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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 34 Documents
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SISTEM INFORMASI TRANSFUSI DARAH BERBASIS WEB MENGGUNAKAN METODE RAPID APPLICATION DEVELOPMENT Nurrohman, Muhammad Yusuf; Hardiani, Tikaridha; Wijayanto, Danur
Networking Engineering Research Operation Vol 9, No 1 (2024): Nero - April 2024
Publisher : Jurusan Teknik Informatika Fakultas Teknik Universitas Trunojoyo Madura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21107/nero.v9i1.28032

Abstract

Blood Transfusion at RS PKU Muhammadiyah Gamping faces several issues in the management, administration, and reporting of medical record data, which are still conducted manually. In an effort to comply with the Indonesian Minister of Health Regulation No. 24 of 2022, which requires all healthcare facilities in Indonesia to implement Electronic Medical Records, a Blood Transfusion Information System has been developed. This system is designed to address the problem of medical record storage at RS PKU Muhammadiyah Gamping, which previously relied on paper records stored in file cabinets. The system development uses the Rapid Application Development (RAD) method, chosen for its ability to expedite the system development process. The RAD method involves sequential phases of analysis, design, development, and transition. This research aims to develop an information system that facilitates the administration and reporting of blood transfusions at RS PKU Muhammadiyah Gamping using the CodeIgniter Framework. The system aims to assist nurses in managing and reporting medical record data related to blood transfusions. Testing using the black-box method showed a 100% success rate, indicating excellent performance.Keywords:.Electronic Medical Records, Blood Transfusion Information System, RS PKU Muhammadiyah Gamping, RAD Method, CodeIgniter Framework
ANALISIS KINERJA ALGORITMA PEMBELAJARAN MESIN ENSEMBEL PADA DATASET MULTI KELAS CITRA JAFFE Azis, Huzain; Alisma, Alisma; Purnawansyah, Purnawansyah; Nirmala, Nirmala
Networking Engineering Research Operation Vol 9, No 2 (2024): Nero - November 2024
Publisher : Jurusan Teknik Informatika Fakultas Teknik Universitas Trunojoyo Madura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21107/nero.v9i2.27872

Abstract

This research aims to develop a facial expression recognition system based on the JAFFE dataset which includes seven classes of emotional expressions, namely happy, sad, angry, afraid, disgusted and neutral expressions. The first step taken is canny segmentation on each dataset to maintain essential information on each face. Next, extraction was carried out using the hu moments method to gain an in-depth understanding of the important characteristics of facial expressions. The next process involves ensemble voting using five classification methods, namely Naive Bayes (NB), Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Gaussian Process Classifier (GPC), and Decision Tree. The results of these five methods are then ensembel using voting techniques, and the final results are evaluated using performance metrics such as accuracy, precision, recall, and F-1 score. Evaluation is carried out by comparing the final results with the original data from the JAFFE dataset, by measuring accuracy , precision, recall, and F1 Score value to evaluate system performance. The results of this research show that the ensemble voting approach using a combination of classification methods is able to significantly improve facial expression recognition capabilities. The resulting accuracy, precision, recall, and F1 Score values provide a comprehensive picture of system performance.  This research contributes to the development of facial emotion recognition technology and can be applied in various contexts. Includes human-computer interaction as well as applications in the fields of artificial intelligence.Keywords: Performance Analysis, Ensemble, Jaffe Image, Classification, Multiclass
IMPLEMENTASI SUPPORT VECTOR MACHINE (SVM) DENGAN QUERY EXPANSION RANKING PADA REVIEW PENGGUNAAN JAMU MADURA Yunitarini, Rika; Fitrianto, Hambali; Ayu Mufarroha, Fifin
Networking Engineering Research Operation Vol 9, No 2 (2024): Nero - November 2024
Publisher : Jurusan Teknik Informatika Fakultas Teknik Universitas Trunojoyo Madura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21107/nero.v9i2.27785

