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Pemanfaatan Uap Panas Insinerator untuk Produksi Pupuk Organik di Panti Asuhan dan Pondok Lansia Al-Maa’uun, Wonosobo Pratiwi, Ilham Ayu Putri; Krisnaputra, Radhian; Aisyah, Nyayu; Bahiuddin, Irfan; Maulana, Arju Ridho; Sena, Zhafran Huda
J-Dinamika : Jurnal Pengabdian Masyarakat Vol 10 No 1 (2025): April
Publisher : Politeknik Negeri Jember

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

Abstract

Panti Asuhan dan Pondok Lansia Al-Maa'uun, Wonosobo menghadapi tantangan dalam mengelola sampah organik secara efisien. Sebagai solusi, program pelatihan ini bertujuan untuk memanfaatkan uap panas insinerator dalam produksi pupuk organik. Metode yang digunakan melibatkan proses insinerasi sampah organik dan anorganik dengan massa awal masing-masing 2 kg. Sampah organik dibakar selama 20 menit, menghasilkan volume pupuk sebesar 400 ml dengan massa 365 g. Di sisi lain, sampah anorganik memerlukan waktu pembakaran lebih lama, yaitu 30 menit. Hasil ini menunjukkan perbedaan signifikan dalam efisiensi pembakaran yang dihasilkan antara kedua jenis sampah. Berdasarkan pembakaran sampah organik, maka akan mengahsilkan pupuk yang berpotensi digunakan sebagai bahan pupuk organik untuk menunjang pertanian dan penghijauan di lingkungan panti asuhan dan pondok lansia. Pelatihan ini tidak hanya menawarkan solusi pengelolaan sampah yang ramah lingkungan, tetapi juga berperan dalam meningkatkan pemahaman masyarakat tentang pentingnya teknologi berbasis insinerator dalam mengelola limbah secara efektif. Program ini diharapkan dapat menginspirasi penerapan teknologi serupa di komunitas lain dengan tujuan keberlanjutan lingkungan dan peningkatan kualitas hidup.
Pengembangan Sistem Monitoring Berbasis Internet of Things untuk Perawatan Berkala Kendaraan dan Alat Berat dengan Fitur Pelaporan Terintegrasi dan GPS Pratama, Handika Yoga; Surojo; M. Hilmi; Bahiuddin, Irfan
Jurnal Teknologi dan Rekayasa Alat Berat Vol 2 No 2 (2025): JTRAB Volume 2, No 2, 2025
Publisher : Department of Mechanical Engineering, Vocational College, Gadjah Mada University.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/jtrab.v2i2.18656

Abstract

This study aims to develop an Internet of Things (IoT)-based hourmeter monitoring system to improve the efficiency and accuracy of periodic maintenance for vehicles and heavy equipment. The system is designed to integrate with a website that enables real-time monitoring of operating hours, reporting, and maintenance notifications. Testing was conducted on one light vehicle and one heavy equipment unit over a period of four days. The developed system was able to accurately record operating time, provide maintenance notifications via alarms, and monitor the unit’s location and performance using GPS data. The test results showed that the system successfully recorded travel distance and average speed, with the light vehicle achieving 14.2 km and 11.9 km/h, while the heavy equipment recorded 1.98 km and 7.17 km/h. The integration of hourmeter monitoring and GPS features proved to enhance supervision effectiveness and maintenance schedule prediction, thus potentially reducing the risk of damage and downtime. This system offers an innovative solution that can be adopted in the heavy equipment industry to support more optimal maintenance management.
Advanced State Estimations for Gravitational Oil/Water Separator Tanks using a Kalman Filter and Multi-Model Hypothesis Testing Cahya, Zaid; Siregar, Parsaulian; Ekawati, Estiyanti; Bahiuddin, Irfan; Cahya, Dito Eka; Nugroho, Tsani Hendro; Taufiqurrohman, Heru; Boudaoud, Mohammed
Jurnal Elektronika dan Telekomunikasi Vol 25, No 1 (2025)
Publisher : National Research and Innovation Agency

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55981/jet.682

Abstract

This paper presents a new application of the Kalman filter with Hypothesis testing for a fast and robust model-based estimator for measuring level interfaces of atmospheric gravitational oil-water separator tanks. A newly developed semi-empirical linearized model is applied in the estimator algorithm. A multi-model hypothesis-testing algorithm for covering more scenarios was deployed. The proposed method provides a cost-effective and straightforward solution for estimating all state variables in an oil-water separator. Our evaluation results demonstrate that the proposed algorithm achieves high accuracy with an observation error of less than 2% and a false alarm rate of 3.3% under 50-70% working conditions. Furthermore, the estimator can effectively handle process noise with a 10% feed offset. The proposed platform requires only a few installed sensors yet can accurately estimate unknown parameters. The proposed approach offers a robust and practical soft sensor solution for gravitational oil/water separators