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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 16 Documents
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Search results for , issue "Vol 11 No 1 (2023): March 2023" : 16 Documents clear
Optimization of Random Forest Prediction for Industrial Energy Consumption Using Genetic Algorithms Sartini Sartini; Luthfia Rohimah; Yana Iqbal Maulana; Supriatin Supriatin; Dewi Yuliandari
PIKSEL : Penelitian Ilmu Komputer Sistem Embedded and Logic Vol 11 No 1 (2023): March 2023
Publisher : LPPM Universitas Islam 45 Bekasi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33558/piksel.v11i1.5886

Abstract

Abstract Saving electrical energy consumption in industries is crucial; hence, the prediction of industrial energy consumption needs to be performed. The random forest method can be applied to steel industry data to predict energy consumption. The purpose of this prediction is to increase energy savings in industries and optimize the performance of the random forest method. The results of the random forest show that the algorithm can predict energy consumption in industries effectively; however, it needs further optimization to achieve better predictions. Therefore, the genetic algorithm method will be used to optimize the previous method. The optimization results indicate that it is successfully conducted in terms of accuracy and kappa level. This optimization is beneficial to society, especially industrial companies.
Storyboard Design of Android-Based Learning Multimedia Integration Application Using Standard Process Tutorial Model Santi Purwanti
PIKSEL : Penelitian Ilmu Komputer Sistem Embedded and Logic Vol 11 No 1 (2023): March 2023
Publisher : LPPM Universitas Islam 45 Bekasi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33558/piksel.v11i1.5893

Abstract

Bina Pribadi Islam is a subject taught in Islamic elementary schools with the aim of improving students' spiritual well-being through guidance from teachers or mentors. Islamic elementary schools have an educational system designed by the Ministry of National Education and adapted to Islamic law. In the process of learning Bina Pribadi Islam, there is a need for a strategy to increase students' motivation and interest by using a multimedia application that includes elements such as text, audio, graphics, video, and animation tailored to the standard process tutorial model. To create an interactive multimedia application, multimedia design should be prepared in accordance with the tutorial model. Therefore, to produce a good application, a storyboard design needs to be created first that is tailored to the tutorial model to facilitate the application's development. Based on the storyboard design created according to the tutorial model, three main menus are produced as the characteristic features of the tutorial model, namely introduction, tutorial material, and exercises. Each menu produces several sub-menus tailored to the material in the Bina Pribadi Islam subject.
Pi Hole on SOE Computer Network using Raspberry Pi 3 Model B+ to Optimize Bandwidth Management and Improve Employee Performance Rahmat Novrianda Dasmen; Darwin Darwin; Irham Irham; Bima Riansyah
PIKSEL : Penelitian Ilmu Komputer Sistem Embedded and Logic Vol 11 No 1 (2023): March 2023
Publisher : LPPM Universitas Islam 45 Bekasi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33558/piksel.v11i1.5911

Abstract

State-Owned Enterprises (BUMN) are government agencies that currently have computer networks and are supported by internet services, which are always used in the work process of BUMN employees. Along with the development of the Internet of Things (IoT) devices, good internet service is very much needed by BUMN as it will affect the performance of BUMN employees. However, the problem that often occurs in internet services in BUMN is slow internet speed, which hinders the work process of BUMN and decreases the performance of BUMN employees. It should be noted that good internet service depends on the bandwidth used, so it is very important to have good bandwidth management applied to the BUMN computer network. So far, BUMN have done several things related to bandwidth management, ranging from access restrictions, even distribution of bandwidth, and the implementation of an authentication system to connect to the BUMN internet network. However, some of these things are still not enough to guarantee no wasteful use of bandwidth, which occurs because when operating some work-supporting websites, various types of digital ads will automatically appear, consuming a significant amount of bandwidth. Therefore, in this Action Research study, a Pi Hole to control digital ads was implemented that can automatically appear and use large amounts of bandwidth. By using Raspberry Pi 3 Model B+, the development of Pi Hole will limit digital ads from appearing when accessing websites or other IoT devices used by BUMN so that bandwidth usage will be more optimal in BUMN work processes. This will also improve the performance of BUMN employees, where with maximum bandwidth, the work process of BUMN can be accelerated.
The Influence of Youtube Ads on Purchase Intention Willy Kristian; RA Dyah Wahyu Sukmaningsih; Eric Gunawan; Rafy Pranadya Annaufal; Rafi Giffari
PIKSEL : Penelitian Ilmu Komputer Sistem Embedded and Logic Vol 11 No 1 (2023): March 2023
Publisher : LPPM Universitas Islam 45 Bekasi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33558/piksel.v11i1.5919

