cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
Ananto Tri Sasongko
Contact Email
ananto@pelitabangsa.ac.id
Phone
+6288980229926
Journal Mail Official
ananto@pelitabangsa.ac.id
Editorial Address
Jl. Inspeksi Kalimalang No.9, Cibatu, Cikarang Sel., Kabupaten Bekasi, Jawa Barat 17530
Location
Kab. bekasi,
Jawa barat
INDONESIA
Jurnal Ilmiah SIGMA: Informatics Engineering Journal of UPB
Published by Universitas Pelita Bangsa
ISSN : 24073903     EISSN : 28291891     DOI : https://doi.org/10.37366/sigma.v16i1
Core Subject : Science,
Computer Science & IT Decision Sciences, Operations Research & Management
Jurnal Ilmiah SIGMA: Informatics Engineering Journal of UPB merupakan jurnal ilmiah yang diterbitkan oleh Program Studi Teknik Informatika Universitas Pelita Bangsa (UPB) Cikarang dengan no p-ISSN 2407-3903 (Media Cetak). Jurnal Ilmiah SIGMA: Informatics Engineering Journal of UPB adalah sebagai salah satu wadah publikasi bagi dosen-dosen yang memiliki penelitian ilmiah di bidang Teknik Informatika, Ilmu Komputer, Sistim Informasi, Artificial Inteligent, Data Mining, Image Processing, Rekayasa Perangkat Lunak. Setiap artikel yang diterbitkan oleh Jurnal Ilmiah SIGMA: Informatics Engineering of UPB telah melalui proses review dan editorial yang ketat serta menghormati ketentuan hukum hak cipta, privasi, dan etika publikasi ilmiah. Jurnal Ilmiah SIGMA : Information Technology Journal of UPB terbit dua kali dalam setahun, yaitu bulan Maret, Juni, September dan Desember.
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 396 Documents
 25   26   27   28   29 
Penerapan Data Mining Menggunakan Metode Algoritma Naive Bayes Untuk Menentukan Kelayakan Kredit Rumah Bersubsidi Muhammad Makmun Effendi
Jurnal SIGMA Vol 11 No 2 (2020): Juni 2020
Publisher : Teknik Informatika, Universitas Pelita Bangsa

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

Abstract

Data mining has been implemented in various fields, including business, education and telecommunications. In the business sector, for example, the results of implementing data mining can help in making decisions about the feasibility of subsidized home loans. In determining the feasibility of subsidized home loans, PT. Gernis Pratama Properti conducts an analysis so that it can be determined whether the subsidized home loan process can be approved or not. Currently there are several obstacles in the assessment process, namely the inaccurate results of the decision at interview stage 1 in the company as an initial stage of the consumer eligibility process. Naive Bayes Algorithm Method is an algorithm found in the classification technique that uses a simple probability method based on the theory of infants with high independent assumptions. The process carried out in this study uses Rapid Miner tools to process data with the Naive Bayes algorithm, from the tests carried out it produces an accuracy of 96.23%. With the application of the Naive Bayes method, it uses data to produce the probability of each criterion for different classes, so that the probability values of these criteria can be optimized to determine the eligibility of "Eligible" and "Eligible" subsidized home loans quickly and efficiently based on the classification made by Naive Bayes method. Keyword : Creditworthiness of subsidies ,Data Mining, Algorithm Naive Bayes.
Rancang Bangun Aplikasi Edukasi Sebagai Media Pembelajaran Mengenal Tanaman Untuk Anak Usia Dini Menggunakan Augmented Reality Berbasis Android Edora Edora
Jurnal SIGMA Vol 11 No 2 (2020): Juni 2020
Publisher : Teknik Informatika, Universitas Pelita Bangsa

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

Abstract

One of the most common knowledge taught to children is art with a variety of materials such as knowing plants. At present the introduction of plants at an early age still uses picture books and explanations explained by teachers. With Augmented Reality (AR) supported by the markerless method. Users don’t need a special marker in the form of black and white, but use 2D images that are presented in the form of magic cards by the author as a reference to issue 3D objects. In this study several devices with different specifications are used to test whether this application can run well or not. From the results of this study that the marker can be read with a smartphone camera with a response time to detect the marker of approximately 1 second. The minimum distance to detect the marker is 20 cm and the maximum distance is 100 cm. For an optimal distance of 30 cm. The minimum angle for marker detection is 30o and the maximum angle is170o. For an optimal angle of 45o-60o. Keywords : : Learning Media, Augmented Reality, Marker, Markerless, Android, Unity 3D
Penerapan Algoritma Naive Bayes Untuk Menentukan Klasifikasi Produk Terlaris Pada Penjualan Pulsa Wahyu Hadikristanto
Jurnal SIGMA Vol 11 No 2 (2020): Juni 2020
Publisher : Teknik Informatika, Universitas Pelita Bangsa

