cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
Ika Oktavia Suzanti
Contact Email
iosuzanti@trunojoyo.ac.id
Phone
+628563212921
Journal Mail Official
nero@trunojoyo.ac.id
Editorial Address
Jln Raya Telang PO BOX 02 Kamal Bangkalan 69162
Location
Kab. bangkalan,
Jawa timur
INDONESIA
NERO (Networking Engineering Research Operation)
Published by Universitas Trunojoyo Madura
ISSN : 23552190     EISSN : 26156539     DOI : https://doi.org/10.21107/nero
Core Subject : Science, Engineering,
Computer Science & IT Decision Sciences, Operations Research & Management Engineering
NERO (Networking Engineering Research Operation) is a scientific journal under the auspices of the Department of Informatics Engineering, Faculty of Engineering, University of Trunojoyo Madura. NERO was first published in April 2014 and is published twice a year in April and November. NERO contains scientific articles covering the fields of Networking, Informatics and Computer Science, Software Engineering, Multimedia, and Intelligent Systems as well as other research results related to these fields.
Arjuna Subject : Ilmu Komputer (semua kategori) - Jaringan Komputer dan Komunikasi
Articles 40 Documents
 1   2   3   4 
Optimasi model machine learning untuk prediksi inhibitor korosi berbasis augmentasi dataset senyawa n-heterocyclic menggunakan KDE Gumelar, Rizky Syah; Akrom, Muhamad; Trisnapradika, Gustina Alfa
NERO (Networking Engineering Research Operation) Vol 10, No 1 (2025): Nero - 2025
Publisher : Jurusan Teknik Informatika Fakultas Teknik Universitas Trunojoyo Madura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21107/nero.v%vi%i.27945

Abstract

This study aims to optimize a machine learning model to predict the corrosion inhibitor effectiveness of N-Heterocyclic compounds.  The main challenge in this modelling is the limited dataset due to the high cost and time required to collect experimental data. To overcome this problem, this research utilizes Kernel Density Estimation (KDE) as a data augmentation technique, generating virtual samples that improve dataset diversity and model predictive performance. The developed dataset includes 11 relevant chemical features such as HOMO, LUMO, and Gap Energy. Linear (MLR, Ridge, Lasso, and ElasticNet) and non-linear (KNR, Random Forest, Gradient Boosting, Adaboost, XGBoost) machine learning models were evaluated based on Root Mean Squared Error (RMSE) and coefficient of determination (R²). The results show that data augmentation using KDE improves prediction accuracy and stability, especially in non-linear models like Random Forest and XGBoost. The application of KDE proved effective in improving the performance of predictive models. It can be recommended as an augmentation method in similar studies that require additional data to improve prediction accuracy.Keywords: Machine Learning, Kernel Density Estimator (KDE), Corrosion Inhibitor, Dataset
Analisis sentimen publik di twitter terhadap pelantikan presiden Prabowo menggunakan algoritma Naïve Bayes Widyassari, Adhika Pramita; Salsabilla, Dea; Amrozi, M. Ali
NERO (Networking Engineering Research Operation) Vol 10, No 1 (2025): Nero - 2025
Publisher : Jurusan Teknik Informatika Fakultas Teknik Universitas Trunojoyo Madura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21107/nero.v10i1.28701

Abstract

Sentiment analysis on social media, especially Twitter, is an effective method to understand public opinion towards political events such as the inauguration of President Prabowo. This study aims to identify the sentiment of the Indonesian people regarding the inauguration of President Prabowo through machine learning-based sentiment analysis using the Multinomial Naïve Bayes algorithm. Data was collected from Twitter with relevant keywords and hashtags, covering the time span before and after the inauguration to capture the dynamics of sentiment changes. The preprocessing process was carried out through text cleaning, removing stop words, tokenization, and stemming to improve model accuracy. The classification results show the distribution of public sentiment, with the majority being neutral (52.63%), followed by positive sentiment (42.98%), and negative (4.39%). The model achieved an accuracy of 75%, showing quite good performance for short text classification. The contribution of this study lies in the application of sentiment analysis to the specific event of the inauguration of the President of Indonesia, with a focus on the critical period before and after the inauguration, which has not been widely studied before. The novelty of this study is the use of real-time Twitter data related to current political events (inauguration of President Prabowo), as well as the emphasis on neutral sentiment which provides a deeper dimension to public understanding. It is hoped that these findings can be the basis for designing more effective public communication strategies on social media.Keywords: naïve bayes, prabowo presidential inauguration, twitter, sentiment analysis
Deteksi dan Klasifikasi Kue Tradisional Indonesia Menggunakan YOLOv8 Mustofa, Arin Ayu Silvyani; Wulanningrum, Resty; Sahertian, Julian
NERO (Networking Engineering Research Operation) Vol 10, No 1 (2025): Nero - 2025
Publisher : Jurusan Teknik Informatika Fakultas Teknik Universitas Trunojoyo Madura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21107/nero.v10i1.30177

