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All Journal Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) JITK (Jurnal Ilmu Pengetahuan dan Komputer) Jurnal ICT : Information Communication & Technology Tematik : Jurnal Teknologi Informasi Komunikasi Prosiding Seminar Nasional Sisfotek (Sistem Informasi dan Teknologi Informasi) Journal of Artificial Intelligence and Engineering Applications (JAIEA) Jurnal Sistim Informasi dan Teknologi AMMA : Jurnal Pengabdian Masyarakat
Dadang Sudrajat
STMIK IKMI Cirebon
Humanities Automotive Engineering Chemistry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Engineering Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Nursing

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Arsitektur Ensemble Convolutional Neural Network untuk Klasifikasi Multi Kelas Penyakit Daun Kopi Ade Irma Purnamasari; Dadang Sudrajat; Yudhistira Arie Wijaya
Prosiding SISFOTEK Vol 9 No 1 (2025): SISFOTEK IX 2025
Publisher : Ikatan Ahli Informatika Indonesia

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Abstract

Coffee leaf disease remains one of the most significant threats to global coffee production, particularly Coffee Leaf Rust (CLR) caused by Hemileia vastatrix. Early and accurate disease detection is essential for maintaining yield stability and ensuring sustainable coffee farming. This study proposes an Ensemble Convolutional Neural Network (CNN) architecture that combines MobileNetV2 and ResNet50 to enhance robustness and generalization in multi-class classification of coffee leaf diseases. The dataset consists of 1,664 images categorized into four classes: miner, nodisease, phoma, and rust, collected from both public repositories and real-field observations. Image preprocessing includes resizing, normalization, and augmentation to increase diversity and reduce overfitting. The ensemble model is trained using the Adam optimizer with a learning rate of 0.0001 and evaluated through accuracy, precision, recall, and F1-score metrics. Results demonstrate that the ensemble CNN outperforms single CNN architectures, achieving an accuracy of 95.6%, precision of 94.4%, and F1-score of 94.2%, even under challenging illumination and noise conditions. Compared to individual models, performance improvement ranges from 2%–4%. The model also maintains higher stability when tested under low-light and noisy images, confirming its robustness in real-world scenarios. This study concludes that ensemble CNN offers a reliable and efficient framework for real-time coffee leaf disease detection and can serve as a foundation for developing intelligent agricultural systems using edge computing.
Analisis Kinerja Algoritma Machine Learning untuk Klasifikasi Prestasi Mahasiswa pada Mata Kuliah Bahasa Inggris Riri Narasati; Dadang Sudrajat; Ahmad Faqih; Indra Wiguna Marthanu; Agus Bahtiar
Prosiding SISFOTEK Vol 9 No 1 (2025): SISFOTEK IX 2025
Publisher : Ikatan Ahli Informatika Indonesia

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This study analyzes the performance of several machine learning algorithms in classifying student achievement in English language courses. The research focuses on comparing the performance of K-Nearest Neighbors (KNN), Naïve Bayes, Random Forest, and Support Vector Machine (SVM) using the K-Fold Cross Validation approach to evaluate accuracy, F1-score, and fairness. The dataset, consisting of students’ final grades, was processed through data pre-processing and feature scaling. Results show that the KNN model with K=5 achieved the highest accuracy of 100%, followed by Naïve Bayes with 95.59%. Statistical tests indicated a significant performance difference between Random Forest and SVM, while fairness evaluation revealed that Random Forest provided the most balanced error distribution. These findings confirm that KNN and Random Forest algorithms are highly effective for academic performance classification based on numerical data. The study highlights the potential of machine learning to enhance adaptive, objective, and equitable educational evaluation systems.
Klasifikasi Telur Fertil dan Infertil Berbasis Hybrid MobileNetV3 dengan Mekanisme Attention dan Texture Fusion Bani Nurhakim; Dadang Sudrajat; Tati Suprapti; Ade Rizki Rinaldi; Agus Bahtiar
Prosiding SISFOTEK Vol 9 No 1 (2025): SISFOTEK IX 2025
Publisher : Ikatan Ahli Informatika Indonesia

