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All Journal TELKOMNIKA (Telecommunication Computing Electronics and Control) IJCIT (Indonesian Journal on Computer and Information Technology) SELAPARANG: Jurnal Pengabdian Masyarakat Berkemajuan Jurnal Inovasi Hasil Pengabdian Masyarakat (JIPEMAS) Journal of Robotics and Control (JRC) Abdi Masyarakat MURHUM : JURNAL PENDIDIKAN ANAK USIA DINI Indonesian Community Journal Jurnal Nasional Teknik Elektro dan Teknologi Informasi eProceedings of Engineering Jurnal Informatika: Jurnal Pengembangan IT
Muhammad Adib Kamali
Telkom University
Aerospace Engineering Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mechanical Engineering Medicine & Pharmacology Nursing Public Health Social Sciences Other

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IoT-based flood disaster early detection system using hybrid fuzzy logic and neural networks Muhammad Adib Kamali; Mochamad Nizar Palefi Ma’ady
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 22, No 4: August 2024
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v22i4.25868

Abstract

A flood stands as one of the most common natural occurrences, often resulting in substantial financial losses to property and possessions, as well as affecting human lives adversely. Implementing measures to prevent such floods becomes crucial, offering inhabitants ample time to evacuate vulnerable areas before flood events occur. In addressing the flood issue, numerous scholars have put forth various solutions, such as the development of fuzzy system models and the es- tablishment of suitable infrastructure. However, when applying a fuzzy system, it often results in a loss of interpretability of the fuzzy rules. To address this issue effectively, we propose to reframe the optimization problem by incorpo- rating stage costs alongside the terminal cost. Results show the proposed model called hybrid fuzzy logic and neural networks (NNs) can mitigate the loss of interpretability. Results also show that the proposed method was employed in a flood early detection system aligned with integrating into Twitter social me- dia. The proposed concepts are validated through case studies, showcasing their effectiveness in tasks such as XOR-classification problems.
Co-Authors Abdulloh Dardum Axel Danu Pramudita Ayu Ainun A'ziziyyah Bernadus Anggo Seno AjiAji Davinsyah Putra Antoro Fachrel Kurniawan Farah Zakiyah Rahmanti Fender Muhammad Adhienugroho Ghozyan Hilman Kurniawan Hanif Guntur Laksono Sentosa Hariyanto, Muhammad Dwi Helisyah Nur Fadhilah Helmy Widyantara Juniyanto, Muhammad Ma'sum Khodijah Amiroh Lora Khaula Amifia Mochamad Nizar Palefi Ma’ady Mohammad Hamim Zajuli Al Faroby Muhammad Dwiky Yanuarezza Muhammad Fajar Shodiq Muhammad Mugi Fakip Anugrah Mustafa Kamal Nilla Rachmaningrum Nur Alifia Rustan Philip Tobianto Daely Prasya Danendra, Revin Rafael Christian Marbun Regita Putri Permata Reynanda Shaquille Purwanto Rizaldy Febry Nugraha Sulthonika Mahfudz Al Mujahidin Teuku Ismail Syuhada Titus Kristanto Wansu Lim Widi Pangestu Danar Wijanarko