Jurnal Pilar Nusa Mandiri

CONTINUOUS INTEGRATION PIPELINE WITH JENKINS

2024-11-01T03:15:43+00:00

The advancement of technology has led to continuous improvement in application development. As a result, there is a growing demand for application software. Tech companies are constantly working on building and updating their existing applications. The development process is prolonged because to the intricate nature of the build and deployment procedures, ensuring that the application software can be accessed and utilized by individuals across the internet. To address these issues, this research aims to construct and enhance a system capable of automating the entire build and deployment process. By eliminating human intervention, potential errors and downtime that may prevent access to the deployed application can be avoided. The system was developed using the RAD or Rapid Application Development method, with the goal of simplifying and expediting the development process. A DevOps Engineer facilitates the implementation of Continuous Integration in order to reduce the duration of the entire Software Development Life Cycle (SDLC) by utilizing an open-source tool named Jenkins. This ensures that the application development process is efficient and adheres to the designated schedule, allowing all users to benefit from timely delivery.

MAPPING OF DOMESTIC AND FOREIGN TOURIST VISITS IN EAST JAVA USING THE DBSCAN METHOD

2024-11-20T09:01:16+00:00

Tourism is important in economic growth and regional development, especially in East Java Province with diverse tourist attractions. However, the mapping of domestic and foreign tourist visit patterns in this province is still limited. For this reason, this study uses the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) method which can group density-based data without determining the number of clusters from the beginning and handle noise. The study aims to map districts/cities in East Java based on the number of tourist visits from 2018 to 2022, using visit data from the East Java Provincial Culture and Tourism Office. The analysis results show that in domestic tourist data, with parameters MinPts = 3 and ε = 1.00, one main cluster is formed consisting of 31 tourist locations and 7 noisy locations. In foreign tourist data, with ε = 0.6 and MinPts = 3, there is one cluster with 30 tourist locations and 8 other locations are categorized as noisy. Noisy locations tend to have higher visits but do not fit into the main cluster. These findings provide important insights for more targeted tourism promotion strategies and efficient resource allocation in East Java.

PERFORMANCE COMPARISON OF RANDOM FOREST REGRESSION, SVR MODELS IN STOCK PRICE PREDICTION

2024-11-22T01:07:58+00:00

The stock market is characterized by high volatility and complexity, making it an intriguing and challenging subject for researchers and practitioners. This study aims to predict stock prices by comparing the performance of two machine learning models: Random Forest Regression and Support Vector Regression (SVR). These models were selected for their ability to handle complex data and high volatility. The dataset used in this study consists of BNI stock data over the last five years (2019–2024), comprising a total of 1,211 data points. Testing was conducted using a cross-validation approach, and model performance was evaluated based on several metrics, including MSE, R², RMSE, MAPE, MAE, and Score. The results indicate that Random Forest Regression outperforms SVR. The model achieved an MAE of 17.766, an RMSE of 22.376, and an R² of 0.997. These findings suggest that Random Forest Regression is more effective in predicting stock prices, particularly in unstable market conditions. This study recommends Random Forest Regression as a reliable model for stock price prediction, with potential applications in other stock markets with similar characteristics.

ONLINE DELIVERY, DINE-IN, AND RESERVATION SYSTEM USING THROW-AWAY PROTOTYPING AT JONG JAVA RESTAURANT

2025-01-08T03:23:51+00:00

The culinary industry continues to grow amidst the increasing purchasing power and consumption of society. Many restaurants still rely on direct sales (dine-in) without utilizing technology to enhance services and expand their market. This study aims to design and build a web-based information system for Restoran Jong Java, which includes dine-in, online delivery, and table reservation features. The system is expected to simplify transaction processes, improve operational efficiency, and provide added value for the restaurant in the industry competition. The system is developed using the throw-away prototyping method. This method allows for iterative system development with direct input from users. Testing is conducted using black-box testing to ensure the system functions as per the specifications, and user acceptance testing is performed through questionnaires with five main types of users: customers, admins, cashiers, waiters, and waitresses. The designed system is capable of supporting the integrated management of dine-in services, reservations, and online deliveries. The tests show that the system meets user needs, with a high level of satisfaction from the respondents. The user acceptance testing in this study shows positive results across different user groups. For the customer group, the average score obtained was 77.25%, the waiter group gave an average score of 83.6%, and for the managers, the system was also well received by them. This system has successfully improved the restaurant's operational efficiency and provided convenience for customers in making orders. It also serves as a technological solution that can help Jong Java expand its market reach and increase competitiveness.

