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All Journal INFORMAL: Informatics Journal SMATIKA Jurnal Manajemen Informatika dan Sistem Informasi Jurnal Informatika dan Rekayasa Elektronik Brilliance: Research of Artificial Intelligence Jurnal Nasional Teknik Elektro dan Teknologi Informasi Jurnal Pengabdian Masyarakat Bidang Sosial dan Humaniora Jurnal Pengabdian Masyarakat Sains dan Teknologi Jurnal ELTEK Recent in Engineering Science and Technology PANDALUNGAN : Jurnal Pengabdian kepada Masyarakat
Dwi Wijonarko
Universitas Jember
Religion Agriculture, Biological Sciences & Forestry Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Materials Science & Nanotechnology Mathematics Mechanical Engineering Social Sciences Other

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Harmoni Multikultural: Membangun Kebersamaan di Tengah Perbedaan untuk Kaum Milenial Katarina Leba; Balthasar Watunglawar; Muhammad ‘Ariful Furqon; Dwi Wijonarko
ABDISOSHUM: Jurnal Pengabdian Masyarakat Bidang Sosial dan Humaniora Vol. 3 No. 4 (2024): Desember 2024
Publisher : Yayasan Literasi Sains Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55123/abdisoshum.v3i4.4217

Abstract

This community service activity is conducted through a religious seminar to strengthen the understanding and implementation of diversity values among young generations. The seminar is designed to respond to the increasing challenges of societal polarisation and intolerance, especially among millennials. Through a series of interactive sessions, participants are invited to explore the concept of multicultural harmony from a religious perspective, emphasizing universal values such as compassion, empathy, and mutual respect. The seminar material covers discussions on the role of religion in promoting peace, strategies to overcome inter-group prejudices and stereotypes, and best practices in building interfaith dialogue. The seminar also addresses the role of technology and social media in facilitating positive interactions between cultures and religions. It is hoped that through this activity, millennials can become active agents of change in building a harmonious and inclusive society while respecting the uniqueness of each cultural identity. Post-seminar evaluations show increased participants' understanding of the importance of togetherness in diversity and a commitment to apply the values of multicultural harmony in daily life.
Mobile Ad-Hoc Network (MANET) Method: Some Trends and Open Issues Wijonarko, Dwi; Arifin, Samsul; Faisal, Muhammad; Pratama, Muhammad Nabil; Priambodo, Okta Nindita; Nugraha, Edwin Setiawan
Recent in Engineering Science and Technology Vol. 3 No. 02 (2025): RiESTech Volume 3 No. 02 Years 2025
Publisher : MBI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59511/riestech.v3i2.108

Abstract

This study analyzes the latest developments and trends in the field of Mobile Ad-Hoc Networks (MANET) through a bibliometric approach using a metadata dataset from publications taken from Scopus between 2021 and 2024. By utilizing VOSviewer to visualize the data, the study identified key keywords that dominated the MANET literature, such as "security", "routing protocols", "mobility", and "5G". The visualization results show several important clusters, including topics related to network security, vehicle networks (VANET), and the application of advanced technologies such as machine learning in network management. Despite the decline in the number of publications in 2023 and 2024, collaboration between authors continues to show a strong trend. The research also highlights various challenges that are still open problems, such as the development of efficient routing protocols, improving network security, and managing resources in a dynamic MANET environment. In addition to the VOSviewer analysis, further exploration was carried out using the built-in visualization tools from the Scopus web platform to enrich the interpretation of emerging topics and research connections. This was followed by a deeper conceptual mapping using Scopus AI, which provided a visual breakdown of interconnected themes such as security issues, routing protocols, and different network types like VANET and FANET. To complement and validate the findings, the study also incorporated evidence based summaries retrieved from Consensus.app, offering additional insights from AI-driven scientific consensus. This multi-platform approach enhances the reliability of the analysis and provides a more comprehensive view of current and future research directions in the MANET domain.
Deteksi Berita Hoaks Berbahasa Indonesia Menggunakan One-Dimensional Convolutional Neural Network Muhammad Zuama Al Amin; Muhammad Ariful Furqon; Dwi Wijonarko
Jurnal Nasional Teknik Elektro dan Teknologi Informasi Vol 14 No 2: Mei 2025
Publisher : Departemen Teknik Elektro dan Teknologi Informasi, Fakultas Teknik, Universitas Gadjah Mada