Abstract

Madura traditional herbal medicine is a traditional herbal medicine made from natural ingredients and is well-known for its efficacy. The popularity of Madura traditional herbal medicine is not only based on the diversity of traditional herbal medicine products and their health benefits, but also on traditional values that have been passed down from generation to generation. One of the most popular Madura traditional herbal medicine is Peluntur traditional herbal medicine. Peluntur traditional herbal medicine is a series of medicinal or herbal products specifically designed as a solution to overcome late menstruation or irregular menstruation, which is often a source of concern for mothers and young women. With the background of the increasing demand for Madura traditional herbal medicine products, a sentiment analysis was conducted on Madura traditional herbal medicine product reviews on the Shopee, Lazada, and Tokopedia applications. This study applies Support Vector Machine and Query Expansion Ranking to achieve the highest accuracy in reviewing the use of Madura traditional herbal medicine. The results obtained for the use of the Support Vector Machine algorithm have an accuracy of 93%, while for the use of the Support Vector Machine and Query Expansion Ranking algorithms at feature selection ratios of 50% and 100% the accuracy increases to 94%.Keywords: Madura traditional herbal medicine, Peluntur traditional herbal medicine, Query Expansion Ranking, Sentiment Analysis, Support Vector Machine
PENGGUNAAN METODOLOGI SCRUM DENGAN PENDEKATAN GOAL-ORIENTED REQUIREMENT ENGINEERING DALAM PENGEMBANG SISTEM INFORMASI KESEHATAN Trinanda, Muhammad Satria Putra; Irawati, Irawati; Hasnawi, Mardiyyah
Networking Engineering Research Operation Vol 9, No 2 (2024): Nero - November 2024
Publisher : Jurusan Teknik Informatika Fakultas Teknik Universitas Trunojoyo Madura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21107/nero.v9i2.26032

Abstract

The information obtained by the public is inadequate, and sometimes they still really need complete information, one of which is about diseases. The provision of media information must be proven accurate and should not be reported by organizations that do not have authority. In this case, health foundations have the right to provide information that is trusted for its authenticity, especially in providing information data regarding tuberculosis disease. The purpose of this research is to develop a tuberculosis tuberculosis information system on the Yamali TB platform with the Goal-Oriented Requirement Engineering (GORE) approach and the Scrum method. Information system development is carried out through several main activities, namely Problem Identification, Problem Analysis, Goal Identification, Backlog Prioritization, System Design, Initial Discussion, Program Design and Final Evaluation using the System Usability Scale assessment method. The results showed that the level of user satisfaction with the information system based on the usability test obtained an average of 77 and was ranked at Grade Scale C, Acceptability Ranges at the Acceptable level, and Adjective Ratings at the Good level which means that the information system developed is well received by users.Keywords: Goal-Oriented Requirement Engineering, Grade Scale, Scrum, System Usability Scale.
KOMPARASI SVM KLASIK DAN KUANTUM DALAM KLASIFIKASI BINER BIJI GANDUM (SEEDS) Akrom, Muhamad
Networking Engineering Research Operation Vol 9, No 1 (2024): Nero - April 2024
Publisher : Jurusan Teknik Informatika Fakultas Teknik Universitas Trunojoyo Madura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21107/nero.v9i1.28082

Abstract

Binary classification is one of the important tasks in machine learning, with wide applications in various fields, including agriculture and food processing. This study compares the performance of the classical Support Vector Machine (SVM) and Quantum Support Vector Machine (QSVM) in wheat grain classification, focusing on accuracy, precision, recall, F1-score, and Area Under the Curve (AUC). The wheat grain dataset consists of physical features relevant to distinguish between two types of grains. The analysis results show that QSVM significantly outperforms classical SVM in all measured metrics, with higher accuracy and a better balance between precision and recall. The superiority of QSVM can be attributed to its ability to handle complex feature interactions and accelerate the training process through quantum algorithms. These findings demonstrate the potential of QSVM as a more effective model for binary classification applications. However, factors such as implementation complexity and availability of quantum computing resources need to be considered. This study provides valuable insights for the development of more efficient classification methods in the context of agriculture and other related fields.Keywords: Quantum Machine Learning, Quantum Support Vector Machine, Classification, Seeds
PERBANDINGAN METODE FUZZY TIME SERIES CHEN DAN METODE EXPONENTIAL SMOOTHING DALAM MEMPREDIKSI CURAH HUJAN DI KABUPATEN PAMEKASAN Tamam, Moh. Badrit; Kuzairi, Kuzairi; Yulianto, Toni; Faisol, Faisol; Yudistira, Ira; Amalia, Rica
Networking Engineering Research Operation Vol 9, No 2 (2024): Nero - November 2024
Publisher : Jurusan Teknik Informatika Fakultas Teknik Universitas Trunojoyo Madura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21107/nero.v9i2.27986

