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Odi Nurdiawan
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Journal : Jurnal Sains Teknologi Transportasi Maritim

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Analisis Dan Penerapan Algoritma K-Means Dalam Strategi Promosi Kampus Akademi Maritim Suaka Bahari: Tuti Hartati*, Odi Nurdiawan, Eko Wiyandi Hartati, Tuti; Nurdiawan, Odi; Wiyandi, Eko
Nama Jurnal Akmi Vol 3 No 1 (2021): Jurnal Sitektransmar Mei 2021
Publisher : LPPM AKMI SUAKA BAHARI CIREBON

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51578/j.sitektransmar.v3i1.30

Abstract

The process of accepting new cadet candidates at the Maritime Academy of Marine Sanctuary every year, produces a lot of data in the form of profiles of prospective cadets. The activity caused a large accumulation of data, it became difficult to identify prospective cadets. This research discusses the application of data mining to generate profiles that have similar attributes. One of the data mining techniques used to identify a group of objects that have the same characteristics is Cluster Analysis. The data clustering method is divided into one or more clusters that have the same characteristics called K-means. The method that the author uses is knowledge discovery in databases (KDD) consisting of Data, Data Cleaning, Data transformation, Data mining, Pattern evolution, knowledge. Implementation of K-means Clustering process using Rapid Miner. Attributes used by NIT, Level, Name, Student Status, Type of Registration, Gender, Place of Birth, Date of Birth, Religion, School Origin, School Origin Department, Religion, GPA, Subdistrict, District/ City, Province. Returns the number of clusters 30 (k=30). From the research results based on davies bouldin test on K-means algorithm resulted in the closest value of 0 is k = 29 with Davies bouldin: 0.070, with the most cluster member distribution in cluster 16 containing cluster members 115 items.
Analisis dan Penerapan Algoritma K-Means Dalam Strategi Promosi Kampus Akademi Maritim Suaka Bahari Tuti Hartati; Odi Nurdiawan; Eko Wiyandi
Nama Jurnal Akmi Vol 3 No 1 (2021): Jurnal sitektransmar Mei 2021
Publisher : LPPM AKMI SUAKA BAHARI CIREBON

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51578/j.sitektransmar.v3i1.31

Abstract

Proses penerimaan calon mahasiswa baru di Akademi Maritim Suaka Bahari setiap tahun menghasilkan data yang sangat banyak berupa profil calon mahasiswa. Kegiatan tersebut menimbulkan penumpukan data dalam jumlah besar, hal ini menjadi kesulitan untuk melakukan identifikasi terhadap calon mahasiswa. Penelitian ini membahas tentang penerapan data mining untuk menghasilkan profil yang memiliki kemiripan atribut yang sama. Salah satu teknik data mining yang digunakan untuk mengidentifikasi suatu kelompok dari objek yang memiliki karakteristik yang sama adalah Analisis Cluster. Metode clustering data dibagi kedalam bentuk satu atau lebih cluster/kelompok yang memiliki karakteristik sama disebut K-means. Metode yang penulis gunakan adalah knowledge discovery in databases (KDD) yang terdiri dari Data, Data Cleaning, Data transformation, Data mining, Pattern evolution, knowledge. Implementasi proses K-means Clustering menggunakan Rapid Miner. Atribut yang digunakan NIT, Tingkat, Nama, Status Mahasiswa, Jenis Pendaftaran, Jenis Kelamin, Tempat Lahir, Tgl Lahir, Agama, Asal Sekolah, Jurusan Asal Sekolah, Agama, IPK, Kecamatan, Kab/Kota, Provinsi. Menghasilkan jumlah cluster 30 (k=30). Dari hasil penelitiam berdasarkan uji davies bouldin pada algoritma K-means menghasilkan nilai paling mendekati 0 adalah k = 29 dengan Davies bouldin: 0.070, dengan persebaran anggota cluster terbanyak ada pada cluster 16 berisi anggota cluster 115 items.
Co-Authors Abdul Robi Padri Ade Bani Riyan Ade Irma Purnamasari Ade Irma Purnamasari Ade Irma Purnamasari Ade Irma Purnamasari Ade Rizki Rinaldi Adisty Tri Putra Agis Maulana Robani Agus Surip Ahmad Faqih Ahmad Faqih Ahmad Faqih Ahmad Zam Zami Ananda Rafly Andi Setiawan Anwar Musaddad Aria Pratama Arif Rinaldi Dikananda Bambang Irawan Basysyar, Fadhil Muhammad Cep Lukman Rohmat Cep Lukman Rohmat Dias Bayu Saputra Dikananda, Arif Rinaldi Dilla Eka Lusiana Dita Rizki Amalia Dwi Teguh Afandi Edi Tohidi Edi Wahyudin Eko Wiyandi Fadhil M. Basysyar Fadrin Helmi Febriansyah, Feggy Fidya Arie Pratama Fidya Arie Pratama Gifthera Dwilestari Haidah Putri Haidar Fakhri Heliyanti Susana Hira Wahyuni Azizah Husein Subandi Ibnu Ubaedila Irfan Ali Irfan Ali Irfan Ali, Irfan Irma Purnamasari, Ade Irvandi Jaelani Sidik Julia Eka Yanti Kaslani Khamim Surya Hadi Kusuma Al Atros Kurniawan Fajar Abdulloh Lukmanul Hakim Martanto . Medina Aprilia Putri Melia Melia Melisa Hikari Mia Fijriani Mohammad Rosihin Amar Muchamad Sobri Sungkar, Muchamad Sobri Mulyana Mulyana Mulyawan Mulyawan Nana Suarna Nana Suarna Nana Suarna Nanda Permatasari Nining Rahaningsih Noval Salim Nur Atikah Nurhadiansyah Nurhadiansyah Nurhadiansyah, Nurhadiansyah Pratama, Fidya Arie Pratiwi, Fitriyani Purnamasari, Ade Irma Putriyana Putriyana R, Nining Rifki Nurcholis Rini Astuti Riyan Suryatana Rohmat, Cep Lukman Rudi Hartono Ruli Herdiana Ruli Herdiana Rully Pramudita Saeful Anwar Saeful Anwar Saeful Anwar, Saeful Saepul Hadi Salsa Billa Agistina Siti Aisyah Tio Prasetiya Tio Prasetya TOMAS TOMAS Topan Hadi Tuti Hartati Tuti Hartati Wiyandi, Eko Yudhistira Arie Wijaya