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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 805 Documents
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Design and Implementation of an IoT-Based Low-Emission Mobile Plastic Melting Machine for Sustainable Paving Block Production in Batam City Lawi, Ansarullah; Aranski, Alvendo Wahyu; Burhan, Rifa’atul Mahmudah; Hernando, Luki; Aritonang, Muhammad Adi Setiawan; Dermawan, Aulia Agung; Kurniawan, Dwi Ely; Leman, Abdul Mutalib
Journal of Applied Informatics and Computing Vol. 10 No. 1 (2026): February 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v10i1.12044

Abstract

Plastic waste accumulation poses a severe environmental burden, particularly in urban and archipelagic regions where centralized treatment infrastructure is limited. While thermal processing offers a pathway for volume reduction and material recovery, inadequate temperature control frequently leads to uncontrolled combustion and the formation of hazardous air pollutants. This study addresses this gap by developing and experimentally validating a low-emission, IoT-enabled mobile plastic melting system designed for decentralized paving block production. The proposed system integrates real-time thermal sensing using a K-type thermocouple and an ESP32-based controller with a compact three-nozzle water spray filtration unit. The control architecture maintains the melting process at approximately 270 °C, thereby preserving polymer viscosity for molding while preventing temperature excursions beyond 300 °C that may initiate combustion and toxic by-product formation. The filtration module operates as a simplified wet scrubber, capturing airborne particulates and simultaneously cooling the exhaust stream. Experimental evaluations confirm that the integrated control–filtration framework achieves stable thermal regulation and substantial suppression of visible exhaust emissions. Under these conditions, molten plastic was consistently transformed into dense paving blocks with smooth surface morphology and without evidence of polymer degradation or charring. The results demonstrate that combining IoT-based thermal governance with low-cost water-spray emission control provides an effective and scalable alternative to open burning for community-level plastic waste recycling. This mobile platform enables environmentally safer conversion of plastic waste into value-added construction materials, offering a practical pathway toward decentralized circular-economy implementation in resource-constrained regions.
Analysis of SMOTE and Random Search on Machine Learning Algorithms for Stroke Disease Diagnosis Dn, Ubaid Khoir Julio; Rahardi, Majid
Journal of Applied Informatics and Computing Vol. 10 No. 1 (2026): February 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v10i1.12046

Abstract

Stroke is a critical medical condition in which false negative predictions may lead to delayed treatment and increased mortality. Therefore, predictive models in the medical domain should prioritize sensitivity (recall) in addition to overall accuracy. This study analyzes the impact of the Synthetic Minority Over-sampling Technique (SMOTE) and Random Search hyperparameter optimization on five machine learning algorithms—Random Forest, XGBoost, Support Vector Machine (SVM), Logistic Regression, and CatBoost—for stroke disease diagnosis. Two experimental scenarios were conducted, namely models trained without SMOTE and models trained with SMOTE applied only to the training data to prevent data leakage. Model performance was evaluated using accuracy, precision, recall, and F1-score, with particular emphasis on recall due to its clinical relevance. In clinical practice, low recall may lead to false negative predictions, where high-risk stroke patients are not identified by the system, potentially resulting in delayed medical intervention. Therefore, recall is emphasized as the primary performance metric in this study. Experimental results demonstrate that SMOTE consistently improves recall across all models, while Random Search further enhances performance. CatBoost achieved the best performance with an accuracy of 96.61%, recall of 97%, and F1-score of 97%. Despite its superior performance, potential overfitting risks are critically discussed. These findings indicate that the proposed approach produces a clinically relevant decision-support model for stroke risk prediction.
Analysis of the Best Social Media Platforms for Promotion Using Machine Learning and RFE Feature Selection: A Comparative Study of Gradient Boosting, XGBoost, CNN, and SVR Putri, Maulina; Hendrawan, Aria
Journal of Applied Informatics and Computing Vol. 10 No. 1 (2026): February 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v10i1.12049

Abstract

This study aims to identify the most effective social media platforms for digital marketing. The use of social media for promotion continues to grow, yet many businesses still struggle to determine which platforms have the greatest impact. Therefore, this study compares the performance of various machine learning platforms to predict the best platform. The algorithms used are Gradient Boosting Regressor, XGBoost Regressor, Convolutional Neural Network (CNN), and Support Vector Regression (SVR) to estimate digital conversion potential based on user reviews, ad reach, and content trend patterns. A Knowledge Discovery in Databases (KDD) workflow is used to identify the most important key factors. This process includes data preprocessing, TF-IDF feature extraction, sentiment analysis, feature engineering, and feature elimination (RFE). The results showed that the CNN algorithm excelled in prediction, with the highest R² score of 0.74 and the lowest RMSE of 14.78. CNN predictions showed YouTube topping the list in terms of conversion potential, followed by Facebook and TikTok. These results highlight the higher promotional effectiveness of video-based platforms and the importance of machine learning in digital marketing decision-making. However, this study is limited by its reliance on static user review and ad reach data, which may not fully capture the dynamic changes of social media platforms.
Real-Time Waste Detection System Using YOLOv12 with Transfer Learning Jovina, Adellia; Lumba, Ester
Journal of Applied Informatics and Computing Vol. 10 No. 1 (2026): February 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v10i1.12050

