Penerapan Algoritma k-Means Clustering untuk Pengelompokan Pembangunan Jalan pada Dinas Pekerjaan Umum dan Penataan Ruang

Kurniadi, Dede; Agustin, Yoga Handoko; Akbar, Hari Ilham Nur; Farida, Ida

doi:10.24246/aiti.v20i1.64-77

Cited by 5 publications

(3 citation statements)

References 11 publications

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“…Penelitian ini memberikan manfaat dalam kesiapan dan kemampuan siswa dalam pembelajaran online. Berdasarkan penelitian yang dilakukan oleh [4] Metode K-Means Clustering digunakan untuk mengelompokkan produk berdasarkan jumlah transaksi penjualan ke dalam dua kelompok atau cluster. Hasil dari analisa clustering ini dimanfaatkan untuk menjaga ketersediaan stok barang serta menentukan strategi guna meningkatkan penjualan produk.…”

Section: Pendahuluanunclassified

Penerapan K-Means Untuk Menganalisis Kepuasan Mahasiswa Terhadap Pembelajaran Daring

Sudrajat,

Irma Purnamasari,

Ali

2024

jati

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Presiden Jokowi menyatakan bahwa Indonesia telah keluar dari masa pandemi COVID-19 dan saat ini memasuki fase endemi. Pendidikan memegang peranan krusial dalam pembangunan masyarakat. Meskipun pembelajaran daring tetap menjadi pilihan utama pada fase endemi, khususnya di STMIK IKMI Cirebon, pengalaman mahasiswa terkait penggunaan platform daring seringkali menimbulkan perbedaan pendapat. Tujuan penelitian ini adalah untuk menyajikan informasi mengenai kepuasan mahasiswa terhadap pembelajaran daring, sebagai upaya evaluasi terhadap sistem pembelajaran tersebut. Dataset yang berhasil dikumpulkan melibatkan 150 responden dari mahasiswa Program Studi Teknik Informatika angkatan 2020 STMIK IKMI Cirebon. Metode penelitian menggunakan Algoritma K-Means klastering, dengan melakukan 5 iterasi untuk menentukan nilai K terbaik. Penentuan tersebut dilakukan dengan membandingkan nilai Davies-Bouldin Index, yang menunjukkan bahwa K2 merupakan nilai K terbaik. Dengan penerapan K-Means, ditemukan 2 klaster yang menggambarkan kepuasan mahasiswa terhadap 5 atribut, yaitu tangible, reliability, responsibility, assurance, dan empathy. Klaster 0 menunjukkan tingkat kepuasan tinggi sebesar 93%, sementara klaster 1 menunjukkan tingkat ketidakpuasan sebesar 7% pada setiap atribut yang digunakan. Penelitian ini diharapkan dapat memberikan wawasan yang lebih mendalam terkait evaluasi dan perbaikan sistem pembelajaran daring di STMIK IKMI Cirebon.

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Section: Pendahuluanunclassified

Penerapan K-Means Untuk Menganalisis Kepuasan Mahasiswa Terhadap Pembelajaran Daring

Sudrajat,

Irma Purnamasari,

Ali

2024

jati

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“…Algoritma K-Means dapat dipergunakan untuk menyelesaiakn data yang cukup besar dengan mudahnya, dimana proses pada algoritma K-Means didasarkan dengan euclidean distance yang artinya berdasarkan dengan jarak terdekat yang dimiliki antara objek yang satu dengan objek yang lainnya. Adapun tahapan proses yang dilakukan pada algoritma K-Means dapat dilihat seperti berikut [22], [25]-[27]: 1. Menentukan nilai K atau banyaknya cluster yang diinginkan dari hasil 2.…”

Section: Algoritma K-meansunclassified

Penerapan Algoritma Clustering K-Means Data Mining dalam Pengelompokan Mahasiswa Penerima Beasiswa

