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INDONESIA
JOURNAL OF APPLIED INFORMATICS AND COMPUTING
Published by Politeknik Negeri Batam
ISSN : -     EISSN : 25486861     DOI : 10.3087
Core Subject : Science,
Computer Science & IT
Journal of Applied Informatics and Computing (JAIC) Volume 2, Nomor 1, Juli 2018. Berisi tulisan yang diangkat dari hasil penelitian di bidang Teknologi Informatika dan Komputer Terapan dengan e-ISSN: 2548-9828. Terdapat 3 artikel yang telah ditelaah secara substansial oleh tim editorial dan reviewer.
Arjuna Subject : -
Articles 695 Documents
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Deployment of Kidney Tumor Disease Object Detection Using CT-Scan with YOLOv5 Kahingide, Hastyantoko Dwiki; Salam, Abu
Journal of Applied Informatics and Computing Vol. 8 No. 1 (2024): July 2024
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v8i1.7771

Abstract

Image processing plays a crucial role in identifying kidney tumors through CT-Scan images. Object detection technology, particularly YOLO, stands out for its speed and accuracy in facilitating more detailed analysis. Using Flask as a web framework offers optimal responsiveness, providing adaptive ease of use, especially in medical image processing. Evaluation of the model shows impressive results, with a mean Average Precision (mAP) of 0.987 for the 'kidney tumor' label. Detection on public data demonstrated high performance with accuracy, precision, recall, and F1-Score of 98.56%, 98.66%, 99.66%, and 99.16%, respectively. This study also utilized clinical data comprising 62 CT-Scan images. Evaluation of the clinical data revealed that YOLOv5 produced an accurate detection model with accuracy, precision, recall, and F1-Score of 95.16%, 96.72%, 98.33%, and 97.52%, respectively. The research shows that both public and clinical data models can accurately detect kidney tumors based on CT-Scan images. The deployment process using the Flask web-based platform allows direct interaction with users through an intuitive interface, enabling users to upload their CT-Scan images and quickly obtain detection results. These test results provide evidence that object detection using YOLOv5 achieves high accuracy in detecting both public and clinical datasets.
Sentiment Analysis of Sirekap Application Users Using the Support Vector Machine Algorithm Setyanto, Joko; Sasongko, Theopilus Bayu
Journal of Applied Informatics and Computing Vol. 8 No. 1 (2024): July 2024
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v8i1.7772

Abstract

In the current era of digitalization, various activities are conducted using technology to aid their execution, including the democratic process scheduled for February 2024. The Komisi Pemilihan Umum (KPU) is utilizing a mobile-based application called Sirekap. During the previous presidential and vice-presidential elections, there were many pros and cons regarding the Sirekap application. A significant number of negative reviews were expressed by the public towards this application. This study employs the SVM algorithm to perform sentiment analysis of Sirekap application users. Before building the model, several steps were undertaken, including data labeling, data preprocessing, splitting the dataset into training and testing data, and performing transformations using Count Vectorizer. Evaluation of the SVM model results shows quite good performance with an accuracy of 81%. For the negative class, the precision and recall values are 87% and 85%, respectively, while for the positive class, the precision and recall values only reach 66% and 70%, indicating a need for improvement in the model's identification of the positive class. Five-fold cross-validation was performed with an average cross-validation score of 79.6% and a standard deviation of 2.14%, indicating the model's consistency across various training data subsets. These findings suggest that the SVM model can effectively perform text classification tasks. Based on the negative word cloud, it can be concluded that the Sirekap application still has many shortcomings affecting the democratic process in February 2024.
Applying the Multi-Attribute Utility Theory (MAUT) to Accurately Determine Stunting Susceptibility Levels in Toddlers Idris, Nur Oktavin; Umasugi, Nurain
Journal of Applied Informatics and Computing Vol. 8 No. 1 (2024): July 2024
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v8i1.7817