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Abstract

Accurate fertile-infertile egg classification is crucial to improve hatching productivity and sorting efficiency. This study proposes MobileFusionV3, a MobileNetV3 architecture enriched with CBAM (Convolutional Block Attention Module) and Hybrid Texture Fusion (LBP and GLCM) to combine deep and texture features to be more robust to candling illumination variations. A dataset of 1,275 candling images (675 fertile, 600 infertile) was subjected to preprocessing (resizing, normalization, background enhancement) and realistic data augmentation (rotation, brightness/contrast changes, Gaussian noise, illumination variations). The model was trained using transfer learning, early stopping, and an evaluation scheme based on accuracy, precision, recall, F1-score, and AUC. The test results showed an accuracy of 97.2%, precision of 96.8%, recall of 97.5%, F1 of 97.1%, and AUC of 0.99, surpassing previous designs that did not use attention mechanisms and texture fusion. Grad-CAM++ analysis confirms the model's focus on physiologically relevant regions (embryonic shadow and air-cell), thus improving the reliability of interpretation. These findings indicate that lightweight, efficient designs based on attention and texture fusion have the potential to be implemented in smart hatchery systems and edge/mobile devices while maintaining high accuracy.
Application of Decision Tree Algorithms to Classify the Sales Results of Kangen Kripik Sme Products Adila G Khiqmatiar Muchsin; Nining Rahaningsih; Irfan Ali; Dadang Sudrajat; Saeful Anwar
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 5 No. 2 (2026): February 2026
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v5i2.1854

Abstract

Micro, Small, and Medium Enterprises (MSMEs) play a vital role in strengthening the national economy; however, many still face challenges in managing and analyzing sales data effectively. This study aims to classify product sales results at UMKM Kangen Kripik Mang Acep by applying the Decision Tree algorithm as a data classification method based on machine learning. A quantitative experimental approach was employed to evaluate the model’s performance using one-year sales data, including attributes such as product variants, sales volume, sales channels, and marketing regions. Data processing was conducted using RapidMiner software following the Knowledge Discovery in Databases (KDD) framework, which includes data selection, preprocessing, transformation, data mining, and model evaluation. The results indicate that the Decision Tree algorithm successfully classified sales regions (Garut, Bandung, and Sumedang) with an accuracy rate of 96.48%, identifying “Units Sold (pcs)” as the most influential attribute for distinguishing marketing areas. These findings demonstrate that the Decision Tree method is not only effective in improving data analysis efficiency but also provides valuable strategic insights for data-driven business decision-making in MSMEs
Pelatihan Manajemen SDM Dan Peningkatan Kapasitas Karyawan Sebagai Upaya Pemberdayaan UMKM Cep Lukman Rohmat; Dadang Sudrajat; Fatkhan Mubarok; Muhammad Agastya
AMMA : Jurnal Pengabdian Masyarakat Vol. 3 No. 2 : Maret (2024): AMMA : Jurnal Pengabdian Masyarakat
Publisher : CV. Multi Kreasi Media

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Abstract

Micro, Small and Medium Enterprises (MSMEs) play an important role in national economic development, particularly in creating jobs and improving people's welfare. However, MSMEs often face various challenges in managing human resources (HR) and increasing employee capacity, which has a direct impact on productivity and business sustainability. This research aims to empower MSME actors through HR management training and employee capacity building. The method used is integrated training that includes workforce needs planning, recruitment and selection, training and development, and performance evaluation. Training activities were carried out in the form of workshops, group discussions, and hands-on practices involving the active participation of MSME actors. The results of the training showed an increase in MSME actors' understanding of the importance of HR management and positive changes in the application of management practices in their respective business environments. In addition, the training also contributes to increasing employee motivation and competence, which in turn improves business performance and competitiveness. With this empowerment program, MSMEs are expected to grow more professional, adaptive, and sustainable. This study recommends continuous assistance and support from the government and related institutions to strengthen the managerial capacity of MSME actors in the long term.
Peningkatan Kompetensi Guru Ciayumajakuning Melalui Bimbingan Teknis Teknologi AI Gemini Dadang Sudrajat; Denni Pratama; Fatkhan Mubarok; Muhammad Zamil Farhan
AMMA : Jurnal Pengabdian Masyarakat Vol. 3 No. 2 : Maret (2024): AMMA : Jurnal Pengabdian Masyarakat
Publisher : CV. Multi Kreasi Media

