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
 32   33   34   35   36 
IMPLEMENTASI SISTEM ABSENSI FACE RECOGNITION BERBASIS WEB PADA BAGIAN KESEJAHTERAAN RAKYAT KABUPATEN BEKASI suprapto; Isarianto; Alhadi Saputra; Handala Simetris Harahap; Ahmad Fauzi
Jurnal SIGMA Vol 15 No 1 (2024): Juni 2024
Publisher : Teknik Informatika, Universitas Pelita Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37366/sigma.v15i1.5067

Abstract

At this time, Bagian Kesejahteraan Rakyat Kabupaten Bekasi still uses finger print attendance, the attendance process using finger print is often problematic on machines that do not detect fingers, so the attendance process is done manually by writing on the attendance form. Plus, to carry out the attendance process, employees have to queue, so it is quite a waste of time. During the Covid-19 pandemic, finger print attendance is still dangerous because of physical contact when going to the attendance process. The attendance process needs to be improved again so that its use is more flexible, safe and efficient. By utilizing face recognition technology, face recognition-based attendance is attendance that is carried out using the detection of parts of the human face. Then in the design of the face recognition-based attendance system, the researcher uses a system modeling with Undefined Modeling Language (UML) and developed with the prototype method. This research shows that with the construction of this web-based facial recognition face attendance system, Bagian Kesejahteraan Rakyat Kabupaten Bekasi can be easier and safer from the Covid-19 outbreak in carrying out attendance in every condition, then in the recapitulation of the list of employees who attend Bagian Kesejahteraan Rakyat Kabupaten Bekasi it is easier because it is already stored in the database.
Penerapan Metode Klasifikasi Dengan Algoritma Decision Tree C4.5 Untuk Mendiagnosa Awal Penyakit Ginjal Kronis Karina Imelda
Jurnal SIGMA Vol 15 No 1 (2024): Juni 2024
Publisher : Teknik Informatika, Universitas Pelita Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37366/sigma.v15i1.5070

Abstract

Patients with kidney disease find it difficult to know because they have to go through a series of laboratory tests with a considerable amount of time at the hospital. The complexity of the detecting process can be made easier using technology with data processing or Data Mining. Data Mining is the process of mining or discovering new information that aims to overcome certain conditions by looking for certain patterns and rules of a large amount of data. To diagnose early patients with chronic kidney disease with Data Mining using the classification method with the Decision Tree C4.5 algorithm. Decision Tree or meaning a decision tree is a prediction model with an hierarchical structure that has the concept of converting data into rules and decision trees, data in decision trees are expressed in tables with attributes and records that state parameters as tree formation criteria. The study used Chronic Kidney Disease data as a dataset and applied the classification method with the Decision Tree C4.5 algorithm. This study uses RapidMiner 9.0.3 data mining tools. The results obtained from this study show an accuracy of 89.05%.
Implementasi Sistem Informasi Penggajian Karyawan Berbasis Desktop Pada PT. Virgi Motor Cikarang Eko Budiarto; Hadikristanto, Wahyu; Syach, Ridwan
Jurnal SIGMA Vol 15 No 1 (2024): Maret 2024
Publisher : Teknik Informatika, Universitas Pelita Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37366/sigma.v15i1.5073

Abstract

PT. Virgi Motor Cikarang adalah perusahaan yang bergerak di bidang penjualan sepeda motor honda, Segmen Ahass / Service, dan penjualan sparepart motor honda.  PT VIRGI MOTOR CIKARANG didirikan sejak tahun 2001. Dalam pengolahan data karyawan selama ini menggunakan sistem penggajian terkomputerisasi namun sederhana yaitu menggunakan software Ms. Excel, sehingga dalam pengolahan datanya mengalami hambatan terjadinya proses kesalahan seperti perhitungan gaji lembur, potongan gaji, gaji pegawai, tunjangan, gaji pokoknya dan laporan gaji harus dihitung dan mengalami proses perhitungan yang berulang – ulang dari tiap karyawannya. Metode yang digunakan penulis dalam penelitian ini menggunakan metode waterfall yang terdiri dari perencanaan, analisis, perancangan, implementasi pemeliharaan. Hasil dari penelitian ini adalah menghasilkan sistem informasi penggajian yang terkomputerisasi yang diberikan kemudahan dalam memberikan informasi data penggajian seperti informasi data karyawan, data jabatan, tunjangan & data penggajian. Pada Sistem Informasi Data Penggajian, penulis menggunakan diagram arus data, ERD, dan laporan dengan menggunakan pemograman Microsoft Visual Studio 2019 dan SQL Server untuk pengolahan data. Setelah peneliti membuat Sistem Informasi Penggajian, penulis berharap agar prosedur kerja dapat lebih mudah bagi pihak-pihak terkait di PT. Virgi Motor Cikarang.
rekayasa perangkat lunak KNOWLEDGE MANAGEMENT SYSTEM SHARING RECORD TEKNISI BERBASIS ANDROID PADA PT. CNC PART TEKNIKA PERMANA, A. YUDI
Jurnal SIGMA Vol 15 No 1 (2024): Juni 2024
Publisher : Teknik Informatika, Universitas Pelita Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37366/sigma.v15i3.1066

