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All Journal JATI (Jurnal Mahasiswa Teknik Informatika) Jurnal Teknik Informatika (JUTIF) AMMA : Jurnal Pengabdian Masyarakat Jurnal Komtika (Komputasi dan Informatika)
Solihudin, Dodi
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Humanities Chemistry Computer Science & IT Engineering Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Nursing

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Pengembangan Website Promosi Produk UMKM Berbasis Online Solihudin, Dodi; Fathurrohman; Muhamad Fajid, Marfelio; Adhari Febrian, Mochamad
AMMA : Jurnal Pengabdian Masyarakat Vol. 2 No. 2 (2023): AMMA : Jurnal Pengabdian Masyarakat (INPRESS)
Publisher : CV. Multi Kreasi Media

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Abstract

Micro, Small, and Medium Enterprises (MSMEs) require effective promotion strategies to enhance the visibility and competitiveness of their products, especially in the digital era. This Community Partnership Program aims to develop product promotion websites for MSMEs based on WordPress. Activities include basic training on using the WordPress platform, designing and developing attractive and informative websites, simple search engine optimization (SEO) of content, and website management training for MSME representatives. It is expected that with this promotional website, MSMEs can expand their online market reach, enhance product brand image, and facilitate interaction with potential consumers, ultimately contributing to increased sales and business growth.
Pelatihan Dasar Microsoft Office Bagi Remaja Putus Sekolah Sebagai Upaya Pemberdayaan Digital Tohidi, Edi; Solihudin, Dodi; Arief Saputra, Mochamad; Haris Maulana Ibrahim, Mochammad
AMMA : Jurnal Pengabdian Masyarakat Vol. 2 No. 3 (2023): AMMA : Jurnal Pengabdian Masyarakat
Publisher : CV. Multi Kreasi Media

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Abstract

Basic skills in using Microsoft Office applications are essential competencies for enhancing job opportunities and participation in various activities in the digital era, especially for out-of-school youth who often have limited access to formal education. This Community Partnership Program aims to empower out-of-school youth through basic Microsoft Office training. This training is designed to provide understanding and practical skills in using Microsoft Word for document processing, Microsoft Excel for data processing and simple calculations, and Microsoft PowerPoint for creating presentations. It is expected that, through this training, out-of-school youth can improve their functional skills, open opportunities for jobs requiring basic administrative abilities, and increase their self-confidence in facing the challenges of the digital era.
Data Augmentation Techniques on the Accuracy of Fertile and Infertile Egg Classification Using Convolutional Neural Networks Nurhakim, Bani; Solihudin, Dodi; Amalia, Dina; Arelia, Irly
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 5 (2025): JUTIF Volume 6, Number 5, Oktober 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.5.5234

Abstract

The classification of fertile and infertile chicken eggs is crucial in the poultry industry to ensure optimal incubation efficiency and hatchability. However, the visual similarity between both egg types under candling conditions poses a significant challenge for manual inspection. This study aims to develop a convolutional neural network (CNN) model using the EfficientNetB4 architecture to automatically classify egg fertility based on image data. The dataset comprises candling images of chicken eggs, which underwent preprocessing steps such as resizing, normalization, and histogram stretching to enhance contrast. To improve model generalization, aggressive data augmentation techniques were applied, including rotation, flipping, zooming, and brightness adjustment. The model was trained in two phases—feature extraction and fine-tuning—using transfer learning and class balancing strategies. Evaluation results demonstrated high performance with an F1-score of 0.95 and balanced classification across both classes. The model's interpretability was further enhanced using Grad-CAM visualization, showing relevant activation regions. These findings indicate that the proposed method is effective in automating egg fertility classification and has potential for broader application in agricultural image diagnostics.
Penerapan Convolutional Neural Network (CNN) Untuk Prediksi Penyakit Tanaman Padi Melalui Citra Daun Sariah, Sariah; Suarna, Nana; Ali, Irfan; Solihudin, Dodi
Jurnal Komtika (Komputasi dan Informatika) Vol 9 No 1 (2025)
Publisher : Universitas Muhammadiyah Magelang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31603/komtika.v9i1.12852

Abstract

Penyakit tanaman padi merupakan salah satu faktor utama yang dapat menurunkan hasil produktivitas, terutama di negara agraris seperti Indonesia. Deteksi dini terhadap penyakit ini sangat penting untuk menghentikan pertumbuhan ekonomi lebih lanjut dan mengurangi kemerosotan ekonomi. Masalahnya, identifikasi tanaman padi secara manual membutuhkan banyak waktu dan tenaga, dan seringkali tidak efisien dalam skala besar. Untuk mengatasi masalah ini, tujuan dari penelitian ini adalah mengembangkan model untuk memprediksi penyakit tanaman yang dapat menganalisis gejala penyakit dari citra daun dengan akurasi yang tinggi, sehingga memungkinkan deteksi penyakit dan mitigasi dampak penyakit yang lebih efektif. Metode yang digunakan dalam penelitian ini yaitu algoritma Convolutional Neural Network (CNN) yang memungkinkan pengumpulan data citra daun padi dari berbagai kondisi kesehatan tanaman padi. Dataset yang digunakan dalam penelitian ini berasal dari sumber sekunder dan citra daun padi yang dikumpulkan secara langsung dilapangan. Dataset ini dianalisis menggunakan teknik augmentasi untuk meningkatkan kualitas dan keberagaman data. Berdasarkan hasil penelitian, model CNN terbaik mampu mendeteksi penyakit tanaman padi dengan akurasi hingga 87,43%. Model ini juga menunjukkan tingkat prediksi dan kepercayaan yang tinggi untuk beberapa penyakit kritis, seperti Blast, Blight, Dan Tungro. Hasil penelitian ini menunjukkan potensi CNN dalam membantu petani mendeteksi penyakit tanaman padi, yang pada akhirnya dapat meningkatkan produktivitas dan mengurangi kerugian.
Co-Authors Adhari Febrian, Mochamad Adniana, Royana Ahmad Faqih Arelia, Irly Arief Saputra, Mochamad Arya Wijaya, Arya Damayanti Damayanti Dina Amalia Eka Permana, Sandy Fathurrohman, Fathurrohman Fazari Hidayat, Nizar Hagi Badra, Muhammad Haris Maulana Ibrahim, Mochammad Herdiana, Ruli Indriya Efendi, Dendy Irfan Ali, Irfan Liawati, Ayu Lukman Rohmat, Cep Muhamad Fajid, Marfelio Narasati, Riri Narasati Nur alamsyah, Deri Nurhakim, Bani Pebdika, Angga Pratama, Denni Prihantono, Willy Puri, Anita Putri Nindya, Nazwa Rifa’I, Ahmad Rohmat, Cep Lukman Saeful Anwar, Saeful Sajidah, Sajidah Sariah Sariah Suarna, Nana Tohidi, Edi Wahyudin, Edi Yulistiano, Irena