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Rahmadya Trias Handayanto
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PIKSEL : Penelitian Ilmu Komputer Sistem Embedded and Logic
Published by Universitas Islam 45 Bekasi
ISSN : 23033304     EISSN : 26203553     DOI : https://doi.org/10.33558/piksel
Core Subject : Science,
Computer Science & IT Decision Sciences, Operations Research & Management
Jurnal PIKSEL diterbitkan oleh Universitas Islam 45 Bekasi untuk mewadahi hasil penelitian di bidang komputer dan informatika. Jurnal ini pertama kali diterbitkan pada tahun 2013 dengan masa terbit 2 kali dalam setahun yaitu pada bulan Januari dan September. Mulai tahun 2014, Jurnal PIKSEL mengalami perubahan masa terbit yaitu setiap bulan Maret dan September namun tetap open access tanpa biaya publikasi. p-ISSN: 2303-3304, e-ISSN: 2620-3553. Available Online Since 2018.
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 489 Documents
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User-Centered Development of a Trustworthy E-Voting Application for Student Elections Jefri Marzal; Suwannit Chareen Chit; A. Zarkasi; Niken Rarasati
PIKSEL : Penelitian Ilmu Komputer Sistem Embedded and Logic Vol. 14 No. 1 (2026): March 2026
Publisher : LPPM Universitas Islam 45 Bekasi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33558/piksel.v14i1.12155

Abstract

Developing a secure and reliable remote electronic voting (e-voting) application presents critical challenges, particularly in ensuring system integrity, security, and user trust. This study focuses on designing and evaluating an e-voting system for student leader elections, with the objective of addressing the key factors that influence voter confidence. The development process followed structured phases: analysis, design, development, implementation, and evaluation. Key concerns, such as transparency, voter anonymity, prevention of duplicate voting (singularity), and result integrity, were addressed. A survey was conducted with 1,000 randomly selected students to assess their primary concerns and level of trust in the system. Results indicate that transparency, singularity, integrity, and anonymity are the most crucial requirements for an e-voting system. To enhance trust, efforts were made from the design phase through socialization, including educational campaigns, application demonstrations, and user training. Rigorous testing was conducted by internal developers and external stakeholders, followed by a public trial. Out of 3,527 survey respondents, 68.04% expressed trust in the application, deeming it “feasible” for adoption. However, areas such as transparency and feedback mechanisms require further improvement to fully address user concerns. This study contributes to the e-voting field by underscoring the importance of user involvement and extensive testing in developing a trustworthy system for student elections.
Energy-Aware Multi-Objective Deployment Optimization of Wireless Sensor Networks Using Direct Radio Graph Medium (DRGM) Modelling Fandi Ali Mustika; Ali Herdian; Prastika Indriyanti; Muhammad Rifqi
PIKSEL : Penelitian Ilmu Komputer Sistem Embedded and Logic Vol. 14 No. 1 (2026): March 2026
Publisher : LPPM Universitas Islam 45 Bekasi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33558/piksel.v14i1.12233

Abstract

Wireless Sensor Networks (WSNs) are widely deployed for large-scale environmental monitoring applications, particularly in remote and maritime areas where manual surveillance is costly and impractical. One of the major challenges in WSN deployment is achieving full sensing coverage and network connectivity while minimizing energy consumption and deployment density. This paper proposes an energy-aware multi-objective deployment optimization model based on Direct Radio Graph Medium (DRGM) modeling. The deployment problem is formulated as a multi-objective optimization task aiming to minimize the number of active sensor nodes while maintaining communication connectivity under predefined sensing and transmission constraints. A genetic algorithm–based optimization mechanism is employed to generate Pareto-optimal deployment solutions. The proposed model is evaluated using NS-2 simulations under various node densities and traffic rates. Simulation results show that the DRGM-based deployment achieves full coverage using only 10 sensor nodes, compared to 50–100 nodes in random deployment, corresponding to a node reduction of up to 90%. Furthermore, the proposed approach significantly reduces network power consumption and radio duty cycles, demonstrating its effectiveness for energy-efficient and scalable WSN deployment in large monitoring areas.
A Comparative Analysis of Sequential Search Algorithm Implementation within the Sales Information Systems of Wira Thrift Store and Rizky Snack Store Mugiarso Mugiarso; Marcellino Hutagalung; Miftahur Rizky; Rakhmat Purnomo; Dwipa Handayani; Rasim Rasim
PIKSEL : Penelitian Ilmu Komputer Sistem Embedded and Logic Vol. 14 No. 1 (2026): March 2026
Publisher : LPPM Universitas Islam 45 Bekasi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33558/piksel.v14i1.12244

