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MDP Student Conference
Published by Universitas Multi Data Palembang
ISSN : -     EISSN : -     DOI : https://doi.org/10.35957/mdp-sc.v2i1.3997
Core Subject : Economy, Science, Engineering,
Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering
MDP Student Conference is a one-year national conference organized by the Universitas Multi Data Palembang. We are inviting teachers, lecturers, researchers, scholars, students, and or other key stakeholders to present and discuss their latest findings, innovations, and best practices as well as fresh ideas on, but not limited to, the listed sub-themes.
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 518 Documents
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Pengembangan Sistem Informasi JDIH Berbasis Web di Kabupaten Pesawaran Agusto, Rio; Efendi, Dwi Marisa; Supriyanto, Supriyanto
MDP Student Conference Vol 4 No 1 (2025): The 4th MDP Student Conference 2025
Publisher : Universitas Multi Data Palembang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35957/mdp-sc.v4i1.11191

Abstract

This research focuses on the development of a web-based Legal Documentation and Information Network System (JDIH) in Pesawaran Regency, integrated with the Legal Service System (SIPE Hukum). Using the Extreme Programming (XP) methodology, the system is developed iteratively to be more responsive to user needs. The technology utilized includes PHP and MySQL to enhance the efficiency of document management and legal information access for both the public and relevant institutions. The research findings indicate that the system can automate legal document management, accelerate information distribution, and improve transparency in legal services. In conclusion, the implementation of this system can optimize efficiency, accessibility, and accuracy in legal document management in Pesawaran Regency.
Penerapan Aplikasi Mobile untuk Pembelajaran Angka dan Berhitung pada Anak Usia Dini Maulana, Fazri; Efendi, Dwi Marisa; Prania, Dona
MDP Student Conference Vol 4 No 1 (2025): The 4th MDP Student Conference 2025
Publisher : Universitas Multi Data Palembang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35957/mdp-sc.v4i1.11195

Abstract

Learning numbers and counting is a fundamental foundation for the cognitive development of early childhood. However, conventional methods that are static and lack interactivity often make children easily bored and struggle to grasp concepts effectively. Therefore, innovation is needed in the form of an interactive mobile application that provides a more engaging, enjoyable, and flexible learning experience, allowing children to learn anytime and anywhere in a more effective way. This study employs a prototyping method, enabling iterative development with user feedback. The result of this research is a mobile-based learning application for early childhood education that presents number and counting materials in the form of text, images, and engaging animations. Additionally, it includes a quiz feature to assess children's understanding.
Penerapan Aplikasi Mobile untuk Pembelajaran Huruf dan Membaca pada Anak Usia Dini Pramudika, Tuta Arya; Efendi, Dwi Marisa; Prania, Dona
MDP Student Conference Vol 4 No 1 (2025): The 4th MDP Student Conference 2025
Publisher : Universitas Multi Data Palembang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35957/mdp-sc.v4i1.11196

Abstract

Early childhood literacy and letter learning is an important phase in their basic literacy development. However, conventional methods are becoming less effective and less interactive as technology advances. This study aims to design and develop a mobile application as a means of interactive and interesting literacy and letter learning for early childhood by utilizing the prototyping development method. This method is implemented through several stages, namely needs analysis, prototype design, prototype evaluation, coding, system testing, user evaluation and improvements based on user feedback. In this study, a mobile application for literacy and reading learning will be created with Android Studio, and with the Kotlin programming language. The results of this study are a mobile application that functions as a tool to assist teachers and parents in the conventional teaching and learning process.
Analisis Sentimen Film Squid Game Melalui Platform X Menggunakan Metode Lexicon Based Anggoro, Deo; Alessandro, Andreas; Aditya, Putra; Wijaya, Andri
MDP Student Conference Vol 4 No 1 (2025): The 4th MDP Student Conference 2025
Publisher : Universitas Multi Data Palembang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35957/mdp-sc.v4i1.11197

