRa network. The contribution of this research is the provision of an advanced and reliable fire detection solution using IoT technology and self-sustaining energy sources, with the ability to transmit data over long distances via LoRa modules.
Sistem Monitoring pH dan Kelembaban Tanah Berbasis IoT untuk Optimasi Pertumbuhan Tanaman Terong: Tanaman Terong Berbasis IoT Sunanto, Sunanto; mitra unik; Desti Mualfah
Computer Science and Information Technology Vol 5 No 3 (2024): Jurnal Computer Science and Information Technology (CoSciTech)
Publisher : Universitas Muhammadiyah Riau

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Abstract

The growth of eggplant plants is greatly influenced by environmental conditions, especially pH and soil moisture. These parameters need to be monitored regularly to ensure optimal conditions for the plant. In this study, an Internet of Things (IoT)-based monitoring system was developed to monitor soil pH and moisture in real-time. The system uses pH sensors and humidity sensors connected to microcontrollers and combined with IoT connectivity to send data to a cloud platform. The data collected can be accessed through a mobile application, allowing farmers or land managers to monitor soil conditions remotely. The results of the implementation show that the system is able to provide accurate and real-time information on soil pH and moisture, as well as provide notifications when these parameters are outside the optimal range. With this system, it is hoped that farmers can take corrective action quickly to maintain the condition of
Application of Random Forest Algorithm to Analyze the Confidence Level of Forest Fire Hotspots in Riau Peatland Unik, Mitra; Sukaesih Sitanggang, Imas; Syaufina, Lailan; Surati Jaya, I Nengah
Jurnal Pengelolaan Sumberdaya Alam dan Lingkungan (Journal of Natural Resources and Environmental Management) Vol 15 No 2 (2025): Jurnal Pengelolaan Sumberdaya Alam dan Lingkungan (JPSL)
Publisher : Pusat Penelitian Lingkungan Hidup, IPB (PPLH-IPB) dan Program Studi Pengelolaan Sumberdaya Alam dan Lingkungan, IPB (PS. PSL, SPs. IPB)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29244/jpsl.15.2.255

Abstract

Forest fires pose a significant challenge in Riau Province, Indonesia, especially in peatland areas. This study employs the Random Forest (RF) algorithm to analyze the confidence levels of hotspots, aiming to predict potential fire occurrences and improve fire management strategies. The research focuses on peatlands spanning 3.86 million ha, using key variables such as NDVI, surface temperature, and peat thickness derived from satellite data. The model achieved an average AUC of 0.732 and a classification accuracy of 70.3%, with medium-confidence hotspots demonstrating the best predictive performance (AUC: 0.707, F1-score: 0.804). However, the model struggled with low-confidence hotspots, reflecting challenges in distinguishing less prominent patterns in the data. Compared to other methods, RF demonstrates strong potential in handling complex environmental datasets, making it a valuable tool for hotspot prediction. This study contributes to understanding forest fire risks in peatlands and provides actionable insights for improving preparedness and mitigation efforts.
Edukasi dan Penerapan Digitalisasi BUMDes: Memanfaatkan Teknologi untuk Kemajuan Desa Suci, Rama Gita; Marlina, Evi; Unik, Mitra; Rodiah, Siti; Armel, R. Septian; Medikawati, Reny; Azmi, Zul; Sarmila, Wingki
Jurnal Pengabdian Kepada Masyarakat Sosial Humaniora Vol 5 No 1 (2025): Juni 2025
Publisher : Fakultas Ekonomi dan Bisnis Universitas Muhammadiyah Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Pengelolaan potensi desa yang dikelola oleh BUMDes harus didukung oleh teknologi digitalisasi. Tujuan dari pengabdian ini untuk memberikan pendalaman terkait implementasi penggunaan aplikasi berbasis digitalisasi dalam menunjang operasional usaha BUMDes. Metode pelaksanaan pengabdian ini menggunakan sosialisasi tata kelola BUMDes, pendampingan penggunaan aplikasi yang dilakukan secara langsung dan terdapat monitoring dan evaluasi penggunaan aplikasi. Hasil pengabdian didapatkan peserta BUMDes yang terlibat sudah mulai mampu memahami penerapan penggunaan aplikasi. Pemanfaatan teknologi dapat menciptakan berbagai kemudahan dalam operasional usaha BUMDes. Kolaborasi antara akademisi, pemerintah, dan BUMDes juga diperlukan untuk mendukung adopsi teknologi secara menyeluruh di wilayah pedesaan.
Exploration of Data Handling Techniques to Improve PM2.5 Prediction Using Machine Learning Unik, Mitra; Sitanggang, Imas Sukaesih; Syaufina, Lailan; Jaya, I Nengah Surati
International Journal of Electronics and Communications Systems Vol. 5 No. 1 (2025): International Journal of Electronics and Communications System
Publisher : Universitas Islam Negeri Raden Intan Lampung, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24042/ijecs.v5i1.25687