Abstract

Waste sorting at the source remains a major challenge in Indonesia due to limited public awareness and the absence of accessible tools for waste classification. While YOLO-based object detection has been widely applied for waste detection, the adoption of the latest YOLO architecture in web-based, real-time public-oriented systems remains limited. This study aims to develop and experimentally evaluate a web-based waste detection system using YOLOv12 with a transfer learning approach to classify waste into organic, inorganic, and hazardous (B3) categories along with their subcategories. The system was developed using the Flask framework and supports image upload and real-time camera-based detection. A real-world dataset was annotated and divided into training, validation, and testing sets for experimental evaluation. The proposed model achieved a precision of 0.86, recall of 0.74, mAP@0.5 of 0.83, and mAP@0.5:0.95 of 0.68, with an average inference time of 0.0187 seconds per image (53.40 FPS). Overall, these results indicate that YOLOv12 with transfer learning provides an effective balance between accuracy and inference speed for web-based real-time waste detection systems, supporting its applicability for practical waste sorting solutions.
Evaluation of Histogram-Based Image Enhancement Methods for Facial Images in Drowsy Driver Using No-Reference Metrics Naufal, Muhammad; Al Azies, Harun; Alzami, Farrikh; Brilianto, Rivaldo Mersis
Journal of Applied Informatics and Computing Vol. 10 No. 1 (2026): February 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v10i1.12055

Abstract

Low-light facial images suffer significant quality degradation, leading to performance degradation in surveillance and face recognition systems, where conventional enhancement methods often produce over-enhancement or unnatural noise artifacts. This study compares three histogram equalization methods, namely HE, AHE, and CLAHE, for low-light facial image enhancement, with evaluation using no-reference quality assessment metrics, including NIQE, LOE, and Entropy, as well as visual analysis and histogram distribution. The results showed that AHE produced the lowest NIQE (4.96 ± 1.38) and the highest entropy (7.86 ± 0.11) but had significant noise artifacts, HE produced an overly even distribution with NIQE of 6.34 ± 1.41, while CLAHE showed the most balanced performance with the lowest LOE (0.07 ± 0.02) and the best visual quality when using the optimal clip limit in the range of 1.2-2.0, providing an optimal trade-off between contrast enhancement, naturalness preservation, and artifact minimization with computational efficiency below 1 ms.
The Impact of the L1/L2 Ratio on Selection Stability and Solution Sparsity along the Elastic Net Regularization Path in High-Dimensional Genomic Data Fahira, Fani; Sadik, Kusman; Suhaeni, Cici; M Soleh, Agus
Journal of Applied Informatics and Computing Vol. 10 No. 1 (2026): February 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v10i1.12059

Abstract

High-dimensional genomic datasets (p>n) pose persistent challenges for predictive modeling and biomarker-oriented feature selection due to multicollinearity and instability of selected feature sets under resampling. Although Elastic Net is widely used to address correlated predictors via combined L1/L2 regularization, the practical role of the L1/L2 mixing ratio (α) is often treated as a secondary tuning choice driven primarily by predictive accuracy. This study investigates how varying α shapes the trade-off among selection stability, solution sparsity, and predictive performance along the Elastic Net regularization path. Experiments were conducted using the publicly available METABRIC breast cancer cohort (n = 1,964) with 21,113 gene expression features and a binary overall survival status outcome. Logistic regression with Elastic Net penalty was fitted across a grid of α values, with the regularization strength (λ) selected by cross-validation. Feature selection stability was evaluated under repeated resampling using the Jaccard index, Dice coefficient, and Adjusted Rand Index (ARI), while sparsity was summarized by the average number of non-zero coefficients; predictive performance was assessed using AUC, accuracy, and F1-score. Results show a monotonic decline in stability as α increases: α = 0.2 yields the highest stability (Jaccard 0.324, Dice 0.487, ARI 0.434), whereas LASSO (α = 1.0) produces the lowest stability (Jaccard 0.278, Dice 0.431, ARI 0.400). In contrast, predictive performance varies only marginally across α (AUC 0.696–0.704; accuracy 0.666–0.671; F1-score 0.738–0.742), while sparsity changes substantially (average selected features 110–204). Coefficient path analyses further illustrate abrupt shrinkage under LASSO versus smoother, group-preserving shrinkage under Elastic Net, consistent with improved reproducibility under lower-to-moderate α. Frequency-of-selection analysis highlights genes repeatedly selected across resampling, supporting interpretability of stable configurations without claiming causal biomarker validity. Overall, the findings demonstrate that α is a substantive modeling choice that materially affects stability and sparsity even when accuracy is similar, motivating stability-aware tuning for high-dimensional genomic prediction and reproducible feature discovery.
Optimizing Email Spam Detection through Handling Class Imbalance with Class Weights and Hyperparameter Using GridSearchCV Nursyam, Muhammad Ridho; Koprawi, Muhammad; Ariyus, Dony
Journal of Applied Informatics and Computing Vol. 10 No. 1 (2026): February 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v10i1.12060