Abstract

Indonesian traditional cakes are part of the cultural heritage, characterized by their rich flavors, unique forms, and significant historical value. However, the lack of recognition among younger generations necessitates a new approach to preservation efforts. This study aims to develop an image processing-based detection system for traditional cake types using the YOLOv8 algorithm. The five types of cakes identified in this research are lumpur cake, lapis cake, wingko cake, dadar gulung cake, and putu ayu cake. The image dataset was obtained through a combination of direct image capture and public datasets, and was manually annotated using the Roboflow platform. The model was trained using the PyTorch framework and evaluated based on precision, recall, F1-score, and mean Average Precision (mAP) metrics. Experimental results show that the system achieved an average mAP of 89.9% and an F1-score of 86.5%, with a relatively low classification error rate. These findings indicate that the YOLOv8 algorithm is effective in detecting visually similar objects and holds significant potential for application in the digital preservation of culinary heritage. The system can also be further developed as a technology-based educational medium to support the conservation of Indonesia’s local culinary wealth.Keywords: YOLOv8, Object Detection, Cake Traditional, Image Processing, Computer Vision
Implementation of RSA Algorithm For Securing Patient Data using QR Code Technology Cobantoro, Adi Fajaryanto; Wicahyono, Fadhlullah Yoga; Zulkarnain, Ismail Abdurrozzaq
NERO (Networking Engineering Research Operation) Vol 10, No 1 (2025): Nero - 2025
Publisher : Jurusan Teknik Informatika Fakultas Teknik Universitas Trunojoyo Madura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21107/nero.v10i1.30052

Abstract

The advancement of digital technology demands improved security systems, especially in protecting users’ personal data. One sector that heavily relies on data confidentiality is the healthcare sector, where patient information must be safeguarded from potential leaks and misuse. This study aims to implement the RSA (Rivest-Shamir-Adleman) cryptographic algorithm in the patient registration system at the dental clinic of Regunawati Cahyaningsih. RSA was chosen for its asymmetric nature and its ability to ensure data security through the use of public and private key pairs. Additionally, this study integrates QR Code technology as a medium for accessing encrypted data, streamlining and accelerating the data authorization process. The encryption process is applied to important information such as NIK, name, address, phone number, and medical records before being stored in the database. Based on the testing results, the system demonstrated excellent performance in terms of functionality, algorithm accuracy, and display speed. Whitebox testing confirmed the accuracy of the RSA algorithm implementation with 100% accuracy. Performance testing using the Largest Contentful Paint (LCP) metric showed very fast load times, ranging from 0.5 to 2.3 milliseconds, with a performance score of 100, making the system highly responsive and optimal in terms of user experience. Therefore, RSA is proven to be effective in enhancing patient data security and maintaining confidentiality within a web-based clinical information system.Keywords: Rivest-Shamir-Adleman Algorithm, Patient Data Security, Whitebox Testing, QR Code
Implementation Of Convolutional Neural Network Algorithm For Tobacco Pest Detection Tamam, Moh Badri; Chafid, Nurul; Hozairi, Hozairi; Aini, Qurrotul; Santoso, Teguh Budi; Kurniawan, Wawan; Kuzairi, Kuzairi
NERO (Networking Engineering Research Operation) Vol 10, No 1 (2025): Nero - 2025
Publisher : Jurusan Teknik Informatika Fakultas Teknik Universitas Trunojoyo Madura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21107/nero.v10i1.30044