Abstract

This research aims to predict rainfall in Pamekasan Regency, Madura, East Java, using two prediction methods: Fuzzy Time Series Chen and the Exponential Smoothing (ES) method, specifically Double Exponential Smoothing (DES). The data used in this study consists of monthly rainfall data from January 2011 to December 2023, covering a period of 13 years. The data was sourced from reliable records that regularly track rainfall in the region. In the analysis, both methods were applied to generate accurate predictions of rainfall patterns in Pamekasan Regency. Based on the calculations and performance evaluation, the best method for predicting rainfall in this region was found to be Double Exponential Smoothing Holt. This method uses two key parameters: alpha at 0.4 and beta at 0.6. After applying this method, a Mean Absolute Percentage Error (MAPE) of 1.479 was obtained, indicating a very low and acceptable level of prediction error. Therefore, it can be concluded that the Double Exponential Smoothing Holt method is an effective and accurate approach for predicting rainfall in Pamekasan Regency based on the historical data used..Keywords: Rainfall; Pamekasan Regency; Prediction; Chen's Fuzzy Time Series and Exponential Smoothing (ES) Method
ANALISIS EFEKTIVITAS APLIKASI MYITS THESIS MENGGUNAKAN CONFIRMATORY FACTOR ANALYSIS UNTUK PENINGKATAN LAYANAN PENYELENGGARAAN UJIAN PADA PROGRAM DOKTOR ILMU KOMPUTER Ambarwati, Lina -; D'layla, Adifa Widyadhani Chanda; Saikhu, Ahmad
Networking Engineering Research Operation Vol 9, No 2 (2024): Nero - November 2024
Publisher : Jurusan Teknik Informatika Fakultas Teknik Universitas Trunojoyo Madura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21107/nero.v9i2.28201

Abstract

In the global era, Information Systems in higher education institutions are a must. Institut Teknologi Sepuluh Nopember (ITS) continuously makes efforts to develop information systems for various academic and non-academic services for the lecturer and students. One of the academic services developed at ITS is myITS Thesis application. myITS Thesis is one of the applications prepared for the management of scholars Final Project. The Computer Science Doctoral Program (PDIK) has implemented the application for examination services including qualification hearings progress, seminars, and closed dissertation hearings. This study aims to measure the effectiveness of using the myITS Thesis application in managing PDIK dissertation research services to stakeholders, especially PDIK scholars. Measurements were carried out by surveying through questionnaires with PDIK participants who used the application in the 2023/2024 academic year. The effectiveness of the application is measured through five factors, namely System Quality (KS), Information Quality(KI), System Use(PS), User Satisfaction(KP), and Individual Impact(DI). The five factors are measured through 30 Question Indicators to 55 respondents. The results of the questionnaire survey were processed using descriptive analysis and CFA modeling. CFA is used to measure validity and reliability through Standardized Loading Factor (SLF), Cronbach Alpha (CA), and Composite Reliability (CR) values. It is concluded from the modeling results based on validity and reliability measurements that the KS factor is valid with CA value=0.931 and reliable with CR value=0.73, the KI factor is valid with CA value=0.923 and reliable with CR value=0.706, the PS factor is valid with CA value=0.95 and reliable with CR value=0.734. While the KP factor is valid with CA value=0.972 and reliable with CR value= 0.814. Therefore, the myITS Thesis application has been quite effective in improving exam administration services.Keywords: questionnaire, descriptive analysis, PDIK, myITS Thesis, CFA modeling
PENENTUAN UKURAN BATCH OPTIMAL UNTUK PELATIHAN YOLOV8 DALAM PENDETEKSIAN OBJEK PADA KENDARAAN OTONOM Jeri, Jeri; Syarif Hidayat, Zaid
Networking Engineering Research Operation Vol 9, No 1 (2024): Nero - April 2024
Publisher : Jurusan Teknik Informatika Fakultas Teknik Universitas Trunojoyo Madura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21107/nero.v9i1.27462

