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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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Clustering and Forecasting Implementation for Medical Consumables Stock Reccomendation Darma, Fahri Setia; Setiadi, Tedy
Journal of Applied Informatics and Computing Vol. 9 No. 4 (2025): August 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i4.9717

Abstract

Managing medical consumables (BMHP) in hospitals can be tricky because the demand often changes unpredictably. This study aims to help hospitals manage their BMHP stocks better by using two techniques: forecasting with Single Exponential Smoothing (SES) and grouping items using Agglomerative Hierarchical Clustering (AHC). SES is used to predict future needs based on previous usage, while AHC groups similar items based on how they're used, which helps make the predictions more accurate. Before applying clustering, the prediction error was quite high, with a MAPE of 61.77% and an MAE of 18,769.80. After clustering, these numbers dropped to 10.06% and 3,987.45, showing a significant improvement. The clustering itself was strong, with a Silhouette Coefficient of 0.727, meaning the item groups made sense. Each group of items got different stock suggestions. Items with high and unstable demand were advised to keep extra safety stock. Items with uncertain patterns needed a more flexible buffer stock. For items with stable use, average trends over the last few months were enough to guide stock planning. This approach helps hospitals avoid both overstock and stockouts by giving more accurate and tailored recommendations. Although this study only used data from one hospital, the results show that combining SES and AHC can make stock management smarter and more efficient.
Comparative Analysis of the C5.0 Algorithm and Other Machine Learning Models for Early Detection of Multi-Class Heart Disease Mardhatillah, Mardhatillah; Aidilof, Hafizh Al-Kautsar; Aidilof, Asrianda
Journal of Applied Informatics and Computing Vol. 9 No. 4 (2025): August 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i4.9753

Abstract

Cardiovascular diseases represent the leading cause of mortality worldwide, making accurate and early detection a critical factor for effective medical intervention and improved patient prognosis. While machine learning (ML) offers promising tools for predictive diagnostics, many existing studies rely on single-algorithm approaches or less-than-robust validation methods, thereby limiting the generalizability and real-world applicability of their findings.This study aims to conduct a rigorous, head-to-head comparative evaluation of multiple machine learning algorithms for the multi-class classification of heart disease, with the goal of identifying the most effective and reliable model for this complex clinical task.We utilized a private dataset comprising 300 patient medical records, each described by 11 clinically relevant features. To ensure a robust and unbiased evaluation, a stratified 5-fold cross-validation methodology was employed. Five widely-used classification algorithms were evaluated: Naïve Bayes (NB), Logistic Regression (LR), Random Forest (RF), a C5.0-analog Decision Tree (DT), and Support Vector Machine (SVM). Model performance was assessed using standard metrics, including accuracy, precision, recall, and F1-score.The comparative analysis revealed that the Naïve Bayes algorithm delivered superior performance, achieving the highest mean accuracy of 43.33% (±4.22%). It also led in other key metrics with a mean precision of 43.40%, recall of 43.64%, and an F1-score of 41.26%. Other algorithms, such as Logistic Regression (40.67% accuracy) and Random Forest (39.33% accuracy), demonstrated competitive performance but were ultimately surpassed by the Naïve Bayes model in this specific multi-class classification context.This research underscores the critical importance of employing robust validation techniques and comprehensive comparative analyses to identify optimal models for clinical applications. The Naïve Bayes algorithm emerges as a strong candidate for developing a reliable clinical decision support system for the early differentiation of various heart conditions, providing a foundation for future data-driven diagnostic tools.
Application of CNN-BiLSTM Algorithm for Ethereum Price Prediction Diash, Hakam Dzakwan; Nathania, Vannesa; Idhom, Mohammad; Trimono, Trimono
Journal of Applied Informatics and Computing Vol. 9 No. 4 (2025): August 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i4.9757

Abstract

The volatile and dynamic Ethereum (ETH) market demands an accurate predictive model to support investment decision making. The complexity of ETH time series data and the influence of various external factors make price prediction a challenge in itself. This study aims to develop an ETH price prediction model using a combined architecture of Convolutional Neural Network (CNN) and also Bidirectional Long Short-Term Memory (BiLSTM). CNN is used to extract local features from historical ETH closing price data, while BiLSTM models bidirectional temporal patterns. The dataset used includes ETH daily price from January 2020 to January 2025, which are obtained from Yahoo Finance and have gone through a normalization process and transformation into sequential form. The model is trained for 100 epochs with an early stopping mechanism to prevent overfitting and evaluated using the MAPE and coefficient of determination (R²) metrics. The evaluation results show that the CNN-BiLSTM model is able to predict ETH prices with a MAPE value of 2.8546% and an R² of 0.9415, indicating high performance in capturing actual data trends. This study shows that the hybrid CNN-BiLSTM approach is effective for Ethereum price prediction.
Development of a Classification Model for Underpriced Issuers Using Machine Learning Algorithms Hidayatullah, Defitra; Jatnika, Ihsan
Journal of Applied Informatics and Computing Vol. 9 No. 4 (2025): August 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i4.9769

