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All Journal Jurnal Informatika Jurnal Komputer, Informasi dan Teknologi Ranah Research : Journal of Multidisciplinary Research and Development
Maria Veronika, Nuri David
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Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Economics, Econometrics & Finance Electrical & Electronics Engineering Law, Crime, Criminology & Criminal Justice Public Health Social Sciences Transportation Other

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Perancangan Sistem Manajemen Jaringan Menggunakan Mikrotik Pada Laboratorium Komputer Fakultas Teknik Universitas Muhammadiyah Bengkulu Jaya, Hendri; Maria Veronika, Nuri David; Mahfuzi, A.R Walad; Toyib, Rozali
Jurnal Komputer, Informasi dan Teknologi Vol. 4 No. 1 (2024): June
Publisher : Penerbit Jurnal Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53697/jkomitek.v4i1.1815

Abstract

Fakultas Teknik Universitas Muhammadiyah Bengkulu saat ini telah memiliki laboratorim komputer yang digunakan untuk menunjang kegiatan perkuliahan di Fakultas Teknik Universitas Muhammadiyah Bengkulu seperti pada mata kuliah Bahasa pemrograman dan mata kuliah lainnya yang menggunakan laboratoium komputer. Jaringan Local Area Network Fakultas Universitas Muhammadiyah Bengkulu sudah terhubung pada internet, dimana semua komputer yang ada pada Local Area Network  terhubung langsung ke modem melalui swith hub, sehingga ketika ada mahasiswa/i yang mendownload akan menganggu mahasiswa/i yang lainnya. Dari permasalah ini maka di bangun Local Area Network berbasis mikrotik pada Fakultas Teknik Universitas Muhammadiyah Bengkulu agar dapat melakukan manajamen jaringan seperti satu username hanya dapat digunakan pada satu perangkat, pembagian bandwith berdasarkan akun, transper data dalam jaringan dan pengaturan lainnya yang diperlukan untuk kelancaran jaringan komputer Fakultas Teknik Universitas Muhammadiyah Bengkulu. Dengan menerapakan sistem manajemen jaringan menggunakan metode simple queue pada jaringan komputer Fakultas Teknik Universitas Muhammadiyah Bengkulu menjadi lebih baik.
Sistem Cerdas Berbasis Image Processing dan deep learning untuk Deteksi Lapisan Lilin pada Permukaan Buah Suganda, Ande; Maria Veronika, Nuri David
Ranah Research : Journal of Multidisciplinary Research and Development Vol. 7 No. 5 (2025): Ranah Research : Journal Of Multidisciplinary Research and Development
Publisher : Dinasti Research

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.38035/rrj.v7i5.1642

Abstract

Grapes are among the most perishable agricultural commodities. To improve shelf life and visual appeal, artificial wax coatings are often applied to their surface. However, these coatings may pose health risks if not properly detected. This study aims to develop an intelligent system based on image processing and deep learning to automatically and non-destructively detect the presence of wax coatings on apples. The dataset consists of a total of 312 apple images, collected using smartphone and digital cameras and expanded through data augmentation to increase variation and training volume. Classification was carried out using a Convolutional Neural Network architecture with input images resized to 150x150 pixels and trained for 20 epochs using the ImageDataGenerator library. The resulting model achieved a training accuracy of up to 99.36% and a validation accuracy of 100%. Testing confirmed that the system can effectively distinguish between waxed and unwaxed apple surfaces by recognizing differences in texture and light reflection. This system shows strong potential for implementation in automated post-harvest quality control within agricultural industries.
Penerapan Metode Convolutional Neural Network Dalam Klasifikasi Kesegaran Ikan Mungkus Berdasarkan Citra Mata dan Insang Ikan Darnita, Yulia; Putra , Febby Andika; Wibowo, Sastya Hendri; Saputera, Surya Ade; Maria Veronika, Nuri David
Jurnal Informatika Vol 25 No 1 (2025): Jurnal Informatika
Publisher : Institut Informatika Dan Bisnis Darmajaya

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Mungkus fish (Sicyopterus stimpsoni) is a type of freshwater fish that is a typical mascot of Kaur Regency, Bengkulu Province. This fish lives in clear, fast-flowing waters, and is known for its ability to stick to rocks using a special structure on its stomach called cupak. Mungkus fish has high economic value and is consumed daily by the local community. However, high demand is not balanced with adequate availability, resulting in increasingly expensive prices. In addition, the lack of public knowledge regarding the assessment of fish freshness causes the risk of consuming fish that is not fresh, which has the potential to endanger health. Traditional assessment of fish freshness based on physical parameters such as eyes, gills, and meat texture is considered less accurate and requires special expertise. Therefore, this study proposes the use of Convolutional Neural Network (CNN) to classify the freshness of mungkus fish based on eye and gill images. CNN is able to extract complex features from images without the need for manual extraction. The application of this method is expected to provide an objective, efficient, and accurate solution in assessing the freshness of mungkus fish, as well as being beneficial for fishermen and consumers.
Co-Authors Darnita, Yulia Jaya, Hendri Mahfuzi, A.R Walad Putra , Febby Andika Sastya Hendri Wibowo Suganda, Ande Surya Ade Saputera Toyib, Rozali