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Rahmadya Trias Handayanto
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rogram Studi Teknik Komputer Fakultas Teknik Universitas Islam 45 Jl. Cut Meutia No. 83 Bekasi 17113
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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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Android-Based Expert Diagnosis System of Children’s Ear, Nose, and Throat Disorders Using Forward Chaining Method Hardiyanto, Arlen Brilliannisa; Andaini, Susy Kuspambudi; Suwarman, Ramdhan Fazrianto
PIKSEL : Penelitian Ilmu Komputer Sistem Embedded and Logic Vol. 12 No. 2 (2024): September 2024
Publisher : LPPM Universitas Islam 45 Bekasi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33558/piksel.v12i2.9654

Abstract

Ear, nose, and throat disorders are a significant health problem, especially in children. This article discusses the development of an expert system as a diagnosis tool for ENT disorders in children by applying the forward chaining method. The system development was carried out using the waterfall method and built based on android using Kodular. The system can diagnose 36 disorders with 42 symptoms obtained by literature study technique from the book First Aid in Your Home written by Dr Tony Smith and Dr Sue Davidson. The result of this system is able to help the process of diagnosing ENT disorders in children accurately based on knowledge from experts through android applications.
Website Security Analysis of SIDESPIN Application Using Vulnerability Scanning Techniques Rasmila, Rasmila; Herawan, Rafi
PIKSEL : Penelitian Ilmu Komputer Sistem Embedded and Logic Vol. 12 No. 2 (2024): September 2024
Publisher : LPPM Universitas Islam 45 Bekasi

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

In the era of globalization, quick and easy access to information is essential, especially through the Internet. Websites have become one of the main media for delivering information globally. Although it provided extensive benefits, the use of websites also brings security risks. Errors in coding and configuration can lead to vulnerabilities that can be exploited by irresponsible parties. This research aims to conduct a security analysis on the SIDESPIN website, owned by the Directorate of Innovation and Business Incubator of Bina Darma University. By applying vulnerability scanning techniques, this research identifies vulnerabilities, analyzes findings, and provides recommendations for improvement. In stages such as scoping, footprinting, vulnerability scanning, vulnerability analysis, and reporting, this research uses tools such as OWASP ZAP, Nikto, and Nessus. The result is a vulnerability analysis along with recommendations that can serve as guidelines to improve the security of the SIDESPIN website
Cloud-Based Financial Reporting System Design for Micro Enterprises in Indonesia Erin, Erin
PIKSEL : Penelitian Ilmu Komputer Sistem Embedded and Logic Vol. 12 No. 2 (2024): September 2024
Publisher : LPPM Universitas Islam 45 Bekasi

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Abstract

The use of information systems has increasingly developed, especially in business. However, micro enterprises in Indonesia still use minimal information systems, especially Accounting Information Systems (AIS) to create financial reports. In fact, using AIS is very important to record all transactions that occur and helps in producing good financial reports. The need for high capital costs and limited knowledge are obstacles to using AIS. Therefore, this research aims to produce a design for building AIS to be able to produce cloud-based financial reports so that micro enterprises can utilize the services provided by the cloud so that they do not need to have capital costs and focus more on business rather than focusing on IT infrastructure to build system. This research was carried out using a qualitative approach with data collection methods using observation using literature studies and system design methods using the Rational Unified Process (RUP) method which was limited to making prototypes. The results of this research are system designs in the form of system architecture and system prototypes
Blood Pressure and Heart Rate Measurement for Hypertension Classification Using the K-Nearest Neighbors Method Based on IoT Nurhadiva, Siti Salwa; Aryanti, Aryanti; Sarjana, Sarjana
PIKSEL : Penelitian Ilmu Komputer Sistem Embedded and Logic Vol. 12 No. 2 (2024): September 2024
Publisher : LPPM Universitas Islam 45 Bekasi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33558/piksel.v12i2.9824