Abstract

This study evaluates the implementation of the Sequential Search algorithm to address manual data management challenges within Indonesian MSMEs, specifically regarding stock tracking inefficiencies. The search system was deployed across two distinct entities: Toko Wira Thrift (apparel sector) and Toko Rizky Snack (food sector) using PHP and MySQL. Blackbox testing was conducted through 14 scenarios covering both positive and negative conditions. The results demonstrate that the algorithm successfully enhanced real-time product search speeds for customers and optimized inventory management to prevent stock expiration, achieving a 100% functional success rate. The testing confirms the system's functionality, asserting that Sequential Search is an effective and flexible solution for improving operational efficiency across various MSME sectors.
Rice Distribution Clustering for Decision Support in Jakarta Suhendra Suhendra; Lukman Hakim; Fandi Ali Mustika
PIKSEL : Penelitian Ilmu Komputer Sistem Embedded and Logic Vol. 14 No. 1 (2026): March 2026
Publisher : LPPM Universitas Islam 45 Bekasi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33558/piksel.v14i1.12245

Abstract

Rice distribution stability plays a strategic role in ensuring urban food security, particularly in metropolitan regions such as Jakarta where supply-demand dynamics are highly complex. This study aims to develop an integrated clustering-based decision support framework to classify regional rice distribution conditions and enhance adaptive allocation strategies. Monthly rice availability and demand data from 2021–2023 across seven administrative regions in Jakarta were analyzed using the K-Means clustering algorithm. Optimal cluster determination employed the Elbow and Silhouette methods. Cluster validation was conducted using Silhouette, Davies–Bouldin, and Calinski–Harabasz indices. A comparative analysis with Hierarchical Clustering (Ward linkage) was also performed. The clustering results were integrated into a Business Intelligence dashboard. Three optimal clusters were identified, representing high-surplus, moderate-surplus, and deficit conditions. K-Means demonstrated superior cluster compactness and separation quality compared to Hierarchical Clustering, with a Silhouette score of 0.62 and a Davies–Bouldin index of 0.41. The proposed framework improves operational transparency and supports evidence-based redistribution policies. This approach also contributes to strengthening adaptive food security management in metropolitan areas.
Multimodal Transfer Learning for Anti-Inflammatory Medicinal Plant Leaf Classification using ResNet50 Umniy Salamah; Nur Ani; Yuwan Jumaryadi; Agustiawan
PIKSEL : Penelitian Ilmu Komputer Sistem Embedded and Logic Vol. 14 No. 1 (2026): March 2026
Publisher : LPPM Universitas Islam 45 Bekasi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33558/piksel.v14i1.12279

Abstract

This study aimed to develop an AI-based image classification model using transfer learning methods to identify seven types of anti-inflammatory plant leaves commonly used in traditional medicine. The novelty of this research lies in approach to integrating Canny Edge detection and Gamma Correction with the ResNet50 architecture for multimodal fusion. The class plants, including Aloe Vera, Annona Muricata, Centella Asiatica, Muntingia Calabura, and Ocimum Basilicum, are known for their therapeutic properties and bioactive compounds. A dataset consisting of 350 images per species was collected, with images divided into training (70%), validation (20%), and testing (10%) sets. Data augmentation techniques such as rotation, flipping, and zooming were applied to improve model generalization. To enhance classification performance, pre-trained convolutional neural network (CNN) models, including ResNet50 and VGG16, were employed for transfer learning. The study also integrated image processing techniques, such as the Laplacian Filter, Canny Edge, and Gamma Correction, to extract additional features and improve the model’s accuracy. Among the different configurations tested, the combination of Canny Edge and Gamma Correction with ResNet50 yielded the best results, achieving a training accuracy of 89.3%, validation accuracy of 88.1%, and test accuracy of 87.0%. In contrast, the use of Laplacian Filter and Canny Edge with ResNet50 led to lower performance, suggesting that multimodal fusion of certain feature extraction methods could enhance classification accuracy. This research highlighted the potential of AI-driven approaches in the classification of medicinal plant leaves and offered a more efficient, accurate method for identifying anti-inflammatory plants used in traditional medicine.
Haversine Algorithm-Based GIS for Nearest Health Facility Mapping in Bekasi Regency Andy Achmad Hendharsetiawan; Dwipa Handayani
PIKSEL : Penelitian Ilmu Komputer Sistem Embedded and Logic Vol. 14 No. 1 (2026): March 2026
Publisher : LPPM Universitas Islam 45 Bekasi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33558/piksel.v14i1.12289

Abstract

The uneven distribution of healthcare facilities in Bekasi Regency makes it difficult for the public to find the nearest services. This study aims to design a Geographic Information System (GIS) to map and find routes to the nearest healthcare facilities. The system implements the Haversine algorithm to calculate the shortest distance precisely based on coordinate points accounting for the earth's curvature. Software development uses the adaptive Extreme Programming (XP) method. System functionality testing was conducted using the Black Box Testing method. The test results indicate that all features on the admin and user interfaces function optimally. In conclusion, integrating the Haversine algorithm into this GIS is proven accurate in recommending the nearest locations, resulting in a practical mobile-based application that facilitates the general public.
A Predictive Model for Type 2 Diabetes Using A Wrapper-Based Feature Selection Method Khairunisa Hilyati; Nuciko Abdul Halim; Wendi Usino
PIKSEL : Penelitian Ilmu Komputer Sistem Embedded and Logic Vol. 14 No. 1 (2026): March 2026
Publisher : LPPM Universitas Islam 45 Bekasi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33558/piksel.v14i1.12293