Setiawan,

Triayudi

2024

JoSYC

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Scholarships are a program intended to help students with economic problems. For universities, especially private universities, scholarships are an attraction or a campus promotional event to attract prospective students to register at the campus. The scholarships provided by the campus are independent scholarships which are based on funding from the university's foundation. This is very important to pay attention to, where apart from the achievements of prospective students, they must also consider their readiness or ability to participate in the learning process that takes place at the university. Therefore, paying attention to the grades obtained from prospective students is very important to pay attention to. Another problem is that the quota given by the foundation for scholarships is also limited, which is not covered by all prospective students who register or submit scholarship applications. In terms of determining or awarding scholarships, there is not yet a reference standard that is used for determination in the decision-making process, so scholarship awards are often misdirected. Mistakes in awarding scholarships are of course very detrimental to the campus. Therefore, this problem should require special attention and treatment. This problem can be easily resolved by finding a pattern of rules for accepting scholarships. Data mining is a process method that is widely used today, this is because data mining is very helpful in the decision making process. The process carried out by data mining is divided into several techniques such as Clustering. Clustering is a way to group new data. The K-Means algorithm carries out a solution process based on grouping, therefore the K-Means algorithm is classified as a clustering part of data mining. The aim of the research to be carried out is to assist in the process of grouping prospective students who will be prioritized in receiving scholarships. Based on the results of this research, it can later help to find students who are truly worthy of receiving the scholarship. The results obtained from the research are that there are 2 (clusters) obtained from the K-Means algorithm process. Where in cluster 1 there are 10 grouping data and in cluster 2 there are 5 grouping data.

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“…The K-Means algorithm has been applied in various studies to cluster data and evaluate the accuracy of the clustering process (Kurniadi et al, 2023), (Tambunan, 2021), (Listiani et al, 2019).Research conducted by Nurjanah (Nurjanah & Arifin, 2021), applied the K-Means method in analysing travel review data. K-Means, as a clustering algorithm, can help identify patterns and groups of words in reviews, enabling a deeper understanding of users' impressions of a place.…”

Section: Literature Reviewmentioning

confidence: 99%

Evaluation of Data Clustering Accuracy using K-Means Algorithm

Suraya,

Sholeh,

Lestari

2023

Int. J. Multidiscipline. Approach. Res. Sci.

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Data clustering is one of the methods in data science that is often used in data analysis. This method is used in making groupings from a collection of datasheets. Data clustering is done to find patterns or relationships between data. This research aims to evaluate the accuracy of data clustering using K-Means algorithm on wine datasheet. Wine datasheet has 13 features that describe the chemical characteristics of three types of wine. The clustering process must produce the best clustering evaluation metrics. The evaluation metric is done through comparison between the clustering results of K-Means algorithm with Davies Bouldin and Silhouette. The research steps involved data standardization, selection of the optimal number of clusters, and assessment of clustering accuracy. The research method uses KDD which consists of pre-processing, transformation, model building and model evaluation. Experimental results show that appropriate parameters and cluster initialization can improve clustering evaluation metrics. The clustering results show that the normalized datasheet produces evaluation metrics for Davies Bouldin 2 groups and Silhouette produces 3 groups. Before normalization, Davies Boulidin results in 7 groups and Silhouette results in 2 groups. In conclusion, this study produced different evaluation metrics between normalized and non-normalized datasheets. The selection of the number of groups chosen depends on the context of the data analysis performed and is selected into 3 groups which can be labelled "Superior Variety", the second group "Intermediate Variety" and the third group "Standard Variety".

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Penerapan Algoritma k-Means Clustering untuk Pengelompokan Pembangunan Jalan pada Dinas Pekerjaan Umum dan Penataan Ruang

Cited by 5 publications

References 11 publications

Penerapan K-Means Untuk Menganalisis Kepuasan Mahasiswa Terhadap Pembelajaran Daring

Penerapan K-Means Untuk Menganalisis Kepuasan Mahasiswa Terhadap Pembelajaran Daring

Penerapan Algoritma Clustering K-Means Data Mining dalam Pengelompokan Mahasiswa Penerima Beasiswa

Evaluation of Data Clustering Accuracy using K-Means Algorithm

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