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REVITALISASI PESANTREN DI ERA DIGITAL DALAM MEWUJUDKAN LINGKUNGAN BERSIH DAN TEKNOLOGI TERAPAN UNTUK INOVASI YANG BERDAMPAK DAN BERKELANJUTAN DI PONPES MIFTAHUL HUDA MALANG Firdausi, Rofiqoh; Huda, Moh. Khoridatul; Iza, Belgis Ainatul; Laili, Nurul
Jurnal Edukasi Pengabdian Masyarakat Vol 4 No 4 (2025): OKTOBER 2025
Publisher : FIP UNIRA MALANG

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36636/eduabdimas.v4i4.8174

Abstract

The revitalization of Islamic boarding schools in the digital era is a strategic initiative to enhance environmental quality and foster the application of applied technology. This community engagement program aims to establish a clean, healthy, and technologically adaptive environment at Miftahul Huda Islamic Boarding School, Malang. Using a participatory approach—consisting of awareness campaigns, training, and continuous mentoring—the program focused on environmental cleanliness, organic waste composting, and the implementation of a digital information system for student administration. The outcomes revealed significant improvements in students’ environmental awareness, successful organic compost production, and the adoption of digital systems to streamline school management. Beyond improving environmental quality, the program equipped students with relevant digital literacy. This sustainable model offers potential replication in other Islamic boarding schools to support impactful innovation.
Uncertainty-Aware Kalman Filtering via Intrusive Polynomial Chaos for Disturbance Estimation Purnawan, Heri; Wakhid, Abdur Rohman; Fiddina, Qori Afiata; Iza, Belgis Ainatul; Sanusi, Tri Muhamad; Cahyaningtias, Sari
ZERO: Jurnal Sains, Matematika dan Terapan Vol 9, No 3 (2025): Zero: Jurnal Sains Matematika dan Terapan
Publisher : UIN Sumatera Utara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30829/zero.v9i3.26488

Abstract

Robust control under parameter uncertainty requires reliable disturbance estimation. This paper proposes an uncertainty-aware method, namely Intrusive Polynomial Chaos-based Kalman Filter (IPC-KF) for systems with probabilistic parameters and measurement noise. The method is evaluated through two numerical case studies and compared with a nominal Kalman filter (KF). Results from 100 realizations, assessed using RMSE and mean variance, show that the IPC-KF achieves estimation accuracy comparable to the nominal KF. For the spring-mass-damper system, the RMSE difference is below , with both methods yielding the same mean variance of . For the F-16 aircraft model, identical RMSE values and a mean variance of are obtained. While IPC-KF captures parameter uncertainty via polynomial chaos, augmenting the state with disturbances does not necessarily improve estimation accuracy. Further studies are needed to assess uncertainty bounds and robustness.