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All Journal Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Jurnal Elektronika Listrik dan Teknologi Informasi Terapan bit-Tech Jurnal Computer Science and Information Technology (CoSciTech) Journal of Computer Scine and Information Technology Jurnal Komtekinfo RJOCS (Riau Journal of Computer Science) CSRID
Yuhandri
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Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Energy Engineering Materials Science & Nanotechnology

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Journal : Jurnal Computer Science and Information Technology (CoSciTech)

 1 
Audit Keamanan Website Menggunakan Acunetix Web Vulnerability (Studi Kasus Di SMK Muhammadiyah 3 Terpadu Pekanbaru) Supriyanto, Boby; Sumijan; Yuhandri
Computer Science and Information Technology Vol 5 No 1 (2024): 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.v5i1.6705

Abstract

Perkembangan teknologi informasi berkembang pesat seiring dengan pertumbuhan penggunanya. Contoh dari perkembangan teknologi adalah penggunaan website untuk mendukung kegiatan pembelajaran. Website merupakan kumpulan halaman web yang dapat diakses secara publik. Website dapat terdiri dari teks, gambar, video, dan media suara lainnya. Namun dengan berkembangnya suatu teknologi, maka perkembangan kerentanan atau serangan terhadap teknologi tersebut juga bertambah. Berdasarkan laporan tahunan monitoring keamanan siber tahun 2021 oleh Badan Siber dan Sandi Negara (BSSN), terdapat lebih dari 1,6 miliar serangan siber yang telah terjadi di Indonesia. Penelitian ini akan menggunakan Acunetix Web Vulnerability Scanner (WVS) untuk mengaudit keamanan website SMK Muhammadiyah 2 Terpadu Pekanbaru (SMK MUTI). Penelitian ini akan mengkaji kelemahan keamanan website SMK MUTI dan membahas bagaimana Acunetix Web Vulnerability dapat membantu dalam meningkatkan tingkat keamanan website tersebut. Metode Vulnerability Assessment (VA) yang digunakan adalah analisis deskriptif, yaitu data yang diperoleh disajikan dalam bentuk tabel, sehingga memungkinkan untuk memperjelas hasil analisis yang dilakukan dalam meng-audit. Berdasarkan data yang diperoleh dari hasil scanning iterasi 1 yang dilakukan, website SMK MUTI berada pada level ancaman 3 tergolong tinggi dengan ditemukan 192 peringatan atau kerentanan, dimana 2 diantaranya berada pada level tinggi dan 11 berada pada level sedang. Berdasarkan audit, dilakukan perbaikan dan pengujian pada penelitian di situs SMK MUTI ini, hasil yang telah dilakukan tingkat ancaman yang dicapai berada pada level 1, dimana pada level tinggi, jumlah kerentanan menjadi 0 dan tingkat dukungan juga menjadi 0, sehingga dapat disimpulkan bahwa situs SMK MUTI saat ini dengan status level 1 dapat bebas dari kerentanan keamanan.
Vision Transformer untuk Identifikasi 15 Variasi Citra Ikan Koi Uthama, Rayhan; Yuhandri; Billy Hendrik
Computer Science and Information Technology Vol 5 No 1 (2024): 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.v5i1.6711

Abstract

This research aims to classify various types of koi fish using Vision Transformer (ViT). There is previous research [1] using Support Vector Machine (SVM) as a classifier to identify 15 types of koi fish with training and testing datasets respectively of 1200 and 300 images. This research was continued by research [2] which implemented a Convolutional Neural Network (CNN) as a classifier to identify 15 types of koi fish with the same amount dataset. As a result, the research achieved a classification accuracy rate of 84%. Although the accuracy obtained from using CNN is quite high, there is still room for improvement in classification accuracy. Overcoming obstacles such as limitations in classification accuracy in previous studies and further exploration of the use of new algorithms and techniques, this study proposes a ViT architecture to improve accuracy in Koi fish classification. ViT is a deep learning algorithm adopted from the Transformer algorithm which works by relying on self-attention mechanism tasks. Because the power of data representation is better than other deep learning algorithms including CNN, researchers have applied this Transformer task in the field of computer vision, one of the results of this application is ViT. This study was designed using class and number datasets retained from two previous studies. Meanwhile, the koi fish image dataset used in this research was collected from the internet and has been validated. The implementation of ViT as a classifier in koi classification in this research resulted in an accuracy level that reached an average of 89% in all classes of test data.
Penerapan Convolutional Neural Network pada Klasifikasi Citra Pola Kain Tenun Melayu Mukhlis Santoso; Sarjon Defit; Yuhandri
Computer Science and Information Technology Vol 5 No 1 (2024): 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.v5i1.6713