PREDICTING SOLAR POWER GENERATION: A MACHINE LEARNING APPROACH FOR GRID STABILITY AND EFFICIENCY

2025-01-06T09:08:21+00:00

In countries with high levels of insolation, the demand for renewable energy sources has driven the rapid emergence and growth of solar power plants. Maintaining grid stability and efficient power management in response to weather variations that affect solar radiation intensity and battery consumption limits remains a major challenge. This study aims to develop a machine learning-based prediction model to estimate the electricity generated by solar power plants using weather data. Four algorithms are utilized: Linear Regression, Random Forest Regressor, Decision Tree Regressor, and Gradient Boosting Regressor. The results show that the Random Forest algorithm produces the best model, with MAE and RMSE values of 0.1114281 and 0.3187232, respectively. This research contributes to the literature, particularly on the relatively unexplored topic of using multiple machine learning models to predict energy output from photovoltaic systems. The findings have the potential to inform more efficient energy policies and improve energy integration technologies for grid-connected solar power systems.

MEASURING INFORMATION TECHNOLOGY GOVERNANCE USING COBIT 2019 FRAMEWORK AT TOURISM INDUSTRY

2025-01-23T09:48:04+00:00

Although PT XYZ has adopted information technology, it has not formally assessed its governance, leading to persistent issues in IT management, human resource capabilities, and alignment with business processes. This study evaluates IT governance at PT XYZ, a company in the travel and tourism industry, where rapid technological advancements have impacted operations. Using the COBIT 2019 framework, the study assessed IT governance through interviews and literature review, focusing on the domains APO04 – Managed Innovation, BAI02 – Managed Requirements Definition, BAI03 – Managed Solution Identification & Build, and BAI05 – Managed Organizational Change. The results indicate that these domains are at level 2, "Largely Achieved," highlighting areas of improvement. This benchmark provides practical recommendations to enhance IT governance and improve integration between IT and business functions. The findings offer PT XYZ actionable steps to strengthen governance practices, improve organizational performance, and better align technology with strategic business goals.

PASSWORD STRENGTH STUDY USING THE ZXCVBN ALGORITHM AND BRUTE-FORCE TIME ESTIMATION TO STRENGTHEN CYBERSECURITY

2025-01-23T10:04:44+00:00

This research analyzes password strength based on its length and complexity using brute force attack simulations. The study begins with collecting password data from various sources to ensure sufficient variation in complexity levels. Next, the passwords are evaluated using the Zxcvbn algorithm, which provides a strength score as well as information about the time required to crack them. The same passwords are also evaluated using Brute-force Time Estimation to calculate the estimated time required to crack the password. After both algorithms have been evaluated, the results are analyzed to find the correlation between the Zxcvbn score and the estimated brute force time. The results of the data analysis are then visualized in the form of graphs or diagrams to facilitate understanding and assessment of password security. This simulation estimates the time required to guess a password, depending on the level of password complexity. Although the simulation results show that long and complex passwords are more secure, the actual strength of the password is highly dependent on the tools used by the attacker. In addition, digital security is not only limited to passwords, but also depends on various loopholes that can be exploited, such as personal data leaks or software vulnerabilities. Therefore, a comprehensive security approach is essential to protect users from potential cyberattacks. This study aims to provide in-depth insights into the strength and vulnerability of passwords and the effectiveness of algorithms in assessing password security.

