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All Journal IJCIT (Indonesian Journal on Computer and Information Technology) Paradigma JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING
Agus Yulianto
STMIK Nusa Mandiri Jakarta
Computer Science & IT Engineering

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Journal : JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING

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Market Basket Analysis for Books Sales Promotion using FP Growth Algorithm, Case Study : Gramedia Matraman Jakarta Firmansyah Firmansyah; agus yulianto
JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Vol 4, No 2 (2021): EDISI JANUARY 2021
Publisher : Universitas Medan Area

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31289/jite.v4i2.4539

Abstract

For retail companies such as Gramedia stores, promotion and strategies to sell books are important, so tools are needed to analyze past sales data. Gramedia does not yet have tools to analyze shopping cart patterns that aim to carry out product promotions appropriately. To promote what books should be promoted using the market basket analysis method or shopping basket analysis. The algorithm used in the data mining process is Frequent Pattern Growth (FP Growth) because it is faster in processing large data. The data analyzed is historical data on book sales from January to March 2020 which is taken randomly (random sampling). The framework used in the data mining process is the Cross Industry Standard Process for Data Mining (CRISP-DM) and the tool used is the Rapid Miner using a market basket analysis framework. With a minimum support of 0.003 and a minimum confidence 0.3 using the FP-Growth algorithm to produce an item set of 7 rules to recommend product promotions. The algorithm results are also in accordance with the business understanding phase of CRISP-DM.
Machine Learning Dengan Decision Tree untuk Prediksi Pembayaran Invoice, Case Study : Gramedia Jakarta Firmansyah Firmansyah; Agus Yulianto
JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Vol 5, No 1 (2021): EDISI JULY 2021
Publisher : Universitas Medan Area

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31289/jite.v5i1.5066

Abstract

Arus keuangan yang lancar merupakan salah satu kunci agar perusahaan tetap bertahan dan memiliki keberlangsungan. Pembayaran atas faktur penjualan adalah salah satu masalah yang dapat mempengaruhi keuangan, jika pembayaran faktur terlambat maka perputaran kas menjadi lambat dan berdampak pada operasional perusahaan. Belum adanya alat yang dapat memprediksi pembayaran faktur di Gramedia menyulitkan bagian keuangan. Dari permasalahan itu, maka diterapkan machine learning untuk memprediksi pembayaran faktur oleh customer, apakah pembayarannya terlambat atau tidak terlambat. Proses dalam data mining menggunakan framework CRISP-DM (Cross Standard Industry for Data Mining). Fitur data yang digunakan sebagai parameter yaitu invoice amount, payment method, paid invoice, average days late dan ratio amount of overdue by amount of balance. Data faktur penjualan diprediksi menggunakan model decision tree algoritma C5.0 dengan hasil akurasi mencapai 71.84%.  Algoritma C5.0 terbukti mampu memprediksi faktur yang pembayarannya terlambat (melewati jatuh tempo) dan pembayarannya tepat waktu (sebelum jatuh tempo).
Co-Authors . Sfenrianto Aji Wahdini Firmansyah Firmansyah Firmansyah Firmansyah