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All Journal International Transactions on Education Technology (ITEE) Jurnal Informatika Polinema (JIP)
Ary Prabowo
Universitas Esa Unggul
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Library & Information Science Social Sciences

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Klasifikasi Penyakit Ayam Berdasarkan Citra Kotoran Menggunakan CNN dengan Xception Shela Atya Mitasya; Ary Prabowo; Vitri Tundjungsari
Jurnal Informatika Polinema Vol. 12 No. 3 (2026): Vol. 12 No. 3 (2026)
Publisher : UPT P2M State Polytechnic of Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33795/jip.v12i3.9247

Abstract

Peternakan ayam di Indonesia menghadirkan potensi ekonomi yang substansial karena tingginya permintaan daging dan telur. Namun, peternakan ini juga menghadapi risiko signifikan dari penyakit yang dapat menginfeksi ayam secara tiba-tiba dan seringkali sulit dideteksi sejak dini. Penelitian ini bertujuan untuk membantu peternak mengidentifikasi penyakit ayam lebih cepat dan akurat dengan memanfaatkan teknologi pemrosesan citra digital. Sistem klasifikasi dibangun menggunakan metode Convolutional Neural Network (CNN) dengan arsitektur Xception, yang dikenal karena efisiensinya dalam mengekstraksi fitur visual. Model tersebut mengklasifikasikan citra menjadi empat kategori: Coccidiosis, Newcastle Disease, Salmonella, and healthy conditions. Proses pelatihan menggunakan dataset dari Kaggle dan sejumlah citra lapangan yang terbatas, dengan pembagian data 80:10:10 untuk pelatihan, validasi, dan pengujian. Model tersebut mencapai akurasi 94%, menunjukkan kinerja terbaik dalam mendeteksi Koksidiosis, sementara Penyakit Newcastle menimbulkan lebih banyak tantangan karena keterbatasan data. Pengujian lebih lanjut dengan 36 foto feses lapangan mengonfirmasi keandalan sistem, terutama dalam mengidentifikasi sampel yang sehat. Sistem ini terintegrasi ke dalam aplikasi seluler atau web yang dapat membantu peternak dalam mendeteksi penyakit ayam secara otomatis dari gambar tinja, yang pada akhirnya mengurangi kerugian dan mendukung praktik peternakan unggas yang lebih sehat.
IoT-Based Home Security System: ESP32-CAM Integration and Real-Time Notification Qori Halimatul Hidayah; Ryan Putra Laksana; Ary Prabowo; Dwi Santika Simatupang
International Transactions on Education Technology (ITEE) Vol. 4 No. 2 (2026): International Transactions on Education Technology (ITEE)
Publisher : Pandawan Sejahtera Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/itee.v4i2.956

Abstract

Conventional home security systems are generally unable to provide real-time monitoring accompanied by visual evidence when suspicious activity occurs, so the user response rate is still low. The development of the Internet of Things (IoT) allows the integration of sensors, cameras and communication platforms to produce more adaptive and responsive security systems. This research aims to develop a prototype IoT-based home security system using ESP32-CAM and PIR sensors integrated with the Telegram Bot API to provide image-based au- tomatic notifications. The research was carried out through the stages of needs identification, literature study, design, prototype development, and functional testing. The system is designed to detect movement via a PIR sensor, take im- ages using the ESP32-CAM, store them on MicroSD media, and send real-time notifications to users via the Telegram platform. Test results show that the sys- tem is able to detect movement accurately, take images with stable quality, and send notifications with a 100% success rate as long as a network connection is available. Moreover, there is also the provision of manually executing /takefoto commands for remotely monitoring the system. This research work contributes towards designing a home security system which is not only cost-effective but is also relatively simple to implement and highly adaptable in the community environment. Future development could involve cloud data storage and video recording capabilities.
Co-Authors Dwi Santika Simatupang Qori Halimatul Hidayah Ryan Putra Laksana Shela Atya Mitasya Vitri Tundjungsari