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All Journal Sainstek JOIV : International Journal on Informatics Visualization JURNAL PENDIDIKAN TAMBUSAI Jurnal Vokasi Informatika (JAVIT) Jurnal Teknik Komputer dan Informatika (JTeKI)
Almasri Almasri
Fakultas Ekonomi dan Ilmu Sosial Universitas Islam Negeri Sultan Syarif Kasim Riau
Computer Science & IT Education Electrical & Electronics Engineering Social Sciences

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Rancang Bangun Sistem Pengukur Ketinggian Permukaan Air untuk Peringatan Dini Banjir Berbasis Internet of Things Guci, Putra Fajar Adhari; Faiza, Delsina; Almasri, Almasri; Anori, Sartika
Jurnal Pendidikan Tambusai Vol. 9 No. 2 (2025): Agustus
Publisher : LPPM Universitas Pahlawan Tuanku Tambusai, Riau, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/jptam.v9i2.31060

Abstract

Penelitian ini bertujuan untuk merancang dan mengimplementasikan alat pendeteksi banjir berbasis Internet of Things (IoT) guna memberikan peringatan dini kepada masyarakat. Sistem ini menggunakan NodeMCU ESP 8266 yang terhubung dengan sensor ultrasonik untuk mengukur ketinggian permukaan air, serta modul komunikasi untuk mengirimkan data secara real-time ke aplikasi pemantauan. Metode waterfall di gunakan dalam proses pengembangan, meliputi tahap Analisis, Perancangan, Implementasi, Pengujian, dan Pemeliharaan. Hasil pengujian menunjukkan bahwa alat dapat mendeteksi kenaikan ketinggian permukaan air secara konsisten dan mengirimkan notifikasi peringatan ke aplikasi setelah ambang batas terlampaui. Sistem ini juga mampu beroperasi secara stabil dengan respons cepat, bekerja dengan baik, dan bisa memberi peringatan dini yang fleksibel bagi pengguna tanpa gangguan. Penggunaan alat ini diharapkan dapat meminimalkan risiko kerugian akibat banjir dengan memberikan waktu yang cukup bagi warga untuk melakukan evakuasi dan persiapan.
Optimizing Educational Assessment: The Practicality of Computer Adaptive Testing (CAT) with an Item Response Theory (IRT) Approach Huda, Asrul; Firdaus, Firdaus; Irfan, Dedy; Hendriyani, Yeka; Almasri, Almasri; Sukmawati, Murni
JOIV : International Journal on Informatics Visualization Vol 8, No 1 (2024)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.8.1.2217

Abstract

This research aims to develop a Computer Adaptive Test (CAT) system using the Items Response Theory (IRT) approach. This study is part of developing a web-based system using the Research and Development (R&D) method, employing the Four-D (4-D) model. At its core, this system is similar to a Computer-Based Test (CBT). Still, the critical difference lies in its ability to randomize and provide questions that align with the test-taker's skill levels using the Items Response Theory (IRT) algorithm. The system employs the 3-PL model from the Items Response Theory, considering the difficulty level of questions, the discriminative power of questions, and the likelihood of guessing or interference in the questions. The examination system randomly assigns questions to students based on their responses to previous questions, ensuring that each test-taker receives a unique question sequence. The exam concludes when a test-taker accurately estimates their ability, i.e., SE <= 0.01, or when all questions have been answered. The outcome of this research is a Computer Adaptive Test (CAT) system based on the Items Response Theory (IRT), which can be used to assess students' learning outcomes. This research was implemented in the Multimedia Department of SMK Negeri 1 Gunung Talang, with 90 students as the research sample. The evaluation of the practicality of this system received very high scores, indicating that the Computer Adaptive Test (CAT) system based on the Items Response Theory (IRT) is considered highly practical and effective in achieving the established measurement goals.
Co-Authors Afdar, Jihan Amaria Agariadne Dwinggo Samala Agus Santoso Alvendri, Dio Anori, Sartika Bambang Widodo Dedy Irfan Delsina Faiza Firdaus Firdaus Guci, Putra Fajar Adhari Hanesman, Hanesman Herliza, Sari Huda, Asrul Malqadri, Doni Marta, Rizkayeni Regivan, Ryan Sukmawati, Murni Yeka Hendriyani