Smart Dairy Cattle Farming and In-Heat Detection through the Internet of Things (IoT)

The Internet of Things (IoT) technology has been being revolutionized in various aspects of agriculture around the world ever since. Its application has already found its success in some countries. On the contrary, this technology has yet to find its substantial breakthrough in the Philippines. This study shows the application of IoT in improving the detection efficiency of standing-heat behaviors of cows through automated detection using Pan-tilt-zoom cameras and a Python-driven Web Application. The dimensions of the barn were measured, and the Cameras' Field of Views (FOVs) were pre-calculated for the strategic positions of the cameras atop of the cowshed. The program detects the cows and any estrus events through the surveillance cameras. The results will be sent to the cloud server to display on the web application for analysis. The web app can allow updates on cow information, inseminations, pregnancy, and calving records, estimate travel time from the user's geolocation to the farm, provide live monitoring and remote camera accessibility and control through the cameras and deliver reliable cross-platform push-notification and call alerts on the user's device(s) whenever an estrus event is detected. Based on the results, the program performed satisfactorily at 50% detection efficiency

A Low-Effort Analytics Platform for Visualizing Evolving Flask-Based Python Web Services

Tens of thousands of web applications are written in Flask, a Python-based web framework. Despite a rich ecosystem of extensions, there is none that supports the developer in gaining insight into the evolving performance of their service. In this paper, we introduce Flask Dashboard, a library that addresses this problem. We present the ease with which the library can be integrated in an already existing web application, discuss some of the visualization perspectives that the library provides and point to some future challenges for similar libraries

Pengembangan sistem prediksi daftar ulang calon mahasiswa baru menggunakan metode ADABOOST

Setiap tahunnya perguruan tinggi melakukan penerimaan mahasiswa baru secara rutin untuk membuka awal tahun ajaran baru. Namun tingginya jumlah mahasiswa yang mengundurkan diri menyebabkan banyaknya jumlah kursi kosong yang tersisa. Pengunduran diri yang terjadi bisa diminimalisir apabila seleksi calon mahasiswa baru dilakukan dengan tepat. Salah satu caranya dengan membuat model prediksi berbasis machine learning untuk membantu proses seleksi kandidat yang berpotensi menyelesaikan proses penerimaan hingga akhir berdasarkan data yang ada. Agar hal tersebut bisa tercapai, dibuatlah model prediksi menggunakan algoritma Adaboost sekaligus membandingkan performanya dengan model alogoritma Decision Tree. Untuk memaksimalkan peforma model, maka dilakukan analisa variabel dengan menggunakan chi square dalam proses feature selection- nya. Hasil akhir menunjukkan bahwa model prediksi Adaboost memiliki peforma yang lebih baik daripada model Decision Tree dengan skor f-measure 90.9%, precision 83.7% dan recall 99.5%. Selain itu didapatkan juga ciri dari kandidat yang cenderung melanjutkan pendaftaran hingga akhir. Sehingga dengan hasil tersebut bisa membantu pihak perguruan tinggi dalam mengambil keputusan dalam proses seleksi calon mahasiswa baru