Abstract

Reports contain facts about news, information, notifications, and forms of activities relating to accountability. Job reports that have not been properly documented are also an obstacle for the company when a technician resigns, making it difficult to distribute the knowledge possessed by the old technician to the new technician. Knowledge Management System is one way to identify, select, disseminate and disseminate important information and expertise in an organization as an effort to develop productivity and work performance so as to increase the competitiveness of the organization. The development of information systems is fast, accurate and up to date available in the plat form, such as Android. In this case, PT. CNC Part Teknika which is engaged in the field of General Trading and Service, still uses a manual system for making information job report. One example of the report method they used paper for media report. However, this reporting method is easily lost and damaged. This research aimed to design application knowledge management system job report based on android system. In designing this application using the XP (Extreme programing) and UML (Unified Modeling Language) methods. This is expected to documenting the knowledge of technicians in handling service and help facilitate the process of making reports is up to date for technician at PT. CNC Part Teknika. This application is useful for technician workers of PT. CNC Part Teknika using a database that can be used easily and quickly.
PENERAPAN DECISION TREE DALAM MENDETEKSI POLA TINGKAT STRESS MANUSIA BERDASARKAN POLA TIDUR MENGGUNAKAN RAPID MINER Ahmad shofwan anshory; Amali; Fauzhan Qhof Pratama; Ridho Pikriyansyah
Jurnal SIGMA Vol 15 No 2 (2024): September 2024
Publisher : Teknik Informatika, Universitas Pelita Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37366/sigma.v15i2.4311

Abstract

According to data from the Health Service Monitor in 2023, stress is one of the most worrying health problems for 30% of respondents. Stress is often associated with sleep patterns. This study aims to identify the relationship between sleep patterns and stress levels in humans using 10 levels: 1-2 (normal), 3-4 (mild), 5-6 (moderate), 7-8 (high), 9-10 (very high). The model used in this study is decision tree, with data covering gender, age, occupation, sleep quality, physical activity level, BMI (Body Mass Index) category, blood pressure, heart rate, daily activities, and sleep disorders. This study is expected to provide valuable information on the relationship between sleep patterns and stress, so that strategies can be developed to improve sleep quality and reduce stress. Based on the data analysis, there are several factors that cause increased stress levels, namely blood pressure, sleep quality, body weight, gender, and daily activities. This can have a significant impact on sleep quality.
Klasifikasi Tingkat Stress Pada Manusia Dengan Menggunakan Algoritma Naive Bayes Pada Rapidminer Andriano, Choky; Pratama, Alvian Saputra; Ramadan, Fadli; Amali, Amali
Jurnal SIGMA Vol 15 No 2 (2024): September 2024
Publisher : Teknik Informatika, Universitas Pelita Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37366/sigma.v15i2.4312

Abstract

Stress is a state of anxiety or mental tension caused by a difficult situation. There are different levels of stress, which indicate how severe and strong the impact is on the body and mind. By recognizing and understanding the level of stress we experience, we can be wiser in distinguishing the type of stress that we are experiencing. The study aims to determine whether classification techniques with the application of the Naïve Bayes algorithm can be used to predict stress levels in humans, as well as to obtain information about accuracy, precision, and recall obtained when conducting patient data testing using Naïva Bayes. The study uses classification and phase-stage techniques in data mining to classify patient data for human stress detection with Naïv Bayes' algorithm using the RapidMiner tool. Using the Naïve Bayes algorithm method for human stress level datasets has been proven to be very effective, producing an accuracy rate of 99.17%. Precision for pred. is low (96.77%, precision for pred . is normal (100.00%, and precision for pred. is high (100.00%. Recall for low 100.00%, recall for normal 97.98%, and recall for high 100.00%. The stress level is determined by humidity, step count, and temperature, thus producing the data.
The Pengembangan Model Prediktif untuk Penyakit Jantung Menggunakan Teknik Data Mining: Abastrak, Pendahuluan, Landasan Teori, Metode Penelitian, Hasil dan Pembahasaan, Kesimpulan, Ucapan Terimakasih, Referensi Pringgandani, Rizal
Jurnal SIGMA Vol 15 No 2 (2024): September 2024
Publisher : Teknik Informatika, Universitas Pelita Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37366/sigma.v15i2.4319

