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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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Analysis of Digital Readiness in the Social Assistance Distribution System with the Unified Theory of Acceptance and Use of Technology (UTAUT) Adiyono, Soni; Latifah, Noor; Laily Fithri, Diana
Journal of Applied Informatics and Computing Vol. 9 No. 2 (2025): April 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i2.9070

Abstract

The adoption of digital systems for social assistance distribution has become increasingly vital in enhancing efficiency and accessibility. This study examines the acceptance of such a system using the Unified Theory of Acceptance and Use of Technology (UTAUT) model, analyzing six key constructs: Performance Expectancy (PE), Effort Expectancy (EX), Social Influence (SI), Facilitating Conditions (FC), Behavioral Intention (BI), and Actual Use (AU). A total of 150 respondents participated in the survey, providing insights into their perceptions of the system. The findings indicate that Performance Expectancy (4.2) received the highest mean score, demonstrating that users perceive the system as beneficial in improving efficiency. Effort Expectancy (4.0) suggests that the system is easy to use, while Social Influence (3.8) highlights the moderate role of external encouragement. Facilitating Conditions (3.9) reveal the availability of infrastructure but also suggest areas for improvement. Additionally, Behavioral Intention (4.1) and Actual Use (4.0) indicate strong user commitment toward system utilization. The study contributes to the understanding of digital technology adoption in social welfare programs and provides recommendations for optimizing system implementation. Future research should explore the long-term impact of digital adoption, assess its effectiveness in different demographic groups, and integrate qualitative insights to deepen the understanding of user experiences. Additionally, expanding the analysis to include external factors such as policy support, economic conditions, and digital literacy could further enhance the model’s applicability.
Aspect-Based Sentiment Analysis of Reviews for Pandawa Beach Using Naive Bayes and SVM Methods Putri, Made Ayu Asri Oktarini; Sumarjaya, I Wayan; Wijayakusuma, I Gusti Ngurah Lanang
Journal of Applied Informatics and Computing Vol. 9 No. 2 (2025): April 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i2.9083

Abstract

The presence of digital technology, especially online platforms such as Google Maps, has changed the way people search for information about tourist destinations, including reviews and ratings from previous visitors. Aspect-based sentiment analysis becomes a very useful tool to understand people's views and feelings towards a place or product based on the reviews given and identify aspects of interest to tourists visiting Pandawa Beach, by utilizing Naive Bayes and Support Vector Machine (SVM) methods. The main objective of this research is to identify sentiment patterns based on aspects such as attraction, accessibility, amenities, and ancillary. Data was collected and labeled according to sentiment and aspects, then processed using preprocessing techniques, extracted by bag-of-words method, and chi-square feature selection. The model evaluation results showed that SVM produced the highest F1-Score value of 79,625%, while the Naive Bayes method reached 73.29%.
Expert System for Diagnosing Shallot Plant Diseases Using the Forward Chaining Method Izzul Haq, Dani Ahmad; Parti Astuti, Yani; Yusa Aditama, Daffa
Journal of Applied Informatics and Computing Vol. 9 No. 2 (2025): April 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i2.9084

Abstract

This research aims to develop an expert system with the forward chaining method to diagnose diseases in shallot plants in Mijen District, Demak Regency. The decline in shallot production caused by disease is one of the main problems faced by local farmers. Therefore, this expert system was developed to help farmers diagnose diseases in their crops more quickly and accurately.The research method used combines qualitative and quantitative approaches. Data on plant diseases and symptoms were obtained through interviews with agricultural extension workers and literature review. Based on the data, a knowledge base was built that was used in the expert system. Forward chaining was applied to trace the relationship between symptoms inputted by the user and possible disease diagnoses. The system was tested using validation data, with an accuracy result of 93.3%, indicating that the system has a high level of agreement with the diagnosis provided by the expert.The results of this study show that the developed web-based expert system can provide practical solutions for farmers to diagnose and treat diseases in shallot plants, so as to increase agricultural productivity and reduce losses due to disease attacks. With web-based implementation, this system can be easily accessed by farmers through computer or mobile devices, providing ease of use in the field.
Comparative Study of the ARIMA Method and Multiple Linear Regression in Metro City Population Growth Projections Saputri, Tri Aristi; Rachma Ajiz, Allien Moetiara; Febritama, Dani
Journal of Applied Informatics and Computing Vol. 9 No. 2 (2025): April 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i2.9097

