cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
KARONA CAHYA SUSENA
Contact Email
karona.cs@unived.ac.id
Phone
+6281541234500
Journal Mail Official
karona.cs@unived.ac.id
Editorial Address
Jl. Meranti Raya No. 32, Sawah Lebar, Kota Bengkulu
Location
Kota bengkulu,
Bengkulu
INDONESIA
Jurnal Media Computer Science
Published by Universitas Dehasen Bengkulu
ISSN : -     EISSN : 28280490     DOI : https://doi.org/10.37676/jmcs
Core Subject : Science,
Computer Science & IT
Jurnal Media Computer Science merupakan jurnal nasional yang diterbitkan oleh Universitas Dehasen Bengkulu sejak tahun 2022. Jurnal Media Computer Science memuat artikel hasil-hasil penelitian di bidang Komputer, Sistem Informasi dan Teknologi. Jurnal Media Computer Science berkomitmen untuk menjadi jurnal nasional terbaik dengan mempublikasikan artikel berbahasa Indonesia yang berkualitas dan menjadi rujukan utama para peneliti.
Arjuna Subject : Ilmu Komputer (semua kategori) - Aplikasi Ilmu Komputer
Articles 291 Documents
 26   27   28   29   30 
Analysis Of Artificial Intelligence Deepfake Risks Regarding The Public's Ability To Distinguish Between Real And AI-Generated Content Using A Quantitative Descriptive Method Muhammad Rizki Akbar; Pahrizal Pahrizal; Yulia Darmi; Rozali Toyib
Jurnal Media Computer Science Vol 5 No 4 (2026): Oktober
Publisher : LPPJPHKI Universitas Dehasen Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37676/jmcs.v5i4.12713

Abstract

The development of Artificial Intelligence (AI) technology has given rise to Deepfake technology capable of producing visual content resembling reality, creating a risk of disinformation and making it difficult for the public to distinguish real from AI-generated content. This study aims to analyze the public's ability to distinguish such content and the relationship between the public's knowledge of Deepfake AI (X1) and their ability to detect Deepfake content (Y1). This study uses a quantitative descriptive method with 100 social media user respondents aged 17-30 years, collected online via Google Forms from June 24 to July 24, 2026. The instrument consisted of five Likert-scale statements measuring X1 and ten pairs of real-Deepfake photos measuring Y1. Results show that detection ability is high (average 77.1%) and knowledge level is also high (average 3.87 on a 1-5 scale). Both instruments were valid and reliable (Cronbach's Alpha for X1 = 0.834; KR-20 for Y1 = 0.712). A Pearson correlation test showed no significant relationship between X1 and Y1 (r = -0.028; p = 0.784 > 0.05), confirmed by simple linear regression (R Square = 0.001), so the null hypothesis (H0) is accepted; conceptual knowledge was shown not to automatically improve actual detection ability. This study recommends improving digital literacy, AI content watermarking regulations, and refining the detection instrument in future research.
Classification of Eligibility for Prospective Recipients of the KIP Kuliah Scholarship Using the K-Nearest Neighbor Method at Dehasen University in Bengkulu Imam Ashari; Herlina Latipa Sari; Devi Sartika
Jurnal Media Computer Science Vol 5 No 4 (2026): Oktober
Publisher : LPPJPHKI Universitas Dehasen Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37676/jmcs.v5i4.12720

Abstract

The Kartu Indonesia Pintar (KIP) Kuliah program aims to help high-achieving students from low-income backgrounds continue their education at the university level. At Dehasen University in Bengkulu, the high number of applicants far exceeds the available quota, while the selection process is still conducted manually by reviewing applications one by one. This situation results in a slow and inefficient selection process that is prone to human error and potentially introduces subjectivity in determining the eligibility of scholarship candidates.This study aims to apply the K-Nearest Neighbor (KNN) algorithm to classify the eligibility of KIP Kuliah scholarship candidates based on socioeconomic and academic data. The research stages included importing a dataset of 665 records, data cleaning, data selection, data transformation, Min-Max normalization, and splitting the data into training and testing sets, followed by classification by testing values of K = 3, 5, 7, and 9 using Euclidean distance calculations. The research results show that the KNN algorithm was successfully applied in the classification process, with the best accuracy achieved at K=7: 63.91%, Precision 53.90%, Recall 63.91%, and F1-Score 57.61%. Thus, the K-Nearest Neighbor (KNN)-based classification system developed can facilitate the initial selection process for prospective recipients of the KIP Kuliah Scholarship more quickly, consistently, and objectively compared to a manual selection process.
Decision Support System for Assessing Service Quality at Sawah Lebar Community Health Center Using the Weighted Product (WP) Method Reno Supardi
Jurnal Media Computer Science Vol 5 No 4 (2026): Oktober
Publisher : LPPJPHKI Universitas Dehasen Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37676/jmcs.v5i4.12724