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Abstract

The advancement of Artificial Intelligence (AI) technology has created significant transformations across various sectors, including education. However, the adoption of AI in the educational sector—particularly in the Ciayumajakuning region (Cirebon, Indramayu, Majalengka, and Kuningan)—still faces several challenges, such as low digital literacy among teachers, limited access to technological training, and a lack of understanding regarding the practical application of AI in learning and school administration contexts. The Gemini AI Technical Training Program is part of a Community Service initiative aimed at enhancing teachers' capacity to use AI technology in a practical and ethical manner within educational environments.
Edukasi Pengelolaan Sampah Organik Dan Anorganik Bagi Masyarakat Desa Cep Lukman Rohmat; Dadang Sudrajat; Ikhwal Alfarizi Suherman; Intan Wangi Nur Qibti
AMMA : Jurnal Pengabdian Masyarakat Vol. 2 No. 2 (2023): AMMA : Jurnal Pengabdian Masyarakat (INPRESS)
Publisher : CV. Multi Kreasi Media

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Abstract

Waste management is an increasingly pressing environmental issue, including at the village level. This Community Partnership Program aims to provide education to villagers regarding the correct management of organic and inorganic waste. Activities include outreach on the types of waste, the negative impacts of improper waste management, methods of waste sorting at the household level, and techniques for processing organic waste into compost and utilizing inorganic waste through recycling. It is expected that, through this program, villagers can increase their awareness and knowledge about the importance of sustainable waste management, and be able to implement independent waste sorting and processing practices, thereby creating a cleaner and healthier village environment.
Peningkatan Kompetensi Akademik Dosen Melalui Pelatihan Systematic Literature Review (SLR) Dadang Sudrajat; Denni Pratama; Luthfi Adianto; Luthfiyyah Iffah Adella
AMMA : Jurnal Pengabdian Masyarakat Vol. 2 No. 2 (2023): AMMA : Jurnal Pengabdian Masyarakat (INPRESS)
Publisher : CV. Multi Kreasi Media

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Abstract

The quality of academic research is significantly influenced by the ability of lecturers to conduct systematic and comprehensive literature reviews. This Community Partnership Program aims to enhance the competence of Kopertip Indonesia lecturers in conducting Systematic Literature Reviews (SLR) as an essential part of the academic research process. This training is designed to provide an in-depth understanding of SLR methodology, including formulating appropriate research questions, effective literature search strategies, study inclusion and exclusion criteria, synthesis and analysis of literature data, and the preparation of high-quality SLR reports. It is expected that, through this training, Kopertip Indonesia lecturers can improve their ability to produce more relevant, comprehensive research that significantly contributes to the advancement of knowledge.
Pelatihan Pembuatan Website dan Pengelolaan Media Sosial Untuk Promosi Produk Lokal Cep Lukman Rohmat; Dadang Sudrajat; Saeful Amri; Selvi Andini
AMMA : Jurnal Pengabdian Masyarakat Vol. 1 No. 03 (2022): AMMA : Jurnal Pengabdian Masyarakat
Publisher : CV. Multi Kreasi Media

