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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 11 Documents
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Search results for , issue "Vol 6 No 1 (2025): Jurnal Computer Science and Information Technology (CoSciTech)" : 11 Documents clear
Analisis Data Mining Untuk Deteksi Diabetes Mellitus Menggunakan Naïve Bayes Yusril Haza Mahendra; Ririen Kusumawati; Imamudin
Computer Science and Information Technology Vol 6 No 1 (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.v6i1.7954

Abstract

Diabetes mellitus is a chronic disease with a continuously increasing global prevalence. it is characterized by high blood glucose levels due to the body's inability to produce or effectively use insulin. the widespread impact on individuals and communities underscores the importance of early detection and proper management. In the digital era, data mining analysis has become a crucial tool in healthcare, enabling the exploration and analysis of health data on a large scale to identify patterns and trends that are difficult to detect manually. in the context of detecting diabetes mellitus, data mining holds great potential for predictive model development. one of the algorithms used is naïve bayes. This study analyzes naïve bayes classification for early symptoms of diabetes mellitus, with the aim of enhancing understanding of risk factors and developing early detection tools. The research findings indicate that naïve bayes has the highest accuracy of 78% with the application of missing value imputation mean. It is hoped that this research will enhance efforts in preventing and managing diabetes mellitus, as well as reducing the burden on individuals and communities as a whole.
Deep Learning Deteksi Dan Klasifikasi Penyakit Daun Tomat Menggunakan ResNet-50 Raynold, Raynold; Alva Hendi Muhammad
Computer Science and Information Technology Vol 6 No 1 (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.v6i1.8501

Abstract

Tomatoes are a popular food around the world, especially in Indonesia. Many tomato farmers experience crop failure due to lack of understanding and delays in recognizing diseases that attack their plants. The purpose of this study is to identify and assess the types of diseases on tomato leaves based on trends, data sources, methodologies, and characteristics used in detecting diseases on tomato leaves. The dataset used is sourced from kaggle consisting of 10 classes and contains a total of 11,000 images. The data division used consists of 90% training data and 10% test data. The augmentation and fine-tuning process is carried out to reduce over fitting. This research uses the ResNet-50 algorithm to detect and classify diseases on tomato leaves. ResNet will compare leaf images to classify them with 10 disease classes in the dataset. From the ResNet method, the average accuracy value is 93%. This shows that the ResNet-50 method for image classification can produce accurate accuracy in solving real-world problems
Pendekatan Transfer Learning untuk Klasifikasi Penyakit Mata Menggunakan Citra dengan CNN InceptionV3 Gunawan, Rahmad; Fathurrahman, Raihan; Widyaningrum, Amelia Ismania Sita; Issandra, Febri; Abdurachman, Muhammad Andhika; Putra, Yogi Ernanda; Naufal
Computer Science and Information Technology Vol 6 No 1 (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.v6i1.8509

Abstract

Eye diseases are a leading cause of vision impairment and blindness worldwide. Therefore, detection of eye diseases is crucial in the prevention of blindness. This study develops an eye disease classification model based on Convolutional Neural Network (CNN) using Transfer Learning with InceptionV3. The dataset consists of 1559 images, divided into 1249 training images and 310 validation images, covering 8 eye disease classes. The model was trained using 40 epochs with the Adam optimizer. Evaluation results show a validation accuracy of 81.29%. While the model performed well, some classes, such as hordeolum, showed lower accuracy, indicating areas that need further improvement. This study confirms that Transfer Learning with InceptionV3 is an effective approach for eye disease classification.
Pengaruh Penerapan Algoritma Pemrograman Dalam Dunia Pekerjaan (Studi Kasus: Metode Deep Learning) Niken Rahma Diasri; Arini Winur Baeti; Ary Prabowo
Computer Science and Information Technology Vol 6 No 1 (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.v6i1.8531

