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All Journal JUTIM (Jurnal Teknik Informatika Musirawas) INTI Nusa Mandiri
Silvy Amelia
UNIVERSITAS BINA SARANA INFORMATIKA
Computer Science & IT Control & Systems Engineering Engineering Other

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PERANCANGAN PROGRAM INSENTIF PADA PT. MASSINDO KARYA PRIMA Nurul Rizal; Silvy Amelia; Luthfia Rohima
JUTIM (Jurnal Teknik Informatika Musirawas) Vol 4 No 2 (2019): JUTIM (Jurnal Teknik Informatika Musirawas) DESEMBER
Publisher : LPPM UNIVERSITAS BINA INSAN

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (476.221 KB) | DOI: 10.32767/jutim.v4i2.633

Abstract

Massindo Karya Prima perusahaan yang bergerak dalam bidang industri membutuhkansekali adanya suatu program yangmenunjang danmemberikankemudahan serta kecepatan dan ketepatan penghitugan hasil insentif untuk administrasi produksi .Untukitulahpenulismencoba merancang sebuah program hasil insentif pada PT.Massindo Karya Prima. Program yang akan dirancang adalah perhitungan hasil insentif sampai dengan penyimpanan data-data lainnya yang berhubungan dengan proses penghitungan hasil insentif hingga pembuatan laporan, sehingga memungkinkan pada saat proses berlangsung tidak terjadilagi kesalahan dalam penginputan data serta penghitungan,kurang akuratnya laporan yan gdibuatdan keterlambatan dalam pencarian data-data yang diperlukan. Perancangan program ini merupakan solusi yang terbaik untuk memecahkan permasalahan- permasalahan yang ada pada perusahaan ini,serta dengan sistem yang terkomputerisasi dapat tercapai suatu kegiatan yang efektif dan efisien dalam menunjang aktifitas pada perusahaan secara tepat dan akurat dengan menggunakan program.
PREDIKSI HARGA PONSEL BERDASARKAN SPESIFIKASINYA MENGGUNAKAN ALGORITMA LINEAR REGRESSION Muhammad Irsyad; Silvy Amelia; Yahya Mara Ardi
INTI Nusa Mandiri Vol. 19 No. 2 (2025): INTI Periode Februari 2025
Publisher : Lembaga Penelitian dan Pengabdian Pada Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/inti.v19i2.6292

Abstract

The rapid advancement of mobile technology tools day by day benefits thousands of smartphone retailers by offering various innovations. This study aims to predict smartphone prices based on their technical features using the linear regression method. The dataset used includes various technical attributes from different smartphone models. The research process involves a data preprocessing stage to clean missing or invalid values and feature transformation to prepare the data for the linear regression process. Subsequently, a linear regression model is developed and tested using cross-validation techniques to evaluate its performance. The metric used to measure the model's prediction accuracy or error is RMSE. The experimental results show an RMSE value of 170.692. The target variable, which is the smartphone price, ranges from the lowest price of 614 to the highest price of 4,361. The RMSE value obtained in this study can be considered fairly good, as it is less than 10% of the actual value or average price. Variables such as RAM, storage size, camera, and processor type significantly influence smartphone prices. However, other factors such as brand and design may also have an impact, albeit to a lesser extent. This study confirms that linear regression can be effectively used to predict smartphone prices based on technical specifications. The findings of this research can assist companies in developing pricing strategies based on smartphone specifications. Additionally, it can help determine which products are suitable for market introduction.
Co-Authors Luthfia Rohima Muhammad Irsyad Nurul Rizal Yahya Mara Ardi