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All Journal Kesatria : Jurnal Penerapan Sistem Informasi (Komputer dan Manajemen)
Y Yuhandri
Universitas Putra Indonesia “YPTK” Padang, Indonesia
Computer Science & IT Control & Systems Engineering

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Metode Tracking 3D Image dalam Teknologi Augmanted Reality untuk Pembelajaran Animasi Sekolah Lanjutan Tingkat Atas Hadrila P A; Y Yuhandri; S Sumijan
Kesatria : Jurnal Penerapan Sistem Informasi (Komputer dan Manajemen) Vol 5, No 2 (2024): Edisi April
Publisher : LPPM STIKOM Tunas Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/kesatria.v5i2.362

Abstract

Augmented reality is a technique that combines two-dimensional and three-dimensional virtual objects into a real three-dimensional scope and then projects these virtual objects in real time. The use of markers in this application is chosen apart from being suitable for implementation as learning and also tends to be fast in terms of reading on the process of the emergence of 3-dimensional objects in visual Form. This research aims to be a learning medium for the 12 Principles of Animation at the high school level. The method used in this research uses the 3D Image tracking method, which has four stages, namely 3 Dimensional Model Development, Marker development, Model implementation into development tools, Augmanted Reality Application Implementation, Dataset consists of 12, 3 Dimensional images representing each of the 12 Principles of Animation. This research produces animated Augmanted Reality digital learning media that can attract student interest and make students understand more quickly. Reference: The use of animated Augmanted Reality learning media makes learning active, creative, effective and fun.
Penerapan Jaringan Syaraf Tiruan Dengan Algoritma Backpropagation Untuk Memprediksi Kunjungan Poliklinik (Studi Kasus Di Rumah Sakit Otak Dr. Drs. M. Hatta Bukittinggi) Eka Ramadhani Putra; Gunadi Widi Nurcahyo; Y Yuhandri
Kesatria : Jurnal Penerapan Sistem Informasi (Komputer dan Manajemen) Vol 5, No 2 (2024): Edisi April
Publisher : LPPM STIKOM Tunas Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/kesatria.v5i2.354

Abstract

Artificial Neural Networks (ANN) are computational models inspired by the structure and function of biological neural networks. ANN can model and learn complex patterns in data. The Backpropagation algorithm is a training algorithm used to optimize weights and biases in ANN.. Use of Python Applications is a popular form of computing used in the fields of science and engineering, including in the development and implementation of ANN. Python provides powerful library for building, training, and deploying ANNs. This research aims to have the ANN Backpropagation Algorithm train data using previously collected polyclinic visit data so that the ANN can learn to predict the burden of polyclinic visits in the future. The method in this research uses the Backpropagation Algorithm. This method has six stages, namely data input, normalization, training, testing, calculating test accuracy, and prediction. The dataset processed in this research comes from the annual report of Rumah Sakit Otak Dr. Drs. M. Hatta Bukittinggi from 2020 to 2022. The dataset consists of 36 months of visits to the polyclinic. The results of this research use the 3-10-1 pattern and can identify or calculate predictions for the next 5 months, 2547 people, 2506 people, 2463 people, 2482 people, and 2495 people. The percentage of predictions for polyclinic patient visits with an accuracy level of computing time requiring 0.001 seconds, an average error of 8.794%, and an average accuracy of 91.706%. Therefore, this research can be a reference in predicting polyclinic patient visits in the future so that it can be a consideration for hospital management.
Teknologi Blockchain dalam Keamanan Sertifikat Menggunakan Smart Contracts dan Distributed Ledger pada Platfrom Edutech Seni Oknora Firza; Y Yuhandri; S Sumijan
Kesatria : Jurnal Penerapan Sistem Informasi (Komputer dan Manajemen) Vol 5, No 2 (2024): Edisi April
Publisher : LPPM STIKOM Tunas Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/kesatria.v5i2.368

