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KARONA CAHYA SUSENA
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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 291 Documents
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Machine Learning Analysis Using the Hybrid K-Means and C4.5 Model in Determining Student Readiness for Educational Field Practice Nilam Agsera; Musli Yanto; Syafri Arlis
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.v5i4.12456

Abstract

The evaluation of student readiness for Educational Field Practice (PLK) has conventionally relied on the subjective assessments of supervising lecturers and mentor teachers, posing a risk of inaccurate outcomes. This traditional framework continues to depend on manual, non-standardized analytical approaches. To address these limitations, this study aims to develop a hybrid K-Means and C4.5 machine learning model to categorize the readiness levels of PLK students at Universitas Negeri Padang. K-Means clustering was initially applied to group evaluation data into three distinct readiness categories, with the resulting cluster labels serving as target variables. Subsequently, the C4.5 algorithm was utilized to extract decision rules and generate a predictive decision tree. The dataset comprises 3,578 student grade records obtained from the Center for Field Experience (Pusat PPL) at Universitas Negeri Padang for the July–December 2025 period, encompassing ten assessment indicator variables ranging from partner school observations to final practice reports. K-Means clustering classified 2,619 students (73.20%) into the "Ready" category, 936 students (26.16%) as "Sufficiently Ready," and 23 students (0.64%) as "Needs Mentoring". The C4.5 model selected the learning media indicator (X4) as the root node, achieving an overall accuracy of 92.32%, a weighted precision of 92.33%, a recall of 92.32%, and an F1-score of 92.33%. These findings demonstrate that the hybrid K-Means–C4.5 approach provides an objective, transparent, and data-driven instrument to support PLK administrators in final evaluations and in determining student readiness status upon completing field practice.
Analysis of Student Interest Recommendations Using K-Means and Perceptron Algorithm at MTSN 3 Solok Hafis Hidayattullah; Rini Sovia; Larissa Navia Rani
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.v5i4.12457

Abstract

Choosing an academic field before entering Vocational High School (SMK) should be supported by information reflecting students' abilities. This study aims to provide an initial field recommendation using K-Means Clustering and Single Layer Perceptron (SLP). K-Means forms three levels of ability, namely High, Medium, and Low, based on nine subject grades. SLP identifies tendencies toward Science, Social, or Creative fields using report card grades and the difference between the two highest field averages. The dataset contains 1,000 records from the 2021/2022 to 2024/2025 academic years. K-Means produced a Silhouette Score of 0.6734 and converged at the seventh iteration. On 150 testing records, SLP achieved 97.33% accuracy, 97.36% precision, 97.33% recall, and a 97.34% F1-score, with 146 correct predictions. Both outputs were combined into an initial recommendation containing ability level and field tendency for students and schools, rather than a final decision. Final selection should consider interests, talents, conditions, and teacher guidance
Analysis of Student Performance Determination Using K-Means Clustering and Perceptron at Private Madrasah Tsanawiyah Koto Anau Zakiya Hikmi; Musli Yanto; Syafri Arlis
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.v5i4.12458

Abstract

The way we normally evaluate how well students are doing at MTsS Koto Anau is not good enough. This is because it does not show us the hidden patterns of how students learn. This study wants to use a way to evaluate how well students are doing. It uses K-Means Clustering and Perceptron together to get an idea of what helps students achieve. We used a two step method. First K-Means Clustering puts students into three groups based on how similar they're in terms of school work. Then we use these groups to teach the Perceptron model. The information we used came from the office at MTsS Koto Anau. It had details about 113 students. Included things like their average grades how often they came to school and their final exam scores. When we tested the model it was very good at putting students into the group. It was 99 percent of the time. This shows that the model is very good, at figuring out which group a student belongs to. The main thing we found out is that using K-Means Clustering and Perceptron is a good way to understand how students learn and predict how well new students will do. This study helps because it gives madrasahs an useful way to analyze student data. This means they can make learning plans that're just right for each student. The MTsS Koto Anau can use this to make sure students get the help they need to do their best.
Analysis Of Closing Price Forecasting For Bank Negara Indonesia Stocks Using A Hybrid K-Means Backpropagation Neural Network Model Alfajri Salim; Sarjon Defit; Gunadi Widi Nurcahyo
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.v5i4.12459

