Penerapan Algoritma K-Medoids dalam Menentukan Daerah Rawan Banjir di Kabupaten Karawang

Flood disasters often occur during the rainy season. Karawang is one area that is often flooded. Based on the risk index from BNPB, the flood disaster in Karawang affected 84% of the community, so efforts need to be made to reduce and overcome flood disasters. These problems are the beginning of efforts that need to be known which areas are prone to flooding. Therefore, this study aims to determine flood-prone areas in Karawang as an initial effort in tackling flood disasters. The research was conducted by classifying flood-prone areas using the k-medoids algorithm. K-Medoids uses the partition clustering method to group lists and objects into a number of clusters. This algorithm uses objects in a collection of objects that represent a cluster. The attributes used are flood-causing factors such as rainfall, elevation (soil height), population density, and distance to the river. The results of the study found three potential floods, namely low, medium, and high. There are 1 sub-district with low flood potential, 24 sub-districts with moderate flood potential, and 5 sub-districts with high flood potential. The test results using the silhouette coefficient get a value of 0.370.

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Segmentasi Buah Mangga Menggunakan MLE dan GMM Sebagai Klasterisasi Pixel

Jurnal Algoritme ◽

10.35957/algoritme.v1i1.435 ◽

2020 ◽

Vol 1 (1) ◽

pp. 57-67

Author(s):

Steven Pranata ◽

Derry Alamsyah

Keyword(s):

Maximum Likelihood ◽

Maximum Likelihood Estimation ◽

Error Rate ◽

Data Distribution ◽

Likelihood Estimation ◽

Clustering Method ◽

Number Of Clusters ◽

Silhouette Coefficient ◽

Good Cluster ◽

Cluster Quality

Segmentation divides an image into parts or segments that are simpler and more meaningful so they can be analyzed further. The solution that has been found is using the Maximum Likelihood Estimation (MLE) method and the Gausian Mixture Model. GMM is a clustering method. GMM is a function consisting of several Gaussian, each identified by k ∈ {1, ..., K}, where K is the number of clusters in our dataset. Maximum Likelihood estimation is a technique used to find a certain point to maximize a function, this technique is very widely used in estimating a data distribution parameter. Tests carried out using mango images with 10 different backgrounds. GMM will cluster the pixels of the mango image to produce averages and covariates. Then the average and covariance will be used by MLE to qualify each pixel of the mango image. In this study GMM and MLE tests were carried out to segment mangoes. Based on the results obtained, the GMM and MLE methods have an error rate of 13.07% for 3 clusters, 8.06% for 4 clusters, and 6.63% for 5 clusters and good cluster quality with silhouette coefficient values of 0.37686 for 3 clusters, 0.29577 for 4 clusters, and 0.26162 for 5 clusters.

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PENINGKATAN KINERJA ALGORITMA K MEANS DENGAN MENGGUNAKAN PARTICLE SWARM OPTIMIZATION DALAM PENGELOMPOKAN DATA PENYEDIAAN AKSES

Electro Luceat ◽

10.32531/jelekn.v6i2.245 ◽

2020 ◽

Vol 6 (2) ◽

pp. 213-227

Author(s):

ARI YUNUS HENDRAWAN

Keyword(s):

Water Quality ◽

Pso Algorithm ◽

Selection Method ◽

Test Results ◽

Community Based ◽

Swarm Optimization ◽

Number Of Clusters ◽

The Third ◽

A Value ◽

Selection Of

Water is one of the things that plays a very important role in human survival, because the Indonesian government has a community-based water supply and sanitation (PAMSIMAS) program, so that all the programs run well need a regional status grouping technique in this thesis. with the K-means algorithm. K-means is a partition algorithm that aims to divide the data into the specified number of clusters, the results of the K means algorithm depend on the selection of the initial klater center but problems that often occur when selecting the initial centroid are randomly drawn from the solution. from the grouping is not quite right. To overcome this problem the author wants to use the PSO algorithm in the initial centroid selector for the K-means algorithm, in this study also compared the selection of the first 3 centroids according to random, second according to government standards the value of high, medium and low drinking water quality then the third method proposed by the PSO algorithm was then tested with Davies Bouldin Index. From the test results, the K-means method with the selection of random initial centroid with a value of 0.208856082, the K-means method with the selection of centroids in accordance with government standards about SAM conditions of 0.280077 and the best selection method is K-means PSO 0, 08383. So testing the PAMSIMAS data using K-means PSO found that the method was more optimal.

