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All Journal Jurnal Buana Informatika Telematika : Jurnal Informatika dan Teknologi Informasi Wikrama Parahita : Jurnal Pengabdian Masyarakat Indonesian Journal of Artificial Intelligence and Data Mining PengabdianMu: Jurnal Ilmiah Pengabdian kepada Masyarakat Applied Technology and Computing Science Journal Community Development Journal International Journal of Informatics and Computation Prosiding Seminar Nasional Multidisiplin Ilmu Respati Jurnal Computer Science and Information Technology (CoSciTech) Journal of Computer, Electronic, and Telecommunication (COMPLETE) Journal of Soft Computing Exploration Jurnal Ilmu Komputer dan Teknologi (IKOMTI) Jurnal Pengabdian Pada Masyarakat
Wijaya, Nurhadi
Universitas Respati Yogyakarta
Religion Aerospace Engineering Agriculture, Biological Sciences & Forestry Arts Humanities Astronomy Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Immunology & microbiology Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Medicine & Pharmacology Neuroscience Nursing Physics Public Health Social Sciences Transportation Veterinary Other

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Optimalisasi Tata Laksana Administrasi Desa Berbasis Teknologi Informasi bagi Perangkat Desa Katekan Gantiwarno Klaten Munir, Agus Qomaruddin; Listiawan, Indra; Wijaya, Nurhadi; Utari, Evrita Lusiana
Wikrama Parahita : Jurnal Pengabdian Masyarakat Vol. 8 No. 1 (2024): Mei 2024
Publisher : Universitas Serang Raya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30656/jpmwp.v8i1.6634

Abstract

Perangkat desa di Desa Katekan, Gantiwarno, Klaten, Jawa Tengah, menghadapi banyak tantangan, pada era digital saat ini terutama dalam hal mendapatkan informasi dan pelayanan administrasi berbasis TIK yang dapat dimanfaatkan secara publik untuk kepentingan masyarakat. Perangkat desa seringkali tidak maksimal dalam memberikan layanan yang optimal, terutama jika mereka tidak mampu memanfaatkan teknologi informasi untuk mendukung proses pelayanan yang lebih baik. Untuk membantu perangkat desa menggunakan tata kelola administrasi berbasis TI, workshop aplikasi kantor diberikan dengan tiga materi pokok pada microsof office yaitu miscrosoft word, microsoft excel dan microsoft power point. Kegiatan yang dilaksanakan diawali tahap persiapan, pembekalan bagi mahasiswa serta pelaksanaan kegiatan berupa pemberian workshop secara periodik. Hasil kegiatan menunjukkan peningkatan pemahaman perangkat desa tetang tatakelola administrasi berbasis teknologi informasi serta pengetahuan perangkat desa untuk mengakses informasi dan pengetahuan dari internet untuk mendukung pelayanan kepada masyarakat.
Identifying Types of Waste as Efforts in Plastic Waste Management Based on Deep Learning Buyung, Irawadi; Munir, Agus Qomaruddin; Wijaya, Nurhadi; Listyalina, Latifah
Telematika Vol 20, No 3 (2023): Edisi Oktober 2023
Publisher : Jurusan Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31315/telematika.v20i3.10804

Abstract

Purpose: This research aims at designing a computer algorithm for automatic waste sorting.Design/methodology/apprach: This research is quantitative and uses secondary data, specifically images of various types of waste. The images will be classified into organic and inorganic waste types with the assistance of a deep learning model. In this research, we propose the EfficientNet method for Waste Type Identification as an Effort in Plastic Waste Management. Experiments were conducted on a secondary dataset from Kaggle.com, which involved classifying various types of waste into 'Plastic' and 'Non-Plastic' categories, showing the effectiveness of the proposed method.Findings/result: The measurement is performed to compute the accuracy of the designed deep learning model in classifying waste images into the appropriate waste types. Based on the research results, our system achieved the highest accuracy of 97% during testing.Originality/value/state of the art: The designed method can perform fast and automatic waste sorting, which is useful in reducing the increasing amount of waste accumulating each year. 
Enhancing cirrhosis detection: A deep learning approach with convolutional neural networks Endah H, Marselina; Wijaya , R. Nurhadi; Khotibul Ahsan, Hilmi
Journal of Soft Computing Exploration Vol. 4 No. 4 (2023): December 2023
Publisher : SHM Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52465/joscex.v4i4.226

