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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 1,006 Documents
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Agentic AI Adoption: Balancing Enthusiasm and Ethical Concerns An Exploratory Study Nadia Azaria; Mambang Mambang; Finki Dona Marleny; Trifebi Shina Sabrila
Journal of Applied Informatics and Computing Vol. 10 No. 3 (2026): June 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v10i3.13070

Abstract

Agentic AI represents a significant paradigm shift in artificial intelligence, transitioning from passive command execution to autonomous goal pursuit. This exploratory study investigates user perceptions toward Agentic AI adoption in Indonesia, focusing on the balance between functional enthusiasm and ethical concerns. The 20 Likert-scale statements were developed based on the Technology Acceptance Model (TAM), Unified Theory of Acceptance and Use of Technology (UTAUT), and AI trust frameworks. These items cover key dimensions including perceived usefulness, ease of use, trust in autonomy, productivity enhancement, data privacy, loss of human control, algorithmic bias, and adoption intention. Utilizing Exploratory Data Analysis (EDA) supplemented with non-parametric tests on a convenience sample of 22 respondents predominantly tech-savvy young adults this study reveals high familiarity with AI tools (54.5% daily users). Respondents showed strong optimism regarding productivity and efficiency (Positive Aspects mean = 3.52), while maintaining notable ethical concerns (Concern Aspects mean = 3.64). The instrument demonstrated excellent reliability (Cronbach’s Alpha = 0.921 overall). Mann-Whitney U tests indicated significant gender differences on certain items, particularly bias concerns. Due to the small sample size and self-selection bias, findings should be interpreted cautiously. This study provides preliminary insights and highlights the need for human-centric design, transparent governance, and culturally appropriate regulations to support responsible Agentic AI adoption in Indonesia.
MobileNetV3 for Durian Seedling Variety Classification Based on Leaf Images with Sobel Edge Detection Preprocessing Rusly Nur Huda; Indah Susilawati
Journal of Applied Informatics and Computing Vol. 10 No. 3 (2026): June 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v10i3.13085

Abstract

Durian is a high-value horticultural commodity in Indonesia, yet variety identification based on leaf morphology remains largely manual and prone to subjective error. This study develops a deep learning-based classification system for durian leaf varieties using MobileNetV3Small, designed for practical deployment on mobile and edge computing devices in agricultural field settings to support real-time variety identification by farmers and agricultural practitioners. A dataset of 1,680 images across four durian varieties Bawor, Musang King, Duri Hitam, and Super Tembaga was constructed through direct field collection and public dataset acquisition, followed by augmentation via rotation and flipping techniques. Preprocessing incorporated background removal, cropping, resizing to 224×224 pixels, and Sobel edge extraction to enhance morphological features such as leaf veins and contours. Transfer learning from ImageNet weights was applied, with training conducted in two phases: a 30-epoch warm-up with frozen base layers, followed by 90-epoch fine-tuning of the 20 deepest layers selected to adapt high-level semantic features while preserving general low-level representations and avoiding catastrophic forgetting using cosine annealing and early stopping. The model achieved a validation accuracy of 98.42% and a macro average F1-score of 0.9842, with only 4 misclassifications out of 253 test images. All misclassifications occurred exclusively between the morphologically similar Bawor and Duri Hitam classes. Comparative analysis against eight prior studies using similar durian leaf classification tasks suggests that the proposed approach achieves competitive performance relative to studies employing larger architectures such as ResNet and ConvNeXt, though direct comparison on an identical dataset was not conducted and remains a direction for future work.
Modeling Chemical Oxygen Demand of River Water in East Kalimantan Using Fixed Effects and Geographically Weighted Panel Regression Memi Nor Hayati; Toha Saifudin; Suyitno Suyitno
Journal of Applied Informatics and Computing Vol. 10 No. 3 (2026): June 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v10i3.13089