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

Abstract

This research is motivated by the progress of the development of communication technology and information is very fast and increasingly cheap so that makes the community for mobile phone pulses become a mandatory requirement at the presente time. Of various pulse products available at the counter RA Cell Pulses Tekomsel, Pulses XL, Pulses Indosat, and Pulses 3 the autors classify as bestseller and non-sellers. The goal is to find out the implementation of data mining using the Naive Bayes algorithm in determining the classification of best-selling products and the result of the accuracy of the data in the sales of pulses. By collecting 600 data into 480 training data and 120 testing data. Data mining is a form of extracting data in classifying a large amount of data, using the RapidMiner application and the Naive Bayes algorithm is a classification method that is widely used because of its simple and high accuracy in classifying data. Based on the result of researh that has been done, the type of product that is most restricted to the sale of pulses by product name is Telkomsel Pulses. The level of classification accuracy with the naive Bayes methodproduces an accuracy value of 97,50%, a precision value of 100%, and a recall value of 93,48% so the Naive Bayes method is good method in this study. Keywords :Pulses, Classification, Data Mining, Naive Bayes
Sistem Pendukung Keputusan Penentuan QCC Terbaik Dengan Metode AHP Pada PT. Century Batteries Indonesia Nurhadi Surojudin
Jurnal SIGMA Vol 11 No 2 (2020): Juni 2020
Publisher : Teknik Informatika, Universitas Pelita Bangsa

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

Abstract

Selection of the Best QCC Group is giving recognition to the best QCC group who actually and extraordinary do Quality Control System activities by making changes that can increase the effectiveness and efficiency of work or production. The selection of the Best QCC Group of PT Century Batteries Indonesia is held annually. In the Best Group assessment process, PT. CBI appointed an assessment team to conduct an assessment of the QCC group. However, the assessment process is still done manually so it takes a long time to process the data. In addition, the assessment is still subjective and not yet relevant to the actual situation. Based on this, in this study a decision support system was built to be used to select the best QCC group selection process at PT Century Batteries. Decision support system built based on web using the programming language PHP and MySQL as the database. The decision making method used is Analytical Hierarchy Process (AHP). This method is used to determine the weight value of each criterion, which is then carried out by a ranking process to determine the best alternative from a number of alternatives. The test carried out in this study is functional testing with the black box testing method. The test results show that the system can run properly according to its function.. Keywords: Analytical Hierarchy Process (AHP), Decision Support System
Pengembangan Sistem Informasi Penjualan Berbasis Web Menggunakan Metode Prototyping Pada Toko Bay Sticker Nanang Tedi Kurniadi
Jurnal SIGMA Vol 11 No 3 (2020): September 2020
Publisher : Teknik Informatika, Universitas Pelita Bangsa

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

Abstract

The Bay Sticker shop is engaged in the cutting sticker business in its sales depending on regular consumers and consumers in the area around the shop, recording reports is still manual so that data search difficulties and data loss often occur and because the Bay Sticker Shop is related to graphic design, the delivery of information often occurs. The purpose of this research is to produce a new sales system at the Bay Sticker Shop by using a web-based application using the prototyping method and the application program design modeling using UML (Unified Modeling Language), and using PHP as a programming language and MySQL as a database. This research produces a sales information system that is fully managed by an administrator in controlling all information related to product data management, consumer data, sales report data, and a special menu for consumers to obtain information and be able to make online purchase transactions. Keywords : Bay Sticker Shop, Website, Prototyping, UML, PHP, MySQL.
Analisa Sentimen Tweet Indonesia Menggunakan Fitur Ekstrasi Dan Teknik Cross Validation Terhadap Model Naïve Bayes Ahmad Turmudi Zy
Jurnal SIGMA Vol 11 No 3 (2020): September 2020
Publisher : Teknik Informatika, Universitas Pelita Bangsa

