scholarly journals Prediksi Waktu Sandar Kapal Di Pelabuhan Batu Ampar, Kota Batam, Provinsi Kepulauan Riau

2019 ◽  
Vol 2 (2) ◽  
pp. 119-134
Author(s):  
Saiful Rizal ◽  
Candra Kurniawan ◽  
Fahrur Rozi
Keyword(s):  
Data Mining ◽  
Decision Tree ◽  
Decision Trees ◽  
Supervised Learning ◽  
Multiple Regression ◽  
Analysis Of Variance ◽  
Dwelling Time

Pelabuhan Batu Ampar merupakan pelabuhan barang terbesar di Kota Batam yang memiliki lalu lintas tertinggi baik untuk kegiatan ekspor maupun kegiatan impor. Waktu tunggu (dwelling time) masih menjadi masalah dalam layanan pelabuhan. Waktu tunggu merupakan salah satu indikator efisiensi pengelolaan pelabuhan. Rata-rata waktu tunggu pelabuhan Batu Ampar untuk kegiatan bongkar pada triwulan I-2015 adalah 7 hari, sedangkan kegiatan muatnya adalah 5 hari. Hal ini yang menjadikan kinerja pelabuhan Batu Ampar masih banyak dikeluhkan, sehingga berakibat banyaknya antrian kapal. Untuk itu, perlu dilakukan analisis guna menghasilkan model yang bisa memberikan gambaran waktu tunggu di pelabuhan dan melakukan evaluasi terhadap model analitik yang telah dibangun. Analisa data sekunder pelabuhan Batu Ampar menggunakan data mining. Metode data mining yang dilakukan menggunakan algoritma supervised learning, yaitu multiple regression dan decision trees. Tujuan umum dari multiple regression adalah untuk mempelajari lebih lanjut tentang hubungan antara beberapa variabel independen atau prediktor dan variabel dependen atau kriteria. Decision trees yang digunakan untuk eksplorasi data pelabuhan ini menggunakan klasifikasi. Klasifikasi decision trees dapat menemukan apakah data mengandung kelas objek yang dipisahkan dengan baik, sehingga kelas dapat diinterpretasikan secara bermakna dalam konteks teori substantif. Dua metode evaluasi model dilakukan untuk dua hasil permodelan yang dibangun. Uji Analysis of Variance (Anova) digunakan untuk evaluasi model multiple regression, sedangkan untuk model decision tree dievaluasi dengan confussion matrix. Hasil analisis data menunjukkan lamanya waktu kapal melakukan bongkar/muat dipengaruhi oleh tiga variabel yaitu jenis ekspedisi, bendera, dan volume. Dengan menggunakan regresi berganda maka dihasilkan model prediksi waktu sandar kapal. Hasil evaluasi model menunjukkan bahwa model yang dibuat signifikan. Dengan tingkat kepercayaan 95% model prediktif yang dibuat akan merepresentasikan nilai sebenarnya. Untuk decision tree, evaluasi menunjukkan model yang dibuat sudah fit, dengan presisi 84,50%.

scholarly journals Data mining untuk memprediksi prestasi siswa berdasarkan sosial ekonomi, motivasi, kedisiplinan dan prestasi masa lalu

2014 ◽  
Vol 4 (2) ◽  
Author(s):  
Heri Susanto ◽  
Sudiyatno Sudiyatno
Keyword(s):  
Data Mining ◽  
Decision Tree ◽  
Multiple Regression ◽  
Economic Status ◽  
Predictive Analysis ◽  
Research Approach ◽  
Significance Level ◽  
Achievement Prediction ◽  
Interaction Detection ◽  
Chi Squared