Abstract

Algoritma, khususnya yang berbasis pada pendekatan deep learning, telah memberikan dampak signifikan dalam berbagai bidang pekerjaan. Studi ini mengeksplorasi pengaruh penerapan algoritma deep learning dalam dunia kerja dengan menyoroti penerapannya di beberapa industri utama seperti manufaktur, kesehatan, keuangan, dan teknologi informasi. Penelitian ini menggunakan pendekatan studi kasus untuk mengidentifikasi bagaimana algoritma tersebut meningkatkan efisiensi, akurasi, dan automasi dalam proses bisnis. Hasil penelitian menunjukkan bahwa penerapan deep learning memberikan keuntungan signifikan dalam prediksi data, analisis keputusan, dan pemrosesan informasi, namun juga menimbulkan tantangan terkait dengan keterampilan tenaga kerja dan etika dalam penggunaannya. Studi ini memberikan wawasan mengenai peran penting algoritma dalam transformasi digital dunia kerja, serta tantangan yang harus dihadapi untuk optimalisasi keuntungannya di masa depan.
Penerapan K-Nearest Neighbors untuk Klasifikasi Kasus Hukum di Pengadilan Federal Australia: Penerapan K-Nearest Neighbors untuk Klasifikasi Kasus Hukum di Pengadilan Federal Australia Karan; Rafi Fadilla; M Alidin; Taslim
Computer Science and Information Technology Vol 6 No 1 (2025): Jurnal Computer Science and Information Technology (CoSciTech)
Publisher : Universitas Muhammadiyah Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

With the development of information technology, especially in the legal field, legal case analysis can now be done more efficiently through the application of machine learning. This study aims to classify legal cases based on the status of Cited and Uncited using the K-Nearest Neighbor (KNN) algorithm. The classification process includes text preprocessing stages, word weighting using the TF-IDF method, and testing the KNN algorithm with various values ​​of the parameter k. The research data was taken from the Federal Court of Australia (FCA) covering legal cases from 2006–2009, with three data sharing scenarios: 90:10, 80:20, and 70:30. The evaluation model was carried out using a confusion matrix with metrics of accuracy, precision, recall, and F1-score. The best results were obtained in the 80:20 scenario with a value of k = 3, resulting in an accuracy of 96.36%, a precision of 96.80%, a recall of 99.49%, and an F1-score of 98.13%. With these results, the KNN algorithm is proven to be effective in supporting the automatic legal document classification process.
Klasifikasi Berita Palsu Menggunakan Pendekatan Hybrid CNN-LSTM Fauzan Salim; Wahyudhy, Adhe Indra
Computer Science and Information Technology Vol 6 No 1 (2025): Jurnal Computer Science and Information Technology (CoSciTech)
Publisher : Universitas Muhammadiyah Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Fake news detection has become a major challenge in the rapidly evolving digital era. This study proposes a hybrid approach combining Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) to enhance the accuracy of fake news classification. CNN is utilized to extract local features from text, while LSTM captures temporal relationships in sequential data. The dataset used is sourced from Kaggle, consisting of 44,919 fake and real news articles. The classification process includes several stages, such as data preprocessing, tokenization, and transforming text into numerical representations before being processed by the hybrid CNN-LSTM model. Evaluation results indicate that the hybrid CNN-LSTM model achieves an accuracy of 99%, outperforming individual CNN and LSTM models. With high precision and recall rates, this method proves to be effective in classifying fake news, significantly contributing to the development of a more accurate and reliable fake news detection system.
GAME RPG “GARUDA QUEST” BERTEMAKAN MITOLOGI INDONESIA BERBASIS ANDROID Dermawan, Ichwan; Ulya Anisatur Rosyidah; Habibatul Azizah Al Faruq
Computer Science and Information Technology Vol 6 No 1 (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.v6i1.8933

Abstract

With the high interest of generation Z in Indonesia in mobile games, this opportunity can be used to introduce various local mythological creatures. One way that can be done is by designing a role-playing game with the theme of Indonesian mythology, which can be played on the Android platform with the game name 'Garuda Quest'. This game was developed using the MDLC (Multimedia Development Life Cycle) method, this method has 6 stages, namely concept, design, material collecting, assembly, testing and distribution. The test results using black box testing show that all the functionalities of the "Garuda Quest" game features run well. The trial on 50 respondents showed that the percentage of feature feasibility was 85.52% and the percentage of playing experience was 82.48%, which shows that the game "Garuda Quest" was well received.
Implementasi Deep Learning Untuk Identifikasi Tanaman Rimpang Menggunakan Metode Convolutional Neural Network Mahendri, Diffa Rahmanda Putra; T. Yudi Hadiwandra
Computer Science and Information Technology Vol 6 No 1 (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.v6i1.8943