Abstract

The high number of social media users on YouTube has made many companies interested in advertising on the platform. However, many YouTube users tend to skip the ads that are displayed. The main objective of this research is to determine the factors within YouTube ads that have an influence on Purchase Intention. This study uses a quantitative research method, with variables including Informativeness, Entertainment, Customization, Irritation, Advertising Value, Flow Experience, Brand Awareness, and Purchase Intention. The results of this research show that Informativeness has a significant influence on Advertising Value but not on Flow Experience. Meanwhile, Entertainment and Customization have a significant influence on both Advertising Value and Flow Experience, and Advertising Value, Brand Awareness, and Flow Experience have a significant influence on Purchase Intention. As for Irritation, this variable does not have a significant influence on Advertising Value and Flow Experience.
Cluster Analysis Using Principal Component Analysis Method and K-Means to Find Out the Compliance Group of Property Tax Rully Pramudita; Nining Rahaningsih; Sekar Puspita Arum; Medina Aprilia Putri; Sok Piseth
PIKSEL : Penelitian Ilmu Komputer Sistem Embedded and Logic Vol 11 No 1 (2023): March 2023
Publisher : LPPM Universitas Islam 45 Bekasi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33558/piksel.v11i1.5924

Abstract

Abstract The village of Kendal has experienced a decline in local income due to the high rate of property tax arrears, with 226 taxpayers (19% of residents) known to have outstanding payments. Additionally, with 1,159 separate residents residing in 10 block areas with varying tax amounts, it has become increasingly difficult for the Village Apparatus to profile taxpayers based on their characteristics. To overcome these problems, a data analysis model based on Machine Learning technology will be developed using the Principal Component Analysis (PCA) Method combined with the K-Means method. The objective of this study is to create a cluster analysis model that can accurately map the characteristics of taxpayers, making it easier for the Village Apparatus to identify and assist residents who need to pay their property tax. This proposed solution will also simplify the reporting process to the central government regarding the estimated regional revenue sourced from property tax.
EfficientNetV2M for Image Classification of Tomato Leaf Deseases Arazka Firdaus Anavyanto; Maimunah Maimunah; Muhammad Resa Arif Yudianto; Pristi Sukmasetya
PIKSEL : Penelitian Ilmu Komputer Sistem Embedded and Logic Vol 11 No 1 (2023): March 2023
Publisher : LPPM Universitas Islam 45 Bekasi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33558/piksel.v11i1.5925

Abstract

Favorable climatic conditions make tomato plants (Solanum Lycopersicon) a widely cultivated horticultural crop in Indonesia. However, the increase in tomato production is often accompanied by a decrease in both the quantity and quality of the plants, which can be caused by a variety of factors such as bacteria, fungi, viruses, and insects like Late Blight and Two-Spotted Spider Mite diseases that attack the tomato leaves. To help farmers identify leaf diseases that have similar characteristics, this study employs image processing with the Convolutional Neural Network (CNN) algorithm and transfer learning models. Specifically, the study uses the EfficientNetV2M transfer learning architecture which has superior parameter efficiency and training speed compared to other transfer learning models. Additionally, this study conducts four experimental scenarios on preprocessing, including green channel + CLAHE, green channel + Gaussian Blur, CLAHE without green channel, and Gaussian Blur without green channel. The dataset used in this study includes 5,176 images with three labels: Tomato Healthy, Tomato Late Blight, and Tomato Two-Spotted Spider Mite. These images were used to train and produce models, which were then tested using a different dataset from the trained dataset. The testing dataset included 30 image samples divided into three labels. Based on the test results of the four models with different scenarios, the best model was found to be the one with the green channel preprocessing scenario + CLAHE, which was able to precisely predict all 30 image samples with high accuracy.
Identification of Website-Based Product Sales Frequency Patterns using Apriori Algorithms and Eclat Algorithms at Rio Food in Bekasi Salwa Nabiila Pramuhesti; Herlawati Herlawati; Tyastuti Sri Lestari
PIKSEL : Penelitian Ilmu Komputer Sistem Embedded and Logic Vol 11 No 1 (2023): March 2023
Publisher : LPPM Universitas Islam 45 Bekasi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33558/piksel.v11i1.5941

