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All Journal Indonesian Journal of Artificial Intelligence and Data Mining Journal of Applied Computer Science and Technology (JACOST) Journal of Computer and Information System (J-CIS)
Rachmini, Siti Aulia
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Computer Science & IT

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Pest Detection on Green Mustard Plants Using Convolutional Neural Network Algorithm Arifin, Nurhikma; Rachmini, Siti Aulia; Rusman, Juprianus
Indonesian Journal of Artificial Intelligence and Data Mining Vol 8, No 2 (2025): July 2025
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/ijaidm.v8i2.30953

Abstract

The productivity of mustard greens is vulnerable to pests and diseases that can threaten the yield and quality of the harvest. This study aims to detect pests on green mustard plants using the Convolutional Neural Network (CNN) method. The dataset used in this research consists of 450 images, with 225 images of pest-infested mustard greens and 225 images of healthy mustard greens. These 450 datasets are divided into 400 training data and 50 testing data. The testing was conducted fifteen times using CNN architectures with 2, 3 and 4 convolutional layers, having filter numbers of (64,32) (64, 32, 16) and (64, 32, 16, 8) respectively, and learning rates ranging from 0.1 to 0.00001 with the Adam optimizer. Based on the testing results of the learning rate and the number of layers, it was found that a learning rate of 0.001 provided the best performance with the highest accuracy and the lowest loss, especially in the model with 3 layers (64, 32, 16), which achieved an accuracy of 94% and a loss of 24.92%. A learning rate that is too high (0.1) or too low (0.00001) results in poor performance and instability, with low accuracy and high loss. Therefore, selecting the appropriate learning rate is crucial to achieving optimal results in model training.
Implementasi Algoritma Horspool pada Kamus Digital Bahasa Daerah Taora Rasjid, Muh.; Hamrul, Heliawati; Rachmini, Siti Aulia; Rinaldi, Rian
Journal of Computer and Information System ( J-CIS ) Vol 7 No 1 (2024): J-CIS Vol. 7 No. 1 Tahun 2024
Publisher : Universitas Sulawesi Barat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31605/jcis.v7i1.3898

Abstract

Indonesia merupakan sebuah negara yang dikenal dimanca negara karena memiliki banyak budaya, agama, suku, dan bahasa yang berbeda-beda, setiap daerah di Indonesia memiliki bahasa daerah tersendiri yang menjadi simbol vokal namun pengguna bahasa daerah semakin menurun dikalangan masyarakat, bahasa daerah semakin ditinggalkan sebagai alat komunikasi utama, termasuk bahasa taora di kabupaten mamasa provinsi sulawesi barat yang seiring perkembangan jaman termasuk dalam bahasa yang terancam punah. Salah satu upaya untuk mempertahankan dan melestarikan bahasa taora adalah pembuatan suatu sistem kamus digital dalam bentuk website, Penelitian ini bertujuan untuk mengimplementasikan algoritma Horspool dalam kamus digital bahasa daerah Taora guna mempercepat dan mempermudah pencarian kata. Pengembangan sistem dilakukan menggunakan metode waterfall, yang meliputi tahapan analisis kebutuhan, desain sistem, implementasi, pengujian, dan pemeliharaan. Pengumpulan data dilakukan melalui wawancara dengan masyarakat Taora, menghasilkan 662 kosakata yang kemudian dimasukkan ke dalam database kamus. Hasil implementasi menunjukkan bahwa algoritma Horspool efektif dalam meningkatkan efisiensi dan akurasi pencarian kata dalam kamus digital bahasa Taora. Sistem ini diharapkan dapat menjadi alat yang signifikan dalam pelestarian bahasa Taora, terutama bagi generasi muda. Pengembangan lebih lanjut diusulkan dalam bentuk aplikasi mobile untuk meningkatkan aksesibilitas dan manfaatnya
Rancang Bangun dan Evaluasi Sistem Smart-Ponik Untuk Monitoring dan Automatisasi Tanaman Hidroponik Berbasis IOT dengan Protokol MQTT QTT Rachmini, Siti Aulia; Rasyid, Muh. Rafli; Insani, Chairi Nur; Rabbani, Alim
Journal of Applied Computer Science and Technology Vol. 6 No. 2 (2025): Desember 2025
Publisher : Indonesian Society of Applied Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52158/tpd2rq94

Abstract

Hydroponic farming, a soil-free farming method that has grown in popularity due to its efficient land use and ability to deliver high-quality yields. However, when managed manually, it often encounters issues such as inaccurate watering, imprecise nutrient regulation, and delays in detecting environmental changes. These factors can lead to reduced productivity and lower crop quality. This research aims to address these issues by developing Smart-Ponik, an Internet of Things (IoT)-based monitoring and automation system for hydroponic cultivation utilizing the Message Queuing Telemetry Transport (MQTT) protocol. The system integrates DHT22, soil moisture, and pH sensors to monitor key environmental parameters and transmits data in real time to a server for visualization through a web-based dashboard and automated notifications. The study employs a Research and Development (R&D) method consisting of needs analysis, system design, implementation, and testing. Experimental results show that the system achieves a 100% data transmission rate without packet loss, with an average latency of 0.00 seconds, and occasional delays of 0.01–0.02 seconds due to network fluctuations. Automated control of pumps and fans records a 95% success rate, while black-box testing demonstrates a 100% functional pass rate. In conclusion, Smart-Ponik proves effective for real-time monitoring and automation of hydroponic environments. The system minimizes manual errors, enhances environmental stability, and supports more consistent crop yields. These findings highlight the potential of IoT-based automation to improve precision agriculture practices and increase the reliability of hydroponic production.
Co-Authors Arifin, Nurhikma Hamrul, Heliawati Insani, Chairi Nur Juprianus Rusman Rabbani, Alim Rasjid, Muh. Rasyid, Muh. Rafli Rinaldi, Rian