Penelitian ini bertujuan untuk membuat prediksi prestasi belajar siswa berdasarkan status sosial ekonomi orang tua, motivasi, kedisiplinan siswa dan prestasi masa lalu menggunakan metode data mining dengan algoritma J48. Sebagai perbandingan, data penelitian dianalisis juga dengan CHAID (Chi Squared Automatic Interaction Detection) dan regresi ganda. Pendekatan penelitian yang digunakan adalah kuantitatif. Subyek penelitian ini adalah siswa tingkat X SMK Negeri 4 Surakarta berjumlah 416 siswa. Teknik pengumpulan data yang digunakan adalah dokumentasi dan angket. Hasil penelitian menunjukkan bahwa analisis prediksi menggunakan decision tree algoritma J48 memiliki akurasi sebesar 95,7%, sedangkan analisis prediksi menggunakan CHAID memiliki tingat akurasi 82,1% dan analisis regresi ganda menghasilkan tingkat signifikansi sebesar 90,6%. Berdasarkan hasil tersebut bisa disimpulkan bahwa metode J48 lebih baik dibandingkan dengan metode CHAID dan regresi ganda. DATA MINING TO PREDICT STUDENT’S ACHIEVEMENT BASED ON SOCIO-ECONOMIC, MOTIVATION, DISCIPLINE AND ACHIEVEMENT OF THE PASTAbstractThis study aims to make student achievement prediction based on socio-economic status of parents, motivation, discipline students and past achievements using data mining methods with the J48 algorithm. For comparison, the data were analyzed also with CHAID (Chi Squared Automatic Interaction Detection) and multiple regression. The research approach is quantitative. The subjects of this study were student-first level at SMK Negeri 4 Surakarta totaled 416 students. Data collection techniques used are documentation and questionnaires. The results showed that the predictive analysis using J48 decision tree algorithm has an accuracy of 95.7%, while the predictive analysis using CHAID has the rank of an accuracy of 82.1% and a multiple regression analysis resulted in a significance level of 90.6%. Based on these results it can be concluded that the J48 method is better than the CHAID and multiple regression methods.

scholarly journals Analisis Rekam Medis Pasien Diabetes Mellitus Melalui Implementasi Teknik Data Mining di RSUP Dr. Sardjito Yogyakarta

2018 ◽  
Vol 2 (2) ◽  
pp. 167
Author(s):  
Marko Ferdian Salim ◽  
Sugeng Sugeng
Keyword(s):  
Diabetes Mellitus ◽  
Data Mining ◽  
Decision Tree ◽  
Decision Trees ◽  
Cross Sectional ◽  
Diabetes Melitus

Latar Belakang: Diabetes mellitus adalah penyakit kronis yang mempengaruhi beban ekonomi dan sosial secara luas. Data pasien dicatat melalui sistem rekam medis pasien yang tersimpan dalam database sistem informasi rumah sakit, data yang tercatat belum dianalisis secara efektif untuk menghasilkan informasi yang berharga. Teknik data mining bisa digunakan untuk menghasilkan informasi yang berharga tersebut.Tujuan: Mengidentifikasi karakteristik pasien Diabetes mellitus, kecenderungan dan tipe Diabetes melitus melalui penerapan teknik data mining di RSUP Dr. Sardjito Yogyakarta.Metode: Penelitian ini merupakan penelitian deskriptif observasional dengan rancangan cross sectional. Teknik pengumpulan data dilakukan secara retrospektif melalui observasi dan studi dokumentasi rekam medis elektronik di RSUP Dr. Sardjito Yogyakarta. Data yang terkumpul kemudian dilakukan analisis dengan menggunakan aplikasi Weka.Hasil: Pasien Diabetes mellitus di RSUP Dr. Sardjito tahun 2011-2016 berjumlah 1.554 orang dengan tren yang cenderung menurun. Pasien paling banyak berusia 56 - 63 tahun (27,86%). Kejadian Diabetes mellitus didominasi oleh Diabetes mellitus tipe 2 dengan komplikasi tertinggi adalah hipertensi, nefropati, dan neuropati. Dengan menggunakan teknik data mining dengan algoritma decision tree J48 (akurasi 88.42%) untuk analisis rekam medis pasien telah menghasilkan beberapa rule.Kesimpulan: Teknik klasifikasi data mining (akurasi 88.42%) dan decision trees telah berhasil mengidentifikasi karakteristik pasien dan menemukan beberapa rules yang dapat digunakan pihak rumah sakit dalam pengambilan keputusan mengenai penyakit Diabetes mellitus.