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Kemampuan Lulusan Sekolah Tinggi Pariwisata Ampta Dalam Menghadapi Persaingan Di Dunia Kerja

Media Wisata ◽

10.36276/mws.v16i1.266 ◽

2021 ◽

Vol 16 (1) ◽

Author(s):

Sri Larasati

Keyword(s):

Communication Skill ◽

Total Population ◽

Sampling Technique ◽

Regression Equations ◽

Test Results ◽

A Value ◽

The Face ◽

Job Competition ◽

Sampling Approach

The purpose of this research is to know wtether knowledge, skill, responsiveness and communication skill influence in job competition for graduate student from Sekolah Tinggi Pariwisata AMPTA. The population in this study are student pf Sekolah Tinggi Pariwisata SMPTA that graduated in 2013 until 2016, the total population in this studt is 702 and 200 respondents who have worked taken as samples, samples were taken with non probability sampling technique and accidental sampling approach. Sampling approach using regression equations obtained results that through test results F (regression analysis jointly) may note that the value of F = 147.104 count with the significance of 0.000 < 0.05. The test results of the test F reinforced with testing the determination of the coefficient that shows the value of 74.6%) (0.746. From Test t (partially) can result in variable knowledge with a value of t = 3,173 and significance of the value 0002 < 0.05, variable skill with a value of t = 2,949 and value the significance of 0.004 < 0.05, variable attitude with a value of t = 2,688 and value the significance of 0.008 < 0.05, variable communication skill with a value of t = value and significance of 4,497 0000 < 0.05. From the above results can be known that the hypothesis posed was not proven in other words that all of these variables can be said to have a positive and significant effect simultaneously as well as partial toward the ability of STP graduates AMPTA in the face of competition in the world of work

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Customer-Based Brand Equity, Product Quality, Price and Customer Loyalty in Electronic Industry of Refrigerator

International Journal of Science, Technology & Management ◽

10.46729/ijstm.v2i6.394 ◽

2021 ◽

Vol 2 (6) ◽

pp. 2143-2154

Author(s):

Bambang Suwarno

Keyword(s):

Product Quality ◽

Multiple Regression ◽

Brand Equity ◽

Customer Loyalty ◽

Test Results ◽

A Value ◽

Library Research ◽

Product Service ◽

Quality Product ◽

Survey Results

The purpose of this study was to analyse the extent of Consumer-Based Brand Equity, Quality Product and Price affects customer loyalty at PT. Panasonic Gobel Indonesia Branch Medan and to compare the theories. The author examined by collecting data using a variety of library research, questionnaires are structured as an instrument to obtain quantitative data with Likert-scale systems are distributed to 68 respondents. Data was then compiled then processed and analysed using multiple regression analysis techniques (multiple regression) through the test of validity, test of reliability and test of classic assumptions. By using the tools of Statistical Product Service Solutions program (SPSS version 22.0). From the survey results revealed that the influential Consumer-Based Brand Equity, Product Quality and Price simultaneously with the results Fhitung 18,457 > Ftabel 2,75 and probability sig. 0,000 < 0,05 then reject ho (accept hi) that the Consumer-Based Brand Equity, Product Quality, Price simultaneously significantly influence Customer Loyalty PT. Panasonic Gobel Indonesia Branch Medan. The Consumer-Based Brand Equity there is for the partial influence on the Customer Loyalty with a value of thitung 3,980 > ttabel 1,997 and sig. 0,000 < 0,05, the Product Quality there is for the partial influence on the Customer Loyalty with a value of thitung 3,874 > ttabel 1,997 and sig. 0,000 < 0,05 and the Price there is for the partial influence on the Customer Loyalty with a value of thitung 2,483 > ttabel 1,997 and sig. 0,002 < 0,05. From the test results are also obtained by the value of determination R square is 0,564 or 56,4%. It is claimed that Consumer-Based Brand Equity, Product Quality and Price is an excellent way to improve Customer Loyalty and also the hypothesis that the authors proposed previously received.