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/jnteti.v14i2.19050

Abstract

The rapid advancement of information technology has enabled global information dissemination and led to a surge in hoax news, particularly in Indonesia. Hoax news poses a significant risk of spreading disinformation, potentially influencing public opinion, social stability, and security. Therefore, an effective technology-based solution is required to detect and identify hoaxes. This study aims to develop and optimize a one-dimensional convolutional neural network (1D-CNN) model to detect hoax news with high accuracy. The dataset comprised 12,151 articles, including 5,276 valid news items and 6,875 hoax news items, collected from reliable sources and anti-hoax platforms. The text preprocessing stages included data cleaning, case folding, punctuation removal, number removal, and stopword removal. The textual data were processed through tokenization and padding stages for model training preparation. The proposed 1D-CNN architecture integrated embedding, Conv1D, batch normalization, globalmaxpooling1d, dense, and dropout layers to enhance generalization capabilities and reduce the risk of overfitting. The model was trained using the Adam optimizer and its performance was evaluated using 10-fold cross-validation. Experimental results showed that the model achieved an average accuracy, precision, recall, and F1 score of 97.74%, 97.75%, 97.74%, and 97.73%, respectively. The developed model outperformed previous methods, namely the convolutional neural network–bidirectional long short-term memory (CNN-BiLSTM), gated recurrent unit (GRU), and conventional methods such as naïve Bayes or support vector machine (SVM), in terms of accuracy and training efficiency. This study demonstrates that the model has a reliable capability in identifying hoax news, both in terms of detection accuracy and performance consistency.
Implementation of YOLO in Cabbage Plant Disease Detection for Smart and Sustainable Agriculture Saputra, Muhammad Andryan Wahyu; Novtahaning, Damar; Narandha Arya Ranggianto; Dwi Wijonarko
Brilliance: Research of Artificial Intelligence Vol. 4 No. 2 (2024): Brilliance: Research of Artificial Intelligence, Article Research November 2024
Publisher : Yayasan Cita Cendekiawan Al Khwarizmi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/brilliance.v4i2.5054

Abstract

Cabbage plants are a commodity needed by the community and an export commodity that must have good quality and be worth selling. There are approaches to create detection systems, namely rule-based and image-based. The use of images allows the system to be reorganized by training data, resulting in a flexible system. The image will be detected by the model and then predict the cabbage plant disease. The data used is image data, namely Alternaria Spots, Healthy, Black Root, and White Rust. Implementation This research tests the YOLO model in making a detection system with the highest precision-confidence result for all labels is 78,5%. While in confusion-matrix testing, the highest result is 0.67 in White Rust disease. This indicates that the YOLO model can identify diseases in cabbage plants based on data that has been trained with great results.
Co-Authors Afif, Hasnan Anang Andrianto Balthasar Watunglawar Betta Wahyu Retna Mulya Betta Wahyu Retna Mulya Betta Wahyu RM Damar Novtahaning Fajar Wahyu Setyo Budi Firdaus, Ananda Dwi Furqon, Muhammad Ariful Ginting, Sonia Br Harry Soepandi Katarina Leba Linda, Kumala Muhammad Andryan Wahyu Saputra Muhammad Faisal Muhammad Zuama Al Amin Mutasyakir, Cahya Rahmadhan Narandha Arya Ranggianto Nugraha, Edwin Setiawan Pratama, Muhammad Nabil Priambodo, Oktanindita Rizal Fathoni Aji RM, Betta Wahyu Samsul Arifin Talitha, Azzura