ejournal.nusamandiri.ac.id (STMIK Nusa Mandiri)
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COMPARATIVE STUDY OF YOLO VERSIONS FOR DETECTING VACANT CAR PARKING SPACES
Full text linkThe increasing vehicle density in urban areas has made parking space availability a significant challenge. With technological advancements, efficient smart parking systems based on object detection have become essential. This study evaluates the performance of YOLO versions 3 to 11 in detecting vacant parking spaces in urban environments, focusing on real-time processing, high accuracy with limited datasets, and adaptability to varying conditions. Using 4,215 annotated images and two test videos, YOLOv7 achieved the highest overall accuracy of 99.57% with an average FPS of 30.79, making it the most effective model for smart parking applications. YOLOv8 and YOLOv11 followed closely, with accuracies of 98.51% and 98.72%, respectively, and average FPS rates of 32.31 and 31.99, balancing precision and speed, which are ideal for real-time applications. Meanwhile, YOLOv5 stood out for its exceptional processing speed of 33.92 FPS. These results highlight YOLO's potential to revolutionize smart parking systems by significantly enhancing both detection precision and operational efficiency.
OPTIMIZING TRANSPORTATION SURVEILLANCE WITH YOLOV7: DETECTION AND CLASSIFICATION OF VEHICLE LICENSE PLATE COLORS
Full text linkOptimizing transportation surveillance requires accurate vehicle license plate color detection and classification; however, existing systems face significant challenges in achieving real-time accuracy and robustness, particularly in crowded traffic scenarios with varying lighting and plate conditions. In Indonesia, vehicle license plates are color-coded based on their usage, including white and black for private vehicles, yellow for public vehicles, red for government vehicles, and green for free-trade areas. Each plate color plays a crucial role in transportation management, enabling proper vehicle identification and regulation. Existing surveillance systems struggle with real-time detection accuracy, especially in distinguishing plate colors in crowded traffic. Traditional methods may not efficiently classify plate colors due to limitations in feature extraction and processing. To address this, this study implements the YOLOv7 model to improve vehicle license plate color detection (black, white, yellow, and red) while distinguishing non-plate vehicles in diverse scenarios. The model's effectiveness is evaluated using precision, recall, and F1-score to ensure robustness for surveillance applications. Results show an average precision of 95.27%, recall of 94.60%, and F1-score of 94.93%, demonstrating strong detection capabilities. Optimizing the Non-Plate category further improves system accuracy, efficiency, and scalability, enhancing transportation monitoring reliability
IMPLEMENTATION MEAN IMPUTATION AND OUTLIER DETECTION FOR LOAN PREDICTION USING THE RANDOM FOREST ALGORITHM
Full text linkLoans and credit are among the most in-demand banking products, making accurate loan prediction systems essential for minimizing bank credit risks and boosting profitability. This study proposed a loan prediction model using the Random Forest algorithm, with mean imputation and 3 outlier detection (Boxplot, Z-score, and Interquartile Range (IQR)) as data pre-processing methods. Using Lending Club loan data from 2014-2021 (466,285 records, split 70/30 for training/testing), model performance was assessed using accuracy, recall, and F1 Score. The proposed approach achieved a 95% prediction accuracy, outperforming previous models at 83%. The best results were obtained using mean imputation with IQR-based outlier detection. However, the determination of the mean imputation mean can be a limitation of this study. This highlights the importance of thorough pre-processing in enhancing prediction accuracy. The study underscores the role of machine learning and financial technology (fintech) in informing credit decisions and support incorporating imputation and outlier handling as standard steps in financial modeling pipelin
SENTIMENT ANALYSIS OF GOVERNMENT ON TIKTOK AND X PLATFORMS WITH SVM AND SMOTE APPROACH
Full text linkThis study aims to analyze public sentiment toward the government on TikTok and X (formerly Twitter) using the Support Vector Machine (SVM) algorithm optimized with the Synthetic Minority Over-sampling Technique (SMOTE). Data were collected through keyword-based scraping of posts containing the word “pemerintah” (government) and processed using standard NLP pre-processing techniques. Results show that SVM combined with SMOTE significantly improves classification accuracy from 61% to 76% on TikTok, and from 74% to 86% on X. Word cloud analysis confirms these findings: TikTok content tends to reflect neutral and positive sentiments, while X contains predominantly negative expressions. These differences highlight platform-specific public discourse characteristics. The findings suggest that public communication strategies should be tailored accordingly: TikTok for positive narrative and outreach, X for monitoring feedback and criticism. This approach demonstrates the effectiveness of machine learning-based sentiment analysis in supporting data-driven public policy communication