Abstract

The degradation of river water quality in East Kalimantan has become an environmental issue related to the Sustainable Development Goals (SDGs), particularly Goal 6 concerning clean water and sanitation. This study aims to model Chemical Oxygen Demand (COD) as an indicator of river water quality using the Fixed Effect Model (FEM) and Geographically Weighted Panel Regression (GWPR) approaches, as well as to compare the performance of both models in representing spatial and temporal heterogeneity. The data used in this study consist of panel data from 31 observation locations over four semesters, with predictor variables including pH, Total Suspended Solid (TSS), Fecal Coliform, Total Dissolved Solid (TDS), and ammonia. FEM estimation was conducted using the within estimator, while the GWPR model employed an adaptive bisquare kernel weighting function. The results show that TSS and ammonia significantly affect COD in the FEM model. In the GWPR model, the effects of predictor variables vary across observation locations, indicating spatial heterogeneity in the factors influencing COD. TSS was identified as the most dominant variable, being significant at 26 locations, followed by ammonia and TDS, which were significant at 18 and 15 locations, respectively. Furthermore, the GWPR model produced a lower RMSE value (1.3777) than the FEM model (2.182011). These findings indicate that GWPR performs better in capturing spatial heterogeneity and temporal information in river water quality and provides more detailed information for supporting location-specific water quality management in East Kalimantan.
Stock Price Modelling of Ciputra Development Tbk. (CTRA) Using Fourier Series Ika Purnamasari; Toha Saifudin; Sri Wahyuningsih; M. Fariz Fadillah Mardianto
Journal of Applied Informatics and Computing Vol. 10 No. 3 (2026): June 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v10i3.13093

Abstract

Stock price data generally show fluctuating and dynamic patterns, making the forecasting process challenging in time series analysis. In addition, stock forecasting is also related to economic activity and investment development that support economic growth. This study applies the Fourier series model to predict daily stock prices of Ciputra Development Tbk (CTRA) during January-December 2025 by considering the Fourier parameter (K). The Fourier series estimator consists of two models, namely a model with trend component and a model without trend component. The data were divided into training and testing sets using an 85:15 ratio. Model selection was performed using Generalized Cross validation (GCV), while model performance was evaluated using Mean Absolute Percentage Error (MAPE) and Root Mean Square Error (RMSE). The results show that the Fourier series model with a trend component outperformed the model without trend component. The optimal model was obtained at K=20 with a minimum GCV value of 357.7702. The model produced a training MAPE of 1.2804% and RMSE of 14.9427, while the testing MAPE and RMSE were 5.0640% and 53.6083, respectively, indicating good predictive accuracy and generalization performance.
Predictive Analytics of Food Retail Seasonal Trends with Advanced Forecasting Modeling Anita Sindar Sinaga; Dameria Esterlina Br Jabat; Amalia Rossa; Dini Auliah
Journal of Applied Informatics and Computing Vol. 10 No. 3 (2026): June 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v10i3.13173

Abstract

Food sales in food retailers generally increase on certain days. Three food categories served as data sources in this study: staple foods, ready-to-eat foods, and dairy products. Predictive analysis of seasonal trends in food retailers shows that macroeconomic factors, seasonal patterns, and religious holiday indicators play a significant role in shaping sales. Staples is the highest-revenue category, while frozen foods has the lowest volume of the three. Each highlighted sector, including dairy, is expected to experience a measurable increase in turnover over the coming period. All models exhibit varying accuracy in predicting 2026 sales compared to actual 2025 sales, evaluated using MAPE, RMSE, and MAE for key products. Moving Average and LSTM tend to be conservative, while ETS and ARIMA are more optimistic but remain limited by limited data. Random Forest also struggles to capture complex relationships. Prophet stands out for its ability to incorporate exogenous variables and handle seasonality, although caution is needed when interpreting future values. The MAPE values ranged from 1.89% to 5.34%, indicating excellent predictive accuracy, as MAPE values below 10% are generally considered high accuracy. The low RMSE and MAE values also indicate a relatively small difference between the 2026 prediction and the actual 2025 values.
Real-Time News Authenticity Verification Using MPNet (Masked and Permuted Pre-training Network)-Based Sentence Embeddings on Digital News Portals Ira Lestari; Herlinah Herlinah; M. Adnan Nur
Journal of Applied Informatics and Computing Vol. 10 No. 3 (2026): June 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v10i3.13193

