Optimalisasi Pelayanan Perpustakaan terhadap Minat Baca Menggunakan Metode K-Means Clustering

Knowledge Discovery in Database (KDD) is a structured analysis process aimed at getting new and correct information, finding patterns from complex data, and being useful. Data mining is at the core of the KDD process. Clustering is a data mining method that is suitable for optimizing library services because it can cluster books effectively and efficiently, with the K-Means algorithm data can be clustered and information from each centroid value of each cluster. Library services can optimize the placement of books so that students can quickly find books according to their reading interest more effectively and can be attracted to other books because they are in one grouping. Meanwhile, the library can prioritize the procurement of the next book. Optimization of library services in the cluster using the K-Means method. Clustering interest in reading has the criteria for the number of books available, borrowed books, and the length of time the books are borrowed. The book data is clustered into 3, namely very interested, in demand, and less desirable. After doing the calculation process from 40 samples of book types, it resulted in 6 iterations, and the final results were 3 clustering, namely cluster 1 of 4 books that were of great interest, cluster 2 of 20 books that were of interest, and cluster 3 of 16 books that were less desirable. This research can be used as a recommendation reference for optimizing library services both for the layout and procurement of books by prioritizing the types of books that are of great interest.

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PENERAPAN ALGORITMA K-MEANS PADA SISWA BARU SEKOLAHMENENGAH KEJURUAN UNTUK CLUSTERING JURUSAN

InfoTekJar (Jurnal Nasional Informatika dan Teknologi Jaringan) ◽

10.30743/infotekjar.v1i2.70 ◽

2017 ◽

Vol 1 (2) ◽

pp. 100-105 ◽

Cited By ~ 3

Author(s):

Fauziah Nur ◽

M. Zarlis ◽

Benny Benyamin Nasution

Keyword(s):

Data Mining ◽

Text Mining ◽

Knowledge Discovery ◽

Naive Bayes ◽

Naïve Bayes ◽

Nearest Neighbour ◽

Knowledge Discovery In Database ◽

Cluster 2

Data mining merupakan teknik pengolahan data dalam jumlah besar untuk pengelompokan.Teknik ini digunakan dalam proses Knowledge Discovery in Database (KDD). Teknik tersebut mempunyai beberapa metode dalam pengelompokannya Naïve-Bayes dan Nearest Neighbour, pohon keputusan (KD-Tree), ID3, K-Means, text mining dan dbscan. Dalam hal ini penulis mengelompokan data siswa baru sekolah menengah kejuruan tahun ajaran 2014/2015. Pengelompokan tersebut berdasarkan kriteria – kriteria data siswa. Pada penelitian ini, penulis menerapkan algoritma K-Means Clustering untuk pengelompokan data siswa baru sekolah menengah kejuruan. Dalam hal ini, pada umumnya untuk memamasuki jurusan hanya disesuaikan dengan nilai siswa saja namun dalam penelitian ini pengelompokan disesuaikan kriteria – kriteria siswa seperti penghasilan orang tua, tanggungan anak orang tua dan nilai tes siswa. Penulis menggunakan beberapa kriteria tersebut agar pengelompokan yang dihasilkan menjadi lebih optimal. Tujuan dari pengelompokan ini adalah terbentuknya kelompok jurusan pada siswa yang menggunakan algoritma K-Means clustering. Hasil dari pengelompokan tersebut diperoleh tiga kelompok yaitu kelompok tidak lulus, kelompok rekayasa perangkat lunak dan kelompok teknik komputer jaringan. Terdapat pusat cluster dengan Cluster-1=1.4;2.2;2.2, Cluster-2= 2.28;1.64;4 dan Cluster-3=5;2;6. Pusat cluster tersebut didapat dari beberapa iterasi sehingga mengahasilakan pusat cluster yang optimal.

