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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 125 Documents
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Optimalisation Strategy For Light Intensity In Solar Cells To Improve Energy Efficiency Siagian, Parlin; Alam, Hermansyah; Fahreza, Muhammad; Frasasti, Ridho Anggu
Jurnal Media Computer Science Vol 4 No 1 (2025): Januari
Publisher : LPPJPHKI Universitas Dehasen Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37676/jmcs.v4i1.8576

Abstract

This study explores the optimisation of light intensity on solar cells to improve their energy efficiency. With the increasing global demand for energy and environmental issues due to dependence on fossil fuels, renewable energy sources, particularly solar energy, have become important. However, the efficiency of solar cells remains a challenge, with typical commercial efficiency ranging from 15% to 22%. This research investigates the impact of light intensity on solar cell performance and identifies strategies to optimise it. Factors such as semiconductor material quality, panel angle and orientation, and external conditions like weather and pollution are considered. The study shows that optimising light intensity can significantly enhance the energy conversion efficiency of solar cells. Methods such as using mirrors to concentrate sunlight and adjusting panel positions are explored to maximise light intensity. These findings contribute to the development of more efficient solar cell technology, aiding the broader adoption of clean energy solutions.
Utilization Of Technology In Ethnomathematics-Based Geogebra Learning Media Sianipar, Hikmatul Fadhilah; Nuranisah, Nuranisah; Silalahi, Trinidad
Jurnal Media Computer Science Vol 4 No 1 (2025): Januari
Publisher : LPPJPHKI Universitas Dehasen Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37676/jmcs.v3i2.8615

Abstract

This study aims to analyze the effectiveness of utilizing GeoGebra technology as a mathematics learning medium integrated with ethnomathematics concepts in enhancing students' understanding of geometry material. The background of this research is based on low student interest and learning outcomes in mathematics subjects, particularly geometry, as well as the need to integrate local culture in learning to create more meaningful contexts. The research method used is Systematic Literature Review (SLR) to review existing literature on the use of technology in ethnomathematics-based learning. A total of 15 relevant articles from the last 5 years were selected and analyzed using SLR to determine technology opportunities in creating culturally-based learning media. The article selection process used strict inclusion and exclusion criteria, with searches through academic databases such as Google Scholar using keywords "ethnomathematics", "GeoGebra", "technology", and "mathematics education". The findings from the literature review indicate that digital technology, particularly GeoGebra, has great potential in developing mathematics learning media. The articles analyzed consistently report improvements in learning interest, conceptual understanding, and appreciation of local culture when technology is used as a bridge between formal mathematics and ethnomathematics.
Implementation of Convolutional Neural Network for Soil Type Category Detection in a Web-Based Plant Recommendation System Sanjaya, Imam; Wahyuni, Yulinar Sri; Parwati, Lusiana Sani
Jurnal Media Computer Science Vol 4 No 2 (2025): Juli
Publisher : LPPJPHKI Universitas Dehasen Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37676/jmcs.v4i2.8637

Abstract

The growth of the agricultural sector in Indonesia is highly dependent on soil fertility, as soil is an important factor in the agricultural sector. However, conventional identification of soil types often takes a long time and requires high costs. To overcome this problem, this research develops a soil classification system using an optimized Convolutional Neural Network (CNN) model to improve soil classification accuracy. The results of this classification become the basis for a Content-Based Filtering (CBF) based recommendation system, in order to provide suggestions for crop types that are suitable for soil types. This research was conducted through several main stages, namely soil image data collection, data preprocessing, CNN model training and CBF-based recommendation system implementation. The CNN model is used to recognize soil texture and color patterns, while CBF is used to match soil characteristics with suitable plant species. System evaluation is conducted using confusion matrix to assess the accuracy of the classification model as well as the effectiveness of the recommendation system. The soil type classification process using CNN with MobileNetV2 architecture achieved an accuracy rate of 96%. This result shows that the architecture is effective in recognizing soil types precisely and can be used to provide appropriate crop recommendations. Thus, this system has the potential to support increased agricultural productivity, both on a small and large scale.
Implementation Of Ratcliff/Obershelp And Cosine Similarity Methods On Image For Detection Lianda, Deri
Jurnal Media Computer Science Vol 4 No 2 (2025): Juli
Publisher : LPPJPHKI Universitas Dehasen Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37676/jmcs.v4i2.8647