Abstract

Particulate matter (PM₂.₅) is one of the most dangerous air pollutants because it can penetrate the respiratory system and cause serious health problems. Amidst the limitations of a real-time and comprehensive air quality monitoring system, a data-driven predictive approach is needed that can accurately project PM₂.₅ concentrations. This study aims to develop a PM₂ concentration prediction model using the Random Forest Regressor (RFR) algorithm optimised through a series of data pre-processing techniques. The pre-processing techniques include outlier detection with four methods (Isolation Forest, Autoencoder ANN, OCSVM, IQR) and missing value handling using three approaches (Spline Cubic Interpolation, Nearest Point Interpolation, Data Removal). The daily data used covered 12 environmental variables (including rainfall, temperature, relative humidity, AOD, and NDVI) from the period of March 2022 to March 2023, with PM₂.₅ as the target. The RFR model was built with 100 decision trees and 10-fold cross-validation to improve accuracy. Results showed the combination of IQR (outlier detection) and data deletion (missing values) produced the best performance with RMSE 0.082, MAE 0.027, and R² 0.886. The most influential variables were temperature (TEMP), relative humidity (RHU), and evapotranspiration (ET). This research contributes to the development of an accurate air quality prediction model, supporting the mitigation of PM₂.₅ pollution impacts on public health
Hotspots and Smoke Detection from Forest and Land Fires Using the YOLO Algorithm ( You Only Look Once ) Dicko Andrean; Mitra Unik; Yoze Rizki
JIM - Journal International Multidisciplinary Vol. 1 No. 1 (2023)
Publisher : Rumah Jurnal

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58794/jim.v1i1.410

Abstract

The term forest and land fires is used to refer to unplanned, controlled and unwanted fires that destroy vegetated areas and their ecosystems triggered by natural or human causes . Early detection of hotspots can reduce the risk of wider forest and land fires. The use of the Deep Learning YOLO ( You Only Look Once ) algorithm is carried out to detect fire and also the smoke it produces. This study tested in 3 ways, 1) 1341 after data augmentation (496 original data), 2) 608 after data augmentation (253 original data), and 3) 1790 after data augmentation (746 original data). Detection of fire and smoke objects in the form of design, implementation and testing resulted in the YOLOv4 framework successfully producing high confidence of up to 97% in the second test. Based on the test results in this study, it is known that the image datasets used for training data greatly affect object detection and affect the confidence value. The more diverse the shape of the object from the image datasets, the lower the confidence value obtained.
Co-Authors Aminuyati Apriansyah Apriansyah Azmi, Zul Baidarus Baskoro, Dhanu Baskoro, Dhanu Deden Girmansyah Desti Mualfah Dhanu Baskoro Diah Angraina Fitri Diah Angraini Putri Dicko Andrean Evans Fuad Evi Marlina, Evi Febby Apri Wenando Hafid, Afdhil Harun Mukhtar Hasanuddin Hasanuddin Hasanuddin Hasanuddin, Hasanuddin, Hayami, Regiolina I Nengah Surati Jaya ijra, Zul Ijra Saryendy Imas Sukaesih Sitanggang Januar Al Amien Januar Al Amien Januar Al Amin Jum’atul Zikri Lailan Syaufina Medikawati, Reny Muhammad Tantaya Nurvelly Rosanti R.Septian Armel Rahmad Firdaus Randra Aguslan Pratama Resiasa, Maskuri Riki Harianto Rizki, Yoze Royan Choiro Yahya Sandy Eka Nuggroho Sarmila, Wingki siti rodiah Soni Suci, Rama Gita Suryanto Suryanto Vicky Gema Larenda Yoze Rizki Yoze Rizki Yulia Fatma