Abstract

Diabetes mellitus continues to show a rising global prevalence, making early detection of diabetes risk essential to prevent serious complications. This research aims to evaluate the effectiveness of a wrapper-based feature selection technique in improving the performance of classification models for early-stage diabetes risk prediction. The feature selection method employed is Recursive Feature Elimination (RFE), which is combined with three classification algorithms: Random Forest, Support Vector Machine (SVM), and Logistic Regression. The dataset used in this research was obtained from RSUD Pemangkat, Sambas Regency, West Kalimantan. The implementation of RFE is expected to identify and eliminate less relevant features, thereby simplifying the model, enhancing interpretability, and improving efficiency without compromising accuracy. This approach is particularly important in medical data analysis, where datasets are often complex and contain numerous clinical variables. Model performance is evaluated using accuracy, F1-score, and Area Under the Curve (AUC) to ensure a comprehensive assessment of classification capability. A comparative analysis is conducted to determine the optimal combination of feature selection method and classification algorithm that yields the best performance. In the scenario of applying the model with all features (baseline), Random Forest showed the best performance compared to other algorithms with an accuracy value of 0.9909, F1-Score of 0.9927, AUC of 0.9995, and sensitivity (recall) of 1.0000, which indicates that all cases of diabetes in the test data were successfully detected without false negative errors. SVM and Logistic Regression produced accuracies of 0.9545 and 0.9273, respectively. Despite having good classification capabilities, SVM tends to produce higher false positives, while Logistic Regression excels in the aspect of model interpretability. With an optimized model, the system has the potential to assist healthcare professionals in screening processes and clinical decision-making more quickly and effectively
Performance Evaluation of YOLOv8 and YOLOv11 for River Waste Detection Herlawati Herlawati; Rahmadya Trias Handayanto
PIKSEL : Penelitian Ilmu Komputer Sistem Embedded and Logic Vol. 14 No. 1 (2026): March 2026
Publisher : LPPM Universitas Islam 45 Bekasi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33558/piksel.v14i1.12295

Abstract

River pollution caused by plastic waste requires an effective and automated monitoring solution. This study proposes an automated waste detection system in river environments by implementing and comparing two deep learning-based object detection models, YOLOv8 and YOLO11. The dataset used is the River Trash dataset (Version 3) from Roboflow, consisting of 415 original images augmented to 1,281 training and 367 validation images across five waste categories: non-plastic, plastic bag, plastic bottle, plastic others, and plastic wrapper sachet. Both models were trained under identical conditions — 10 epochs, image size 640×640, batch size 16, and AdamW optimizer — to ensure a fair comparison. Performance was evaluated using Precision, Recall, mAP@50, and mAP@50-95. Results show that YOLOv8 achieved higher detection accuracy with Precision 0.880, Recall 0.889, mAP@50 0.946, and mAP@50-95 0.942, outperforming YOLO11 which recorded 0.879, 0.800, 0.910, and 0.909 respectively. However, YOLO11 demonstrated greater efficiency with fewer parameters (2.59M vs 3.01M) and faster inference speed (249ms vs 265ms), making it more suitable for edge device deployment. These findings confirm that both models are capable of detecting plastic waste in complex river environments, with YOLOv8 recommended for accuracy-critical applications and YOLO11 for resource-constrained real-time monitoring systems.
A Comparative Study of Machine Learning-Based Student Dropout Risk Prediction Prima Dina Atika
PIKSEL : Penelitian Ilmu Komputer Sistem Embedded and Logic Vol. 14 No. 1 (2026): March 2026
Publisher : LPPM Universitas Islam 45 Bekasi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33558/piksel.v14i1.12299

Abstract

Student dropout is a critical issue in higher education, affecting both institutional performance and student success. This study aims to develop a classification model for predicting student dropout risk and to compare the performance of several machine learning algorithms. A quantitative experimental approach was employed using a dataset that integrates academic records and Learning Management System (LMS) activity. The dataset exhibits imbalanced characteristics, with approximately 20% of instances belonging to the dropout class. The classification algorithms evaluated in this study include Naïve Bayes, Decision Tree, Random Forest, and K-Nearest Neighbor (KNN). Model performance was assessed using Accuracy, Precision, Recall, and ROC-AUC metrics to ensure a comprehensive evaluation. The results indicate that Naïve Bayes achieved the best performance with an accuracy of 86.40% and a ROC-AUC value of 0.934, followed by Random Forest with a ROC-AUC of 0.907. All models demonstrated high recall values (above 90%), indicating strong capability in identifying students at risk of dropout. These findings highlight the importance of selecting appropriate algorithms and evaluation metrics when dealing with imbalanced datasets. This study contributes by utilizing a more realistic dataset with noise and imbalance, as well as integrating academic and behavioral data to improve prediction performance. The proposed approach can support early intervention strategies to reduce student dropout rates in higher education.

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