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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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Machine Learning Approaches to Sentiment Analysis of Mental Health Discussions on Platform X Jumaryadi, Yuwan; Fajriah, Riri; Salamah, Umniy; Priambodo, Bagus; Lystha, Arie
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.v13i2.11350

Abstract

Sentiment analysis on mental health issues is crucial for understanding public perceptions of healthcare services. This study analyzed tweets related to mental health on platform X in 2025 using SVM, Random Forest, and Naive Bayes algorithms. Data was collected through web scraping with Python, then evaluated using a confusion matrix with accuracy, precision, f1-score, and recall metrics. The classification results showed a distribution of sentiment: positive (3,667 tweets), neutral (838 tweets), and negative (704 tweets). A comparative analysis of the three algorithms revealed that SVM achieved the highest accuracy (78.69%), followed by Random Forest (75.04%) and Naive Bayes (70.44%), proving the superiority of SVM in classifying mental health sentiment. These findings provide valuable insights for stakeholders in improving mental healthcare services based on public feedback, while also offering a reference for effective sentiment analysis methods for social media data.
Machine Learning Predictive Model for Analyzing the Influence of Academic Performance on Course Completion in Algorithms and Programming Arifin, Rita Wahyuni; Safitri, Nadya; Farisi, Imam
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.v13i2.11463

Abstract

The success of students in core computer science courses such as Algorithms and Programming is a critical factor in their academic journey, as it reflects both mastery of fundamental concepts and readiness for more advanced studies. Academic performance in this course is not only shaped by grades but also by behavioral and psychological attributes that influence learning outcomes. This study investigates the influence of academic performance on graduation in Algorithms and Programming using a predictive machine learning approach. The dataset includes 106 student records encompassing academic variables (attendance, average grades, assignment scores), psychological factors (motivation, anxiety toward examinations), and behavioral indicators (discussion participation, AI tool usage, online learning activities). The research adopts the SEMMA methodology, consisting of sampling, exploration, modification, modeling, and assessment. Several classification algorithms were tested, and Random Forest was selected as the primary model due to its strong performance and interpretability. The results indicate that academic achievement variables, particularly average grades and attendance, significantly influence graduation. Additionally, non-academic factors such as motivation, discussion activity, and exam anxiety contribute to predictive outcomes. The model achieved an accuracy of around 91% and an AUC score of 0.93, confirming its reliability in distinguishing between students who passed and those who did not. These findings highlight that academic performance influences success in algorithm and programming courses.
User Interface Implementation Using User Centered Design (UCD) with Usability Testing Evaluation Purwanti , Santi; jaja, Jaja; Rakhmayudhi , Rakhmayudhi
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.v13i2.11467

Abstract

User Interface (UI) design plays a crucial role in enhancing user interaction, efficiency, and satisfaction in digital information systems. This study aims to design and evaluate the login module of the membership information system for Kelompok Seni Sigertengah using the User Centered Design (UCD) approach. The research methodology follows the UCD framework based on ISO 9241-210, which includes understanding the usage context, identifying user requirements, designing prototypes, and conducting evaluations through usability testing. Data were collected through observation, interviews, documentation studies, and prototype testing involving five representative users. The results show that the questionnaire scores ranged from 4.0 to 4.8, with an overall score of 4.43 out of 5, categorized as “Very Good.” Qualitative findings further indicate that the UI is easy to use, reflects local artistic identity, and improves both user comfort and system access efficiency. Therefore, the application of UCD has proven effective in producing a user interface design that meets user needs and is feasible to be implemented as a development model for UCD-based UI in arts and culture MSMEs.
An Efficient Vehicle Counting System Based on YOLO Deep Learning Model Miswanto, Miswanto; Ruseno, Ndaru; Shufiyah , Khotimah Nurhaliza
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.v13i2.11494

Abstract

Deep Learning is a popular Machine Learning algorithm that is widely used in many areas in current daily life. Its robust performance and ready-to-use frameworks and architectures enables many people to develop various Deep Learning-based software or systems to support human tasks and activities. Traffic monitoring is one area that utilizes Deep Learning for several purposes. By using cameras installed in some spots on the roads, many tasks such as vehicle counting, vehicle identification, traffic violation monitoring, vehicle speed monitoring, etc. can be realized. In this paper, we discuss a Deep Learning implementation to create a vehicle counting system without having to track the vehicles movements. To enhance the system performance and to reduce time in deploying Deep Learning architecture, hence pretrained model of YOLOv5 is used in this research due to its good performance and moderate computational time in object detection. This research aims to create a simple vehicle counting system to help human in classify and counting the vehicles that cross the street. The counting is based on four types of vehicles, i.e. car, motorcycle, bus, and truck, while previous research counts the car only. As the result, our proposed system capable to count the vehicles crossing the road based on video captured by camera with the highest accuracy of 97.72%
Breast Cancer Classification in Ultrasound Images Using Convolutional Neural Network (CNN) with Watershed Transform Method Fadillah , Muhammad Iqbal; Syafii , Ahmad Faishal; Abdullah , Indra Nugraha
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.v13i2.11499

