Article Per Year (5 Year)
p-Index From 2019 - 2024
0.751
P-Index
This Author published in this journals
All Journal Journal Information System Development JURNAL TEKNOLOGI DAN ILMU KOMPUTER PRIMA (JUTIKOMP) Progresif: Jurnal Ilmiah Komputer Jurnal Minfo Polgan (JMP)
Ferawaty Ferawaty
Universitas Pelita Harapan
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Engineering Library & Information Science Mathematics Social Sciences

Published : 4 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 4 Documents
Search

 1 
Analisis Status Karyawan Produksi di PT. Universal Indofood Product menggunakan Metode Naive Bayes Brian Emerson; Ferawaty Ferawaty; Robin Robin
Jurnal Minfo Polgan Vol. 9 No. 2 (2020): Article Research
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (214.617 KB) | DOI: 10.33395/jmp.v9i2.10945

Abstract

Karyawan merupakan aset perusahaan. Karyawan adalah asset yang sangat penting dalam mendukung kelancaran dan proses produksi sebuah perusahaan berbasis industri sehingga tanpa adanya karyawan, kelancaran dan proses produksi suatu perusahaan akan menjadi terganggu atau industry bahkan tidak dapat dapat beroperasi sama sekali. Penelitian ini mengklasifikasikan data karyawan dengan sample data PT.Universal Indofood Product yang diteliti dengan metode Naive Bayes dimana data uji karyawan produksi yang diteliti menghasilkan rata-rata klasifikasi 56.79% karyawan tetap dan 40.74% karyawan kontrak sehingga dapat disimpulkan bahwa karyawan tetap paling banyak dari dataset.
Prediksi Klasifikasi Perawatan pada Dataset Kanker Payudara Coimbra Memakai Metode Naive Bayes Ferawaty Ferawaty; Wenripin Chandra; Kelvin Ivanka
Journal Information System Development Vol 5, No 1 (2020): Journal Information System Development (ISD)
Publisher : UNIVERSITAS PELITA HARAPAN

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

Abstract

Breast cancer is a dreaded disease and a major cause of death. In this study, the Naïve Bayes method is used to predict the category of breast cancer treatment for the Breast Cancer Coimbra Dataset. Test results involving nine variables in the dataset resulted in 44.8% of the "Healthy Controls" category and 55.2% of the "Patient" category.Keywords : Breast Cancer, Naive Bayes, Coimbra, Classification.
ANALISIS PENJUALAN PAKAIAN PADA PERUSAHAAN PERSEORANGAN PRIMA JAYA LESTARI MENGGUNAKAN METODE AUTOREGRESSIVE INTEGRATED MOVING AVERAGE (ARIMA) Ferawaty Ferawaty; Jeslyn Jeslyn
JURNAL TEKNOLOGI DAN ILMU KOMPUTER PRIMA (JUTIKOMP) Vol. 3 No. 2 (2020): Jutikomp Volume 3 Nomor 2 Oktober 2020
Publisher : Fakultas Teknologi dan Ilmu Komputer Universitas Prima Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34012/jutikomp.v3i2.1621

Abstract

Since 1993, PP Prima Jaya Lestari is a business engaged in convection and garment, especially batik and men's shirts. Currently, PP Prima Jaya Lestari has used a computerized system to record and manage business transaction data. However, the system is unable to provide information on the influence of millennial clothing trends on to increasing convection sales at the company. To solve the problem in PP Prima Jaya Lestari, the method that being used is Auto Regressive Integrated Moving Average (ARIMA) method. In the working procedure, the stationarity of the data in the mean can be done by identifying the data plot and the ACF data form. If ACF shows a slowdown pattern means the data is not stationary in the mean, so it requires differencing so that the data becomes stationary in the mean. Conversely, if the ACF shows a rapidly descending pattern, the data is statistical in the mean Based on the results of the tests conducted in this study obtained information that by using the MAE method to calculate the difference in clothing sales between the real value and the predicted result value, information was obtained that the type of plain shirts and men's tile motif shirts have a greater MAE value when compared to the type of men's batik shirts and men's lyrical motif shirts, which means that the error rate of predictions against plain shirt types and men's tile motif shirts is much greater. This is because the type of men's batik shirts and men's lyrical shirts have a sales turnover that tends to be more stable. Meanwhile, the type of plain shirts and men's tile motif shirts have a more volatile sales turnover.
Implementasi Haversine Formula dan Algoritme Dijkstra pada Aplikasi Vehicle Help Sebagai Layanan Konsultasi Pemilik Kendaraan Ferawaty Ferawaty; Albert Cenderawan
Progresif: Jurnal Ilmiah Komputer Vol 19, No 1: Februari 2023
Publisher : STMIK Banjarbaru

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35889/progresif.v19i1.1027

Abstract

Owners of motorized vehicles who still use conventional methods to find information on the location of refueling and vehicle service workshops, receive inaccurate and not up-to-date information. The purpose of this research is to develop the Vehicle Help application to make it easier for motorists to search for information on the location of the nearest fuel filling station and service shop. This study implements and tests the accuracy of the Haversine Formula and Dijkstra's algorithm in recommending the location of the nearest fuel filling station and service shop. The system development methodology used is the Waterfall model. The results of the study show that the mobile-based Vehicle Help application that was built can provide easy and complete information on where to fill up fuel and vehicle service workshops through digital mapping and available recommendation features. In addition, the results of the Haversine Formula test and Dijkstra's algorithm in recommending gas stations and service shops get an accuracy of 90% from 10 trials.Keywords: Location Search; Digital Map; Service Workshop; Vehicle Help; Mobile Application AbstrakPemilik Kendaraan bermotor yang masih memanfaatkan cara konvesional dalam mencari informasi lokasi tempat pengisian Bahan Bakar Minyak (BBM) dan bengkel servis kendaraan, memperoleh informasi yang tidak tepat dan tidak up-to-date. Tujuan penelitian ini adalah mengembangkan aplikasi Vehicle Help untuk mempermudah pengendara melakukan pencarian informasi lokasi tempat pengisian BBM dan bengkel servis terdekat. Penelitian ini mengimplementasikan dan menguji akurasi Haversine Formula dan algoritme Dijkstra dalam merekomendasikan lokasi tempat pengisian BBM dan bengkel servis terdekat. Metodologi pengembangan sistem yang diguanakan adalah dengan model Waterfall. Hasil penelitian menunjukkan bahwa aplikasi Vehicle Help berbasis mobile yang dibangun dapat memberikan informasi tempat pengisian BBM dan bengkel servis kendaraan secara mudah dan lengkap melalui pemetaan digital dan fitur rekomendasi yang tersedia. Selain itu, hasil pengujian Haversine Formula dan algoritme Dijkstra dalam merekomendasikan SPBU dan bengkel service mendapatkan akurasi sebesar 90% dari 10 kali percobaan.Kata kunci: Pencarian Lokasi; Peta Digital; Bengkel Servis; Vehicle Help; Aplikasi Mobile
Co-Authors Albert Cenderawan Brian Emerson Jeslyn Jeslyn Kelvin Ivanka Robin Robin Wenripin Chandra