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Jurnal Computer Science and Information Technology (CoSciTech)
Published by Universitas Muhammadiyah Riau
ISSN : 2723567X     EISSN : 27235661     DOI : https://doi.org/10.37859/coscitech
Core Subject : Science,
Computer Science & IT
Jurnal CoSciTech (Computer Science and Information Technology) merupakan jurnal peer-review yang diterbitkan oleh Program Studi Teknik Informatika, Fakultas Ilmu Komputer, Univeritas Muhammadiyah Riau (UMRI) sejak April tahun 2020. Jurnal CoSciTech terdaftar pada PDII LIPI dengan Nomor ISSN 2723-5661 (Online) dan 2723-567X (Cetak). Jurnal CoSciTech berkomitmen menjadi jurnal nasional terbaik untuk publikasi hasil penelitian yang berkualitas dan menjadi rujukan bagi para peneliti. Jurnal CoSciTech menerbitkan paper secara berkala dua kali setahun yaitu pada bulan April dan Oktober. Semua publikasi di jurnal CoSciTech bersifat terbuka yang memungkinkan artikel tersedia secara bebas online tanpa berlangganan.
Arjuna Subject : Ilmu Komputer (semua kategori) - Aplikasi Ilmu Komputer
Articles 358 Documents
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Optimalisasi Desain Basis Data E-Commerce untuk Menjamin Integritas Data (Studi Kasus: Web E-Commerce Rie.charge) Renanti, Medhanita Dewi; Pratini, Choirun Nisa Putri; Nisrina, Siti Nayla Alikha; Zaki, Farel Muhammad; Luthfiana, Daffa Erdiyan; Baruna A., Mochamad Emil
Computer Science and Information Technology Vol 6 No 3 (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.v6i3.10643

Abstract

E-commerce platforms require accurate data management, but often face the challenge of data redundancy that threatens system integrity. This study aims to design an optimal database for the Rie.charge e-commerce case study. The research method used is the Database Life Cycle (DBLC) approach, with a focus on logical design. This process systematically applies normalization techniques to transform data from Unnormalized Form (UNF), through First Normal Form (1NF) and Second Normal Form (2NF) , to Third Normal Form (3NF). The results show that the 3NF design was successfully achieved, as evidenced by the redundancy rate analysis, which shows a significant and effective reduction in data redundancy. This optimal design has been proven to successfully eliminate data anomalies (insertion, update, deletion) and ultimately ensure data integrity.
The IMPLEMENTASI YOLOV8 NANO PADA SISTEM MONITORING BUDIDAYA JAMUR TIRAM BERBASIS IOT Nopiandi, Andi; Yasin, Fakhriyal Riyandi; Prayoga, Rizki Haddi; Somantri, Somantri; Kharisma, Ivana Lucia
Computer Science and Information Technology Vol 6 No 3 (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.v6i3.10673

Abstract

Oyster mushrooms are one of the agricultural commodities with high economic value and are widely cultivated in Indonesia. However, the conventional process of monitoring their growth is still carried out manually, which requires considerable time and labor while also being prone to errors in decision-making. To address this issue, this study developed an automatic oyster mushroom growth monitoring system using Internet of Things (IoT) and Artificial Intelligence (AI) technologies. The system uses a DHT22 sensor to measure temperature and humidity, a BH1750 sensor to measure light intensity, and an ESP32-CAM module to capture mushroom images. The data is transmitted through the ESP32 and analyzed using Python, while the images are processed by a YOLOv8 Nano model to classify mushroom growth stages into baglog, young mushrooms, and ready-to-harvest mushrooms. The monitoring results are displayed in real time on a dashboard and stored in a MySQL database. The model training results show fairly good performance, with an average precision of 0.69, recall of 0.78, and a mean Average Precision (mAP@0.5) of 0.71. Further testing was conducted on 15 test images for each mushroom stage, and all images were successfully detected according to their actual classes. Additionally, tests conducted on 10 negative images (without mushroom objects) also supported the system’s reliability. The success of the system is further supported by stable network connectivity for data transmission, adequate lighting in the cultivation room during image capture, and automatic adjustment of temperature and humidity according to the mushroom growth phase. This system demonstrates its capability to monitor mushroom growth conditions automatically and accurately, offering a practical solution for supporting more modern and efficient mushroom cultivation practices.
Klasifikasi Citra Penyakit Daun Tomat Menggunakan Metode Convolutional Neural Network (CNN) Dengan Arsitektur VGG-19 Fitri Handayani; Baidarus, Baidarus; Sunanto, Sunanto; Putra, Bayu Anugerah; Anggraini, Chelina; Taufiq, Reny Medikawati
Computer Science and Information Technology Vol 6 No 3 (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.v6i3.10699