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

Abstract

Sentiment analysis is a science in the field of natural language processing studies to analyze data in the form of positive and negative opinions with the aim of getting results in decision making. One of the media in sentiment analysis research is twitter. The main problem in sentiment analysis classification is how to choose the right features and validation in the test. The model used for this research is Naïve Bayes. Naïve Bayes can be combined with feature extraction. In testing the feature extraction of CountVectorizer and TFIDFVectorizer is compared using the Cross Validation technique to improve the Naïve Bayes classification. Value measurement is done by comparing between testing without validation and using validation. Accuracy can be measured using confusion matrix, precision and recall. The results of the study show that using the TF- IDFVectorizer feature extraction is better than the CountVectorizer with the highest accuracy of 85.98% and for the final test the extraction feature with Cross Validation is better than not using Cross Validation with the highest accuracy value of 97.67%. Thus, testing the extraction feature that is best used is the TF-IDFVectorizer and by using the Cross Validation technique it can improve the performance of the Naïve Bayes model in the sentiment analysis of Indonesian-language twitter so that it. Keywords : Sentiment analysis, twitter, Naïve Bayes, feature extraction, Count Vectorizer, TF-IDF Vectorizer, Crosss Validation.
Menentukan Prediksi Kelulusan Siswa Dengan Membandingkan Algoritma C4.5 Dan Naive Bayes Studi Kasus SMKN. 1 Cikarang Selatan Muhammad Makmun Effendi
Jurnal SIGMA Vol 11 No 3 (2020): September 2020
Publisher : Teknik Informatika, Universitas Pelita Bangsa

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

Abstract

The process of identifying information using statistical techniques, and machine learning is the meaning of data mining. Data mining can be applied in various fields of life such as health, business, and education. One of the applications of data mining in the field of education is to predict the graduation of school students. Prediction of student graduation using data derived from transcripts of the final grades of each student, while the attributes used are the average value of Indonesian, English, and Mathematics lessons from semester 1 to semester 5 as well as the history of SP that has been obtained during the student is in school. In this study, two data mining methods are used, namely the C4.5 algorithm and Naïve Bayes algorithm. The use of the two methods in this study aims to compare the performance of the two algorithms in predicting student graduation based on the level of accuracy, precision, and recall obtained. From the test results using data testing as much as 222 data which states that the C4.5 algorithm has an accuracy value of 98.64%, 100% precision and 100% recall, while Nave Bayes has an accuracy level of 97.75%, precision 95.52% and recall. 95.52%. And if the test uses 890 training data, it will state that the C4.5 algorithm has an accuracy level of 98.99%, precision 98.68% and recall 98.68% while nave Bayes has an accuracy level of 97.42%, precision 99, 39% and recalls 99.39%. From the above comparison, the C4.5 algorithm has an accuracy level that tends to be higher than the nave Bayes algorithm, so it was decided that in predicting student graduation, the C4.5 algorithm is better than the nave Bayes algorithm in predicting student graduation data. Keywords: Data mining, Clacification, C4.5 Algoritma, Naive Bayes
Pengembangan Sistem Pakar Diagnosa Penyakit Asma Dengan Metode Forward Chaining Berbasis Android Suherman Suherman
Jurnal SIGMA Vol 11 No 3 (2020): September 2020
Publisher : Teknik Informatika, Universitas Pelita Bangsa

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

Abstract

Expert system is a branch of artificial intelligence that learns how to adopt the way an expert thinks and reason in solving a problem, and makes a decision or draws conclusions from a number of facts. Until now there have been several results of the development of expert systems in various fields according to one's expertise. In this study a system application will be designed experts to diagnose asthma. The development of an expert application system for asthma diagnosis is one of the applications of a computerized system in the field of medicine. The aim of this research is to develop a medical knowledge-based system in diagnosing asthma that can be displayed in expert system-based application software. So that it can simplify the counseling process for ordinary people to know the early detection of symptoms of asthma and solutions or treatments that can be done independently. The reasoning of this expert system application uses forward chaining inference techniques. Where in this forward chaining begins with the initial information (initial symptoms) and moves forward to match further information until finding information in accordance with the rules, then will conclude in the form of a description of the type of disease and solution. In developing expert systems, conventional approaches to methodology will be used Expert System Development Life Cycle (ESDLC) from Durkin (1994). The results of this research are the application of an expert system for diagnosing asthma has facilities that can help extension workers in providing counseling to the public to find out early detection of symptoms of asthma, based on the type of asthma that attacks and solutions or ways of treatment that can be done independently. Keywords : ESDLC, forward chaining, asthma, expert system.
Klasifikasi Analisis Sentimen Terhadap Calon Presiden 2019 Pada Media Sosial Twitter Menggunakan Metode Algoritma Naïve Bayes Donny Maulana
Jurnal SIGMA Vol 11 No 3 (2020): September 2020
Publisher : Teknik Informatika, Universitas Pelita Bangsa

