cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
-
Contact Email
coscitech@umri.ac.id
Phone
+6285225539224
Journal Mail Official
coscitech@umri.ac.id
Editorial Address
Program Studi Teknik Informatika Fakultas Ilmu Komputer Gedung Rektorat Lt. 4, Universitas Muhammadiyah Riau Jl. Tuanku Tambusai, Pekanbaru, Riau
Location
Kota pekanbaru,
Riau
INDONESIA
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
 31   32   33   34   35 
Perbandingan Random Forest Regressor Dan Decision Tree Regressor Untuk Prediksi Hasil Panen Rizki Faizal; Abdullah, Asrul; Pangestika, Menur Wahyu
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.v6i2.9966

Abstract

Uncertainty in crop yields due to environmental factors remains a major challenge in Indonesia's agricultural sector. This study aims to compare the performance of the Random Forest Regressor and Decision Tree Regressor algorithms in predicting cultivated crop yields. The dataset used was sourced from Kaggle, consisting of 300,000 rows with features such as crop type, soil type, rainfall, fertilizer use, irrigation, and weather conditions. The system was developed using Python and Streamlit. The methodology includes data preprocessing, model training, and evaluation using the Mean Absolute Error (MAE) metric. The test results show that the Decision Tree Regressor achieved a lower MAE (0.43) compared to the Random Forest Regressor (0.48), resulting in more accurate predictions on this dataset. Feature analysis indicates that rainfall and crop type are the most influential factors. Although Random Forest is generally known for its stability, this study demonstrates that Decision Tree can outperform it within the context of the dataset used. The developed system is expected to assist farmers and policymakers in planning agricultural production more efficiently and in a data-driven manner.
Klasifikasi Penyakit Daun Tanaman Timun Berbasis Convolutional Neural Network (CNN) Yanto, Maryogi; Siregar, Alda Cendekia; Abdullah, Asrul
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.v6i2.9982

Abstract

Penyakit daun pada tanaman mentimun merupakan salah satu tantangan utama dalam meningkatkan hasil panen, terutama di Kalimantan Barat. Identifikasi penyakit secara manual seringkali tidak akurat dan memakan waktu. Penelitian ini bertujuan untuk mengembangkan sistem klasifikasi otomatis untuk penyakit daun mentimun berbasis Convolutional Neural Network (CNN) menggunakan arsitektur VGG-16. Dataset terdiri dari 2.000 citra daun mentimun yang dikategorikan ke dalam lima kelas: Bercak Daun Bakteri, Penyakit Bulai Berbulu, Daun Sehat, Penyakit Mosaik, dan Penyakit Bulai Tepung. Metode yang diterapkan meliputi praproses (pengubahan ukuran, augmentasi, normalisasi), pelatihan model, pengujian, dan evaluasi menggunakan metrik akurasi, presisi, recall, dan skor F1. Model mencapai akurasi 88% pada data pelatihan, 84% pada data validasi, dan 81,50% pada data pengujian. Model yang telah dilatih kemudian diintegrasikan ke dalam aplikasi berbasis web menggunakan Streamlit untuk memfasilitasi klasifikasi interaktif. Hasilnya menunjukkan bahwa Jaringan Saraf Konvolusional (CNN) efektif dalam mengklasifikasikan penyakit daun mentimun secara otomatis dan dapat diterapkan sebagai solusi teknologi di bidang pertanian.
PENILAIAN RESIKO KEAMANAN APLIKASI WEB MENGGUNAKAN STANDAR ISO/IEC 27005 : 20022 PADA LAYANAN ORGANISASI: PENILAIAN RESIKO KEAMANAN APLIKASI WEB MENGGUNAKAN STANDAR ISO/IEC 27005 : 20022 PADA LAYANAN ORGANISASI Chandra, Nungky; Mohamad Yusuf
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.v6i2.9994

Abstract

The problem of information security vulnerability and threat risks is increasing, so it is necessary to be able to analyze the risk situation of future information security threats and vulnerabilities, especially for application services of a community organization. Research on the application of information security risk analysis based on the ISO/IEC 27005: 2022 framework in an organization's service applications. ISO/IEC 27005: 2022 is an international standard used for guidelines for implementing the most effective information security risk analysis process compared to other information security risk assessment method frameworks. The results of the assessment are to measure the level of information security risk of an organization's service application so that it can be used as material for improvements in carrying out information security prevention and control measures so that vulnerability gaps and threats of information security attacks can be reduced. The results of this study can describe the risk value in the organization's service application with 3 high-risk categories, namely in financial transaction data (risk value 20), customer database (risk value 16), and server configuration (risk value 15). And medium risk values are found in public APIs (risk value 12) and internal report data (risk value 6).
Implementasi Sistem Informasi Berbasis Web Pada Pengelolaan Arsip Bagian Sertifikasi Balai BPOM Pekanbaru Sukrianto, Darmanta; Oktarina, Dwi
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.v6i3.9164

