cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
Elsa Aditya
Contact Email
redaksijurnalupu@gmail.com
Phone
+6285175205250
Journal Mail Official
redaksijurnalupu@gmail.com
Editorial Address
JL. KL. Yos Sudarso Km. 6,5 No. 3A, Tanjung Mulia, Medan, Sumatera Utara, 20241
Location
Kota medan,
Sumatera utara
INDONESIA
CSRID
Published by Universitas Potensi Utama
ISSN : 20851367     EISSN : 2460870X     DOI : https://doi.org/10.22303/csrid
Core Subject : Science,
Computer Science & IT
CSRID (Computer Science Research and Its Development Journal) is a scientific journal published by LPPM Universitas Potensi Utama in collaboration with professional computer science associations, Indonesian Computer Electronics and Instrumentation Support Society (IndoCEISS) and CORIS (Cooperation Research Inter University).
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 129 Documents
 9   10   11   12   13 
Implementation of Analytical Hierarchy Process (AHP) Method on Teacher Performance Appraisal Decision Support System Erliyana Nurrahma; Berlilana, Berlilana
CSRID (Computer Science Research and Its Development Journal) Vol. 16 No. 3 (2024): October 2024
Publisher : LPPM Universitas Potensi Utama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22303/csrid-.16.3.2024.330-343

Abstract

Assessing the quality of education in a school often hinges on evaluating the performance of its teaching staff. Teachers play a pivotal role as professional educators shaping the learning experiences of students. Their performance evaluations not only serve as a benchmark for excellence but also as criteria for career advancements, such as promotions to higher positions or recommendations for teacher certification programs. To ensure these assessments are fair, consistent, and objective, it's crucial to employ a reliable evaluation method. The Analytical Hierarchy Process (AHP) emerges as a robust tool for this purpose. AHP offers a structured framework that facilitates comprehensive decision-making by allowing decision-makers to prioritize various criteria based on their relative importance. In the context of this study, data for the AHP analysis was gathered through questionnaires distributed to respondents. These questionnaires likely covered multiple aspects of teaching performance, such as instructional effectiveness, classroom management, and professional development. After collecting and analyzing the data using the AHP method, the results provided a weighted ranking of the teachers. According to the AHP analysis, Teacher C emerged as the top performer with a weight of 0.7604 or 76.04%. This indicates that Teacher C excelled in the evaluated criteria and demonstrated superior teaching skills. Following closely, Teacher B secured the second spot with a weight of 0.2079 or 20.79%. Lastly, Teacher A received the lowest priority with a weight of 0.0517 or 5.17%. These findings not only highlight the strengths and areas for improvement among the teachers but also offer valuable insights for school administrators to make informed decisions regarding promotions, certifications, and professional development opportunities.
Analisis Kepuasan Pengguna Terhadap Aplikasi Dolan Banyumas Menggunakan Metode End User Computing Satisfaction (EUCS) dan DeLone and McLean Fadillah, Septiya Nur; Hidayah, Debby Ummul; Saputra, Jeffri Prayitno Bangkit
CSRID (Computer Science Research and Its Development Journal) Vol. 16 No. 3 (2024): October 2024
Publisher : LPPM Universitas Potensi Utama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22303/csrid-.16.3.2024.344-366

Abstract

Technology has an important role in modern life, including in the tourism sector. The Dolan Banyumas application, which was launched by the Banyumas district Youth, Sports, Culture and Tourism Department, is one of the innovations to improve the regional tourism experience. The Dolan Banyumas application was created to make it easier for travelers to find a guide to enjoy the beauty of Banyumas with complete facilities. This research aims to identify problem points experienced by users and propose potential improvements for the application. Apart from that, this research also aims to determine the level of user satisfaction with the Dolan Banyumas application and examine several factors that have an influence on user satisfaction. This research uses the End User Computing Satisfaction (EUCS) and DeLone and McLean methods. The population in this study were users of the Dolan Banyumas application in the Banyumas district, with 82 respondents being the research sample. Data analysis was carried out using PLS-SEM with Microsoft Excel tools for demographic data and SmartPLS for statistical analysis. The research results show that the Dolan Banyumas application has several problem points that need to be improved, such as content, accuracy, format, ease of use, system quality and service quality variables which show a low level of satisfaction. However, the timeliness and information quality variables show a good level of satisfaction. So it can be concluded that the factors that influence user satisfaction with the Dolan Banyumas application use the End User Computing Satisfaction (EUCS) and DeLone and McLean methods, namely information quality and timeliness.
Analisis Tanggapan Customer Service Laptop Di Instal Murah Purwokerto Menggunakan Algoritma Naïve Bayes Pratiwi, Widya Dian; Debby Ummul Hidayah; Primandani Arsi
CSRID (Computer Science Research and Its Development Journal) Vol. 16 No. 3 (2024): October 2024
Publisher : LPPM Universitas Potensi Utama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22303/csrid-.16.3.2024.367-380