Abstract

Cirrhosis, a prevalent and life-threatening liver condition, demands early detection for effective intervention. This study investigates the potential of machine learning algorithms, including Convolutional Neural Networks (CNN), Support Vector Machines (SVM), Decision Trees, K-Nearest Neighbors (KNN), Gaussian Naive Bayes (GNB), and Gradient Boosting (GBoost), in cirrhosis prediction using a dataset from Kaggle containing 418 observations and 20 attributes. Performance evaluation involves metrics like accuracy, precision, recall, and F1-score, revealing CNN's superior performance with an 84% accuracy rate. The study highlights the importance of algorithm selection and feature engineering in medical diagnosis. Moreover, a comparison with traditional machine learning techniques underscores CNN's prowess in this domain. Beyond cirrhosis, CNNs offer promise for automating feature extraction from medical imagery and recognizing complex patterns, potentially transforming diagnostic accuracy in healthcare.
Performance Comparison of Convolutional Neural Network with Traditional Machine Learning Methods in Adult Autism Detection Wijaya, Nurhadi; Muliani, Sri Hasta; Nurain, Maisarah
TEKNOLOGI DITERAPKAN DAN JURNAL SAINS KOMPUTER Vol 7 No 1 (2024): June
Publisher : Universitas Nahdlatul Ulama Surabaya

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Abstract

Diagnosing Autism Spectrum Disorder (ASD) in adults is a challenging task, requiring precise and efficient early detection methods. However, there is limited research in this area. Hence, this study seeks to address this gap by evaluating the effectiveness of Convolutional Neural Networks (CNNs) compared to traditional machine learning techniques for detecting autism in adults. The study introduces a CNN-based model and conducts a performance comparison with conventional algorithms such as Support Vector Machine (SVM), Decision Tree, K-Nearest Neighbors (KNN), Gaussian Naive Bayes (GNB), and Gradient Boosting (GBoost). The objectives are to evaluate the efficacy of CNNs in adult autism detection, identify algorithm strengths and weaknesses, and explore healthcare implications. The research utilizes the Autism Screening on Adults dataset, with 704 records and 21 features, employing preprocessing steps to optimize data quality. The proposed CNN model encompasses convolutional layers, max-pooling, dropout, and dense layers, while baseline algorithms serve as benchmarks. Evaluation metrics include the Confusion Matrix and Classification Report. The CNN model achieved remarkable accuracy (99%) and precision in adult autism detection, outperforming traditional algorithms. SVM emerged as the closest competitor but fell short. This study underscores CNN's potential for precise autism detection in adults, with implications for early intervention and telehealth applications. The research highlights CNNs' effectiveness and superiority over traditional machine learning algorithms, suggesting their promise for accurate diagnosis. Future research opportunities include expanding datasets, optimizing model parameters, and addressing ethical considerations for practical healthcare implementation
Optimizing Breast Cancer Detection: A Comparative Study of SVM and Naive Bayes Performance Diqi, Mohammad; Hiswati, Marselina Endah; Hamzah, Hamzah; Ordiyasa, I Wayan; Mulyani, Sri Hasta; Wijaya, Nurhadi; Wanda, Putra
TEKNOLOGI DITERAPKAN DAN JURNAL SAINS KOMPUTER Vol 7 No 1 (2024): June
Publisher : Universitas Nahdlatul Ulama Surabaya