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Abstract

Improving the competitiveness of local products in the digital era requires micro, small, and medium enterprises (MSMEs) to be able to utilize information technology in their marketing processes. However, many local business actors still rely on conventional promotional methods and have not yet optimized the use of digital media. This Community Service Program (PKM) aims to assist partner MSMEs in increasing their product visibility through website creation and social media management as promotional tools. The program was carried out in several stages: identifying partner needs, training in the creation and management of websites using WordPress, training in content design and marketing strategies via social media (Instagram, Facebook, and WhatsApp Business), and mentoring in content creation and account management. During the program, partners were equipped with basic skills to build a simple online store website, copywriting techniques, product photography using smartphones, and the use of free design tools such as Canva. The results of the activity show that partners who previously had no online presence now have active websites displaying product catalogs, business profiles, and contact information. In addition, the partners’ social media accounts became more active and strategically managed, featuring consistent and engaging promotional content. This activity also increased partners’ understanding of the importance of digital identity and content-based marketing strategies. The PKM provided direct impact in the form of increased consumer trust in local brands, enhanced sales potential, and better preparedness of MSMEs to compete in the digital market. Moving forward, this training can be replicated in various local business communities as part of a technology-based community economic empowerment effort.
Pelatihan Analisis Bibliometrik Berbasis Vosviewer dan Publish or Perish Bagi Dosen Kopertip Indonesia Dadang Sudrajat; Bani Nurhakim; Suteja; Syaiful Imanudin
AMMA : Jurnal Pengabdian Masyarakat Vol. 1 No. 03 (2022): AMMA : Jurnal Pengabdian Masyarakat
Publisher : CV. Multi Kreasi Media

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Abstract

Strengthening the research capacity of university lecturers is a crucial aspect of improving the quality of higher education in Indonesia. One relevant skill to support research development and scientific publication is the ability to conduct bibliometric analysis. This analysis is useful for mapping research trends, identifying scientific collaborations, and determining potential research topics through the study of references and citations. This Community Service Program (PKM) aims to provide training in bibliometric analysis using the software tools VOSviewer and Publish or Perish for lecturers affiliated with Kopertip Indonesia (Coordinator of Indonesian Private Universities). The training was conducted both online and offline, using a combination of theoretical and practical approaches. The materials covered included basic bibliometric concepts, methods for accessing scholarly publication data (e.g., from Google Scholar and Scopus), and hands on practice using Publish or Perish to extract bibliometric data and VOSviewer to visualize the analysis results in the form of term maps and author collaboration networks. The training was also complemented by case studies from various fields of science. Evaluation results indicated that the training improved participants’ understanding and skills in using both tools. Participating lecturers reported increased confidence in mapping research topics, selecting relevant journals, and planning more targeted publication strategies. This activity not only enhanced digital research competencies but also encouraged lecturers to be more productive in contributing to national and international scientific outputs. In the future, this type of training can be replicated across other higher education environments as part of efforts to strengthen a data-driven academic ecosystem.
Co-Authors Abdul Aziz Abrar Hiswara Ade Irma Purnamasari Ade Rizki Rinaldi Adila G Khiqmatiar Muchsin agus bahtiar Ahmad Faqih Ahmad Rifai Ajudin Arif Rinaldi Dikananda Arif Rinaldi Dikananda Bani Nurhakim Bani Nurhakim Cep Lukman Rohmat Denni Pratama Dian Ade Kurnia Dian Ade Kurnia Fadhil Muhammad Basysyar Fatihanursari Dikananda Fatkhan Mubarok Gifthera Dwilestari Harry Budi Santoso Hery Widijanto Ikhwal Alfarizi Suherman Indra Wiguna Marthanu Intan Wangi Nur Qibti Irfan Ali Jayawarsa, A.A. Ketut Kaslani Khaerul Anam Khaerul Anam Lana Surlanto Luthfi Adianto Luthfiyyah Iffah Adella Martanto Maulana Sujarwadi Muhammad Agastya Muhammad Zamil Farhan Nana Suarna Nining Rahaningsih Nisa Dienwati Nuris Novianti Indah Putri Odi Nurdiawan Othman bin Mohd Raditya Danar Dana Rinovian Rais Riri Narasati Rudi Kurniawan Rudi Kurniawan Saeful Amri Saeful Anwar Salman Alfarizi Sandy Eka Permana Selvi Andini suroso, amat Suteja Syaiful Imanudin Tati Suprapti Ujang Supriatna Umi Hayati Yudhistira Arie Wijaya Zen Munawar