Utilizing Fuzzy Decision Trees in Decision Making

2011 ◽  
pp. 2024-2030
Author(s):  
Malcolm J. Beynonm
Keyword(s):  
Data Mining ◽  
Decision Making ◽  
Decision Tree ◽  
Decision Trees ◽  
Time Series Data ◽  
Series Data ◽  
Fuzzy Decision ◽  
Fuzzy Environment ◽  
Complex Decision Making ◽  
Fuzzy Decision Trees

The seminal work of Zadeh (1965), namely fuzzy set theory (FST), has developed into a methodology fundamental to analysis that incorporates vagueness and ambiguity. With respect to the area of data mining, it endeavours to find potentially meaningful patterns from data (Hu & Tzeng, 2003). This includes the construction of if-then decision rule systems, which attempt a level of inherent interpretability to the antecedents and consequents identified for object classification (See Breiman, 2001). Within a fuzzy environment this is extended to allow a linguistic facet to the possible interpretation, examples including mining time series data (Chiang, Chow, & Wang, 2000) and multi-objective optimisation (Ishibuchi & Yamamoto, 2004). One approach to if-then rule construction has been through the use of decision trees (Quinlan, 1986), where the path down a branch of a decision tree (through a series of nodes), is associated with a single if-then rule. A key characteristic of the traditional decision tree analysis is that the antecedents described in the nodes are crisp, where this restriction is mitigated when operating in a fuzzy environment (Crockett, Bandar, Mclean, & O’Shea, 2006). This chapter investigates the use of fuzzy decision trees as an effective tool for data mining. Pertinent to data mining and decision making, Mitra, Konwar and Pal (2002) succinctly describe a most important feature of decision trees, crisp and fuzzy, which is their capability to break down a complex decision-making process into a collection of simpler decisions and thereby, providing an easily interpretable solution.

Security in Mission Critical Communication Systems

2017 ◽  
pp. 270-291 ◽  
Author(s):  
Karen Medhat ◽  
Rabie A. Ramadan ◽  
Ihab Talkhan
Keyword(s):  
Wireless Communications ◽  
Decision Tree ◽  
Unsupervised Learning ◽  
Decision Trees ◽  
Supervised Learning ◽  
Binary Tree ◽  
Communication Systems ◽  
Clustering Algorithm ◽  
Critical Systems ◽  
Mission Critical

This chapter introduces two different algorithms to detect intrusions in mission critical communication systems to guarantee their security. The first algorithm is a classification algorithm which applies the concept of supervised learning. The second algorithm is a clustering algorithm which applies the concept of unsupervised learning. The algorithms detect intrusions using a set of detection rules that are structured in the form of decision trees. The algorithms are described in details and their results on well-known dataset are introduced. An enhancement for the J48algorithm is also introduced, where the decision tree for the algorithm is changed to a binary tree. The change enhances the complexity to reach a decision. The chapter includes a brief introduction about the security in Mission critical systems and the reason behind securing such systems. It introduces different methodologies that were introduced to detect intrusions in wireless communications.

scholarly journals Penerapan Principal Component Analysis untuk Peningkatan Kinerja Algoritma Decision Tree pada Iris Dataset

2020 ◽  
Vol 1 (2) ◽  
pp. 55-58
Author(s):  
Putri Kurnia Handayani
Keyword(s):  
Data Mining ◽  
Principal Component Analysis ◽  
Decision Tree ◽  
Supervised Learning ◽  
Principal Component ◽  
Component Analysis

Data mining merupakan salah bidang ilmu yang bermanfaat untuk pengenalan pola/knowledge yang tersimpan dalam database. Klasifikasi merupakan salah satu peran dalam bidang data mining. Termasuk ke dalam supervised learning, klasifikasi digunakan untuk memprediksi objek yang belum memiliki kelas/label. Penggunaan algoritma decision tree untuk proses mining dataset bunga iris dikarenakan kemudahan dalam representasi knowledge yang dihasilkan. Selain itu, decision tree juga termasuk ke dalam eager learner sehingga akurasi dari knowledge yang dihasilkan lebih baik. Penggunaan principal component analysis (PCA) dalam optimasi algoritma decision tree, dilakukan saat preprocessing dataset. PCA berfungsi untuk mereduksi dimensi, fitur yang saling berkorelasi akan dipertahankan. Penggunaan dataset publik bunga iris diambil dari UCI Repository. Berdasarkan hasil perhitungan, akurasi algoritma decision tree setelah dilakukan optimasi dengan PCA terhadap dataset bunga iris sebesar 95.33%.

scholarly journals Mate-tree: un algoritmo para la tarea de clasificación basado en operadores algebraicos y primitivas SQL [Mate-tree: an Algorithm for classification task based on algebraic operators and SQL primitives]

