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Muhammad Faishol Amrulloh
Prodi Teknik Informatika, Universitas Yudharta Pasuruan
Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Social Sciences Other

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RANCANG BANGUN MONITORING SUHU KAMBING DIPETERNAKAN BHR FARM MENGGUNAKAN INTERNET OF THINGS (IOT) Asyadulloh; Arief Tri A; Muhammad Faishol A
Kohesi: Jurnal Sains dan Teknologi Vol. 1 No. 1 (2023): Kohesi: Jurnal Sains dan Teknologi
Publisher : CV SWA Anugerah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.3785/kjms.v1i1.39

Abstract

Iklim di Indonesia yang tropis sangat mendukung perkembangan ternak kambing. Dalam iklim tropis ada dimana adalah musim penghujan, musim ini kerentanan suhu yang menurun menjadi dingin dan menjadi salah satu penyebab kesehatan hewan ternak menurun. Oleh karena itu diperlukan suatu system deteksi suhu yang dapat mengukur suhu terkan secara realtime. Penelitian ini bertujuan untuk memantau suhu hewan ternak yang akan menjadi informasi awal bagi peternak .Sistem ini memanfaatkan teknologi Internet Of Things dengan menggunakan NodeMCU ESP8266 sebagai pemroses data dan selanjutnya dikirimkan menuju Firebase menggunakan jaringa internet dan sensor suhu infrared sebagai pengukur suhu pada ternak. Hasil penelitian ini berupa prototype perangkat sistem deteksi suhu hewan ternak yang dapat menginformasikan notifikasi alarm sesuai dengan level suhu. Indikator lampu LED akan menyala sesuai dengan level suhu pada hewan ternak, kemudian alarm akan muncul pada buzzer dan smartphone melalu aplikasi rancangan peneliti. Dengan demikian prototype sistem monitoring suhu ini diharapkan dapat bermanfaat agar para peternak dapat segera melakukan penanganan untuk menekan angka kematian pada hewan ternak pada masa musim penghujan.
Perancangan dan Implementasi Sistem Monitoring Jaringan di MA Darut Taqwa Berbasis Web yang Mengintegrasikan dengan API MikroTik Nurus Sobah; Muhammad Faishol Amrulloh
BIOS : Jurnal Teknologi Informasi dan Rekayasa Komputer Vol 4 No 2 (2023): September
Publisher : Puslitbang Sinergis Asa Professional

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37148/bios.v4i2.75

Abstract

MA Darut Taqwa is a school located in Pasuruan Regency, which has implemented a centralized internet network with large bandwidth. The high number of users accessing the network at MA Darut Taqwa requires a real-time monitoring system to ensure the quality and condition of the network on MikroTik. However, this school has not fully used real-time network management, so it requires a user monitoring system using the MikroTik API which is integrated through PHP and MySQL-based websites. This will be very useful for administrators in monitoring the network effectively. The implementation of the proposed system is the development of a PHP-based website that interacts with MikroTik devices via the API. Real-time monitoring is obtained by continuously querying and updating data, as well as providing administrators with up-to-date information about network usage. The results of this study show that the website application functions properly with the results that the website can provide real-time internet speed information on the proxy interface and can manage hotspots and Point-to-Point Protocol (PPP).
Classification of Coffee Leaf Diseases using the Convolutional Neural Network (CNN) EfficientNet Model Muhammad Imron Rosadi; Lukman Hakim; M. Faishol A.
IAIC International Conference Series Vol. 4 No. 1 (2023): SEMNASTIK 2023
Publisher : IAIC Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34306/conferenceseries.v4i1.627

Abstract

Coffee leaf disease is a problem that needs attention because it affects the quality and productivity of the coffee harvest and is detrimental to farmers. Therefore, a system is needed to identify types of coffee leaf diseases using artificial intelligence. There are four types of coffee leaf diseases, namely Miner leaf, Phoma leaf, Rust leaf, and Nodisease leaf. The research used the EfficientNet Architecture Convolutional Neural Network (CNN) method to detect types of disease on coffee leaves. This method was chosen because it is capable and reliable in processing digital images for pattern recognition. The dataset used is 1,464 images with dimensions of 2048 x 1024 pixels with RGB type which are divided into 1,264 training data and 400 testing data. Several architectures used in EfficientNet are EfficientNet B0, EfficientNet B1, EfficientNet B2, EfficientNet B3, EfficientNet B4. Parameters used are Lanczos resampling, Epoch 25, Learning Rate 0.0001, Loss Function Sparse Categorical Cross Entropy, Optimizer Adam. The results of training data testing, namely the CNN EfficientNet B1 Architecture Model method, got the best accuracy of 97% and a loss of 0.1328 and testing data testing got an accuracy of 0.97% and a loss of 0.1328. The architecture of the EfficientNet B1 model is better than other architectural models, namely VGG16, ResNet50, MobileNetv2, EfficientNet B0, EfficientNet B2, EfficientNet B3, EfficientNet B4, EfficientNet B5, EfficientNet B6, EfficientNet B7.
Co-Authors Ahmad Rif’an Firdaus Ainun Gita Risqi Ilma Arief Tri A Asyadulloh Asy’ari Fandi Ahmad Dafina Agustina Lukman Hakim M Sirodjudin Moch. Lutfi Mochamad Sirodjudin Muhammad Febriansyah Muhammad Imron Rosadi Muhammad Mahrus Ali Nur Moh Sholeh Nurus Sobah Rifqiyah Rifqiyah Try Hadyanto