Abstract

The quality of healthcare services is a crucial factor in enhancing public satisfaction with the services provided by Community Health Centers (Puskesmas). Puskesmas Sawah Lebar requires a system to assist in assessing service quality in an objective, measurable, and systematic manner. This study aims to develop a Decision Support System (DSS) for evaluating service quality at Puskesmas Sawah Lebar by applying the Weighted Product (WP) method. The WP method facilitates decision-making based on predetermined criteria, enabling the generation of service quality scores and rankings. The criteria employed in this study encompass aspects such as service timeliness, staff friendliness, facilities, responsiveness, and information clarity. Research data were obtained through [questionnaires/interviews/observations] involving service users at Puskesmas Sawah Lebar. Each criterion was assigned a weight based on its level of importance, followed by a calculation process using the Weighted Product method to derive preference values ​​for each alternative. The results indicate that the developed system assists the health center in conducting service quality assessments more rapidly, objectively, and in a structured manner. With this decision support system in place, the assessment results can serve as a basis for evaluation, helping Puskesmas Sawah Lebar improve the quality of services provided to the community.
Implementation Of Marker-Based Tracking And Fisher-Yates Shuffle Algorithms In An Augmented Reality-Based Solid Geometry Learning Application Using Unity For Mobile Platform Ryandika Anugrah; Edy Hermansyah; Rizka Tri Alinse
Jurnal Media Computer Science Vol 5 No 4 (2026): Oktober
Publisher : LPPJPHKI Universitas Dehasen Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37676/jmcs.v5i4.12727

Abstract

Teaching three-dimensional shapes still faces challenges in visualizing three-dimensional objects, causing students to struggle with understanding abstract concepts. This study aims to design and develop an Augmented Reality (AR)-based geometric shapes learning application using Unity on the Android platform by implementing the Marker-Based Tracking and Fisher-Yates Shuffle algorithms. Marker-Based Tracking is used to detect markers in real-time and display three-dimensional geometric shapes, while the Fisher-Yates Shuffle algorithm is applied to randomize the order of questions in the assessment feature. This study employed the Research and Development (R&D) method with the Waterfall development model, which includes the stages of requirements analysis, design, implementation, testing, and maintenance. Data collection was conducted through observation, interviews, and a literature review. Application testing was conducted using Black Box Testing to evaluate system functionality and the System Usability Scale (SUS) to measure the application’s usability. The results show that the application was successfully developed using Unity and Vuforia on the Android platform. The implementation of Marker-Based Tracking successfully detected markers and displayed 3D geometric objects in real time, while the Fisher-Yates Shuffle successfully generated variations in the question order within the quiz feature. The results of the black-box testing showed that all of the application’s main features functioned 100% in accordance with the test scenarios. Meanwhile, the results of the SUS test administered to 20 students yielded an average score of 78.0, which falls into the “Acceptable” category with a Grade B (Good) rating. Thus, the developed application has a good level of usability and can be used as an interactive learning tool to help students understand three-dimensional geometry concepts.
Mobile-Based Drug Inventory Management And Prediction System Using Multiple Linear Regression (Case Study: Idola Bunda Pharmacy) Rinaufal Liandisa; Yupianti Yupianti; Prahasti Prahasti
Jurnal Media Computer Science Vol 5 No 4 (2026): Oktober
Publisher : LPPJPHKI Universitas Dehasen Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37676/jmcs.v5i4.12728

Abstract

Manual drug inventory recording can delay recapitulation, increase recording errors, and make it difficult to estimate stock requirements for the next period. This study aimed to develop a mobile-based drug inventory management and prediction system for Idola Bunda Pharmacy and to apply Multiple Linear Regression to estimate inventory from historical data. A Research and Development design with a quantitative approach was employed using the Rapid Application Development model. Data were collected through observation, interviews, literature review, and transaction records covering purchases, sales, and inventory from January to December 2025. The system was implemented with PHP, MySQL, and Progressive Web Application technology. Sales and purchases were treated as independent variables, while inventory was the dependent variable. Testing with Woods Peppermint Syrup produced a January 2026 inventory prediction of 22 bottles compared with an actual value of 26 bottles. The Mean Absolute Percentage Error was 15.38%, which falls within the good prediction category, and black-box testing indicated that the main system functions operated according to the expected scenarios. These findings show that integrating transaction management and predictive analysis in a mobile-accessible application can support more structured inventory control and provide practical information for procurement decisions intended to reduce overstock and stockout risks.
Predicting Consumer Demand At Nayla Buah Store, Ratu Agung District, Bengkulu City Using Machine Learning Technology Yulia Darnita; Muntahanah Muntahanah; Dandi Sunardi; Ahmad Fauzi
Jurnal Media Computer Science Vol 5 No 4 (2026): Oktober
Publisher : LPPJPHKI Universitas Dehasen Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37676/jmcs.v5i4.12750

