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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 230 Documents
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Implementasi Automated Conflict Testing Untuk Mencegah Double Booking Pada Sistem Penjadwalan Klinik Gigi Sulthan Zahran Sunata; Wahyu Catur Wibowo
Jurnal Media Computer Science Vol 5 No 3 (2026): Juli
Publisher : LPPJPHKI Universitas Dehasen Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37676/jmcs.v5i3.11809

Abstract

Double booking poses a significant risk to dental clinic scheduling systems, as it disrupts service workflows, increases patient wait times, and reduces practitioner operational efficiency. While healthcare scheduling literature extensively covers slot optimization, resource allocation, and no-show issues, there is limited research demonstrating how conflict prevention rules are automatically validated within implemented clinic systems. Conversely, software testing literature tends to focus on general automation rather than executable testing for rule-based conflict checking. This study aims to implement automated conflict testing to verify double-booking prevention mechanisms in a Django REST API-based dental clinic scheduling system. The methodology involves automated testing at both the model and API levels, utilizing scenarios derived from conflict-checking business rules. Test results indicate that all scenarios passed and that conflict-checking rules were consistently applied to both initial booking and rescheduling processes. The study's primary contribution is an executable, structured automated conflict-testing approach that confirms the acceptance of back-to-back slots, the rejection of active overlaps, and the subjection of rescheduling requests to conflict validation.
Densenet201 Feature Extraction With Soft Voting Ensemble For Accurate Rice Leaf Disease Classification Nelly Khairani Daulay; Novi Lestari; Rusdiyanto Rusdiyanto
Jurnal Media Computer Science Vol 5 No 3 (2026): Juli
Publisher : LPPJPHKI Universitas Dehasen Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37676/jmcs.v5i3.11876

Abstract

Rice leaf diseases are one of the major factors contributing to reduced agricultural productivity and economic losses for farmers. Manual disease identification generally requires expert knowledge and is often difficult to perform efficiently in field conditions. Therefore, this study aims to develop a rice leaf disease classification system by combining DenseNet201 as a feature extractor and a Voting Ensemble approach as the classifier. The dataset consisted of 1,470 rice leaf images categorized into five classes: Bacterial Leaf Blight, Brown Spot, Healthy Leaf, Leaf Blast, and Tungro. The dataset was divided using a stratified split strategy into 80% training data, 10% validation data, and 10% testing data. Image augmentation was applied only to the training set, increasing the number of training samples to 7,056 images. DenseNet201 was employed to extract image features into 1,920-dimensional feature vectors, which were subsequently classified using Logistic Regression, Support Vector Machine (SVM), Hard Voting, and Soft Voting. Experimental results showed that Logistic Regression achieved an accuracy of 95.24%, while SVM achieved 95.92%. Hard Voting obtained an accuracy of 95.24%, whereas Soft Voting achieved the best performance with an accuracy of 95.92%, precision of 95.75%, recall of 95.70%, F1-score of 95.71%, and ROC-AUC of 99.76%. Furthermore, the best-performing model was deployed in a Streamlit-based application for automatic rice leaf disease identification. The results demonstrate that the combination of DenseNet201 and Soft Voting provides an accurate and effective approach for rice leaf disease classification and has strong potential as an early disease detection tool in agriculture.
Does CFTC Regulation Reduce Prediction Market Anomalies? A Benford’s Law And DiD Analysis Al Berlant Ghulam Fania Akhmad; Ridka Dhenabayu
Jurnal Media Computer Science Vol 5 No 3 (2026): Juli
Publisher : LPPJPHKI Universitas Dehasen Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37676/jmcs.v5i3.11900

Abstract

The rapid growth of prediction markets raises concerns about data integrity and susceptibility to manipulation. This study examines whether Commodity Futures Trading Commission (CFTC) regulation of Polymarket reduces market anomalies measured through Benford's Law conformity. Employing a quasi-experimental nonequivalent control group design with a difference-in-differences (DiD) estimator, the study exploits the CFTC Amended Order of 25 November 2025 as the treatment on Polymarket, with Kalshi (regulated since 2020) as the control group. Daily price and volume data for both platforms were retrieved from Dune Analytics for January 2023–June 2026 (over 1.4 billion observations) and transformed into monthly Mean Absolute Deviation (MAD) per platform per category; the volatility index (VIX) serves as a covariate. DiD estimates on the theoretically valid volume outcome reveal no significant regulatory effect (β = +0.0038; p = 0.073); thus the hypothesis that regulation reduces anomalies is not supported, as Benford conformity was already high and improved on both platforms due to market maturation. Placebo and pre-trends robustness tests confirm the validity of the design. This study constitutes the first Benford–DiD quasi-experimental test in prediction markets.
A Comparative Analysis of User Sentiment and Review Patterns on The Dana and Seabank Apps Using The Naive Bayes, Support Vector Machine and Logistic Regression Algorithms Nur Ayu Siti Hardianti; Dedy Abdullah
Jurnal Media Computer Science Vol 5 No 3 (2026): Juli
Publisher : LPPJPHKI Universitas Dehasen Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37676/jmcs.v5i3.11946