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TCLUST: Trimming Approach of Robust Clustering Method

Malaysian Journal of Fundamental and Applied Sciences ◽

10.11113/mjfas.v8n4.154 ◽

2014 ◽

Vol 8 (4) ◽

Author(s):

Muhamad Alias Md. Jedi ◽

Robiah Adnan

Keyword(s):

Clustering Algorithm ◽

Likelihood Function ◽

R Package ◽

Clustering Method ◽

Number Of Clusters ◽

Robust Clustering ◽

Scatter Matrix ◽

Group Assignment ◽

Log Likelihood ◽

Clustering Approach

TCLUST is a method in statistical clustering technique which is based on modification of trimmed k-means clustering algorithm. It is called “crisp” clustering approach because the observation is can be eliminated or assigned to a group. TCLUST strengthen the group assignment by putting constraint to the cluster scatter matrix. The emphasis in this paper is to restrict on the eigenvalues, λ of the scatter matrix. The idea of imposing constraints is to maximize the log-likelihood function of spurious-outlier model. A review of different robust clustering approach is presented as a comparison to TCLUST methods. This paper will discuss the nature of TCLUST algorithm and how to determine the number of cluster or group properly and measure the strength of group assignment. At the end of this paper, R-package on TCLUST implement the types of scatter restriction, making the algorithm to be more flexible for choosing the number of clusters and the trimming proportion.

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HUBUNGAN ACCESS BLOCK DENGAN PERBURUKAN KONDISI PASIEN DI INSTALASI GAWAT DARURAT RSUD ULIN BANJARMASIN

DINAMIKA KESEHATAN JURNAL KEBIDANAN DAN KEPERAWATAN ◽

10.33859/dksm.v11i1.528 ◽

2020 ◽

Vol 11 (1) ◽

pp. 110-119

Author(s):

Julianto Julianto ◽

Izma Daud ◽

Sari Milyati

Keyword(s):

Research Method ◽

Sampling Technique ◽

P Value ◽

Test Results ◽

Hospital Emergency ◽

Cross Sectional ◽

Analytical Design ◽

A Value ◽

Relationship Of ◽

The Relationship

Abstrak Latar Belakang :Access block adalah situasi dimana pasien yang mengalami lama rawat di IGD 6 karena kurangnya akses ke ruang rawat inap dan kepadatan jumlah pasien yang tidak terkendali, dampaknya ditemukan bahwa semakin lama access block semakin tinggi pula perburukan pasien dalam 24 jam.Tujuan : ini bertujuan untuk mengetahui hubungan access block dengan perburukan kondisi pasien di Instalasi Gawat Darurat RSUD Ulin Banjarmasin.Metode :Penelitian menggunakan desain analitik dengan pendekatan cross sectional. Jumlah sampel yang diambil berjumlah 40 orang dengan teknik pengambilan accidental sampling. Analisis data melalui uji spearman rank.Hasil : Hasil uji dipapatkan nilai p hitung 0,000 (0,05) ada hubungan antara access block dengan perburukan kondisi pasien di instalasi gawat darurat RSUD Ulin Banjarmasi.Simpulan: Berdasarka hasil penelitian Ada hubungan antara access block dengan perburukan kondisi pasien di Instalasi Gawat Darurat RSUD Ulin Banjarmasin dengan nilai P= 0,000 nilai α = 0,05 dengan nilai koefisien korelasi 0,588 Kata kunci: Access Block, Perburukan Pasien. Abstract Background :Access block is a situation where patients who experience length of stay in the ED 6 due to lack of access to the inpatient room and uncontrolled density of patients, it is found that the longer the access block the higher the deterioration of patients in 24 hours.Purpose: This study aims to determine the relationship of access block with deteriorating condition of patients in Emergency Installation Ulin Hospital Banjarmasin.Method : The research method uses an analytical design with a cross sectional approach. The number of samples taken is 40 people using the accidental sampling technique. Data analysis through spearman rank test.Result: The test results obtained p value calculated 0,000 (0.05) there is a relationship between the access block and deterioration of the patient's condition at the Ulin Banjarmasin Hospital emergency department.Conclusion: Based on the results of the study, there is a relationship between the access block and the worsening of the patient's condition at the Emergency Department of RSUD Ulin Banjarmasin with a value of P = 0.000 value α = 0.05 with a correlation coefficient value of 0.588. Keywords: Access Block, Patient worsening