Abstract

The dissemination of fake news (hoaxes) on digital news portals represents a significant challenge in the digital era, as it may mislead the public and reduce trust in circulating information. The rapid and open nature of digital media enables unverified information to spread widely within a short period of time, while manual verification processes require substantial time and effort. This study proposes a semantic similarity-based approach to support real-time news verification using the Multilingual MPNet model. The proposed approach utilizes content text as input, followed by keyword extraction using KeyBERT to represent the core information of the news. The extracted keywords are employed in a news scraping process to obtain comparative news articles from digital news portals. A dataset consisting of 200 Indonesian news articles, including 100 factual news articles and 100 hoax news articles, was used for evaluation. Subsequently, semantic similarity measurement is conducted to evaluate the degree of semantic relevance between the test news and the scraped news. Evaluation metrics were applied to assess the effectiveness of the proposed approach. The findings demonstrate that semantic text representation using Multilingual MPNet effectively supports hoax detection and provides relevant supporting evidence in the form of semantically related news articles, enabling users to access comparative news sources that support the verification process. Experimental results show that the proposed approach achieved an accuracy of 83.5%, precision of 97.18%, recall of 69.0%, F1-score of 80.70%, and an AUC of 0.695, indicating that Multilingual MPNet can effectively support news verification through semantic similarity analysis.
Evaluating LSB and MSB Steganography in Retinal Fundus Images Through Image Quality Assessment and VGG19-Based Classification Gilang Faturrahman; Muhammad Naufal; Wahyu Aji Eko Prabowo; Sindhu Rakasiwi
Journal of Applied Informatics and Computing Vol. 10 No. 3 (2026): June 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v10i3.13198

Abstract

The security of medical image data within electronic medical record systems has become a critical issue due to the increasing threat of health data breaches. Steganography is a promising technique for protecting patient information by concealing secret data within medical images without significantly altering their visual appearance. However, the application of steganography to retinal fundus images, which carry high diagnostic value, has never been comprehensively evaluated in terms of image quality or its impact on artificial intelligence-based diagnostic model performance. This study compares Least Significant Bit (LSB) and Most Significant Bit (MSB) steganography methods applied to 3,200 retinal fundus images from the Retinal Fundus Multi-disease Image Dataset (RFMiD) dataset across four payload levels (0.1-0.4 bpp), evaluated using PSNR, SNR, SSIM, and FSIM for image quality, and VGG19 classification accuracy and AUC for diagnostic impact. Results show LSB achieves substantially superior image quality (PSNR: 59.97-65.93 dB; SNR: 49.34-55.30 dB; SSIM: 0.9981-0.9997; FSIM: 0.9999-1.0000) compared to MSB (PSNR: 12.98-18.99 dB; SNR: 2.35-8.37 dB; SSIM: 0.5979-0.9003; FSIM: 0.5342-0.7500), while VGG19 classification accuracy remains stable for both methods (LSB: 0.8938-0.9000; MSB: 0.8953-0.9031) with a maximum difference of 0.62% from baseline. This study demonstrates that LSB is the more appropriate steganography method for retinal fundus images, delivering superior visual quality while preserving VGG19 diagnostic capability.
Performance Comparison of Random Forest, Support Vector Machine, and K-Nearest Neighbors Algorithms in Ten-Minute Rainfall Prediction for Urban Flood Mitigation in South Tangerang Tri Nurmayati
Journal of Applied Informatics and Computing Vol. 10 No. 4 (2026): August 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v10i4.10809

