Home appliances recommendation system based on weather information using combined modified k-means  and elbow algorithms

Jaafar, Basim Amer; Gaata, Methaq Talib; Jasim, Mahdi Nsaif

doi:10.11591/ijeecs.v19.i3.pp1635-1642

Cited by 10 publications

(6 citation statements)

References 12 publications

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“…Kmeans merupakan algoritma sederhana dengan waktu proses yang cepat dan menghasilkan cluster yang optima [8]. K-Means adalah algoritma iteratif; itu terus mengulangi langkah-langkah di atas sampai tidak ada perubahan lokasi centroid [9], Tujuan dari metode clustering data ini adalah untuk meminimalkan fungsi tujuan yang ditetapkan dalam proses clustering [10]. Algoritma K-means memberikan metode sederhana untuk mengeksekusi solusi perkiraan [11].…”

Section: Pendahuluanunclassified

Algoritma K-Means Clustering Penggunaan Bandwidth Internet (Studi Kasus di Pemerintah Daerah Kabupaten Padang Pariaman)

Mubarak,

Defit,

Nurcahyo

2023

Explore. jurnal. sistem. inf. dan. telematika

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To support government activities, a fast and precise network connection is needed. So it requires a wide network bandwidth. Bandwidth management needs to be done so that the network speed remains stable. This study aims to look at the pattern of bandwidth usage in the Regional Government of Padang Pariaman Regency using K-Means Clustering. The data is taken from the Cacti application, an open-source, web-based network monitoring software. The total extracted datasets used are 32 OPD data (Regional Apparatus Organizations) in the Regional Government of Padang Pariaman Regency in 2022. The available data is then processed to obtain cluster targets by utilizing the data mining concept using the K-Mean Clustering method. Bandwidth usage data grouping in Padang Pariaman Regency uses the Clustering method with the K-Means algorithm with the attributes Name OPD, Inbound Average, Inbound Maximum, Outbound Average, Outbound Maximum used in the process of calculating and dividing data into 3 clusters with high bandwidth usage categories, low, and medium. Calculations are done manually and then tested with RapidMiner software. The results of the manual calculations obtained the same number of cluster members as the calculations with the RapidMiner software.

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Section: Pendahuluanunclassified

Algoritma K-Means Clustering Penggunaan Bandwidth Internet (Studi Kasus di Pemerintah Daerah Kabupaten Padang Pariaman)

Mubarak,

Defit,

Nurcahyo

2023

Explore. jurnal. sistem. inf. dan. telematika

View full text Add to dashboard Cite

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“…However, the mastery of the K-means algorithm and its implementation pleads in its favor much more than other unsupervised algorithms such as hierarchical or fuzzy clustering algorithms. The other research used Elbow method to determine the optimal number of clusters [11], [12] . Despite the use of the elbow method to determine the optimal k value, any change in the dataset requires a new search for the optimal value.…”

Section: Classical Clustering Recommendation Researchmentioning

confidence: 99%

A cluster validity for optimal configuration of Kohonen maps in e-learning recommendation

Mawane¹,

Naji²,

Ramdani³

2022

IJEECS

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the first block of our unsupervised deep collaborative recommendation (UDCF) system and proposes a platform whose goal is to try to find the adequate parameters of the Kohonen maps, to create homogeneous clusters in profile data and results, the homogeneity is verified thanks to the very low variance rate of the results obtained by the cluster population and a second criterion which is the high prediction rate of collaborative recommendation. Although the revision concerns only the clustering block, and the use of a symmetrical autoencoder without searching for its optimization, the result obtained (82.33%) for the optimal configurations with high homogeneity of the Kohonen map is equivalent to the optimized result of the UDCF and even better than the classical recommendation methods

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“…The distance is used to measure the difference of the K clusters in the dataset. [17]. The dataset, for example X, contains multiple data points.…”

Section: K-means Clustering Algorithmmentioning

confidence: 99%

Food recommendation system based on nutritional needs of human beings and user preferences

Jasim

Hamid

2022

ijhs

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Introduction: Nowadays the food types became so diverse and complicated, so human needs+ professional assistance to make his best choices especially after foods became global parameter. Food Recommendation System is a smart system that provides the best suggestions to the beneficiaries to know the best choices to their needs. Moreover, the human activities and lifestyle are affected by another types of dietaries in other foods. There is need for everybody to know what the nutrition is he/she needs. So, this research responding to these needs. The goal of the proposed system is to propose a system that provides recommendations for foods that are rich in nutritional components that people need in their daily lives based on computational model and expert preferences. Objective: The research aims to design and implement a food recommendation system has the ability to coordinate both user preferences and data clustering techniques to produce high accuracy recommendations. Material and methods: The proposed method focuses on merging computational model and user preferences to give the user the best recommended list of food options. Clustering techniques are approaches used in Recommendation system applications to group different foods according to the similarities in nutrition values.

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Home appliances recommendation system based on weather information using combined modified k-means and elbow algorithms

Cited by 10 publications

References 12 publications

Algoritma K-Means Clustering Penggunaan Bandwidth Internet (Studi Kasus di Pemerintah Daerah Kabupaten Padang Pariaman)

Algoritma K-Means Clustering Penggunaan Bandwidth Internet (Studi Kasus di Pemerintah Daerah Kabupaten Padang Pariaman)

A cluster validity for optimal configuration of Kohonen maps in e-learning recommendation

Food recommendation system based on nutritional needs of human beings and user preferences

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