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All Journal International Journal of Electrical and Computer Engineering International Journal of Power Electronics and Drive Systems (IJPEDS) Bulletin of Electrical Engineering and Informatics Angkasa: Jurnal Ilmiah Bidang Teknologi Buletin Ilmiah Sarjana Teknik Elektro KURVATEK Aviation Electronics, Information Technology, Telecommunications, Electricals, and Controls (AVITEC) Indonesian Journal of Electrical Engineering and Computer Science SPEKTA (Jurnal Pengabdian Kepada Masyarakat : Teknologi dan Aplikasi) Prosiding Seminar Nasional Rekayasa Teknologi Industri dan Informasi ReTII
Oni Yuliani
Institut Teknologi Nasional Yogyakarta
Aerospace Engineering Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Electrical & Electronics Engineering Energy Engineering Environmental Science Industrial & Manufacturing Engineering Materials Science & Nanotechnology Mechanical Engineering Physics Social Sciences Transportation

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Desain dan Implementasi Alat Pemantauan Cuaca Self-Sustain Berbasis IoT untuk Dukungan Data Cuaca Real-Time Yuliani, Oni; Pratama, Bagus Gilang; Sari, Sely Novita
Retii 2025: Prosiding Seminar Nasional ReTII ke-20 (Edisi Penelitian)
Publisher : Institut Teknologi Nasional Yogyakarta

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Abstract

Perubahan iklim dan dinamika cuaca ekstrem menuntut ketersediaan sistem pemantauan cuaca yang akurat, berkelanjutan, dan mudah diakses. Sistem konvensional yang bergantung pada infrastruktur listrik dan operasional manual sering kali menghadapi keterbatasan di wilayah terpencil. Sebagai respons terhadap tantangan tersebut, dikembangkan alat pemantauan cuaca self-sustain berbasis Internet of Things (IoT) yang mampu menyediakan data atmosfer secara real-time melalui integrasi sensor otomatis dan sumber energi surya. Sistem menggunakan sensor DHT22, BMP280, BH1750, anemometer digital, dan rain sensor yang dihubungkan ke mikrokontroler ESP32 dan dikirim ke cloud platform (ThingSpeak dan Blynk) untuk visualisasi data daring. Pengujian dilakukan selama tujuh hari di lingkungan terbuka Kampus ITNY dengan interval pengambilan data setiap lima menit. Hasil menunjukkan akurasi pengukuran dalam batas ±5% dibandingkan data BMKG, efisiensi energi 84,7%, dan tingkat keberhasilan transmisi data 97,6%. Sistem mampu beroperasi mandiri hingga 78 jam tanpa sinar matahari, membuktikan efektivitas rancangan self-sustain berbasis energi terbarukan. Penelitian ini mendukung pengembangan sistem pemantauan cuaca yang efisien, hemat energi, dan berkelanjutan untuk mendukung mitigasi bencana dan perencanaan sumber daya berbasis data real-time.
Classification Based on Artificial Neural Network for Regency Road Maintenance Priority Bagus Gilang Pratama; Sely Novita Sari; Oni Yuliani
Aviation Electronics, Information Technology, Telecommunications, Electricals, and Controls (AVITEC) Vol 7, No 3 (2025): November (Special Issue)
Publisher : Institut Teknologi Dirgantara Adisutjipto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28989/avitec.v7i3.3056

Abstract

The priority classification of road maintenance is an important issue in regional infrastructure management. This study developed a classification model based on Artificial Neural Network (ANN) to determine the priority of district road maintenance automatically based on actual condition data. The data covered 141 road sections, reduced from 15 to 9 main variables using Principal Component Analysis (PCA), and normalized with the Min-Max Scaler. The ANN model consists of 10 input neurons, 30 hidden neurons, and 5 priority class outputs. The data is divided in a 55-15-35 ratio for training, validation, and testing. The model produces 92% accuracy, 91.7% accuracy, 90.4% recall, and 90.9% F1-score. These findings demonstrate the reliability of ANN in multi-class classifications to support more efficient road maintenance decision-making. The novelty lies in the integration of actual field data, multi-criteria classification, and the application of ANN in the context of complex and underexplored district roads in the literature.
Co-Authors Anwar, Gilang Khairul Arif Basuki Bagus Gilang Pratama, Bagus Gilang Budi Utama, Budi Joko Firdaus Joko Prasojo Kadiman, Sugiarto Karan Gunawan Permana, Irvan Ardhi Qodri, Muhammad Fatih Ratna Kartikasari Sely Novita Sari Sugiarto Kadiman Suwarti, Diah Taufik, Tito Tiyastono Trie Handayani, Trie Yogi Sarwo Wibowo Yusuf Ari Bahtiar