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Abstract

This study evaluates the performance of Support Vector Machine (SVM) and Naive Bayes algorithms in classifying breast cancer using the Breast Cancer Wisconsin dataset. Both models exhibited high accuracy, with Naive Bayes achieving a slightly higher overall accuracy of 97% and demonstrating a balanced performance between precision and recall. The SVM model showed strong proficiency in detecting positive cases, with an overall accuracy of 95%, though it faced minor challenges in recall for negative cases. These results highlight the effectiveness of both algorithms in breast cancer detection, emphasizing the significance of model selection based on specific diagnostic requirements. Although there are limitations, such as the small sample size and assumptions made in the model, the findings provide useful insights into the use of machine learning in medical diagnostics. This supports the idea that these models have the potential to enhance early detection and treatment results. Future research should focus on utilizing larger, more diverse datasets, exploring advanced feature processing techniques, and integrating additional algorithms to enhance further the accuracy and reliability of breast cancer detection systems.
Implementation of KNN Algorithm for Occupancy Classification of Rehabilitation Houses Nurhadi Wijaya; Joko Aryanto; Kasmawaru Kasmawaru; Anang Faktchur Rachman
International Journal of Informatics and Computation Vol. 4 No. 2 (2022): International Journal of Informatics and Computation
Publisher : University of Respati Yogyakarta, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35842/ijicom.v4i2.36

Abstract

The 2010 eruption of Mount Merapi and the resulting rain lava in Central Java's Kab. Sleman DIY and Magelang Regency damaged homes and infrastructure. According to the Head of BNPB Regulation No. 5, the Community Rehabilitation and Reconstruction and Community-Based Settlement program plan is utilized to repair and rebuild properties damaged by the 2011 Merapi eruption. Two thousand five hundred sixteen residences that will stay in the area have been built permanently due to this initiative. Occupancy rates (permanent occupancy) are used by the World Bank's Key Performance Indicators (KPI) to gauge a program's effectiveness. The database has information on how the software was used and proved successful. Databases, essential tools for introducing new data patterns and revealing previously hidden information, are used in data mining. This study applies the KNN algorithm to classify the house's occupancy status data after Mount Merapi's eruption. The accuracy results obtained from the classification of 82.03%, and the performance of the results through the AUC obtained a value of 0.935.
Implementation of Deep Learning for Classification of Mushroom Using CNN Algorithm Imam Mahfudz I'tisyam; Nurhadi Wijaya; Rike Pradila
International Journal of Informatics and Computation Vol. 5 No. 1 (2023): International Journal of Informatics and Computation
Publisher : University of Respati Yogyakarta, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35842/ijicom.v5i1.42

Abstract

Mushrooms are a type of low-level plant that lacks chlorophyll. One of the advantages of fungi is that they are commonly utilized as food items in the community. This paper discussed the implementation of CNN for the classification of mushrooms. The project aims to develop a robust system that can automate the labor-intensive task of mushroom classification. The CNN model will be trained on a large dataset of annotated mushroom images, learning to extract meaningful features and patterns for accurate categorization. To evaluate the performance of the developed system, a comprehensive set of metrics, including accuracy, precision, recall, and F1 score, will be used. The dataset will be split into training, validation, and testing sets to assess the model's generalization ability to unseen data. Based on the experimental result, the average accuracy rate in the Agaricus Portobello test was % -99.89 %, % -99.89 % in the Amanita Phalloides test, % -99.59 % in the Cantharellus Cibarius test, % -98.89 % in the Gyromitra Esculenta test, % -99.96 % in the Hygrocybe Conica, and % -99.93 % in the Omphalotus Orealius.
Fake News Detection in Health Domain Using Transformer Models Sri Hasta Mulyani; Suwarto; Hamzah; R.Nurhadi Wijaya; Rodiyah; Wita Adelia
International Journal of Informatics and Computation Vol. 6 No. 2 (2024): International Journal of Informatics and Computation
Publisher : University of Respati Yogyakarta, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35842/ijicom.v6i2.89

