Perbandingan Metode Fuzzy C-Means Dan K-Means Untuk  Pemetaan Daerah Rawan Kriminalitas Di Kota Semarang

Firdaus, Hana Sugiastu; Nugraha, Arief Laila; Sasmito, Bandi; Awaluddin, Moehammad

doi:10.14710/elipsoida.2021.9219

Cited by 6 publications

(8 citation statements)

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“…Penelitian terdahulu lainnya antara lain penelitian Anis Hoerunnisa (dkk) pada komparasi algoritma K-Means dan K-Medoids dalam analiss pengelompokkan daerah rawan kriminalitas di Indonesia, penelitian tersebut menunjukkan bahwa algoritma K-Means lebih optimal dengan DBI 0, 463 dibandingkan dengan K-Medoids dengan DBI 1, 089 [12]. Peenelitian Hana Sugiastu Firdaus (dkk) pada perbandingan metode Fuzzy C-Means dan K-Means untuk pemetaan daerah rawan kriminalitas di kota semarang, dimana pada penelitian tersebut berdasarkan nilai uji menunjukkan bahwa metode Fuzzy C-Means dengan nilai uji sebesar 0,818 lebih baik dibandingan metode K-Means [13].…”

Section: Tinjauan Pustaka 21 Penelitian Terdahuluunclassified

Perbandingan Algoritma K-Means Dan K-Medoids Untuk Klasterisasi Produksi Telur Ras Ayam Kampung Di Provinsi Sumatera Selatan

Andini,

Talita Aminisari,

Aditya Febrianza Husin

et al. 2024

jati

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Telur adalah salah satu bahan pangan yang memegang peranan penting dalam memenuhi kebutuhan protein dan asupan gizi bagi manusia. Berdasarkan data produksi telur pada tahun 2020 hingga 2022 Produksi telur di Provinsi Sumatera Selatan masih terbilang rendah jika dibandingkan dengan daerah lain, berdasarkan permasalahan tersebut maka dilakuakn penelitian ini menggunakan teknik data mining dengan metode clustering menggunakan aplikasi Rapidminer untuk menganalisis pola produksi telur ayam kampung di provinsi Sumatera Selatan pada tahun 2020 hingga 2022 agar dapat mengoptimalkan produksi telur pada setiap wilayah. Penelitian ini menerapkan perbandingan algoritma K-Means dan K-Medoids, tujuan melakukan perbandingan yaitu untuk menentukan algoritma mana yang terbaik dalam klasterisasi produksi telur rasa ayam kampung di Sumatera Selatan dengan membandingkan nilai Davies Bouldin Index (DBI) untuk menentukan nilai K (jumlah cluster) terbaik. Hasil Pengujian menunjukkan algoritma K-Means menjadi algoritma terbaik untuk klasterisasi telur ras ayam kampung di provinsi sumatera selatan dengan hasil nilai Davies Bouldin Index (DBI) 0.193 dengan lima buah cluster dibandingkan dengan K-Medoids sebesar 0.268 dengan tiga cluster

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Section: Tinjauan Pustaka 21 Penelitian Terdahuluunclassified

Perbandingan Algoritma K-Means Dan K-Medoids Untuk Klasterisasi Produksi Telur Ras Ayam Kampung Di Provinsi Sumatera Selatan

Andini,

Talita Aminisari,

Aditya Febrianza Husin

et al. 2024

jati

View full text Add to dashboard Cite

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“…The Fuzzy C-Means algorithm has the highest silhouette index accuracy comparison of 0.2559, which is better than the k-medoids algorithm [8] and has an accuracy rate of 76% when compared to the k-means algorithm [7]. The Fuzzy C-Means algorithm is excellent at detailed grouping [9] based on the data attributes it possesses [10], and it performs exceptionally well in finding high-level clusters [11] Additionally, it can map interactions between different cluster shapes [12] and demonstrate test results with a best percentage of 71.23% [13]. The Silhouette Index is shown to have a strong structure category value of 0.84321191, indicating that the Fuzzy C-Means algorithm is well-suited for grouping product data into three clusters [14].…”

