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All Journal Indonesian Journal of Information System Jati Emas (Jurnal Aplikasi Teknik dan Pengabdian Masyarakat)
Fahrudin Mukti Wibowo
Universitas Telkom
Agriculture, Biological Sciences & Forestry Computer Science & IT Economics, Econometrics & Finance Engineering Public Health Social Sciences

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Improving Cattle Farmers Knowledge of Animal Weight Monitoring Using IoT: Peningkatan Pengetahuan Peternak Sapi dalam Monitoring Berat Badan Hewan Ternak Berbasis IOT Wahyu Andi Saputra; Muhamad Azrino Gustalika; Faizah Faizah; Silvia Van Marsally; Dedy Agung Prabowo; Fahrudin Mukti Wibowo
JATI EMAS (Jurnal Aplikasi Teknik dan Pengabdian Masyarakat) Vol. 10 No. 3 (2026): Jati Emas (Jurnal Aplikasi Teknik dan Pengabdian Masyarakat)
Publisher : DPD Jatim Perkumpulan Dosen Indonesia Semesta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36339/je.v10i3.536

Abstract

The Mukti Mandiri Livestock Group is located in Karanggitung Village, Banyumas Regency. This group focuses on beef cattle farming. Currently, the group faces various obstacles, including the lack of ability to accurately calculate cattle weight. This causes the group to often rent scales or entrust their cattle to other cattle groups to determine their weight. Furthermore, the Mukti Mandiri Group often chooses to estimate the weight of cattle due to the high rental price of scales. This impacts the selling price of cattle to collectors, resulting in farmers only earning a small profit. Therefore, a weighing device is needed to assist the Mukti Mandiri Livestock Group. This community service activity was carried out in order to provide IoT (Internet of Things)-based weighing devices to facilitate the cattle weighing process. The activity consisted of 5 stages: socialization, technology implementation, training, mentoring and evaluation, and the sustainability of the community service program. The results of this community service showed that 51% of respondents answered strongly agree, 46% agreed, and 3% quite agreed, in terms of increasing the knowledge of livestock farmers regarding the community service activity and the use of IoT-based weighing devices. It is hoped that this community service activity can continue in the management aspect for RPH (Animal Slaughterhouse) and Juleha (Halal Slaughterhouse) so that it can encourage participation from other livestock breeders in the use of technology in the livestock sector.
Adaptive Integration of Distributed Deep Q-Networks for Enhancing OLSR Routing in Dynamic Mobile Ad-Hoc Networks Alon Jala Tirta Segara; Arief Rais Bahtiar; Muhammad Raafi'u Firmansyah; Fahrudin Mukti Wibowo
Indonesian Journal of Information Systems Vol. 8 No. 2 (2026): February 2026
Publisher : Program Studi Sistem Informasi Universitas Atma Jaya Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24002/ijis.v8i2.11760

Abstract

Adaptive routing in Mobile Ad-Hoc Networks (MANETs) poses considerable difficulty owing to the network's dynamic characteristics, lack of stable infrastructure, and swift topology alterations. The Optimized Link State Routing (OLSR) protocol provides a proactive routing mechanism via topology dissemination and MultiPoint Relay (MPR) selection. Nevertheless, it exhibits diminished responsiveness to real-time topology alterations, as it depends on periodic updates and does not explicitly account for link quality. This paper suggests the incorporation of the Deep Q-Network (DQN) methodology into OLSR as a reinforcement learning strategy to improve routing adaptability and efficiency. The DQN model employs network metrics like latency, ETX, buffer occupancy, and neighbor count as state inputs, with actions determined by Q-values obtained via environmental interactions. Simulations conducted with NS-3 and PyTorch demonstrate that OLSR-DQN enhances Packet Delivery Ratio (PDR) by as much as 20%, decreases delay by 15–25%, and markedly boosts throughput in dynamic MANET situations. Keywords: MANET, OLSR, Deep Q-Network, adaptive routing, reinforcement learning
Co-Authors Alon Jala Tirta Segara Arief Rais Bahtiar Dedy Agung Prabowo Faizah Faizah Muhamad Azrino Gustalika Muhammad Raafi'u Firmansyah Silvia Van Marsally Wahyu Andi Saputra