Abstract

Rhizome plants are spices widely used by Indonesian people as cooking ingredients or traditional medicine. These plants havesimilar appearances, making them difficult to distinguish for some people. Errors in identifying rhizome plants can lead topoisoning, allergies, or unwanted side effects. To simplify identifying these plants, a system is needed to detect and differentiatetypes of rhizome plants, which can be achieved using Convolutional Neural Networks (CNN) with the YOLO algorithm. CNN isa Machine Learning technique capable of identifying objects based on their visual features, enabling efficient differentiation ofrhizome plants. The image dataset used is divided into six classes, with a total of 700 images. Model testing produced resultswith a precision of 98%, recall of 99%, and mAP50-95 of 96%. Future research is expected to increase dataset variety to avoidoverfitting.
Pemanfaatan Artificial Intelligence Bagi Dunia Pendidikan Di Era Society 5.0: Utilization of Artificial Intelligence for the World of Education in the Era of Society 5.0 Dara Sawitri
Computer Science and Information Technology Vol 6 No 1 (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.v6i1.8968

Abstract

The development of digital technology in the Society 5.0 era has had an impact on significant changes in various aspects of life, including education. Artificial Intelligence (AI) has become an innovative technology that plays an important role in increasing the success and effectiveness of learning. Where artificial intelligence makes it possible for individual and distance learning, digitalization of education administration, and the assessment process to become faster, objective and more efficient. The existence of artificial intelligence brings students a more participatory learning experience. For educators, artificial intelligence makes it easy to provide references and learning resources that are aligned to the individual needs of students. Artificial intelligence has a moderate role in creating a more responsive, effective and personalized learning system. However, the application of artificial intelligence for education also has challenges, especially in remote areas, such as the availability of facilities and infrastructure, data management ethics, as well as the spread of technology in the form of fast internet access and understanding digital literacy. In the Society 5.0 era, artificial intelligence technology has provided many opportunities to improve the quality of learning. With artificial intelligence, more personal methods can be applied that actively discuss and contribute based on technology. With artificial intelligence in the era of society 5.0, it is hoped that the education system will be more flexible so that it can adapt its methods and curriculum to existing needs and developments in order to create highly skilled and professional human resources.
Implementasi Access Control List (ACL) Sebagai Metode Proteksi dan Traffic Control Pada Infrastruktur Jaringan Local Area Network (LAN) Miftahur Rahman; Moh. Dasuki
Computer Science and Information Technology Vol 6 No 1 (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.v6i1.9102

Abstract

PT. Hidatech Indonesia merupakan salah satu perusahaan di Kabupaten Jember yang berjalan di bidang teknologi yaitu menyediakan layanan kursus, pelatihan, pembuatan software, dan penjualan hardware. Infrastruktur jaringan pada perusahaan tersebut kerap mengalami penyerangan cyber seperti jaringan trouble, server down, dan gangunan operasional lainnya. Hal ini disebabkan banyaknya user yang mengakses jaringan tersebut tanpa adanya proteksi dan kontrol lalu lintas jaringan. Oleh sebab itu, dibutuhkan strategi untuk memproteksi atau melindungi dan mengkontrol traffic jaringan komputer dari serangan siber, salah satu strateginya adalah dengan menerapkan Access Control List (ACL). Adapun tahapan atau metode penelitian yang dilakukan pada penelitian ini meliputi pengumpulan data, desain, implementasi, dan pengujian. Menghasilkan penelitian bahwa Jaringan divisi ruang pimpinan (192.168.10.0) dapat mengakses server FTP (192.168.50.20) maupun server Web (192.168.50.20). Jaringan divisi ruang pemasaran (192.168.20.0) hanya diijinkan akses server Web (192.168.50.20). Jaringan divisi soft. development (192.168.30.0) dan divisi course pelatihan (192.168.40.0) tidak diijinkan mengakses keduanya server Web dan server FTP, sementara divisi server memiliki akses penuh ke semua divisi didalam jaringan tersebut dengan persentase keberhasilannya adalah 100%. Dari hasil penelitian ini diharapkan dapat diterapkan terhadap jaringan riil sebagai keamanan dan kontrol lalu lintas pada jaringan di PT. Hidatech.

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