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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Techno.Com: Jurnal Teknologi Informasi TELKOMNIKA (Telecommunication Computing Electronics and Control) Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) Jurnal Pengabdian UntukMu NegeRI RABIT: Jurnal Teknologi dan Sistem Informasi Univrab CIRCUIT: Jurnal Ilmiah Pendidikan Teknik Elektro Seminar Nasional Teknologi Informasi Komunikasi dan Industri JITK (Jurnal Ilmu Pengetahuan dan Komputer) MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer ALGORITMA : JURNAL ILMU KOMPUTER DAN INFORMATIKA IJISTECH (International Journal Of Information System & Technology) EDUMATIC: Jurnal Pendidikan Informatika Jurnal Pengabdian Kepada Masyarakat MEMBANGUN NEGERI Journal of Electronics, Electromedical Engineering, and Medical Informatics Indonesian Journal of Electrical Engineering and Computer Science Computer Science and Information Technologies Journal of Education Informatic Technology and Science Jurasik (Jurnal Riset Sistem Informasi dan Teknik Informatika) IJISTECH Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer) Jurnal Computer Science and Information Technology (CoSciTech) Jurnal Pendidikan dan Teknologi Indonesia SATIN - Sains dan Teknologi Informasi Jurnal Pendidikan Dirgantara
Edi Ismanto
Universitas Muhammadiyah Riau
Religion Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Mathematics Social Sciences Other

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Journal : Jurnal Computer Science and Information Technology (CoSciTech)

 1 
Pendekatan Machine Learning Dengan Menggunakan Algoritma Xgboost (Extreme Gradient Boosting) Untuk Peningkatan Kinerja Klasifikasi Serangan Syn Rahmad Gunawan Gunawan; Erik Suanda Handika; Edi Ismanto
Jurnal CoSciTech (Computer Science and Information Technology) Vol 3 No 3 (2022): 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.v3i3.4356

Abstract

Denial of Service (DoS) adalah salah satu serangan cyber populer yang ditargetkan pada situs web organisasi terkenal dan berpotensi memiliki biaya ekonomi dan waktu yang tinggi. Dalam makalah ini, beberapa metode pembelajaran mesin termasuk model ensemble dan pengklasifikasi deep learning berbasis autoencoder dibandingkan dan disetel menggunakan optimasi Bayesian. Kerangka autoencoder memungkinkan untuk mengekstrak fitur baru dengan memetakan input asli ke ruang baru. Metode tersebut dilatih dan diuji baik untuk klasifikasi biner dan multi-kelas pada kumpulan data Digiturk dan Labris, yang baru-baru ini diperkenalkan untuk mendeteksi berbagai jenis serangan DdoS. Semakin penting koneksi data melalui Internet membuat kebutuhan akan keamanan jaringan data semakin meningkat. Salah satu tools yang penting adalah Intrusion detection systems (IDS). Sistem Deteksi Intrusi (IDS) adalah proses pemantauan lalu lintas jaringan dalam sistem untuk mendeteksi pola dan aktivitas yang mencurigakan yang memungkinkan ada serangan dalam sistem itu. beberapa jenis serangan, yaitu Botnet, UDP, SYN, broadcast, sleep deprivation, dan serangan bertubi-tubi. klasifikasi pertama, hasilnya menunjukkan bahwa baik Precision (PR) dan Recall (RE) adalah 89% untuk Algoritma Random Forest. Akurasi rata-rata (AC) dari model yang kami usulkan adalah 89% yang luar biasa dan cukup baik. Pada klasifikasi kedua, hasilnya menunjukkan bahwa baik Precision (PR) dan Recall (RE)sekitar 90% untuk algoritma XGBoost. Akurasi rata-rata (AC) dari model yang kami sarankan adalah 90% pada dataset CICDDoS2019.
Development of Microsoft Office Virtual Reality (VR) Application with Four-D (4D) Approach Ismanto, Edi; Al Rian, Rahmad; Septian Alza
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.v5i1.6816

Abstract

Virtual Reality (VR) technology has made significant advancements in recent decades. With its evolving potential, VR can transform how we learn, work, play, and interact with the world around us. The development of VR applications for Microsoft Office training holds significant relevance, especially for students of Madrasah Tsanawiyah (MTs) Darul Hikmah Pekanbaru. Microsoft Office training is essential for MTs Darul Hikmah Pekanbaru students as it serves as a practical necessity in preparing them for academic and professional endeavors. One relevant challenge is how to integrate VR technology with appropriate learning methods, such as the Four D (4D) method, to make the training experience more effective and efficient. Therefore, this research aims to identify and address these issues and explore the potential of VR applications with the 4D method to enhance users' practical and intuitive Microsoft Office skills. The development of VR applications for Microsoft Office training using the Four D (4D) method, comprising the Define, Design, Develop, and Disseminate stages, has yielded highly favorable results based on comparisons of measurements from subject matter experts, media experts, and participants. From the measurement and validation results, this VR application has received high feasibility ratings. Subject matter experts rated it 100%, media experts rated it 91.83%, and participants rated it 96% from the VR application trial.
Pengembangan Media Pembelajaran Berbasis Game Edukasi Tema 4 Tentang Bangun Ruang Di Kelas 2 SD Bella, Bella Fitria Sari; Melly Novalia; Edi Ismanto
Computer Science and Information Technology Vol 5 No 2 (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.v5i2.7529