Abstract

This study develops a classification model to identify underpriced Initial Public Offering (IPO) issuers so that it can help investor decision-making. Using the CRISP-DM methodology, this research uses a sample of 209 non-banking IPO issuers of the OJK E-IPO platform (since establishment until December 31, 2024). In addressing the problem of class imbalance (161 underpriced and 48 not underpriced), SMOTE was used. The model utilizes nine features: Year-on-Year, IHSG, IPO price, ratio of shares issued, age of the firm, size of the firm, sales growth, Return on Assets (ROA), Debt to Equity Ratio (DER), and Asset Turnover Ratio (ATO). Seven classifier algorithms were compared based on accuracy, precision, sensitivity, recall, F1-score, and AUC. Random Forest had the best performance with 89.2% accuracy, 88.9% Macro Average F1-score, and an AUC of 0.946. The findings suggest that the Random Forest model accurately identifies underpriced IPO issuers as a good investment decision-making tool. This research demonstrates that machine learning concepts can be implemented to classify underpriced issuers in Indonesia, continuing previous studies that contributed to understanding the correlation and significance of certain variables to underpricing.
Minimalist DevStack Deployment: An Analysis of Performance and Swap Utilization Kristyanto, Marco; Fajrin, Ahmad Miftah
Journal of Applied Informatics and Computing Vol. 9 No. 4 (2025): August 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i4.9792

Abstract

Cloud technology offers significant advantages; however, its high implementation costs and high hardware requirements pose barriers to small-scale deployments and educational institutions. This study addresses these challenges by investigating the performance of OpenStack deployed via DevStack on a single-node server equipped with an Intel Core i7 processor, 16 GB of RAM, and a 500 GB solid-state drive (SSD) under resource-constrained conditions. We implemented a resource tuning approach by turning off non-essential services (including Cinder, Heat, and Tempest) and adjusting Nova's memory configurations to minimize overhead. Real-time system monitoring was performed using Prometheus and Grafana to examine trends in CPU, memory, and swap utilization across three configurations: default, optimized (RAM=1024 MB), and minimalist (RAM=512 MB). Our empirical results show that the optimized setup enhances system efficiency, decreasing CPU use and memory usage from 86% to 70.90% while maintaining the ability to run up to ten virtual machines with varying operating systems (e.g., CirrOS, Ubuntu 24.04 Server LTS). However, the minimalist configurations, which aim for aggressive swap utilization and reach 100% swap saturation when running 8 VMs under idle workloads, consequently compromise overall system responsiveness despite lower CPU usage. Efficiency in this context is defined as conserving RAM and CPU usage without degrading basic system responsiveness. This highlights a critical trade-off between RAM conservation and overall system responsiveness. This research provides practical insights into designing cost-effective and lightweight OpenStack environments. It establishes a crucial threshold for memory optimization, preventing performance degradation caused by excessive swap usage, particularly in resource-constrained research settings.
Implementation of Support Vector Machine for Classifying User Reviews on the Sentuh Tanahku Application Febriani, Ayu; Khotibul Umam; Mokhammad Iklil Mustofa
Journal of Applied Informatics and Computing Vol. 9 No. 4 (2025): August 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i4.9832

Abstract

User reviews play a crucial role in the development of digital public service applications, as they reflect user satisfaction and service quality. This study aims to classify user reviews of the Sentuh Tanahku application into two sentiment categories, namely positive and negative, by applying the Support Vector Machine (SVM) algorithm. A total of 13,231 reviews obtained from Kaggle were processed through text preprocessing stages including case folding, tokenizing, stopword removal, and stemming. The TF-IDF technique was employed to convert text data into numerical vectors, followed by classification using SVM with hyperparameter tuning via RandomizedSearchCV. The evaluation results showed that the SVM model achieved an accuracy of 91% on training data and 84% on testing data. To assess its performance, the study compared SVM with baseline algorithms, namely Naïve Bayes and Logistic Regression. The comparison revealed that Logistic Regression and Naïve Bayes outperformed SVM with accuracy scores of 88.84% and 88.68%, respectively. Despite this, SVM remained competitive in maintaining balanced metrics across both classes. These findings highlight that algorithm performance in sentiment classification is highly influenced by the nature of the dataset. This study is expected to contribute as a reference for improving user opinion analysis methods in Indonesian-language public service applications.
Prediction of Cyberbullying in Social Media on Twitter Using Logistic Regression Prayudani, Santi; Adha, Lilis Tiara; Ariyani, Tika; Lubis, Arif Ridho
Journal of Applied Informatics and Computing Vol. 9 No. 4 (2025): August 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i4.9842

