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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 7 Documents
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Search results for , issue "Vol 9, No 1 (2024): Nero - 2024" : 7 Documents clear
SELEKSI FITUR ALGORITMA GENETIKA DALAM KLASIFIKASI DATA REKAM MEDIS PCOS MENGGUNAKAN SVM Novianti, Fahriza; Ulinnuha, Nurissaidah
NERO (Networking Engineering Research Operation) Vol 9, No 1 (2024): Nero - 2024
Publisher : Jurusan Teknik Informatika Fakultas Teknik Universitas Trunojoyo Madura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21107/nero.v9i1.25399

Abstract

A hormonal imbalance causes a woman with polycystic ovarian syndrome (PCOS) to have an ovum or egg that does not mature normally. It usually occurs during the reproductive period, but is often difficult to detect due to lack of awareness. Therefore, it is important to detect this condition early so that proper treatment or prevention can be done. One way to diagnose PCOS is through the use of medical data. In this study, 40 variables were used, including hormonal data, ultrasound results, and other medical information. The method used was Support Vector Machine (SVM), which is able to handle non-linear data with a kernel. To improve accuracy, features were selected using a genetic algorithm, which resulted in 19 significant variables. By applying the selected variables as input, the classification produced the best model with 94.26% accuracy, 87.57% sensitivity, and 97.52% specificity. Without the feature selection process, SVM classification only has an accuracy of 82.46%, sensitivity of 60.91%, and specificity of 97.25%. From the findings of this research, it can be seen that the genetic algorithm feature selection method can improve SVM classification performance. Keywords: Genetic Algorithm, Classification, PCOS, Feature Selection, SVM.
PREDIKSI NASABAH KREDIT USAHA RAKYAT MENGGUNAKAN ALGORITMA C4.5 yadi, yadi
NERO (Networking Engineering Research Operation) Vol 9, No 1 (2024): Nero - 2024
Publisher : Jurusan Teknik Informatika Fakultas Teknik Universitas Trunojoyo Madura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21107/nero.v9i1.25348

Abstract

Banking is a financial institution that collects all public funds in the form of deposits and manages these funds to maintain liquidity and security in processing funds aimed at maximizing profits. Banks must provide financial traffic services needed by all customers for both internal and external transactions. Some programs offered by banks in providing financial services include the provision of micro-business credit (KUR) aimed at improving the community's economy. However, the problem that arises in the potential provision of KUR assistance is that it often misses the target, resulting in many customers not optimally receiving financial services. C4.5 Algorithm is an accurate data mining method used for data prediction and processing for decision making. This research aims to predict banking customers in providing KUR using the C4.5 algorithm. The methodology used is the Cross-Industry Standard Process Model for Data Mining, employing the C4.5 algorithm. The prediction results of micro-business credit recipients using the C4.5 algorithm are excellent, as seen from the calculation of entropy value of 0.97 and gain value of 0.69, as well as the formation of decision trees with several determinant data sets such as data from the Ministry of Home Affairs, OJK's Slik, repayment capacity, types of businesses, and locations. The optimization of the C4.5 algorithm in data processing helps in determining customers more optimally, reducing mis-targeted micro-business credit assistance.Keywords: Customer, Algorithm C4.5, Data mining
PEMBENTUKAN POHON KEPUTUSAN UNTUK PENERIMA BANTUAN BERAS MISKIN MENGGUNAKAN ALGORITMA DECISION TREE C4.5 Avianto, Donny; Wibowo, Adityo Permana
NERO (Networking Engineering Research Operation) Vol 9, No 1 (2024): Nero - 2024
Publisher : Jurusan Teknik Informatika Fakultas Teknik Universitas Trunojoyo Madura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21107/nero.v9i1.28020

Abstract

Beras Miskin (Raskin) merupakan salah satu program pemerintah yang bertujuan untuk memberikan bantuan pangan pokok kepada masyarakat kurang mampu. Namun, tantangan besar dalam implementasi program ini adalah ketidaktepatan sasaran, di mana terdapat kasus di mana warga yang seharusnya menerima bantuan malah tidak mendapatkannya, sementara sebagian yang tidak memenuhi syarat justru menerima bantuan. Penelitian ini bertujuan menghasilkan model pohon keputusan yang dapat membantu proses klasifikasi penerima bantuan beras miskin secara lebih mudah dan akurat, sehingga penyaluran program Raskin menjadi lebih tepat sasaran. Pembuatan model dilakukan menggunakan aplikasi RapidMiner Studio versi 10.3 dengan menerapkan algoritma pembentuk Decision Tree C4.5. Dalam menentukan kelayakan penerima, aplikasi menggunakan tujuh kriteria utama: tingkat kesejahteraan, jumlah tanggungan, jenis pekerjaan, sarana sanitasi, sumber air, jenis atap, dan jenis lantai. Algoritma C4.5 pada penelitian ini dilatih menggunakan 100 data pelatihan dan diuji dengan 20 data uji, menghasilkan akurasi sebesar 79,17% dengan faktor yang paling menentukan dalam prediksi adalah jenis lantai. Penelitian ini juga memvisualisasikan pohon keputusan yang terbentuk secara utuh untuk memudahkan interpretasi hasil prediksi dan peluang peningkatan di masa depan.
SISTEM INFORMASI TRANSFUSI DARAH BERBASIS WEB MENGGUNAKAN METODE RAPID APPLICATION DEVELOPMENT Nurrohman, Muhammad Yusuf; Hardiani, Tikaridha; Wijayanto, Danur
NERO (Networking Engineering Research Operation) Vol 9, No 1 (2024): Nero - 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
KOMPARASI SVM KLASIK DAN KUANTUM DALAM KLASIFIKASI BINER BIJI GANDUM (SEEDS) Akrom, Muhamad
NERO (Networking Engineering Research Operation) Vol 9, No 1 (2024): Nero - 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
PENENTUAN UKURAN BATCH OPTIMAL UNTUK PELATIHAN YOLOV8 DALAM PENDETEKSIAN OBJEK PADA KENDARAAN OTONOM Jeri, Jeri; Syarif Hidayat, Zaid
NERO (Networking Engineering Research Operation) Vol 9, No 1 (2024): Nero - 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
PREDIKSI ANEMIA DARI PIXEL GAMBAR DAN LEVEL HEMOGLOBIN MENGGUNAKAN RANDOM FOREST CLASSIFIER Azis, Huzain; Rismayanti, Nurul
NERO (Networking Engineering Research Operation) Vol 9, No 1 (2024): Nero - 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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