cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
Rahmadya Trias Handayanto
Contact Email
rahmadya.trias@gmail.com
Phone
-
Journal Mail Official
piksel.unisma@gmail.com
Editorial Address
rogram Studi Teknik Komputer Fakultas Teknik Universitas Islam 45 Jl. Cut Meutia No. 83 Bekasi 17113
Location
Kota bekasi,
Jawa barat
INDONESIA
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
 17   18   19   20   21 
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.
User Experience Evaluation on Production Performance Monitoring System Using Honeycomb Method Moh. Sofyan Sauri; Arie Hidaya Putra; Emny Harna Yossy
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.v11i1.6927

Abstract

In the era of digitalization in industries, companies are implementing the Digital Factory Operating System in various aspects, including monitoring production performance on the sachet line. Previously, operators manually carried out the production performance monitoring process using paper forms, and admins re-entered the data into the system. Although the monitoring process has begun to be carried out digitally using available tablets, its usage still needs improvement. Therefore, it is necessary to evaluate the user experience of the Production Performance Monitoring Information System to determine user satisfaction. The Honeycomb method is used in this study, which assesses seven aspects, namely accessible, credible, desirable, findable, usable, useful, and valuable. The results show the average final score of each aspect assessed, and the highest score of 66.45% is for the accessible and useful aspects. The study shows that the user experience evaluation of the Production Performance Monitoring Information System using the honeycomb method is generally good, but improvements are still needed in some aspects, such as accessibility and desirability.
Usability Analysis on Health Tracking Application using User Experience Questionnaire in Jakarta Area Richard Richard; Aditya Kusumadwiputra; Adela Zahwa Firdaus Suherman
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.v11i1.6929

Abstract

The study aims to investigate user satisfaction with the Health Tracking application in Jakarta, which is a vital tool used by the government to monitor the health of Indonesians. The research will employ a User Experience Questionnaire to gather feedback from a minimum of 400 respondents who are users of the Health Tracking application selected through random sampling. The analysis of the survey results will evaluate the User Interface (UI) and User Experience (UX) of the application and provide valuable insights that can be used to enhance the app's future development. The UEQ Data Analysis Tool will be utilized to analyze data. Based on the findings, it can be concluded that users are highly satisfied with the Health Tracking application's UI/UX. However, improvements can be made to enhance the perspicuity aspect of the app, along with maintaining or improving other factors. The results of this study can serve as a benchmark for the future development of the Health Tracking application.
Identifying Factors Affecting the Relationship between Department and Graduation Level of Informatics Engineering Students using Apriori Algorithm: A Case Study at Pamulang University Thoyyibah T
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.v11i1.6933

Abstract

To cultivate the next generation of leaders, it is essential for teenagers to receive a high level of education. Typically, this education is acquired through attending lectures that produce a high GPA, which is considered a valuable achievement for students. The level of graduation achieved within the appropriate timeframe can also impact campus accreditation, especially for engineering students, particularly those pursuing informatics engineering. To improve graduation rates, it is necessary to use data mining to identify patterns and trends among graduating students. The a priori algorithm was used in this study to analyze school majors, the length of study, and student graduation rates. Through this algorithm, it was possible to identify one or more rules that can be used as benchmarks for predicting graduation rates. Based on the results and discussions of 30 students, the most effective rule for predicting graduation is a combination of the student's previous school major, a study period of 4 years or less, a GPA of 2.51-3.00, and passing all courses on time. Using the a priori algorithm, the rule was found to have a confidence value of 16 and a support value of 71.4%. This indicates that the rule is a reliable predictor of student graduation rates.
Sentiment Analysis of Sentence-Level using Dependency Embedding and Pre-trained BERT Model Fariska Zakhralativa Ruskanda; Stefanus Stanley Yoga Setiawan; Nadya Aditama; Masayu Leylia Khodra
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.v11i1.6938

Abstract

Sentiment analysis is a valuable field of research in NLP with many applications. Dependency tree is one of the language features that can be utilized in this field. Dependency embedding, as one of the semantic representations of a sentence, has shown to provide more significant results compared to other embeddings, which makes it a potential way to improve the performance of sentiment analysis tasks. This study aimed to investigate the effect of dependency embedding on sentence-level sentiment analysis through experimental research. The study replaced the Vocabulary Graph embedding in the VGCN-BERT sentiment classification system architecture with several dependency embedding representations, including word vector, context vector, average of word and context vectors, weighting on word and context vectors, and merging of word and context vectors. The experiments were conducted on two datasets, SST-2 and CoLA, with more than 19 thousand labeled sentiment sentences. The results indicated that dependency embedding can enhance the performance of sentiment analysis at the sentence level.

Page 19 of 31 | Total Record : 304

 17   18   19   20   21 

Filter by Year

2013 2025


Reset
Filter By Issues
All Issue Vol. 13 No. 2 (2025): September 2025 Vol. 13 No. 1 (2025): Maret 2025 Vol. 12 No. 2 (2024): September 2024 Vol. 12 No. 1 (2024): March 2024 Vol. 11 No. 2 (2023): September 2023 Vol 11 No 2 (2023): September 2023 Vol. 11 No. 1 (2023): March 2023 Vol 11 No 1 (2023): March 2023 Vol 10 No 2 (2022): September 2022 Vol 10 No 1 (2022): March 2022 Vol 9 No 2 (2021): September 2021 Vol 9 No 1 (2021): Maret 2021 Vol. 8 No. 2 (2020): September 2020 Vol 8 No 2 (2020): September 2020 Vol 8 No 1 (2020): Maret 2020 Vol 7 No 2 (2019): September 2019 Vol 7 No 1 (2019): Maret 2019 Vol 6 No 2 (2018): September 2018 Vol 6 No 1 (2018): Maret 2018 Vol 5 No 2 (2017): September 2017 Vol 5 No 1 (2017): Maret 2017 Vol 4 No 2 (2016): September 2016 Vol. 4 No. 1 (2016): Maret 2016 Vol 4 No 1 (2016): Maret 2016 Vol 3 No 2 (2015): September 2015 Vol 3 No 1 (2015): Maret 2015 Vol 2 No 2 (2014): September 2014 Vol 2 No 1 (2014): Maret 2014 Vol 1 No 2 (2013): September 2013 Vol. 1 No. 2 (2013): September 2013 Vol 1 No 1 (2013): Januari 2013 Vol. 1 No. 1 (2013): Januari 2013 More Issue