Metode Boost-K-means untuk Clustering Puskesmas berdasarkan Persentase Bayi yang Diimunisasi

Dinas Kesehatan Kabupaten/Kota adalah satuan kerja pemerintahan daerah kabupaten/kota yang bertanggung jawab menyelenggarakan urusan pemerintahan dalam bidang kesehatan di kabupaten/kota. Pelayanan kesehatan adalah upaya yang diberikan oleh Puskesmas kepada masyarakat, mencakup perencanaan, pelaksanaan, evaluasi, pencatatan, pelaporan, dan dituangkan dalam suatu sistem. Pada penelitian ini, akan digunakan data persentase bayi yang diimunisasi yang merupakan salah satu layanan dari Puskesmas. Pelayanan imunisasi ini merupakan pelayanan imunisasi dasar meliputi BCG, DPT/HB1-3, polio 1-4 dan campak. Data persentase bayi yang diimunisasi belum memiliki pengelompokan sehingga pada penelitian ini akan diterapkan metode clustering untuk melakukan pengelompokan Puskesmas berdasarkan persentase bayi yang diimunisasi. Data persentase bayi dari masing-masing Puskesmas dijadikan data uji yang akan diterapkan pada proses multi-clustering dengan metode boost-clustering. Output dari penerapan metode ini akan dibandingkan dengan output metode clustering dasar k-means, hasil clustering akan diukur menggunakan metode silhouette index. Evaluasi menggunakan metode silhouette index dilakukan pada dataset puskesmas. Analisis dilakukan dengan melihat hasil evauasi dataset yang sudah diimplementasikan kedalam algoritma cluster dasar k-means dan algoritma multiclustering boost-k-means. Berdasarkan hasil evaluasi, diperoleh nilai silhouette index 0,798102756 untuk k-means dan 0,789901932 untuk boost-k-means, dengan ini algoritma yang diusulkan memiliki kualitas hasil clustering minimal sama atau lebih baik dari single clustering k-means dengan jumlah iterasi yang lebih sediki

Solar Energy Validation for Strategic Investment Planning via Comparative Data Mining Methods: An Expanded Example within the Cities of Turkey

Energy supply together with the data management is one of the key challenges of our century. Specifically, to decrease the climate change effects as energy requirement increases day by day poses a serious dilemma. It can be adequately reconciled with innovative data management in (renewable) energy technologies. The new environmental-friendly planning methods and investments that are discussed by researchers, governments, NGOs, and companies will give the basic and most important variables in shaping the future. We use modern data mining methods (SOM and K-Means) and official governmental statistics for clustering cities according to their consumption similarities, the level of welfare, and growth rate and compare them with their potential of renewable resources with the help of Rapid Miner 5.1 and MATLAB software. The data mining was chosen to make the possible secret relations visible within the variables that can be unpredictable at first sight. Here, we aim to see the success level of the chosen algorithms in validation process simultaneously with the utilized software. Additionally, we aim to improve innovative approach for decision-makers and stakeholders about which renewable resource is the most suitable for an exact region by taking care of different variables at the same time

Enhancement of spam detection mechanism based on hybrid k-mean clustering and support vector machine

Spam e-mails are considered a serious violation of privacy. It has become costly and unwanted communication. Support vector machine (SVM) has been widely used in e-mail spam classification, yet the problem of dealing with huge amounts of data results in low accuracy and time consumption as many researches have demonstrated. This paper proposes a hybrid approach for e-mail spam classification based on the SVM and -mean clustering. The experiment of the proposed approach was carried out using spambase standard dataset to evaluate the feasibility of the proposed method. The result of this combination led to improve SVM and accordingly increase the accuracy of spam classification. The accuracy based on SVM algorithm is 96.30 % and the proposed hybrid SVM algorithm with -mean clustering is 98.01 %. In addition, experimental results on spambase datasets showed that improved SVM (ESVM) significantly outperforms SVM and many other recent spam classification methods