Abstract

Tomatoes, known as Solanum lycopersicum in Latin, are a type of horticultural commodity with high economic value in Indonesia.Tomato production can decrease due to leaf diseases that are hard to identify manually because the symptoms of different diseases often appear similar. The purpose of this study is to apply a deep learning-based tomato leaf disease classification system using the Convolutional Neural Network (CNN) VGG-19 architecture. The dataset was obtained from Kaggle and contains 6,600 images of tomato leaves divided into six disease classes and one healthy leaf class. The research stages include preprocessing (resizing, normalization), data augmentation, dataset division (80% training, 20% testing), model training with transfer learning, and fine-tuning for optimization. The evaluation using the confusion matrix and classification report includes accuracy, precision, recall, and F1-score. Test results show that the VGG-19 model achieved 97% accuracy on the test data, with an average precision, recall, and F1-score of 0.97. These findings show that VGG-19 effectively identifies tomato leaf diseases and could be applied in web- or mobile-based detection systems to help farmers with early diagnosis and proper treatment.
Klasifikasi serangan DDoS dengan metode random forest dan teknik class weight pada dataset CICDDoS2019 Mualfah, Desti; Ardiansyah, Rudi; Gunawan, Rahmad
Computer Science and Information Technology Vol 6 No 3 (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.v6i3.10731

Abstract

The rapid advancement of information technology has significantly influenced various aspects of life, including an increasing reliance on network-based services. However, this dependence has also led to the emergence of more complex cybersecurity threats, one of the most prominent being Distributed Denial of Service (DDoS) attacks. These attacks can disrupt service availability by overwhelming target systems with excessive traffic. A major challenge in detecting DDoS attacks lies in the wide variety of attack patterns and the class imbalance that commonly occurs in network traffic datasets. To address these issues, a machine learning–based approach capable of handling complex attack behaviors while compensating for imbalanced data distribution is required. One potential solution is the use of the Random Forest algorithm with class-weight techniques, applied to the CICDDoS2019 dataset. The research procedure includes data collection and exploration, preprocessing steps such as handling missing and infinite values, encoding categorical attributes, and feature normalization. The dataset is then divided into training and testing subsets before being processed by the Random Forest model. Model evaluation is conducted using a confusion matrix along with accuracy, precision, recall, and F1-score metrics. Experimental results show that incorporating class weight significantly improves model performance, achieving an accuracy of 99.98%, precision of 99.98%, recall of 99.97%, and an F1-score of 99.97%. These findings demonstrate that the proposed approach is highly effective for accurately detecting and classifying DDoS attacks.
Perbandingan Kinerja Model GARCH Dan LSTM Dalam Memprediksi Volatilitas Harian IHSG Sitorus, Gabriel; Yolanda, Yolanda Angel lina Sitorus; Gracia, Gracia Domini Simarmata
Computer Science and Information Technology Vol 6 No 3 (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.v6i3.10741