2012 ◽  
Author(s):  
Ricardo Timarán Pereira
Keyword(s):  
Data Mining ◽  
Decision Tree ◽  
Decision Trees ◽  
Information Gain ◽  
Classification Task ◽  
Classification Algorithms ◽  
Classification Rules ◽  
Decision Tree Classification ◽  
Expensive Process ◽  
Algebraic Operators

Resumen La clasificación basada en árboles de decisión es el modelo más utilizado y popular por su simplicidad y facilidad para su entendimiento. El cálculo del valor de la métrica que permite seleccionar, en cada nodo, el atributo que tenga una mayor potencia para clasificar sobre el conjunto de valores del atributo clase, es el proceso más costoso del algoritmo utilizado. Para calcular esta métrica, no se necesitan los datos, sino las estadísticas acerca del número de registros en los cuales se combinan los atributos condición con el atributo clase. Entre los algoritmos de clasificación por árboles de decisión se cuentan ID-3, C4.5, SPRINT y SLIQ. Sin embargo, ninguno de estos algoritmos se basan en operadores algebraicos relacionales y se implementa con primitivas SQL. En este artículo se presenta Mate-tree, un algoritmo para la tarea de minería de datos clasificación basado en los operadores algebraicos relacionales Mate, Entro, Gain y Describe Classifier, implementados en la cláusula SQL Select con las primitivas SQL Mate by, Entro(), Gain() y Describe Classification Rules, los cuales facilitan el cálculo de Ganancia de Información, la construcción del árbol de decisión y el acoplamiento fuerte de este algoritmo con un SGBD. Palabras ClavesÁrboles de Decisión, Minería de Datos, Operadores Algebraicos Relacionales, Primitivas SQL, Tarea de Clasificación.  Abstract Decision tree classification is the most used and popular model, because it is simple and easy to understand. The calculation of the value of the measure that allows selecting, in each node, the attribute with the highest power to classify on the set of values of the class attribute, is the most expensive process in the used algorithm. To compute this measure, the data are not needed, but the statistics about the number of records in which combine the test attributes with the class attribute. Among the classification algorithms by decision trees are ID-3, C4.5, SPRINT and SLIQ. However, none of these algorithms are based on relational algebraic operators and are implemented with SQL primitives. In this paper Mate-tree, an algorithm for the classification data mining task based on the relational algebraic operators Mate, Entro, Gain and Describe Classifier, is presented. They were implemented in the SQL Select clause with SQL primitives Mate by, Entro(), Gain() y Describe Classification Rules. They facilitate the calculation of the Information Gain, the construction of the decision tree and the tight coupled of this algorithm with a DBMS.KeywordsDecision Trees, Data Mining, Relational Algebraic Operators, SQL Primitives, Classification Task. 

scholarly journals Study of The ID3 and C4.5 Learning Algorithms

2020 ◽  
Vol 1 (2) ◽  
pp. 29-43
Author(s):  
Y. Fakir ◽  
M. Azalmad ◽  
R. Elaychi
Keyword(s):  
Data Mining ◽  
Decision Making ◽  
Data Analysis ◽  
Decision Tree ◽  
Decision Trees ◽  
Information Gain ◽  
Learning Algorithms ◽  
Large Data ◽  
Classification Algorithms ◽  
Important Data

Data Mining is a process of exploring against large data to find patterns in decision-making. One of the techniques in decision-making is classification. Data classification is a form of data analysis used to extract models describing important data classes. There are many classification algorithms. Each classifier encompasses some algorithms in order to classify object into predefined classes. Decision Tree is one such important technique, which builds a tree structure by incrementally breaking down the datasets in smaller subsets. Decision Trees can be implemented by using popular algorithms such as ID3, C4.5 and CART etc. The present study considers ID3 and C4.5 algorithms to build a decision tree by using the “entropy” and “information gain” measures that are the basics components behind the construction of a classifier model

Security in Mission Critical Communication Systems

2020 ◽  
pp. 125-147
Author(s):  
Karen Medhat ◽  
Rabie A. Ramadan ◽  
Ihab Talkhan
Keyword(s):  
Wireless Communications ◽  
Decision Tree ◽  
Unsupervised Learning ◽  
Decision Trees ◽  
Supervised Learning ◽  
Binary Tree ◽  
Communication Systems ◽  
Clustering Algorithm ◽  
Critical Systems ◽  
Mission Critical