Abstract

As cases of cyberbullying on social media increase, there is a need for efficient measures to detect the vice. This research aims to establish the application of machine learning algorithms in analyzing text on social media to determine potentially harmful comments using logistic regression. The first and most important research question of this study is to assess the extent to which the model is capable of correctly identifying the comments that contain features of cyberbullying and those that do not. The data set included comments from different social media sites and was preprocessed before further analysis was conducted on it. Exploratory Data Analysis was applied in the study to establish relationships and textual features with bullying behavior. As with any other model, after training and testing the model, the results were analyzed using parameters like precision, precision, gain, and F1 statistics. The outcomes of this study revealed that the use of logistic regression models can give a fairly satisfactory level of accuracy in identifying cyberbullying. In light of this, this study underscores the need to use machine learning algorithms to minimize negative actions in cyberspace.
Design of an IoT-Based Air Quality System with Web Integration in a Palm Oil Mill Environment Rohim, Sigid Nur; Saputro, Uyock Anggoro
Journal of Applied Informatics and Computing Vol. 9 No. 4 (2025): August 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i4.9876

Abstract

This research aims to design and implement an Internet of Things (IoT)-based air quality monitoring system with real-time data integration to web applications in the PT Gemareksa Mekarsari palm oil mill environment. This system utilizes NodeMCU ESP32 as the main microcontroller connected with MQ-2 and MQ-135 sensors to detect CO, CH₄, and NH₃ gases. Data is sent in real-time to the ThingSpeak platform and displayed through a responsive web dashboard. Testing was conducted over two days at three different location points (open area, fruit processing, and factory office) with a total of 45 measurements. Results showed that the system was able to transmit data with a delivery accuracy rate of 86.67%, with most data received without delay.. The detected gas concentrations were within safe limits, although mild fluctuations occurred, especially in the fruit processing area. The system also showed stable performance in displaying data on mobile and desktop devices. Thus, this system can be an effective solution for automatic and real-time industrial air monitoring, and support efforts to mitigate health risks due to air pollution.
Security Risk Analysis of QRIS Implementation in Public Locations Using ISO 31000:2018 Framework M. Fadhli Ma'arif; Melwin Syafrizal; Jeki Kuswanto; Aiko Nur Hendry Yansyah
Journal of Applied Informatics and Computing Vol. 9 No. 4 (2025): August 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i4.9877

Abstract

This study aims to analyze the security risks associated with the implementation of the QRIS (Quick Response Indonesia Standard) payment system in public spaces and provide appropriate mitigation recommendations. The research employs a case study approach with a qualitative research design to explore the perceptions of users and business owners regarding the potential risks involved. Data were collected through semi-structured interviews, risk perception surveys, and document analysis related to QRIS security policies and practices. The findings reveal that the primary risks faced by users and business owners include QR code manipulation, social engineering attacks, unstable internet connections, and low digital literacy. Based on the identified risks, the study suggests several mitigation strategies, including the use of dynamic QRIS, user security education, infrastructure improvements, and the implementation of regular audits. In conclusion, to enhance security and user trust in QRIS, a comprehensive approach is needed, incorporating technical, procedural, and educational aspects in an integrated manner.
SMOTE and Weighted Random Forest for Classification of Areas Based on Health Problems in Java Setiawan, Erwan; Sartono, Bagus; Notodiputro, Khairil Anwar
Journal of Applied Informatics and Computing Vol. 9 No. 4 (2025): August 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i4.9933

Abstract

Random Forest (RF) is a popular Machine Learning (ML) approach extensively employed for addressing classification issues. Nevertheless, the RF method for classification problems demonstrates suboptimal performance in cases of data imbalance. There are several approaches to enhance RF performance when coping with data imbalance issues, such as using weighting and oversampling. This research explores the intervention of RF in addressing data imbalances, focusing on case studies of health problem classification in Java This study aims to develop models to analyze the health status of regions using RF, WRF, SMOTE-RF, and SMOTE-WRF methods. The objective is to compare the performance of these models and identify the best model for classifying DBK and Non-DBK categories in Java. The research results show that SMOTE-WRF is the most effective model in classifying DBK, achieving an accuracy level of 93.62%, sensitivity of 85.71%, precision of 75%, F-score of 80%, and AUC of 93.57%. The three key variables in the SMOTE-WRF model entail access to adequate sanitation, egg and milk consumption, and the number of doctors

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