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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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Optimasi algoritma deteksi spam email dengan BERT-MI dan jaringan dense Florentina Yuni Arini
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.v6i2.9460

Abstract

Email spam detection is a critical challenge in maintaining the security and efficiency of digital communication. This research proposes and evaluates an optimized pipeline for email spam detection by integrating Bidirectional Encoder Representations from Transformers (BERT) for feature extraction, Mutual Information (MI) for feature selection to reduce dimensionality, and a dense neural network for classification. The Lingspam dataset, consisting of 2893 emails (2412 ham and 481 spam), was used in the experiments with an 80% training and 20% testing data split. Text features were extracted using BERT (bert-base-uncased), resulting in a 768-dimensional embedding, which was then reduced to the 200 most relevant features using MI. A dense neural network model with a 256-128-64-32-1 neuron architecture was trained using the Adam optimizer, binary cross-entropy loss function, and techniques such as early stopping and class weights to handle class imbalance. Evaluation results on the test data demonstrated very high performance, achieving an accuracy of 99.14%, precision of 0.9596, recall of 0.9896, F1-score of 0.9744, and ROC-AUC of 0.9995. This approach indicates that the combination of BERT-MI with a dense network can achieve accuracy comparable to more complex methods, but with the potential for a simpler and more efficient architecture.
Implementasi CNN untuk identifikasi penyakit daun jagung Gumelar, Gilang; Tito Sugiharto; Iwan Lesmana
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.v6i2.9462

Abstract

Maize is an important commodity in Indonesia's agricultural sector. However, disease attacks on the leaves can reduce the quality and quantity of the harvest. At SMK Negeri 1 Kuningan, disease identification is still done manually, so there is a risk of errors. This research aims to design and build an Android application to automatically detect corn leaf diseases using the Convolutional Neural Network (CNN) algorithm. The development method used is Rapid Application Development (RAD), with a CNN model based on MobileNetV2 architecture trained using a dataset of diseased and healthy corn leaf images. Evaluation using test images resulted in an accuracy of 96.2%. The model was able to detect five categories: leaf spot, downy mildew, leaf blight, leaf rust, and healthy leaves. The F1-Score is 94% Leaf Spot, 96% Leaf Blight, 96% Healthy Leaf, 97% Leaf Blight, and 96% Leaf Rust, respectively. The precision and recall values of all classes are above 94%. These results show that the integration of CNN in mobile applications is effective in helping the automatic identification of corn leaf diseases in an educational environment.
KLASIFIKASI JENIS TANAMAN PHILODENDRON BERDASARKAN CITRA DAUN MENGGUNAKAN ALGORITMA CNN Alif, Muhammad Alif Fathan; Tito Sugiharto; Iwan Lesmana
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.v6i2.9484

Abstract

Accurate identification of ornamental plants is becoming important as public interest in tropical plant collections increases, one of which is from the Philodendron genus. This ornamental plant has many varieties that are often difficult to distinguish due to visual similarities in the shape and pattern of their leaves. This research aims to develop a system for Philodendron type classification based on leaf images using the Convolutional Neural Network (CNN) algorithm to help the identification process. The method used is with a dataset of 5000 leaf images of five Philodendron species, which are divided into 80% training data, 10% validation data, and 10% test data. A CNN model with MobileNetV2 FPNLite SSD architecture was implemented and trained for 50,000 steps, then optimised for mobile devices using TensorFlow Lite. Performance analysis was conducted using confusion matrix to evaluate accuracy, precision, recall, and F1-Score metrics. The results show that the developed model is able to accurately classify leaf images, both in the form of static images and in real-time. This system has been successfully implemented in an Android application that is expected to be a practical identification tool for general users and ornamental plant enthusiasts.
INTEGRASI QDRANT VECTOR DATABASE DAN DEEPSEEK AI UNTUK CHATBOT OTOMATIS PADA APLIKASI E-COMMERCE Abdurrafiq Sujana, Azhar; Indra Yustiana; Alun Sujjada
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.v6i2.9668

