Klasifikasi Rasa Susu Almond Berdasarkan Minat Pelanggan Menggunakan Algoritma C4.5 di Home Made MamiNia Ice Pematangsiantar

Almond milk is a plant-based milk drink made from almonds. Almond juice has a paler color and a thicker texture than regular milk. As for the benefits of almond milk in the world of health, namely to increase breast milk production, prevent high blood pressure, strengthen immunity, protect bone health, maintain baby's heart health, prevent free radicals, facilitate digestion, make skin bright after childbirth. Data mining is part of the Knowledge Discovery in Database (KDD) process stage. Therefore, the authors provide a classification solution in Data Mining is done using the Algorithm C4.5. With the Algorithm C4.5 you will get a Decision Tree that is easy to understand and easy to understand. Thus, it can help the owner determine the almond milk production plan that is most in demand and without worrying about excess goods or shortages of ingredients.

PREVENDO A TAXA DE CONVERSÃO DE LEADS NO SETOR DA EDUCAÇÃO COM TÉCNICAS DE APRENDIZAGEM DE MÁQUINA

A crise provocada pela COVID-19 acelerou processos de mudanças na economia global, levando a alterações nas empresas em estruturas, modelo de negócios e rotinas. Particularmente, Pequenas e Medias Empresas (PMEs) têm enfrentado desafios de encontrar caminhos para a jornada de transformação digital e adaptação na era da indústria 4.0, o que as leva a precisar de apoio para integrar suas transformações. O objetivo do trabalho é prever a probabilidade de conversão de leads usando Aprendizagem de Máquina (ML) com o propósito de melhorar o processo das oportunidades de fechamento de matrículas nas PMEs do setor da educação. O trabalho tem fundamentação no Modelo de Transformação Digital para as PMEs (MTD_PMEs), abordagem específica na tecnologia ML e Knowledge Discovery in Database (KDD). A metodologia envolve uma sequência de três etapas do processo de KDD_AZ. Os dados foram coletados de um polo de uma universidade do sul do Brasil. Resultados indicam que os 8 atributos utilizados são significativos para prever a conversão de leads. A técnica de ML, Regressão Logística chegou a uma precisão bruta de 100%, contribuindo assim para o aumento da taxa de conversão, ganho de tempo das equipes e filtragem de leads “improváveis”, e ainda ajuda o marketing a melhorar sua mira para trazer leads qualificados/quentes

Optimalisasi Pelayanan Perpustakaan terhadap Minat Baca Menggunakan Metode K-Means Clustering

Knowledge Discovery in Database (KDD) is a structured analysis process aimed at getting new and correct information, finding patterns from complex data, and being useful. Data mining is at the core of the KDD process. Clustering is a data mining method that is suitable for optimizing library services because it can cluster books effectively and efficiently, with the K-Means algorithm data can be clustered and information from each centroid value of each cluster. Library services can optimize the placement of books so that students can quickly find books according to their reading interest more effectively and can be attracted to other books because they are in one grouping. Meanwhile, the library can prioritize the procurement of the next book. Optimization of library services in the cluster using the K-Means method. Clustering interest in reading has the criteria for the number of books available, borrowed books, and the length of time the books are borrowed. The book data is clustered into 3, namely very interested, in demand, and less desirable. After doing the calculation process from 40 samples of book types, it resulted in 6 iterations, and the final results were 3 clustering, namely cluster 1 of 4 books that were of great interest, cluster 2 of 20 books that were of interest, and cluster 3 of 16 books that were less desirable. This research can be used as a recommendation reference for optimizing library services both for the layout and procurement of books by prioritizing the types of books that are of great interest.

PENENTUAN POLA PENJUALAN KIPAS KAPAL DENGAN MENGGUNAKAN ALGORITM APRIORI

<p><em>Penelitian ini dilakukan guna untuk mengetahui pola penjualan </em><em>kipas kapal</em><em> pada PT.</em><em>XYZ</em><em> berdasarkan hasil penjualan yang dilakukan setiap harinya. Penelitian ini akan menginformasikan kepada perusahaan barang apa saja yang sering terjual secara bersamaan dan membantu perusahaan untuk menyusun strategi dan solusi untuk </em><em>kipas kapal</em><em> yang sedikit terjual.</em><em> Data Mining merupakan proses pencarian data, penggalian informasi serta pengetahuan yang berasal dari data yang besar yang disebut sebagai Knowledge Discovery in Database disingkat KDD. Penggunaan metode data mining sangat banyak di pergunakan untuk menyelesaikan suatu masalah yang dimana diantaranya dengan menerapkan algoritma </em><em>Apriori </em><em>yang dapat mempermudah pemilik usaha dalam mendapatkan suatu </em><em>pola penjualan</em><em>. Diharapkan dengan menerapkan </em><em>algoritma apriori </em><em>dapat mengetahui pola penjualan </em><em>kipas kapal</em><em> pada PT. </em><em>XYZ</em><em>.</em></p>

