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All Journal SIGMA: Information Technology Journal Journal of Practical Computer Science (JPCS) SAINTEK
Aswan S Sunge
Universitas Pelita Bangsa
Automotive Engineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Engineering Industrial & Manufacturing Engineering

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Optimasi Algoritma Genetika Dalam Memprediksi Minat Baca Siswa Pada Perpustakaan SMK Negeri 1 Gantar Dengan Metode Decision Tree Lina Yulita; Aswan S Sunge; Nisa Nurhidayanti
Journal of Practical Computer Science Vol. 2 No. 1 (2022): Mei 2022
Publisher : DPPM Universitas Pelita Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37366/jpcs.v2i1.949

Abstract

Students are one part of the world of education that cannot be separated from reading activities. Each school certainly seeks to provide reading facilities such as school libraries, as well as libraries owned by SMK Negeri 1 Gantar aim to be able to foster and increase students' interest in reading books in the school library. But at the moment the library of SMK Negeri 1 Gantar tends to be minimal in number of visitors, this could be due to the lack of student awareness of the importance of reading books or there are other factors that can influence such as service, type of book, comfort, collection of books and so on. Then conducted a study that aims to find out how much interest in reading students of SMK Negeri 1 Gantar library using genetic algorithm optimization with the decision tree method. The data used in this study are visitor data owned by the library of SMK Negeri 1 Gantar as many as 290 data, the process of testing the method using RapidMiner 9.2. Based on the results of testing on research in predicting students' reading interest in the library of SMK Negeri 1 Gantar, the results obtained from the C4.5 algorithm or decision tree are accuracy by 84.48% and after being optimized using genetic algorithms the accuracy increases by 12.07% so that the accuracy value obtained from optimization of 96.55%. Then it can be concluded that the genetic algorithm optimization technique in value succeeded in increasing the accuracy of the C4.5 algorithm or decision tree in predicting students' interest in reading at the SMK Negeri 1 Gantar library. Keyword: Reading interest, library, C4.5 algorithm, genetic algorithm.
Analisis Sentimen Tentang Mobil Listrik Dengan Metode Support Vector Machine Dan Feature Selection Particle Swarm Optimization Ahmad Santoso; Agung Nugroho; Aswan S Sunge
Journal of Practical Computer Science Vol. 2 No. 1 (2022): Mei 2022
Publisher : DPPM Universitas Pelita Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37366/jpcs.v2i1.1084

Abstract

Analisis sentimen twitter merupakan teknik untuk mengidentifikasi sentimen atau pendapat dalam tweet dan kemudian mengategorikannya ke dalam tweet positif atau tweet negatif salah satu topik yang dibahas pada social media twitter adalah mobil listrik, mobil listrik memiliki beberapa kelebihan dibandingkan dengan mobil bahan bakar fosil. Mobil listrik ini menuai banyak komentar dari masyarakat sehingga menimbulkan pro dan kontra di sosial media twitter. Penelitian ini dilakukan tujuannya untuk mengetahui pendapat masyarakat terhadap mobil listrik. Apakah pendapat tersebut lebih mengarah ke positif atau negatif dan untuk mengetahui nilai accuracy, AUC dari penggunaan metode Support Vector Machine dan feature selection Particle Swarm Optimization pada Software RapidMiner Studio. di dalam penelitian ini dapat diketahui bahwa 94,25% pengguna twitter setuju dan 5,75% pengguna twitter tidak setuju terhadap kehadiran mobil listrik. Penggunaan feature selection Particle Swarm Optimization pada metode support vector machine untuk menganalisis sentimen masyarakat mengenai mobil listrik dapat meningkatkan nilai accuracy dan AUC. Dimana nilai accuracy yang awalnya sebesar 82,51% menjadi 86,07%, terjadi kenaikan sebesar 3,56%. Sedangkan nilai AUC yang awalnya sebesar 0,844 menjadi 0,862 terjadi kenaikan sebesar 2,13%. Kata kunci: Analisis Sentimen, Text Mining, Support Vector Machine, Particle Swarm Optimization, Mobil Listrik.
Pengembangan Sistem E-Learning Menggunakan Metode Research & Development Pada MTs. Roiyatul Mujahidin Wahyu Hadikristanto; Nur Azizah; Aswan S Sunge
Jurnal SIGMA Vol 13 No 2 (2022): Juni 2022
Publisher : Teknik Informatika, Universitas Pelita Bangsa

