ANALISA DATA EKSPEDISI PAKET DENGAN MENERAPKAN ALGORITMA ROUGH SET (STUDI KASUS: JNE AGEN MENTENG MEDAN)

2019 ◽  
Vol 3 (1) ◽  
Author(s):  
Hasrat Selpia Simorangkir
Keyword(s):  
Data Mining ◽  
Rough Set ◽  
Microsoft Excel ◽  
General Rules ◽  
New Knowledge ◽  
Analyze Data

In analyzing data with data mining using the Rough Set algorithm which has results in the form of rules (rules). The process of determining the rules in the Rough Set method starts from processing the Microsoft Excel database, and continues testing with the Rough Set method. Thus producing General Rules which will become new knowledge in this research.With the existence of data mining using the rough set penilus algorithm, it can analyze data and provide solutions to the weaknesses faced in the menteng Medan field agents that have occurred so that it can be used as an alternative to solving problems in packet expedition encountered so far.Keyword: Data Mining, Descision System, Equivalen Class, ,General Rules, Rough set,Reduction

scholarly journals Factors Analysis And Profit Achievement For Trading Company By Using Rough Set Method

2017 ◽  
Vol 1 (1) ◽  
pp. 16
Author(s):  
Muhammad Ardiansyah Sembiring ◽  
Zulfi Azhar
Keyword(s):  
Data Mining ◽  
Equivalence Class ◽  
Rough Set ◽  
Discovery Process ◽  
Discernibility Matrix ◽  
Trading Company ◽  
Factors Analysis ◽  
General Rules ◽  
New Knowledge ◽  
System Equivalence

This research has been done to analysis the financial raport fortrading company and it is  intimately  related  to  some  factors  which  determine  the profit of company. The result of this reseach is showed about  New Knowledge and perform of the rule. In  discussion, by followed data mining process and using Rough Set method. Rough Set is to analyzed the performance of the result. This  reseach will be assist to the manager of company with draw the intactandobjective. Rough set method is also to difined  the rule of discovery process and started the formation about Decision System, Equivalence Class, Discernibility Matrix,  Discernibility Matrix Modulo D, Reduction and General Rules. Rough set method is efective model about the performing analysis in the company. Keywords : Data Mining, General Rules, Profit,. Rough Set.

scholarly journals Penerapan Machine Learning dengan Konsep Data Mining Rough Set (Prediksi Tingkat Pemahaman Mahasiswa terhadap Matakuliah)

2021 ◽  
Vol 5 (1) ◽  
pp. 317
Author(s):  
Mokhamad Ramdhani Raharjo ◽  
Agus Perdana Windarto
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Learning Outcomes ◽  
Rough Set ◽  
Teaching Methods ◽  
Student Understanding ◽  
New Knowledge ◽  
The Subject ◽  
Level Of Understanding ◽  
Extract Information

The Rough Set (RS) method is part of machine learning that analyzes the uncertainty of the dataset used to determine the attributes of important objects (classification). The purpose of this study was to extract information from the rough set using the Rosetta application in predicting cases of students' level of understanding of the course. The attributes used are communication (F1), learning atmosphere (F2), learning media (F3), appearance (F4), and teaching methods (F5). Sources of data obtained from the output of the Journal of Physics: Conference Series, 1255 (1). https://doi.org/10.1088/1742-6596/1255/1/012005. The results of the application of the Rough Set method in determining the prediction of the level of student understanding of the course, produce new knowledge, namely learning outcomes based on the subject. There are 15 Reductions with 90 Generate Rules. But overall, the attributes that affect the level of student understanding of the subject are communication (F1) and learning media (F3)

Analisis Kinerja Guru Dalam Menentukan Guru Berprestasi di SMP Negeri 29 Padang Dengan Menggunakan Metode Rough Set

2018 ◽  
Vol 9 (1) ◽  
pp. 1925-1931
Author(s):  
Sri Haryati ◽  
Yanti Yusman ◽  
Sri Nadriati
Keyword(s):  
Data Mining ◽  
Equivalence Class ◽  
Rough Set ◽  
Discernibility Matrix ◽  
Decision System ◽  
General Rules

Penelitian ini dilakukan dengan data mining untuk menemukan pengetahuan  (knowledge) baru berupa aturan (rule) menggunakan metode rough set dalam menganalisa kinerja guru untuk menentukan guru berprestasi di SMP Negeri 29 Padang. Hasil dari penelitian ini berupa rule yang akan menentukan guru  sangat berprestasi, berprestasi dan tidak berprestasi. Proses penemuan rule dalam metode Rough Set dimulai dari pembentukan Decision System yang merupakan data awal dari beberapa atribut, kemudian dibentuk  Equivalence Class ,Discernibility Matrix, Discernibility Matrix Modulo D kemudian terakhir General Rules dan menggunakan Perangkat Lunak Rosetta. Hasil dari General Rules inilah kemudian yang akan menjadi pengetahuan baru dalam penelitian ini.

