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All Journal Khazanah Informatika: Jurnal Ilmu Komputer dan Informatika Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Jurnal Nasional Komputasi dan Teknologi Informasi Jurnal Abdi Insani Jurnal Bakti Nusa Jurnal Karya Pengabdian Teknik Mesin Mars: Jurnal Teknik Mesin, Industri, Elektro dan Ilmu Komputer
I Made Budi Suksmadana
Universitas Mataram
Aerospace Engineering Agriculture, Biological Sciences & Forestry Humanities Automotive Engineering 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 Environmental Science Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Neuroscience Transportation

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Implementasi Deteksi Penyakit Tanaman Menggunakan Aplikasi Harvest Scan Berbasis Analisis Visual dengan Integrasi Cloud Computing Erna Wati; I Made Budi Suksmadana
Jurnal Nasional Komputasi dan Teknologi Informasi (JNKTI) Vol 7, No 6 (2024): Desember 2024
Publisher : Program Studi Teknik Komputer, Fakultas Teknik. Universitas Serambi Mekkah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32672/jnkti.v7i6.8292

Abstract

Abstrak - Cloud computing memainkan peran penting dalam mendukung aplikasi Harvest Scan untuk deteksi penyakit tanaman melalui analisis visual. Dengan memanfaatkan teknologi ini, Harvest Scan memproses dan menyimpan data gambar tanaman secara efisien, memungkinkan analisis kesehatan tanaman dan memberikan rekomendasi perawatan secara real-time kepada pengguna. Hasil uji menunjukkan akurasi deteksi sebesar 75%, yang membuktikan kemampuan aplikasi dalam mengenali dan mengklasifikasikan berbagai masalah tanaman secara cepat dan tepat. Teknologi cloud mendukung pengelolaan data skala besar dan pemantauan kondisi tanaman berkelanjutan, memberikan solusi praktis dan tepat waktu bagi petani untuk meningkatkan produktivitas dan kualitas hasil pertanian. Studi ini mengkaji bagaimana integrasi cloud computing dalam Harvest Scan mempercepat proses deteksi dan meningkatkan akurasi diagnosis, serta membahas tantangan dan manfaat implementasinya di sektor pertanian.Kata kunci: Kesehatan tanaman, deteksi penyakit, teknologi pertanian, analisis gambar, dan Harvest Scan.  Abstract  - Cloud computing plays a crucial role in supporting the Harvest Scan application for plant disease detection through visual analysis. By leveraging this technology, Harvest Scan efficiently processes and stores images of plants, enabling health analysis and providing real-time care recommendations to users. Test results indicate a detection accuracy of 75%, demonstrating the application's ability to quickly and accurately recognize and classify various plant issues. Cloud technology supports the management of large-scale data and continuous monitoring of plant conditions, providing practical and timely solutions for farmers to enhance productivity and the quality of agricultural yields. This study examines how the integration of cloud computing in Harvest Scan accelerates the detection process and improves diagnostic accuracy, while also discussing the challenges and benefits of its implementation in the agricultural sector.Keywords: Plant health, disease detection, agricultural technology, image analysis, and Harvest Scan.
RadReader: An Enhanced AlexNet-Based GUI Application for Pneumonia Prediction in Thoracic X-Ray Images Giri Wahyu Wiriasto; Ahdiat Aunul Hipzi; I Made Budi Suksmadana; Misbahuddin; Indira puteri Kinasih; Putu Aditya Wiguna
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 9 No 6 (2025): December 2025
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v9i6.7023

Abstract

Recent advancements in radiology applications have led to user-friendly interfaces, improving pneumonia diagnosis by accurately differentiating between viral and bacterial pneumonia from thoracic X-rays. This approach enhances diagnostic precision and efficiency while offering intuitive real-time interaction for radiologists. This study aims to achieve two objectives: (i) developing a desktop-based radiology reader application, and (ii) modifying the alexNet architecture for classifying pneumonia based on thoracic X-ray datasets with the output encompassing pneumonia and normal cases. The desktop application assists radiologists in efficient image analysis and is developed using python–Tkinter. Integrate enhanced of AlexNet models which has been modified to better differentiate. The modified alexNet includes changes like adding max pooling in specific blocks and adjusting hidden layer neuron count. The dataset consists of 7442 images, with 4484 positive pneumonia and 2958 normal images obtained from the Mendeley websites. The enhanced alexNet (EAM) model achieves impressive results: 95.36% accuracy, 95.34% precision, 95.28% recall, and 95.31% F1-score for classifying bacterial pneumonia.
Co-Authors A. Sjamsjiar Rachman Agil Maulana Nanda Riady Ahdiat Aunul Hipzi Bagus Widhi Dharma S Bulkis Kanata Cipta Ramadhani Danar Hadi Bachtiar Erna Wati Giri Wahyu Wiriasto I Ketut Wiryajati I Made Ary Swantika I Made Ginarsa I Nengah Suhartawan I Nyoman Wahyu Satiawan Ida Ayu Sri Adnyani Ida Bagus Fery Citarsa Ida Bagus Fery Citarsa Ida Bagus Fery Citarsa Ida Rofaida Indira puteri Kinasih Indira Puteri Kinasih M. Laylul Mustagfirin Misbahuddin, Misbahuddin Ni Made Seniari Paniran Paniran Putu Aditya Wiguna Supriono . Supriono Supriono Supriono Supriono Supriono Supriono Supriono Supriyatna Supriyatna Warindi D