Abstract

This research aims to develop a hypertension classification system based on measuring blood pressure and heart rate using the K-Nearest Neighbor (KNN) method and storing data on the Thingspeak cloud server. Hypertension is a major health problem that requires regular monitoring for effective prevention and management. This research created a tool that can measure blood pressure and heart rate using the KNN method as a classification. The data was collected using a sensor device integrated with Thingspeak for real-time data storage and analysis. The KNN method is used to classify measurement data into optimal, normal, prehypertension, grade 1 hypertension, and grade 2 hypertension categories. The implementation of data storage on the Thingspeak cloud server allows easy data access and efficient analysis, and supports continuous health monitoring. In conclusion, the system developed can be an effective tool in monitoring and classifying hypertension, and has the potential to be applied on a wider scale for public health management.  
Smart Dispenser Using Voice Recognition as an Assistive Device for the Visually Impaired with a Second-Order IIR Filter Algorithm Angelina, Fifteen; Aryanti, Aryanti; Lindawati , Lindawati
PIKSEL : Penelitian Ilmu Komputer Sistem Embedded and Logic Vol. 12 No. 2 (2024): September 2024
Publisher : LPPM Universitas Islam 45 Bekasi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33558/piksel.v12i2.9825

Abstract

This research developed a Smart Dispenser with Voice Recognition technology as a tool to help the visually impaired in taking drinking water. The system is designed using a Voice Recognition sensor, an ISD 1820 sensor, and a microcontroller to enable the operation of the dispenser via voice commands. The test was carried out to analyze the performance of the system in recognizing voice commands and removing water. The results showed a voice detection accuracy rate of 28%, with factors such as environmental noise and sound intonation affecting the effectiveness of the system. The study also identified several areas of improvement, including the use of a DC water pump to increase the flow rate, the addition of an automatic feature to stop the flow of water when a glass is taken, and the use of a DC fan to maintain the sensor's optimal temperature. Further development is needed to improve the accuracy and efficiency of the system so that it can more effectively help the visually impaired in daily life.
Monitoring and Information System for Automatic Clothes Drying Based on the Internet of Things Mila, Mila; Taqwa, Ahmad; Suroso, Suroso
PIKSEL : Penelitian Ilmu Komputer Sistem Embedded and Logic Vol. 12 No. 2 (2024): September 2024
Publisher : LPPM Universitas Islam 45 Bekasi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33558/piksel.v12i2.9828

Abstract

This research develops an Internet of Things (IoT)-based automatic clothes drying monitoring and information system to overcome the challenges of drying clothes due to uncertain weather changes. The system integrates NodeMCU ESP8266 as the main controller with various sensors such as rain, light, temperature, and humidity sensors to detect environmental conditions. The servo motor is used as a driving force to move the clothesline. The smartphone-based user interface was developed using the MIT App Inventor to enable remote monitoring and control. The test results showed that the system successfully detected weather changes and responded automatically, providing real-time information to users. Implementing IoT technology in this system allows for more efficient and practical management of clotheslines, overcoming the limitations of conventional drying methods.
Naive Bayes Algorithm and TF-IDF for Detecting Plagiarism in Journal Articles Azzahrah, Ladysa; Lindawati, Lindawati; Sholihin, Sholihin
PIKSEL : Penelitian Ilmu Komputer Sistem Embedded and Logic Vol. 12 No. 2 (2024): September 2024
Publisher : LPPM Universitas Islam 45 Bekasi