Abstract

This study aims to compare the effectiveness of the ARIMA (Autoregressive Integrated Moving Average) method and multiple linear regression in projecting population growth in Metro City, Lampung. The analysis utilizes population data from 2010 to 2022, sourced from the Central Statistics Agency and the Population and Civil Registration Office. The methodologies employed include ARIMA modelling and multiple linear regression, with model evaluation conducted using metrics such as Mean Absolute Percentage Error (MAPE) and Root Mean Square Error (RMSE). The findings indicate that the multiple linear regression model predicts an average population growth of 2,200 individuals per year, resulting in a total projection of 185,032 by 2030. In contrast, the ARIMA (2,1,1) model forecasts a total population of 169,500 for the same year. The conclusion drawn from this research suggests that while both methods possess distinct advantages, ARIMA is more effective in capturing seasonal patterns and long-term trends, whereas multiple linear regression offers greater interpretability. This study recommends the complementary use of both methods to enhance the accuracy of population growth projections.
Effect of Virtual Sample Generation in Predicting Corrosion Inhibition Efficiency on Pyridazine Aldiansah, Ilham Pratama; Akrom, Muhamad
Journal of Applied Informatics and Computing Vol. 9 No. 2 (2025): April 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i2.9131

Abstract

The purpose of this research is to study how the application of virtual sample generation using the linear interpolation and gaussian noise augmentation method impacts the improvement of prediction model performance in the case of corrosion inhibition efficiency using pyridazine. Random Forest Regressor, Gradient Boosting Regressor, and Bagging Regressor are the models used. The coefficient of determination (R2) values for each model are -0.06, 0.05, and 0.12 on the initial data; the RMSE values are 34.80, 32.90, and 31.65, respectively. After the use of virtual sample development, the R2 values significantly increased to 0.99, 0.96, and 0.99, while the RMSE values significantly decreased to 1.59, 2.88, and 1.25. The research results show that the linear interpolation method can enrich the dataset without altering the data distribution pattern, this method significantly improves the model's accuracy. This performance improvement demonstrates the ability of virtual sample generation to overcome the limitations of the original data; ultimately, this results in a more accurate and reliable predictive model. In the field of material efficiency prediction especially for material technology applications and corrosion control this research helps develop data augmentation methods for similar cases.
Twitter Sentiment Classification towards Telecommunication Provider Users in Indonesia Syah Putra, Fernanda Mulya; Rakasiwi, Sindhu; Ariyanto, Noval
Journal of Applied Informatics and Computing Vol. 9 No. 2 (2025): April 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i2.9143

Abstract

Internet services have become essential for communication and information sharing. Nowadays, daily activities are conducted through the internet. This study aims to gain a better understanding of the components that influence user perception and satisfaction using textual, sentiment, and statistical analysis techniques. By applying machine learning algorithms such as Naïve Bayes and Support Vector Machine (SVM), this research analyzes customer perceptions of telecommunication service providers in Indonesia. The dataset consists of 300 tweets obtained from the Kaggle platform. The objective is to identify elements that affect customer satisfaction, particularly those related to network stability and service quality. Data preprocessing is carried out using methods such as case folding, normalization, stemming, and stopword removal to enhance sentiment analysis model performance. The results show that SVM outperforms Naïve Bayes in precision and recall, achieving an accuracy of 90% compared to Naïve Bayes' 87%. This demonstrates SVM's ability to classify positive and negative sentiments more accurately. Common topics found in the analysis include customer satisfaction with network stability and affordable pricing, while dissatisfaction arises from poor connectivity and slow customer service response. These findings provide valuable insights for service providers to improve service quality and enhance customer satisfaction. Real-time sentiment analysis using machine learning has great potential, and this study highlights how telecommunication companies can leverage strategic recommendations to improve service quality and retain customers.
Implementation of Naive Bayes Algorithm for Early Detection of Stunting Risk Mirantika, Nita; Trisudarmo, Ragel; Syamfithriani, Tri Septiar
Journal of Applied Informatics and Computing Vol. 9 No. 2 (2025): April 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i2.9144