Abstract

The development of financial technology (fintech) in Indonesia has driven a significant increase in the use of digital financial applications, particularly e-wallets and digital banks. The DANA application as a representative of e-wallet services and SeaBank as a representative of digital banking services have attracted millions of users with different service characteristics. These differences in service characteristics potentially generate different patterns of user sentiment and reviews. This study aims to analyze and compare user sentiment and review patterns of the DANA and SeaBank applications using three machine learning algorithms: Naive Bayes, Support Vector Machine (SVM), and Logistic Regression. User review data were collected from the Google Play Store and processed through text preprocessing stages including case folding, tokenization, stopword removal, and stemming. Text features were extracted using the TF-IDF method before being classified into positive, negative, and neutral sentiment categories. Model performance evaluation was conducted using accuracy, precision, recall, and F1-score metrics. The comparative analysis results are expected to provide a comprehensive overview of differences in user sentiment based on the type of fintech service, while also identifying the most optimal machine learning algorithm for sentiment analysis of digital financial application reviews.
The Implementation Of K-Means Clustering Method To Product Sales Data At Pratama Elektronik Shop, Taba Lagan Village, Central Bengkulu Regency Yoga Putra Pratama; Jusuf Wahyudi; Eko Suryan
Jurnal Media Computer Science Vol 5 No 3 (2026): Juli
Publisher : LPPJPHKI Universitas Dehasen Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37676/jmcs.v5i3.11974

Abstract

The implementation of K-Means Clustering method to product sales data at Pratama Elektronik shop, Taba Lagan Village, Central Bengkulu Regency, can assist store management in grouping sales data automatically and in a computerized manner, as well as providing information regarding product sales patterns that can be used to support decision-making, particularly in inventory management. The clustering results consisted of three groups: Cluster C1 (high-sales category), Cluster C2 (medium-sales category), and Cluster C3 (low-sales category). Based on testing using 100 sales data records from Pratama Elektronik shop, Taba Lagan Village, the results showed that 76 products were classified in the low-sales category, 21 products were classified in the medium-sales category, and only 5 products were classified in the high-sales category.
Application Of The Term Frequency-Inverse Document Frequency (TF-IDF)-Based Support Vector Machine (SVM) Method For Sentiment Classification Of Customer Reviews On My Lova Bengkulu Muhammad Sihab; Prahasti Prahasti; Ahmad Asyhari
Jurnal Media Computer Science Vol 5 No 3 (2026): Juli
Publisher : LPPJPHKI Universitas Dehasen Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37676/jmcs.v5i3.11981

Abstract

This study aims to apply a Term Frequency-Inverse Document Frequency (TF-IDF)-based Support Vector Machine (SVM) method for sentiment classification of My Lova Bengkulu customer reviews. The research data consisted of 109 reviews obtained from Google Reviews, which were then subjected to a preprocessing process involving cleaning and stemming. Next, weighting was performed using TF-IDF, and classification was carried out using the SVM algorithm. The results showed that 93 reviews (85.32%) were positive, while 16 reviews (14.68%) were negative. The model achieved an Accuracy of 81.82%, Precision of 81.80%, Recall of 100%, and an F1-Score of 90.00%. These results demonstrate that the TF-IDF-based SVM method is capable of effectively classifying customer sentiment.
Implementasi Data Mining Algoritma K-Nearest Neighbor Pada Penentuan Pemberian Sanksi Disiplin Di Rumah Sakit Umum Daerah Dr. M. Yunus Bengkulu Fiqi Aprianto; Jusuf Wahyudi; Ila Yati Beti
Jurnal Media Computer Science Vol 5 No 3 (2026): Juli
Publisher : LPPJPHKI Universitas Dehasen Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37676/jmcs.v5i3.12078