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Penerapan Data Mining Untuk Prediksi Penjualan Mobil Menggunakan Metode K-Means Clustering

Jurnal Nasional Komputasi dan Teknologi Informasi (JNKTI) ◽

10.32672/jnkti.v3i3.2428 ◽

2020 ◽

Vol 3 (3) ◽

pp. 187-201

Author(s):

Sufajar Butsianto ◽

Nindi Tya Mayangwulan

Keyword(s):

Data Mining ◽

Clustering Data ◽

Cluster 2

Penggunaan mobil di Indonesia setiap tahunnya selalu meningkat dan membuat perusahaan otomotif berlomba-lomba dalam peningkatan penjualannya. Tujuan dari penelitian ini untuk mengelompokan data penjualan kedalam sebuah cluster dengan metode Data Mining Algoritma K-Means Clustering. Data Penjualan nantinya akan dikelompokan berdasarkan kemiripan data tersebut sehingga data dengan karakteristik yang sama akan berada dalam satu cluster. Atribut yang digunakan adalah brand dan penjualan. Cluster yang terbentuk setelah dilakukan proses K-Means Clustering terbagi menjadi tiga cluster yaitu Cluster 0 jumlah anggota 235 dengan presentase 26% dikategorikan Laris, Cluster 1 jumlah anggota 604 dengan presentase 67% dikategorikan Kurang Laris, dan Cluster 2 jumlah angota 61 dengan presentase 7% dikategorikan Paling Laris, dari proses clustering diatas dapat diperoleh validasi DBI (Davies Bouldin Index) dengan nilai 0,341

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Implementation of data mining as a support of business application strategy

Journal of Applied Information, Communication and Technology ◽

10.33555/ejaict.v5i1.49 ◽

2018 ◽

Vol 5 (1) ◽

pp. 47-55

Author(s):

Florensia Unggul Damayanti

Keyword(s):

Data Mining ◽

Random Forest ◽

Business Strategy ◽

Input Parameter ◽

Data Mining Algorithm ◽

Complex Data ◽

Business Decision ◽

Marketing Department ◽

Business Application ◽

Complex Data Sets

Data mining help industries create intelligent decision on complex problems. Data mining algorithm can be applied to the data in order to forecasting, identity pattern, make rules and recommendations, analyze the sequence in complex data sets and retrieve fresh insights. Yet, increasing of technology and various techniques among data mining availability data give opportunity to industries to explore and gain valuable information from their data and use the information to support business decision making. This paper implement classification data mining in order to retrieve knowledge in customer databases to support marketing department while planning strategy for predict plan premium. The dataset decompose into conceptual analytic to identify characteristic data that can be used as input parameter of data mining model. Business decision and application is characterized by processing step, processing characteristic and processing outcome (Seng, J.L., Chen T.C. 2010). This paper set up experimental of data mining based on J48 and Random Forest classifiers and put a light on performance evaluation between J48 and random forest in the context of dataset in insurance industries. The experiment result are about classification accuracy and efficiency of J48 and Random Forest , also find out the most attribute that can be used to predict plan premium in context of strategic planning to support business strategy.

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Analisis Data Pembayaran Kredit Nasabah Bank Menggunakan Metode Data Mining

Jurnal ULTIMA InfoSys ◽

10.31937/si.v4i1.238 ◽

2013 ◽

Vol 4 (1) ◽

pp. 18-27

Author(s):

Ira Melissa ◽

Raymond S. Oetama

Keyword(s):

Data Mining ◽

Knowledge Discovery ◽

Knowledge Discovery In Database

Data mining adalah analisis atau pengamatan terhadap kumpulan data yang besar dengan tujuan untuk menemukan hubungan tak terduga dan untuk meringkas data dengan cara yang lebih mudah dimengerti dan bermanfaat bagi pemilik data. Data mining merupakan proses inti dalam Knowledge Discovery in Database (KDD). Metode data mining digunakan untuk menganalisis data pembayaran kredit peminjam pembayaran kredit. Berdasarkan pola pembayaran kredit peminjam yang dihasilkan, dapat dilihat parameter-parameter kredit yang memiliki keterkaitan dan paling berpengaruh terhadap pembayaran angsuran kredit. Kata kunci—data mining, outlier, multikolonieritas, Anova

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Preprocessing Profiling Model for Visual Analytics

10.5753/sibgrapi.est.2020.12991 ◽

2020 ◽

Author(s):