PENERAPAN KOMPOR MINYAK JELANTAH SEBAGAI SUBSTITUSI BAHAN BAKAR PADA INDUSTRI UMKM MAKANAN
Full text linkIn daily life, stoves are essential tools used in cooking. They play an important role in energy use for households and small businesses, especially MSMEs (Micro, Small, and Medium Enterprises). In Samarinda and surrounding areas, food-related MSMEs are growing rapidly, ranging from snacks to trendy beverages. One of the major components of production costs is fuel. The 3-kg LPG cylinder, commonly called the "melon gas," is widely used due to its affordability and availability in urban areas. However, frequent shortages cause price instability and make it unreliable. To address this, a community service program was conducted through education and mentoring on the use of stoves fueled by used cooking oil as an alternative. Emission test results showed that a stove using used cooking oil reached 303°C with O₂ at 11.13%, CO at 9 ppm, and NaO at 16 ppm in 1 minute. In comparison, a gas stove reached 310.2°C with O₂ at 12.28%, CO at 61 ppm, and no NaO detected. These results show that used cooking oil can be a cost-effective alternative for MSMEs. It not only provides a solution to the unstable LPG supply but also helps reduce environmental impact by reusing waste oil from food processing. The used-oil stove has been well received by local entrepreneurs as an affordable and eco-friendly option
DESIGN OF WEB-BASED CAR RENTAL INFORMATION SYSTEM USING EXTREME PROGRAMMING AT CV. NUGROHO
Full text linkThis study discusses the development of a web-based car rental information system for CV. Nugroho Trans Surabaya using the Extreme Programming (XP) methodology. The system was designed to address issues in the previous manual rental process, such as transaction recording on paper, which was prone to errors and delays in data management. The developed system includes key features such as car booking, fleet data management, rental confirmation, and payment integration. Testing was conducted through various methods, including performance testing, usability testing, and security testing. Performance testing using PageSpeed Insights in desktop mode showed the following scores: performance 93, accessibility 84, best practices 93, and SEO 82. Meanwhile, testing with GTmetrix yielded a performance score of 96%, a structure score of 72%, a Largest Contentful Paint (LCP) time of 909 ms, and a fully interactive time of 1.3 seconds, indicating excellent speed and interface stability. In terms of security, testing with Pentest Tools indicated an overall medium risk level, with 1 medium risk finding, 5 low-risk findings, and 13 informational findings, and no high-risk vulnerabilities. The application of the XP method enabled adaptive system development tailored to user needs and iterative changes. This system has proven to increase the company’s operational efficiency by up to 40%, based on faster transaction completion times compared to the manual system. However, some limitations remain, such as user interface constraints and suboptimal integration of online payment channels. For future research, it is recommended to improve user experience, optimize the mobile interface, enhance server security protection, and expand system features to support the broader business growth of CV. Nugroho Trans Surabay
EVALUATING REGRESSION AND NEURAL NETWORKS FOR FIVE TRAIT TEXT-BASED PERSONALITY PREDICTION
Full text linkThe aim of this study is to evaluate the effectiveness of several predictive modeling techniques in mapping the five major personality traits (extraversion, neuroticism, agreeableness, conscientiousness, and openness) from text-based data. The dataset consists of text-based features extracted from publicly available social media posts, providing a realistic basis for personality prediction. Performance was measured using mean absolute error (MAE), mean squared error (MSE), and R² score to evaluate prediction accuracy and generalization quality, along with training time for computational efficiency. The research compares linear regression, ridge regression, random forest, and neural networks implemented in PyTorch. Results indicate that ridge regression and random forest outperform linear regression and neural networks in error metrics and explained variance, with ridge regression offering a favorable balance between accuracy and training time. Random forest achieves slightly better accuracy but with significantly longer training duration, reducing its practicality for real-time use. Despite theoretical advantages in modeling non-linear relationships, neural networks showed suboptimal results, likely due to limited hyperparameter tuning and dataset size. These findings highlight trade-offs among model complexity, accuracy, and efficiency, suggesting ridge regression as a pragmatic choice for current personality prediction from text while encouraging future research on advanced neural architectures and enhanced dataset