Abstract

Regional General Hospital (RSUD) Dr. M. Yunus Bengkulu is a government-owned health care facility with a strategic role in providing health services to the public, making employee discipline an important factor to maintain. The determination of disciplinary sanctions for employees at RSUD Dr. M. Yunus Bengkulu is still carried out manually based on recorded violation data, which requires a relatively long time and has the potential to create subjectivity in decision making. This study aims to apply the data mining method using the K-Nearest Neighbor (K-NN) algorithm to classify employee disciplinary sanctions based on five assessment attributes, namely years of service, number of tardiness incidents, number of unexplained absences, SOP violations, and service ethics violations. The system was developed using the Waterfall development method and implemented as a web-based application using the PHP programming language and MySQL database. Testing was conducted on 20 training data and 20 testing data using a K value of 5, calculated from the square root of the number of training data. The test results show that the K-Nearest Neighbor algorithm is able to classify employee disciplinary violation data into four sanction categories, namely no sanction, verbal warning, written warning, and disciplinary sanction, with an accuracy rate of 90%. Black box testing results also show that all system functions run according to requirements. This research is expected to help the management of RSUD Dr. M. Yunus Bengkulu make decisions on employee disciplinary sanctions in a more objective, consistent, and measurable manner.
Comparative Analysis Of Fuzzy Time Series And Double Exponential Smoothing Methods In Predicting Medicine Demand At Community Health Centers Aldo Zantohodi; Lena Elfianty; Rizka Tri Alinse
Jurnal Media Computer Science Vol 5 No 3 (2026): Juli
Publisher : LPPJPHKI Universitas Dehasen Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37676/jmcs.v5i3.12140

Abstract

Drug inventory management in primary health centers requires accurate forecasting to reduce stock-outs, overstock, and logistics inefficiency. This study compares Cheng Fuzzy Time Series (FTS) and Holt Double Exponential Smoothing (DES) for forecasting drug demand at Pajar Bulan Public Health Center. Using LPLPO data, 84 valid drug items were analyzed with January-December 2025 as training data and January-April 2026 as testing data. DES produced a lower MAE, but FTS achieved a better MAPE and was superior on 65.5% of items with valid MAPE. The results indicate that FTS is more robust for fluctuating active drug demand, while DES is suitable for more stable trend patterns.
Application Of The K-Means Algorithm in Clustering Medical Records Of BPJS Participants At Bhayangkara Hospital In Bengkulu Wahyu Rizki Rasuanto; Devi Sartika; Dimas Aulia Trianggana
Jurnal Media Computer Science Vol 5 No 1 (2026): Januari
Publisher : LPPJPHKI Universitas Dehasen Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37676/jmcs.v5i1.8941

Abstract

Grouping is to separate labels from unknown data and grouping is expected to be able to identify data groups to then be labeled as desired. Cluster analysis is a multivariate analysis technique to find and organize information about variables so that they can be relatively grouped into homogeneous groups or "clusters" can be formed.The purpose of data clustering work can be divided into two, namely grouping for understanding and grouping for use. If the goal is for understanding, the formed groups must capture the natural structure of the data, usually the grouping process in this goal is only an initial process to then be continued with core work such as summarization (average, standard deviation), class labeling in each group to then be used as classification training data and so on. K-Means is one of the clustering algorithms included in the Unsupervised Learning group which is used to divide data into several groups with a partition system. This algorithm accepts input in the form of data without class labels. In the K-Means algorithm, the computer groups the data that is its input without first knowing the target class. The input received is data or objects and k desired groups (clusters).
Sistem Informasi Pelayanan Kesehatan Berbasis Web Di Puskesmas Sambi Dengan Laravel Dwiki Reza Nova Alvianto; Ihsan Cahyo Utomo
Jurnal Media Computer Science Vol 5 No 1 (2026): Januari
Publisher : LPPJPHKI Universitas Dehasen Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37676/jmcs.v5i1.8973

Abstract

Sambi Health Center is one of the first-level health care facilities that has an important role in providing basic medical services to the community. However, in its implementation, the service process is still carried out manually, especially in recording patient data, managing queues, and delivering information. This has an impact on low efficiency, potential data input errors, and delays in service. To answer these problems, this study developed a web-based health service information system using the Laravel framework. The system is designed to automate the service process, minimize errors, and speed up the workflow. The Model-View-Controller (MVC) architecture is used to separate logic and interface, making the system easier to develop and maintain. The test results using the blackbox method and System Usability Scale (SUS) obtained a score of 90.4 with the category "Best Imaginable". The system has proven effective in increasing the speed of service, ease of data access, and supporting the digitalization of health services in real time.

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