Sandi, Tommi Alfian Armawan
STMIK Nusa Mandiri Jakarta

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IMPLEMENTASI ALGORITMA APRIORI TERHADAP DATA PENJUALAN PADA PERUSAHAAN RETAIL Putra, Jordy Lasmana; Raharjo, Mugi; Sandi, Tommi Alfian Armawan; Ridwan, Ridwan; Prasetyo, Rizal
Jurnal Pilar Nusa Mandiri Vol 15 No 1 (2019): PILAR Periode Maret 2019
Publisher : PPPM Nusa Mandiri

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Abstract

The development of the business world is increasingly rapid, so it needs a special strategy to increase the turnover of the company, in this case the retail company. In increasing the company's turnover can be done using the Data Mining process, one of which is using apriori algorithm. With a priori algorithm can be found association rules which can later be used as patterns of purchasing goods by consumers, this study uses a repository of 209 records consisting of 23 transactions and 164 attributes. From the results of this study, the goods with the name CREAM CUPID HEART COAT HANGER are the products most often purchased by consumers. By knowing the pattern of purchasing goods by consumers, the company management can increase the company's turnover by referring to the results of processing sales transaction data using a priori algorithm
CLUSTERING KESETIAAN PELANGGAN DENGAN MODEL RFM (RECENCY, FREQUENCY, MONETARY) DAN K-MEANS Sandi, Tommi Alfian Armawan; Raharjo, Mugi; Putra, Jordy Lasmana; Ridwan, Ridwan
Jurnal Pilar Nusa Mandiri Vol 14 No 2 (2018): PILAR Periode September 2018
Publisher : PPPM Nusa Mandiri

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Abstract

Bisnis merupakan kegiatan yang tidak pernah berhenti, segala sesuatu yang menjadi peluang usaha pun dapat dijadikan bisnis yang menjanjikan kepada pelaku bisnis, semakin maju dan berkembangnya dunia usaha, membuat sebagian dari pebisnis gulung tikar, banyak faktor yang membuat mereka kesusahan dalam mempertahankan bisnisnya, diantaranya adalah pelanggan. Penelitian ini bertujuan untuk menentukan pelanggan potensial dan loyal kepada pelaku usaha, pelanggan yang potensial ditentukan dengan segmentasi pelanggan. Model RFM (Recency, Frequency, Monetary) digunakan untuk mencari atribut yang cocok untuk segementasi pelangan dan melalukan klastering menggunakan algoritma K-Means, model yang di keluarkan oleh K-Means pelanggan yang potensial memiliki nilai frekuensi yang besar. Menggunakan Davies bouldin index untuk membantu tingkat akurasi pada data klister.
Implementasi Metode Decision Tree Klasifikasi Data Mining Untuk Prediksi Peminatan Jurusan Robotika oleh Mahasiswa RAHARJO, MUGI; ridwan, Ridwan; Putra, Jordy Lasmana; Sandi, Tommi Alfian Armawan
JURNAL TEKNIK KOMPUTER Vol 5, No 2 (2019): JTK - Periode Agustus 2019
Publisher : Universitas Bina Sarana Informatika

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Abstract

Specialization of majors in a study program becomes something important must be an option for a student, for that they must think carefully before choosing the majors. Because later this thing can determine the success or failure of a student to understand what they learned to apply to during the final project. In the past few years there has been a question about the problem of electing majors in the Computer Technology Study Program. Because almost every year the majority of interest voters in majors are interested in computer network majors rather than robotics majors. majoring in majors, so the authors analyzed and retrieved data from 145 student samples in the electronic practicum course and chose 7 attributes in this study because this course was very influential on the interest in the robotics department in the Computer Technology study program. The author uses the classification tree Decision method to predict interest in students. Therefore, with this research, the authors hope that in the future with the results of this analysis can be found a solution to the problem of why students are more inclined to choose the interests of departments other than robotics, whether due to factors or other factors. Keywords: Computer Technology, Analysis, Classification