Abstract

Learning media at SD Negeri 164 Pekanbaru City refers to blackboards and books that are already available, but do not yet have effective learning strategies. Lack of student interest in learning in the teaching and learning process which is not in accordance with the current development and needs of students. With increasingly advanced and rapid technological developments, humans can create various tools to carry out activities that support productivity. Like making educational game learning media. This research aims to produce a learning media product for the snakes and ladders educational game theme 4 about building space and to determine the feasibility test, practicality and effectiveness of the educational game snakes and ladders theme 4 about building spaces in class 2 of SD Negeri 164 Pekanbaru City. The research method used is the Research and Development (R&D) research method with the ADDIE (Analysis, Design, Development, Implementation, and Evaluation) development model. In the feasibility test, an assessment is carried out by two experts, namely a media expert and a material expert to determine the suitability of the product. Where the results of the feasibility test for media experts got a result of 82% in the "Very Good" category and for material experts a result of 93% in the "Very Good" category. In the practicality test, it was carried out by teachers and students, where the teacher got a practicality result of 93% in the "Very Practical" category and the students got a result of 80% in the "Practical" category. The effectiveness test was carried out by giving pretest and post-test questions, where the pretest got a result of 64% and the post-test got a result of 92.2%. So this learning media is feasible, practical and effective to use to support teaching and learning activities.
Analisis Perbandingan Model Fully Connected Neural Networks (FCNN) dan TabNet Untuk Klasifikasi Perawatan Pasien Pada Data Tabular Ismanto, Edi; Abdul Fadlil; Anton Yudhana
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

Abstract

Electronic Health Records (EHR) store tabular data that is rich in information and play a critical role in supporting decision-making within the healthcare field, particularly for patient care classification. This study evaluates the performance of two artificial intelligence models, Fully Connected Neural Networks (FCNN) and TabNet, in processing tabular data for patient care classification tasks. The findings reveal that both models demonstrate strong performance, with TabNet showing a slight advantage. TabNet achieves an accuracy of 0.74, marginally surpassing FCNN's 0.73. Furthermore, TabNet excels in precision (0.74 vs. 0.72), recall (0.72 vs. 0.71), and F1-Score (0.73 vs. 0.71), highlighting its greater reliability in minimizing false positives and accurately detecting positive cases with a better balance between precision and recall. With its architecture specifically tailored for tabular data and its capacity for direct interpretability, TabNet offers enhanced efficiency and ease of implementation compared to FCNN, which demands more complex data preprocessing. For future research, it is suggested to employ larger and more diverse datasets, explore data with higher feature complexity, and conduct comprehensive hyperparameter tuning to further improve the performance of both models.
Peramalan Harga Emas Berbasis Time Series Menggunakan Arsitektur LSTM Deep Learning Diva Arifal Adha; Adam Ramadhan; Habil Maulana; Patlan Putra Humala Harahap; Edi Ismanto
Computer Science and Information Technology Vol 6 No 2 (2025): 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.v6i2.9980

Abstract

Gold is one of the most influential commodities in the global economy. Its high price volatility poses a significant challenge for investors, financial analysts, and policymakers in formulating effective strategies and making accurate decisions. Therefore, an accurate prediction method is needed to forecast future gold price movements. This study aims to forecast gold prices using a deep learning approach with the Long Short-Term Memory (LSTM) algorithm. The LSTM model is capable of learning long-term dependencies in time-series data, making it highly suitable for modeling complex and dynamic financial data. The data used in this study consists of daily historical gold prices obtained from reliable sources. A preprocessing phase was carried out to clean and normalize the data before training the model. Furthermore, this study compares the performance of the LSTM model with the Multilayer Perceptron (MLP) model to examine differences in prediction accuracy. Evaluation metrics such as Mean Squared Error (MSE) and Root Mean Squared Error (RMSE) were used to assess model performance. The results show that the LSTM model provides more accurate predictions compared to MLP, with lower error values and better model stability. In conclusion, the deep learning approach, particularly the LSTM model, can serve as an effective alternative for gold price forecasting and support data-driven decision-making in the financial sector.
Co-Authors Abdul Fadlil Adam Ramadhan Agus Satria Ajeng Safitri Al Rian, Rahmad Ambiyar, Ambiyar Amelia Agustina Amran, Hasanatul Fu'adah Anton Yudhana Bella, Bella Fitria Sari Deprizon, Deprizon Diah Eka Ratna Diva Arifal Adha Dwi Sanggar Wati, Anisa Effendi, Noverta Eka Pandu Cynthia Eka Pandu Cynthia Eka Pandu Cynthia Erik Suanda Handika Gunawan, Rahmad Habil Maulana Hadhrami Ab Ghani Hadhrami Ab. Ghani Hammam Zaki Harun Mukhtar Herdani, Inka friska Herlandy, Pratama Benny Herman Januar Al Amien Januar Al Amien Januar Al Amien Khairul Anshari Kitagawa, Kodai Lisman, Muhammad Melly Novalia Melly Novalia Melly Novalia, Melly Mohamad, Mohd Saberi Nuraeni, Eneng Nurul Izrin Binti Md Saleh Nurul Izrin Md Saleh Nurul Safira, Natasya Oriana, Larisa Patlan Putra Humala Harahap Pratama Benny Herlandi Pratama Benny Herlandy Putri Ramahdani, Anggi Rahmad Al Rian Rahmad Al Rian Rahmad Alrian Rahmad Gunawan Gunawan Remli, Muhammad Akmal Renita Rahmadani Resmi Darni Ridhollah, Farhan Septian Alza Siti Niah Soni Sri Fitria Retnawaty Sunanto Sunanto Suryadila, Lusi Tri Wahono Vitriani Vitriani Vitriani Vitrian Wan Salihin Wong, Khairul Nizar Syazwan Wandi Syahfutra Yeeri Badrun