Abstract

Stock price movements are dynamic in nature, and their active patterns make them difficult to predict accurately. These conditions have driven the development of forecasting models capable of recognizing data characteristics before generating predictions with a low error rate. This study aims to analyze how the hybrid K-Means and Backpropagation Neural Network model works in forecasting stock closing prices. The dataset used consists of historical closing prices of Bank Negara Indonesia (BNI) stock over a 5-year period, comprising a total of 1,447 data points. Based on the results of this study, the model achieved an RMSE of 109.26, an MAE of 83.30, and a MAPE of 2.1458%, indicating that the model is capable of predicting stock prices with a low error rate based on these evaluation metrics. The addition of klasters to the neural network training process helps the model recognize data characteristics, resulting in more stable predictions. This study provides a hybrid-model-based approach to stock closing price forecasting that can serve as a reference for developing time-series prediction models in the field of finance.
Comparison of K-Nearest Neighbor and Naive Bayes Algorithms for Vocational High School 4 Padang Student’s Majoring Specialization Ryan Firmansyah; Agung Ramadhanu; Billy Hendrik
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.v5i4.12460

Abstract

Determining a student's major of specialization at State Vocational High School (SMKN) Negeri 4 Padang is still done subjectively, so there is a risk of causing inaccurate placement which has an impact on student learning achievement. A Data Mining approach is needed to analyze academic grade patterns in a structured and objective manner to support majoring decisions. This research aims to implement and compare the performance of the K-Nearest Neighbor (KNN) algorithm and the Naive Bayes Classifier in predicting students' majors. The research method applies a standard preprocessing approach, which includes data cleaning, normalization, and training-test data sharing. The classification model was then evaluated using the Stratified 5-Fold Cross Validation and Confusion Matrix methods to assess the accuracy and reliability of predictions. The dataset consists of 68 records of SMKN 4 Padang students with five core subject variables and three target major specialization classes. Experimental results show that the Naive Bayes Classifier produces superior performance with an average total accuracy of 64.71%. Meanwhile, the K-Nearest Neighbor algorithm (K=12) obtained an accuracy of 47.06%. The contribution of this research can provide an automated decision support system model based on objective academic data for schools in determining major interest.
Sentiment Analysis Of Public Reviews Of Public Services At The Dpmptsp Of Bengkulu Province Using The Naive Bayes Method Khairullah Khairullah; Muhammad Husni Rifqo; Ujang Juhardi; Na’im Mukarrom Nawwar
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.v5i4.12471

Abstract

This study aims to analyze public sentiment toward public services at DPMPTSP of Bengkulu Province using the Naive Bayes method. The research data were obtained from public reviews collected during the 2025–2026 period and processed using Natural Language Processing (NLP) techniques, including case folding, cleaning, tokenizing, stopword removal, and stemming. Furthermore, the data were represented using the TF-IDF method and classified into positive, negative, and neutral sentiments using the Naive Bayes algorithm. The results indicate that the Naive Bayes method is capable of classifying public review sentiments effectively and providing an overview of public satisfaction and perceptions regarding the services provided. The findings of this study are expected to serve as evaluation material for DPMPTSP of Bengkulu Province in its efforts to improve the quality of public services.
Implementation Of Eye Tracking For Cheating Detection In Computer Vision-Based Exam Simulations Yovi Apridiansyah; Gerry Mandala Putra; Gunawan Gunawan; Ardi Wijaya
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.v5i4.12474