METODE ROUGH SET UNTUK MENGANALISA PROBLEMATIKA GURU DALAM MENGGUNAKAN MEDIA PEMBELAJARAN BERBASIS KOMPUTER

2020 ◽  
Vol 1 (1) ◽  
pp. 19-25
Author(s):  
Wita Yulianti
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Data Mining ◽  
Rough Set ◽  
Computer Technology ◽  
Computer Based Learning ◽  
Learning Techniques ◽  
Computer Based ◽  
Source Of Information ◽  
Analyze Data

The development of various forms of information technology, one of them in the field of Artificial Intelligence in the form of data mining. Data mining is a process that employs one or more computer learning techniques (a machine learning) to analyze data and extract knowledge (knowledge) automatically. Data mining is used to analyze the problems of teachers in the use of computer-based learning media. To find out the problem of teachers in the use of computer-based learning media, this study used the calculations in the form of data mining by the method of rough set. Applying data mining rough set to explore the knowledge, which is a source of information in decision making. With this method rough set problems that occurred in the teacher can be overcome in the use of computer-based learning media. Each school can determine the development of teachers in the use of learning media computer technology.

scholarly journals IMPLEMENTASI DATA MINING ROUGH SET DALAM MENENTUKAN TINGKAT KERUSAKAN ALAT DAN BAHAN KIMIA (STUDI KASUS DI LABORATORIUM RESEP SMK S-16 FARMASI BENGKULU)

2019 ◽  
Vol 2 (1) ◽  
Author(s):  
Marnis Nasution
Keyword(s):  
Data Mining ◽  
Rough Set ◽  
Know How ◽  
New Knowledge ◽  
Extent Of Damage ◽  
The Cost

Data mining is one of the methods used in extracting the data in generating new knowledge fromstored data. The extent of damage to the equipment and chemicals that affect the cost of school often donot know how much it cost the school. With data mining that generates knowledge in the form of somerules and help the school to predict the extent of damage and the cost of chemicals that can be in.

PREDIKSI KUALITAS AIR SUNGAI CILIWUNG DENGAN MENGGUNAKAN ALGORITMA POHON KEPUTUSAN

2021 ◽  
Vol 12 (2) ◽  
Author(s):  
Mohammad Haekal ◽  
Henki Bayu Seta ◽  
Mayanda Mega Santoni
Keyword(s):  
Data Mining ◽  
Decision Tree ◽  
Cross Validation ◽  
Online Monitoring ◽  
Training Set ◽  
Microsoft Excel ◽  
Test Set

Untuk memprediksi kualitas air sungai Ciliwung, telah dilakukan pengolahan data-data hasil pemantauan secara Online Monitoring dengan menggunakan Metode Data Mining. Pada metode ini, pertama-tama data-data hasil pemantauan dibuat dalam bentuk tabel Microsoft Excel, kemudian diolah menjadi bentuk Pohon Keputusan yang disebut Algoritma Pohon Keputusan (Decision Tree) mengunakan aplikasi WEKA. Metode Pohon Keputusan dipilih karena lebih sederhana, mudah dipahami dan mempunyai tingkat akurasi yang sangat tinggi. Jumlah data hasil pemantauan kualitas air sungai Ciliwung yang diolah sebanyak 5.476 data. Hasil klarifikasi dengan Pohon Keputusan, dari 5.476 data ini diperoleh jumlah data yang mengindikasikan sungai Ciliwung Tidak Tercemar sebanyak 1.059 data atau sebesar 19,3242%, dan yang mengindikasikan Tercemar sebanyak 4.417 data atau 80,6758%. Selanjutnya data-data hasil pemantauan ini dievaluasi menggunakan 4 Opsi Tes (Test Option) yaitu dengan Use Training Set, Supplied Test Set, Cross-Validation folds 10, dan Percentage Split 66%. Hasil evaluasi dengan 4 opsi tes yang digunakan ini, semuanya menunjukkan tingkat akurasi yang sangat tinggi, yaitu diatas 99%. Dari data-data hasil peneltian ini dapat diprediksi bahwa sungai Ciliwung terindikasi sebagai sungai tercemar bila mereferensi kepada Peraturan Pemerintah Republik Indonesia nomor 82 tahun 2001 dan diketahui pula bahwa penggunaan aplikasi WEKA dengan Algoritma Pohon Keputusan untuk mengolah data-data hasil pemantauan dengan mengambil tiga parameter (pH, DO dan Nitrat) adalah sangat akuran dan tepat. Kata Kunci : Kualitas air sungai, Data Mining, Algoritma Pohon Keputusan, Aplikasi WEKA.