Abstract

Image is a tool for communicating in conveying certain messages from the image maker, so that images can be used as language in the technical field. Important pictures are stored by the user on a storage medium. The problem that occurs is that an image can be copied for personal gain, for example forging scanned images on important documents that can be misused. This study uses the Ratcliff/Obershelp and Cosine Similarity methods to detect similarities and differences in image images. So that with the implementation of the Ratcliff/Obershelp and Cosine Similarity methods for detecting similarities and differences in images, the image similarities can be known.
Effectiveness And Efficiency Of LLM Models Vs Traditional Machine Learning In Sentiment Analysis Of Indonesian Language Product Reviews Nurohim, Galih Setiawan; Amin, Budi Al; Setyadi, Heribertus Ary
Jurnal Media Computer Science Vol 4 No 2 (2025): Juli
Publisher : LPPJPHKI Universitas Dehasen Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37676/jmcs.v4i2.8681

Abstract

This research aims to conduct a comparative analysis of the performance and efficiency of several machine learning models in the task of sentiment analysis on Indonesian language customer reviews. In the digital business era, a quick and accurate understanding of customer opinions is a strategic asset for making decisions, from product development to marketing strategy. Four models were evaluated: two Transformer-based models (agufsamudra/indo-sentiment-analysis and ayameRushia/bert-base-indonesian-1.5G-sentiment-analysis-smsa), Naive Bayes, and K-Nearest Neighbors (KNN) on a dataset of 5,400 product reviews. The evaluation metrics used are Accuracy, Precision, Recall, and F1-Score. The results show that the Naive Bayes model and the Transformer model 'agufsamudra/indo-sentiment-analysis' achieve the highest performance with an F1-Score and accuracy of around 95%, significantly outperforming other Transformer models (90%) and KNN (47%). The crucial finding of this research is that the performance of the classical Naive Bayes model is equivalent to the state-of-the-art Transformer model. From an accounting and business perspective, this implies that solutions with much higher computational efficiency (Naive Bayes) can provide a more optimal Return on Investment (ROI) for large-scale implementation of customer sentiment monitoring systems.
Pengelolaan Brand Awereness Aplikasi Ezurance PT Jasaraharja Putera Semarang Melalui Strategi Public Relation Suryatiana, Putri Karima; Mardiana, Lisa
Jurnal Media Computer Science Vol 4 No 2 (2025): Juli
Publisher : LPPJPHKI Universitas Dehasen Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37676/jmcs.v4i2.8723

Abstract

This Public relations strategy plays a strategic role in building corporate image and increasing brand awareness, especially in the context of digital services such as the Ezurance application owned by PT Jasaraharja Putera Semarang Branch. This study aims to analyze the implementation of communication strategies carried out by the company in promoting Ezurance to the public. The approach used is PDCA (Plan, Do, Check, Action), which is applied in stages starting from the planning, implementation, evaluation, to corrective action. The strategies implemented include digital promotion through social media, participation in local events, dissemination of information through booths, and use of print media. In addition, the utilization of brand ambassadors that match the company's image is an innovative step to expand audience reach and increase campaign appeal. The results of the study indicate that the PR strategies implemented by the company have gradually succeeded in increasing brand awareness, although challenges remain due to the low level of digital literacy among the public in understanding insurance. Therefore, continuous public education and periodic evaluation are needed to ensure that the PR strategy implemented remains relevant to the development of digital trends and the changing needs of the audience
Deep Learning-based Sentiment Analysis of Public Comments on Military Education Using RoBERTa Algorithm and Rule-Based Hybird Parameters Hidayat, Jose Julian; Setyowati, Cindy; Amin, Muhammad Dikaisa Ibnu; Bimasakti, Khodir; Werdana, Aditya Pratama
Jurnal Media Computer Science Vol 4 No 2 (2025): Juli
Publisher : LPPJPHKI Universitas Dehasen Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37676/jmcs.v4i2.8769

Abstract

Social media such as Instagram has become an important digital public space for people to voice opinions on various policy issues, including the military education policy, which has recently become highly debated, especially in West Java and even outside Java. The purpose of this research is to develop a sentiment analysis model for public comments on Instagram regarding military education policy using a deep learning approach. m The RoBERTa model was trained and tested using classification performance metrics such as accuracy, precision, recall, and f1-score. The test results show that the model achieved an accuracy of 97%, with the highest f1-score value in the positive category at 0.98. The results show that RoBERTa can effectively classify sentiment based on public opinion on social media. This method can not only provide an overview of public responses, but can also be used as a tool in the decision-making process or public policy evaluation based on real-time digital opinion analysis.
Decision Support System for Prioritising the Use of Village Funds in Improving Local Infrastructure Using the Analytic Hierarchy Process (AHP) Method Rizqi, Alif Faiq; Aspriyono, Hari; Suryana, Eko
Jurnal Media Computer Science Vol 4 No 2 (2025): Juli
Publisher : LPPJPHKI Universitas Dehasen Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37676/jmcs.v4i2.8799