Abstract

This study implements an eye tracking system based on computer vision to detect cheating behavior during computer-based examination simulations. The developed system utilizes the MediaPipe Face Mesh library to detect more than 468 facial landmark points without requiring specialized hardware, then extracts visual behavior features including gaze direction, gaze-outside-screen duration, saccade count, and PERCLOS (Percentage of Eye Closure) values from examinee video recordings using OpenCV and Python through a Tkinter-based GUI. Cheating detection is performed using a rule-based threshold approach, without requiring machine learning model training. Testing was conducted on a dataset consisting of 30 examination simulation videos of approximately 10–20 seconds each, comprising 15 videos labeled as cheating and 15 videos labeled as normal ground truth. System performance was evaluated using a confusion matrix with the cheating class as the positive class. The results show that the system achieves an accuracy of 93.33%, precision of 100%, recall (sensitivity) of 86.67%, F1-score of 92.86%, and specificity of 100%, with True Positive of 13, True Negative of 15, False Positive of 0, and False Negative of 2. These results indicate that the PERCLOS value is the most dominant indicator in the cheating detection process in this study. This study demonstrates that a threshold-based eye tracking approach offers a lightweight, transparent, and affordable cheating detection solution compared to approaches requiring specialized hardware or deep learning models that require large computational resources.
Analysis Of The Performance Of The Wifi Network At The Faculty Of Engineering, Muhammadiyah University Of Bengkulu, Using Qos (Quality Of Service) Parameters Pitria Hasanah; A.R. Walad Mahfuzhi; Dedy Abdullah; RG Guntur Alam; Ruliyani Ruliyani
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.v5i4.12493

Abstract

The quality of the Wi-Fi network plays an important role in supporting academic activities at the Faculty of Engineering, Muhammadiyah University of Bengkulu. This study aims to analyze Wi-Fi network performance using the Quality of Service (QoS) method based on parameters like throughput, packet loss, delay, and jitter. Measurements were taken on six access points located on the 1st, 2nd, and 3rd floors during busy and quiet hours using the Wireshark application. The measurement results were analyzed and compared with TIPHON standards to determine network quality. The research results showed that the average throughput value of 770.19 Kbps falls into the Fair category, packet loss at 0.01% is in the Very Good category, delay of 32.49 ms is in the Very Good category, and jitter of 32.65 ms is in the Good category. Based on this analysis, an average index of 3.25 was obtained, so the overall Wi-Fi quality at the Faculty of Engineering, Muhammadiyah University of Bengkulu is in the Good category according to TIPHON standards and can support academic activities.
The Effect of Information Quality and Service Quality on User Satisfaction with the Public Service Information System at the National Unity and Political Affairs Agency of Palembang City Poni Cornelia; Tri Oktarina
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.v5i4.12508

Abstract

Digitalizing public services does not automatically ensure that a service will be adopted or evaluated satisfactorily. This study examines the effects of information quality and service quality on user satisfaction with the Public Service Information System of the National Unity and Politics Agency of Palembang City by adapting selected constructs from the DeLone and McLean model. A cross-sectional quantitative survey involved 141 users purposively selected from a population of 218 users. The instrument comprised 14 Likert-scale items measuring information quality, service quality, and user satisfaction. The data were analyzed using PLS-SEM through measurement and structural model assessment. All indicator loadings ranged from 0.736 to 0.858; AVE values ranged from 0.585 to 0.663; and composite reliability ranged from 0.849 to 0.908. Information quality had a positive and significant effect on user satisfaction (beta=0.442; t=7.100; p<0.001), as did service quality (beta=0.460; t=8.777; p<0.001). Together, the two constructs explained 44.6% of the variance in user satisfaction. The results identify regularly updated service information and responsive user support as improvement priorities. The findings are limited to people who had used the system and do not represent every success dimension in the DeLone and McLean model.
Design Of A Talent And Interest Assessment (Abm) Test Application At Smkn 3 Bengkulu City Using The Fisher-Yates Shuffle Algorithm Yuza Reswan; Muhammad Husni Rifqo; Khairullah Khairullah; Ade Rangga Saputera
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.v5i4.12520

Abstract

In the world of education, especially at the Vocational High School (SMK) level, students are expected to be able to choose a major that suits their talents and interests so that the learning process can take place optimally and produce graduates who are competent in their fields. The development of web-based or computer-based applications for the implementation of assessment tests is an effective solution. Assessment test applications can provide convenience in presenting questions, randomizing the order of questions so that the results are more objective. However, in reality, there are still many students at vocational schools in Bengkulu City who do not know the direction and goals they want to achieve, especially in continuing their further studies. How to design and build an aptitude and interest assessment (ABM) test application by applying the Fisher-Yates Shuffle algorithm at SMK N 3 Bengkulu City.

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