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PERANCANGAN DAN SIMULASI JARINGAN IP CAMERA MENGGUNAKAN FITUR VLAN STUDI KASUS CV ROZITECH MULTIMEDIA INDONESIA Gangga Aditya Saputra; Deni Sutaji
Jurnal Media Akademik (JMA) Vol. 4 No. 1 (2026): JURNAL MEDIA AKADEMIK Edisi Januari
Publisher : PT. Media Akademik Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62281/9cmqd019

Abstract

Studi ini menguraikan desain dan pemodelan jaringan kamera IP yang menggunakan teknologi Virtual Local Area Network (VLAN) untuk meningkatkan efisiensi, keamanan, dan integrasi dalam sistem CCTV yang didirikan di Desa Leran oleh CV. Rozitech Multimedia Indonesia. Penelitian ini terutama membahas kendala jarak yang menghambat CCTV 11 dan CCTV 12 untuk terhubung melalui jalur konverter media saat ini, sehingga memerlukan implementasi Titik Distribusi Optik (ODP) dan Unit Jaringan Optik (ONU) sebagai rute alternatif untuk meminimalkan biaya dan durasi instalasi. Penelitian ini bertujuan untuk menciptakan jaringan kamera IP berbasis VLAN terintegrasi yang mendukung berbagai jalur fisik sambil mempertahankan segmen logis yang kohesif melalui konfigurasi subnet /23. Metodologi Siklus Hidup Pengembangan Jaringan (NDLC)—yang terdiri dari analisis, desain, simulasi, implementasi, pemantauan, dan optimasi—diimplementasikan untuk menjamin proses pengembangan yang sistematis dan terukur. Simulasi jaringan dilakukan dengan Cisco Packet Tracer 8.2.2 untuk menilai pengaturan VLAN, ketepatan topologi, interkonektivitas perangkat, dan stabilitas sistem secara keseluruhan. Temuan simulasi menunjukkan bahwa semua kamera IP berkomunikasi secara efektif di dalam VLAN yang sama, menampilkan latensi yang konsisten, tidak ada kehilangan paket, dan komunikasi tanpa gangguan antara perangkat yang terletak di jalur transmisi yang berbeda. Hasilnya menunjukkan bahwa segmentasi VLAN, jika dipadukan dengan teknik subnetting yang efektif, dapat meningkatkan kinerja jaringan dan mengurangi kompleksitas perutean. Penelitian ini menunjukkan bahwa desain yang diusulkan layak untuk penggunaan praktis dan memiliki potensi signifikan untuk kemajuan di masa depan, termasuk sistem pemantauan otomatis, integrasi penyimpanan cloud, dan penggabungan jaringan yang ditentukan perangkat lunak untuk peningkatan skalabilitas.
PENINGKATAN KOMPETENSI GURU DAERAH TERPENCIL MELALUI PELATIHAN PENGEMBANGAN KONTEN E-LEARNING Suryanti, Sri; Sutaji, Deni; ., Muyasaroh; Arifani, Yudhi; Zamzamy, Muhammad
Martabe : Jurnal Pengabdian Kepada Masyarakat Vol 4, No 1 (2021): Martabe : Jurnal Pengabdian Kepada Masyarakat
Publisher : Universitas Muhammadiyah Tapanuli Selatan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31604/jpm.v4i1.85-93

Abstract

This Community Service activity was backgrounded by three things namely 1) obtained SPADA Grant for the development and impelementation of online learning in 2018; 2) the results of evaluation of devotion activities in 2018 and 2019 namely 80% of teachers understand mobile learning based on Augmented Reality; 3) the fact that there is a gap in the quality of education that occurs in remote areas with urban areas, so it is necessary to increase the competence of teachers in terms of learning innovation through the development of e-learning content for teachers in remote areas of Bawean Island located 80 miles or 135 km north of Java island. The geographical location and transport conditions connecting Bawean island with Gresik are the main obstacles for teachers on Bawean island to keep up with the development of science and technology.This Community Service Program  was implemented through the stage of Strengthening the concept of e-learning followed by needs analysis workshop, e-learning system design workshop, e-learning content development workshop, Upload development results to LMS system, then implementation with assistance from the service team and continued implementation independently by the school. The result of this activity were 1) increased teacher understanding of e-learning and examples of its implementation; 2) produced an e-learning design consisting of the design of the materials of each meeting, video tutorials, discussion activities for students, and evaluation tools; 3) teachers are able to implement e-learning independently.
The Effect of Resampling on Classifier Performance: anEmpirical Study Pujianto, Utomo; Akbar, Muhammad Iqbal; Lassela, Niendhitta Tamia; Sutaji, Deni
Knowledge Engineering and Data Science
Publisher : citeus

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

Abstract

An imbalanced class on a dataset is a common classification problem. The effect of using imbalanced class datasets can cause a decrease in the performance of the classifier. Resampling is one of the solutions to this problem. This study used 100 datasets from 3 websites: UCI Machine Learning, Kaggle, and OpenML. Each dataset will go through 3 processing stages: the resampling process, the classification process, and the significance testing process between performance evaluation values of the combination of classifier and the resampling using paired t-test. The resampling used in the process is Random Undersampling, Random Oversampling, and SMOTE. The classifier used in the classification process is Naïve Bayes Classifier, Decision Tree, and Neural Network. The classification results in accuracy, precision, recall, and fmeasure values are tested using paired t-tests to determine the significance of the classifier's performance from datasets that were not resampled and those that had applied the resampling. The paired t-test is also used to find a combination between the classifier and the resampling that gives significant results. This study obtained two results. The first result is that resampling on imbalanced class datasets can substantially affect the classifier's performance more than the classifier's performance from datasets that are not applied the resampling technique. The second result is that combining the Neural Network Algorithm without the resampling provides significance based on the accuracy value. Combining the Neural Network Algorithm with the SMOTE technique provides significant performance based on the amount of precision, recall, and f-measure. T