Abstract

The rise of fake news in the health sector poses a serious threat to public well-being and accurate health communication. This study investigates the effectiveness of transformer models, particularly BERT (Bidirectional Encoder Representations from Transformers), in detecting fake news related to health. By leveraging the advanced contextual understanding of BERT, we aim to enhance the accuracy of fake news detection in this critical domain. Our approach involves training the BERT model on a curated dataset of health news articles, followed by rigorous evaluation on its ability to differentiate between genuine and misleading content. The results reveal that the transformer-based model significantly outperforms traditional methods, achieving high accuracy and robust performance metrics. This research underscores the potential of transformer models in combating health misinformation and provides a foundation for future improvements in automated fake news detection systems.
Pelatihan Penggunaan LMS untuk Peningkatan Kualitas Layanan Perkuliahan di Fakultas Sains dan Teknologi, Universitas Respati Yogyakarta: Training on Using LMS to Improve the Quality of Lecture Services at the Faculty of Science and Technology, Universitas Respati Yogyakarta Ordiyasa, I Wayan; Sugiarto, Raden Bagus Nurhadi Wijaya; Winardi, Sugeng; Meliala, Dyan Avando; Utari, Evrita Lusiana; Sahal, Ahmad
PengabdianMu: Jurnal Ilmiah Pengabdian kepada Masyarakat Vol. 10 No. 2 (2025): PengabdianMu: Jurnal Ilmiah Pengabdian kepada Masyarakat
Publisher : Institute for Research and Community Services Universitas Muhammadiyah Palangkaraya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33084/pengabdianmu.v10i2.8500

Abstract

Training on the Use of Learning Management Systems (LMS) is essential for enhancing the quality of academic services in an era of increasingly adopting technology. The integration of LMS with conventional methods, known as blended learning, which combines distance learning, regular classes, and LMS, results in a more effective and efficient learning process. With the shift towards digital learning, LMS use becomes crucial for improving the efficiency, accessibility, and quality of academic services. Through e-learning, students not only listen to lectures but also actively observe, perform, demonstrate, and more. Teaching materials can be virtualized in various formats to create more engaging and dynamic content, motivating students to delve deeper into the learning process. This training aims to equip educators and administrative staff with knowledge of LMS features and potential, enabling them to maximize its use for content delivery, facilitating teacher-student interaction, and enhancing course management and evaluation. The training methods include presentations on basic LMS concepts, demonstrations of key features, and hands-on practice sessions that allow participants to actively engage in the learning process. Additionally, interaction between participants and facilitators is enhanced through discussions and Q&A sessions, ensuring deep understanding and practical skills in LMS usage to improve academic service quality. Consequently, this training is expected to provide a solid foundation for educational institutions to meet challenges and leverage the opportunities offered by the digital era in providing quality academic services.
Stimulasi Penggunaan Aplikasi Smartwebcalc Dalam Pengaturan Makan Pada Orangtua Atlet Sari, Siska Puspita; Afriani, Yuni; Puspaningtyas, Desty Ervira; Sugiarto , R. Nurhadi Wijaya
Jurnal Pengabdian Pada Masyarakat Vol 10 No 1 (2025): Jurnal Pengabdian Pada Masyarakat
Publisher : Universitas Mathla'ul Anwar Banten

