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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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Apriori Algorithm Analysis to Determine Purchasing Patterns at Beleven Farma Pharmacy Lufiah, Fara; Indah, Dwi Rosa; Firdaus, Mgs Afriyan
Journal of Applied Informatics and Computing Vol. 9 No. 1 (2025): February 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i1.8871

Abstract

Beleven Farma Pharmacy is a place that provides medicines and other health products such as supplements, vitamins and also various health tests. As a newly established pharmacy, no innovations have been made to improve sales strategies. Analysis of purchasing patterns can produce information that helps pharmacies in determining product bundling recommendations as well as determining product layout. This research applies the a priori algorithm method and uses rapidminer tools to identify drug purchasing patterns from transaction data at the Beleven Farma pharmacy. The Knowledge discovery in database (KDD) method is used as a reference in the data processing process. Based on tests carried out by the author, the resulting rules are that if you buy hemaviton you will buy vice with 4% support and 91% confidence and if you buy amoxicillin you will buy paracetamol with 4% support and 64% confidence. Thus, the resulting information can be used to support decision making in determining marketing strategies so as to increase sales at pharmacies.
Development of YOLO-Based Mobile Application for Detection of Defect Types in Robusta Coffee Beans Nugroho, Eko Dwi; Verdiana, Miranti; Algifari, Muhammad Habib; Afriansyah, Aidil; Firmansyah, Hafiz Budi; Rizkita, Alya Khairunnisa; Winarta, Richard Arya; Gunawan, David
Journal of Applied Informatics and Computing Vol. 9 No. 1 (2025): February 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i1.8886

Abstract

Improving the quality of Robusta coffee beans is a crucial challenge in the coffee industry to ensure that consumers receive high-quality products. However, the identification of defects in coffee beans is still largely performed manually, making the process error-prone and time-consuming. This study aims to develop a YOLO-based mobile application to detect defects in Robusta coffee beans quickly and accurately. The method employed in this study is YOLO, a deep learning-based object detection algorithm known for its real-time object detection capabilities. The application was tested using a dataset of Robusta coffee beans containing various defects, such as broken, black, and wrinkled beans. The test results indicate that the application achieves high detection accuracy, with the black bean class achieving 95.3% accuracy, while the moldy or bleached bean class records the lowest accuracy at 62.2%. This application is expected to assist farmers and coffee industry stakeholders in improving the quality of Robusta coffee beans and enhancing the efficiency of the sorting process.
Utilization of Machine Learning for Predicting Corrosion Inhibition by Quinoxaline Compounds Fadil, Muhamad; Akrom, Muhamad; Herowati, Wise
Journal of Applied Informatics and Computing Vol. 9 No. 1 (2025): February 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i1.8894

Abstract

Corrosion is a significant issue in both industrial and academic sectors, with widespread negative impacts on various aspects, including economics and safety. To address this problem, the use of corrosion inhibitors has proven effective. This study explores the application of Machine Learning (ML) methods based on Quantitative Structure-Properties Relationship (QSPR) to develop a predictive model for the efficiency of quinoxaline compounds as corrosion inhibitors. By conducting a comparative analysis among three algorithms: AdaBoost Regressor (ADB), Gradient Boosting Regressor (GBR), and Extreme Gradient Boosting Regressor (XGBR), and optimizing parameters through hyperparameter tuning using Grid Search and Random Search, this research demonstrates that the XGBR model yields the most superior prediction results. The XGBR optimized with hyperparameter tuning using Grid Search achieved the highest R² value of 0.970 and showed the lowest RMSE, MSE, MAD, and MAPE values of 0.368, 0.135, 0.119, and 0.273, respectively, indicating high predictive accuracy. These results are expected to contribute to the development of more effective methods for identifying corrosion inhibitor candidates.
Public Sentiment Analysis of the Free Meal Program: A Comparison of Naive Bayes and Support Vector Machine Methods on the Twitter (X) Social Media Platform Saleh, Muhammad Farhan; Imanda, Rahmi
Journal of Applied Informatics and Computing Vol. 9 No. 1 (2025): February 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i1.8895

