Jurnal SIGMA

Implementasi Sistem Informasi Penggajian Karyawan Berbasis Desktop Pada PT. Virgi Motor Cikarang

2024-08-20T14:56:08+00:00

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.

IMPLEMENTASI SISTEM ABSENSI FACE RECOGNITION BERBASIS WEB PADA BAGIAN KESEJAHTERAAN RAKYAT KABUPATEN BEKASI

2024-08-20T03:52:26+00:00

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

2024-08-20T11:15:14+00:00

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%.