Abstract

This study discusses the role of movies as a medium in conveying social and emotional messages, with a focus on the Squid Game series. This research applies Lexicon-Based sentiment analysis to evaluate audience reactions to the series using 10,000 tweets from Twitter (X). The results of the analysis showed that 36.3% of the comments had a positive sentiment, 46.4% were neutral, and 17.2% were negative. The majority of neutral sentiment indicates that many viewers were interested but did not have strong opinions, while the significant positive sentiment indicates a favorable reception to the movie. These results provide insight into how audiences responded to the themes of economic inequality and social oppression in the movie. This analysis highlights the relevance of sentiment analysis in understanding audience responses to social issues, as well as how films can reflect deeper cultural and social issues.
Sistem Pakar Diagnosa Penyakit Pencernaan pada Manusia Menggunakan Metode Certainty Factor Laita, Vera April; Efendi, Dwi Marisa; Ngajiyanto, Ngajiyanto
MDP Student Conference Vol 4 No 1 (2025): The 4th MDP Student Conference 2025
Publisher : Universitas Multi Data Palembang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35957/mdp-sc.v4i1.11199

Abstract

Digestive diseases are one of the most common and diverse groups of diseases, covering a variety of conditions that affect the organs in the human digestive system. These diseases can range from mild disorders to serious conditions that require immediate medical attention. Delays in diagnosis and inappropriate treatment can lead to serious complications, as well as burden the health system. Digestive diseases are often difficult to diagnose because their symptoms can be similar to other conditions or vary from one individual to another. The examinations and tests needed for a proper diagnosis may also be unavailable or difficult to access. This study was conducted using the Development method using Extreme Programming, data collection, system analysis, database design, design of an expert system for diagnosing digestive diseases in humans using the certainty factor method, and the programming language used is Java programming. The development of this expert system for diagnosing digestive diseases in humans increases the accuracy and speed of the digestive disease diagnosis process and provides assistance to medical practitioners in making decisions related to diagnosis and treatment.
Perancangan Sistem Pakar Penyakit Demam Berdarah Menggunakan Metode Gradient Boosting Decision Tree Valentina, Cecilia; Tjen, Jimmy
MDP Student Conference Vol 4 No 1 (2025): The 4th MDP Student Conference 2025
Publisher : Universitas Multi Data Palembang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35957/mdp-sc.v4i1.11200

Abstract

Dengue fever is a disease transmitted through mosquito bites and can cause high death rates in several countries . This disease is most commonly found in countries with a tropical climate. Therefore, technology utilization has been implemented to help people to predict dengue fever. This research design an expert system using the Gradient Boosting Decision Tree (GBDT) method to classification a symptoms of dengue fever. This research used a dataset from Kaggle website and this data was analyzed and resulted in accuracy of 89%, a recall of 88,79%, and a precision of 69,96%. So, it was able to provide an accurate prediction of dengue fever through the GBDT method. The classification result was then adapted into mobile based application with a UI/UX design so that it can directly interact with users.
Perancangan UIUX Aplikasi Pemantau Kelembaban Tanah dan Suhu Udara Pada Tanaman Buah Naga Sabila, Qolbi Sin; Tjen, Jimmy
MDP Student Conference Vol 4 No 1 (2025): The 4th MDP Student Conference 2025
Publisher : Universitas Multi Data Palembang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35957/mdp-sc.v4i1.11201

Abstract

The increase in the number of dragon fruit commodities is greatly influenced by the growth and quality of the plant itself. Soil moisture and air temperature are the main factors that must be considered by farmers in the cultivation of dragon fruit plants. There is a need for regular monitoring of dragon fruit plants to maximize the growth of these plants. This research aims to design a User Interface (UI) display for the application of monitoring soil moisture and air temperature in dragon fruit plants. This application is based on Android to be more flexible when used. This monitoring application is supported by the use of IoT technology sensor devices connected to a microcontroller, this application can present real-time data about plant conditions, such as soil moisture levels and air temperature in a visual form that is easy to understand for users. The result of this research is a UI design with ease of providing information to users so that it can enable users to improve decision-making accuracy in monitoring soil moisture and air temperature in dragon fruit plants. This is expected to have a positive impact on the productivity of users in caring for dragon fruit plants and can maintain the quality of the harvest.
Value Positioning Advantage: A Resource Advantage Theory of Competition Perspective Salsabila, Cika Tania; Ferdinand, Augusty Tae
MDP Student Conference Vol 4 No 2 (2025): The 4th MDP Student Conference 2025
Publisher : Universitas Multi Data Palembang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35957/mdp-sc.v4i1.11203