Abstract

The development of the digital era is dominated by advances in information technology, so customer service is becoming increasingly important in the technology industry. This research aims to analyze customer responses to customer service using the Naïve Bayes classification method, with a focus on the Purwokerto Cheap Installation service. The Naïve Bayes algorithm is used to systematically understand customer response patterns, which is important for companies in increasing customer satisfaction. In the context of the continuously developing information technology industry, understanding customer response patterns is crucial for maintaining competitive advantage. The Naïve Bayes classification method is used in the analysis, with stages including data collection, data labeling, preprocessing, classification using Naïve Bayes, and analysis of the results. The data used are customer ratings and reviews of the Purwokerto Cheap Installation Shop, with a total of 50 data. After data labeling, the research results show that around 66% of customers describe their satisfaction through the ratings and reviews given, which are classified as positive. This research is expected to provide theoretical and practical contributions in understanding customer responses to technology services. Using the Naïve Bayes classification method, this research reveals customer response patterns that are important for companies to maintain a competitive advantage in the ever-growing information technology industry.
Evaluasi Performa Otomatisasi Skema Basis Data Dengan Model dan Migrasi Django Dalam Aplikasi Proyek Akhir Gat; Prasetya, Wahyu Sindu; Wibowo, Vellen
CSRID (Computer Science Research and Its Development Journal) Vol. 17 No. 2 (2025): Juni 2025
Publisher : LPPM Universitas Potensi Utama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22303/csrid-.17.2.2025.270-284

Abstract

Effective management of database schemas is essential to ensure the scalability, performance, and integrity of academic applications. However, for systems with complex entities and large volumes of data, the use of framework-based schema automation remainsrelatively untested. Examiner, Guidance, Evaluation, Final Project, Lecturer, and Student are the six main entities that make up the final project management application, and the purpose of this study is to assess how well Django's models and migration tools can automate database schemas for this application. The case study methodology was used on two linked datasets with 1,000 and 10,000 entries, respectively. During the stages of installation, testing, and analysis, a descriptive-analytical approach was employed. Unit, functional, integration, and performance tests were conducted using MariaDB, Django 5.1.2, and a digital stopwatch. For the dataset with 1,000 entries, the read operation averaged 0.00010 seconds, the update operation 0.00439 seconds, and the delete operation 0.00124 seconds. The results demonstrate that the models remain consistent, migrations proceed smoothly, and all CRUD operations are completed with an effective average time. For the dataset containing 10,000 entries, the average operation times were 0.00045 seconds per operation, 0.00013 seconds for reads, 0.04535 seconds for updates, and 0.00345 seconds for deletions. In summary, Django can be effectively applied to large-scale academic applications, as it consistently and efficiently automates complex database schemas.
Implementasi Deep Learning Untuk Identifikasi Jenis Biji Kopi Menggunakan Metode Convolutional Neural Network Pratama, Munawwar Anugrah; Hadiwandra, T. Yudi
CSRID (Computer Science Research and Its Development Journal) Vol. 17 No. 3 (2025): Oktober 2025
Publisher : LPPM Universitas Potensi Utama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22303/csrid-.17.3.2025.387-398

