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All Journal Proceedings of KNASTIK Bulletin of Electrical Engineering and Informatics Prosiding Seminar Nasional Sains Dan Teknologi Fakultas Teknik Jurnal Teknologi Informasi dan Ilmu Komputer Telematika JUITA : Jurnal Informatika Jurnal Ilmiah Dinamika Rekayasa (DINAREK) Jurnal Eksplora Informatika Jurdimas (Jurnal Pengabdian Kepada Masyarakat) Royal EDUMATIC: Jurnal Pendidikan Informatika Jurnal Abdi Insani Jurnal Teknik Informatika (JUTIF) Journal of Applied Data Sciences Jurnal Pendidikan dan Teknologi Indonesia Jurnal Pengabdian Masyarakat Indonesia JUSTIN (Jurnal Sistem dan Teknologi Informasi) Pamasa: Jurnal Pengabdian kepada Masyarakat Journal of Electronics Technology Exploration (JOeTEx) Mengabdi: Jurnal Hasil Kegiatan Bersama Masyarakat Journal of Industrial and Mechanical Engineering Journal of Current Research in Blockchain
Lasmedi Afuan
Informatika, Fakultas Teknik, Universitas Jenderal Soedirman, Indonesia
Agriculture, Biological Sciences & Forestry Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mathematics Mechanical Engineering Public Health Social Sciences Other

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Edukasi Geowisata Bukit Pentulu Indah-Karangsambung, Kabupaten Kebumen-Jawa Tengah Widagdo, Asmoro; Andreas, Roy; Kharisun, Kharisun; Rif’an, Muhammad; Afuan, Lasmedi
Pamasa : Jurnal Pengabdian Pada Masyarakat Vol 2 No 2 (2024): Desember 2024
Publisher : Fakultas Ilmu Budaya Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20884/1.pamasa.2024.2.2.12952

Abstract

Pengunjung obyek wisata Bukit Pentulu Indah di Karangsambung-Kabupaten Kebumen perlu mendapatkan edukasi unsur-unsur geologis pada lokasi-lokasi yang dikunjungi. Sebagian besar pengunjung belum memahami fenomena geologi yang diamati. Pengabdian pada masyarakat sosialisasi geo-wisata Bukit Pentulu Indah ini dilakukan melalui rangkaian alur dari kaji pustaka, observasi lapangan, sosialisasi pada pengunjung dan kemudian penulisan karya pengabdian ini. Tujuan sosialisasi pada geo-wisatawan tentang aspek geowisata Bukit Pentulu Indah telah menambahkan pengetahuan/pemahaman tentang makna geologis pada lokasi geo-wisata. Pengunjung tidak hanya datang dan menikmati pemandangan, namun mereka juga memahami dan memperoleh pencerahan akan aspek geoscience dibalik fenomena yang mereka lihat.
PATTERN CLASSIFICATION SIGN LANGUAGE USING FEATURES DESCRIPTORS AND MACHINE LEARNING Nurhadi, Nurhadi; Winanto, Eko Arip; Said, Rahaini Mohd; Jasmir, Jasmir; Afuan, Lasmedi
Jurnal Teknik Informatika (Jutif) Vol. 5 No. 2 (2024): JUTIF Volume 5, Number 2, April 2024
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2024.5.2.1228

Abstract

Sign language is way of communication for the deaf and speech impaired. In Indonesia, the utilization of a standardized language involves the incorporation of American Sign Language (ASL). ASL is employed for various communication needs, ranging from basic alphanumeric fingerspelling (A-Z and numbers) to the more complex SIBI form (comprising gesture vocabulary) in everyday interactions as well as formal contexts. This surge in the digitization of sign language underscores the ongoing advancements in research and development. The challenge in this research lies in the ability to recognize American Sign Language (ASL) with diverse intensities and invariant backgrounds. Therefore, the study emphasis is on proposing a suitable segmentation method comparison for multi-intensity ASL cases. Subsequently, global feature descriptor methods, including Color Histogram, Hu Moments, and Haralick Texture techniques, are applied for feature extraction. The result of the Logistic Regression method versus the supervised Random Forest checks accuracy and suitability in identifying ASL fingerspelling. The findings of this research is predictive value of logistic regression is 48%, with class Y having the highest precision (0.86), class V having the lowest accuracy (0.16), and class L having the highest recall (0.73). The maximum precision in classes B, F, H, I, K, Y, and Z is 1.00, and the lowest in class U is 0.58, while the highest recall is in class G, which is 1.00. The lowest is in class V, while the predictive value from the random forest is 86 percent. Class H has the greatest f1 score (0.99), while class U has the lowest f1 score (0.64). The Random Forest method outperforms the two methods suggested in the paper, according to the comparison.
THE EFFECT OF UNIGRAM AND BIGRAM IN THE NAÏVE BAYES MULTINOMIAL FOR ANALYZING OF COMMENT SENTIMENT OF GOJEK APPLICATION IN GOOGLE PLAY STORE Adyatma, Adrian Dwinanda; Afuan, Lasmedi; Maryanto, Eddy
Jurnal Teknik Informatika (Jutif) Vol. 4 No. 6 (2023): JUTIF Volume 4, Number 6, Desember 2023
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2023.4.6.1310

