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All Journal International Journal of Electrical and Computer Engineering Journal of the Indonesian Mathematical Society Sinkron : Jurnal dan Penelitian Teknik Informatika Building of Informatics, Technology and Science MES: Journal of Mathematics Education and Science Journal of Research in Mathematics Trends and Technology Journal of Applied Data Sciences
Saib Suwilo
Department of Mathematics, University of Sumatera Utara
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Mathematics

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VERTEX EXPONENTS OF A CLASS OF TWO-COLORED HAMILTONIAN DIGRAPHS Syahmarani, Aghni; Suwilo, Saib
Journal of the Indonesian Mathematical Society Volume 18 Number 1 (April 2012)
Publisher : IndoMS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22342/jims.18.1.105.1-19

Abstract

pdf abstractDOI : http://dx.doi.org/10.22342/jims.18.1.105.1-19
Feature Extraction Method GLCM and LVQ in Digital Image-Based Face Recognition Sukiman, T. Sukma Achriadi; Suwilo, Saib; Zarlis, Muhammad
Sinkron : jurnal dan penelitian teknik informatika Vol. 4 No. 1 (2019): SinkrOn Volume 4 Number 1, October 2019
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (399.88 KB) | DOI: 10.33395/sinkron.v4i1.10199

Abstract

The face is one of the media to identify someone, a human face has a very high level of variability. Many methods have been introduced by researchers and scientists in recognizing one's face, one of the methods introduced is the Feature Extraction of Gray Level Co-Occurrence Matrix (GLCM) and Learning Vector Quantization (LVQ). GLCM feature extraction is used for data extraction/learning process whereas a data analysis process (face recognition, cropping and storing data) the LVQ method is used for the data training process where the data that has been processed in GLCM feature extraction which still has large dimensions are processed to be smaller dimensions. So this test uses data of 190 photos and gets a match of 90%, the authors conclude that the GLCM feature extraction and LVQ method can very well recognize faces contained in the database.
Analysis of Braycurtis, Canberra and Euclidean Distance in KNN Algorithm Pulungan, Annisa Fadhillah; Zarlis, Muhammad; Suwilo, Saib
Sinkron : jurnal dan penelitian teknik informatika Vol. 4 No. 1 (2019): SinkrOn Volume 4 Number 1, October 2019
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (308.901 KB) | DOI: 10.33395/sinkron.v4i1.10207

Abstract

Classification is a technique used to build a classification model from a sample of training data. One of the most popular classification techniques is The K-Nearest Neighbor (KNN). The KNN algorithm has important parameter that affect the performance of the KNN Algorithm. The parameter is the value of the K and distance matrix. The distance between two points is determined by the calculation of the distance matrix before classification process by the KNN. The purpose of this study was to analyze and compare performance of the KNN using the distance function. The distance functions are Braycurtis Distance, Canberra Distance and Euclidean Distance based on an accuracy perspective. This study uses the Iris Dataset from the UCI Machine Learning Repository. The evaluation method used id 10-Fold Cross-Validation. The result showed that the Braycurtis distance method had better performance that Canberra Distance and Euclidean Distance methods at K=6, K=7, K=8 ad K=10 with accuracy values of 96 %.
VERTEX EXPONENTS OF A CLASS OF TWO-COLORED HAMILTONIAN DIGRAPHS Aghni Syahmarani; Saib Suwilo
Journal of the Indonesian Mathematical Society Volume 18 Number 1 (April 2012)
Publisher : IndoMS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22342/jims.18.1.105.1-19

Abstract

pdf abstractDOI : http://dx.doi.org/10.22342/jims.18.1.105.1-19
Dimensi Partisi pada Graf Payung Rumahorbo, Yuli; Suwilo, Saib; Mardiningsih, Mardiningsih; Nasution, Putri Khairiah
MES: Journal of Mathematics Education and Science Vol 9, No 2 (2024): Edisi April
Publisher : Universitas Islam Sumatera Utara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30743/mes.v9i2.8613