Abstract

The retail industry, including fruit stores, is significantly influenced by fluctuating consumer demand dynamics. An inability to accurately predict demand can lead to various operational issues for fruit stores, namely overstocking and understocking. Therefore, the ability to accurately forecast demand is crucial for business owners. The Random Forest Regressor and K-Nearest Neighbor (KNN) methods provide an evaluation of which algorithm is most suitable for predicting demand. Research results indicate that factors such as fruit type, day of the week, promotions, and weather influence the volume of consumer demand for fruit. These variables can serve as a basis for predicting fruit demand at the store. The prediction results from both models can be used to inform stock planning and operational decision-making. By forecasting daily demand more accurately, the store can mitigate the risk of losses associated with both overstocking and understocking. Overall, the Random Forest Regressor is the most recommended model for predicting fruit demand at Nayla Buah Store, as it offers higher accuracy and stability, and is capable of processing data variations more effectively.
Automated Data Extraction From Student Id Cards (KTM) Using Optical Character Recognition (OCR) And Regular Expressions Based On TKINTER GUI Andes Putra Gunawan; Yulia Darmi; Ardi Wijaya; Yuza Reswan
Jurnal Media Computer Science Vol 5 No 4 (2026): Oktober
Publisher : LPPJPHKI Universitas Dehasen Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37676/jmcs.v5i4.12751

Abstract

This research aims to design and implement an automatic data extraction system from student ID card (KTM) images using a combination of Tesseract-based Optical Character Recognition (OCR) and Regular Expression, with a graphical user interface built using Tkinter. The developed system consists of an image preprocessing stage (text-zone cropping, skew correction with protection against textured backgrounds, and Otsu binarization), OCR text extraction using a multi-source and multi-page-segmentation-mode strategy, and data field extraction using tiered Regular Expression patterns supported by a keyword-mapping dictionary for the Study Program and Faculty fields. Testing was conducted on 100 student ID card images from Universitas Muhammadiyah Bengkulu using confusion matrix evaluation metrics, namely Precision, Recall, F1-Score, and Accuracy for each data field. The test results show that the system achieved an overall accuracy of 93.8%, with a Precision of 94.86%, Recall of 98.72%, and F1-Score of 96.75%. Per-field accuracy results show that the Student ID Number (NPM) field reached 100%, Faculty 98%, Study Program 95%, Place/Date of Birth 94%, and Name 82%. This research also identified and resolved a cross-platform issue in the OCR library that caused the loss of spaces and punctuation marks in the extraction results when run on the Windows operating system. The findings indicate that the combination of OCR and Regular Expression, specifically designed to match the visual characteristics of student ID cards, is able to produce an accurate and reliable data extraction system suitable for supporting academic administration processes.
Predicting Daily Patient Visits at Community Health Centers Using Machine Learning Algorithms to Optimize Medical Staff Scheduling Zahza Ivana Putri; Edy Hermansyah; Dimas Aulia Trianggana
Jurnal Media Computer Science Vol 5 No 4 (2026): Oktober
Publisher : LPPJPHKI Universitas Dehasen Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37676/jmcs.v5i4.12756

Abstract

Optimal health care at primary-level health facilities depends heavily on the readiness of human resources. At the Lempuing Community Health Center in Bengkulu, there is a significant operational imbalance due to unpredictable fluctuations in the number of daily patient visits. Medical staff scheduling has thus far been conducted conventionally without considering future trends in patient arrivals. This results in long lines on certain days, while also causing labor inefficiency during periods of low patient volume. This study employs the Cross-Industry Standard Process for Data Mining (CRISP-DM) methodology. The modeling approach utilizes the Random Forest algorithm. The computational process leverages historical data on daily patient visits over the past three months, specifically from October through December 2025. In addition to building a computational prediction model, this study also integrates the forecasting results with the Standard Workload (SBK) formula. Based on the results of the Random Forest model testing, the evaluation performance showed a Mean Absolute Error (MAE) of 7.27 patients per day and a Root Mean Squared Error (RMSE) of 10.25 patients. The coefficient of determination obtained was 37.10%, which reasonably indicates the level of complexity and the highly stochastic nature of historical health data, which is susceptible to random external factors. Feature importance analysis revealed that the H-7 and H-1 Visit variables are the most dominant parameters influencing spikes in patient numbers. The system is effectively capable of transforming digital prediction figures into recommendations for medical staff allocation, ranging from 1 to 3 per day.
Implementation of the Greedy Algorithm for Subject Scheduling at SMP Negeri 57 North Bengkulu Yessi Indah Radianti; Reno Supardi; Desi Mahdalena
Jurnal Media Computer Science Vol 5 No 4 (2026): Oktober
Publisher : LPPJPHKI Universitas Dehasen Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37676/jmcs.v5i4.12790

