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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 35 Documents
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Search results for , issue "Vol 6 No 3 (2025): Jurnal Computer Science and Information Technology (CoSciTech)" : 35 Documents clear
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.
Deteksi Spam Email Multibahasa: Menggunakan Cross-Lingual Transfer Learning Mahalisa, Galih; Alfah, Rina; Sanjaya, Hendra
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.v6i3.10107

Abstract

Targeting the challenge of text classification in Indonesian, which often faces a scarcity of adequate labeled data, this research adapts the pre-trained language model BERT-base-multilingual-cased, which was trained on a large multilingual corpus. The strategy involves two stages: first, the model is fine-tuned on a rich English-language spam dataset, and second, the trained model is then further fine-tuned using a much smaller Indonesian-language dataset. Quantitative evaluation results show that the model achieved very good and consistent performance in both languages. On the English dataset, the model reached an Accuracy of 0.9738 and an F1-score of 0.9436. More significantly, on the Indonesian dataset, the model achieved an Accuracy of 0.9492 with an F1-score of 0.9494. The comparable performance between the two languages, despite the Indonesian dataset being much smaller, proves that the semantic knowledge acquired from the source language (English) can be efficiently transferred for the same classification task in the target language (Indonesian). This research provides a strong demonstration of how transfer learning can bridge the data resource gap and has important implications for the development of NLP applications in the context of low-resource languages
Klasifikasi Buah dan Sayuran Multi-Label Menggunakan CNN: Mengatasi Class Imbalance dengan Focal Loss Syafarina, Gita Ayu; Purnomo, Indu Indah; Hasbi, Muhammad
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.v6i3.10116

Abstract

Investigates the effectiveness of Focal Loss as a solution to the problem of class imbalance in multi-label fruit and vegetable classification tasks. Using a ResNet50-based Convolutional Neural Network (CNN) architecture, two models were trained and evaluated: one using Focal Loss and another using Binary Cross-Entropy (BCE) Loss as a baseline. To address the availability of multi-label datasets, a synthetic multi-label dataset was created by combining images from existing single-label datasets. Experimental results show that the model trained with Focal Loss achieved an accuracy of 0.9390 and an F1-score of 0.9863, outperforming the BCE Loss model which only reached an accuracy of 0.8850 and an F1-score of 0.9718. The comparative analysis indicates that Focal Loss, with its ability to focus the training process on difficult examples, effectively addresses class imbalance and produces superior performance. This study concludes that Focal Loss is an effective tool for multi-label classification tasks and highlights the existing limitations, including the synthetic nature of the dataset and the limited training duration, which underscore the need for further research
Implementasi logika fuzzy mamdani dan simple additive weighting (saw) pada sistem pakar berbasis web untuk deteksi dini gangguan neurologis Harits, Muhammad Harits Firdaus; Thohir, Muhammad Ikhsan; Sujjada, Alun
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.v6i3.10130

Abstract

Neurological disorders such as low back pain, vertigo, ischemic stroke, epilepsy, and peripheral neuropathy affect the central and peripheral nervous systems and have the potential to reduce quality of life and be fatal if not detected early. In Indonesia, the high prevalence is not balanced with access to early diagnosis due to limited medical personnel, costs, and waiting times. This study developed a web-based expert system for early detection of five neurological disorders using the Mamdani Fuzzy Method for inference and Simple Additive Weighting (SAW) for symptom ranking. The diagnosis process includes fuzzification, rule evaluation, aggregation, centroid defuzzification, and SAW calculation. The system was tested through black box testing and accuracy evaluation using MAE, RMSE, and F1 Score. The results showed an MAE value of 2.8%, RMSE 2.83%, and F1 Score 0.75, which proves the system is accurate, consistent with manual calculations, and easy to use. With a user-friendly interface, this system has the potential to be a pre-diagnosis tool that increases public awareness and supports medical personnel in decision-making.

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