Abstract

In sentiment classification systems that use Naïve Bayes Classifier, a commonly used feature extraction method is TF-IDF with unigram and bigram, where the two is used separately. In the reality, most of texts contain single or composed word,so it is needed to use the combination of unigram and bigram to maximize the accuracy of the classification results. In this research, the impact and performance improvement between classification systems using unigram or bigram solely and those using a combination of both are studied. Using 1000 data of reviews with ratings 1 (negative) and 5 (positive) from Gojek users on the Google Play Store, and performing performance validation with K-Fold at K=10, the system that uses the combined TF-IDF feature extraction of unigrams and bigrams achieves the best performance among the three systems with an accuracy of 0.84, however the accuracy of the system that uses unigrams solely has accuracy of 0.83, and 0.7 for the system that uses bigram. From the results of the research, it can be concluded that the use of the combination of unigram and bigram can increase the accuracy of the classification result.
PENERAPAN BIOTEKNOLOGI PRODUK SUSU YOGURT DAN KEJU UNTUK MENINGKATKAN NILAI TAMBAH DAN DAYA SAING KELOMPOK TANI SUPRAH DI DUSUN SILEMBU BANJARNEGARA Uletika, Niko Siameva; Arkan, Naofal Dhia; Putera, Radita Dwi; Afuan, Lasmedi; Zulfa, Mulki Indana
Jurnal Abdi Insani Vol 12 No 12 (2025): Jurnal Abdi Insani
Publisher : Universitas Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/abdiinsani.v12i12.3309

Abstract

This dedication aims to analyze the implementation of digital transformation in Nagari Bukit Buai Tapan through the integration of regional profiles, management of organic compost, and the use of family medicinal plants (Toga) as an effort to support sustainable development. The methodology used includes socialization of programs to local stakeholders, training in nagari and community devices in the use of digital information systems, regional mapping with simple software, as well as direct practice of cultivating toga and making household compost. This program is implemented in a participatory manner by involving nagari devices, farmer groups, health cadres, youth, and beneficiary households. The results of this service indicate that the digital information system is successfully operated by the Nagari device to manage population data and local potential. The regional mapping produces administrative maps that are printed and installed in the Nagari office and are available in digital format. More than 20 households planted toga, including ginger, turmeric, and lemongrass, while three units of household composter began to be actively used with kitchen waste and EM4. Analysis shows a significant increase in public knowledge related to herbal -based health and environmentally friendly waste management. The discussion confirms that the integration of digitalization, organic compost, and toga not only strengthens data-based governance, but also encourages food independence, health, and preservation of local wisdom. This finding implies that holistic digital nagari transformation models can be an effective strategy to strengthen governance, improve welfare, and support sustainable development. This research contributes conceptual and practical to the development of local village based on local wisdom that can be replicated in other nagari.  
Milkfish Freshness Detection Based On Eye Images Using Convolutional Neural Network (CNN) With Mobilenetv3 Architecture On A Mobile Application Musaadah, Khalimah; Afuan, Lasmedi; Permadi, Ipung
Journal of Electronics Technology Exploration Vol. 3 No. 2 (2025): December 2025
Publisher : SHM Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52465/joetex.v3i2.649