Abstract

Breast cancer is one of the deadliest diseases, especially for women. Early diagnosis of breast lesions and differentiation of malignant nodules from benign nodules and normal nodules are important for breast cancer prognosis. In diagnosing this disease, one radiological method, namely medical image analysis using ultrasonography, can be used to determine early diagnosis of breast cancer. Breast cancer ultrasound images have several characteristics, such as color, shape, size, and texture, which make segmentation difficult due to object accumulation. This study implements a Convolutional Neural Network classification algorithm and modified watershed segmentation to separate nodules or tumors in breast cancer. From the segmentation performance test with Watershed Transform, the average ZSI index was 40% for malignant images and 60% for benign images. The results of the VGG architecture for classification modeling showed 47% for watershed segmentation and 80% without watershed segmentation.
Hybrid YOLOv8 and SSD for Real-Time Digitalization of PPE Usage Compliance Detection in Workers (Non-Maximum Suppression Method) Adityas, Yazi; Aryadi, Dimas; Soetanto , Hari
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.v13i2.11502

Abstract

This study aims to develop an automated deep learning-based system to monitor compliance with the use of Personal Protective Equipment (PPE) in the manufacturing industry. Manual monitoring, which has been carried out so far, is considered inefficient and prone to error. This system compares three approaches: the YOLOv8 model, SSD Mobile Net, and a hybrid method that combines the two. The dataset consists of 700 images covering eight classes related to PPE use. The results show that the hybrid method performs best with: 1. Accuracy: 95.1%, 2. Precision: 98.7%, 3. Recall: 97.2%, and F1-Score: 94.5%. Although its detection speed (18 FPS) is slightly lower than SSD (29 FPS), its detection quality is superior. The system has been implemented in a web application that can run in real-time using a webcam, equipped with an alarm and “SAFE” or “NO SAFE” notifications. This system is expected to be an accurate and efficient digital solution to improve work safety.
Smart Drip Irrigation System with Integrated Soil pH and Moisture Sensors Using RS485 Communication Muhammad Rafindha Aslam; Hasibuan, Faisal Candrasyah; Rifqi Muhammad Fikri
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.v13i2.11507

Abstract

Agricultural development requires reliable methods for measurable soil condition monitoring to support crop growth. Soil moisture and soil pH are critical parameters that significantly affect plant development. Manual observation of these parameters is inefficient and often produces inconsistent data. This study designed a smart drip irrigation system integrating soil moisture and soil pH sensors with RS485 communication using the MAX485 module. Arduino Nano was applied as the sensor node for data acquisition, and ESP32 was used as the master controller for decision-making in irrigation and fertilization. System validation employed Lavios VT-05 as the reference instrument. Experimental results showed that soil moisture sensors provided consistent readings with minimal deviation across different conditions, while soil pH sensors maintained high accuracy compared to standard buffer solutions. RS485 communication was tested up to 7 meters and demonstrated stable performance. These findings confirm that the system is capable of accurate soil monitoring and reliable communication for smart drip irrigation applications.
Implementation of Proxy at XYZ Inc. Study: Experiment on Network Performance Optimization Fandi Ali Mustika; Muhammad Rifqi; Yuwan Jumaryadi; Febryo Ponco Sulistyo; Indah Ramadhani; Eko Prasetyo Pratomo
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.v13i2.11535

Abstract

The rapid development of information technology demands companies to have a network infrastructure that is not only fast but also secure and efficient. PT. XYZ, as a company engaged in distribution and customer service, faces challenges in managing increasing data traffic while maintaining the stability and security of its internal network. One strategic solution to address this issue is the implementation of a proxy server. A proxy server functions as an intermediary between users and the internet, enabling it to filter data requests, store cache, and restrict access to unwanted content. Thus, the implementation of a proxy server can improve bandwidth efficiency, accelerate access to frequently used websites, and strengthen network security systems through traffic monitoring and restriction. This research aims to implement and evaluate the effectiveness of using a proxy server within PT. XYZ’s environment. The evaluation results show that the use of a proxy server can enhance network efficiency and provide better protection against cyber threats. With a more controlled and secure system, the company can run its operations more optimally and sustainably.
Data-Driven Insights for Higher Education Marketing: Segmenting Applicant Pools Using K-Means Clustering Shadiq, Jafar; Wicaksono, Harjunadi; Budiarto, Rahmat; Salamah, Raisha Nur; Fakhrudin, Zidan Al Buqhori
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.v13i2.11545

Abstract

This research aims to optimize marketing strategies for new student recruitment at Bina Insani University (BiU), which faces intense competition. The current marketing efforts are generic and inefficient. Utilizing the CRISP-DM framework, this study applies the K-Means clustering data mining method to analyze primary data from applicants from 2021 to 2024. The analysis focuses on the attributes of previous school major, information source, and location. The findings successfully identified four distinct segments of prospective students: the "Proactive Outreach Segment," reached through school presentations; the "Social Network & Affiliation Segment," influenced by friends and relatives; the "Academic Recommendation Segment," who rely on guidance from teachers; and the "Digital & Non-Technical Segment," who actively seek information on social media. Based on the unique profile of each cluster, this study provides recommendations for specific and targeted marketing strategies to improve the effectiveness and efficiency of student recruitment
Digital Reporting System for Product Quality Using Normal Distribution and EWMA Control Chart Handayani, Dwipa; Andy Achmad Hendharsetiawan; Muhammad Yasir
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.v13i2.11611

Abstract

The collection and reporting of product quality at CV. Sinar Agung Teknik is still done manually, which is time-consuming, inefficient, and prone to errors. This study aims to digitize the reporting process to improve efficiency, accuracy, and speed up information distribution between departments. The system developed uses a normal distribution algorithm in product quality data analysis because it is in line with the characteristics of quantitative data that is distributed close to normal. The research stages include needs identification, form digitization, statistical analysis integration, and employee training. The results of the implementation show that the product quality reporting digitization system is capable of improving the accuracy of analysis, speeding up reporting, reducing human error, and increasing operational efficiency, with positive feedback from users regarding its ease of use and benefits in quality data management.  

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