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All Journal Jurnal Computer Science and Information Technology (CoSciTech) Jurnal Pengabdian Sosial
Widiarto, Tasya Evrillia
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Religion Computer Science & IT Economics, Econometrics & Finance Education Law, Crime, Criminology & Criminal Justice Public Health Social Sciences

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Penerapan LACP dalam Meningkatkan Redundansi dan Throughput Jaringan Ethernet di PT Sawarganet Nugraha, Nugraha; Widiarto, Tasya Evrillia
Jurnal Pengabdian Sosial Vol. 2 No. 9 (2025): Juli
Publisher : PT. Amirul Bangun Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59837/rfjhqn90

Abstract

Kegiatan pengabdian ini bertujuan untuk meningkatkan keandalan dan efisiensi jaringan pada PT Sawarganet melalui penerapan teknologi Link Aggregation Control Protocol (LACP). Permasalahan utama yang dihadapi mitra adalah belum adanya sistem cadangan yang efektif ketika terjadi gangguan pada koneksi utama jaringan. Untuk menjawab permasalahan tersebut, dilakukan pendampingan teknis berupa konfigurasi dua antarmuka fisik menjadi satu antarmuka logis menggunakan metode Link Aggregation dengan protokol LACP. Simulasi gangguan dilakukan secara terencana, dan proses pemantauan jaringan menggunakan perangkat lunak Cacti dan SmokePing dilakukan selama 24 jam penuh untuk melihat respons sistem terhadap kegagalan koneksi. Hasil dari kegiatan ini menunjukkan bahwa sistem mampu mengalihkan trafik secara otomatis ke jalur cadangan tanpa mengganggu layanan serta menjaga kestabilan throughput. Kegiatan ini tidak hanya memberikan solusi teknis kepada mitra, tetapi juga meningkatkan pemahaman dan keterampilan tim teknis dalam mengelola infrastruktur jaringan yang lebih tangguh dan andal.
Implementasi Vision Transformer untuk Klasifikasi Jenis Jerawat pada Citra Wajah Berbasis Web Widiarto, Tasya Evrillia; Imam Sanjaya; Alamsyah, Zaenal
Computer Science and Information Technology Vol 7 No 2 (2026): Jurnal Computer Science and Information Technology (CoSciTech)
Publisher : Universitas Muhammadiyah Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37859/coscitech.v7i2.11982

Abstract

Acne Vulgaris is a widespread skin condition that can affect not only physical skin health but also a person's self-confidence. Because manually distinguishing acne types still demands specialized expertise, an artificial-intelligence-based approach is needed to support this classification task. This research applies a Vision Transformer (ViT-B/16) architecture to categorize acne lesions from facial images into four groups: normal, papule, pustule, and nodule. A total of 4,000 images were used as the dataset and processed through a transfer-learning strategy initialized with pre-trained ImageNet weights. The model was trained across 20 epochs with the Adam optimizer and a learning rate of 0.001. Testing showed that the model reached an accuracy of 86%. The resulting model was then embedded into a web-based application to streamline the acne identification workflow. These outcomes confirm that Vision Transformer can classify acne types reliably and holds promise as an automated early-screening tool for facial skin conditions.
Co-Authors Imam Sanjaya Nugraha Nugraha Zaenal Alamsyah