Abstract

Throughout the world, heart disease is still a health problem. To study the development of a predictive model using the Decision Tree algorithm, clinical data from patients with a history of heart disease was collected and analyzed. The decision tree model achieved an accuracy of 80.88%, indicating the ability to correctly predict the target category in the majority of cases. These results suggest that there is potential for earlier intervention and prevention. Additional evaluation is needed to understand the components that influence the results and improve model performance. This research helps improve heart disease predictions using data mining techniques.
A Analisis Sentimen Ulasan Pengguna Aplikasi Tokopedia Berbasis Algoritma Naive Bayes Serta Pendekatan Klasifikasi Sentimen Positif Dan Negatif: - Pendahuluan, Metodelogi penelitian , Hasil dan pembahasan , kesimpulan Aditiya, Rangga; Riffani, Sidik; Maulana, Faris; Amali
Jurnal SIGMA Vol 15 No 2 (2024): September 2024
Publisher : Teknik Informatika, Universitas Pelita Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37366/sigma.v15i2.4320

Abstract

Changes in consumer behavior in shopping for daily necessities have driven the growth of e-commerce applications as the main platform for transactions. Tokopedia, as one of the leading e-commerce applications in Indonesia with a large number of users, is the main focus of this study. This study aims to analyze the sentiment of Tokopedia user reviews published on the Google Play Store using a data mining approach with Naive Bayes algorithm.Positive and negative sentiment classification methods are used to understand the views and evaluations of users on Tokopedia services. Review Data is extracted, processed, and trained using the Naive Bayes algorithm to classify reviews into positive or negative sentiments.
Analisa Klasifikasi Data Harga Handphone Menggunakan Algoritma Regresi Linier Syauqi, Fauzan
Jurnal SIGMA Vol 15 No 2 (2024): September 2024
Publisher : Teknik Informatika, Universitas Pelita Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37366/sigma.v15i2.4324

Abstract

The rapid advancement of technology and communication over time has significantly increased the utilization and application of technology in various fields and aspects of life, such as the use of smartphones. Mobile phone classification is often performed using linear regression methods to predict prices based on various features. However, the accuracy of mobile phone price classification using linear regression, as well as the impact of parameters such as maximal_depth and criteria types like gain_ratio, accuracy, and gini_index on the classification results, remains unknown. The objectives of this research are to determine the accuracy of mobile phone price classification using the linear regression algorithm and to understand the influence of the maximal_depth parameter and criterion types (gain_ratio, accuracy, and gini_index) on the classification results. The classification method that will be applied in this research is linear regression. The results of this research are expected to provide a clear picture of the accuracy of mobile phone price prediction using the linear regression algorithm, as well as how the parameters maximal_depth and criterion types contribute to the model's performance. Consequently, this research will contribute to the selection and application of more appropriate classification methods for predicting mobile phone prices Keywords: data mining, rapidminer, classification, cellphone, linear regression algorithm
Penerapan Algoritma Decision Tree untuk Prediksi Kelulusan Mahasiswa Berdasarkan Data Akademik Menggunakan RapidMiner Laela Nur Rohmah; Sara Khusnul Mumtazah; Alvina Damayanti; Amali
Jurnal SIGMA Vol 15 No 2 (2024): September 2024
Publisher : Teknik Informatika, Universitas Pelita Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37366/sigma.v15i1.4332

Abstract

Higher education has an important role in the long-term development of each individual. One of the most important indicators of success for a high-performing educational institution is student achievement. There are several factors that might influence student achievement, including academic, demographic, and socioeconomic factors. This study employs the Decision Tree algorithm, which is one of several effective algorithms for making predictions or analyzing large amounts of data. This study aims to determine whether the Decision Tree algorithm can be used to predict student achievement by gathering information on accuracy, precision, and recall obtained during data collection. This study used RapidMiner tools to create a Decision Tree model and was carried out with the following steps: data collection, data analysis, Decision Tree modeling, method development, and results evaluation. Data collection on the dataset will be divided into two parts: 70% for training and 30% testing. The results of the study on the decision tree algorithm show that it has a good performance with a high accuracy of 73.17%. It also performs well in predicting graduate students with a precision of 74.05% and a recall of 93.82%, as well as dropout students with a precision of 73.02% and a recall of 80.05%.

Page 34 of 40 | Total Record : 396

 32   33   34   35   36 

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): Desember 2019 Vol 10 No 2 (2019): Juni 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