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All Journal IKRA-ITH Informatika : Jurnal Komputer dan Informatika Antivirus : Jurnal Ilmiah Teknik Informatika
Allya Putri Nadila Gustin
Universitas Singaperbangsa Karawang
Computer Science & IT

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DESIGN OF AN INTERNET OF THINGS (IOT)-BASED AUTOMATIC CLASSROOM AIR QUALITY MONITORING AND NOTIFICATION SYSTEM Jouvanytha Aswar Afendy; Muhammad Hanif; Allya Putri Nadila Gustin
Antivirus : Jurnal Ilmiah Teknik Informatika Vol 20 No 1 (2026): Mei 2026
Publisher : Universitas Islam Balitar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35457/52nvvb47

Abstract

Kesehatan dan konsentrasi belajar dapat terpengaruh oleh kualitas udara yang buruk di ruang kelas.  Karena tidak dapat memberikan data secara real-time, pengawasan manual seringkali kurang efektif.  Tujuan penelitian ini adalah untuk mengembangkan sistem yang berbasis Internet of Things (IoT) yang dapat melacak dan melaporkan kualitas udara secara cepat dan akurat.  Mikrokontroler ESP32 digunakan dalam sistem, yang terhubung dengan sensor MQ135 untuk mengidentifikasi gas berbahaya, sensor DHT22 untuk mengukur suhu dan kelembapan, dan LCD 16x2 dan LED untuk memberikan tampilan visual. Diuji melalui simulasi Wokwi, data sensor dikirim secara real-time ke aplikasi Blynk melalui jaringan Wi-Fi. Hasil pengujian menunjukkan bahwa sistem mampu menampilkan data dengan stabil, memberikan notifikasi otomatis ketika nilai udara melewati ambang batas aman, dan bekerja secara responsif dengan jeda waktu pembaruan sekitar dua hingga tiga detik.  Dinilai bahwa sistem ini adalah cara yang efektif, efisien, dan mudah digunakan untuk memantau kualitas udara di ruang kelas.
Klasifikasi Jenis Penyakit Tanaman Padi Menggunakan Arsitektur Mobilnetv2 Berbasis Transfer Learning Di Desa Karangraharja, Kabupaten Bekasi Allya Putri Nadila Gustin; Jajam Haerul Jaman; Garno Garno
IKRA-ITH Informatika : Jurnal Komputer dan Informatika Vol. 10 No. 2 (2026): IKRAITH-INFORMATIKA Vol 10 No 2 Juli 2026
Publisher : Fakultas Teknik Universitas Persada Indonesia YAI

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

Abstract

Rice is an important food commodity in Indonesia, but its productivity is often affected by plant diseases that can reduce yields. In Bekasi Regency, particularly Karangraharja Village, rice plant disease identification is still done manually through visual observation of leaves, making it subjective and dependent on farmer experience. This condition has the potential to cause errors in disease classification and management. Technological developments allow the classification of rice disease types to be carried out using image processing. The purpose of this study was to classify rice plant diseases based on leaf images, according to their disease class using transfer learning techniques. The research stages were carried out using the CRISP-DM methodology, which includes business understanding, data understanding, data preparation, modeling, evaluation, and deployment. The image data used amounted to 6.166 rice leaf images. The classification process was carried out using the MobileNetV2 architecture. The best scenario was obtained by the fine-tuning method with a data division of 80% training, 10% validation, and 10% testing, which resulted in an accuracy of 99% on the testing data and 95% on the new data.
Co-Authors Garno, Garno Jaman, Jajam Haerul Jouvanytha Aswar Afendy Muhammad Hanif