ejournal.nusamandiri.ac.id (STMIK Nusa Mandiri)
Not a member yet
1504 research outputs found
Sort by
INTELLIGENT SYSTEM TO DETERMINE THE BEST LECTURER USING ADDITIVE RATIO ASSESSMENT ALGORITHM
Get PDFThe quality of a lecturer's performance is one of the keys to institutional success that must be continuously improved. The performance assessment of lecturers in the Informatics study program of the Faculty of Information Technology, Andalas University faces obstacles in processing quantitative and qualitative data so that it is vulnerable to subjectivity including research productivity, teaching effectiveness, contributions to community service and additional activities. In addition, limitations in a systematic evaluation system result in unfairness and lack of transparency in the decision-making process. The research objective is to create a technology-based approach by applying the Additive Ratio Assessment method based on a Decision Support System. The ARAS method was chosen because it is able to determine effective final results based on multiple criteria that have been determined. The application of the ARAS method consists of 5 stages, namely determining the decision matrix, normalizing the decision matrix, weighting the normalization results, determining the optimum function value and ranking results. The results obtained are alternative data consisting of A1,A2,A3,A4,A5,A6,A7,A8,A9,A10,A11 and 8 criteria and weighting, namely the last education (10%), functional position (15%), certification (20%), number of publications (15%), author order (15%), publication index quality (10%), research grants (10%) and PkM (5%). The ranking results with the highest value in order 1-5 are 0.113875, 0.109785, 0.104235, 0.099005, 0.094715. The final conclusion of this research is that the ARAS method is able to prove the best lecturer assessment to be more efficient, transparent and subjective to be applied in the Andalas University Informatics study program
ANALISIS KUALITAS WEBSITE PORTAL MEDIA ONLINE MILENIANEWS.COM MENGGUNAKAN STANDAR ISO 9126
Get PDFSoftware quality can be assessed based on two main criteria, namely conformance to specifications and the ability to meet user needs. One of the international standards used to assess software quality is ISO 9126, which includes six main aspects: functionality, reliability, usability, efficiency, maintainability, and portability. In this journal, four aspects are taken to examine the quality of an online media portal website milenianews.com. The research methods include black-box testing for functionality, stress testing for reliability, Likert Scale-based questionnaire for usability, and GTMetrix for efficiency. The results showed that the functionality aspect scored 100%, indicating that all functions run according to specifications. The reliability aspect shows a 100% success rate on sessions, pages, and hits, indicating excellent performance under high usage conditions. Usability scored 79%, which falls into the good category, reflecting an interface that is easy to use and understand by users. The efficiency aspect obtained grade B with a performance score of 75% and structure 91%, indicating quite good performance, although there is room for improvement, especially in the load time of 2.5 seconds and total blocking time of 192 ms. Overall, the milenianews.com online media portal has met ISO 9126 quality standards and is declared suitable for use. These results show the importance of implementing international standards-based quality testing to ensure an optimal user experience
IMPLEMENTASI MODEL DeiT UNTUK MEMBEDAKAN GAMBAR BUATAN AI DAN MANUSIA PADA ILUSTRASI ANIMASI 2D
Get PDFThe development of artificial intelligence (AI) has influenced various fields, including art and visual design. AI Generative Art, which mimics human styles, has sparked debates on originality, artistic value, as well as legal and ethical challenges. Therefore, methods are needed to distinguish between AI-generated and human-made images, particularly in 2D animation illustrations. This study proposes the use of Data-efficient Image Transformers (DeiT) for image classification. Two models tested are DeiT Base and DeiT Tiny, using a dataset of 6,000 images equally divided between AI and human categories. The dataset is split into training (70%), validation (15%), and testing (15%). Experimental results show that DeiT Base achieves over 95% accuracy with fast convergence and optimal loss function stability. Meanwhile, DeiT Tiny attains around 93% accuracy, being more computationally efficient despite requiring more epochs for stability. Compared to previous models using a larger dataset (11,000 images per category) but achieving only 80% accuracy, DeiT performs better in both accuracy and computational efficiency, even with a smaller dataset. In conclusion, DeiT is effective for classifying 2D animation images. DeiT Base excels in accuracy and convergence speed, while DeiT Tiny is more resource-efficient, making it an ideal choice for environments with computational constraints
PENERAPAN POLA FIBONACCI UNTUK PENGATURAN QOS (QUALITY OF SERVICE) JARINGAN