GENERATION Z'S AND ANDROID OS: HOW USER EXPERIENCE, SECURITY, AND SYSTEM PERFORMANCE SHAPE SATISFACTION

2025-01-07T04:50:47+00:00

This paper investigates Generation Z's opinion of the Android OS through an analysis of system performance, security, and user experience and their influence on general satisfaction. With Technology Adaptation (Z) as a mediator, the study focuses on three main factors: User Experience (X1), Security and Privacy (X2), and System Performance and Stability (X3), specifically among students who use Android devices. Data were collected through a structured questionnaire, and the analysis was conducted using the Partial Least Squares (PLS) method to evaluate the relationships between the variables. The findings reveal that, in addition to frequent updates, Generation Z's satisfaction is significantly influenced by the accessibility, performance, and security features of Android. The results highlight the importance of a positive user experience and robust security measures in enhancing user satisfaction. Continuous development in these areas is crucial for improving user engagement and contentment with Android devices.

DETERMINATION OF POTENTIAL BUSINESS LOCATIONS USING DATA MINING CLUSTERING

2025-02-01T18:10:26+00:00

Potential locations for businesses are highly sought after by business people to set up, expand their business, or establish a new business. Limited information on potential business locations is still a problem faced by many business people in making business decisions. The purpose of this research is to overcome the limitations of potential business location information. The approach used is the K-Means data mining clustering method which is compared to the Gaussian Mixture Model. The dataset used is residential, road access data and business points that already exist around the location. Both clustering methods are compared to the model evaluation method to determine the model with the best performance. The results show that the clustering method with the K-Means algorithm is the clustering model with the best performance. The results of the clustering resulted in 2 clusters, one of which is a cluster of potential business locations of 1041 locations. The conclusion of this study is that data mining clustering can be used to determine the optimal business location cluster. The results of this study can be recommended for business people to look for potential business locations, and for local governments to publicize potential business locations in order to attract investors from outside.

FINE-TUNING RESNET50V2 WITH ADAMW AND ADAPTIVE TRANSFER LEARNING FOR SONGKET CLASSIFICATION IN LOMBOK

2025-03-05T01:14:08+00:00

This study aims to develop a classification system for traditional Lombok songket fabric patterns using the ResNet50V2 architecture, optimized through fine-tuning and the AdamW optimizer. The data were collected directly from songket artisans in Lombok and categorized into three groups based on the origin of the patterns: Sade, Sukarara, and Pringgasela. The model was trained with data augmentation techniques, including rotation, shifting, and zooming, to increase data diversity. During the training process, fine-tuning was applied to the last layer of ResNet50V2, and optimization was performed using AdamW with a learning rate of 0.0001. The model was evaluated using a confusion matrix, classification report, and analysis of accuracy and loss. The experimental results showed that the model achieved 100% accuracy at the 15th epoch. Furthermore, experiments with different parameters (epochs, batch size, and learning rate) demonstrated that the 15th epoch provided the best results with 100% accuracy, while using higher epochs (30 and 40) did not necessarily yield better outcomes. This model is effective in identifying songket fabric patterns with good classification results for each class. Although the results are excellent, increasing the dataset size and exploring more complex model architectures could further enhance performance. Overall, this study demonstrates the significant potential of deep learning technology in classifying songket patterns with reliable accuracy in real-world applications.

EVALUATING FIKOM THESIS ADVISORY QUALITY WITH MANAGEMENT BY OBJECTIVES AT UNIVERSITAS MUSLIM INDONESIA

2025-02-14T05:14:45+00:00

The Faculty of Computer Science (FIKOM) at Universitas Muslim Indonesia (UMI) faces significant challenges in enhancing the quality of thesis supervision due to an increasing student population. This study utilizes the Management by Objectives (MBO) approach to evaluate and improve faculty supervision quality. MBO involves setting clear goals, monitoring progress, providing feedback, and evaluating performance based on Key Performance Indicators (KPIs), Customer Satisfaction Scores (CSAT), and Customer Effort Scores (CES). Data was gathered from questionnaires distributed to 211 FIKOM students currently writing or who have completed their theses. The findings reveal that MBO implementation significantly enhances communication between faculty and students, clarifies supervision goals, and boosts student satisfaction. The structured and directed approach of MBO makes the supervision process more efficient, leading to higher quality thesis completions. Additionally, the research underscores the importance of aligning supervision schedules and methods to better fit both faculty and student needs, thus mitigating issues related to faculty workload and student guidance. The study concludes that adopting MBO in thesis supervision processes can substantially improve both the effectiveness and satisfaction of academic guidance at FIKOM UMI.

