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All Journal Sinkron : Jurnal dan Penelitian Teknik Informatika Indonesian Journal of Computer Science JUSTIN (Jurnal Sistem dan Teknologi Informasi)
Eni Irfiani, Eni
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Computer Science & IT

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Application of Apriori Algorithms to Determine Associations in Outdoor Sports Equipment Stores Irfiani, Eni
Sinkron : Jurnal dan Penelitian Teknik Informatika Vol 3 No 2 (2019): SinkrOn Volume 3 Number 2, April 2019
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (360.281 KB) | DOI: 10.33395/sinkron.v3i2.10089

Abstract

A good sales strategy has an effect on increasing the number of sales of goods. Problems that often occur in outdoor sports equipment stores are the difficulty in determining sales strategies because there is not much interest in outdoor sports in the community. In addition, the amount of inventory in the store is excessive, which affects the sales cycle of goods. One way to help determine strategy is to use apriori algorithm. In this method can determine consumer shopping behavior patterns. Apriori algorithms are part of the data mining analysis association. This algorithm is used to determine association rules. In the study, a combination of sports equipment purchased by consumers will be determined. Determination of the combination starts from 1 itemset to 3 itemsets, the combination of rule association produces different sales transaction patterns. The results of the study in the form of a combination of consumer shopping behavior patterns that will be used as recommendations for shop owners in determining the sales strategy. The resulting Rule association will help sales promotions and add the amount of inventory that many customers buy.
Algoritma K-Means Untuk Clustering Rute Perjalanan Wisata Pada Agen Tour & Travel Irfiani, Eni; Indriyani, Fintri
Indonesian Journal of Computer Science Vol. 9 No. 1 (2020): April 2020
Publisher : STMIK Indonesia Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (358.38 KB) | DOI: 10.33022/ijcs.v9i1.244

Abstract

Government support for the development of tourism has an impact on the growth of business opportunities for travel agents. Along with the advancement of the domestic travel sector, tour & travel agent business forms have sprung up that influence business competition between travel agents. The problem with tour & travel agents is the lack of information about tourist routes that are most in-demand by customers. To solve this problem the method used to classify the most desirable travel routes using the method of data mining is clustering with the K-Means algorithm. Based on the results of the study found three groups of travel routes, namely the most desirable travel routes by 20%, the trips that are in demand by 30% and less desirable trips by 50%.
Algoritma K-Means Clustering untuk Menentukan Nilai Gizi Balita Irfiani, Eni; Rani, Siti Sulistia
JUSTIN (Jurnal Sistem dan Teknologi Informasi) Vol 6, No 4 (2018)
Publisher : Jurusan Informatika Universitas Tanjungpura

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (877.506 KB) | DOI: 10.26418/justin.v6i4.29024

Abstract

Gizi  sangat dibutuhkan bagi tubuh manusia, terutama pada usia balita dan anak-anak, nilai gizi yang seimbang sangat baik dalam proses tumbuh kembang anak, meningkatkan kemampuan belajar yang baik, serta memberikan dampak positif untuk perkembangannya di masa depan. Saat ini masih kurangnya pengetahuan dasar orang tua dan para kader Posyandu mengenai nilai gizi seimbang pada balita, belum adanya pengelompokkan data berdasarkan karakteristik nilai gisi balita. Pengklasteran (clustering) merupakan metode yang cukup popular dan paling sering digunakan dalam pengolahan data pencitraan medis, biometrik dan bidang yang terkait oleh karena kesederhanaannya serta cukup efektif dalam mengelompokan data dengan  ukuran besar berdasarkan kecepatan pemrosesan dengan menempatkan objek-objek ke dalam kelas-kelas yang memiliki kemiripan. Dalam penelitian ini klasterisasi data nilai gizi balita pada Posyandu dengan acuan parameter tinggi badan balita dan berat badan balita menggunakan algoritma k-Means. Dengan menggunakan K-Means dapat mengklasifikasi nilai gizi balita secara umum agar dapat digunakan sebagai landasan pencegahan dini bagi para kader posyandu menanggulangi gizi buruk atau obesitas. Hasil klasterisasi tersebut dapat membantu para kader Posyandu dan orang tua balita dalam penanganan dini kondisi gizi balita dengan kategori obesitas, gizi lebih, gizi baik, gizi kurang dan gizi buruk.
Co-Authors Fintri Indriyani, Fintri Rani, Siti Sulistia Rani, Siti Sulistia