PENENTUAN PRIORITAS PENGEMBANGAN DESA WISATA RINTISAN KOTA PURWAKARTA MENGGUNAKAN METODE MULTI ATTRIBUTE UTILITY THEORY
Full text linkA Tourism Village can be understood as a village that organizes tourism activities due to the tourist attractions arising from the characteristics of the local community’s life, including various attractions available within the village itself. Each tourism village needs to be supported by tourist attractions, accessibility, and amenities, which include the potential of cultural tourism as well as natural tourism. The purpose of this research is to design and develop a Decision Support System for Determining the Priority of Pioneer Tourism Village Development using the Multi Attribute Utility Theory (MAUT) method. The urgency of this research lies in the need for DISPORAPARBUD Purwakarta to have a tool to assist in making strategic decisions related to the development of pioneer tourism villages. The methodology used in developing this system is the Waterfall model. The system is designed using PHP with the CodeIgniter 3 framework, MySQL as the database, and Unified Modeling Language (UML) for system modeling. The main criteria used in the MAUT calculation include public facilities, homestay management, local crafts, local arts, and local food. The results of the calculation using the MAUT method show that Batu Nunggal Margaluyu Tourism Village ranks first with a preference value of 100, Sasanakerta Tourism Village ranks second with a value of 96, and Sumbersari Tourism Village ranks third with a value of 90. Therefore, these three tourism villages are the best recommendations to be prioritized in the development of tourism villages in Purwakar
RANCANG BANGUN APLIKASI PREDIKSI TAGIHAN AIR BERBASIS WEB MENGGUNAKAN REGRESI LINIER BERGANDA
Full text linkThe management of water billing in the PAMSIMAS service in Sidobandung Village is still conducted manually and does not provide early information regarding bill estimates, often resulting in delayed payments by customers. This study aims to design and develop a web-based water bill prediction application using the Multiple Linear Regression (MLR) method, capable of delivering fast, accurate, and accessible billing estimates. The dataset used in this research consists of historical monthly water usage and billing data from January to December 2024, with a structure comprising 231 rows of customer data and 30 feature columns. The research stages include data preprocessing, model training using MLR, integration of the model into a web-based system, and evaluation of prediction results using the Mean Squared Error (MSE) and R-squared ( ) metrics. Evaluation results showed that the model achieved an MSE of 18,882 and an of 0,8, indicating a fairly good and stable prediction performance. The system allows customers to log in, view predicted water bills for the 13th month based on previous data, and access graphical visualizations of usage and cost trends. Meanwhile, the admin can efficiently manage customer data through a dedicated dashboard. With the implementation of this application, the management and prediction process of water billing becomes more transparent, efficient, and helps customers in planning their water expenses more precisely
RANCANG BANGUN SISTEM INFORMASI MANAJEMEN DISTRIBUSI QURBAN
Full text linkOne of the most important aspects of Eid al-Adha celebrations is the distribution of sacrificial meat. However, the process of distributing sacrificial meat often faces various challenges, such as inaccurate data collection, difficulty in tracking the amount of sacrificial meat, and a lack of transparency and efficiency. The objective of this study is to design and develop an application that can enhance efficiency, accuracy, and accountability in the distribution of sacrificial meat through the systematic use of information technology. This study employs the waterfall method, which involves several sequential stages: needs analysis, system design, implementation, and testing. This system was developed to support the performance of the sacrificial committee in managing data on sacrificial animals, information on recipients, the distribution process of meat, and the documentation of all activities in a digital and real-time manner. In the user interface (front end), the Next.js/React.js framework is combined with Tailwind CSS to produce a responsive and user-friendly interface. Meanwhile, the server side (back end) was developed using Laravel as a reliable and efficient PHP framework, and MySQL as a database to store all information related to distribution. The result of this research is a web-based application prototype featuring animal sacrifice data collection, beneficiary data recording, and distribution report generation. It is hoped that this application will facilitate more organized and effective distribution of sacrificial mea