Abstract

Subject scheduling is one of the essential activities in supporting the teaching and learning process in schools. Manual scheduling often presents several challenges, such as requiring considerable time, difficulties in allocating class periods, and the possibility of schedule conflicts. This study aims to develop a web-based subject scheduling system by implementing the Greedy Algorithm at SMP Negeri 57of North Bengkulu. The system was developed using the PHP programming language and MySQL database. The research methods included requirements analysis, system design using Data Flow Diagrams (DFD) and Entity Relationship Diagrams (ERD), system implementation, and functionality testing using the Black Box Testing method. The Greedy Algorithm was implemented to assist the scheduling process by gradually selecting available scheduling alternatives until a feasible schedule was generated. The results showed that the system was capable of managing teacher, subject, class, and class-period data in an integrated manner and generating lesson schedules more quickly and systematically than the manual process. With the implementation of this system, the subject scheduling process at SMP Negeri 57of North Bengkulu became more effective and efficient.
An Application For Predicting Children’s Nutritional Status In The Free Nutritious Meals Programme Using Machine Learning Muntahanah Muntahanah; A.R. Walad Mahfuzi; Pahrizal Pahrizal; Sinta Yopita Utari
Jurnal Media Computer Science Vol 5 No 4 (2026): Oktober
Publisher : LPPJPHKI Universitas Dehasen Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37676/jmcs.v5i4.12807

Abstract

The Free Nutritious Meal Program (Program Makan Bergizi Gratis - MBG) serves as a strategic initiative to enhance human resource quality from an early age through targeted nutritional intervention for school children. However, implementation efficiency at the school level remains hindered by conventional and manual monitoring of student nutritional status. This manual approach risks calculation errors in anthropometric indices and delays the early detection of nutritional issues. This study aims to design, develop, and evaluate the reliability of a web-based decision support system capable of automatically predicting elementary school students' nutritional status to support targeted distribution within the MBG program. The modeling technique utilizes the K-Nearest Neighbor (KNN) algorithm, processing four primary input variables: gender, age (months), weight (kg), and height (cm), in accordance with the Body Mass Index-for-Age (BMI-for-Age) anthropometric standards established by Permenkes RI No. 2 of 2020. The data preprocessing stage incorporates automatic unit conversion of height from centimeters to meters (Heightmeters = Heightcm / 100), Label Encoding, and Min-Max Scaling to normalize attribute scales and prevent variable dominance during Euclidean Distance calculation. Computational performance testing was conducted using a 4 x 4 Multi-class Confusion Matrix with a K=5 configuration on test samples from SDN 27 Bengkulu City. Experimental evaluation results demonstrate that the system achieved an Overall Accuracy of 90.91%, a Macro Average Precision of 96.88%, and a Macro Average Recall of 87.50%. These metrics prove that the KNN modeling approach possesses high precision and reliability in predicting four nutritional categories: thinness, normal, overweight, and obesity. In conclusion, the developed decision support application is scientifically validated and suitable for deployment as a digital ecosystem for schools and program managers to monitor students' physical health dynamics accurately, consistently, and in real time.

Page 28 of 30 | Total Record : 291

 26   27   28   29   30 

Filter by Year

2022 2026


Reset
Filter By Issues
All Issue Vol 5 No 4 (2026): Oktober Vol 5 No 3 (2026): Juli Vol 5 No 2 (2026): April Vol 5 No 1 (2026): Januari Vol 4 No 2 (2025): Juli Vol 4 No 1 (2025): Januari Vol 3 No 2 (2024): Juli Vol 3 No 1 (2024): Januari Vol 2 No 2 (2023): Juli Vol 2 No 1 (2023): Januari Vol 1 No 2 (2022): Juli Vol 1 No 1 (2022): Januari More Issue