PROJECT MANAGEMENT OF STEEL PLATE WAREHOUSE INVENTORY INFORMATION SYSTEM

2025-03-04T04:30:44+00:00

Information system project management is an activity of available resources from an information system solution development project so that a system solution can be produced that meets predetermined objectives. From the findings in the field that the process of applying the inventory information system project management is still constrained because the previous business process lacks support and some still use ms. office excel in recording the process of entering and exiting goods becomes an obstacle if many transactions occur in a day, and inventory data tends to have differences from the warehouse and head office, so this study aims to apply the inventory information system project management that has been developed using the waterfall development method can function optimally and effectively by implementing the Project Management Body of Knowledge (PMBOK) method where the focus of discussion is work breakdown structure analysis, activity of arrow analysis, and project cost estimate analysis. The results of this study obtained the results of stage-based WBS analysis, activity of arrow analysis with 58 days, while project cost estimate analysis with 14% for the communication stage, 20% for the planning stage, 57% for the modeling stage, 4% for the construction stage, 5% deployment stage.

AGGLOMERATIVE HIERARCHICAL CLUSTERING FOR REGIONAL GROUPING IN CENTRAL JAVA BASED ON WELFARE INDICES

2025-03-03T06:58:00+00:00

Central Java Province comprises 35 regencies/cities with diverse welfare characteristics. These variations present challenges for the government in formulating targeted development policies. This study aims to group regions in Central Java based on welfare indices to support more effective policy planning. The Agglomerative Hierarchical Clustering method with the Average Linkage approach is applied to cluster the regions based on three attributes: Human Development Index, Uninhabitable Houses, and Economic Growth Rate. Data were obtained from the Central Java Provincial Social Service and the official website of the Central Statistics Agency (BPS) and processed using the proposed method. Experimental results indicate three clusters with proportions: 32 regions in cluster 1 (91.4%), 2 regions in cluster 2 (5.7%), and 1 region in cluster 3 (2.9%). Regions with higher welfare dominate the first cluster, while the second and third clusters include regions facing more significant welfare challenges. Clustering results were evaluated using the Silhouette Score (0.535) and Davies-Bouldin Index Score (0.610), demonstrating that the applied method effectively grouped regions based on the specified attributes. The findings of this study are anticipated to lay the groundwork for more directed and effective development policies.

STUDENT ATTENDANCE BASED ON FACE RECOGNITION USING THE CONVOLUTIONAL NEURAL NETWORK METHOD

2025-03-07T09:03:19+00:00

Mataram University of Technology (UTM) still relies on a manual attendance process, such as signing paper-based attendance lists, which are prone to fraud and difficult to manage on a large scale. This study develops a face recognition-based attendance system using Convolutional Neural Network (CNN), which can automatically recognize visual patterns and unique facial features. CNN has advantages in extracting significant facial features, allowing it to recognize faces under various lighting conditions and viewing angles. The dataset used consists of 5,820 facial images from 97 students, with 60 augmented images per student. The results indicate that this system can be implemented in a lecture environment, achieving a validation accuracy of 98.5% at the 150th epoch. However, the model has some limitations, such as a relatively small dataset size and challenges in recognizing faces under extreme lighting conditions or unusual angles, which can affect accuracy in real-world applications. Additionally, although this system has the potential for real-time implementation, further optimization is required to ensure fast and accurate responses on a large scale. To overcome these limitations, future research can explore the use of direct camera input to enhance efficiency and user experience. Furthermore, improving dataset quality by incorporating variations in lighting and image angles, as well as exploring alternative deep learning architectures such as Vision Transformers (ViT) or Swin Transformer, can enhance model performance and generalization. By implementing these improvements, the facial recognition-based attendance system can be more optimal in enhancing accuracy and ease of use in academic environments.