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PENERAPAN TEXT MINING UNTUK MELAKUKAN CLUSTERING DATA TWEET AKUN BLIBLI PADA MEDIA SOSIAL TWITTER MENGGUNAKAN K-MEANS CLUSTERING

Jurnal Gaussian ◽

10.14710/j.gauss.v10i4.30409 ◽

2022 ◽

Vol 10 (4) ◽

pp. 583-593

Author(s):

Syiva Multi Fani ◽

Rukun Santoso ◽

Suparti Suparti

Keyword(s):

Social Media ◽

Text Mining ◽

Virtual Networks ◽

Number Of Clusters ◽

Silhouette Coefficient ◽

Twitter Account ◽

Computer Based ◽

Twitter Users ◽

Clustering Data ◽

Coefficient Method

Social media is computer-based technology that facilitates the sharing of ideas, thoughts, and information through the building of virtual networks and communities. Twitter is one of the most popular social media in Indonesia which has 78 million users. Businesses rely heavily on Twitter for advertising. Businesses can use these types of tweet content as a means of advertising to Twitter users by Knowing the types of tweet content that are mostly retweeted by their followers . In this study, the application of Text Mining to perform clustering using the K-means clustering method with the best number of clusters obtained from the Silhouette Coefficient method on the @bliblidotcom Twitter tweet data to determine the types of tweet content that are mostly retweeted by @bliblidotcom followers. Tweets with the most retweets and favorites are discount offers and flash sales, so Blibli Indonesia could use this kind of tweet to conduct advertising on social media Twitter because the prize quiz tweets are liked by the @bliblidotcom Twitter account followers.

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The Effect Of Counseling On The Adherence Of Therapeutic Hypertension Patients

Jurnal Keperawatan ◽

10.22219/jk.v11i2.11943 ◽

2020 ◽

Vol 11 (2) ◽

Author(s):

Gita Sekar Prihanti ◽

Novi Puspita Sari ◽

Nur Indah Septiani ◽

Laura Putri Risty L. Tobing ◽

Annisa Rahayu Adrian ◽

...

Keyword(s):

Family Support ◽

Statistical Tests ◽

Adherence Rate ◽

Test Results ◽

A Value ◽

Significant Difference ◽

Post Test ◽

Lifestyle Education ◽

Nonparametric Statistical ◽

Adherence To Therapy

Failure of therapy is a result of bad adherence medication. Non-adherence to therapy is a major factor that is suspected to result in uncontrolled blood pressure in hypertensive patients resulting in more serious complications. Therefore it is important to increase the adherence rate of treatment in patients with hypertension in the treatment process. For this reason, the need for interventions to improve adherence with several aspects that can be changed. This study uses One Group Pretest-Posttest Design using 100 samples. Data derived from questionnaires containing 25 items of adherence to therapy, 5 items of knowledge, 4 items of trust, 3 items of motivation, 10 items of family support with nonparametric statistical tests used were Mc Nemar test. Mc Nemar test results indicate that there is a significant difference between adherence at the pre-test and at the post-test after counseling with a significance value (p = 0,000). The results also showed that there was a difference in knowledge with a significance value (p = 0.001), motivation with a value (p = 0.031) and family support with a value (p = 0,000). The education with counseling about knowledge, trust, motivation and family support is effective in increasing compliance. There are other changeable factors such as lifestyle education, patient doctor relationships, and the use of smartphone applications for self-reported therapy can improve adherence in patients thereby minimizing therapy failure. Other educational methods that can be used besides counseling are counseling and dissemination of social media information.

