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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 Metode Certainty Factor Menggunakan Rank Order Centroid Pada Sistem Pakar Pendeteksi Turnover Intention Berbasis WEB Muhammad Maulana Akbar; Moh. Dasuki; Miftahur Rahman
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.v6i2.9869

Abstract

Turnover intention, or the tendency of employees to resign, poses a significant challenge for companies—especially when dealing with Generation Z, who tend to have lower job commitment and are more likely to switch jobs. This study aims to develop a web-based expert system to detect the level of employee turnover intention by integrating the Certainty Factor (CF) and Rank Order Centroid (ROC) methods. The CF method is used to handle uncertainty in questionnaire assessments, while ROC is implemented to optimize the weights among aspects, namely Thinking of Quitting, Intention to Search for Alternatives, and Intention to Quit. The system is built based on 36 questionnaire statements and tested on 34 respondents. The results show that the system provides more proportional and realistic interpretations compared to the non-optimized approach. Accuracy testing indicates that 27 out of 34 system results match manual assessments, yielding an accuracy rate of 79.41%. These findings suggest that the system performs reliably and can serve as a practical tool for the early detection of turnover intention in the workplace.
Peramalan Harga Emas Berbasis Time Series Menggunakan Arsitektur LSTM Deep Learning Diva Arifal Adha; Adam Ramadhan; Habil Maulana; Patlan Putra Humala Harahap; 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.v6i2.9980

Abstract

Gold is one of the most influential commodities in the global economy. Its high price volatility poses a significant challenge for investors, financial analysts, and policymakers in formulating effective strategies and making accurate decisions. Therefore, an accurate prediction method is needed to forecast future gold price movements. This study aims to forecast gold prices using a deep learning approach with the Long Short-Term Memory (LSTM) algorithm. The LSTM model is capable of learning long-term dependencies in time-series data, making it highly suitable for modeling complex and dynamic financial data. The data used in this study consists of daily historical gold prices obtained from reliable sources. A preprocessing phase was carried out to clean and normalize the data before training the model. Furthermore, this study compares the performance of the LSTM model with the Multilayer Perceptron (MLP) model to examine differences in prediction accuracy. Evaluation metrics such as Mean Squared Error (MSE) and Root Mean Squared Error (RMSE) were used to assess model performance. The results show that the LSTM model provides more accurate predictions compared to MLP, with lower error values and better model stability. In conclusion, the deep learning approach, particularly the LSTM model, can serve as an effective alternative for gold price forecasting and support data-driven decision-making in the financial sector.
Sistem Pendukung Keputusan Untuk Pemilihan Mahasiswa Terbaik di Fakultas Sains dan Teknologi Universitas Muhammadiyah Kalimantan Timur Menggunakan Metode AHP: Desain Sistem, Penentuan Kriteria dan Alternatif, Analytic Hierarchy Process (AHP), Validasi Sistem, Kriteria dan Alternatif yang Digunakan, Perhitungan Nilai Eigen, Perhitungan Nilai Konsisten Ratio, Hasil Perangkingan, Implementasi Dalam Sistem Pendukung Keputusan Boy, Fachri
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.v6i2.9999

Abstract

Penelitian ini bertujuan untuk mengetahui pengaruh pemilihan mahasiswa terbaik pada Fakultas Sains dan Teknologi. Pemilihan mahasiswa terbaik dapat dinilai berdasarkan IPK. Untuk membantu pihak universitas dalam menentukan mahasiswa terbaik, maka dibuatlah Sistem Pendukung Keputusan dengan menggunakan kriteria IPK, TOEFL, LIFE SKILLS, dan MENGAJI dengan menggunakan metode AHP. Metode ini digunakan karena salah satu kemungkinan penyelesaian dari permasalahan tidak terstruktur. Hasil dari penentuan ini adalah mahasiswa dengan predikat tertinggi merupakan mahasiswa dengan lulusan terbaik. Objek penelitian ini adalah Fakultas Sains dan Teknologi di Universitas Muhammadiyah Kalimantan Timur. Jenis penelitian ini adalah penelitian kuantitatif. Populasi dalam penelitian ini adalah nama-nama mahasiswa yang lulus dalam dua tahun terakhir dari tahun 2021-2022 sampai dengan tahun 2022-2023 di Fakultas Sains dan Teknologi. Sampel penelitian dipilih dengan menggunakan Analytic Hierarchy Process. Penelitian ini menggunakan data sekunder yang diperoleh dalam penelitian ini melalui data MKDU dan Program Studi Bahasa Inggris.
PAPER Perancangan Sistem Informasi Ticket Bookin Online Travel Umrah Studi Kasus PT.Sukkari Halal Tour: PAPER Perancangan Sistem Informasi Ticket Bookin Online Travel Umrah Studi Kasus PT.Sukkari Halal Tour raja, abrar; Elsi Titasari Br Bangun
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.v6i2.10006