Abstract

The ever-increasing development of the digital environment means that educational certificates are often vulnerable to forgery, manipulation, or even loss of integrity. Blockchain is a technology that allows for a distributed database that can only be accessed by a certain number of computer network nodes. Distributed Ledger Technology (DLT) is a system that captures and distributes data over multiple data stores (Ledgers), where each storage has identical data records. Smart Contracts is a Blockchain protocol that allows developers to create and execute financial agreement codes on the Blockchain. This contract will be activated by all parties involved. This research aims to improve the security of certificate authenticity on the edutech platform at Inatechno. The methods applied are Smart Contract and Distributed Ledger. The dataset processed in this research comes from Inatechno. The dataset consists of 48 data on certificate participants who have taken part in training activities at Inatechno. The results of this research are that Blockchain technology can increase certificate security on the Edutech platform. The resulting system can automate the verification process and reduce the risk of counterfeiting. Therefore, this research can be a reference that Blockchain technology using Smart Contracts and Distributed Ledger can be an effective solution in increasing certificate security on the Edutech platform. This implementation can provide significant benefits in supporting the need for security and integrity of certificate data, opening up the potential for further development in the context of digital education.
Analisis Perbandingan Optimalisasi Port Knocking Dan Honeypot dengan Iptables Pada Server Untuk Keamanan Jaringan Anjun Dermawan; Y Yuhandri; S Sumijan
Kesatria : Jurnal Penerapan Sistem Informasi (Komputer dan Manajemen) Vol 5, No 2 (2024): Edisi April
Publisher : LPPM STIKOM Tunas Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/kesatria.v5i2.364

Abstract

Computer network systems are designed to share resources together, so that the security of resources on the server must be maintained and the resources used must be optimized. The aim of this research is to analyze the comparative level of optimization of Port knocking and Honeypot using the IPTables method for network security on servers with different CPU and memory resources. The security methods used in this research are Port knocking, Honeypot and IPTables. The data used includes ports that were successfully attacked as well as resource usage before and after IPTables implementation on a server with 2 CPU resources and 1507284KiB memory obtained from previous research. The results of this research show that 80% of ports cannot be attacked while 20% of ports, namely port 22, are designed to be attacked. The server CPU and Memory resource usage graph shows a decrease after implementing IPTables from Denial of Service (DoS) and Brute force testing. On a server with 1 CPU and 1015852KiB of memory resources, CPU usage decreased by 36%, and memory usage decreased by 41%. Meanwhile, on a server with 4 CPU resources and 6036624 KiB of memory, CPU usage decreased by 41%, and memory usage decreased by 46%. This shows increased effectiveness compared to using just the Port knocking and Honeypot methods. It is hoped that this research can be a guide in measuring server optimization in overcoming Denial of Service (DoS) and Brute force attacks
Penerapan Teorema Bayes Pada Sistem Pakar Untuk Mendeteksi Dini Penyakit Tuberkulosis (Studi Kasus Di Rs. Tentara Dr. Reksodiwiryo Padang) Fadil Idensia; Y Yuhandri; Billy Hendrik
Kesatria : Jurnal Penerapan Sistem Informasi (Komputer dan Manajemen) Vol 5, No 2 (2024): Edisi April
Publisher : LPPM STIKOM Tunas Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/kesatria.v5i2.369

Abstract

Tuberculosis (TB) is an infectious disease that is still a global health problem, including in Indonesia. Early detection of this disease is crucial for effective treatment. In order to improve early detection of TB, this research aims to apply the Bayes Theorem method to the development of an expert system. The case study was conducted at Dr. Reksodiwiryo, Padang, where the percentage of Tuberculosis based on the method has been identified. The Bayes Theorem method is implemented in an expert system to provide early diagnosis to patients suspected of having TB. Expert system testing was carried out to evaluate the accuracy of the diagnosis, with an average calculation result using Bayes' theorem of 80%. The results of this research indicate that the application of Bayes' Theorem in an expert system can be an effective tool in early detection of Tuberculosis. The practical implication of this research is to increase the capabilities of the Dr. Army Hospital. Reksodiwiryo Padang in treating TB early and accurately, as well as contributing to efforts to prevent and control this disease more efficiently.
Co-Authors Anjun Dermawan Billy Hendrik Eka Ramadhani Putra Fadil Idensia Gunadi Widi Nurcahyo Hadrila P A S Sumijan Seni Oknora Firza