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All Journal Applied Technology and Computing Science Journal Jurnal Matematika UNAND Warta Pengabdian Andalas: Jurnal Ilmiah Pengembangan Dan Penerapan Ipteks
Radhiatul Husna
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Computer Science & IT Engineering Mathematics Medicine & Pharmacology Social Sciences Other

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Journal : Applied Technology and Computing Science Journal

 1 
Development of a Rice Leaf Disease Detection Application Using Python-Based Computer Vision and YOLO Fajar Maulana; Yomei Hendra; Guswita Helmi; Radhiatul Husna; Amma Liesvarastranta Haz; Evianita Dewi Fajrianti
TEKNOLOGI DITERAPKAN DAN JURNAL SAINS KOMPUTER Vol 8 No 2 (2025): December
Publisher : Universitas Nahdlatul Ulama Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33086/atcsj.v8i2.8486

Abstract

Rice is one of Indonesia’s primary agricultural commodities and is highly vulnerable to various leaf diseases, including blast, blight, brown spot, and tungro, which can significantly reduce crop productivity. To address this issue, an automated and accurate detection system is needed to assist farmers in identifying rice leaf diseases at an early stage. This study aims to develop a rice leaf disease detection application using computer vision technology based on Python and the YOLO (You Only Look Once) algorithm. The research methodology consisted of several stages: problem identification, data acquisition, data exploration, model development, evaluation, and deployment. The dataset was obtained from Roboflow and comprised five classes: blast, blight, brown spot, healthy, and tungro. The YOLO model was trained using Google Colab with optimized parameters to enhance detection performance. Experimental results demonstrate that the proposed model achieved an accuracy of 95% and a mean Average Precision (mAP) of 95%, indicating strong performance in detecting and classifying rice leaf diseases. The system was implemented as a web-based application using Flask and Bootstrap, allowing users to upload images of rice leaves and obtain real-time detection results. This application enables farmers to identify plant diseases quickly and accurately, facilitating timely and effective intervention to minimize crop losses.
Simulation and Optimization of Review Intervals Based on the Mathematical Model of the Ebbinghaus Forgetting Curve Radhiatul Husna; Nabila Gusti Rohima; Alwan Ronan; Mursyid Nur Fahmi; Amma Liesvarastranta Haz; Evianita Dewi Fajrianti
TEKNOLOGI DITERAPKAN DAN JURNAL SAINS KOMPUTER Vol 8 No 2 (2025): December
Publisher : Universitas Nahdlatul Ulama Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33086/atcsj.v8i2.8491

Abstract

Humans naturally experience memory decay over time due to the brain’s limited capacity, a phenomenon first systematically quantified by Hermann Ebbinghaus in 1885 through the forgetting curve, which illustrates the exponential decline of retention in the absence of reinforcement. This curve demonstrates that newly acquired information fades rapidly unless reviewed, negatively affecting educational outcomes as students struggle to retain knowledge in the long term. Spaced repetition, involving scheduled review sessions, has emerged as an effective strategy to counteract forgetting; however, optimal review intervals are often determined intuitively rather than derived mathematically. This study aims to model memory retention dynamics using an extended Ebbinghaus forgetting curve formulated through a learning and forgetting differential equation model, estimate parameters from empirical data, and optimize review intervals to enhance long-term retention. Data were collected through questionnaires from 15 students in the 2023 Mathematics Study Program at Andalas University enrolled in Real Analysis I, yielding parameter estimates of learning rate (α = 0.70), forgetting rate (λ = 0.30), initial knowledge level (K(0) = 100%), and maximum knowledge capacity (K_max = 100%). The model was solved analytically, and numerical simulations compared three strategies: no review, random review, and optimal review at an 80% retention threshold. The optimal review time was found to be t = 1.098 days (approximately 26 hours and 32 minutes), corresponding to the point at which retention declines to 80%. Simulations showed that no review leads to near-zero retention over time, random review produces inconsistent improvements, and the optimal review strategy maintains retention above 80% efficiently. Overall, the mathematically derived optimal review strategy significantly outperforms alternative approaches, providing a personalized, evidence-based method to improve learning efficiency and long-term memory stability while demonstrating the value of integrating psychological memory theory with mathematical optimization for practical educational applications.
Co-Authors Ade Putri Admi Nazra Alwan Ronan Amma Liesvarastranta Haz ANJAR DWI NASTITI Arrival Rince Putri Asdi, Yudiantri Bahri, Susila Baqi, Ahmad Iqbal Devianto, Dodi Efendi Efendi Eka Asih Kurniati Evianita Dewi Fajrianti Fajar Maulana . Fernando Sihotang Finti Warni Guswita Helmi Hazmira Yozza Hendra, Yomei Ikhlas Pratama Sandi Lidya Pratiwi Maiyastri, Maiyastri Mawanda Almuhayar Mursyid Nur Fahmi Nabila Gusti Rohima Nadifa Rahadatul 'Aisy Narwen Narwen Nova Noliza Bakar Nurwijayanti RAMADHAL NOFRINA PUTRI Riri Lestari Siti Asyiah Fudhila Nengsih Syafwan, Mahdhivan Yanuar, Ferra Zulakmal, Zulakmal