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

Abstract

The existence of Twitter has been widely used by various levels of society in recent years. The public's habit of posting tweets to evaluate the presidential candidates is one of the media in representing the public response to the presidential candidates. Therefore in this study an analysis of public sentiments towards the 2019 presidential candidates will be revealed through the Twitter social network. The analysis was carried out using a tweet classification that contained public sentiment towards the 2019 presidential nomination, namely jokowi and prabowo. The classification method used in this study is Naive Bayes Classification (NBC). NBC is used to get the classification of positive and negative responses to the twitter and get a preference value from the community towards the 2019 presidential candidates. The results of the jokowi data percentage test methods are 25%, 50%, 75%, and 100% of the amount of data from the training data yielding an accuracy of 64.67%, 70.57%, 87.56%, 97.50% and for the test results the percentage of Pre -owo data 25%, 50%, 75%, and 100% of the amount of data from the training data resulted in an accuracy of 64.57 %, 81.67%, 64.22%, 62.67%. And for the results of testing the positive response of the people on Twitter with a value of perference value of 53% for Jokowi and 48% for Prabowo. Therefore sentiment classification using the Naive Bayes classification method can be used to measure the public response to the performance of 2019 presidential candidates. Keywords: Twitter, naive bayes, sentiment analysis
Kelemahan Metode Enkripsi Message Digest 5 Terhadap Kriptanalisis Modern Asep Muhidin
Jurnal SIGMA Vol 11 No 3 (2020): September 2020
Publisher : Teknik Informatika, Universitas Pelita Bangsa

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

Abstract

This study examines encryption techniques using the MD5 method. So popular is this method that after several decades of its release this method is still often used, even though a year since its release was announced about the potential weaknesses of this method. The discussion of weaknesses in the MD5 method in this study is referring to research conducted by Dobertin H. About the potential errors in this method. This study examines the weakness of encryption techniques using the MD5 method that has been published by other studies before. The results and conclusions in this study can be used as a reference for information system makers to pay attention to the aspect of data security and make modifications in such a way as to prevent the potential weaknesses discussed in this study. Keywords: Cryptography, Data security, MD5, MD5 potential weaknesses

Page 27 of 40 | Total Record : 396

 25   26   27   28   29 

Filter by Year

2016 2025


Reset
Filter By Issues
All Issue Vol 16 No 2 (2025): September 2025 Vol 16 No 1 (2025): Juni 2025 Vol 15 No 3 (2024): Desember 2024 Vol 15 No 2 (2024): September 2024 Vol 15 No 1 (2024): Maret 2024 Vol 15 No 1 (2024): Juni 2024 Vol 14 No 4 (2023): Desember 2023 Vol 14 No 3 (2023): September 2023 Vol 14 No 2 (2023): Juni 2023 Vol 13 No 4 (2022): Desember 2022 Vol 13 No 3 (2022): September 2022 Vol 13 No 2 (2022): Juni 2022 Vol 13 No 1 (2022): Maret 2022 Vol 12 No 4 (2021): Desember 2021 Vol 12 No 3 (2021): September 2021 Vol 12 No 2 (2021): Juni 2021 Vol 12 No 1 (2021): Maret 2021 Vol 11 No 3 (2020): September 2020 Vol 11 No 2 (2020): Juni 2020 Vol 11 No 1 (2020): Desember 2020 Vol 10 No 4 (2019): Desember 2019 Vol 10 No 3 (2019): September 2019 Vol 10 No 2 (2019): Juni 2019 Vol 10 No 2 (2019): Desember 2019 Vol 10 No 1 (2019): Maret 2019 Vol 9 No 4 (2019): Juni 2019 Vol 9 No 3 (2019): Maret 2019 Vol 9 No 2 (2018): Volume 9 Nomor 2 Desember 2018 Vol 9 No 2 (2018): Desember 2018 Vol 9 No 1 (2018): September 2018 Vol 8 No 2 (2018): Maret 2018 Vol 8 No 1 (2018): Maret 2018 Vol 8 No 4 (2017): Desember 2017 Vol 8 No 3 (2017): September 2017 Vol 8 No 2 (2017): Juni 2017 Vol 8 No 1 (2017): Maret 2017 Vol 7 No 2 (2016): September 2016 Vol 7 No 1 (2016): Maret 2016 More Issue