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Abstract

This study examines the implementation of a combination of Naïve Bayes, TF-IDF, and cosine similarity algorithms in detecting plagiarism in journal articles. Technological advances have increased the risk of plagiarism, which poses a serious threat to the integrity of science. The purpose of this study is to explain in detail the implementation of the algorithm to detect plagiarism, as well as measure the effectiveness of its combination. The method used involves the development of a Python-based system that is implemented through a website. The dataset consists of one hundred abstracts of Indonesian-language journal articles on the Internet of Things (IoT) taken from Mendeley software. The plagiarism limit is set at a maximum threshold of 20%. Implementation is carried out through data preprocessing stages, extraction of text features using a combination of Naïve Bayes and TF-IDF, and measurement of similarity with cosine similarity. The results show that this combination of algorithms has proven to be effective in detecting plagiarism rates in journal article abstracts, providing high accuracy in measuring text similarity. The developed system is able to better extract text features through the combination of Naïve Bayes and TF-IDF, and accurately measure the similarity of text in various test scenarios. This research contributes to the development of fast and accurate plagiarism detection technology, especially in fields that require complex text analysis.
IoT-Based Medical Health Monitoring System with a Web Interface Inayah, Cantika Tri; Handayani, Ade Silvia; Nasron, Nasron
PIKSEL : Penelitian Ilmu Komputer Sistem Embedded and Logic Vol. 12 No. 2 (2024): September 2024
Publisher : LPPM Universitas Islam 45 Bekasi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33558/piksel.v12i2.9830

Abstract

Health technology is increasingly developing along with the development of information and communication technology, where these advances are used to improve the quality of health services, especially in remote patient monitoring through telemedicine, M-Health and Telehealth. The development of the times has also brought telemedicine to a wider realm through M-Health and Telehealth which uses digital technology and wireless communication to enable monitoring of patient conditions, besides that websites are also used for wireless monitoring and data collection of patients because they have wide accessibility. This study aims to design a website that can integrate the data of the size of health sensor devices into an Internet of Things-based platform to display body temperature, blood oxygen, and ECG data in real-time. The test results show that all website features are running well, where the website runs well depending on the network used.
Image Identification System for Beef and Pork Using a Convolutional Neural Network Fauzi, Nadiyah Salsabila; Salamah, Irma; Hadi, Irawan
PIKSEL : Penelitian Ilmu Komputer Sistem Embedded and Logic Vol. 12 No. 2 (2024): September 2024
Publisher : LPPM Universitas Islam 45 Bekasi

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

In the modern era, assurance of the halalness of meat products has become a fundamental need for Indonesian Muslims, as awareness and sensitivity towards the consumption of halal products increases. This has led to the development of innovative solutions to ensure the authenticity of beef and distinguish it from pork. This research presents an Android-based meat image identification tool that relies on the Convolutional Neural Network (CNN) algorithm to process and analyze images. The research includes hardware design, deep learning model with CNN algorithm, and Android application for real-time integration of detection results. This tool is equipped with an LCD screen and speaker to display identification results. The results show the accuracy of the CNN model reaches 99% in distinguishing beef and pork on the test dataset. In real-time testing of the tool using fresh beef and pork samples, the system achieved 92% accuracy, demonstrating good performance under practical conditions. The system provides a reliable and practical solution for consumers to verify the type of meat, while contributing to efforts to ensure the halalness of food products in society.
Vehicle Class Prediction at Toll Gate Using Deep Learning Nisa, Suci Lutfia; Soim, Sopian; Agung, Muhammad Zakuan
PIKSEL : Penelitian Ilmu Komputer Sistem Embedded and Logic Vol. 12 No. 2 (2024): September 2024
Publisher : LPPM Universitas Islam 45 Bekasi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33558/piksel.v12i2.9833

Abstract

In the era of digitalization and automation, efficiency in the traffic management system at toll gates is very important. One of the efforts to improve this efficiency is to develop an automatic vehicle class detection system using deep learning technology, especially Convolutional Neural Network (CNN). This research aims to design and implement a CNN model that can identify and classify the types of vehicles passing through toll gates. The model development process includes collecting and annotating vehicle image data, data pre-processing, and CNN model training and testing. The evaluation results show that the developed model can achieve an accuracy of about 96% in detecting vehicle classes, so it can be integrated with the toll gate system to increase the speed and accuracy in the vehicle classification process. Thus, this solution is expected to reduce the waiting time of toll users and improve operational efficiency.

Page 26 of 31 | Total Record : 304

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