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All Journal Aksi Kita: Jurnal Pengabdian Kepada Masyarakat MURAKOM
Akhiruddin Pulungan
Universitas Graha Nusantara
Economics, Econometrics & Finance Environmental Science Social Sciences Other

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AgriVision AI: Sistem Deteksi Dini Penyakit Tanaman Kedelai (Glycine max L.) Menggunakan Deep Learning Berbasis Smartphone Akhiruddin Pulungan; Surya Handayani; Muhammad Noor Hasan Siregar; Siti Hardianti Wahyuni; Meiliana Friska; Jumaria Nasution
Aksi Kita: Jurnal Pengabdian kepada Masyarakat Vol. 2 No. 4 (2026): JULI-AGUSTUS
Publisher : Indo Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63822/23zy5a46

Abstract

Produktivitas tanaman kedelai (Glycine max L.) di Indonesia masih menghadapi berbagai kendala, salah satunya adalah serangan penyakit daun yang dapat menyebabkan penurunan hasil panen secara signifikan. Identifikasi penyakit yang masih dilakukan secara manual sering kali memerlukan waktu, bergantung pada pengalaman petani, dan berpotensi menghasilkan diagnosis yang kurang akurat. Penelitian ini bertujuan mengembangkan AgriVision AI, sebuah sistem deteksi dini penyakit tanaman kedelai berbasis smartphone yang memanfaatkan teknologi Deep Learning dengan algoritma Convolutional Neural Network (CNN). Metode penelitian menggunakan pendekatan Research and Development (R&D) yang meliputi tahap analisis kebutuhan, pengumpulan data, perancangan sistem, implementasi model, pengujian, dan evaluasi. Dataset yang digunakan terdiri atas citra daun kedelai sehat, karat daun, bercak daun, dan hawar daun yang diperoleh dari sumber publik dan citra lapangan. Hasil penelitian menunjukkan bahwa model CNN mampu melakukan klasifikasi penyakit dengan tingkat kinerja yang sangat baik, ditunjukkan oleh nilai accuracy sebesar 94,8%, precision 93,6%, recall 94,1%, dan F1-score 93,8%. Analisis confusion matrix memperlihatkan bahwa sebagian besar citra berhasil diklasifikasikan dengan benar pada setiap kelas dengan tingkat kesalahan yang relatif rendah. Integrasi model ke dalam aplikasi smartphone memungkinkan proses identifikasi penyakit dilakukan secara cepat, praktis, dan real-time. Sistem ini juga menyediakan informasi gejala serta rekomendasi pengendalian penyakit sehingga dapat membantu petani dalam pengambilan keputusan. Dengan demikian, AgriVision AI berpotensi mendukung penerapan smart farming dan meningkatkan efektivitas pengelolaan kesehatan tanaman kedelai.  
Analisis Sentimen Pelanggan terhadap Produk UMKM pada Marketplace Menggunakan Algoritma Naïve Bayes Akhiruddin Pulungan; Muhammad Noor Hasan Siregar; Hery Dia Anata Batubara
MUARA KOMPUTER : Jurnal Ilmiah Ilmu Komputer & Elektronika Vol. 2 No. 3 (2026): MUARA KOMPUTER : Jurnal Ilmiah Ilmu Komputer & Elektronika
Publisher : CV MUARA EDUKASI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64365/murakom.v2i3.484

Abstract

The rapid growth of online marketplaces has provided significant opportunities for Micro, Small, and Medium Enterprises (MSMEs) to market their products to a broader audience. Customer reviews posted on marketplaces contain valuable information regarding customer satisfaction, experiences, and perceptions of the products and services offered. However, the increasing volume of reviews makes manual analysis inefficient and time-consuming. Therefore, an automated method is needed to process and analyze review data effectively. This study aims to analyze the sentiment of MSME reviews on marketplaces using the Naïve Bayes algorithm and Natural Language Processing (NLP) techniques. This research employs a text mining approach consisting of data collection, preprocessing stages including case folding, tokenization, stopword removal, and stemming, followed by term weighting using the Term Frequency-Inverse Document Frequency (TF-IDF) method. The processed data are then classified into three sentiment categories: positive, negative, and neutral, using the Naïve Bayes algorithm. Model performance is evaluated using a confusion matrix as well as accuracy, precision, recall, and F1-score metrics. The results indicate that the Naïve Bayes model performs well in classifying sentiment from marketplace reviews. Based on the confusion matrix, the model correctly classified 176 out of 200 testing data instances. The evaluation results show an accuracy of 88.00%, precision of 87.25%, recall of 86.80%, and F1-score of 87.02%. The best performance was achieved in the positive sentiment class, with a precision of 92.96%, recall of 94.29%, and F1-score of 93.62%. These findings demonstrate that the Naïve Bayes method combined with NLP techniques is effective for conducting sentiment analysis of MSME reviews on marketplaces. This study is expected to assist MSME owners in understanding customer opinions more quickly and accurately, thereby providing valuable insights for improving product quality and service performance.
Co-Authors HERY DIA ANATA BATUBARA Jumaria Nasution Meiliana Friska Muhammad Noor Hasan Siregar Siti Hardianti Wahyuni Surya Handayani