Article Per Year (5 Year)
p-Index From 2020 - 2025
0.444
P-Index
This Author published in this journals
All Journal IT (INFORMATIC TECHNIQUE) JOURNAL CSRID
Wahyu Saptha Negoro
Universitas Potensi Utama
Aerospace Engineering Computer Science & IT Decision Sciences, Operations Research & Management

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
SISTEM PENDUKUNG KEPUTUSAN KELAYAKAN KENAIKAN GAJI PEGAWAI MENGGUNAKAN METODE WASPAS Wahyu Saptha Negoro; Linda Wahyuni; Fujiati Fujiati
IT (INFORMATIC TECHNIQUE) JOURNAL Vol 9, No 1 (2021): IT JOURNAL APRIL 2021
Publisher : Universitas Potensi Utama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22303/it.9.1.2021.1-12

Abstract

Gaji pegawai merupakan hasil kinerja yang diperoleh pegawai selama bekerja, adapun dari kenaikan gaji dikarenakan dari kelayakan atau keberhasilan pegawai dalam mencapai progress yang telah ditentukan oleh pihak perusahaan. Suatu upaya untuk meningkatkan kinerja pada PT. Dwi Tama Prima Sakti adalah memberikan kenaikan gaji kepada pegawai  yang layak mendapatkannya. Dalam pemberian kenaikan gaji kadang kala tidak sesuai dengan hasil kinerjanya. Pemberian kenaikan gaji sering kali dipukul rata  atau kadang kala pemberian kenaikan gaji hanya dengan memperkirakannya saja, tanpa penerapan perhitungan. PT. Dwi Tama Prima Sakti bergerak di bidang rental construction equipment dimana perusahaan ini menerima jasa pengerjaan tanah, memindahkan bahan bangunan, serta rental alat untuk bangunan yang mana dalam penentuan kenaikan gaji pegawainya belum menggunakan aplikasi komputer. Penentuan kriteria dalam pemilihan kelayakan dari kenailkan gaji ada 4 kriteria diantaranya masa kerja, prestasi, beban kerja dan pendidikan. Sehingga dari 20 data pegawai yang bisa diambil keputusan Layak atau tidaknya  dalam  penentuan kenaikan gaji diranking lebih besar dari nilai 8 terhadap perhitungan metode WASPAS.
JARINGAN SYARAF TIRUAN DENGAN LEARNING VECTOR QUANTIZATION (LVQ) UNTUK KLASIFIKASI DAUN: ARTIFICIAL NEURAL NETWORKS USING LEARNING VECTOR QUANTIZATION (LVQ) FOR LEAF CLASSIFICATION Soeheri; Rita Sari; Wahyu Saptha Negoro; Yuhandri
Computer Science Research and Its Development Journal Vol. 16 No. 1 (2024): February 2024
Publisher : LPPM Universitas Potensi Utama

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

Abstract

Leaves are one part of a plant species that is commonly used to classify plant and plant species. The process of assisting various types of leaves usually involves experts using a herbarium, which is a collection of preserved plant specimens. Leaf classification is the detection of different types of leaves, where there are 2 types of leaves including Magnolia Soulangeana and Invillea leaves. The training data contains 30 images consisting of 15 each of the 2 types of leaves, then the test data contains 20 images which are also taken from the 2 types of leaves. So that the total images used are 50 leaf images. The leaf classification uses feature extraction and the method used in the classifier is Learning Vector Quantization (LVQ) which is a pattern classification method in which each output unit represents a particular class or group. The test results showed that the process of calling Magnolia Soulangeana and Bougainvillea leaves in this experiment was successful with 80% detection Keywords—Leaf classification, Learning Vector Quantization, Artificial Neural Networks, Feature extraction.
Co-Authors Fujiati Fujiati Linda Wahyuni Rita Sari Soeheri Yuhandri