Alessandra Maciel Paz Milani ◽

Fernando V. Paulovich ◽

Isabel Harb Manssour

Keyword(s):

Data Mining ◽

Data Analysis ◽

Visual Analytics ◽

Data Preprocessing ◽

Interview Study ◽

Raw Data ◽

Important Stage ◽

Analysis Process

Analyzing and managing raw data are still a challenging part of the data analysis process, mainly regarding data preprocessing. Although we can find studies proposing design implications or recommendations for visualization solutions in the data analysis scope, they do not focus on challenges during the preprocessing phase. Likewise, the current Visual Analytics processes do not consider preprocessing an equally important stage in their process. Thus, with this study, we aim to contribute to the discussion of how we can use and combine methods of visualization and data mining to assist data analysts during the preprocessing activities. To achieve that, we introduce the Preprocessing Profiling Model for Visual Analytics, which contemplates a set of features to inspire the implementation of new solutions. In turn, these features were designed considering a list of insights we obtained during an interview study with thirteen data analysts. Our contributions can be summarized as offering resources to promote a shift to a visual preprocessing.

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Analisis Data Sebaran Bandwidth Menggunakan Algoritma Dbscan Untuk Menentukan Tingkat Kebutuhan Bandwidth Di Kabupaten Purwakarta

Rabit : Jurnal Teknologi dan Sistem Informasi Univrab ◽

10.36341/rabit.v5i2.1388 ◽

2020 ◽

Vol 5 (2) ◽

pp. 130-137

Author(s):

Teguh Iman Hermanto ◽

Yusuf Muhyidin

Keyword(s):

Data Mining ◽

Data Selection ◽

Cluster 2

Berdasarkan data yang tercatat pada tahun 2018 terdapat 43 organisasi perangkat daerah di kabupaten Purwakarta yang sudah mendapatkan bandwidth internet. Setiap organisasi perangkat daetah yang telah mendapatkan bandwidth mempunyai tingkat kebutuhan yang berbeda – beda ,namun saat ini jumlah pembagian bandwidth dan tingkat kebutuhan belum dapat dikelompokan. Tujuan dari penelitian ini untuk menetukan tingkat kebutuhan bandwidth di Purwakarta dengan cara melakukan analisis data mining terhadap data yang ada menggunakan algoritma DBSCAN sehingga akan terbentuk cluster yang yang dibagi berdasarkan tingkat kebutuhan. Pada penelitian ini metode analisis yang digunakan yaitu SEMMA (Sample, Explore, Modify, Model, Assess) tahapan SEMMA meliputi Data Selection, Pre-processing / cleaning, Transformation, Data Mining dan Assess / Evaluation. Hasil dari analisis menggunakan nilai minpts = 5 dan nilai epsilon = 3. Cluster yang terbentuk yaitu sebanyak 2 cluster, cluster 1 terdapat sebanyak 15 organisasi perangkat daerah dengan tingkat kebutuhan bandwidth rendah dan cluster 2 terdapat 21 organisasi perangkat daerah dengan tingkat kebutuhan bandwidth sedang, dan Noise terdapat 7 organisasi perangkat daerah dengan kebutuhan bandwidth yang terlalu tinggi.

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Clustering Fasilitas Kesehatan Berdasarkan Kecamatan Di Karawang Dengan Algoritma K-Means

BINA INSANI ICT JOURNAL ◽

10.51211/biict.v8i1.1488 ◽

2021 ◽

Vol 8 (1) ◽

pp. 83

Author(s):

Bagus Muhammad Islami ◽

Cepy Sukmayadi ◽

Tesa Nur Padilah

Keyword(s):

