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All Journal Journal of Information Systems and Informatics JATI (Jurnal Mahasiswa Teknik Informatika) Jurnal Infortech Journal of Computer Networks, Architecture and High Performance Computing Jurnal Responsif : Riset Sains dan Informatika Devotion: Journal of Research and Community Service Malcom: Indonesian Journal of Machine Learning and Computer Science
Hafizh Cahaya Putra, Vito
Universitas Satu
Computer Science & IT Decision Sciences, Operations Research & Management Education Library & Information Science

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Enhanced Plant Disease Detection Using Computer Vision YOLOv11: Pre-Trained Neural Network Model Application Al Husaini, Muhammad; Rachmat Raharja , Agung; Cahaya Putra , Vito Hafizh; Lukmana, Hen Hen
Journal of Computer Networks, Architecture and High Performance Computing Vol. 7 No. 1 (2025): Article Research January 2025
Publisher : Information Technology and Science (ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/cnahpc.v7i1.5146

Abstract

This study investigates the application of YOLOv11, a cutting-edge deep learning model, to enhance the detection of plant diseases. Leveraging a comprehensive dataset of 737 images depicting tomato leaves affected by various diseases, YOLOv11 was trained and evaluated on key performance metrics such as precision, recall, and mAP. Experimental results the model was trained and evaluated on key metrics including accuracy (75.6%), precision (0.80), recall (0.77), and mAP@0.5 (75.6%). Experimental through base architectural such as enhanced feature extraction with C2 modules, improved multi-scale detection using SPPF layers, and optimized non-maximum suppression techniques. These improvements enable the model to achieve stable precision and recall for each class, even in challenging scenarios with overlapping objects and diverse environmental conditions. By addressing practical usability challenges, this system offers a scalable, accessible, and impactful solution for precision agriculture, paving the way for sustainable with this pretrained model. This study underscores the potential of deep learning-based models, particularly YOLOv11, in transforming the way monitoring and disease management are approached, demonstrating its ability to stable accuracy and operational efficiency in real-world applications. Furthermore, the practical usability of the YOLOv11-based system addresses challenges in the domain of precision plant detection desease. By providing a scalable, accessible, and highly efficient solution, the model offering a significant advancement toward sustainable agricultural practices.
SISTEM PERINGATAN DINI BANJIR BANDANG DI WILAYAH PENAMBANGAN PASIR VULKANIK MENGGUNAKAN INTERNET OF THINGS Putra, Vito Hafizh Cahaya; Kanugrahan, Ghanim; Wahyu, Ari Purno
Jurnal RESPONSIF: Riset Sains & Informatika Vol 6 No 1 (2024): Jurnal Responsif : Riset Sains dan Informatika
Publisher : LPPM Universitas Adhirajasa Reswara Sanjaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51977/jti.v6i1.1498

Abstract

Banjir bandang menjadi ancaman serius yang mengintai bagi siapapun yang beraktivitas disungai, seperti aktivitas penambang pasir vulkanik yang kerap kali mendatangkan korban yaitu secara mendadak banjir bandang datang secara tiba-tiba dari dataran tinggi. Banyak penelitian untuk membuat sistem peringatan dini berbasis Internet of Things (IoT) yang telah dilakukan umumnya menggunakan Water Flow Sensor yang kurang tepat untuk banjir bandang, tidak menggunakan platform Thingspeak dan membutuhkan pembangkit tenaga listrik konvensional. Penelitian ini bertujuan untuk membuat prototipe sistem peringatan dini banjir bandang bagi penambang pasir vulkanik berbasis Internet of Things menggunakan alarm dan media sosial twitter, serta memanfaatkan sensor Ultrasonic, sensor IR (Infrared) Line Tracking, dan modul jaringan ESP8266 menggunakan protokol HTTP (Hypertext Transfer Protocol) untuk terhubung ke platform Thingspeak. Terdapat Solar Cell yang digunakan untuk mengisi daya berbagai perangkat menggunakan cahaya matahari. Setelah dilakukan pengujian pada sistem ini menggunakan Black Box Testing dengan hasil semua perangkat dapat bekerja dengan baik. Peringatan dini melalui alarm terbukti dapat memberikan peringatan secara cepat, dan informasi bencana dapat diakses melalui twitter, serta hasil pendeteksian sensor ditampilkan melalui grafik.
PREDIKSI HARGA SAHAM BBCA MENGGUNAKAN METODE LONG SHORT-TERM MEMORY DAN GATED RECURRENT UNIT Hariyanti, Ifani; Putra, Vito Hafizh Cahaya; Raharja, Agung Rachmat
Jurnal RESPONSIF: Riset Sains & Informatika Vol 7 No 1 (2025): Jurnal Responsif : Riset Sains dan Informatika
Publisher : LPPM Universitas Adhirajasa Reswara Sanjaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51977/jti.v7i1.1901

Abstract

Penelitian ini bertujuan untuk memprediksi harga saham PT Bank Central Asia Tbk (BBCA) menggunakan model Long Short-Term Memory (LSTM) dan Gated Recurrent Unit (GRU). Data harga saham diambil dari Yahoo Finance (2010–2023) sebanyak 3.464 data, mencakup atribut Tanggal, Open, High, Low, Close, Adj Close, dan Volume. Data diproses menggunakan MinMax Scaler sebelum pelatihan model. Model dievaluasi menggunakan MAE, RMSE, dan MAPE untuk mengukur performa prediksi. Hasil penelitian menunjukkan bahwa GRU lebih unggul dibandingkan LSTM dalam memprediksi harga saham BBCA, dengan akurasi prediksi yang lebih mendekati nilai aktual. Dari hasil eksperimen pelatihan model menggunakan dataset harga saham BBCA harian dengan berbagai kombinasi hyperparameter yang ditetapkan, ditemukan bahwa model dengan metrik evaluasi terendah adalah model LSTM dengan batch size 64 dan epoch 20. Model ini memberikan nilai MAE sebesar 158.508342, RMSE sebesar 208.687816, dan MAPE sebesar 2.248164%. Temuan ini diharapkan berkontribusi pada pengembangan analisis keuangan di Indonesia.
Co-Authors Agung Rachmat Raharja Al Husaini, Muhammad Al-Husaini, Muhammad Ari Purno Wahyu Wibowo Ce, Win Febriyanti Panjaitan Hariyanti, Ifani Hery Oktafiandi Kanugrahan, Ghanim Lukmana, Hen Hen Mokhamad Hendayun Oktafiandy, Hery Permai, Antika Yudi Ramdhani Yustianto , Purnomo