This chapter introduces two different algorithms to detect intrusions in mission critical communication systems to guarantee their security. The first algorithm is a classification algorithm which applies the concept of supervised learning. The second algorithm is a clustering algorithm which applies the concept of unsupervised learning. The algorithms detect intrusions using a set of detection rules that are structured in the form of decision trees. The algorithms are described in details and their results on well-known dataset are introduced. An enhancement for the J48algorithm is also introduced, where the decision tree for the algorithm is changed to a binary tree. The change enhances the complexity to reach a decision. The chapter includes a brief introduction about the security in Mission critical systems and the reason behind securing such systems. It introduces different methodologies that were introduced to detect intrusions in wireless communications.

Top 10 data mining techniques in business applications: a brief survey

Kybernetes ◽  
2017 ◽  
Vol 46 (7) ◽  
pp. 1158-1170 ◽  
Author(s):  
Wei-Chao Lin ◽  
Shih-Wen Ke ◽  
Chih-Fong Tsai
Keyword(s):  
Data Mining ◽  
Decision Tree ◽  
Supervised Learning ◽  
Bankruptcy Prediction ◽  
Classification And Regression Tree ◽  
Support Vector ◽  
Content Type ◽  
Data Mining Techniques ◽  
Business Applications ◽  
Learning Techniques

Purpose Data mining is widely considered necessary in many business applications for effective decision-making. The importance of business data mining is reflected by the existence of numerous surveys in the literature focusing on the investigation of related works using data mining techniques for solving specific business problems. The purpose of this paper is to answer the following question: What are the widely used data mining techniques in business applications? Design/methodology/approach The aim of this paper is to examine related surveys in the literature and thus to identify the frequently applied data mining techniques. To ensure the recent relevance and quality of the conclusions, the criterion for selecting related studies are that the works be published in reputed journals within the past 10 years. Findings There are 33 different data mining techniques employed in eight different application areas. Most of them are supervised learning techniques and the application area where such techniques are most often seen is bankruptcy prediction, followed by the areas of customer relationship management, fraud detection, intrusion detection and recommender systems. Furthermore, the widely used ten data mining techniques for business applications are the decision tree (including C4.5 decision tree and classification and regression tree), genetic algorithm, k-nearest neighbor, multilayer perceptron neural network, naïve Bayes and support vector machine as the supervised learning techniques and association rule, expectation maximization and k-means as the unsupervised learning techniques. Originality/value The originality of this paper is to survey the recent 10 years of related survey and review articles about data mining in business applications to identify the most popular techniques.

Decision Tree Applications for Data Modelling

2011 ◽  
pp. 437-442
Author(s):  
Man Wai Lee ◽  
Kyriacos Chrysostomou ◽  
Sherry Y. Chen ◽  
Xiaohui Liu
Keyword(s):  
Data Mining ◽  
Health Care ◽  
Decision Tree ◽  
Decision Trees ◽  
Care Management ◽  
Health Care Management ◽  
Business Management ◽  
Theoretical Background ◽  
Critical Discussion ◽  
Future Research

Many organisations, nowadays, have developed their own databases, in which a large amount of valuable information, e.g., customers’ personal profiles, is stored. Such information plays an important role in organisations’ development processes as it can help them gain a better understanding of customers’ needs. To effectively extract such information and identify hidden relationships, there is a need to employ intelligent techniques, for example, data mining. Data mining is a process of knowledge discovery (Roiger & Geatz, 2003). There are a wide range of data mining techniques, one of which is decision trees. Decision trees, which can be used for the purposes of classifications and predictions, are a tool to support decision making (Lee et al., 2007). As a decision tree can accurately classify data and make effective predictions, it has already been employed for data analyses in many application domains. In this paper, we attempt to provide an overview of the applications that decision trees can support. In particular, we focus on business management, engineering, and health-care management. The structure of the paper is as follows. Firstly, Section 2 provides the theoretical background of decision trees. Section 3 then moves to discuss the applications that decision trees can support, with an emphasis on business management, engineering, and health-care management. For each application, how decision trees can help identify hidden relationships is described. Subsequently, Section 4 provides a critical discussion of limitations and identifies potential directions for future research. Finally, Section 5 presents the conclusions of the paper.

Sign in / Sign up

Export Citation Format

Share Document