Abstract

This study aims to determine the optimal batch size in training the YOLOv8 model for object detection in autonomous vehicles. With the increasing need for accurate and efficient object detection technology, this study explores the effect of batch size variation on the performance of the YOLOv8 model. The dataset used in this study is a traffic simulation dataset from CARLA, obtained from the Roboflow universe, consisting of 1719 images divided into training, validation, and testing data. The research methodology includes data collection, pre-processing, and data analysis using the YOLOv8 technique with different hyperparameter settings. The results showed that increasing the number of epochs and batch size contributed to the increase in the mean Average Precision (mAP) value of the model. The best training scheme was identified with the highest mAP value of 98.2%, using 100 epochs, batch size 32, and image resolution 640x640. These findings provide important insights for further development in object detection technology, as well as provide guidance for researchers who want to optimize training parameters for object detection models using YOLOv8 in the context of autonomous vehicles. This research is expected to serve as a reference for future studies in this field.Kata kunci: YOLOv8, object detection, autonomous vehicle, optimal batch size, CARLA dataset, mean Average Precision (mAP), hyperparameters, model training
PERBANDINGAN KINERJA ALGORITMA APRIORI DAN EQUIVALENCE CLASS TRANSFORMATION (ECLAT) DALAM MENEMUKAN POLA PEMBELIAN PADA DATA TRANSAKSI MINIMARKET Handika, I Putu Susila; Susila Satwika, I Kadek
Networking Engineering Research Operation Vol 9, No 2 (2024): Nero - November 2024
Publisher : Jurusan Teknik Informatika Fakultas Teknik Universitas Trunojoyo Madura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21107/nero.v9i2.28055

Abstract

This study compares the performance of the Apriori and ECLAT algorithms in analyzing sales transaction data from a minimarket. The research focuses on examining both algorithms' efficiency in terms of execution time and memory usage when identifying frequent itemsets and generating association rules. Given the limited variety of products sold in a minimarket, a lower minimum support (0.001) and minimum confidence (0.005) were applied to ensure meaningful results, as higher thresholds resulted in no significant findings. The first test evaluated the time required to find frequent itemsets, revealing that ECLAT consistently outperformed Apriori with an average execution time of 0.71634 seconds compared to Apriori's 4.88256 seconds. The second test assessed the time taken to generate association rules, where ECLAT again showed slightly better performance, averaging 0.01352 seconds versus Apriori's 0.01618 seconds. Memory usage tests showed that ECLAT was more efficient, using an average of 0.12436 MB to find frequent itemsets and 0.01052 MB to generate association rules, compared to Apriori's 0.1385 MB and 0.01136 MB, respectively. The results indicate that the ECLAT algorithm is generally more effective for analyzing sales transactions in a minimarket environment, particularly when handling large datasets and when computational efficiency is critical. The findings provide valuable insights for selecting the appropriate algorithm to optimize marketing strategies and inventory management in retail settings.Keywords: Market Basket Analysis, Apriori, Assocation Rule, ECLAT
PREDIKSI ANEMIA DARI PIXEL GAMBAR DAN LEVEL HEMOGLOBIN MENGGUNAKAN RANDOM FOREST CLASSIFIER Azis, Huzain; Rismayanti, Nurul
Networking Engineering Research Operation Vol 9, No 1 (2024): Nero - April 2024
Publisher : Jurusan Teknik Informatika Fakultas Teknik Universitas Trunojoyo Madura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21107/nero.v9i1.27916

Abstract

Anaemia is a widespread blood disorder characterized by a deficiency of red blood cells or hemoglobin, which can lead to severe health complications if not diagnosed and treated promptly. This research aims to develop a machine learning model to predict anaemia based on hemoglobin levels and image pixel distributions, leveraging a dataset from Kaggle. The dataset includes features such as percentages of red, green, and blue pixels in images and hemoglobin levels. We applied a Random Forest Classifier, a robust machine learning algorithm, and evaluated its performance using 5-fold cross-validation. The data pre-processing involved removing irrelevant columns, encoding categorical variables, and scaling numerical features. The model achieved a mean accuracy of 97.05%, precision of 97.02%, recall of 97.05%, and F1-score of 96.88%, indicating its high reliability in predicting anaemia. Visualizations such as Correlation Heatmaps, 3D PCA, Parallel Coordinates Plots, 3D t-SNE, and Violin Plots were used to understand feature relationships and distributions. These results underscore the potential of machine learning in providing a non-invasive, cost-effective diagnostic tool for anaemia, especially in resource-limited settings. Future research should address dataset imbalance and potential biases, explore additional features, and test other machine learning models to further enhance the predictive accuracy. This study contributes to the field of medical diagnostics by demonstrating the efficacy of integrating hemoglobin levels and image data for anaemia prediction, paving the way for improved early detection and treatment strategies.Keywords: Anaemia, Hemoglobin, Machine Learning, Random Forest.

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