Abstract

Indonesia is one of the largest coffee producers in the world, with various types of coffee beans such as Arabica, Robusta, and Liberica. Each type of coffee bean has unique characteristics that influence the taste, aroma, and overall quality of the coffee. However, many people are still unable to visually distinguish between these types of beans. This research aims to develop a Deep Learning-based system using the Convolutional Neural Network (CNN) method with the Xception architecture to identify coffee bean types from images. The dataset was obtained from direct image collection and online sources, then processed through preprocessing and data augmentation stages. The model training process was conducted using transfer learning techniques to improve classification performance. The resulting model is capable of classifying coffee bean images into three main categories with an accuracy 81.63%. The system is implemented as a web interface using Flask, allowing users to upload images of coffee beans and obtain classification results via a website. This study demonstrates that the CNN method with Xception architecture is effective for visual recognition of coffee bean types and can be a solution to help the general public in identifying different coffee bean varieties. This study aims to develop a deep learning–based system using the Convolutional Neural Network (CNN) method with the Xception architecture to identify coffee bean types from images. A total of 600 images of Arabica, Robusta, and Liberica beans were collected from primary and online sources, and then divided into training (80%), validation (10%), and testing (10%) sets. The dataset was processed through image preprocessing and augmentation techniques such as rotation, flipping, zooming, and brightness adjustment to improve model generalization. The training was performed using a transfer learning approach, followed by fine-tuning several deeper layers to enhance feature extraction. Evaluation was conducted using a confusion matrix and F1-score to validate class-wise performance. The model achieved an accuracy of 81.63% using the testing dataset. In practical implementation through a Flask-based website, the system achieved above 90% accuracy for several input angles, indicating strong recognition ability under controlled image conditions. This work demonstrates that the CNN Xception model is effective for visual identification of coffee bean types and can be applied as a practical solution to assist the general public, farmers, and coffee industry practitioners. Future enhancement may include expanding bean classes, optimizing architecture, and real-world testing.
Diabetes Diagnosis Expert System Based on Family History Analytic Hierarchy Process (AHP) Method Saragih, Reagan Surbakti; Nufus, Inayah chayatun; Samsir, Samsir
CSRID (Computer Science Research and Its Development Journal) Vol. 17 No. 2 (2025): Juni 2025
Publisher : LPPM Universitas Potensi Utama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22303/csrid-.17.2.2025.229-242

Abstract

An expert system is a branch of artificial intelligence (AI) designed to replicate the decision-making abilities of a human expert in a specific domain. It utilizes a rule-based approach by incorporating expert knowledge and experience into a computer system, allowing non-expert users to analyze and solve complex problems efficiently. One of the critical applications of expert systems is in the healthcare sector, especially in supporting early diagnosis of chronic diseases such as Diabetes Mellitus. Diabetes Mellitus is a metabolic disorder characterized by elevated blood glucose levels caused by insufficient insulin production or the body's inability to effectively use insulin. It is classified into two main types: Type 1 Diabetes Mellitus (Insulin Dependent) and Type 2 Diabetes Mellitus (Non-Insulin Dependent). Key factors contributing to the onset of diabetes include genetic predisposition, obesity, and unhealthy lifestyle habits. To assist the public in self-diagnosing the risk of diabetes, a web-based expert system was developed using the Analytic Hierarchy Process (AHP), a structured decision-making method that helps prioritize multiple criteria. In this system, symptoms such as frequent thirst, weight loss, and family history of diabetes are assessed and weighted using AHP to determine a person's risk level. The system is implemented using PHP programming language and MySQL database. Users interact with the system by answering a set of predefined questions, and based on their responses, the system calculates and displays the diagnosis result with corresponding risk categories.This expert system aims to raise public awareness and provide an accessible tool for early detection and prevention of diabetes, especially in regions with limited access to healthcare professionals.
Expert System for Early Detection of Depression Using Psychological Symptoms Certainty Factor Method Rambe, Nisa indriani; Samsir, Samsir; Subagio, S.
CSRID (Computer Science Research and Its Development Journal) Vol. 17 No. 2 (2025): Juni 2025
Publisher : LPPM Universitas Potensi Utama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22303/csrid-.17.2.2025.243-255

Abstract

Depressive disorders in the elderly often go undetected due to early symptoms that resemble normal aging processes. The absence of an early detection system becomes a major obstacle to prompt treatment. This study aims to design an expert system for early detection of depression in the elderly using the Certainty Factor (CF) method. The dataset was collected from 60 patient complaint narratives and validated by three professional psychologists with over five years of experience in geriatric psychiatry. The system design process includes symptom extraction using Natural Language Processing (NLP), CF value calculation for each symptom, and classification of depression risk (low, moderate, high). The system architecture consists of a knowledge base, inference engine, and user interface. Validation was conducted through diagnostic accuracy testing and user evaluation using a Focus Group Discussion (FGD). The results showed a validity level of 73%, and 88.6% of respondents agreed that the system can assist in early diagnosis. The novelty of this study lies in the integration of NLP and Certainty Factor tailored to the narrative patterns of the elderly, combined with a user-friendly interface design. This system is expected to serve as a supportive tool for psychologists and families in the early detection of depression in elderly individuals.
Model Klasifikasi Machine Learning Berbasis Multiple Measurement Distance Arwansyah, Arwansyah; Susanto, Cucut; Nurdiansah, Nurdiansah
CSRID (Computer Science Research and Its Development Journal) Vol. 17 No. 2 (2025): Juni 2025
Publisher : LPPM Universitas Potensi Utama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22303/csrid-.17.2.2025.256-269