Abstract

This study discusses the development of a Decision Support System (DSS) to assist the Karya Bakti Village government in determining priorities for the use of Village Funds for local infrastructure development. The background of this research stems from a common problem in villages, namely suboptimal fund allocation due to subjective and conventional decision-making processes. The objective of this research is to design a DSS based on the Analytic Hierarchy Process (AHP) method so that decision-making becomes more objective, measurable, and in line with the urgent needs of the community. The benefits of this research are expected to assist the village in determining infrastructure development priorities and serve as a scientific reference for academics or researchers in similar fields. The results of this research are a web-based system that has been successfully developed and tested using the black box method, with all features functioning as intended. This system facilitates the comparison process between criteria and infrastructure project alternatives using AHP, and produces clearer and more measurable development priorities. In conclusion, the application of this DSS has proven effective in improving transparency and efficiency in decision-making at the village level, and has the potential to be applied to other villages with adjustments to local characteristics.
Analisis Faktor-Faktor Keterlambatan Pada Pelaksanaan Pekerjaan Proyek Modernisasi Gedung Serba Guna Pt Pusri Palembang Siddiq, Andika Maulana; Firdaus, Firdaus
Jurnal Media Computer Science Vol 4 No 2 (2025): Juli
Publisher : LPPJPHKI Universitas Dehasen Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37676/jmcs.v4i2.8802

Abstract

Construction project delays often result in financial losses and schedule disruptions. This study analyzes the factors causing delays in the modernization project of the Multi-Purpose Building of PT Pupuk Sriwidjaja (Pusri) Palembang using Structural Equation Modeling (SEM). The findings reveal that ineffective communication is the dominant factor, contributing 39.9% to project delays. Inefficient execution accounts for 37.5%, while insufficient workforce skills significantly contribute 33.2%. Additionally, the interaction among these three variables exacerbates the impact of delays. This study highlights the importance of improving coordination among project stakeholders, enhancing project execution efficiency, and developing workforce capacity. These measures are crucial to preventing similar delays in future projects.
Application Of Data Mining In Grouping Data On The Need For Social Welfare Services (Ppks) At The Dharma Guna Center In Bengkulu Wahyuni, Mera; Yulianti, Liza; Alinse, Rizka Tri
Jurnal Media Computer Science Vol 4 No 2 (2025): Juli
Publisher : LPPJPHKI Universitas Dehasen Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37676/jmcs.v4i2.8814

Abstract

Sentra Dharma Guna Bengkulu is an institution under the auspices of the Ministry of Social Affairs of the Republic of Indonesia that provides social rehabilitation services for people with disabilities, including therapy (physical therapy and occupational therapy) and training. At Sentra Dharma Guna Bengkulu, data collection is carried out on PPKS (Social Welfare Service Recipients) every month to determine the development of the PPKS based on 5 (five) assessment aspects, namely physical aspects, spiritual aspects, psychological aspects, social aspects, and vocational aspects. Every month the development of PPKS Mentally Disabled (PDM) is assessed against 5 assessment aspects to determine whether PPKS is in the severe, moderate or mild group. The application of data mining in grouping data on Social Welfare Service Recipients (PPKS) at the Dharma Guna Bengkulu Center can help collect data and assess the development of PPKS, especially People with Mental Disabilities (PDM), can help analyze and group PPKS data, especially People with Mental Disabilities (PDM), and can provide information on the results of grouping PPKS data, especially People with Mental Disabilities (PDM) every month. From the test data used, namely PPKS data for People with Mental Disabilities (PDM) in October 2024 as many as 49 PPKS, the results of data grouping were obtained using the K-Means Clustering Method which has been divided into 3 groups. The number of Cluster C1 data (Severe Group) consists of 9 PPKS data, Cluster C2 (Moderate Group) consists of 26 PPKS data, and Cluster C3 (Light Group) consists of 14 PPKS data.

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