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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 304 Documents
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Optimization of Classification Models for Customer Sentiment on Train Suite Class Compartments Using SMOTE and Particle Swarm Optimization Setiawan, Kiki; Miswanto, Miswanto; Zakaria, Aditya
PIKSEL : Penelitian Ilmu Komputer Sistem Embedded and Logic Vol. 13 No. 2 (2025): September 2025
Publisher : LPPM Universitas Islam 45 Bekasi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33558/piksel.v13i2.11617

Abstract

This study uses three algorithms, namely Naive Bayes (NB), K-Nearest Neighbour (KNN), and Support Vector Machine (SVM). Then, the three methods are supplemented with the use of SMOTE (Synthetic Minority Oversampling Technique) and Particle Swarm Optimization (PSO), which will later be compared with the three methods to obtain good accuracy results. It is hoped that the use of SMOTE in this study can be a solution in handling imbalanced data, because the influence of imbalanced data is very large on the results of the model obtained, since algorithm processing that does not take into account data imbalance will tend to be dominated by the major class and ignore the minor class. Similarly, the use of Particle Swarm Optimization is expected to increase attribute weights and improve the accuracy of an algorithm and data classification. The model that obtained the best evaluation results was the Support Vector Model using SMOTE and Particle Swarm Optimization, with an accuracy value of 81.15%.
Comparative Study of PCA, t-SNE, and UMAP for CNN Feature Representation of Image Classification Herlawati, Herlawati; Handayanto, Rahmadya Trias
PIKSEL : Penelitian Ilmu Komputer Sistem Embedded and Logic Vol. 13 No. 2 (2025): September 2025
Publisher : LPPM Universitas Islam 45 Bekasi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33558/piksel.v13i2.11634

Abstract

Currently, the use of Deep Learning is widespread across various domains, with Convolutional Neural Networks (CNNs) as one of its main pioneers due to the principle of convolution. Recent methods continue to emerge with steadily increasing accuracy, in some cases approaching perfection. However, their implementation is often limited by the lack of sufficient computational resources in many environments. Moreover, the growing demand for explainable AI compels researchers to explore approaches that reveal the inner workings of deep learning models rather than treating them as mere black boxes. In this study, a simple CNN model is employed as a testbed for examining the feature extraction process through convolution, which is subsequently transformed into a user-friendly two-dimensional representation. The dataset used in this study is the Cats and Dogs dataset from Kaggle, which contains 25,000 labeled images equally distributed between the two classes. The dimensionality reduction methods utilized include Principal Component Analysis (PCA), t-distributed Stochastic Neighbor Embedding (t-SNE), and Uniform Manifold Approximation and Projection (UMAP). The results demonstrate that UMAP achieves superior performance compared to PCA and t-SNE, with the highest silhouette score and a lower Davies–Bouldin index, indicating more compact and well-separated feature clusters.
Water Quality Measurement based on Internet of Thing Fajri, Misbahul; Jumaryadi, Yuwan; Parlina, Anne
PIKSEL : Penelitian Ilmu Komputer Sistem Embedded and Logic Vol. 13 No. 2 (2025): September 2025
Publisher : LPPM Universitas Islam 45 Bekasi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33558/piksel.v13i2.11642

Abstract

Good water quality is crucial for living things, including temperature, pH, and TDS, which are constantly changing due to various factors. These three water parameters are crucial for maintaining water quality within a certain threshold to ensure that an ecosystem meets specified standards. Measuring water quality is essential to anticipate these changes as desired. Internet of Things (IoT) technology allows continuous monitoring of water parameters at any time and can be accessed anywhere with a network connection via computer or smartphone. In this proposed research, an IoT-based system based on ESPHome will be developed for water quality measurement in aquarium water and its ecosystem. The proposed research detects, records, and displays water pH and TDS parameters, including temperature, using an ESP8266 microcontroller. The system utilizes sensors to detect water parameters; the system utilizes an ESP8266 microcontroller and a WiFi connection that sends data to a cloud-based server with a Homeassistant dashboard. The research results are well-functioning in both hardware and software and are easily accessible.
Comparative Study of Logistic Regression, Neural Network, and Deep Learning in Predicting Hypertension Risk Atika, Prima Dina
PIKSEL : Penelitian Ilmu Komputer Sistem Embedded and Logic Vol. 13 No. 2 (2025): September 2025
Publisher : LPPM Universitas Islam 45 Bekasi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33558/piksel.v13i2.11646

Abstract

Hypertension is a major risk factor for cardiovascular diseases, and early detection is crucial for effective management. This study compares the predictive performance of three modeling techniques—Logistic Regression (LR), Neural Network (NN), and Deep Learning (DL)—in estimating the risk of hypertension. The dataset, obtained from Kaggle, consists of demographic and clinical variables with binary labels indicating the presence or absence of hypertension. Each model was trained and evaluated using RapidMiner, with performance assessed through accuracy and Root Mean Squared Error (RMSE). The results indicate that the Neural Network outperformed both Deep Learning and Logistic Regression, achieving the highest accuracy (99.88%) and the lowest RMSE (0.124). These findings suggest that shallow neural networks can provide reliable and efficient predictions for hypertension risk, sometimes even surpassing more complex deep learning architectures.  

Page 31 of 31 | Total Record : 304

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