Abstract

This study focus to design an online umrah ticket booking information system of PT.Sukkari Halal Tour to enhance service efficiency and information transparency. The Prototype method was employed, covering stages such as requirement gathering, prototype development, evaluation, coding, testing, and implementation. The result include the design of a use case diagram, activity diagram, sequence diagram, and class diagram, which comprehensively illustrate the system flow. The system accomondates two actor: pilgrims and administrator with features like online registration, document verification, digital payment, and travel packages management. The research concludes that this system simplifies administrative processes and improves user experience.
Recommendation Implementation of a Digital Book Recommendation System Using Item-Based Collaborative Filtering in a University Library Application.: Item-Based Collaborative Filtering, recommendation system, digital library, Pearson correlation, MAE. Mutsna, Mutsna; Mufti Ari Bianto; M. Cahyo Kriswantoro
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.v6i2.10011

Abstract

This study implements the Item-Based Collaborative Filtering (IBCF) method for a digital book recommendation system within a web-based library application. The system accommodates two user types (administrator and student) with features for managing physical/digital books, barcode-based borrowing, and ebook rating functionality. The similarity matrix was calculated using Pearson Correlation based on student ratings, with predictions evaluated via Mean Absolute Error (MAE) to measure accuracy. Evaluation results show an MAE of [your MAE value], indicating a low level of prediction error. Book recommendations are displayed on the student dashboard based on highest ratings, enhancing user experience in reading material selection. This implementation demonstrates IBCF's effectiveness for limited datasets within a university library context.
The Implementasi Model Vision Transfomers Pada Klasifikasi Jenis Kulit Wajah Berbasis Website Dila Aura Futri; Ivana Lucia Kharisma; Somantri
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.v6i2.10026

Abstract

Skin type misidentification often leads to inappropriate skincare product selection, which can negatively affect skin health. This study aims to develop a web-based automatic facial skin type classification system using the Vision Transformer (ViT) architecture. The model implemented is ViT Base Patch 16, pre-trained on the ImageNet dataset and fine-tuned using 10,000 facial images evenly distributed across four classes: normal, dry, oily, and combination. The dataset underwent augmentation and normalization during preprocessing. The training results showed an accuracy of 78% on the test data, with the best performance in the combination skin class (F1-score of 0.86) and the lowest in the normal skin class (F1-score of 0.72). The model was integrated into a Flask-based system that enables users to classify their skin type by either uploading an image or capturing it via camera. System testing was conducted using functional testing and API testing via Postman. The results demonstrated that all key features of the system functioned properly, and the API successfully returned classification responses in JSON format. This system can assist users in identifying their skin type and serve as a reference for selecting appropriate skincare ingredients.
Rancang Bangun Aplikasi Pengolahan Data Nilai Siswa Berbasis Web dengan Metode Agile pada Sekolah Dasar 011 Kebun Agung: Design and Construction of a Web-Based Student Grade Data Processing Application Using the Agile Method at Kebun Agung 011 Elementary School Aji, Muhamad Aji Romadhon
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.v6i2.10094