Klasifikasi Pengguna Shopee Berdasarkan Promosi Menggunakan Naïve Bayes

Online shopping is a transaction of buying and selling goods or services through intermediary media, namely social networks. There has been a change in consumption patterns and the way people spend their money, which was originally conventional to switch to E-Commerce services due to several factors, namely the increasing public interest in online shopping due to the COVID-19 virus outbreak, and throughout 2019 E-Commerce users who made transactions reached 168.3 million people. . Based on iprice report data in 2020, Shopee is the most visited E-Commerce with a total of 129,320,800 visitors. Shopee is only a third party that provides a place to sell and payment facilities, therefore Shopee is not responsible for marketing the products sold. To attract consumers, sellers need attractive promotions. Therefore, research is needed to classify E-Commerce users. The purpose of this research is to classify E-Commerce users based on the promotion used using the Naïve Bayes algorithm with the Knowledge Discovery in Database (KDD) methodology. Nine test scenarios were carried out with cross validation which showed that the best performance was a test scenario using 3 folds which resulted in performance with an accuracy value of 88.73%, and with a kappa value of 0.451 which was included in the moderate category. Based on these results, the model generated by the Naïve Bayes algorithm is quite consistent.

Diabetes Prediction System using Classification Techniques & Healthcare Consultation using Artificial Intelligence

Diabetes is a global diseases that has affected over 388 million people and cause many deaths and serious condition. This is due to the late detection and diagnosis of the disease as it causes a delay in treatment and becomes harder to prevent it from worsening. It is important to detect the disease at an early stage and start early treatment to prevent it from becoming life-threatening. The aim of this project is to produce a system that can accurately predict the disease in real-time for the user and provide online consultation by doctors and chatbots which will help prevent major illnesses in future. The project targets anyone who may want to check whether they have the disease or not. It also serves as a platform for doctors to provide online consultation to their clients. The project will follow the Knowledge Discovery in Database approach. Implementing the system will reduce time consumption, produce real-time results cost-freely & early detection of diabetes. The project is expected to produce a functional system which accurately predicts diabetes based on the data entered in real-time to minimize visits to clinics and cut the cost of the test while providing online health consultation.

Algoritma K-Means Untuk Mengelompokkan Suplier Obat (Studi Kasus: RS. Prima Husada Cipta Medan)

<p><em>Obat merupakan zat yang berasal dari tumbuhan, hewan, mineral maupun zat kimia tertentu yang dapat digunakan untuk mengurangi rasa sakit, memperlambat proses penyakit dan atau menyembuhkan penyakit. Obat-obat yang diterima oleh RS. Prima Husada Cipta Medan merupakan obat yang telah dikirimkan oleh suplier-suplier nya. Dengan banyaknya data tersebut, maka bagian Farmasi RS. Prima Husada Cipta Medan mengalami kesulitan untuk menentukan tingkat pengiriman terhadap masing-masing supplier. Dari permasalahan yang ada, maka penulis ingin menerapkan data mining dengan algoritma K-Means (Clustering) menggunakan aplikasi RapidMiner untuk mengelompokkan data supplier, yang awalnya tidak tersusun/terstruktur bisa menjadi data yang terstruktur, selain itu penggalian informasi pada sebuah data yang berukuran sangat besar (memiliki jumlah field dan jumlah record yang banyak) tidak dapat dilakukan dengan mudah, maka daripada itu teknologi data mining adalah salah satu alat bantu untuk penggalian data berukuran besar dengan tingkat kerumitan yang cukup mudah. Pengolahan data mining yang dilakukan pada penelitian ini menggunakan tahapan Knowledge Discovery in Database (KDD), agar dapat menghasilkan informasi sesuai dengan tahapan yang telah ditentukan. Penelitian ini juga menggunakan tools RapidMiner agar dapat dilakukannya pengujian dengan perhitungan manual dan dengan menggunakan tools RapidMiner. Hasil akhir dari penelitian ini berbentuk informasi mengenai tingkat pengiriman dari para supplier yang terbagi menjadi 3 kelompok pengiriman yaitu tinggi, sedang, dan rendah.</em></p>

GOVERNMENT POLICIES MODELING IN CONTROLLING INDONESIA'S COVID-19 CASES USING DATA MINING

Since the positive case of covid-19 in Indonesia, the government has taken several policies with the purpose of controlling the spread of the covid-19 virus, which has been regulated in Government Regulation No. 21 of 2020. The purpose of research is to obtain a model of government policy in controlling cases of covid by using data mining classification techniques, and obtain attributes that have the greatest weight, as well as look at the impact of policies that have been carried out by the government on the cases of covid-19 in Indonesia. The methodology used in the research is Knowledge Discovery In Database (KDD). Based on the research that has been done, it can be concluded that the policies that have been done by the government in controlling cases of covid-19 can be said to be successful, the C4.5 algorithm is the algorithm that gives the best results compared to the Deep Learning algorithm, as well as the attribute that has the greatest weight is cancel public events. Secondary data will be used in this research.

Implementation of the C4.5 Algorithm to Predict Student Achievement at SMK Negeri 6 Surakarta

Data mining is a knowledge used to get information from multiple data. C.45 Algorithm is one of data mining algorithm to classify data to many categories. Implementation of data mining not only could be used in industrial section but it could be used to in educational section (educational data mining) to help teacher and student improve their learning quality. This research purposed to know the implementation of data mining to predict student achievement from many factors could be effected . The research use Knowledge Discovery in Database method and it would be analyzed by accuration calculated from classify model that be form. Result of the research is the rules that formed by the decision tree and it could predict student achievement . Teacher could use it to give special treatment to student who got not good prediction.