The production of fish-based food processing has become a commodity for restaurants, restaurants, catering and home consumption, but there are still many people who don’t know how fish can be processed in various dishes for their daily needs. To find out how to make fish-based dishes, the researchers provide a solution to cooking any kind of food, starting from the grouping of types of dishes, the basic ingredients that must be prepared, how to cook them, to the address of the cooking link with ingredients from fish. This study aims so that people can cook various menus whose basic ingredients come from fish. This research uses clustering algorithm, k-means and k-medoids. The stages of this research consisted of data collection, data selection, modeling, data training, data testing and evaluation. The object in the study of menu data for various processed fish dishes consisted of 978 datasets of processed fish dishes. The data used for data relating to fish food ingredients with fish food attributes and the number of likes via the website, the fish dataset is sourced from https://ipm.bps.go.id/data/dataset/ikan. From the two algorithms, the best accuracy results are -1.777 for the k-means algorithm, while -1.535 results are obtained for the accuracy of the k-medoids algorithm.
Soil moisture is a parameter needed by plants in terms of plant growth. In determining the appropriate soil moisture for plants requires a control system. This study uses a comparison of KNN and decision tree algorithms with the aim of being able to determine soil calcification with yield parameters namely moist and dry, so that it has good accuracy compared to the accuracy of the Decision Tree algorithm with an accuracy of 55.77% with dry class recall of 62.69% moist 51.92% dry precision class 58.33% humid 47.37% and K-Nearest Neighbor with 72.69% accuracy dry class recall 80.60% humid 63.16% dry precision class 72.00% humid 73.47%. The results of the above model testing can be concluded that the K-Nearest Neighbor is the most accurate algorithm for classification of moist soil
The process of accepting new cadet candidates at the Maritime Academy of Marine Sanctuary every year, produces a lot of data in the form of profiles of prospective cadets. The activity caused a large accumulation of data, it became difficult to identify prospective cadets. This research discusses the application of data mining to generate profiles that have similar attributes. One of the data mining techniques used to identify a group of objects that have the same characteristics is Cluster Analysis. The data clustering method is divided into one or more clusters that have the same characteristics called K-means. The method that the author uses is knowledge discovery in databases (KDD) consisting of Data, Data Cleaning, Data transformation, Data mining, Pattern evolution, knowledge. Implementation of K-means Clustering process using Rapid Miner. Attributes used by NIT, Level, Name, Student Status, Type of Registration, Gender, Place of Birth, Date of Birth, Religion, School Origin, School Origin Department, Religion, GPA, Subdistrict, District/ City, Province. Returns the number of clusters 30 (k=30). From the research results based on davies bouldin test on K-means algorithm resulted in the closest value of 0 is k = 29 with Davies bouldin: 0.070, with the most cluster member distribution in cluster 16 containing cluster members 115 items.
Smartphone market in Indonesia is growing rapidly nowadays. A huge variety of brands from many vendors and smartphone companies compete to gain market share, such as Apple Incorporation. One of consideration in choosing smartphone is to give attention about product attributes (product quality, feature, and design). The objective of this research was to determine and analyze the influence of product attributes towards purchasing decision of iPhone, especially iPhone 5s. The type of research used is quantitive. The sampling methods used are purposive and incidental sampling. The research was conducted by analizying data from 50 respondents selected according to specific criteria such as iPhone 5s users, student of collage, and domicilied in Depok. The result of simple correlation analysis i.e 0,786 revealed that there is a positive relation between two variables, so that product attributes and purchasing decisions has a strong correlation. Based on the result obtained from coefficient determination, the value of R Square is 0,618 so the product attributes influencing purchasing decisions iPhone 5s i.e 61,8%.Key words: Smartphone, Product Attributes, Purchasing DecisionAbstrakPasar smartphone di Indonesia saat ini telah mengalami perkembangan yang sangat pesat. Berbagai macam merek dari banyak vendor dan perusahaan smartphone mencoba untuk mendapatkan pangsa pasar, seperti halnya Apple Incorporation. Salah satu pertimbangan konsumen dalam memilih smartphone adalah dengan memperhatikan atribut produk (kualitas, fitur, dan desain produk). Tujuan diadakannya penelitian ini adalah untuk mengetahui dan menganalisis pengaruh atribut produk terhadap keputusan pembelian iPhone, khususnya iPhone 5s. Metode penelitian yang digunakan adalah metode kuantitatif dengan teknik sampling, yaitu purposive dan insidental sampling. Penelitian dilakukan dengan menganalisis data dari 50 responden terpilih yang sesuai dengan kriteria, yaitu merupakan pengguna iPhone 5s, Mahasiswa, dan berdomisili di Kota Depok. Hasil dari analisis korelasi sederhana mengungkapkan bahwa terdapat hubungan linear yang positif sebesar 0,786 dari kedua variabel sehingga atribut produk dan keputusan pembelian memiliki hubungan yang kuat. Berdasarkan uji koefisien determinasi diperoleh nilai R Square sebesar 0,618 sehingga atribut produk mempengaruhi keputusan pembelian iPhone 5s sebesar 61,8%..Kata kunci: Smartphone, Atribut Produk, Keputusan Pembelian.
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