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All Journal Jupiter Jurnal SIMADA (Sistem Informasi dan Manajemen Basis Data) International Journal of Artificial Intelligence Research I-ROBOT Cyberspace: Jurnal Pendidikan Teknologi Informasi JOURNAL OF APPLIED INFORMATICS AND COMPUTING SELAPARANG: Jurnal Pengabdian Masyarakat Berkemajuan Prosiding Seminar Nasional Darmajaya ILKOM Jurnal Ilmiah JURIKOM (Jurnal Riset Komputer) JOURNAL OF SCIENCE AND SOCIAL RESEARCH Jurnal Informasi dan Komputer IJISCS (International Journal Of Information System and Computer Science) Jurnal Teknologi Komputer dan Sistem Informasi BERNAS: Jurnal Pengabdian Kepada Masyarakat Jurnal Pengabdian Dharma Wacana Sinar Sang Surya: Jurnal Pusat Pengabdian kepada Masyarakat International Research on Big-data and Computer Technology (IRobot) Journal Computer Science and Informatic Systems : J-Cosys International Journal Software Engineering and Computer Science (IJSECS) Jurnal Pengabdian Multidisiplin Ilmu Komputer untuk Masyarakat Prosiding Seminar Nasional Pengabdian Kepada Masyarakat Jurnal Masyarakat Madani Indonesia Jurnal Pengabdian Kolaborasi dan Inovasi IPTEKS Bulletin of Network Engineer and Informatics (BUFNETS) PROSIDING SEMINAR NASIONAL PENELITIAN DAN PENGABDIAN KEPADA MASYARAKAT (SNPPM) UNIVERSITAS MUHAMMADIYAH METRO Jurnal TAM (Technology Acceptance Model)
Saputri, Tri Aristy
Informatics Engineering Of STMIK Dharma Wacana Metro, Metro City, Lampung
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IoT-Based Monitoring for Optimizing Yield of Gogo Rice (Oryza sativa, L.) Handayani, Etik Puji; Saputri, Tri Aristy; Sutomo, Budi
International Journal of Artificial Intelligence Research Vol 9, No 1.1 (2025)
Publisher : Universitas Dharma Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29099/ijair.v9i1.1.1677

Abstract

Advancements in Internet of Things (IoT) technology have introduced new opportunities in precision agriculture, particularly for enhancing the productivity of upland rice (Oryza sativa, L.) cultivated on marginal lands. This study aims to integrate an IoT-based monitoring system with the application of biochar and Trichoderma harzianum to optimize soil parameters and water resource efficiency. The monitoring system utilizes Trico Master and Slave devices to measure real-time environmental parameters, including soil pH, soil moisture, soil temperature, and air temperature. The results reveal that the application of biochar at a dosage of 1 kg/m² increased soil pH from an average of 7.0 to 8.7, creating a conducive environment for the activity of Trichoderma harzianum. This microorganism demonstrated its ability to improve soil quality by decomposing organic matter and enhancing nutrient absorption by plants. Additionally, the IoT-based automated irrigation system maintained soil moisture levels above 45% while reducing water usage by up to 30% compared to manual irrigation methods. In conclusion, the integration of IoT technology with biochar and Trichoderma harzianum significantly improved upland rice yield, resource efficiency, and the sustainability of agricultural systems. This study presents an innovative and sustainable approach to supporting future food security, particularly in resource-limited environments
Utilizing IoT Technology for Soil Moisture Management through Integration of pH and Moisture Sensors in an Android Application for Rice Farming Budi Sutomo; Tri Aristi Saputri; Ilham Wahyu Satria
International Journal Software Engineering and Computer Science (IJSECS) Vol. 5 No. 1 (2025): APRIL 2025
Publisher : Lembaga Komunitas Informasi Teknologi Aceh (KITA)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/ijsecs.v5i1.3538

Abstract

In this study, to help address the challenges involved in rice production (especially optimizing soil and crops in dryland areas that are prone to water scarcity and variable soil pH), we leveraged IoT technology. An IoT soil moisture and pH monitoring system to track soil moisture status in real time using ESP8266 microcontroller along with dedicated sensors coupled with Blynk as a user interface. The system provides instant alerts to farmers on mobile devices about irrigation and soil pH modifications, thereby minimizing the direct dependence on time-consuming maintenance of vegetation monitoring. The results from a trial of 28 upland rice plots in dryland agricultural areas showed that the irrigation alert system provided timely irrigation alerts, improved water use efficiency by up to 30% and increased yield by 15–20% compared to conventional techniques. The significance of these findings in terms of practical applications are water resource management, optimal soil conditions for rice farming and to promote sustainable agricultural practices on the other hand. Furthermore, the system can be applied to other crops in a similar manner to enhance food security at national and local scales despite climate change and resource constraints.
OPTIMASI ENERGI DENGAN IOT DAN FUZZY LOGIC PADA AC SMART DI GEDUNG DHARMA WACANA Saputri, Tri Aristi; Sutomo, Budi; Hairunnisa, Afifah
Jurnal SIMADA (Sistem Informasi dan Manajemen Basis Data) Vol. 7 No. 2 (2024): Jurnal SIMADA (Sistem Informasi dan Manajemen Basis Data)
Publisher : LP2M Institut Informatika Dan Bisnis Darmajaya