Abstract

Archives play a crucial role in the administrative and management activities of an organization as they contain information about daily operations. The BBPOM (Food and Drug Supervisory Agency) in Pekanbaru is a technical unit responsible for overseeing food and drug products in accordance with legal regulations. One of the divisions at BBPOM Pekanbaru is the Certification Division, which is tasked with evaluating the production facilities of food and drug products. Currently, the Certification Division still uses a simple archiving management system, where all physical documents are scanned and stored in Google Drive, while the physical documents are kept in filing cabinets. Although the arrangement is neat, there is still a higher risk of data damage or loss, and it also requires a large storage space. Developing an information system for efficient archiving takes time, but by using the Rapid Application Development (RAD) method, the system can be completed quickly. This web-based archiving information system is designed to make data searches faster by using a search feature for finding document numbers, names, and types. The system also facilitates monitoring of storage spaces and maintaining archived documents, while minimizing the risk of document damage or loss, as access to the archiving system requires a username and password for login.
Implementasi CNN untuk Identifikasi Penyakit Daun Cabai: Implementation of CNN for Chili Leaf Disease Identification Salya Nur Alamsyah, Aqmal; Iwan, Lesmana; Rio, Priantama
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.v6i3.9381

Abstract

Disease detection in chili plants is a crucial step in preventing damage that can reduce productivity and cause economic losses for farmers. This study presents the design of an Android-based application called Chili Leaf Disease App that can automatically detect chili leaf diseases. The application uses a Convolutional Neural Network (CNN) algorithm with the MobileNetV2 architecture to classify leaf diseases through images captured directly from the camera or uploaded from the gallery. The dataset used consists of 4,000 chili leaf images across four disease classes. Testing results show that the model achieves an accuracy of 97.5%. The system was developed using the Rapid Application Development (RAD) method, chosen for its shorter development cycle, flexibility, and ability to increase user involvement. This approach enables efficient, fast, and user-responsive application development. The application is expected to help farmers detect diseases early and take preventive action more quickly to maintain plant health.
Klasifikasi kecanduan smartphone mahasiswa universitas esa unggul menggunakan machine learning dan sas-sv Verrel, Anggoro; Maulana, Irfan Zidny; Liu, Vico Andrean; Prabowo, Ary
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.v6i3.9817

Abstract

The digital era has made smartphones an inseparable part of students' lives, but it also raises the risk of addiction that negatively impacts academic achievement and mental health. This research aims to develop and evaluate machine learning models capable of classifying the level of smartphone addiction among Esa Unggul University students. Data were collected from 32 student respondents through an online questionnaire adopting the validated psychometric instrument, the Smartphone Addiction Scale-Short Version (SAS-SV). Addiction levels were categorized into two classes: 'High', which refers to the gender-specific addiction risk threshold from Kwon et al. (2013), and 'Moderate', which includes scores below that threshold. Four classification algorithms—Logistic Regression, K-Nearest Neighbors (KNN), Decision Tree, and Random Forest—were implemented to compare their performance. To address class imbalance in the data, the SMOTE oversampling technique was applied to the training data. Model evaluation was based on accuracy, precision, recall, and F1-score. The research results show that the Logistic Regression model achieved the best performance with an accuracy of 1.0000 and an average F1-score of 1.00 on the test data. Nevertheless, it should be noted that this perfect performance was obtained from a very limited test data size (8 samples), so generalization requires further validation. Feature importance analysis from the Random Forest model identified that the question related to Planned tasks/work often interrupted by smartphone use (Q0) was the most dominant predictor. These results indicate that machine learning models based on psychometric scales have initial potential as a screening and exploratory tool to identify students at risk of smartphone addiction, but require extensive development and validation on larger data before practical implementation.
Rancang Bangun Sistem Penilaian Kenaikan Jabatan Karyawan Berbasis Web di PT Timbul Mandiri Agung dengan Metode Weighted Scoring Fakhriyatin, Iklil; Simatupang, Dwi Sartika
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.v6i3.9824

Abstract

This study aims to design and develop a web-based employee promotion evaluation system at PT Timbul Mandiri Agung using the Weighted Scoring method. The system was developed to improve efficiency, accuracy, and transparency in the evaluation process, which was previously conducted manually. The Weighted Scoring method enables objective decision-making by assigning weights to evaluation criteria such as performance, work experience, skills, attitude, discipline, and attendance. The study adopted the Waterfall model for system development, including requirements analysis, system design, implementation, testing, and evaluation. The results indicate that the proposed system enhances evaluation accuracy, accelerates decision-making processes, and increases employee trust in the company’s evaluation system.
Deteksi Serangan Dalam Ekosistem Iot Melalui Analisis Multi-Class Dengan Model Xgboost Dan Penerapan Teknik Imbalance Ratio Pada Dataset IoTID20 Amien, Januar Al; Sunanto, Sunanto; Rangkuti, Muhammad Al-Ikhsan; Soni, Soni
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.v6i3.9861