Abstract

Dimensi metrik, dimensi partisi, dan bilangan kromatik-lokasi dari suatu graf merupakan tiga macam konsep dimensi dalam graf yang berkaitan. Untuk memperoleh cara pandang baru terhadap permasalahan penentuan dimensi metrik graf, Chartrand, Salehi, dan Zhang pada tahun 2000 memperkenalkan suatu konsep baru yang selanjutnya dikenal sebagai dimensi partisi graf. Andaikan G(V,E) suatu graf terhubung dengan himpunan titik V dan himpunan sisi E. Diberikan partisi Π dari V(G) dengan k kelas komponen dalam bentuk Π={L_1,L_2,⋯,L_k}. Representasi dari titik t terhadap Π didefinisikan sebagai vektor dengan k komponen dapat ditulis dalam bentuk r(t│Π)=(d(t,L_1 ),d(t,L_2 ),⋯,d(t,L_k )), dimana k merupakan bilangan bulat positif. Untuk suatu graf G terhubung dan suatu subhimpunan L⊂V(G), partisi Π disebut partisi pembeda dari graf G jika semua representasi dari titik t∈V(G) berbeda terhadap Π. Bilangan bulat positif terkecil k adalah dimensi partisi pada graf G yang dinotasikan dengan pd(G). Pada penelitian ini akan ditentukan dimensi partisi pada graf payung U_(m,n) (1) dan U_(m,n) (2). Graf U_(m,n) (1) merupakan suatu graf hasil penggabungan sebuah graf roda W_(1,n) dan lintasan P_n. Graf U_(m,n) (2) merupakan suatu graf hasil penggabungan sebuah graf kipas F_(1,n) dan lintasan P_n.
THE CYCLE LENGTH OF SPARSE REGULAR GRAPH Christy, Claudia; Suwilo, Saib; Tulus, Tulus
Sinkron : jurnal dan penelitian teknik informatika Vol. 6 No. 3 (2022): Article Research Volume 6 Number 3, July 2022
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v7i3.11595

Abstract

Let be a reguler graph with girth . Set of cycle length in Graf is denoted by . Graph is a sparse graph if and only if . Furthermore, it was obtained the number of cycle length of sparse reguler graph which denoted is .
Graph-Based Modeling for Optimal Strategy in Online Buying Tarigan, Ruffiana; Mardiningsih; Suwilo, Saib
Sinkron : jurnal dan penelitian teknik informatika Vol. 6 No. 3 (2022): Article Research Volume 6 Number 3, July 2022
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v7i3.11601

Abstract

Discount is a type of online purchase promotion that is presented based on the total value of consumer purchases. In this paper, an online purchase optimization problem will be studied, where a buyer is interested in buying several items (x≥2) by considering the total value discounts from different retailers, so that result in significant cost savings. The comparison shopping websites can be an alternative for consumers to find and compare information on items they want to buy from many online retailers. An integer programming formulation is proposed to obtain a near-optimal model of the online purchase problem. Then this formulation was developed into a graph-based modeling which was presented to build an optimization model (OptiGraph). The OptiGraph model obtained consists of the OptiNode set (subgraph) SG_a,SG_b,SG_c (retailer a, b, and c which contains nodes m_1 and m_2 in each subgraph representing the item to be purchased) and the OptiEdge set which describes the relationship between nodes in the subgraph. All nodes and edges contain the constraint function properties of the integer programming formulation of the online purchase problem with discount.
The Maximum Degree of an Exponentially Distributed Random Graph Harahap, Desti Alannora; Suwilo, Saib; Mardiningsih
Sinkron : jurnal dan penelitian teknik informatika Vol. 6 No. 3 (2022): Article Research Volume 6 Number 3, July 2022
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v7i3.11602

Abstract

Let G G (n, p) be a graph on n vertices where each pair of vertices is joined independently with probability p for 0 < p < 1 and q = 1 p. In this work, we introduce weighted random graf G with exponential distribution and investigate that the probability that every vertex of G has degree at most np + b√pqn is equal to 0.595656764.
The search for alternative algorithms of the iteration method on a system of linear equation Tarigan, Aam Jon Mintase; Mardiningsih , Mardiningsih; Suwilo, Saib
Sinkron : jurnal dan penelitian teknik informatika Vol. 6 No. 4 (2022): Article Research: Volume 6 Number 4, October 2022
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v7i4.11817

