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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 10 Documents
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Search results for , issue "Vol 11 No 3 (2020): September 2020" : 10 Documents clear
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

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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

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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

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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

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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

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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

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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
Penerapan Data Mining Untuk Menganalisis Data Bencana Gempa Bumi Di Kepulauan Maluku Pada BMKG Menggunakan Naïve Bayes Algorithm Nurhadi Surojudin
Jurnal SIGMA Vol 11 No 3 (2020): September 2020
Publisher : Teknik Informatika, Universitas Pelita Bangsa

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Abstract

Earthquakes are natural phenomena that cannot predict the location, scale, depth of the hypocenter and the place affected by the earthquake. Until now, there is no precise theory that can be used to predict this. The collection of data on earthquake events in the Maluku Islands provides an opportunity for writers to take part in problem problems, namely by applying the big data theorem (data mining) by applying the naïve Bayes algorithm application technique using a tool that can know the level of accuracy in making earthquake predictions. The research phase begins with the earthquake data process. Then the big data is normalized and the training dataset and testing dataset are generated. Then upload the training data set on the tool and do modeling using classification techniques with the application of Hebrew by testing the training data which is then evaluated with the test data set so that the final results of the research are obtained. Based on the research it can be seen that the annual earthquake disaster in 2019-2020 in the Maluku Islands, namely North Maluku Province with the truth of the prediction data of 71.1%, in North Maluku Province there was an earthquake disaster on Ternate Island with the correctness of the prediction data of 78, 6%, In Maluku Province, there was an earthquake on Seram Island with the accuracy of the prediction data of 92%. Based on research, it can be denied that Yahoo Naïve Bayes can be used properly for earthquakes. Keywords: Ring Of Fire, Data Mining, Normalisasi, Data Set, Data training, Data testing, Weka, Classification, Algoritma Naïve Bayes.
Implementasi Sistem Monitoring Absensi Berbasis Rfid Proxymity Muhtajuddin Danny
Jurnal SIGMA Vol 11 No 3 (2020): September 2020
Publisher : Teknik Informatika, Universitas Pelita Bangsa

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Abstract

The fact is that there are still many companies that use manual attendance records, that is, by using an attendance record at the time of entry and completion of work time. Reducing efficiency and company accuracy in optimizing their productivity This final project aims to design a prototype of an RFID attendance system that is integrated with a database to support the employee disciplinary attitude improvement program as the first step in improving the overall work performance of the company. This prototype RFID attendance system consists of several main components, namely tags that will be used as a substitute for ID cards and readers that are used to read information regarding employee attendance, database integration in this system will allow data to be stored automatically into the database. The result of this final project is a prototype of an RFID attendance system that has a function to store employee attendance data, with a maximum reading distance of 2 cm with a chance of success of 1 and a minimum reading interval of 2 seconds to perform its function optimally. Keywords: RFID, absence, otomatically
Penerapan Algoritma Naive Bayes Pada Analisa Penyebab Kurang Dan Lebihnya Penggunaan Cutting Tool (Study Kasus Di PT. Sumiden Sintered Component Indonesia (SSI) Edy Widodo
Jurnal SIGMA Vol 11 No 3 (2020): September 2020
Publisher : Teknik Informatika, Universitas Pelita Bangsa

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Abstract

PT.Sumiden Sintered Component Indonesia (SSI) Is a company engaged in automotive component parts, and this company is also one of a group of companies from Japan namely Sumitomo Corporation in collaboration with local companies Santini Group. PT.SSI was founded in 2012 in the manufacture of component parts with metallurgical technology. Metallurgical technology with this synthesis is a new technology that has existed in Indonesia. PT.SSI has difficulty in processing data using cutting tools which often results in excess and underuse due to inaccurate data. To support this problem, the authors apply the Naive Bayes method to provide a solution in analyzing the problem of the lack and excess use of cuting tools at PT SSI. The data taken in this study is based on data in 2017 and 2018. This research is expected to help SSI companies in analyzing the problem of less and more use of cutting tools. That way, the application of this method is expected to help the user in doing his work. Naive Bayes Method Is a simple probabilistic classification that calculates a set of probabilities by adding up the frequency and combination of given dataset values. The algorithm uses the Bayes theorem and assumes all the attributes are independent or not interdependent given by the value of the class variable. In the above problem, the choice of using the Naive Bayes algorithm is due to the amount of data used in this study. Because the calculation of Naive Bayes algorithm only requires a small amount of training data to estimate parameters. Keyword : Naive Bayes, Prediction, Cutting Tool.
Perancangan Aplikasi Untuk Menganalisis Penyakit Menggunakan Pengobatan Tanaman Herbal Dan Cara Mengolahnya Dengan Certainty Factor Berbasis Android A. Yudi Permana
Jurnal SIGMA Vol 11 No 3 (2020): September 2020
Publisher : Teknik Informatika, Universitas Pelita Bangsa

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Abstract

Applications regarding diseases and herbal plants do not have a diagnostic system that can assist the selection process. Most of the processes used have not used disease analysis / diagnosis. Most of them provide information about diseases or about herbal plants directly. So often users have to look for diseases or plants first to find the information. The method used in designing information systems for diseases and herbal plants is a structured programming method using UML diagrams. When building this application, it takes software such as Apache as a web server, MYSQL as a database, Eclipse. The steps taken are analyzing the needs required by the application, designing according to the needs analysis, building an application program in accordance with the previously made designs, and testing the application. The results of this thesis will present that a disease information system and herbal plants can be developed using Eclipse and MYSQL database. This application that has been built can help the process of analyzing / diagnosing diseases and providing information about what herbal plants can be used as medicine and how to process these plants so that they are really easy for the user to consume. Keyword: Disease and Herbal Plants, Android, Eclipse, Apache

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