Abstract

Stunting is a condition of impaired growth and development in toddlers due to prolonged nutritional deprivation. In the Kota Timur Community Health Center, stunting is traditionally assessed based solely on body weight and height, neglecting other crucial factors such as socioeconomic status, maternal nutrition during pregnancy, history of illness, and dietary intake. This limited approach leads to inaccurate decision-making and misdiagnoses of stunting. This research applied the Multi-Attribute Utility Theory (MAUT) to identify stunting susceptibility levels in toddlers by integrating various determinants, including body weight, height, socioeconomic conditions, maternal nutrition during pregnancy, morbidity, and dietary intake. MAUT effectively integrates multiple criteria and manages data uncertainties through its utility concept, allowing for comparison across different alternatives to facilitate accurate decision-making. The results showed that Arbi, Manaf, and Aisyah were susceptible to stunting, with evaluation scores of 0.028, 0.288, and 0.299, respectively, while Daffa and Zayyan were not susceptible, with scores of 0.900 and 0.966, respectively. Therefore, the system utilizing MAUT to determine stunting susceptibility levels in toddlers can be adopted by health workers at the Kota Timur Community Health Center to enable efficient, quick, and accurate diagnosis by integrating multiple determinants of stunting susceptibility.
Analysis of User Experience in the Design of the AMGM Lab Mobile Application Using the User Experience Questionnaire (UEQ) for Enhanced Efficiency Ilmiana, Indah Rahma; Ratnasari, Chanifah Indah
Journal of Applied Informatics and Computing Vol. 8 No. 1 (2024): July 2024
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v8i1.7848

Abstract

AMGM mobile Lab application is a design application supporting documentation of water sample management data. There is a management support system in the form of an intranet. Nevertheless, access to the system is restricted to the office. Naturally, this is less effective since officers must first return to the office in order to enter sample data, and this cannot be done in real-time while they are out in the field. Thus, in order to facilitate these operations, mobile application development is required. User experience analysis is necessary to provide an inventive UX design, which is then required to satisfy laboratory requirements and the company's expectations for the application design. The objective of this study is to use user experience research as a foundation for future application development. In order to do user experience analysis, the User Experience Questionnaire (UEQ) approach is used. The test results reveal that the assessment falls into the excellent category for attractiveness (1.86) and dependability (1.82). The efficiency (1.85) and novelty (1.24) scales are categorized as good. The perspicuity (1.71) and stimulation (1.21) measures are categorized as above average. The mean of the entire scale exceeds 0.8, indicating that people assess all features positively.
Social Media Analysis for Effective Information Dissemination and Promotions Using TOPSIS Latifah Nurhasanah, Reka Hani; Anshor, Abdul Halim; Muhidin, Asep
Journal of Applied Informatics and Computing Vol. 8 No. 1 (2024): July 2024
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v8i1.7863

Abstract

Social media has become an essential tool for spreading news and promotions. This research aims to evaluate the effectiveness of using social media as a strategy for disseminating information and promoting products using the TOPSIS method. Initial data was collected from a survey of social media users. The data was gathered through questionnaires distributed to various groups, including students, entrepreneurs, and office workers. The TOPSIS method was used to analyze the data and identify the most effective social media channels for information dissemination and promotion. The findings indicate that Facebook is the most effective platform for disseminating information, followed by Instagram and Twitter. Conversely, Instagram is the most effective platform for content promotion, followed by Facebook and YouTube. This study has significant implications for businesses and organizations that use social media for information dissemination and promotions. The TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) method was used to evaluate and rank platforms based on criteria such as reach, accessibility, topicality, ease of use, creativity, informativeness, adaptability, transactionability, and security. The results show that TikTok is the best social media platform with the highest preference score of 0.755, followed by Facebook in second place, Instagram in third place, Twitter in fourth place, Telegram in fifth place, and YouTube in sixth place.
Comparison of EfficientNetB7 and MobileNetV2 in Herbal Plant Species Classification Using Convolutional Neural Networks Arnandito, Seno; Sasongko, Theopilus Bayu
Journal of Applied Informatics and Computing Vol. 8 No. 1 (2024): July 2024
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v8i1.7927

Abstract

This study compares the performance of EfficientNetB7 and MobileNetV2 in classifying herbal plant species using Convolutional Neural Networks (CNNs). The primary objective was to automatically identify herbal plant species with high accuracy. Based on the evaluation results, both EfficientNetB7 and MobileNetV2 achieved approximately 98% accuracy in recognizing herbal plant species. While both models demonstrated excellent performance in precision, recall, and F1-score for most plant species, EfficientNetB7 showed a slight edge in some evaluation metrics. These findings provide valuable insights into the potential implementation of CNN architectures in automatic plant recognition applications, particularly for developing widely applicable web-based systems for herbal plant identification.
Pricing and Producer-Retailer Supply Chain Coordination: A Game Theory Approach Utama, KF. Sunny Cahya; Lukitosari, Valeriana
Journal of Applied Informatics and Computing Vol. 8 No. 1 (2024): July 2024
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v8i1.8050