Abstract

Transformasi digital telah mendorong e-commerce untuk meningkatkan kualitas layanan pelanggan (customer service. Salah satu solusi yang ditawarkan yaitu munculnya teknologi chatbot sebagai program berbasis kecerdasan buatan yang mampu berinteraksi secara otomatis dengan pengguna. Penelitian ini mengembangkan chatbot dengan memanfaatkan Qdrant Vector Database sebagai vector untuk menyimpan dan mencari informasi berbasis konteks, dan OpenRouter API Key model DeepSeek AI sebagai akses chatbot. Chatbot ini dirancang untuk menjawab pertanyaan umum terkait informasi produk, stok, pengiriman, metode pembayaran serta pertanyaan umum lainnya dalam 24/7. Hasil penelitian menunjukan bahwa penerapan sistem ini membantu meningkatkan layanan otomatis kepada pengguna untuk mencari informasi terkait produk, pengiriman, pembayaran dan informasi umum lainnya, khususnya di luar jam operasional.
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).
Analisis Kinerja Algoritma K-Nearest Neighbors (KNN) dan Random Forest untuk Klasifikasi Kondisi Cuaca Asha Yuda, Agim Sahrija; Muhammad Desfriyan Arif Rosady; Nabil Ibrahim Faisal; 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.v6i2.9827

Abstract

The development of information technology has encouraged the use of machine learning algorithms in various fields, including in the analysis and prediction of weather conditions. This study aims to analyze and compare the performance of two machine learning algorithms, namely K-Nearest Neighbors (KNN) and Random Forest, in the classification of weather conditions based on historical meteorological data. The dataset used includes features such as rainfall, maximum temperature, minimum temperature, and wind speed, with target categories in the form of weather types such as rain, sunny, fog, drizzle, and snow. The process includes data pre-processing, feature scaling, training and test data sharing, and model training using the scikit-learn library. Performance evaluations are conducted using accuracy, precision, recall, and F1-score metrics. The results showed that the Random Forest model had higher accuracy (82%) than KNN (78%), with more stable performance in the majority class. However, both models experienced significant performance declines in minority classes due to data imbalances. The study recommends further optimizations such as class balancing and model parameter selection to improve the overall accuracy of weather classification.
Perancangan Sistem Informasi Penyewaan Bus Berbasis Web di Pt. Dzakki Buana Tour Abdulfattah, Faiz; Diansyah, Risnal; Jahpal, Habib; Balqis Windra, Tamimah; Khusnul Khotimah, Anisa
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.v6i2.9831

Abstract

The development of information technology has had a significant impact on various sectors, including services, operational efficiency, and data management. Information technology enables business processes to be conducted more quickly, accurately, and in an integrated manner, while also reducing the risk of errors from manual record-keeping. PT Dzakki Buana Tour, a company providing tourist bus rental services based in Pekanbaru, still carries out its business processes manually using tools such as Word, Excel, and Spreadsheets. This often leads to data entry errors, scheduling conflicts, and delays in service. This study aims to analyze and design a web-based bus rental application for PT Dzakki Buana Tour. The result of this research is a system design that includes client data management, rental processing, departure scheduling, and bus and driver management. The system design is illustrated using UML modeling tools such as use case diagrams, activity diagrams, and class diagrams. This system is expected to improve the efficiency and accuracy of the bus rental operations at PT Dzakki Buana Tour.
Identifikasi penyakit tanaman tomat melalui citra daun menggunakan DenseNet201 Okamisar; Hayami, Regiolina; Fuad, Evans
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.v6i2.9965

Abstract

This study focuses on implementing the DenseNet201 algorithm for disease classification in tomato plants using leaf images from PlantVillage dataset. The agricultural sector plays a central role in the Indonesian economy, with tomatoes being one of the important horticultural crops. However, tomato productivity is often hindered by various plant diseases. Accurate disease diagnosis is crucial for improving production stability. Image processing-based approaches, such as Convolutional Neural Network (CNN), have facilitated effective plant disease diagnosis. In this study, the PlantVillage dataset consisting of 18,835 tomato leaf images is utilized. The data is divided into training (10,000 images), validation (7,000 images), and test (500 images) sets. A classification model is constructed using the DenseNet201 architecture with some modifications. The results show that the DenseNet201 model achieves an accuracy of 95.20% on the testing data, with an overall F1-score of 0.95. Compared to previous studies using VGG16 (77.2%), InceptionV3 (63.4%), and MobileNet (63.75%), the DenseNet201 model demonstrates a significant performance improvement. This study concludes that DenseNet201 is highly effective in classifying tomato plant diseases and has the potential to be implemented in widespread plant disease diagnosis applications.

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