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All Journal Syntax Jurnal Informatika Bianglala Informatika : Jurnal Komputer dan Informatika Akademi Bina Sarana Informatika Yogyakarta Indonesian Journal on Software Engineering (IJSE) Cakrawala : Jurnal Humaniora Bina Sarana Informatika Paradigma Bina Insani ICT Journal Information Management For Educators And Professionals (IMBI) KOMPUTIKA - Jurnal Sistem Komputer JATI (Jurnal Mahasiswa Teknik Informatika) Reputasi: Jurnal Rekayasa Perangkat Lunak SPEED - Sentra Penelitian Engineering dan Edukasi JUSTIN (Jurnal Sistem dan Teknologi Informasi)
Ela Nurelasari
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Arts Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Library & Information Science Social Sciences Other

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Journal : information management for educators and professionals imbi

 1 
Algoritma Assosiasi K-Means dan FP-Growth untuk Analisis Keranjang Pasar pada Penjualan Produk Alumunium Ela Nurelasari
INFORMATION MANAGEMENT FOR EDUCATORS AND PROFESSIONALS : Journal of Information Management Vol 1 No 2 (2017): INFORMATION MANAGEMENT FOR EDUCATORS AND PROFESSIONALS (Juni 2017)
Publisher : Penelitian dan Pengabdian Masyarakat Universitas Bina Insani

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (441.309 KB)

Abstract

Abstrak: Alumunium banyak digunakan di berbagai bidang, sehingga bermunculan perusahaan-perusahaan untuk mengolah aluminium. Semakin banyak usaha-usaha dalam bidang yang sama menimbulkan persaingan usaha. Untuk mengatasinya diperlukan strategi pemasaran yang baik. Salah satu penelitian yang banyak dilakukan yaitu menganalisis keranjang belanja untuk menentukan rekomendasi produk kepada pelanggan. Banyak peneliti menggunakan aturan asosiasi dengan apriori atau fp-growth dalam menganalisis keranjang belanja. Tetapi untuk dataset yang besar, hasil dari penerapan asosiasi menjadi kurang akurat. Oleh karena itu dataset yang besar akan disegmentasi dengan k-means agar dataset yang besar dibagi menjadi beberapa segmen yang lebih kecil. Hasil yang didapat dalam menganalisis keranjang belanja dengan menerapkan algoritma k-means dan algoritma fp-growth terbukti dapat meningkatkan akurasi dari 70% menjadi 90 %,80% dan 90%. Rekomendasi produk yang tepat dapat membantu dalam strategi pemasaran, khususnya dalam bidang promosi produk dan untuk membantu perencanaan produksi produk. Kata kunci: algoritma k-means, algoritma fp-growth, keranjang belanja, rekomendasi produk. Abstract: Aluminum is widely used in a variety of things, so that the emerging companies to process aluminum. More and more businesses in the same pose competition. To overcome this need a good marketing strategy. One study done analyzing Market Basket to determine product recommendations to customers. Many researchers associate a priori approach or fp-growth in analyzing market basket. But for a large dataset, the results of the application of the association becomes less accurate. Therefore, a large dataset to be segmented k-means that large datasets are divided into several smaller segments. Analyzing the results obtained in the market basket by applying k-means algorithm and fp-growth algorithm is shown to improve the accuracy of 70% to 90%, 80% and 90%. Appropriate product recommendations to assist in the marketing strategy,especially in the field of promotional products and to assist production planning. Keywords: k-means algorithm, fp-growth algorithm, market basket, product recommendation.
Implementasi Metode K-Means Clustering Dengan Davies Bouldin Index Pada Analisis Faktor Penyebab Perceraian Esty Purwaningsih; Ela Nurelasari
INFORMATION MANAGEMENT FOR EDUCATORS AND PROFESSIONALS : Journal of Information Management Vol 7 No 2 (2023): INFORMATION MANAGEMENT FOR EDUCATORS AND PROFESSIONALS (JUNI 2023)
Publisher : Penelitian dan Pengabdian Masyarakat Universitas Bina Insani

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51211/imbi.v7i2.2307

Abstract

Basically, divorce is the release of the marital relationship between partners. In this country, the number of divorce cases has reached its peak in the last six years. Many reasons can lead to divorce, such as financial problems, leaving a partner, domestic violence, or polygamy. In this study, the K-Means clustering method, which is assisted by the Davies Bouldin index, shows an advantage in solving clustering problems. Rapid Studio software is used to process secondary data. The data were tested with the values k=3, k=5, and k=7. The results showed that the k=3 group obtained a value of -0.419, the k=5 group obtained a value of -0.423, and the k=7 group obtained a value of -0.337. Thus, it can be concluded that the K-Means clustering method using the Davies Bouldin index has a value of k=7, which is the best cluster compared to the values of k=3 and k=5. The following clusters were generated from research conducted on the K-Means method with a value of k = 7 using the Davies Bouldin Index: Cluster_0 consists of "Provinsi Jawa Barat", Cluster_1 consists of "Kota Tasikmalaya", Custer_2 consists of "Cirebon" and "Indramayu", Cluster_3 consists of "Tasikmalaya", "Kuningan" and "Subang", Cluster_4 consists of "Bogor", "Cianjur", "Sumedang"
Co-Authors Ahmad Al Kaafi Algani, Ahmad Artika Surniandari Esty Purwaningsih Gumilang Al Farabi, Difa Junian Tamba, Hiskia Lisda Widiastuti Nisawa Dewi Paramita - AMIK BSI Jakarta Rifki Azmi - UBSI Taat Kuspriyono