Abstract

Supply chain process is interdependent. Starting from procurement of raw materials, production, distribution, and finally the goods reaching consumers will influence each other. The costs of goods and services will be somewhat impacted by these social habits. Producers and merchants must comprehend these social behaviors in order to properly establish prices and the distribution of goods. This includes determining the prices of goods and services. Offering manufacturers who participate in cooperative advertising schemes money for a percentage of the costs connected with local advertising encourages retailers to launch additional promotional activities. The aim of this research is to investigate how cooperative pricing and advertising can improve supply chain coordination using consumer demand functions. A model based on game theory that takes the dynamics of power in the supply. A series of numerical simulations is presented to illustrate the optimal solution of channel members based on scenarios that illustrate, understand and compare the fundamental results of the game models. The results of this research are that retail price decisions are influenced by the level of competition and product differentiation. The results show that retail margins depend on local ( ) and national ( ) advertising effectiveness values. In addition, retailers can gain greater profits by setting higher prices in conditions of low price elasticity but must consider consumer sensitivity to price to maximize profits.
Musical Instrument Classification using Audio Features and Convolutional Neural Network Giri, Gst. Ayu Vida Mastrika; Radhitya, Made Leo
Journal of Applied Informatics and Computing Vol. 8 No. 1 (2024): July 2024
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v8i1.8058

Abstract

This research classifies acoustic instruments using Convolutional Neural Network (CNN). We utilize a dataset from Kaggle containing audio recordings of piano, violin, drums, and guitar. The training set consists of 700 guitar, percussion, violin, and 528 piano samples. The test set contains 80 samples of each instrument. Features such as Mel spectrograms, MFCCs, and other spectral and non-spectral characteristics are extracted using the Librosa package. Three feature sets"”spectral-only, non-spectral-only, and a combined set"”are employed to evaluate the efficacy of CNN models. Various CNN configurations are tested by adjusting the number of convolutional filters, learning rates, and epochs. The combined feature set achieves the highest performance, with a validation accuracy of 71.8% and a training accuracy of 76.9%. In comparison, non-spectral features achieve a validation accuracy of 68.4%, and spectral-only features achieve 69.3%. These findings highlight the benefits of using a comprehensive feature set for accurate classification.
Classification Vehicle Tire Quality using Convolutional Neural Networks Pratiwi, Vila Rusantia; Rijati, Nova
Journal of Applied Informatics and Computing Vol. 8 No. 1 (2024): July 2024
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v8i1.8074

Abstract

Tires are a very important component in a vehicle because they are related to driving safety. Defective tires often cause accidents ranging from minor to fatal accidents. Convolutional Neural Network (CNN) is a type of neural network that is used to detect and recognize objects in an image. CNN can imitate the image recognition system in the human visual cortex, making it suitable for identification and classification of image data. This research aims to develop and evaluate a CNN model that is able to classify vehicle tires as 'defective' or 'good'. Model uses a total of 1856 tire images from kaggle.com and is labeled 'defective' or 'good'. Dataset is split using four different data split ratios (60:40, 70:30, 80:20, and 90:10) to determine the optimal distribution that improves the generalization ability of the model. Model evaluation uses accuracy, precision and recall matrices, which are calculated based on the confusion matrix results from testing on 300 data samples. Research results show that the model achieves the best performance at a split ratio of 80:20, with an accuracy of 76.67%, precision of 77.33%, and recall of 76.32%.
Computational Analysis of IT Governance Audit Using COBIT 4.1 Framework: A Customer Perspective Wati, Vera; Febriani, Siska; Sari, Eka Yulia
Journal of Applied Informatics and Computing Vol. 8 No. 1 (2024): July 2024
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v8i1.8135

Abstract

A company's performance can be measured by the number and satisfaction of customers, which helps in maintaining customer relationships. Indicators such as customer satisfaction, perception of service, and loyalty can be derived from the Customer Perspective of the Balance Scorecard (BSC). Conducting an IT governance audit is essential to understand how customers perceive a service. The use of the COBIT 4.1 Framework for IT governance audits is recognized for its detailed process, both for business and governance purposes, to avoid vulnerabilities and threats, thereby increasing customer satisfaction. Effective IT governance plays a crucial role in enhancing customer satisfaction and achieving organizational success. This research aims to analyze IT governance audits from a customer perspective using the COBIT 4.1 framework, with a focus on aligning IT strategy with business goals to meet customer expectations. The research method involves key processes in PO8 (Manage Quality) and PO10 (Manage Project) to determine quality standards and influential budgets. Integration with computational techniques for data analysis and IT audit algorithms is carried out to build strong IT governance practices. The computational audit results show maturity levels of 2.59 for PO8 and 3.02 for PO10, indicating areas needing improvement in product quality management and project execution to better meet customer needs. These findings underscore the importance of integrating computational insights to optimize IT governance frameworks and improve organizational performance, especially in customer retention through enhanced project quality management.

Page 20 of 70 | Total Record : 695

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