Abstract

Elementary schools are formal educational institutions for children aged 6–12 years and play a crucial role in the early stages of education. With the rapid advancement of information technology across various sectors, including education, the integration of technology into school administrative processes has become increasingly important—particularly in managing student grade data. This study was conducted to provide a solution for SD 011 Kebun Agung by developing a web-based student grade management system that enables faster, more accurate, and more accessible input, processing, and dissemination of grade information for teachers and parents. The application was designed and developed using the Agile software development methodology, with Python as the programming language and Django as the framework, and was deployed on the PythonAnywhere platform for online access. Testing using the black-box method indicated that all system features operated effectively in accordance with user requirements. Moreover, interviews with teachers indicated that the application significantly improved the efficiency of grade processing and optimized the communication of academic information to parents. Therefore, the system developed through this study successfully accommodates the needs of SD 011 Kebun Agung in enhancing the efficiency and effectiveness of student grade data management.
Klasifikasi Penyakit Daun Kentang dengan Transfer Learning Menggunakan CNN optimalisasi Arsitektur MobileNetV2 Gunawan, Rahmad; Fauzan Salim; Wahyudhy, Adhe Indra; Wibowo, Angga Yudha; Yordan, Gibril; Filamori, Refly Fauzan
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.v6i2.8599

Abstract

Potatoes are a major food crop with high economic value, but they are susceptible to various Diseases impacting potato leaves can significantly influence their quality and productivity. This research focuses on identifying diseases in potato leaves through the Convolutional Neural Network (CNN) approach, leveraging transfer learning with the MobileNetV2 architecture. The dataset utilized comprises 4,072 images of potato leaves. categorized into three groups: non-infected leaves (healthy ), Early Blight-infected leaves, and Late Blight-infected leaves. The dataset is processed through data augmentation and normalization to enhance data quality. The resulting model demonstrates excellent performance, achieving an accuracy of 95.31%, a precision of 95.81%, a recall of 95.31%, and an F1-Score of 95.38%. These findings indicate the approach demonstrates its ability to identify the condition of potato leaves with a low classification error rate, especially in the healthy category. However, there are challenges in classifying between Early Blight and Late Blight that require further analysis and method improvement. This study contributes to the development of efficient and accurate plant disease detection systems.
Korelasi Kasus Harian Covid-19 dan Pergerakan Saham Perusahaan Vaksin di Pasar Global Menggunakan Long Short-Term Memory (LSTM) Gigih Setyaji; Kusrini
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.v6i2.9231

Abstract

The COVID-19 outbreak has had a significant impact on stock price fluctuations in the pharmaceutical industry, particularly among vaccine-producing companies. This study evaluates the relationship between the number of daily COVID-19 cases and the stock price movements of global vaccine companies, with a primary focus on AstraZeneca (AZN). The predictive model employed is Long Short-Term Memory (LSTM), a deep learning algorithm based on time series data. To achieve more accurate predictions, automatic hyperparameter tuning was performed using the Optuna method. Based on the evaluation results, the model demonstrated high predictive performance, with a Mean Squared Error (MSE) of 1.131777, Mean Absolute Error (MAE) of 0.773518, Root Mean Squared Error (RMSE) of 1.063850, and a coefficient of determination (R²) of 0.974614. Additionally, the model was able to realistically forecast the AZN stock price trend for the next 30 days. These results prove that the optimized LSTM model can serve as an effective prediction tool for analyzing the impact of the pandemic on the capital market.
Penerapan Learning Vector Quantization Dalam Pengolahan Citra Digital Untuk Deteksi Penyakit Kulit Rizki Akbar Pratama; Barry Ceasar Octariadi; Syarifah Putri Agustini Alkadri
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.v6i2.9270

Abstract

Skin, as the largest human organ, covers more than two square meters and accounts for about 15% of body mass. Consisting of three main layers of epidermis, dermis, and subcutaneous tissue, the skin serves as a physical shield and barrier against infection, injury, and UV radiation. Skin diseases such as chickenpox, monkey pox, measles and herpes are medical challenges that require quick and accurate diagnosis. This study used 520 digital images (130 per category) from Mendeley Data and online sources. The Learning Vector Quantization (LVQ) algorithm was applied for image classification based on the extracted features. Results showed an overall accuracy of 90.74%, with respective accuracies: 97% (chickenpox), 98% (monkey pox), 91% (measles), and 100% (herpes). Evaluation using confusion matrix resulted in accuracy, precision, recall, and F1-score values of 0.91, indicating strong model performance. These findings demonstrate the potential of LVQ as a digital image-based skin disease diagnosis tool.

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