Abstract

Rainfall prediction with very high temporal resolution, such as ten-minute intervals, is of great urgency in urban flood mitigation, especially in densely populated areas such as South Tangerang. Most previous studies still focus on daily or monthly rainfall prediction, so this study attempts to fill this gap by comparing the performance of three machine learning algorithms: Random Forest (RF), Support Vector Machine (SVM), and K-Nearest Neighbors (KNN) in predicting ten-minute rainfall. A dataset of 52,703 entries was obtained from an automated weather station (AWS) with predictor variables of temperature, humidity, and air pressure. The analysis process was carried out through preprocessing stages (missing value imputation, Min–Max normalization), data splitting (80:20), and 10-fold cross-validation using Orange Data Mining software. Model performance evaluation used Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and coefficient of determination (R²). The results showed that Random Forest performed best with an RMSE of 0.671 and MAE of 0.090, followed by KNN with an RMSE of 0.683 and MAE of 0.083, while SVM performed significantly lower (RMSE of 2.553 and MAE of 2.511). However, the negative R² values for all models indicate limitations in explaining rainfall data variability, which is likely influenced by skewed data distribution (zero-inflated) and AWS sensor noise. These findings indicate that Random Forest is relatively more reliable than other models, but its accuracy is still not optimal for operational applications. Future research is recommended to adopt a hybrid or deep learning approach (e.g., LSTM), add other meteorological variables such as wind speed and solar radiation, and integrate the models into IoT-based early warning systems to support urban flood mitigation.
Optimization of Diabetes Mellitus Classification Using the Random Forest and SMOTE-ENN Methods Imam Fadhur Rahman; Egia Rosi Subhiyakto; Cinantya Paramita
Journal of Applied Informatics and Computing Vol. 10 No. 4 (2026): August 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v10i4.11387

Abstract

Diabetes Mellitus is a non-communicable disease (NCD) that has turned into a worldwide health issue with a steadily rising prevalence. Timely identification is essential for minimizing the risk of complications and the financial strain on the healthcare system. This research focuses on creating a precise and dependable diabetes classification model through the Random Forest algorithm by implementing a series of systematic data preprocessing methods. This methodology utilizes a dataset obtained from Kaggle, consisting of 768 samples. The steps taken include addressing missing values using Multiple Imputation by Chained Equations (MICE) , removing outliers with the Z-Score and Interquartile Range (IQR) techniques , selecting features based on ANOVA F-value to identify the eight most significant features , and balancing classes through the Synthetic Minority Over-sampling Technique-Edited Nearest Neighbours (SMOTE-ENN) to correct dataset imbalance. The assessment of the Random Forest model revealed outstanding performance, attaining an accuracy of 94.3% and an Area Under Curve (AUC) value of 0.98. These findings suggest that the model possesses strong discriminative capability to differentiate between diabetic and non-diabetic individuals. This research concludes that the Random Forest algorithm, when backed by suitable data preprocessing, is very efficient and could be utilized in clinical decision support systems as an early tool for diabetes screening.
SEO Strategy Using Content Pillars and Long-Tail Keywords to Boost Organic Traffic Nuri Cahyono; Kamarudin Kamarudin
Journal of Applied Informatics and Computing Vol. 10 No. 4 (2026): August 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v10i4.11792

Abstract

The issue of low content visibility in organic search results remains a major challenge in website management, particularly when content structures are disorganized and keyword selection is suboptimal. This study employs a descriptive quantitative approach through the implementation of content pillar and long-tail keyword strategies, complemented by structural connectivity analysis using a Content Relevance Matrix and Interlink Density (ILC) measurement. The research process includes topic mapping, the development of pillar and cluster articles, the evaluation of internal link density, and the analysis of organic performance using Google Search Console. The results indicate that clusters with the highest connectivity levels, such as C3 and C5, demonstrate a strong correlation with organic traffic performance, while the calculated ILC value of 53.57% reflects an efficient internal linking structure that enhances crawlability. Traffic evaluation further shows a significant outcome, with the highest value reaching 14,608 organic visits, aligning with the strengthened semantic relevance within the content structure.

Page 91 of 101 | Total Record : 1006

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