Abstract

This study compares the performance of the Generalized Autoregressive Conditional Heteroskedasticity (GARCH) and Long Short-Term Memory (LSTM) models in predicting daily volatility of the Jakarta Composite Index (JCI) for the 2016–2025 period. Volatility is an important indicator in assessing market risk and uncertainty, so accurate prediction methods are needed by investors, analysts, and policymakers. The JCI closing price data is converted into log returns and processed through cleaning, normalization, and sequence formation stages for modeling purposes. The GARCH(1,1) model is used to capture the nature of volatility clustering through a conditional variance approach, while LSTM is utilized to study non-linear patterns and long-term relationships in time series. The results show that GARCH(1,1) is able to describe volatility patterns in general, but is less responsive to sudden changes in volatility. In contrast, the LSTM model provides superior prediction performance with low prediction errors and high coefficient of determination values. These findings indicate that the deep learning approach is more effective in modeling the volatility dynamics of the Jakarta Composite Index (JCI) than traditional econometric methods, especially under volatile market conditions.   Keywords: JCI Volatility, GARCH, LSTM, Time Series Forecasting, Deep Learning
The Implementation of cloud computing in enhanching the creative industry in lamongan city Atikah Ardlianti, Rana; Andriani, Adelina Zian
Computer Science and Information Technology Vol 6 No 3 (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.v6i3.10773

Abstract

The current pace of science and technology development is accelerating and has become a crucial aspect of societal life. Lamongan is one of the cities in East Java with a variety of scattered Creative Industries. One of the most popular Creative Industries among the public is the F&B Industry. In this study, the F&B business selected is a Lamongan Soto stall (Warung Soto Lamongan). After conducting the research, several issues were found to arise because the services regarding ordering and payment at this Soto stall are still conventional, leading to sub-optimal processes and time-consuming queues. The same also applies to the processes of payment, promotion, and even bookkeeping. The most common and easy-to-implement technology is cloud computing, as this technology is user-friendly and can enhance business efficiency. The purpose of this study is to offer a solution to Creative Industry players so they can grow and compete by implementing a technology-based platform.
Perencanaan SI pada Website Alat Kopi Gaharu Menggunakan Analisis SWOT dan CSF Rafli, Muhammad; Sihombing, Karen Michelle; Handayani, Titis
Computer Science and Information Technology Vol 6 No 3 (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.v6i3.10799

Abstract

The utilization of information systems and information technology (IS/IT) by MSMEs is still not optimal, especially in supporting website-based marketing and sales. UMKM Gaharu Coffee Tools faces problems such as limited website features, less-than-optimal access speed, and the lack of integration between the ordering and payment systems, which impacts operational effectiveness and business competitiveness. This research aims to design an SI/IT strategic plan for the Gaharu Coffee Tools website to enhance business competitiveness. The research methods used include interviews and observations, which were analyzed using the SWAT and Critical Success Factors (CSF) methods. The results of the SWAT analysis show a difference of 1.38 between strengths and weaknesses and a difference of 1.10 between opportunities and threats, placing the website in the aggressive (SO) quadrant. The CSF analysis yielded three main priorities: capitalizing on the trend of home brewing, expanding the online market internationally, and managing a structured product catalog. In conclusion, targeted website development aligned with SI/IT priorities has the potential to enhance user experience, expand market reach, and sustainably strengthen the competitiveness of MSMEs.
Analisis Penerapan Algoritma Random Forest Dalam Klasifikasi Prakiraan Cuaca Saputra, Deny Saputra; Pangestika, Menur Wahyu; Octariadi, Barry Ceasar
Computer Science and Information Technology Vol 6 No 3 (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.v6i3.10846

Abstract

Weather plays an important role in various aspects of life, such as agriculture and transportation. However, weather prediction remains challenging because it is influenced by many complex factors. Extreme weather events, such as storms and floods, can cause significant losses, making accurate weather forecast classification systems essential. This study applies the Random Forest algorithm to improve prediction accuracy and optimizes it using Grid Search Cross Validation. The method used is CRISP-DM, consisting of six main stages. The data were obtained from the Meteorological, Climatological, and Geophysical Agency (BMKG), containing features such as temperature, humidity, wind speed, cloud cover, visibility, and wind direction, with the labels Weather Condition and Region Name serving as indicators of the classified weather category and location. The final evaluation uses a confusion matrix, yielding an accuracy of 98.84% on the training data and 95.33% on the testing data, demonstrating stable performance and strong generalization capability.

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