Abstract

Agriculture plays a vital role in increasing Gross Domestic Product (GDP), providing employment, contributing to foreign exchange earnings, and supporting environmental conservation. Indonesia has great potential as an agricultural country where population majority relies on agricultural sector for their livelihood. Pamekasan Regency is center of tobacco production development in East Java, with a tobacco plantation area of over 30,000 hectares. However, pest attacks such as caterpillars often damage tobacco plants, reducing productivity and leaf quality. This study implemented AI technology, specifically Convolutional Neural Networks (CNN), to detect caterpillar pests in tobacco plants in Pamekasan. The main focus is on AI development in computer vision using deep learning techniques. The CNN training process involves several stages: convolution, ReLU layers, subsampling/pooling layers, and fully connected layers. The test scenario was conducted by dividing data by 85% training, 10% validation, and 5% testing, as well as tuning parameters for the learning rate and epochs. The model achieved a maximum accuracy of 85% without overfitting at a learning rate of 0.001 and epochs 15. This demonstrates that the CNN deep learning method can effectively identify disease features in tobacco plants. The application of this technology can increase productivity and efficiency in the agricultural sector, supporting a sustainable economy and ecology.Keywords: convolutional neural network, image detection, tobacco pest.
Optimasi model machine learning untuk prediksi inhibitor korosi berbasis augmentasi dataset senyawa n-heterocyclic menggunakan KDE Gumelar, Rizky Syah; Akrom, Muhamad; Trisnapradika, Gustina Alfa
NERO (Networking Engineering Research Operation) Vol 10, No 1 (2025): Nero - 2025
Publisher : Jurusan Teknik Informatika Fakultas Teknik Universitas Trunojoyo Madura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21107/nero.v%vi%i.27945

Abstract

This study aims to optimize a machine learning model to predict the corrosion inhibitor effectiveness of N-Heterocyclic compounds.  The main challenge in this modelling is the limited dataset due to the high cost and time required to collect experimental data. To overcome this problem, this research utilizes Kernel Density Estimation (KDE) as a data augmentation technique, generating virtual samples that improve dataset diversity and model predictive performance. The developed dataset includes 11 relevant chemical features such as HOMO, LUMO, and Gap Energy. Linear (MLR, Ridge, Lasso, and ElasticNet) and non-linear (KNR, Random Forest, Gradient Boosting, Adaboost, XGBoost) machine learning models were evaluated based on Root Mean Squared Error (RMSE) and coefficient of determination (R²). The results show that data augmentation using KDE improves prediction accuracy and stability, especially in non-linear models like Random Forest and XGBoost. The application of KDE proved effective in improving the performance of predictive models. It can be recommended as an augmentation method in similar studies that require additional data to improve prediction accuracy.Keywords: Machine Learning, Kernel Density Estimator (KDE), Corrosion Inhibitor, Dataset
Analisis sentimen publik di twitter terhadap pelantikan presiden Prabowo menggunakan algoritma Naïve Bayes Widyassari, Adhika Pramita; Salsabilla, Dea; Amrozi, M. Ali
NERO (Networking Engineering Research Operation) Vol 10, No 1 (2025): Nero - 2025
Publisher : Jurusan Teknik Informatika Fakultas Teknik Universitas Trunojoyo Madura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21107/nero.v10i1.28701

Abstract

Sentiment analysis on social media, especially Twitter, is an effective method to understand public opinion towards political events such as the inauguration of President Prabowo. This study aims to identify the sentiment of the Indonesian people regarding the inauguration of President Prabowo through machine learning-based sentiment analysis using the Multinomial Naïve Bayes algorithm. Data was collected from Twitter with relevant keywords and hashtags, covering the time span before and after the inauguration to capture the dynamics of sentiment changes. The preprocessing process was carried out through text cleaning, removing stop words, tokenization, and stemming to improve model accuracy. The classification results show the distribution of public sentiment, with the majority being neutral (52.63%), followed by positive sentiment (42.98%), and negative (4.39%). The model achieved an accuracy of 75%, showing quite good performance for short text classification. The contribution of this study lies in the application of sentiment analysis to the specific event of the inauguration of the President of Indonesia, with a focus on the critical period before and after the inauguration, which has not been widely studied before. The novelty of this study is the use of real-time Twitter data related to current political events (inauguration of President Prabowo), as well as the emphasis on neutral sentiment which provides a deeper dimension to public understanding. It is hoped that these findings can be the basis for designing more effective public communication strategies on social media.Keywords: naïve bayes, prabowo presidential inauguration, twitter, sentiment analysis
Deteksi dan Klasifikasi Kue Tradisional Indonesia Menggunakan YOLOv8 Mustofa, Arin Ayu Silvyani; Wulanningrum, Resty; Sahertian, Julian
NERO (Networking Engineering Research Operation) Vol 10, No 1 (2025): Nero - 2025
Publisher : Jurusan Teknik Informatika Fakultas Teknik Universitas Trunojoyo Madura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21107/nero.v10i1.30177