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Pengaruh Kualitas Pelayanan Terhadap Kepuasan Pelanggan PT. Langit Membiru Wisata Bogor

PARAMETER ◽

10.37751/parameter.v6i1.160 ◽

2021 ◽

Vol 6 (1) ◽

pp. 38-49

Author(s):

Rini Ardista

Keyword(s):

Customer Satisfaction ◽

Service Quality ◽

T Test ◽

Primary Data ◽

Test Results ◽

A Value ◽

Primary Data Source ◽

Data Source

This research aims to determine and analyze the effect of service quality on customer satisfaction of PT. Langit Membiru Wisata Bogor, to find out how big the influence and relationship between service quality and customer satisfaction. The sample used was 100 respondents taken through t-test or interviews, observations, and questionnaires at PT. Langit Membiru Wisata Bogor and the primary data source processed with SPSS 25. The t-test results show the effect of service quality on customer satisfaction, this is evidenced by the variable (X) service quality and variable (Y) customer satisfaction has a value less than 0.05, namely (0.00 <0.05), this is proven with the value in the t test obtained t count 91.624> t table 5.293, so it can be concluded that the hypothesis can be accepted. Abstrak Penelitian ini bertujuan untuk mengetahui dan menganalisis pengaruh kualitas pelayanan terhadap kepuasan pelanggan PT. Langit Membiru Wisata Bogor, untuk mengetahui seberapa besar pengaruh dan hubungan antara kualitas pelayanan dengan kepuasan pelanggan. Sampel yang digunakan adalah 100 responden yang diambil melalui uji-t atau wawancara, observasi, dan angket pada PT. Langit Membiru Wisata Bogor dan sumber data primer diolah dengan SPSS 25. Hasil uji t menunjukkan adanya pengaruh kualitas pelayanan terhadap kepuasan pelanggan, hal ini dibuktikan dengan variabel (X) kualitas pelayanan dan variabel (Y) kepuasan pelanggan memiliki nilai lebih kecil dari 0,05 yaitu (0,00 < 0,05) hal ini terbukti dengan nilai pada uji t diperoleh t hitung 91,624 > ttabel 5,293, sehingga dapat disimpulkan bahwa hipotesis dapat diterima.

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Penerapan Metode Silhouette Coefficient untuk Evaluasi Clustering Obat

PENA TEKNIK: Jurnal Ilmiah Ilmu-Ilmu Teknik ◽

10.51557/pt_jiit.v6i2.659 ◽

2021 ◽

Vol 6 (2) ◽

pp. 48

Author(s):

Solmin Paembonan ◽

Hisma Abduh

Keyword(s):

Data Set ◽

Number Of Clusters ◽

Silhouette Coefficient ◽

Data Group

Dalam penelitian ini menggunakan metode k-means, metode ini dapat digunakan untuk menjadikan beberapa obat yang mirip menjadi suatu kelompok data tertentu. Salah satu cara untuk mengetahui tingkat kemiripan data adalah melalui perhitungan jarak antar data. Semakain kecil jarak antar data semakin tinggi tingkat kemiripan data tersebut dan sebaliknya semakin besar jarak antar data maka semakin rendah tingkat kemiripannya. Tujuan akhir clustering adalah untuk menentukan kelompok dalam sekumpulan data yang tidak berlabel, karena clustering merupakan suatu metode unsupervised dan tidak terdapat suatu kondisi awal untuk sejumlah cluster yang mungkin terbentuk dalam sekumpulan data, maka dibutuhkan suatu evaluasi hasil clustering. Berdasarkan evaluasi yang dilakukan terhadap hasil clustering dengan nilai dari silhouette coeficient = 0,4854. In this study using the k-means method, this method can be used to make several similar drugs into a certain data group. One way to determine the level of similarity of the data is through the calculation of the distance between the data. The smaller the distance between the data, the higher the level of similarity between the data and vice versa, the greater the distance between the data, the lower the similarity level. For a number of clusters that may be formed in a data set, an evaluation of the results of clustering is needed. Based on the evaluation carried out on the results of clustering with the value of the silhouette coefficient = 0.4854.

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