Abstract

Indonesia has abundant fishery resources, making it one of the world's largest producers and consumers of fish. One of the most commonly consumed types is milkfish (Chanos chanos). Before consumption, it is important to determine the freshness level of the fish. This freshness can be identified using a Convolutional Neural Network (CNN) model with the MobileNetV3 architecture, which is efficient and suitable for mobile application implementation. This study aims to detect the freshness level of milkfish based on eye images using the MobileNetV3 CNN architecture implemented in a mobile application. The dataset used consists of 500 images, divided into training, validation, and testing sets with proportions of 70%, 20%, and 10%, respectively. The data underwent preprocessing, including resizing and image augmentation, to increase data variation. The model was developed using hyperparameter tuning with both random search and grid search methods. The results show that random search achieved better performance with a training accuracy of 92.88%, validation accuracy of 89.90%, and an overall test accuracy of 91%. The trained model was successfully implemented into a mobile application named ScanBang, which can classify the freshness level of milkfish and display its confidence score in a practical and user-friendly manner.
Network-Based Risk Scoring of Blockchain Nodes Using Graph Neural Networks (GNN) Widodo, Slamet; Afuan, Lasmedi
Journal of Current Research in Blockchain Vol. 3 No. 1 (2026): Regular Issue March 2026
Publisher : Bright Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jcrb.v3i1.54

Abstract

Blockchain technology has introduced a decentralized and transparent mechanism for recording transactions; however, the increasing volume and interconnectivity of blockchain networks also raise the risk of fraudulent and high-risk activities. This study proposes a Graph Neural Network (GNN)-based framework to evaluate the risk levels of blockchain nodes by integrating both transactional attributes and structural relationships. Using a dataset of 10,000 blockchain records and approximately 412,000 edges, the network was modelled as a graph in which each node represents an address and edges denote transaction or similarity links. As baselines, Random Forest and XGBoost models were employed, achieving accuracies of 0.94 and 0.95, respectively, with F1-scores of 0.93 and 0.94. These models effectively captured individual node patterns but lacked awareness of inter-node dependencies. The proposed GNN model demonstrated the highest overall performance, with an accuracy of 0.96 and an F1-score of 0.95, by learning from both node attributes and their topological context. This approach enabled the identification of high-risk nodes that traditional models failed to detect. The results confirm that network-based learning significantly enhances the accuracy and interpretability of blockchain risk analysis. The proposed GNN framework provides a scalable foundation for real-time blockchain monitoring, anomaly detection, and governance systems, contributing to improved transparency and resilience within decentralized financial ecosystems.
Co-Authors Abidin, Dodo Zaenal Adi Pangestu Adyatma, Adrian Dwinanda Afrizal Nehemia Toscany Ahmad Ashari Ahmad Fauzi Ridlwan Aji, Pandu Wahyu Alfarez Marchelian, Reyno Alkaf, Zakiyyan Andreas, Roy Anin Ammbya Soulani Arief Kelik Arief Kelik Nugroho Arief Kelik Nugroho Arief Kelik Nugroho Arief Kelik Nugroho Arkan, Naofal Dhia As'ad, Mohamad Faris Asmoro Widagdo, Asmoro Bangun Wijayanto Bintang Pradana Yosua, Panky Dadang Iskandar Dadang Iskandar Daffa Ammar Muaafii Daffa Naufaldi Al Rasyid Didit Suprihanto, Didit Dodi Sandra Eddy Maryanto Eddy Maryanto Fandy Setyo Utomo Faris Akbar Abimanyu Febri Sutomo Ferry Darmawan Hidayat, Nurul Indah Cahya Febriani Indyastuti, Devani Laksmi Ipung Permadi Ipung Permadi Ipung Permadi Ipung Permadi Iqbal Iqbal Irfan Agus Tiawan Jasmir, Jasmir Joe, Michael Khanza, Muthia Kharisun, Kharisun Kurniawan, Yogiek Indra Maria Ulfa Chasanah Muhammad Fikri Rivaldi Muhammad Luthfi Muhammad Randy Cahya Mardika Muhammad Zein Albalki Muhammad, Katon Mulki Indana Zulfa, Mulki Indana Musaadah, Khalimah Najmudin Nandha Arwiansyah Nasichatul Umayah Niko Siameva Uletika Nofiyati Nofiyati, Nofiyati Nofiyati, Nofiyati Nur Chasanah Nurhadi Nurul Hidayat Nurul Hidayat Nurul Ismailiah Priandika Ratmadani Anugrah Purnama, Benni R. Rizal Isnanto Rahayu, Swahesti Puspita Rif’an, Muhammad Rista Afifah Rochmat Mulyo Sugihono Said, Rahaini Mohd Sari, Enjelita Sharipuddin, Sharipuddin Siti Nurhayati Slamet Widodo SRI LESTARI Susi Setianingsih Teguh Cahyono Tuti Alawiyah Victoria Angela Sugianto Wahid, Arif Mu'amar Yohanes Suyanto Yunindar, Galih Arditiya Zahira Hasyati, Adila