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All Journal Tamilis Synex: Multidimensional Collaboration
Jessica Dumpel
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Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Social Sciences

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Optimalisasi Keuntungan Penjualan Produk Gelang dan Kalung di The Beadeary Jayapura Menggunakan Metode Grafik Hazrin Armehzan; Samanta Deliana; Wafiq Azizah Tuahuns; Khoiratul Masyruah; Jessica Dumpel; Heru Sutejo
Tamilis Synex: Multidimensional Collaboration Vol. 2 No. 03 (2024): Tamilis Synex: Multidimensional Collaboration
Publisher : CV Edujavare Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70610/tls.v2i03.358

Abstract

Optimalisasi keuntungan merupakan tujuan utama dalam menjalankan sebuah usaha. Salah satu cara untuk mencapai hal tersebut adalah dengan mengoptimalkan jumlah penjualan produk. The Beadeary Jayapura merupakan sebuah usaha kerajinan tangan dengan dua produk utama, yaitu kalung dan gelang. Penelitian ini bertujuan untuk mengoptimalkan jumlah penjualan kalung dan gelang agar diperoleh keuntungan yang maksimal. Metode yang digunakan adalah metode grafik dengan langkah-langkah meliputi identifikasi variabel keputusan dan fungsi tujuan, penggambaran fungsi kendala, penentuan daerah feasible, dan penentuan titik optimal dengan garis isoprofit. Hasil penelitian menunjukkan bahwa untuk memperoleh keuntungan maksimal yaitu sebesar Rp2.000.000,00 per bulan dari hasil penjualan produk gelang dan kalung.
PENERAPAN ALGORITMA NAIVE BAYES TERHADAP TARGET PENJUALAN HANDPHONE MENGGUNAKAN APLIKASI RAPID MINER J. Anggun Rumboirusi; Nahema Yaroseray; Kartensia Firli Rumboirusi; Jessica Dumpel; Lamberth Anthoni Yores Rumbino; Mariani Regina Sisilia Lengkey; Heru Sutejo
Tamilis Synex: Multidimensional Collaboration Analysis of the Influence of Performance on Company Value and Purchasing Decisions in the Digital Er
Publisher : CV Edujavare Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70610/tls.v2i1.625

Abstract

This research aims to analyze cellphone sales predictions using the Naive Bayes algorithm which is implemented through the RapidMiner application. The dataset used consists of sales data with various relevant features, such as product descriptions, categories, and sales labels (sold or not sold). The research process involves several main stages, namely data retrieval (Retrieve), dividing data into train and test (Split Data), applying the Naive Bayes model, and evaluating performance using metrics such as accuracy, precision, and recall. The test results show that the Naive Bayes model succeeded in achieving accuracy, precision and recall levels of 100%. This indicates that the model has very good performance in classifying test data. However, to ensure the validity of the model, an analysis was carried out on the possibility of overfitting and suggestions for improvements such as using a larger dataset and testing using cross-validation. This research proves that the Naive Bayes method can be an effective and efficient solution for analyzing sales data patterns, especially in cases with structured and clear data patterns. The implementation of the results of this research can be applied as a basis for decision making in marketing strategy and inventory management.
Co-Authors Hazrin Armehzan Heru Sutejo J. Anggun Rumboirusi Kartensia Firli Rumboirusi Khoiratul Masyruah Lamberth Anthoni Yores Rumbino Mariani Regina Sisilia Lengkey Nahema Yaroseray Samanta Deliana Wafiq Azizah Tuahuns