Abstract

This study aims to bridge previous inconsistent research regarding the role of social media marketing on business performance. The design in this study is applied with a quantitative approach. The theoretical model was tested on 200 samples of Coffeeshop businesses in Semarang City, Indonesia. Data analysis was carried out with AMOS software. The novelty of this study lies in the variables and indicators that are deduced from the resource advantage theory of competition. We combine simple mediation effect testing that produces destination competitiveness to be a good intermediary in the influence of social media marketing on business performance. The rejection of the two hypotheses leaves a gap for future studies to be able to be explored further.
Prediksi GDP dengan RF dan XGBoost Berdasarkan Aspek Sosial, Ekonomi, dan Lingkungan Mubarok, M. Husni; Septian, Firza
MDP Student Conference Vol 4 No 1 (2025): The 4th MDP Student Conference 2025
Publisher : Universitas Multi Data Palembang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35957/mdp-sc.v4i1.11206

Abstract

This study aims to analyze Gross Domestic Product (GDP) prediction using Random Forest and XGBoost algorithms by considering social, economic, and environmental variables. The dataset was obtained from Kaggle and includes 22 independent variables influencing GDP. The model was developed with Whale Optimization Algorithm (WOA) optimization to improve prediction accuracy. Experiments were conducted on the Google Colab platform, and evaluation metrics included Mean Absolute Error (MAE), Mean Squared Error (MSE), and R-Squared (R²). The results show that XGBoost with WOA optimization achieves higher prediction accuracy compared to Random Forest. Key factors influencing GDP were identified through feature correlation analysis. In conclusion, the combination of machine learning and metaheuristic-based optimization methods enhances GDP prediction accuracy, providing valuable insights for economic policymakers.
Custom LiDAR Dataset for 3D Object Recognition in Restricted Spaces Using Voxel-RCNN Firman, Firman; Susilawati, Helfy; Setyawan, Arief Suryadi; Haqiqi, Mokh. Mirza Etnisa
MDP Student Conference Vol 4 No 1 (2025): The 4th MDP Student Conference 2025
Publisher : Universitas Multi Data Palembang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35957/mdp-sc.v4i1.11209

Abstract

Autonomous vehicles play a crucial role in logistics, agriculture, and warehousing, requiring precise object detection and recognition for safe navigation in confined spaces. Traditional 2D sensor-based methods and simple LiDAR applications often struggle with depth perception and classification accuracy, limiting real-time decision-making. This study addresses these challenges by developing a custom LiDAR-based dataset for object recognition within the Voxel-RCNN framework, focusing on six object categories to enhance recognition accuracy. The Voxel-RCNN model was trained on this custom dataset without architectural modifications, assessing its generalization to non-standard data and performance in constrained environments. The training process demonstrated stable convergence, with loss decreasing from 6.09 to 2.37 after 600 epochs. Quantitative evaluations using BEV and 3D Average Precision (AP) revealed strong performance in detecting structured objects like cars (68.14% BEV AP, 55.83% 3D AP in Easy cases) but significant challenges with occluded and irregularly shaped objects such as trees and cyclists. Despite these challenges, the study highlights the potential of Voxel-RCNN for 3D object recognition in autonomous navigation. Future improvements include dataset augmentation, multi-scale feature fusion, and advanced voxelization techniques to enhance recognition performance. These findings contribute to the advancement of LiDAR-based perception systems, supporting safer and more intelligent autonomous vehicle operations.

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