Abstract

The use of electronic computerized media is growing along with advances in hardware and software as an analytical tool with various algorithms and methods for classifying and measuring objects in various contexts. This progress aims to overcome the weaknesses that exist in conventional methods used in the identification process. The identification process can be applied to various objects, one of which is an image object. An image is a visual representation of an object formed through a combination of RGB (red, green, blue) colors. RGB color components or features have a range of values from 0 to 255 in an image. Weaving is a type of fabric that is specially made with distinctive motifs. Malay weaving motifs have a lot of diversity, this diversity makes it difficult to distinguish the motifs of these fabrics.This study aims to recognize and distinguish the pattern of Malay woven fabric. The method used in this research is Convolutional Neural Network (CNN). The CNN method has several stages, namely Convolution Layer, Pooling Layer, Rectifed Linear Unit (ReLU) Function, Fully-Connected Layer, Transfer Learning, Optimizer and Accuracy. The dataset used in this research is sourced from Tenun Putri Mas Bengkalis. The dataset used consists of 1000 images of weaving motifs which are divided into 80% training data and 20% testing data, from the existing dataset divided into three categories of weaving motifs namely pucuk rebung, elbow clouds and elbow keluang. The results in this study are considered good because they produce accuracy with a result of 95% with an epoch value of 15. From the results of good enough accuracy, it is hoped that it can help the community in recognizing Malay weaving motifs.
Analisis Data Forensik Pada Rekaman CCTV Menggunakan Metode National Institute Of Standard Techology (NIST) Ilham Asy'ari; Yuhandri; Sumijan
Computer Science and Information Technology Vol 5 No 3 (2024): 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.v5i3.7779

Abstract

CCTV (Closed-Circuit Television) recordings have become one of the important instruments in monitoring and securing various places such as companies, commercial buildings, public institutions, and households. CCTV recordings are often vital evidence in investigating crimes, accidents, or other incidents. However, in addition to the visual content stored in CCTV recordings, metadata also plays an essential role in forensic analysis and event reconstruction. The NIST method has developed several techniques and guidelines for forensic metadata analysis on CCTV recordings. This research aims to explore and apply the forensic metadata analysis methods recommended by NIST (National Institute of Standards and Technology) in the context of CCTV recordings. By involving forensic data analysis techniques and information security principles, this study will delve into the potential of metadata analysis in supporting criminal investigations, event reconstructions, and meeting the security standards established by NIST. This research is crucial in the context of digital security and modern forensic investigations. The outcome of applying the NIST methods in forensic data analysis of CCTV recordings is the preparation of an official report derived from the stages outlined in the NIST method, so that the report can serve as a reference in court, and the authenticity of the digital evidence can be validated. By applying the NIST method in forensic data analysis of CCTV recordings, the case handling process becomes structured and adheres to procedures, with a valid report ensuring the integrity of the digital evidence.
Co-Authors Aal, Defrizal Ade Dwi Dayani Akbari Wafridh Auriga, Wira Billy Hendrik Dian Maharani, Dian Edwar, Eggy Febyanti Eriyanto, Joko Fernando Ramadhan Fikri, Dzaki Al Gunadi Widi Nurcahyo Gunadi Widi Nurcahyo, Gunadi Hardianto, Romi Harkamsyah Andrianof Hendrik, Billy Idir Fitriyanto Idir Ilham Asy'ari Indah Dwi Putri Karseno, Doni Mardison Meiditra, Irzon Mey Yuki Lestari Mukhlis Santoso Pulungan, Akhiruddin Rahmansyah, Rizky Ramadhan, Prestian Ramadhanu, Agung Resnawita Riati, Itin Ridho, Ridho Afwan Rita Sari Rita Sari Rusydi, Rezki Sahri, Alfi Sajida, Mayang Sari, Fitri P. Sarjon Defit Siregar, Diffri Soeheri Soeheri Sonia Indhira Sovia, Rini Sumijan Supriyanto, Boby Syafri Arlis Syaputra, Eka B. Tri Agusti Farma Uthama, Rayhan Veri, Jhon Very, Jhon Wahyu Saptha Negoro Wahyu Saptha Negoro Yanti, Salma Nofri Yuda, Fitra Yuda