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All Journal MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer Jurnal Bumigora Information Technology (BITe) Jurnal: International Journal of Engineering and Computer Science Applications (IJECSA)
Mayadi Mayadi
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering

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Developing Application in Anticipating DDoS Attacks on Server Computer Machines Anthony Anggrawan; Raisul Azhar; Bambang Krismono Triwijoyo; Mayadi Mayadi
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol 20 No 2 (2021)
Publisher : LPPM Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (875.407 KB) | DOI: 10.30812/matrik.v20i2.410

Abstract

The use of server computer machines in companies is primarily a web hosting server that is very easy to experience threats, especially external security threats such as attempts to infiltrate, hacking, viruses, and other malicious attacks. Having a secure server is indispensable for working online and especially if involved in business-related network transactions. The Server's realization to be safe from threats is to protect the server machine's security on the hardware and software side and pay attention to network security that goes to the server machine. Generally, firewall applications on router devices have configuration limitations in securing the network, namely non-integrated applications. In other words, it is necessary to manage the perfect firewall configuration to anticipate Distributed Daniel attacks of Service (DDoS) attacks. Therefore, this study aims to integrate existing firewall applications for router devices into an integrated program to secure the network. The methodology used is the Network Development Life Cycle (NDLC). The research results on this developed application program can overcome DDoS attacks without setting up a firewall on the router device and can automatically monitor DDoS attack activities from outside the Server. Securing servers from DDoS attacks without setting up a firewall on the router device and automating the monitoring of DDoS attack activity from outside the Server are the novelties of this study that have not been available in previous studies.
Sistem Pendukung Keputusan Seleksi Penerimaan Karyawan dengan Metode AHP dan Pembobotan Fuzzy Apriani Apriani; I Gde Dharos Santana Dharma; Mayadi Mayadi; Ni Gusti Ayu Dasriani
Jurnal Bumigora Information Technology (BITe) Vol 4 No 1 (2022)
Publisher : Prodi Ilmu Komputer Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/bite.v4i1.1915

Abstract

Proses perekrutan karwayan merupakan salah satu kegiatan rutin yang dilakukan oleh sebuah perusahaan dalam memenuhi salah satu target dan capaiannya.Oleh karena itu proses perekrutan yang obyektif, transparan serta profesional harus dilakukan demi pemenuhan sumber daya manusia yang sesuai dengan kriteria yang dibutuhkan.Namun hal ini terkadang tidak sesuai dengan harapan sehingga perusahaan merasa kesulitan dalam menempatkan karyawan sesuai yang dibutuhkan.Penelitian ini menggunkan metode Analytical Hierarchy Process dengan tahapan analisis kebutuhan,desain sistem,implementsi dan pengujian. Jumlah data yang digunakan pada penelitian ini adalah 140 data. Dari data tersebut dilakukan perhitungan oleh sistem dan didapatkan 115 data penerima beasiswa dengan tingkat akurasi sebesar 94,07%. Kebaharuan dari sistem ini adalah sudah menggunakan sistem yang terintegrasi dengan database, sehingga data akan menjadi lebih aman, proses perangkingan juga akan menjadi lebih cepat karena dengan adanya sistem ini pengambil keputusan akan langsung mendapatkan hasil perangkingan dari sistem, dan di sistem ini juga menggunakan 2 metode yang berbeda sehingga hasil seleksi dapat lebih akurat. Tujuan penelitian menghasilkan sebuah Sistem Penerimaan Karyawan dengan menerapkan metode AHP dan Fuzzy di dalamnya sehingga memudahkan pihak HRD dalam pengambilan keputusan seleksi karyawan,mempercepat waktu dalam pengambilan keputusan dan menilai calaon-calon yang akan direkrut menjadi lebih akurat dan efisien
Feature Extraction in Eye Images Using Convolutional Neural Network to Determine Cataract Disease Fitra Rizki Ramdhani; Khasnur Hidjah; Muhammad Zulfikri; Hairani Hairani; Mayadi Mayadi; Ni Gusti ayu Dasriani; Juvinal Ximenes Guterres
International Journal of Engineering and Computer Science Applications (IJECSA) Vol. 4 No. 2 (2025): September 2025
Publisher : Universitas Bumigora Mataram-Lombok

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/ijecsa.v4i2.5064

Abstract

The eye is one of the vital human senses and serves as the main organ for vision. One of the visual impairments that requires special attention is blindness, and cataracts are a major cause of it. A cataract is a condition in which the eye’s lens becomes cloudy due to changes in the lens fibers or materials inside the capsule. This cloudiness blocks light from entering the eye and reaching the retina, significantly interfering with vision. Early detection of cataracts is essential to prevent blindness. An efficient image-based classification model is needed for cataract detection. This study aims to test the Convolutional Neural Network (CNN) model for early cataract detection by exploring the use of several optimization algorithms: Adaptive Moment Estimation (Adam), Root Mean Square Propagation (RMSprop), Adaptive Gradient Algorithm (AdaGrad), and Stochastic Gradient Descent (SGD). The research method follows an experimental approach, where eye image datasets are trained using the same CNN architecture but with different parameter configurations. The results show that the Adam optimizer, with a data split of 70% for training, 15% for validation, and 15% for testing over 50 epochs, produced the best results, achieving accuracies of 94%, 93%, and 93%, respectively. Other optimizers performed reasonably well but could not match Adam's stability and accuracy. The implication of this research is that the choice of optimizer and hyperparameter configuration plays a crucial role in improving the performance of image-based cataract detection models.
Co-Authors Anthony Anggrawan Apriani Apriani Ayu Dasriani, Ni Gusti Azhar, Raisul Fitra Rizki Ramdhani Hairani Hairani I Gde Dharos Santana Dharma Juvinal Ximenes Guterres Khasnur Hidjah Muhammad Zulfikri Triwijoyo, Bambang Krismono