Penerapan Metode Klasifikasi Decision Tree dan Algoritma C4.5 dalam Memprediksi Kriteria Nasabah Kredit Mega Auto Finance

2020 ◽  
Vol 7 (2) ◽  
pp. 200
Author(s):  
Puji Santoso ◽  
Rudy Setiawan
Keyword(s):  
Data Mining ◽  
Decision Tree ◽  
Microsoft Excel ◽  
Customer Data ◽  
Data Mining Techniques ◽  
C4.5 Algorithm ◽  
Marketing Costs ◽  
Excel Format ◽  
Data Mining Application

One of the tasks in the field of marketing finance is to analyze customer data to find out which customers have the potential to do credit again. The method used to analyze customer data is by classifying all customers who have completed their credit installments into marketing targets, so this method causes high operational marketing costs. Therefore this research was conducted to help solve the above problems by designing a data mining application that serves to predict the criteria of credit customers with the potential to lend (credit) to Mega Auto Finance. The Mega Auto finance Fund Section located in Kotim Regency is a place chosen by researchers as a case study, assuming the Mega Auto finance Fund Section has experienced the same problems as described above. Data mining techniques that are applied to the application built is a classification while the classification method used is the Decision Tree (decision tree). While the algorithm used as a decision tree forming algorithm is the C4.5 Algorithm. The data processed in this study is the installment data of Mega Auto finance loan customers in July 2018 in Microsoft Excel format. The results of this study are an application that can facilitate the Mega Auto finance Funds Section in obtaining credit marketing targets in the future

scholarly journals Clustering Fasilitas Kesehatan Berdasarkan Kecamatan Di Karawang Dengan Algoritma K-Means

2021 ◽  
Vol 8 (1) ◽  
pp. 83
Author(s):  
Bagus Muhammad Islami ◽  
Cepy Sukmayadi ◽  
Tesa Nur Padilah
Keyword(s):  
Data Mining ◽  
Developing Countries ◽  
Data Processing ◽  
Health Problems ◽  
Health Facilities ◽  
Microsoft Excel ◽  
Two Factors ◽  
Clustering Data ◽  
Index Value ◽  
Cluster 2

Abstrak: Masalah kesehatan yang ada di dalam masyarakat terutama di negara- negara berkembang seperti Indonesia dipengaruhi oleh dua faktor yaitu aspek fisik dan aspek non fisik. Berdasarkan data yang diperoleh dari karawangkab.bps.go.id data dibagi menjadi 3 cluster yaitu sedikit, sedang dan terbanyak. Algoritma yang digunakan adalah K-Means cluster yang diimplementsikan menggunakan Microsoft Excel dan Rapidminer Studio. Hasil pengolahan data fasilitas kesehatan di karawang menghasilkan 3 cluster dengan cluster 1 yang mempunyai fasilitas kesehatan sedikit sebanyak 23 kecamatan, cluster 2 yang mempunyai fasilitas kesehatan sedang sebanyak 5 kecamatan dan cluster 3 yang mempunyai fasilitas kesehatan terbanyak terdapat 2 kecamatan. Kinerja yang dihasilkan dari algoritma K-means menghasilkan nilai Davies Boildin Index sebesar 0,109.   Kata kunci: clustering, data mining, fasilitas kesehatan, K-Means.   Abstract: Health problems that exist in society, especially in developing countries like Indonesia, are built by two factors, namely physical and non-physical aspects. Based on data obtained from karawangkab.bps.go.id the data is divided into 3 clusters, namely the least, medium and the most. The algorithm used is the K-Means cluster which is implemented using Microsoft Excel and Rapidminer Studio. The results of data processing of health facilities in Karawang produce 3 clusters with cluster 1 which has 23 sub-districts of health facilities, cluster 2 which has medium health facilities as many as 5 districts and cluster 3 which has the most health facilities in 2 districts. The performance resulting from the K-means algorithm results in a Davies Boildin Index value of 0.109.   Keywords: clustering, data mining, health facilities, K-Means.

scholarly journals Data Mining dalam Akurasi Tingkat Kelayakan Pakai terhadap Peralatan Perangkat Keras

2020 ◽  
pp. 83-88
Author(s):  
Nurhidayat ◽  
Sarjon Defit ◽  
Sumijan
Keyword(s):  
Data Mining ◽  
Information System ◽  
Rough Set ◽  
Access Point ◽  
Learning Processes ◽  
Discernibility Matrix ◽  
Student Work ◽  
Hard Drives ◽  
Good Shape ◽  
Equivalency Class

Hardware is a computer that can be seen and touched in person. Hardware is used to support student work and learning processes. The hardware should always be in good shape. If any damage should be done quickly. The benefits of this study provide a viable level of data against hardware tools. The purpose of this study determines that hardware that is worth using quickly and precisely so easily can be repaired and replaced. Hard-processed action consists of 12 projectors, 2 units of access point, 6 units of monitors, and 20 CPU units. To see the level of appropriateness regarding hard drives requires a rough set algorithm with that stage: information system; Decision system; Equivalency class; Discernibility matrix; Discernibility Matrix module D; Reduction; Generate Rules. The results of the 40 devices of study STMIK Indonesia Padang subtract college have 10 rules of policy on whether the hardware is still viable, repaired or replaced. So using a rough set algorithm is particularly appropriate to apply in a verifiable level of accuracy to fast and precise hardware.

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