Abstract

Sales reports that are not managed automatically may hinder businesses from accurately determining their progress in the short or long term. With increasing community needs for a product, business owners have an opportunity to market their products to a larger audience. The abundance of data highlights the need for information to produce patterns that can be used as a reference for making decisions in buying products on the website. Data mining algorithms can provide support for analysis, which can help avoid inaccurate business progress reports. In this study, the Apriori and Eclat algorithms were applied to analyze frequent itemsets in association rule mining. The dataset used in this study consists of 20 transaction data from frozen food sales. The results showed that the combination of Nugget and Chicken Sausage itemsets were the most frequent, with higher support, confidence, and lift ratio values than the others. These results can be used as product recommendations that are most in demand by customers.
Decision Support System Design for Informatics Student Final Projects Using C4.5 Algorithm Rafika Sari; Hasan Fatoni; Khairunnisa Fadhilla Ramdhania
PIKSEL : Penelitian Ilmu Komputer Sistem Embedded and Logic Vol 11 No 1 (2023): March 2023
Publisher : LPPM Universitas Islam 45 Bekasi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33558/piksel.v11i1.5954

Abstract

Academic consultation activities between students and academic supervisors are necessary to help students carry out academic activities. Based on the transcript of grades obtained, many students do not choose the appropriate final project/thesis specialization fields based on their academic abilities, resulting in a lot of inconsistencies between the course grades and the final project specialization fields. The purpose of this research is to minimize the subjectivity aspect of students in choosing their final project academic supervisors and minimize the inconsistencies between the course grades and the final project specialization fields. The method used in this research is classification data mining using the Decision Tree and C4.5 Algorithm methods, with the attributes involved being courses, course grades, and specialization courses. The C4.5 Decision Tree algorithm is used to transform data (tables) into a tree model and then convert the tree model into rules. The implementation of the C4.5 Decision Tree algorithm in the specialization field decision support system has been successfully carried out, with an accuracy rate of 70% from the total calculation data. The data used in this research is a sample data from several senior students in the Informatics program at Ubhara-Jaya. The results of the research decision support system can be used as a good recommendation for the Informatics program and senior students to direct their final project research. It is expected that further research will use more sample data so that the accuracy rate will be better and can be implemented in website or mobile-based applications.
Factors Influencing Students' Intention to use Online Tutoring Applications in Jakarta RA Dyah Wahyu Sukmaningsih; Adam Kurniawan; Ronald Ronald
PIKSEL : Penelitian Ilmu Komputer Sistem Embedded and Logic Vol 11 No 1 (2023): March 2023
Publisher : LPPM Universitas Islam 45 Bekasi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33558/piksel.v11i1.6318

Abstract

As the COVID-19 pandemic has disrupted traditional learning methods, many students have turned to online tutoring as a supplementary source of education. This study aims to identify the factors that influence students' intention to use online tutoring applications. Data was collected from 401 student respondents in Jakarta through a questionnaire, and analyzed using smart PLS. The results show that perceived brand orientation, interactive course features, course quality, perceived usefulness, perceived ease of use, and trust all have a significant impact on students' intention to use online tutoring applications. These findings have implications for the design and promotion of online tutoring applications, as well as for policymakers and educators seeking to support student learning in the era of COVID-19.
The Weighted Product Method and the Multi-Objective Optimization on the Basis of Ratio Analysis Method for Determining the Best Customer Mugiarso Mugiarso; Rasim Rasim
PIKSEL : Penelitian Ilmu Komputer Sistem Embedded and Logic Vol 11 No 1 (2023): March 2023
Publisher : LPPM Universitas Islam 45 Bekasi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33558/piksel.v11i1.6325

Abstract

The objective of this study is to compare the effectiveness of the Weighted Product (WP) and Multi-Objective Optimization on the Basis of Ratio Analysis (MOORA) methods in determining the best customers. Onesnet, the case study service provider, provides discounts and rewards to eligible customers to support this objective. The problem addressed in this study is how to determine the most relevant method for selecting eligible customers for bonuses. To achieve this, sensitivity testing was conducted by altering the weights of each criterion in both methods and observing the percentage changes of the results. The Weighted Product method multiplies the rating of each connected attribute, which is raised to the appropriate attribute weight, to decide. Data for this study was collected through interviews and observations at Onesnet and processed using the Rank Order Centroid (ROC) method for weighting, and the WP and MOORA methods for evaluating and selecting a decision. The WP and MOORA methods produced different total values and rankings, but the modeling with either method can be used equally for selecting the best customers. While there was a 60% similarity in data between the two methods, the WP method is recommended over MOORA, as it prioritizes customers with high loyalty criteria as the best customers.

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