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Abstract

MTs Roiyatul Mujahidin is a tsanawiyah madrasa that forms quality human resources (HR). The learning system that takes place at MTs Roiyatul Mujahidin is done face-to-face and there is no online learning media that utilizes information technology that supports students' understanding of the main lessons by the teacher. The problem that has occurred so far is the ineffectiveness of teaching and learning activities in schools, so there is a need for additional hours of learning. Therefore, it is necessary to create a Web-Based E-Learning Information System at MTs ROIYATUL MUJAHIDIN using PHP and MySQL dataase which can be accessed anytime and anywhere so as to support the educational process at MTs Roiyatul Mujahidin and facilitate the dissemination of knowledge to students. E-Learning is a distance learning using internet technology, the development method used by the author is the Research & Development method, which is a method that uses a systematic and sequential approach starting from the development carried out in several stages, namely design, production, and evaluation. Keywords: Employee Recruitment, Classification, C5.0 Algorithm
Analisis Sentimen Terhadap Masyarakat Indonesia Di Masa PPKM Menggunakan Algoritma Naïve Bayes Ahmad Turmudi Zy; Aswan S Sunge; Riani Riani; Edy Widodo
Jurnal SIGMA Vol 13 No 2 (2022): Juni 2022
Publisher : Teknik Informatika, Universitas Pelita Bangsa

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Abstract

Abstract Coronavirus is a group of viruses that can cause disease in animals or humans. Several types of coronavirus are known to cause respiratory tract infections in humans ranging from coughs and colds to more serious ones such as Middle East Respiratory Syndrome (MERS) and Severe Acute Respiratory Syndrome (SARS). One of the topics currently being discussed by the public, including on social media Twitter, is the government's policy regarding the Enforcement of Restrictions on Community Activities (PPKM). PPKM is a policy of the Government of Indonesia to deal with COVID-19 that has been made since early 2021. The implementation of PPKM raises pros and cons from the community. Based on the results of the SRMC survey reported through the saifulmunjani.com page, it was stated that nationally, 44% chose to strictly implement PPKM even though on the other hand income decreased, and 40% chose to stop PPKM with an increased risk of COVID-19 transmission. Based on the problems that occurred, it became the basis of this research which aims to find out how the public sentiment towards the implementation of PPKM policies in Indonesia through tweets and comments on the Twitter social media platform using sentiment analysis Keywords: PPKM, Naïve bayes, Covid19
Analisis Pemilihan Jurusan Siswa Dengan Metode Klasifikasi Algoritma C5.0 (Studi Kasus : SMK Ma’arif Nu Al – Mawardi Bekasi) Aswan S Sunge
Jurnal SIGMA Vol 11 No 1 (2020): Desember 2020
Publisher : Teknik Informatika, Universitas Pelita Bangsa

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Abstract

The study, entitled "Analysis of the Selection of Student Majors with the C5.0 Algorithm Classification Method (Case Study: SMK Ma'arif NU Al - Mawardi Bekasi)" aims to find out information on student majors viewed based on the criteria set by the school using the classification algorithm C5.0 with a decision tree method. The data used in this study were 115 data of students belonging to SMP, with the testing process using Rapid Miner 9.7. Based on the test results of research in predicting majors in junior high school students, from the research using the C5.0 algorithm or decision tree method, the dominant pattern and factor in selecting student majors was obtained, namely based on the value of interest with an accuracy level of 95.65%. So it can be concluded that analyzing the majors of junior high school students at SMK Ma'arif NU Al-Mawardi Bekasi using the C5.0 algorithm classification technique with the decision tree method is considered successful. Keywords : Focusing, SMK, Classification, Algorithm C5.0.
Penerapan Data Mining Untuk Mempermudah Produksi Diapers Dengan Menggunakan Algoritma Regresi Linier (Studi Kasus Pada PT. Sinergi Adimitra Jaya Cibitung, Bekasi) Tahyani Tahyani; Aswan S Sunge; Miftah Wangsadanureja
Prosiding Sains dan Teknologi Vol. 1 No. 1 (2022): Seminar Nasional Sains dan Teknologi (SAINTEK) ke 1 - Juli 2022
Publisher : DPPM Universitas Pelita Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37366/SAINTEK0101.176179

Abstract

Diapers manufacturers are competing to make improvements while at the same time finding the latest formulas, it can be seen by the many brands/types of diapers on the market. Likewise, the material used by the company must import high quality materials. Material usage prediction activities are often things that must be considered so that the production and inventory processes run smoothly. So that how the calculation or estimation process can be calculated manually and with the help of the RapidMiner application or tools. Estimation methods used in Data Mining include the Linear Regression Algorithm. The test results on the SAP B material prediction, it is known that the error presentation rate using the MAPE formula is known to be 8.27% or an accuracy rate of 91.3%. Then the Linear Regression method can be used to predict material requirements quickly and efficiently. Keywords: Data Mining, Linear Regression, RapidMiner
Co-Authors Agung Nugroho Ahmad Santoso Ahmad Turmudi Zy Edy Widodo Lina Yulita Miftah Wangsadanureja Nur Azizah Nurhidayanti, Nisa Riani Riani Tahyani Tahyani Wahyu Hadikristanto