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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 8 Documents
 1 
Search results for , issue "Vol 9, No 2 (2024): Nero - 2024" : 8 Documents clear
MULTI-CRITERIA RECOMMENDER SYSTEM BERBASIS METODE WEIGHTED SUM DAN PARETO FRONT UNTUK MANAJEMEN SUMBER DAYA AIR Putri, Astrid Novita
NERO (Networking Engineering Research Operation) Vol 9, No 2 (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.v9i2.27840

Abstract

AbstractClean water is used daily to meet individual needs such as cooking, drinking, bathing, and more. Clean water is essential to support human metabolism, which impacts health. However, obtaining clean water has become increasingly difficult due to high population growth and rising demand, coupled with limited availability.This study develops a multi-criteria recommender system model that considers various criteria or attributes to provide valuable recommendations, facilitating better decision-making based on suitable recommendations regarding water production and consumption. Using the Pareto front and weighted sum methods, this model balances trade-offs among criteria. The results of this study offer an optimal solution for both consumers and water resource management in Semarang City to achieve balance, with W1 minimizing water consumption and W2 maximizing production. The recommended optimal solution is W1 = 0.5 and W2 = 0.5, yielding water consumption of 1,064,910.4 m³/ha and production yield of 14,933,601 tons/ha. Other findings include W1 = 0.1 and W2 = 0.9, yielding water consumption of 11,115,920 m³/ha and production yield of 16,341,636 tons/ha. W1 = 0.4 and W2 = 0.6, yielding water consumption of 11,115,920 m³/ha and production yield of 16,341,636 tons/ha, W1 = 0.7 and W2 = 0.3, yielding water consumption of 10,649,104 m³/ha and production yield of 14,933,601 tons/ha.These outcomes indicate optimal solutions based on different weighting balances between consumption and production criteria.Keywords: Multi-criteria recommender system, pareto front, water resource management
IMPLEMENTASI QSVM DALAM KLASIFIKASI BINER PADA KASUS KANKER PROSTAT Hilmy, Nur Amalina Rahmaputri; Akrom, Muhamad
NERO (Networking Engineering Research Operation) Vol 9, No 2 (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.v9i2.27781

Abstract

Quantum Machine Learning (QML) is increasingly attracting attention as a potential solution to improve computational performance, especially in handling complex and big data-driven classification tasks. In this study, the Quantum Support Vector Machine (QSVM) algorithm is applied to prostate cancer classification, with the results compared to the classical Support Vector Machine (SVM) model. QSVM shows superiority in accuracy, reaching 0.93, compared to the classical SVM which has an accuracy of 0.91. In addition, QSVM produces precision, recall, and F1-score values of 0.83, 0.95, and 0.88, respectively, higher than the precision of 0.82, recall of 0.93, and F1-score of 0.87 of the classical SVM. These findings indicate that QSVM is more effective in handling high-dimensional data and complex classification, thus demonstrating the great potential of QML in medical applications, especially in cancer classification and biomarker discovery.Keywords: Quantum Machine Learning, Quantum Support Vector Machine, Klasifikasi, Kanker Prostat
ANALISIS KINERJA ALGORITMA PEMBELAJARAN MESIN ENSEMBEL PADA DATASET MULTI KELAS CITRA JAFFE Azis, Huzain; Alisma, Alisma; Purnawansyah, Purnawansyah; Nirmala, Nirmala
NERO (Networking Engineering Research Operation) Vol 9, No 2 (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.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
NERO (Networking Engineering Research Operation) Vol 9, No 2 (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.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
NERO (Networking Engineering Research Operation) Vol 9, No 2 (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.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.
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
NERO (Networking Engineering Research Operation) Vol 9, No 2 (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.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
NERO (Networking Engineering Research Operation) Vol 9, No 2 (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.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
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
NERO (Networking Engineering Research Operation) Vol 9, No 2 (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.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

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