Data Mining ◽

Developing Countries ◽

Data Processing ◽

Health Problems ◽

Health Facilities ◽

Microsoft Excel ◽

Two Factors ◽

Clustering Data ◽

Index Value ◽

Cluster 2

Abstrak: Masalah kesehatan yang ada di dalam masyarakat terutama di negara- negara berkembang seperti Indonesia dipengaruhi oleh dua faktor yaitu aspek fisik dan aspek non fisik. Berdasarkan data yang diperoleh dari karawangkab.bps.go.id data dibagi menjadi 3 cluster yaitu sedikit, sedang dan terbanyak. Algoritma yang digunakan adalah K-Means cluster yang diimplementsikan menggunakan Microsoft Excel dan Rapidminer Studio. Hasil pengolahan data fasilitas kesehatan di karawang menghasilkan 3 cluster dengan cluster 1 yang mempunyai fasilitas kesehatan sedikit sebanyak 23 kecamatan, cluster 2 yang mempunyai fasilitas kesehatan sedang sebanyak 5 kecamatan dan cluster 3 yang mempunyai fasilitas kesehatan terbanyak terdapat 2 kecamatan. Kinerja yang dihasilkan dari algoritma K-means menghasilkan nilai Davies Boildin Index sebesar 0,109. Kata kunci: clustering, data mining, fasilitas kesehatan, K-Means. Abstract: Health problems that exist in society, especially in developing countries like Indonesia, are built by two factors, namely physical and non-physical aspects. Based on data obtained from karawangkab.bps.go.id the data is divided into 3 clusters, namely the least, medium and the most. The algorithm used is the K-Means cluster which is implemented using Microsoft Excel and Rapidminer Studio. The results of data processing of health facilities in Karawang produce 3 clusters with cluster 1 which has 23 sub-districts of health facilities, cluster 2 which has medium health facilities as many as 5 districts and cluster 3 which has the most health facilities in 2 districts. The performance resulting from the K-means algorithm results in a Davies Boildin Index value of 0.109. Keywords: clustering, data mining, health facilities, K-Means.

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Designing Public Library of Binjai City by Metaphorical Architecture

International Journal of Architecture and Urbanism ◽

10.32734/ijau.v3i2.3738 ◽

2019 ◽

Vol 3 (2) ◽

pp. 232-240

Author(s):

Imam Faisal Pane ◽

Febrina

Keyword(s):

Public Library ◽

Building Construction ◽

Library Services ◽

Concept Design ◽

Visual Form ◽

The Public ◽

Physical Form ◽

Alternative Approaches ◽

Reading Interest

The lack of public reading interest makes people rarely come to visit the public library. The public library of The Binjai city as a media to increase people's knowledge, in fact, that still using traditional planning and structuring and has a less comfortable atmosphere. The Binjai city public library requires to improve its facilities and infrastructures related to the standards of a public library, so that possible to increase people's interest in visiting the library. The designer can make several alternative approaches to design an object, one of that is a metaphorical approach. Choosing of metaphor theme is a theme approachment by taking the book as a visual form that applicated to an element of the building. The strong Malay culture of Binjai city as a local heritage can be applicated in designing the public library of Binjai City. The concept design of the Binjai City Public Library is applied to accommodate library services and activities. By taking a representation physical form of the book as part of the building construction, giving all facilities and services can be functioned to attract more people's attention to visit the library.

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Goods Stock Management using the K-Means Algorithm Method

Jurnal Teknologi ◽

10.35134/jitekin.v9i2.15 ◽

2020 ◽

Vol 10 (1) ◽

pp. 22-45

Author(s):

Dhio Saputra

Keyword(s):

Data Mining ◽

Data Processing ◽

Test Results ◽

Stock Management ◽

Clustering Method ◽

Sales Data ◽

Using Data ◽

Cluster 2

The grouping of Mazaya products at PT. Bougenville Anugrah can still do manuals in calculating purchases, sales and product inventories. Requires time and data. For this reason, a research is needed to optimize the inventory of Mazaya goods by computerization. The method used in this research is K-Means Clustering on sales data of Mazaya products. The data processed is the purchase, sales and remaining inventory of Mazaya products in March to July 2019 totaling 40 pieces. Data is grouped into 3 clusters, namely cluster 0 for non-selling criteria, cluster 1 for best-selling criteria and cluster 2 for very best-selling criteria. The test results obtained are cluster 0 with 13 data, cluster 1 with 25 data and cluster 2 with 2 data. So to optimize inventory is to multiply goods in cluster 2, so as to save costs for management of Mazayaproducts that are not available. K-Means clustering method can be used for data processing using data mining in grouping data according to criteria.

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