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30653/jppm.v10i1.1141

Abstract

Komponen pendukung tercapainya prestasi optimal adalah asupan gizi yang sesuai. Pemenuhan asupan gizi yang tepat secara jumlah, jenis, dan periode latihan dibutuhkan oleh atlet. Atlet sepakbola belum memenuhi asupan gizinya secara tepat dan kebutuhan cairan sehari masih kurang. Orang tua atlet terlebih pada atlet remaja memiliki peran besar dalam menyiapkan gizi yang tepat. Banyak orang tua atlet yang belum mengetahui menu yang tepat yang dibutuhkan oleh atlet remaja. Penggunaan teknologi informasi menjadi penting untuk dilakukan. Aplikasi teknologi berbasis web yang sudah dikembangkan sebelumnya dan digunakan sebagai media edukasi adalah smartwebcalc. Smartwebcalc ini berfungsi memberikan informasi yang lebih mudah dipahami terkait perhitungan kebutuhan gizi dan pemenuhan asupan gizi pada atlet berdasar usia, jenis kelamin, dan status aktivitas atlet. Kegiatan pengabdian masyarakat ini dilaksanakan pada bulan 29 Juni 2024 di Maguwoharjo football academy. Tujuan kegiatan ini memberikan pendampingan pengaturan menu seimbang melalui smartwebcalc. Kegiatan pengabdian ini dilakukan pada orang tua atlet sebanyak 12 orang. Metode pendidikan dan pelatihan digunakan pada kegiatan ini, yang terdiri dari: pendampingan penggunaan smartwebcalc dalam pengaturan menu seimbang dan stimulasi menu gizi seimbang pada atlet. Pengaturan menu yang dibuat oleh orang tua atlet sudah benar sesuai jadwal makan, menu makanan, banyaknya gram dalam ukuran rumah tangga (URT). Setiap kali makan utama terdiri dari sumber karbohidrat, protein hewani, protein nabati, lemak, sayur dan buah. The supporting component for achieving optimal performance is appropriate nutritional intake. Fulfillment of the right nutritional intake in terms of quantity, type, and training period is needed by athletes. Football athletes have not met their nutritional intake properly and their daily fluid needs are still lacking. Parents of athletes, especially adolescent athletes, have a big role in preparing the right nutrition. Many parents of athletes do not know the right menu needed by adolescent athletes. The use of information technology is important to do. A web-based technology application that has been developed previously and used as an educational medium is smartwebcalc. Smartwebcalc functions to provide easier-to-understand information regarding the calculation of nutritional needs and fulfillment of nutritional intake in athletes based on age, gender, and athlete activity status. The aim of this activity is to provide assistance in setting a balanced menu via smartwebcalc. This community service activity was carried out on June 29, 2024 at the Maguwoharjo football academy. This service activity was carried out on 12 parents of athletes. The education and training methods used in this activity, which consisted of: assistance in using smartwebcalc in setting a balanced menu and stimulation of a balanced nutritional menu for athletes. The menu arrangements made by the athlete's parents are correct according to the eating schedule, food menu, number of grams in the Household Measure (URT). At each meal the main meal consists of a source of carbohydrates, animal protein, vegetable protein, fat, vegetables anda fruit.
Co-Authors Agus Qomaruddin Munir Ahmad Wakhid Anang Faktchur Rachman Anggie Puspita Dewi Desty Ervira Puspaningtyas Endah H, Marselina Evrita Lusiana Utari, Evrita Lusiana Hamzah Hamzah Hamzah Hamzah Herliana Riska I Wayan Ordiyasa Ikhwan Mustiadi Imam Mahfudz I'tisyam Irawadi Buyung, Irawadi Jessica Paskalina Joko Aryanto kasmawaru kasmawaru Khotibul Ahsan, Hilmi Listiawan, Indra Listyalina, Latifah Marselina Endah Hiswati Martin, Benedicto Reynaka Filio Meliala, Dyan Avando Mohammad Diqi Muliani, Sri Hasta Nurain, Maisarah Nurkhasanah Putra Wanda, Putra Reggy Chrismanta Rike Pradila Rodiyah Sahal, Ahmad Siska Puspita Sari Siska Puspita Sari Sri Hasta Mulyani Sugeng Winardi Suwarto Trinidya, Fike Windi Vensia Putri Wita Adelia Yuni Afriani Yuni Afriani, Yuni