Abstract

This study aimed to develop an early detection model for stunting risk in children in Kuningan Regency using the Naïve Bayes algorithm. The model used 3,155 data with a division of 50% training data and 50% testing data, utilizing five predictor variables: gender, age, weight, height, and nutritional intake. The results demonstrated an accuracy of 66.8%, precision of 62.4%, and recall of 69.5%, indicating that the model performs adequately but requires further refinement to enhance predictive quality. Improvements can be achieved by incorporating additional variables, such as environmental factors, sanitation, and maternal nutritional status, as well as optimizing data preprocessing techniques. The findings provide a scientific basis for the Kuningan Regency Health Office to design targeted intervention strategies, including regular screening programs, specific nutritional interventions, and community health education. Effective implementation of these strategies requires collaborative efforts among local government, community health centers (puskesmas), integrated health posts (posyandu), and other stakeholders to ensure a holistic and sustainable approach to stunting prevention. This study highlights the potential of data-driven models in supporting evidence-based public health policies and interventions.
Implementation of Apriori Algorithm in Identifying Purchase Relationships at Bluder Cokro Pakuwon Mall Ivana, Angeline; Maryati, Indra
Journal of Applied Informatics and Computing Vol. 9 No. 2 (2025): April 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i2.9154

Abstract

Bluder Cokro Store, located at Pakuwon Mall, specializes in traditional bluder bread with a wide range of flavor variations. This study aims to identify consumer purchasing patterns at the store to enhance promotional strategies and optimize product placement. The research applies the Cross Industry Standard Process for Data Mining (CRISP-DM) methodology, which includes phases such as business understanding, data understanding, data preparation, modeling, evaluation, and deployment. The dataset used consists of 4,371 transactions from October to December 2024. This study uses the Apriori algorithm to find patterns of association between products, with the goal of determining the scope of correlation between products and frequently co- purchased items. The results reveal nine significant association rules, with the strongest relationship observed between coklat keju and keju, having a support value of 0.100394 and a lift of 1.31. These findings indicate that strategic product placement and bundling promotions can enhance sales performance. Optimizing the store layout by placing coklat keju near coklat can increase purchase likelihood, while targeted discounts, such as "Buy coklat keju, get 10% off keju," can drive transaction values. This study serves as a recommendation framework rather than an experimental validation, offering insights on how transaction data and association rule mining can inform business decisions. The findings offer actionable insights for improving store layouts and promotional effectiveness, making this research valuable for retailers.
Implementation of K-Means Clustering in Grouping Sales Data at Zura Mart Miranda, Miranda; Sriani, Sriani
Journal of Applied Informatics and Computing Vol. 9 No. 2 (2025): April 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i2.9160

Abstract

The efficiency of inventory management and targeted marketing strategies relies on understanding sales patterns and stock levels dynamically. This study proposes a K-Means Clustering-based approach combined with a real-time stock monitoring system to classify products adaptively. The dataset consists of 87 products with variables including total sales, average sales, and remaining stock. The analysis process begins with data normalization to standardize parameter scales, followed by the application of the Elbow Method, which determines the optimal number of clusters as three. The clustering results indicate that Cluster C0 (21 products) has high sales but low stock, Cluster C1 (59 products) has stable sales with moderate stock, and Cluster C2 (7 products) has low sales but abundant stock. These findings not only provide strategic insights for inventory optimization but also serve as the foundation for developing an automated recommendation system that links clustering results with adaptive promotional strategies and restock prediction. Thus, this study contributes to enhancing Zura Mart's business efficiency through the integration of data-driven decision-making in inventory management and marketing.
Named Entity Recognition for Medical Records of Heart Failure Using a Pre-trained BERT Model Manurung, Mikael Triartama; I Gusti Ngurah Lanang Wijayakusuma; I Putu Winada Gautama
Journal of Applied Informatics and Computing Vol. 9 No. 2 (2025): April 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i2.9170

Abstract

This study aims to develop a Named Entity Recognition (NER) model based on a pre-trained BERT model for medical records of heart failure patients. The focus of this research is to classify essential medical entities from unstructured medical record texts. The classification covers four categories: objective data (patient identity, laboratory test results, and objective examination data), subjective data (patient complaints), prescriptions, and diagnoses (diagnosis codes and descriptions). The methodology employs Natural Language Processing (NLP) techniques using Transformer-based architectures, such as Bidirectional Encoder Representation from Transformers (BERT). The developed model is evaluated based on entity label prediction accuracy and medical entity classification performance. The results indicate that the BERT-based NER model performs well, achieving an entity prediction accuracy of 84.82%. Furthermore, the model effectively classifies medical entities from input texts in alignment with expected medical entities. This research is expected to contribute significantly to medical data management, assist healthcare professionals in clinical decision-making, and serve as a reference for the development of AI-based healthcare technology in Indonesia.

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