Abstract

The problems of nutrition, including stunting, remain a challenge in Indonesia. Therefore, Prabowo and Gibran launched the 2024 Free Meal Program, which provides free lunch to every school child as well as pregnant mothers. This research analyzes public sentiment towards this program using data from X with Naïve Bayes and Support Vector Machine (SVM) methods. The data was analyzed through crawling, preprocessing, labeling, and feature extraction using TF-IDF. The results showed a predominance of positive sentiment towards the program, with SVM performing better in sentiment classification, achieving 86.42% accuracy compared to Naïve Bayes with 67.9%. The findings can guide policymakers in improving the communication strategy and implementation of the Free Meal Program to make it more impactful for Indonesians.
Colors and Texture Feature Extraction Using Learning Vector Quantization 3 Algorithm in Optimization of Beef Identification Suendri, Suendri; Susanti, Eka; Hartono, Agung
Journal of Applied Informatics and Computing Vol. 9 No. 1 (2025): February 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i1.8903

Abstract

The Assessment Institute for Foods, Drugs, and Cosmetics of the Indonesian Ulama Council (LPPOM MUI) is responsible for conducting research, evaluations, and determining the halal status of products in accordance with Islamic teachings. In Indonesia, where religious diversity is prevalent, the halal certification process is crucial, particularly due to differences in the halal status of certain foods, such as beef and pork, across religions. One of the challenges in this process lies in ensuring a rapid and accurate determination of various types of meat, including beef, pork, goat, and buffalo, which currently tends to be time-consuming within the LPPOM MUI Halal Center. To address this issue, there is a need for a technological solution that can quickly and accurately identify different types of meat, thereby reducing consumer uncertainty when selecting halal products. This study aims to develop an Android-based application utilizing the Learning Vector Quantization 3 (LVQ3) method to facilitate the classification of meat types by analyzing patterns specific to beef, pork, goat, and buffalo. This system is expected to expedite the halal verification process, thereby supporting more efficient and accurate decision-making in the halal certification sector.
Comparing Machine Learning Algorithms to Enhance Volumetric Water Content Prediction in Low-Cost Soil Moisture Sensor Setiawan, Iman; Musa, Mohammad Dahlan Th.; Afriza, Dini Aprilia; Hafidah, Siti Nur
Journal of Applied Informatics and Computing Vol. 9 No. 1 (2025): February 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i1.8905

Abstract

Measuring soil moisture is possible either with directly using gravimetric test or indirectly using soil moisture sensor. Direct measurements offer accuracy but are not efficient in field measurements. On the other hand, indirect measurement offers remote measurement that will facilitate the user but lacks in accuracy. This research aims to compare and identify the best machine learning model that can improve indirect measurement (soil moisture sensor prediction) using direct measurement (gravimetric test) as a response variable. This research uses linear regression, K-Nearest Neighbours (KNN) and Decision Tree models. The three models were then compared based on Root Mean Square Error (RMSE). The results suggested that KNN (0.02939128) had the smallest RMSE value followed by decision tree (0.05144186) and linear regression model (0.05172371).
Coffee Beans Classification Using Convolutional Neural Networks Based On Extraction Value Analysis In Grayscale Color Space Santoso, Bagus Raffi; Sari, Christy Atika; Rachmawanto, Eko Hari
Journal of Applied Informatics and Computing Vol. 9 No. 1 (2025): February 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i1.8916

Abstract

Coffee is a vital agricultural commodity, and precise classification of coffee beans is crucial for quality assessment and agricultural practices. In this study, we propose a methodology utilizing Convolutional Neural Networks (CNN) based on ResNet-101 architecture for coffee bean classification. The novelty of our approach lies in the integration of comprehensive feature extraction from grayscale coffee bean images, including mean, standard deviation, skewness, energy, entropy, and smoothness, with the transfer learning capabilities of CNN. Through this integration, we achieved exceptional classification performance, with the CNN model attaining accuracy, recall, precision, and F1-score metrics of 99.44% and 100% on the training data, and 100% on the testing data. These results underscore the robustness and generalization capability of our methodology in accurately classifying coffee bean types. While the dataset used in this study is experimental, the comprehensive feature extraction and the effectiveness of the CNN architecture suggest the potential for accurate classification of coffee bean types beyond the experimental data, provided the new data shares similar characteristics to the collected samples. For future research, we recommend exploring the integration of two transfer learning techniques within CNN architectures to further enhance coffee bean classification systems. Specifically, leveraging pre-trained CNN models as a foundation for feature extraction, while simultaneously fine-tuning specific layers to adapt to the nuances of coffee bean classification tasks, could offer improved model performance and scalability.
Sentiment Analysis on Google Reviews Using Naïve Bayes, K-Nearest Neighbors, and Logistic Regression to Improve Novotel Services Dhamma, Yonathan Arya; Barus, Simon Prananta
Journal of Applied Informatics and Computing Vol. 9 No. 1 (2025): February 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i1.8923