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

Abstract

Efficient energy management in air conditioning (AC) systems is a critical challenge in the modern era, particularly in educational buildings such as Dharma Wacana Hall. This study aims to design and implement an Internet of Things (IoT)-based Smart Air Conditioning system utilizing fuzzy logic to optimize energy management. The system employs DHT22 sensors and ESP32 microcontrollers to monitor indoor and outdoor temperatures in real-time. The collected data were analyzed using the Mamdani fuzzy logic method, resulting in automated AC adjustments. Over a 29-day testing period, the system demonstrated its capability to adapt cooling intensity based on environmental conditions, achieving an average energy savings of 4% on moderate-temperature days. However, on days with higher cooling demands, energy consumption exceeded that of conventional systems. In conclusion, the system successfully improved energy efficiency and thermal comfort in educational buildings, although further development is needed to enhance sensor accuracy and system resilience to internet dependency. This research contributes to the application of IoT technologies for energy efficiency, particularly in the educational sector.
Optimization of Intelligent Traffic Control Based on iot and Reinforcement Learning for Congestion Reduction in Smart Cities Tri Aristi Saputri; Budi Sutomo; Dimas Akbar Maulana; Hendika Purnomo
ILKOM Jurnal Ilmiah Vol 18, No 2 (2026)
Publisher : Prodi Teknik Informatika FIK Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v18i2.3260.320-332

Abstract

Traffic congestion has become a major challenge in Indonesian urban areas due to rapid vehicle growth and the limited adaptability of conventional traffic signal control systems. Most existing Deep Reinforcement Learning (DRL)-based traffic signal control studies adopt a free-phase selection approach, which assumes full agent freedom in determining signal phases — an assumption fundamentally incompatible with fixed phase-sequence regulations in Indonesian urban infrastructure — and rely on synthetic traffic data that fails to represent motorcycle-dominated traffic conditions. Furthermore, existing DQN-based approaches treat all traffic density conditions uniformly, without utilizing IoT-derived density categories for context-aware decision-making. To address these gaps, this study proposes a manual phase rotation mechanism with constrained actions (15, 30, and 60 seconds) compatible with existing fixed-phase infrastructure without hardware modifications, real-world IoT CCTV data from four intersections in Metro City processed using the YOLOv11 model to generate Low, Medium, and High traffic density categories as a representative training foundation for Indonesian urban traffic conditions, and a category-based action bias mechanism that adjusts DQN Q-value estimates according to IoT-derived traffic density, enabling context-aware signal duration selection. The DQN agent interacts with the SUMO simulation environment through the TraCI interface, receiving real-time traffic states comprising vehicle count, queue length, waiting time, average speed, density category, and delta queue, and selecting optimal green signal durations based on an epsilon-greedy exploration strategy and experience replay mechanism over 1,100 training episodes. Training yielded a 39.2% improvement in total reward and a 6.6% reduction in average waiting time. The best-performing model, obtained at episode 1050, achieved an 8.6% reduction in average waiting time and an 11.7% increase in traffic throughput compared to the fixed-time baseline. These results demonstrate that the proposed framework contributes three concrete advances for adaptive traffic signal control, a constrained-action DQN that is fully compatible with real-world fixed-phase infrastructure, a real-world IoT CCTV dataset as a representative data foundation for Indonesian traffic conditions, and a category-based bias mechanism for context-aware control — collectively offering a deployable, infrastructure-compatible, and replicable solution for traffic authorities and local governments advancing the smart city agenda in Indonesia.
Co-Authors Ajiz, Allien Mostiara Rachman Aninda Amelia Apsiswanto, Untoro Arief Adjie Wicaksono Bina, Dara Budi Sutomo Budi Sutomo Budi Sutomo Budi Sutomo Budi Sutomo Budi Sutomo, Budi Cici Novita Sari Dadang Asep Fitriantoro Dede Mulyana Deo Pratama Dimas Akbar Maulana Febri Sugandi Febritama, Dani Firman Eka Saputra, Firman Eka Hairunnisa, Afifah Handayani, Etik Puji Hendika Purnomo Ilham Wahyu Satria Kasmiatun, Kasmiatun Kohoiriah, Siti Krisnarini Krisnarini, Krisnarini M Nur Ikhsanto Mirantika, Fika Moetiara R.A, Allien Muharni, Sita Nofisa, Hendri Noviyanti Noviyanti Nurdiyanto, Heri Okke Stevanus Pardosi, Joseph Kevin Fratama Perdana, Andreas Pradana, Windu Adi Pusfitasari, Desi Rachma Ajiz, Allien Moetiara Rakhmiati Rakhmiati Ridwan Yusuf Rudy Kurniawan Saputra, Bima Arif Sofyan Rofiq Sulistiyanto Sulistiyanto Sulistiyanto Sulistiyanto, Sulistiyanto Sulistiyanto, Mr. Syaputra, Muhammad Adie Tri Budi Hartono Usep Saprudin Usep Saprudin Yosi Ardianto Yusnira, Dera Rara