Abstract

This research focuses on attack detection in the Internet of Things (IoT) ecosystem using the XGBoost algorithm and the Imbalance Ratio technique on the IoTID20 dataset. The main goal is to overcome the problem of data imbalance that is common in IDS datasets and improve accuracy in classifying attack types. The methodology used includes data preprocessing, feature selection, and applying the Imbalance Ratio technique to handle class imbalance in the IoTID20 dataset. Next, the XGBoost model is implemented with the scale_pos_weight parameter to handle the class imbalance problem. This model is trained on training data and evaluated using metrics such as accuracy, precision, recall, and F1-score. The research results show that the combination of the XGBoost algorithm and the Imbalance Ratio technique is able to overcome data imbalance problems effectively. The resulting model achieved an accuracy rate of 99.32%, precision 99.32%, recall 99.32%, and F1-score 99.32% in classifying attack types on the IoTID20 dataset. These results demonstrate excellent capabilities in detecting attacks and distinguishing between normal and anomalous traffic in the IoT ecosystem. This research contributes to improving IoT network security by applying an effective Machine Learning approach to accurately detect attacks, while also addressing data imbalance problems that often occur in IDS datasets.
Implementasi Convolutional Neural Network untuk Deteksi Penyakit pada Daun Cengkeh Berbasis Mobile: Bahasa Indonesia Junmulyana, Satria; Fergina, Anggun; Insany, Gina Purnama
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.v6i3.9895

Abstract

Clove (Syzygium aromaticum) is a spice crop that has high economic value, but faces serious threats from various diseases that can reduce yields. Early detection of disease in clove plants is very important to prevent greater losses. This research aims to develop a disease detection system for clove plants using Convolutional Neural Network (CNN) implemented in a mobile application. This method is expected to provide a faster and more accurate solution compared to traditional detection methods that are often inefficient. This research was conducted by collecting datasets of infected and healthy clove leaf images, which were then used to train the CNN model. The results show that the developed CNN model is able to achieve high disease detection accuracy, and can be integrated with mobile technology to facilitate farmers in identifying diseases in real-time. Thus, this research not only contributes to increasing agricultural productivity, but also supports the application of digital technology in the agricultural sector. The results of this research are expected to benefit farmers, researchers, and the agricultural industry as a whole.
Perbandingan model SARIMA dan Prophet dalam memprediksi jumlah kunjungan wisatawan mancanegara ke Indonesia berdasarkan data deret waktu bulanan Alfaridzi, M Ilmi; Gunawan, Rahmad; Alfian, Haris; Mirano, Muhammad Fitter; Nazifah, Hayatun; Wahyuni, Sri; Illahi, Kevanda Sondani
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.v6i3.9963

Abstract

Forecasting international tourist arrivals is a critical aspect of tourism planning and policy-making. This study compares two time series forecasting methods, Seasonal Autoregressive Integrated Moving Average (SARIMA) and Prophet in modeling and predicting the monthly number of international tourists visiting Indonesia, based on data from January 2018 to May 2025. The methodology includes data preprocessing, stationarity testing using the Augmented Dickey-Fuller test, and selecting optimal SARIMA parameters based on the lowest AIC. Model performance was evaluated using MAE and RMSE on the testing data from January to May 2025. The results indicate that SARIMA outperforms Prophet, with a lower average MAE of 1336.41 and RMSE of 1616.67, compared to Prophet’s MAE of 5591.33 and RMSE of 5739.71. Based on this evaluation, SARIMA was selected as the best model and used to project international tourist visits for the period June to December 2025. These findings highlight SARIMA’s superior ability to capture seasonal patterns in tourism data, making it a reliable tool for short-term tourism forecasting in Indonesia.

Page 33 of 36 | Total Record : 358

 31   32   33   34   35 

Filter by Year

2020 2025


Reset
Filter By Issues
All Issue Vol 6 No 3 (2025): Jurnal Computer Science and Information Technology (CoSciTech) Vol 6 No 2 (2025): Jurnal Computer Science and Information Technology (CoSciTech) Vol 6 No 1 (2025): Jurnal Computer Science and Information Technology (CoSciTech) Vol 5 No 3 (2024): Jurnal Computer Science and Information Technology (CoSciTech) Vol 5 No 2 (2024): Jurnal Computer Science and Information Technology (CoSciTech) Vol 5 No 1 (2024): Jurnal Computer Science and Information Technology (CoSciTech) Vol 4 No 3 (2023): Jurnal Computer Science and Information Technology (CoSciTech) Vol 4 No 2 (2023): Jurnal Computer Science and Information Technology (CoSciTech) Vol 4 No 1 (2023): Jurnal Computer Science and Information Technology (CoSciTech) Vol 3 No 3 (2022): Jurnal Computer Science and Information Technology (CoSciTech) Vol 3 No 2 (2022): Jurnal Computer Science and Information Technology (CoSciTech) Vol 3 No 1 (2022): Jurnal Computer Science and Information Technology (CoSciTech) Vol 2 No 2 (2021): Jurnal Computer Science and Information Technology (CoSciTech) Vol 2 No 1 (2021): Jurnal Computer Science and Information Technology (CoSciTech) Vol 1 No 2 (2020): Jurnal Computer Science and Information Technology (CoSciTech) Vol 1 No 1 (2020): Jurnal Computer Science and Information Technology (CoSciTech) More Issue