Abstract

This study aims to explore and develop a K-Nearest Neighbors (KNN)-based classification model using various distance calculation methods, namely Euclidean, Manhattan, Minkowski, and Hamming Distance. To improve the model’s accuracy, the results from each distance method are combined using a weighted average technique. The datasets used are the Iris and Breast Cancer datasets obtained from the UCI Machine Learning Repository. Preprocessing is carried out using normalization with StandardScaler to ensure uniform feature scaling. The model is tested using cross-validation techniques and evaluated using accuracy metrics and a confusion matrix to assess classification performance. Based on the experimental results, the use of multiple distance methods combined with a weighted average approach yields improved accuracy compared to the conventional KNN method that relies on a single distance calculation. The findings of this study indicate that the combination of distance methods in KNN can enhance model performance in classification tasks. This study is expected to contribute to the development of a more adaptive KNN algorithm tailored to diverse data characteristics.
Perancangan Manajemen Bandwidth Pada Jaringan Internet Menggunakan Hierarchical Teken Bucket (HTB) Di SMAN 12 Pekanbaru Siregar, Vivi Devina; Marpaung, Noveri Lysbetti; Hutabarat, Sakti; Ervianto, Edy; Nurhalim, Nurhalim; Amri, Rahyul
CSRID (Computer Science Research and Its Development Journal) Vol. 17 No. 3 (2025): Oktober 2025
Publisher : LPPM Universitas Potensi Utama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22303/csrid-.17.3.2025.421-431

Abstract

In today’s digital era, the availability of a stable and efficient internet network is a crucial need to support teaching and learning activities in educational environments. SMAN 12 Pekanbaru, as one of the schools implementing technology-based learning, is experiencing issues related to uneven bandwidth distribution, especially during peak usage hours. This often disrupts access to essential school services such as online exams and digital learning platforms. This study aims to design a bandwidth management system using the Hierarchical Token Bucket (HTB) method with the Mikrotik RB750Gr3 device to optimize internet bandwidth distribution at SMAN 12 Pekanbaru. The methodology involves analyzing the existing network issues, designing a new network topology, configuring HTB, measuring Quality of Service (QoS) parameters, and conducting data analysis. he results show an improvement in network performance, with more stable average throughput, reduced delay and jitter, and minimal packet loss during peak hours. The implementation of the HTB method proves effective in providing better control over bandwidth allocation based on user and service priorities, thereby supporting more efficient and effective digital-based teaching and learning activities.
Sistem Deteksi URL Phishing Menggunakan Random Forest dan Gradient Boosting untuk Pencegahan Kejahatan Dunia Maya Khairunnisya, Aqilla; Lindawati, Lindawati; Zefi, Suzan
CSRID (Computer Science Research and Its Development Journal) Vol. 17 No. 3 (2025): Oktober 2025
Publisher : LPPM Universitas Potensi Utama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22303/csrid-.17.3.2025.296-310

Abstract

Phishing attacks through malicious URLs have become a critical cybersecurity threat, resulting in substantial financial losses and data exposures on a global scale. Conventional approaches like blacklisting and rule-based detection often fall behind as phishing methods become more advanced, including zero-day phishing URLs. In this research, machine learning models based on Random Forest and Gradient Boosting are designed and tested to accurately identify phishing URLs. The dataset, obtained from Kaggle, consists of 11,430 URLs with extracted features representing URL characteristics such as length, subdomain count, HTTPS status, and domain age. The two models underwent training and validation with the help of stratified train-test splits and cross-validation techniques. To evaluate the models, several performance indicators—such as accuracy, precision, recall, F1-score, and ROC AUC—were applied. Results from the experiments reveal that Gradient Boosting slightly exceeds the performance of Random Forest, achieving an accuracy of 98.0%, precision of 98.1%, and an F1-score of 98.0%. The best-performing model was integrated into a web application built with Streamlit, providing real-time phishing detection for end-users. This research contributes to developing adaptive and efficient phishing URL detection systems, enhancing cybersecurity defenses against evolving phishing threats. The implementation demonstrates practical applicability and ease of use for non-expert users.

Page 12 of 13 | Total Record : 129

 9   10   11   12   13 

Filter by Year

2022 2026


Reset
Filter By Issues
All Issue Vol. 18 No. 1 (2026): Februari 2026 Vol. 17 No. 3 (2025): Oktober 2025 Vol. 17 No. 2 (2025): Juni 2025 Vol. 17 No. 1 (2025): Februari 2025 Vol. 16 No. 3 (2024): October 2024 Vol. 16 No. 2 (2024): June 2024 Vol. 16 No. 1 (2024): February 2024 Vol. 15 No. 3 (2023): October 2023 Vol. 15 No. 2: June 2023 Vol. 15 No. 1: February 2023 Vol. 14 No. 3: October 2022 More Issue