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All Journal SIGMA: Information Technology Journal SAINTEK
Tedi Kurniadi, Nanang
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Automotive Engineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Engineering Industrial & Manufacturing Engineering

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Penerapan Data Mining Untuk Prediksi Pola Pembelian Pelanggan Menggunakan Algoritma Apriori (Studi Kasus: Toko Jihan) Arista, Ratna; Nugroho, Agung; Tedi Kurniadi, Nanang
Jurnal SIGMA Vol 14 No 3 (2023): September 2023
Publisher : Teknik Informatika, Universitas Pelita Bangsa

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Abstract

Determining the combination of items and the layout of goods based on consumer purchasing trends is one solution for Toko Jihan in developing marketing strategies so as to increase sales at the store. The algorithm that can be used to find any combination of items that are often purchased together at a time is the Apriori Algorithm, the apriori algorithm is a market basket analysis algorithm used to generate association rules, with an "if then" pattern. In the apriori algorithm, frequent itemset-1, frequent itemset-2, and frequent itemset-3 are determined to obtain association rules from previously selected data. To get the frequent itemset, each data that has been selected must meet the minimum support and minimum confidence requirements. In this study using different minimum support and minimum confidence comparisons based on existing transaction data using a minimum support of 20% and a minimum confidence of 80% resulted in four association rules. One example is if the consumer buys cooking oil, coffee then 87% (certainty of consumers in buying items) will buy eggs. Keywords: Association Rule Mining, Apriori Algorithm, Support, Confidence.
Perbandingan Model Klasifikasi Tuned dan Untuned Dalam Prediksi Penyakit Jantung Sunge, Aswan Supriyadi; Tedi Kurniadi, Nanang; Zahrotul Kamalia, Antika; Naya, Candra; Afriantoro, Irfan; Suwarno, Agus
Prosiding Sains dan Teknologi Vol. 3 No. 1 (2024): Seminar Nasional Sains dan Teknologi (SAINTEK) ke 3 - Januari 2024
Publisher : DPPM Universitas Pelita Bangsa

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Penyakit jantung merupakan salah satu penyakit yang mematikan, biarpun bisa dicegah namun sulit untuk diprediksi, maka dari itu dibutuhkan suatu model prediksi jantung dengan penggunaan Machine Learning dengan metode Classifier dengan model Random Forest dan Decision Tree lalu hasilnya divalidasi kembali agar bisa membandingkan hasilnya dengan (Tuned) atau tanpa (Untuned) dengan GridSearchCV. Hasil yang didapat bahwa model Random Foreset lebih tinggi dibanding Decision Tree, diharapkan dengan hasil penelitian bisa dijadikan pedoman dalam prediksi jantung dikemudian hari.
Co-Authors Afriantoro, Irfan Agus Suwarno, Agus Arista, Ratna Naya, Candra Nugroho, Agung Sunge, Aswan Supriyadi Zahrotul Kamalia, Antika