Abstract

Indonesian traditional cakes are part of the cultural heritage, characterized by their rich flavors, unique forms, and significant historical value. However, the lack of recognition among younger generations necessitates a new approach to preservation efforts. This study aims to develop an image processing-based detection system for traditional cake types using the YOLOv8 algorithm. The five types of cakes identified in this research are lumpur cake, lapis cake, wingko cake, dadar gulung cake, and putu ayu cake. The image dataset was obtained through a combination of direct image capture and public datasets, and was manually annotated using the Roboflow platform. The model was trained using the PyTorch framework and evaluated based on precision, recall, F1-score, and mean Average Precision (mAP) metrics. Experimental results show that the system achieved an average mAP of 89.9% and an F1-score of 86.5%, with a relatively low classification error rate. These findings indicate that the YOLOv8 algorithm is effective in detecting visually similar objects and holds significant potential for application in the digital preservation of culinary heritage. The system can also be further developed as a technology-based educational medium to support the conservation of Indonesia’s local culinary wealth.Keywords: YOLOv8, Object Detection, Cake Traditional, Image Processing, Computer Vision
Implementation of RSA Algorithm For Securing Patient Data using QR Code Technology Cobantoro, Adi Fajaryanto; Wicahyono, Fadhlullah Yoga; Zulkarnain, Ismail Abdurrozzaq
NERO (Networking Engineering Research Operation) Vol 10, No 1 (2025): Nero - 2025
Publisher : Jurusan Teknik Informatika Fakultas Teknik Universitas Trunojoyo Madura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21107/nero.v10i1.30052

Abstract

The advancement of digital technology demands improved security systems, especially in protecting users’ personal data. One sector that heavily relies on data confidentiality is the healthcare sector, where patient information must be safeguarded from potential leaks and misuse. This study aims to implement the RSA (Rivest-Shamir-Adleman) cryptographic algorithm in the patient registration system at the dental clinic of Regunawati Cahyaningsih. RSA was chosen for its asymmetric nature and its ability to ensure data security through the use of public and private key pairs. Additionally, this study integrates QR Code technology as a medium for accessing encrypted data, streamlining and accelerating the data authorization process. The encryption process is applied to important information such as NIK, name, address, phone number, and medical records before being stored in the database. Based on the testing results, the system demonstrated excellent performance in terms of functionality, algorithm accuracy, and display speed. Whitebox testing confirmed the accuracy of the RSA algorithm implementation with 100% accuracy. Performance testing using the Largest Contentful Paint (LCP) metric showed very fast load times, ranging from 0.5 to 2.3 milliseconds, with a performance score of 100, making the system highly responsive and optimal in terms of user experience. Therefore, RSA is proven to be effective in enhancing patient data security and maintaining confidentiality within a web-based clinical information system.Keywords: Rivest-Shamir-Adleman Algorithm, Patient Data Security, Whitebox Testing, QR Code
A Comparative Analysis of K-Nearest Neighbors and Random Forest Methods for Recommendations on Selecting Islamic Boarding Schools Based on Student Interest Profiles (primary and middle school students at xxx) Hamidah, Mas Nurul; Tias, Rahmawati Febrifyaning; Zainal, Rifki Fahrial
NERO (Networking Engineering Research Operation) Vol 10, No 2 (2025): Nero - 2025
Publisher : Jurusan Teknik Informatika Fakultas Teknik Universitas Trunojoyo Madura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21107/nero.v10i2.30548

Abstract

KNN and Random Forest are one of the classification methods, in this study will compare 2 methods in machine learning namely KNN and Random forest to recommend the type of Islamic boarding school based on student interests, the application of a comparison of 2 classification methods in the recommendation system for selecting the type of Islamic boarding school based on student interests at the Elementary and Middle School levels of Xxx, The types of Islamic boarding schools are salafi, khalafi and mixed, with attributes such as academic tendencies, religious interests, extracurricular involvement, and family background. application of machine learning methods to support decision making in selecting Islamic boarding schools that are in accordance with student character, which is still rarely found in Islamic educational institutions. Performance evaluation is carried out using the Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) metrics. The test results show that the Random Forest algorithm gives better results with an MAE of 0.23 and an RMSE of 0.57, compared to KNN which has an MAE of 0.6 and an RMSE of 0.96. Thus, Random Forest shown to be more effective in providing recommendations for selecting appropriate Islamic boarding schools, and can be used as a basis for developing a decision support system for Islamic boarding school-based schools.Keywords: KNN, Machine Learning, Random Forest, Islamic boarding schools

Page 4 of 4 | Total Record : 40

 1   2   3   4 

Filter by Year

2023 2025


Reset
Filter By Issues
All Issue Vol 10, No 2 (2025): Nero - 2025 Vol 10, No 1 (2025): Nero - 2025 Vol 9, No 2 (2024): Nero - 2024 Vol 9, No 1 (2024): Nero - 2024 Vol 8, No 2 (2023): Nero - 2023 Vol 8, No 1 (2023): Nero - 2023