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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 40 Documents
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RANCANG BANGUN SISTEM PAKAR DIAGNOSA HAMA DAN PENYAKIT TANAMAN BAWANG MERAH DAN CABAI MENGUNAKAN METODE FORWARD CHAINING Fatah, Doni Abdul; Rifqi, Khoirur; Sawaki, Sawaki; Irhamni, Firli
NERO (Networking Engineering Research Operation) Vol 8, No 2 (2023): Nero - 2023
Publisher : Jurusan Teknik Informatika Fakultas Teknik Universitas Trunojoyo Madura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21107/nero.v8i2.26789

Abstract

Sistem pakar merupakan suatu program komputer cerdas yang menggunakan pengetahuan dan prosedur inferensi untuk memecahkan masalah-masalah yang cukup kompleks, sehingga membutuhkan keahlian dan pengalaman seorang pakar untuk menyelesaikannya. Dalam bidang pertanian, sistem pakar dapat dimanfaatkan untuk membantu petani dan penyuluh pertanian dalam mengidentifikasi dan mengatasi berbagai masalah terkait hama dan penyakit pada tanaman. Penelitian ini bertujuan untuk merancang dan mengembangkan sebuah sistem pakar untuk mendiagnosis hama dan penyakit pada tanaman bawang merah dan cabai menggunakan metode forward chaining. Sistem pakar ini dirancang untuk menjadi alat bantu yang dapat membantu petani dan penyuluh pertanian dalam mengidentifikasi jenis hama dan penyakit yang menyerang tanaman, serta memberikan solusi penanganannya secara cepat dan akurat. Sistem pakar yang dihasilkan dalam penelitian ini merupakan sistem berbasis aturan (rule-based) yang mampu mengidentifikasi jenis hama dan penyakit berdasarkan gejala-gejala yang diamati, serta memberikan rekomendasi penanganannya. Hasil pengujian menunjukkan bahwa sistem pakar ini memiliki tingkat akurasi sebesar 90% dalam mendiagnosis hama dan penyakit pada tanaman bawang merah dan cabai. Dengan demikian, sistem pakar dapat menjadi alat bantu yang efektif bagi petani dan penyuluh pertanian dalam upaya meningkatkan produktivitas tanaman bawang merah dan cabai.Kata kunci: bawang merah, cabai, forward chaining, hama, penyakit, sistem pakar
PERHITUNGAN KOLONI BAKTERI SUSU SEGAR PADA RUANG WARNA YCBCR Fitri, Zilvanhisna Emka; Sahenda, Lalitya Nindita; Holili, Rexy Solehudin Abdi; Rukmi, Dyah Laksito
NERO (Networking Engineering Research Operation) Vol 8, No 2 (2023): Nero - 2023
Publisher : Jurusan Teknik Informatika Fakultas Teknik Universitas Trunojoyo Madura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21107/nero.v8i2.19094

Abstract

The problem with fresh milk on the SPR farm is the manual milking process, which causes the milk to be less hygienic and becomes an ideal growing medium for microbes. Therefore, it is necessary to carry out a procedure for checking the microbiological status as an indicator of food safety. To test for microbial contamination in fresh milk, namely the Total Plate Count (TPC) test, but in this study the focus is on the calculation of bacterial colonies using digital image processing techniques. The stages of the research carried out are the preprocessing process (cropping and color conversion to YCbCr space), image enhancement (addition of brightness and inverse image), the segmentation combination process (gray degree and channel area thresholding) and colony calculation using labeling based on the proximity of 8 neighbors to the feature area. From the results of the study, it was found that bacterial colonies had a wide area range of 150 ≤ area ≤ 8000. A comparison of manual TPC calculations with the system has been carried out on 5 test samples and obtained an average error difference of 0.176.Keywords : channel area thresholding, bacterial colonies, fresh milk, TPC, YCbCr
ANALISIS KEAMANAN WEBSITE DENGAN INFORMATION SYSTEM SECURITY ASSESSMENT FRAMEWORK (ISSAF) DAN OPEN WEB APPLICATION SECURITY PROJECT (OWASP) Nugroho, Verseveranda Setyo; Christanto, Febrian Wahyu
NERO (Networking Engineering Research Operation) Vol 8, No 2 (2023): Nero - 2023
Publisher : Jurusan Teknik Informatika Fakultas Teknik Universitas Trunojoyo Madura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21107/nero.v8i2.19712

Abstract

Meningkatnya penggunaan platform digital dalam setiap kegiatan manusia memunculkan kembali perhatian yang pada dasarnya diperlukan di setiap aktifitas atau hal yang dikerjakan manusia, seperti keamanan. Keamanan juga tentu saja hadir dalam kegiatan yang sudah terdigitalisasi atau dapat disebut juga sebagai Cybersecurity. Keamanan dalam Cybersecurity penting karena berkaitan dengan data pribadi (Privacy), integritas (Integrity), akses atau verifikasi (Authentication), kerahasiaan (Confidentiality) dan ketersediaan (Availability).  DiamantePro Digital Creative bertempat di Jakarta adalah perusahaan platform digital yang menyediakan jasa undangan atau Invitation seperti undangan pernikahan, meeting internal maupun seminar. Pada dasarnya undangan memerlukan adanya identitas pribadi dari individu yang akan diundang. Maka dari itu untuk melindungi data pengguna dari kebocoran data identitas, DiamantePro Digital Creative memerlukan adanya pengujian penetrasi melalui website mereka. Metode penetrasi tingkat keamanan website dapat menggunakan Information Systems Security Assessment Framework (ISSAF) dan Open Web Application Security Project (OWASP). Hasil pengujian membuktikan bahwa website diaundangkamu.com tidak dapat ditembus karena memiliki fitur security yang mumpuni seperti Naga Cyber Defense dari hostingan rumahweb.com.Kata kunci: Pengujian Penetrasi, ISSAF, OWASP
PENERAPAN ALGORITMA LINEAR SEARCH DI APLIKASI SECONDHAND Agustin, Nely Dwi; Cobantoro, Adi Fajaryanto; Setyawan, Mohammad Bhanu; Nurfitri, Khoiru
NERO (Networking Engineering Research Operation) Vol 8, No 2 (2023): Nero - 2023
Publisher : Jurusan Teknik Informatika Fakultas Teknik Universitas Trunojoyo Madura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21107/nero.v8i2.21089