Abstract

The application of artificial intelligence (AI) has been widely used in various industrial sectors, including the hospitality industry. One of the applications that is widely used in the hospitality industry is sentiment analysis. Sentiment analysis is carried out by analyzing feedback data from hotel guests or customers. The results of this sentiment analysis are important for decision makers to improve and improve their services. This study aims to obtain sentiment analysis results from Novotel hotel Google reviews based on machine learning by comparing three algorithms, namely Naïve Bayes, K-Nearest Neighbors (KNN), and Logistic Regression. The stages carried out in this study are data collection, data labeling, exploratory data analysis (EDA), data preprocessing, text representation, data sharing, modeling, model training, model evaluation, selection of the most accurate model, visualization of the most accurate model, interpretation of results and writing research reports. The dataset used was 1200 reviews, only 1190 reviews were used in the analysis. From the training results, the model produced by the Logistic Regression algorithm was the most accurate, namely 94.54% with unigrams (n = 1). Here are the results of each category, positive as many as 723 reviews (60.76%), negative as many as 218 reviews (18.32%), and neutral as many as 249 reviews (20.92%). Thus, most of the sentiment towards the service is positive, but some services need to be fixed and improved for customer satisfaction. The next research, the research area is expanded and the use of Deep Learning.
Analysis of Copy-move Manipulation in Digital Images using Scale Invariant Feature Transform (SIFT) and SVD-matching Methods Efendi, Muhamad Masjun; Nukman, Nukman
Journal of Applied Informatics and Computing Vol. 9 No. 1 (2025): February 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i1.8937

Abstract

In recent years, more and more data has been created in digital form, allowing for easier control over storage and manipulation thanks to technological advancements. Unfortunately, these advancements also bring with them many risks, especially those related to the security of digital files. One of the concerns of many organisations is digital forgery, as it is increasingly easy to create fake images without leaving obvious traces of manipulation. One form of image forgery known as 'copy-move' is considered one of the most difficult problems in forgery detection. In this case, a portion of an image is copied and pasted at another location in the same image to hide unwanted objects in the scene. In this paper, we propose a method that automatically detects duplication areas within the same image. Duplication detection is performed by identifying local characteristics of the image (key points) using the Scale Invariant Feature Transform (SIFT) method and matching identical features using the Singular Value Decomposition (SVD) method. The results obtained show that our proposed hybrid method is robust to geometric transformations and is able to detect duplication areas with high performance.
Optimization of Decision Tree Algorithm for Chronic Kidney Disease Classification Based on Particle Swarm Optimization (PSO) Aulia Fitri, Laili; Baita, Anna
Journal of Applied Informatics and Computing Vol. 9 No. 1 (2025): February 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i1.8940

Abstract

The body's most important vital organ is the kidney. The kidneys are responsible for maintaining acid and alkaline balance, regulating blood pressure, and filtering blood to prevent the accumulation of metabolic waste in the body. However, chronic kidney disease does not always show symptoms and signs but can progress to kidney failure. Algorithm-based predictive methods in data processing show great potential in the health field to predict various diseases, one of which is kidney disease. One of the techniques in data mining is classification. One of the classification algorithms in data mining that is often used to detect diseases is Decision Tree. In this study, it is expected that by combining these two methods, it will make a new contribution to the Decision Tree algorithm that is optimized with Particle Swarm Optimization (PSO) for the selection of relevant features, and improve the weaknesses in the model to improve more accurate predictions. By performing feature selection with the Particle Swarm Optimization (PSO) algorithm, it is shown that the use of Particle Swarm Optimization (PSO) can improve the accuracy and performance of the Decision Tree algorithm in the chronic kidney disease classification process. The accuracy of the Decision Tree algorithm with feature selection using Particle Swarm Optimization (PSO) is higher, reaching 0.967%, compared to the accuracy of Decision Tree without Particle Swarm Optimization (PSO) feature selection which is only 0.95%. This shows that Particle Swarm Optimization (PSO) is effective in selecting relevant features so that it can significantly improve model performance.

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