Abstract

The system of linear equations is a set of linear equations consisting of coefficients and variables. The coefficients in the system of linear equations exist in the form of real numbers and some are complex numbers. The system of linear equations has some form of solving or solution, ie a single solution, many solutions and no solutions. One of the most common problems encountered in systems of linear equations. Using modern mathematical methods, often a complex problem can be reduced to a system of linear equations.There are basically two groups of methods that can be used to solve a linear equation. The first method is known as the direct method, ie the method that searches for the completion of a linear equation in finite step. These are guaranteed to work and are recommended for general use. The second group is known as the indirect method or the method of iteration, which starts from an early settlement. Then try to fix almost in infinity, but convergent steps. The iterative methods are used to solve large Linear Equations Systems. And the proportion of zero is large, as are many systems encountered in the Linear Equation System. Therefore it takes an Alternative Algorithm in Iteration Method
Improved Accuracy In Data Mining Decision Tree Classification Using Adaptive Boosting (Adaboost) Riansyah, Muhammad; Suwilo, Saib; Zarlis, Muhammad
Sinkron : jurnal dan penelitian teknik informatika Vol. 7 No. 2 (2023): Research Article, Volume 7 Issue 2 April, 2023
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v8i2.12055

Abstract

The Decision Tree algorithm is a data mining method algorithm that is often applied as a solution to a problem for a classification. The Decision Tree C5.0 algorithm has several weaknesses, including: the C5.0 algorithm and several other decision tree methods are often biased towards modeling whose features have many levels, some problems for the model can occur such as over-fit or under-fit challenges, big changes to decision logic can result in small changes to data training, C5.0 can experience modeling inconvenience, data imbalance causes low accuracy in C5.0 algorithm. The boosting algorithm is an iterative algorithm that gives different weights to the distribution of training data in each iteration. Each iteration of boosting adds weight to examples of misclassification and decreases weight to examples of correct classification, thereby effectively changing the distribution of the training data. One example of a boosting algorithm is adaboost. The purpose of this research is to improve the performance of the Decision Tree C5.0 classification method using adaptive boosting (adaboost) to predict hepatitis disease using the Confusion matrix. Tests that have been carried out with the Confusion Matrix use the Hepatitis dataset in the Decision Tree C5.0 classification which has an accuracy rate of 80.58% with a classification error rate of 19.15%. Whereas in the Decision Tree C5.0 classification Adaboost has a higher accuracy rate of 82.98%, a classification error rate of 17.02%. This difference is caused by the adaboost algorithm, because the adaboost algorithm is able to change a weak classifier into a strong classifier by increasing the weight of the observations, and adaboost is also able to reduce the classifier error rate.
Co-Authors Aghni Syahmarani Aghni Syahmarani Ahmad Zaki Mubarak, Ahmad Zaki Christy, Claudia Efendi, Syahril Elly Rosmaini Erna Budhiarti Nababan Feren, Feren Gayus Simarmata, Gayus Harahap, Desti Alannora Hasibuan, Shubuhan Syukri Herman Mawengkang Mardiansyah, Heru Mardiningsih Mardiningsih , Mardiningsih Mardiningsih Mardiningsih Muhammad Zarlis, Muhammad Nasution, Putri Khairiah Opim Salim Sitompul Pasaribu, Suhendri Prandana, Randy Pujiana, Perli Pulungan, Annisa Fadhillah Putri, Mimmy Sari Syah Rezeki, Ema Sri Riansyah, Muhammad Rizka Fitria, Rizka Rumahorbo, Yuli Sukiman, T. Sukma Achriadi Sutarman Sutarman Tarigan, Aam Jon Mintase Tarigan, Ruffiana Tulus Tulus Yanti, Maulida