Abstract

Inflation in Indonesia has been increasing year by year. The high inflation rate has an impact on the increasing production costs of finished goods, resulting in higher prices which this is not balanced by good sales of new products. This has led to a plan to create a secondhand e-commerce platform. This research aims to implement a search feature that facilitates the search for used goods based on keywords. The implementation process involves the use of the Linear/Sequential Search algorithm in the JavaScript programming language and PostgreSQL as the data storage used. In practice, when the keyword matches the data in the database, the search results will be displayed. If there is no data match, the search will not find any relevant products. The result of this research is the availability of the SecondHand application, with a search feature using the Linear/Sequential Search algorithm, which helps facilitate the interaction between sellers and buyers. The results of white box and postman/grey box testing show that the search feature and its functions work well, producing valid outputs, and have a short execution time of around 613.5 ms or 0.6135 seconds based on the results of five tests.Keywords : Linear/Sequential Search Algorithm, Used Goods, SecondHand.
KLASIFIKASI CITRA PNEUMONIA MENGGUNAKAN ARSITEKTUR CONVOLUTIONAL NEURAL NETWORK (CNN) Pratama, Aan Rachmatullah; Cobantoro, Adi Fajaryanto
NERO (Networking Engineering Research Operation) Vol 8, No 2 (2023): Nero - 2023
Publisher : Jurusan Teknik Informatika Fakultas Teknik Universitas Trunojoyo Madura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21107/nero.v8i2.18992

Abstract

Pneumonia adalah infeksi atau peradangan akut pada bagian jaringan paru yang disebabkan oleh berbagai mikroorganisme seperti bakteri, virus, parasit, jamur, kerusakan fisik paru ataupun bahan kimia. Pneumonia dapat menyerang orang dewasa maupun anak-anak, banyak kasus yang terjadi, terutama pada Negara berkembang dimana kebanyakan mengandalkan energi yang berpontensi menyebabkan polusi udara yang akan berdampak pada pernafasan manusia. Klasifikasi citra Pneumonia dari hasil rontgen dengan algoritma Convolutional Neural Network yang memiliki metode alur pemecahan masalah yang menyerupai pola pikir manusia. Pada program ini melakukan penelitian tentang membandingkan performa dari kedua model arsitektur Convolutional Neural Network arsitektur AlexNet dengan GoogleNet. Pada hasil confusion matrix mendapatkan hasil tingkat akurasi 0,79 untuk arsitektur Alexnet dan untuk arsitektur GoogLeNet mendapatkan hasil akurasi 0,78. Umumnya akurasi dari GoogLeNet lebih tinggi namun pada penelitian ini AlexNet mendapatkan akurasi yang lebih tinggi, namun GoogLeNet memiliki loss yang lebih rendah, loss dan Accuracy diperngaruhi callback yang didalamanya terdapat epoch. Pada hasil implementasi kedua model dari web app menggunakan flask dan Google colab, dari jumlah masukan 16 citra 15 prediksi dilakukan benar dan 1 salah mendapatkan hasil akurasi 0,94.Kata kunci : AlexNet, CNN, GoogLeNet, Pneumonia
KLASIFIKASI DIAGNOSIS DIABETES MELITUS MENGGUNAKAN METODE NAÏVE BAYES DENGAN SELEKSI FITUR BACKWARD ELIMINATION Nugroho, Hendro; Yuliastuti, Gusti Eka; Pradana, Andrean Firman
NERO (Networking Engineering Research Operation) Vol 8, No 2 (2023): Nero - 2023
Publisher : Jurusan Teknik Informatika Fakultas Teknik Universitas Trunojoyo Madura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21107/nero.v8i2.21110

Abstract

Diabetes mellitus is a dangerous disease caused by high sugar levels (hyperglycemia). Hyperglycemia can cause sufferers to experience chronic disease, damage to organs in the body. Diabetes mellitus is a dangerous disease, so it is very interesting to classify diabetes mellitus using the Naïve Bayes method with Backward Elimination (BE) feature selection. The Diabetes mellitus dataset used in the research consisted of 101 data with 5 attributes consisting of age, Current Blood Sugar (GDS), 2 hours after eating/Post Pradial (PP), Fasting Blood Sugar (GPD) levels, and Low Density Lipoprotein (LDL) . To get classification results, there are several steps taken, namely data input, BE feature selection, 8-Fold Cross Validation, Naïve Bayes and results testing. From the classification results, testing was carried out using the accuracy, precision and recall calculation method. To find out the results of classification performance, four test scenarios were carried out, namely the first scenario, Naïve Bayes combined with BE and 8-Fold Cross Validation, accuracy of 77%, second scenario, Naïve Bayes combined with 8-Fold Cross Validation, accuracy of 78.1%, third scenario, Naïve Bayes combined with BE accuracy is 86% and the fourth scenario of Naïve Bayes classification accuracy is 90%, so the accuracy of Naïve Bayes classification with BE feature selection is better.Keywords: Diabetes melitus, Naïve bayes, Backward Elimination. 8-Flod Cross Validation.
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.
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

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