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 214 Documents
 18   19   20   21   22 
Sentiment Analysis of On-Demand Ride-Hailing Systems using Support Vector Machine and Naïve Bayes Bhagaskara Farhan Wiguna; Herlawati Herlawati; Ajif Yunizar Pratama Yusuf
PIKSEL : Penelitian Ilmu Komputer Sistem Embedded and Logic Vol 11 No 2 (2023): September 2023
Publisher : LPPM Universitas Islam 45 Bekasi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33558/piksel.v11i2.7384

Abstract

Gojek is one of Indonesia's most popular online transportation, founded in 2010. The Gojek application has been downloaded one hundred forty-two million times with more than two million drivers and four hundred thousand partners in food delivery services. Due to the increasing use of the Gojek application and the importance of knowing user views about the services provided by the application. In this research, the sentiment analysis is using Support Vector Machine and the Naïve Bayes method to classify positive sentiment and negative sentiment. The target label focus on positive and negative labels to aims avoid the bias that exists in neutrally labeled reviews on the Gojek Application. The research process includes data collection, pre-processing the data, weighting with Term Frequency-Invers Document Frequency, Support Vector Machine, and Naïve Bayes training by dividing the data into 90% training data and 10% testing data and then evaluating the results using a confusion matrix. The results of testing using the Support Vector Machine algorithm resulted in 90% accuracy, 94% recall, 91% precision, and 94% f1-score, therefore the Naïve Bayes algorithm produces 77% accuracy, 96% recall, 77% precision, and 85% f1-score.
Classifying Half-Unemployment Levels in Indonesian Provinces: A K-Means Approach for Informed Policy Decisions Suhardjono Suhardjono; Hari Sugiarto; Dewi Yuliandari; Adjat Sudradjat; Luthfia Rohimah
PIKSEL : Penelitian Ilmu Komputer Sistem Embedded and Logic Vol 11 No 2 (2023): September 2023
Publisher : LPPM Universitas Islam 45 Bekasi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33558/piksel.v11i2.7390

Abstract

Half-level unemployment refers to individuals who work part-time and are not fully employed. Increasing the half-poverty rate from year to year can lead to challenges in the lives of these individuals. The issue arising with the rise in the half-poverty rate is the government's difficulty in prioritizing areas that require intervention to address these problems. Consequently, an increase in the half-poverty rate can have adverse consequences. Therefore, it is necessary to categorize underemployment rate data obtained from public sources, specifically from data.go.id, using the widely recognized clustering method known as K-Means. The purpose of this categorization is to identify and classify provinces with a significant prevalence of half-poverty levels. This classification will assist the government in making informed decisions when addressing individuals who meet the half-poverty criteria. The results were obtained by grouping the data from the first to the eighteenth iteration into three categories: 'large' (C1), 'medium' (C2), and 'small' (C3) in terms of half-poverty levels. Group C1 comprises 17 provinces with a high half-poverty rate, while C2 includes only 2 provinces, and C3 covers 16 provinces with a significant half-poverty rate. Based on these findings, it is advisable for the Indonesian government to consider implementing policies aimed at reducing the poverty level by half. Priority should especially be given to the C1 group when creating employment opportunities for the province's residents
Machine Learning-Based Classification for Scholarship Selection Asriyanik Asriyanik; Agung Pambudi
PIKSEL : Penelitian Ilmu Komputer Sistem Embedded and Logic Vol 11 No 2 (2023): September 2023
Publisher : LPPM Universitas Islam 45 Bekasi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33558/piksel.v11i2.7393

Abstract

University of Muhammadiyah Sukabumi (UMMI) is a university that accepts KIP scholarship every year. However, KIP student applicants always exceed the quota, so it requires a re-selection process to determine KIP Shcolarship Awardee. UMMI does not have a clear method to support decisions in the selection process for KIP Shcholarship Awardee. To solve this problem, a classification modeling process will be carried out from previous data using machine learning algorithms, namely with Decision Tree (DT) and Support Vector Machine (SVM) algorithms. The general method for its development uses the SEMMA method (Sample, Explore, Modify, Model, Assess). Starting with collecting a dataset of KIP recipients studying at UMMI from 2021-2022 which amounted to 519 data with 16 attributes. From the results of exploration, the main attributes that became features for modeling were DTKS Status, P3KE Status, Combined income of father and mother and achievement. These attributes are converted into numeric data for easy data modeling. The results of K-Fold Cross-Validation for the DT model in the case of classification of KIP Kuliah recipients resulted in an accuracy of 78.44% of the entire test dataset, a precision of 0.73107 indicating that 73.11% of the model's predictions were correct, recall (sensitivity level) of 78.45% and an F1 score of 73.20%. The results of modeling and validation with SVM are 80.17% accuracy, 84.44% precision and 80.17% recall. The SVM model shows slightly better in terms of accuracy and precision, both models show competitive performance in classifying KIP scholarship recipients studying at UMMI.
Optimization of Village Budget Plan Selection Based on Priorities Using Method Promethee and Borda Dwi Yanti Laily; Muhammad Dedi Irawan
PIKSEL : Penelitian Ilmu Komputer Sistem Embedded and Logic Vol 11 No 2 (2023): September 2023
Publisher : LPPM Universitas Islam 45 Bekasi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33558/piksel.v11i2.7890

Abstract

The rapid information technology that is happening now has a significant impact on human life, including various activities that occur at the village office. The village head is responsible for managing funds at the village level including the collection and accountability of funds in accordance with the provisions of law no.33 of 2004 concerning the balance of central and regional governments. This study aims to implement a decision support system by combining two methods, namely the promethee and borda methods in selecting village budget plans in Tegal Sari village based on priority. The method used in this research is the Research and Development (R&D) method. The method used for calculations is the PROMETHEE and Borda methods. The PROMETHEE method is used to manage individual decisions from each decision maker, while the Borda method is used to manage group decisions resulting from the PROMETHEE ranking method. The criteria in this study are Cost, Completion Time, Impact on Regions/Society, Profits from Sustainable Investment, Factors of Interest. The results obtained from this research are Pemb. Health Sarpras as an alternative priority for the village budget.

Page 22 of 22 | Total Record : 214

 18   19   20   21   22 

Filter by Year

2013 2023


Reset
Filter By Issues
All Issue Vol 11 No 2 (2023): September 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 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 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 1 (2013): Januari 2013 More Issue