Abstract

Email spam is a major problem in digital communication that can disrupt productivity, burden network resources, and pose a security threat. This research focuses on optimizing spam email detection using a machine learning approach by addressing class imbalance through class weighting and hyperparameter tuning using GridSearchCV. To improve model accuracy and sensitivity, a combination of diverse datasets is applied to provide a wider scope of training data. The models used in this study include Support Vector Machine (SVM), Random Forest, Multinomial Naive Bayes (MNB), and XGBoost. Evaluation is carried out based on metrics such as accuracy, precision, recall, and F1-score, before and after hyperparameter tuning. The experimental results show that SVM produces the highest accuracy after tuning, reaching 97.10%, compared to 96.73% before hyperparameter tuning. In addition, Random Forest, MNB, and XGBoost also show significant improvements, with each model achieving better performance after tuning. Overall, this study shows that dataset merging and class weight adjustment can significantly improve the model's ability to detect spam, as well as provide a basis for implementing the model in a more effective email spam detection system.
Behavioural Predictors of Forward Head Posture Risk: A Correlation, Machine Learning, and Clustering Analysis Putri Lo, Angel Aprilia; Christian, Christian
Journal of Applied Informatics and Computing Vol. 10 No. 1 (2026): February 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v10i1.12089

Abstract

Forward Head Posture (FHP) has become increasingly common among university students due to prolonged digital device use and inadequate ergonomic behaviour. This study aims to identify the behavioural factors that most strongly predict neck tension, which is used as an indicator of FHP risk, among laptop users at Universitas Ciputra. A total of 141 survey responses were collected, capturing digital lifestyle patterns that include screen exposure, posture habits, ergonomic awareness, physical activity, and screen-related symptoms. The analysis followed a complete methodological sequence that involved data preprocessing, correlation testing, supervised machine-learning modelling, and K-Means clustering. The results show that headache after screen use, frequency of head-down posture, ergonomic knowledge, and weekly exercise emerged as the most influential behavioural predictors of neck tension, with head-down posture demonstrating the strongest association (r = 0.437). Correlation testing supported three of the four hypotheses, while the Random Forest model achieved the highest predictive performance (71.01% cross-validated accuracy). The clustering analysis revealed two distinct behavioural subgroups with different ergonomic risk profiles. These findings highlight specific behavioural targets that can support ergonomic-awareness efforts and help reduce the likelihood of FHP development in academic environments.
Application of the Hybrid Entropy–VIKOR Method for Urban EV Charging Station Prioritization in Central Java Purbaningtyas, Ivana; Cholil, Saifur Rohman
Journal of Applied Informatics and Computing Vol. 10 No. 1 (2026): February 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v10i1.12094

Abstract

The rapid growth of electric vehicles (EVs) in Indonesia necessitates strategic and data-driven planning of public electric vehicle charging stations (EVCS/SPKLU), particularly in urban areas with high mobility and economic activity such as Central Java Province. This study aims to determine priority locations for EVCS development using an objective hybrid Multi-Criteria Decision Making (MCDM) approach. Official secondary data from the Central Java Provincial Statistics Agency (BPS) for the 2023-2024 period are employed, involving 12 urban areas as decision alternatives. Criteria weighting is performed using the Entropy method to minimize subjectivity, while alternative ranking is conducted using the VIKOR method to obtain the best compromise solution. Six criteria are considered, including installed electrical capacity, population density, motor vehicle density, gross regional domestic product (GRDP) per capita, percentage of regional area, and the number of commercial facilities. The results indicate that Cilacap Regency (Q = 0.000), Banyumas Regency (Purwokerto) (Q = 0.271), and Tegal Regency (Q = 0.492) are the highest-priority locations for EVCS development. Ranking validation using the Normalized Discounted Cumulative Gain (NDCG) yields a value of 0.963, indicating a very high level of agreement with the reference ranking, while the Spearman rank correlation coefficient of 0.832 reflects a strong positive consistency. The novelty of this study lies in integrating up-to-date regional statistical indicators with a fully objective Entropy-VIKOR framework complemented by ranking validation, providing a reliable data-driven decision-support tool for policymakers and investors in regional EVCS infrastructure planning.
Numerical Investigation of Nonlinear Parabolic Dynamical Wave Equations Using Modified Variational Iteration Algorithm-II Mohammed, Sizar Abid; Ali, Nawzad Hasan
Journal of Applied Informatics and Computing Vol. 10 No. 1 (2026): February 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v10i1.12107

Abstract

In this study, the Modified Variational Iteration Algorithm-II (MVIA-II) is implemented as a robust numerical scheme for solving nonlinear Parabolic partial differential equations. The study focuses on the implementation of an auxiliary parameter h into the correction functional to control the convergence region of the approximate series solution. To validate the efficiency of this semi-numerical approach, two fundamental models arising in mathematical physics and biology are investigated: The Allen-Cahn equation and the Newell-Whitehead equation. The results are compared with exact analytical solutions and other existing numerical methods. The error analysis demonstrates that the proposed algorithm yields high accuracy with minimal computational overhead, making it a promising tool for simulating nonlinear dynamical wave phenomena.

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