Section: Introductionmentioning

confidence: 99%

Fuzzy Clustering-Based Grouping for Mapping the Distribution of Student Success Data

Mustakim,

Aini,

Batubara

et al. 2024

MALCOM

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Learning activities are the main activity in the overall teaching and learning process in schools. This is because whether or not the achievement of educational goals depends on how the learning process is carried out by students. The uneven level of student success in learning is one of the problems in the school's efforts to realize the vision and mission of SMKN 5 Pekanbaru in preparing skilled graduates to be able to work in certain sectors by the public interest and the industrial world. In this study, mapping and grouping student grade data was carried out using the Fuzzy C-Means algorithm to provide information to the school in making the right decisions and optimizing the learning process. Furthermore, clustering was carried out in several experiments K=3 to K=7, and obtained the best validity value tested with the Silhouette Index of 0.4277 located at K=5. Then the distribution of cluster 5 on student score data was obtained with details, namely cluster 1 with a capacity of 1 student, cluster 2 with a capacity of 27 students, cluster 3 with a capacity of 1 student, cluster 4 with a capacity of 10 students, cluster 5 with a capacity of 23 students.

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“…Previous research studies used the Fuzzy C-Means and K-Means methods to map crime-prone areas in Semarang City, then validated with the results of processing tests, namely the Partition Coefficient Index Test on Fuzzy C-Means and Silhouette Index in K-Means [9]. The research with the K-Means method used for regional clustering prone to natural disasters in Indonesia which is divided into 3 clustering groups (high, medium, low) and uses the Silhouette Index to test the level of validity [4].…”

Section: Introductionmentioning

confidence: 99%

Application of K-Means and Fuzzy C-Means Algorithms to Determine Flood Vulnerability Clusters (Case Study: Kutai Kartanegara Regency)

Nurjanah,

Anggriani,

Hasanah

2024

BAREKENG: J. Math. & App.

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Flooding show situation where areas that are not usually inundated, such as farmland and settlements, and city district areas, become inundated due to water. Floods can to occur when the flow of water on rivers or waste channels overrun its normal measurements. This study describes the K-Means and Fuzzy C-Means Algorithm methods for clustered flood-prone areas built on Districts in Kutai Kartanegara Regency. This research begins with data collection in the character of rainfall, land elevation, the number of victims affected, the quantity of damaged houses, the quantity of damage to facilities and the quantity of flood events. Before the data is processed using these two methods, data normalization will be carried out in a dataset which aims to shape the data into positional values from the same range. K-Means and Fuzzy C-Means are accustomed to identifying groups in each sub-district in Kutai Kartanegara Regency that have a level of vulnerability to floods. At this stage, 3 initial clusters were carried out, namely high, medium, and low vulnerability clusters. The validity test produces a Silhouette Index value of 0.574283589 and a Partition Coefficient Index of 0.78905. The outcome of the K-Means method with the standard deviation within and between clusters are 0.5131 and the Fuzzy C-Means method for the standard deviations within and between clusters is 0.3489. based uppon value of the silhouette index, partition coefficient index and standard deviation within and between clusters it results that Fuzzy C-Means is the best method of this study.

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Perbandingan Metode Fuzzy C-Means Dan K-Means Untuk Pemetaan Daerah Rawan Kriminalitas Di Kota Semarang

Cited by 6 publications

References 0 publications

Perbandingan Algoritma K-Means Dan K-Medoids Untuk Klasterisasi Produksi Telur Ras Ayam Kampung Di Provinsi Sumatera Selatan

Perbandingan Algoritma K-Means Dan K-Medoids Untuk Klasterisasi Produksi Telur Ras Ayam Kampung Di Provinsi Sumatera Selatan

Fuzzy Clustering-Based Grouping for Mapping the Distribution of Student Success Data

Application of K-Means and Fuzzy C-Means Algorithms to Determine Flood Vulnerability Clusters (Case Study: Kutai Kartanegara Regency)

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