Get PDFIn managing network quality of service (QoS), this research uses the Fibonacci pattern to optimize delay control and bandwidth allocation. QoS is very important in contemporary network management, especially considering the increasing demand for stable and effective data services. This study prioritizes data based on traffic levels using a Fibonacci algorithm simulation. Each priority is assigned a value corresponding to the Fibonacci sequence, which allows for resource allocation that is more in line with network load.The simulation was conducted under normal and overload conditions. The research results show that conventional methods, such as round-robin and weighted fair queuing, can improve QoS efficiency with the Fibonacci pattern by up to 15%. This improvement primarily focuses on managing important data packets such as real-time communication and video streaming, and reducing latency. Additionally, this technique is better at adapting to traffic changes.The research results show that the Fibonacci pattern can be an innovative method for managing network QoS, especially for complex priority needs. By using the Fibonacci pattern as a data priority management technique, this research helps improve network quality of service (QoS). This method is capable of improving bandwidth allocation efficiency and reducing latency by up to 15% compared to conventional approaches such as Round-Robin and Weighted Fair Queuing. The main contribution of this research is to offer a new approach based on Fibonacci patterns that can be adapted to the dynamics of network traffic
EVALUATING FIKOM THESIS ADVISORY QUALITY WITH MANAGEMENT BY OBJECTIVES AT UNIVERSITAS MUSLIM INDONESIA
Get PDFThe 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
CONTINUOUS INTEGRATION PIPELINE WITH JENKINS
Get PDFThe 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
FINE-TUNING RESNET50V2 WITH ADAMW AND ADAPTIVE TRANSFER LEARNING FOR SONGKET CLASSIFICATION IN LOMBOK
Get PDFThis 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
ARCHITECTURAL DESIGN USING THE ZACHMAN FRAMEWORK AT MINING EQUIPMENT INDUSTRY
Get PDFThe mining equipment industry requires the effective integration of Information Systems (IS) and Information Technology (IT) into its business processes to achieve a competitive advantage. This study focuses on Enterprise Architecture (EA) planning to align IS/IT implementation with the company's vision and mission. The Zachman Framework is utilized to map the organization’s systems comprehensively, considering six perspectives and addressing 5W+1H (What, Why, When, Where, Who, and How). The research methodology includes data collection through interviews with key stakeholders and observations of core and supporting business activities. These data are analyzed using the Value Chain to assess the current state of the organization. The findings reveal gaps in the existing business processes and the misalignment of IS/IT initiatives with the organization’s goals. Based on these analyses, the study develops an Enterprise Architecture design that proposes a structured approach to IS/IT implementation. The result of this research is a detailed proposal for the development of a tailored application to optimize business processes, improve operational efficiency, and ensure better alignment between IS/IT initiatives and organizational objectives. This study provides practical recommendations for the mining equipment industry to enhance its competitive edge through strategic IS/IT integration
SENTIMENT ANALYSIS OF JAKLINGKO APP REVIEWS USING MACHINE LEARNING AND LSTM
Get PDFApplication-based transportation services have rapidly developed in recent years, with various studies indicating that service quality and user experience play a crucial role in the adoption of this technology. Previous research has analyzed user satisfaction with digital transportation applications, highlighting factors such as ease of use, service reliability, and the effectiveness of fare systems. This study aims to analyze user sentiment toward the JakLingko application to assess satisfaction levels and identify aspects that need improvement. Utilizing a dataset of 200 user reviews, this research applies data preprocessing techniques to clean and organize the information before performing sentiment classification. The machine learning models used include Naïve Bayes, Random Forest, Support Vector Machine, Logistic Regression, Decision Tree, and Long Short-Term Memory (LSTM), categorizing sentiment into positive, negative, and neutral. The analysis results indicate a dominance of negative sentiment in user reviews, reflecting a significant level of dissatisfaction with the application. This highlights major challenges in the implementation of transportation applications, potentially affecting public adoption and trust in the service. Therefore, besides providing insights into user perceptions, this study also proposes improvement strategies aimed at enhancing features and the overall user experience. Given the high proportion of negative sentiment, this research emphasizes the importance of improving the accuracy of sentiment analysis models to generate deeper and more precise insights. These findings can serve as a foundation for designing policies and strategies to improve application-based transportation services, ultimately enhancing service quality and expanding user adoption
THE ROLE OF AI (ARTIFICIAL INTELLIGENCE) FOR ALZHEIMER: A SYSTEMATIC REVIEW
Get PDFAlzheimer's disease (AD) is the most common type of dementia and represents a significant global health problem due to its profound impact on patients' quality of life and the heavy burden it places on health care. Alzheimer's is characterized by a progressive decline in cognitive function and memory, ultimately disrupting daily activities and leading to dependence on long-term care. This systematic literature review aims to explore the role of AI in diagnosing and managing Alzheimer’s disease. The method used in this study refers to the PICO framework to highlight various studies on the role of AI for Alzheimer's disease. Recent breakthroughs in the field of artificial intelligence (AI), particularly machine learning (ML) and deep learning, offer promising innovative approaches to improve diagnosis, monitoring, and understanding of Alzheimer's disease