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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 CIRCUIT: Jurnal Ilmiah Pendidikan Teknik Elektro Seminar Nasional Teknologi Informasi Komunikasi dan Industri JITK (Jurnal Ilmu Pengetahuan dan Komputer) ALGORITMA : JURNAL ILMU KOMPUTER DAN INFORMATIKA JURNAL PENDIDIKAN TAMBUSAI 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
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 has the capability to transform the way 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, as well as 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%, while 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: 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.
Pengembangan Media Pembelajaran Virtual Reality Materi Sistem Pencernaan Manusia di SMP Maulana, M.Rizky; Ismanto, Edi; Novalia, Melly
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.v6i2.9696

Abstract

The use of virtual reality (VR) technology is one of the innovative approaches in the development of learning media. This technology is able to present more real and interactive visualizations so that it makes it easier to understand complex materials such as the human digestive system and abstract concepts in science lessons. The use of technology-based media such as VR is important so that teachers and students can keep up with the times and take advantage of technological advances in the teaching and learning process, especially at SMP Muhammadiyah 1 Pekanbaru. This research aims to design and develop virtual reality-based learning media on human digestive system materials for grade VIII junior high school students. The research was conducted using the Research and Development (R&D) method using a 4D model which includes the stages of define, design, develop, and disseminate. The media developed has been validated by media experts and material experts, and tested on students. The results of the study showed excellent quality with a feasibility rate of 98% from media experts, 96% from material experts, and 93% from students. Thus, the virtual reality-based learning media developed was declared valid, very feasible, and effective to support the learning of digestive system materials.
Perbandingan Model Machine Learning Untuk Klasifikasi Deteksi Penyakit Jantung Fatihul Ihsan, Tengku Fawwaz; Ilham Ramadhan; Davie Rizky Akbar; 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.v6i2.9811

Abstract

Heart disease is one of the leading causes of death in the world, so early detection is an important aspect in prevention efforts. This study aims to build a heart disease risk prediction model based on patient clinical data using the Random Forest algorithm. The dataset used consists of 303 data with 13 features such as blood pressure, cholesterol, maximum heart rate, and others, as well as one nested target attribute. The data processing process includes cleaning invalid values ​​such as question marks ('?') which are changed to missing values, and deleting incomplete data to maintain the integrity of the dataset. After going through data exploration and correlation analysis between features, the model is trained using the Random Forest algorithm because of its ability in multiclass classification and resistance to overfitting. The initial evaluation results show that the model has good prediction accuracy with a score reaching 0.89. This study proves that the Random Forest-based machine learning approach is effective in helping the process of systematically identifying heart disease risks, so it has the potential to be a decision support tool in the field of preventive health.
Pemodelan Prediktif Diabetes Menggunakan Pendekatan Multimodel Machine Learning dan Deep Learning Fadli Rahmad Hidayatullah; Afandi Alsyar; Riski Amin Putra; Winson Ardhika Ramadhani; 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.v6i2.9812

Abstract

This study discusses the implementation and evaluation of various machine learning algorithms along with one deep learning model for predicting diabetes based on patient medical data. The dataset underwent Preprocessing steps including categorical feature Encoding, feature scaling, and train-test split. The algorithms compared in this study include Logistic regression, Decision Tree, Random Forest, and K-Nearest Neighbors (KNN). Additionally, a Multilayer Perceptron (MLP) model was developed using Keras to explore a deep learning approach with the use of epochs and batch size. The model performance was evaluated using accuracy, precision, and recall metrics, along with learning curve visualizations to analyze model convergence during training. The evaluation results showed that the Random Forest model achieved the highest accuracy among traditional algorithms, while the MLP provided competitive results with strengths in generalization. Visualization of loss and accuracy per epoch offered deeper insight into model behavior throughout the training process. This study demonstrates that a combination of proper data Preprocessing techniques and appropriate model selection significantly influences prediction accuracy. The findings may serve as an early reference for the development of data-driven medical prediction systems and support computer-assisted clinical decision-making (clinical decision support systems).
Co-Authors Abdul Fadlil Adam Ramadhan Afandi Alsyar Agus Satria Ajeng Safitri Al Rian, Rahmad Ambiyar, Ambiyar Amran, Hasanatul Fu'adah Anton Yudhana Bella, Bella Fitria Sari Celvin Arafat Davie Rizky Akbar Delopinli, Crystian Deprizon, Deprizon Diah Eka Ratna Diva Arifal Adha Dwi Sanggar Wati, Anisa Effendi, Noverta Eka Pandu Cynthia Eka Pandu Cynthia Erik Suanda Handika Fadli Rahmad Hidayatullah Fatihul Ihsan, Tengku Fawwaz Fikri Abdul Jafar Gunawan, Rahmad Habil Maulana Hadhrami Ab Ghani Hadhrami Ab. Ghani Hammam Zaki Harun Mukhtar Herdani, Inka friska Herlandy, Pratama Benny Herman Ilham Ramadhan Januar Al Amien Januar Al Amien Januar Al Amien Khairul Anshari Kitagawa, Kodai Lisman, Muhammad Maulana, M.Rizky Melly Novalia Melly Novalia Melly Novalia, Melly Mohamad, Mohd Saberi Muhammad Cavin Ramadhan Muhammad Ridwansyah Nuraeni, Eneng Nurul Izrin Binti Md Saleh Nurul Izrin Md Saleh Nurul Safira, Natasya Oriana, Larisa Patlan Putra Humala Harahap Pramudya, Muhammad Rayenra Azthi Pratama Benny Herlandi Pratama Benny Herlandy Putri Ramahdani, Anggi Rahmad Al Rian Rahmad Al Rian Rahmad Alrian Rahmad Gunawan Gunawan Rahmadani, Delia Syaf Ramadani, Tasya Remli, Muhammad Akmal Renita Rahmadani Resmi Darni Ridhollah, Farhan Riski Amin Putra Rohima Zalti, Ulfani Rose Darmakusuma, Dinda Safitri, Ajeng Septian Alza Septiawan, Raffi Siti Niah Soni Sri Fitria Retnawaty Sunanto Sunanto Suryadila, Lusi Tri Wahono Vitriani Vitriani Vitriani Vitrian Vitriani, Vitriani Wan Salihin Wong, Khairul Nizar Syazwan Wandi Syahfutra Winson Ardhika Ramadhani Yeeri Badrun