Implementation of Simple Additive Weighting (SAW) Method and Profile Matching for Employee Selection

Employees are an important element in a company that determines the progress of a company. With good quality employees in a company, it is easier to achieve desired goals of a company. Conventional (manual) recruitment method is vulnerable to non-technical factors such as frequent duplicate data or invalid data. In such condition, a Decision Support System (DSS) will be helpful in making decision process valid and reliable. In this paper, a Simple Addictive Weighting (SAW) method and Profile Matching were proposed to solve employee selection problem. This research was conducted at UPT Career Development and Entrepreneurship Universitas Brawijaya Malang, using data collected from written test selection in 2019. The effectiveness of both methods is analyzed by means of confusion matrix. SAW method give Accuracy rate of 94.7%, Precision rate of 87.5%, Recall rate of 91.3% and F-measure rate of 89.4%. On the other hand, Profile Matching method obtained the Accuracy rate of 90.4.7%, Precision rate of 81.4%, Recall rate of 81.4% and F-measure rate of 81.4%. From these results, it can be concluded that both methods have a high accuracy value accompanied by a high precision value when used for the selection process. This system can also reduce the bias of the same data very well, as can be seen from the high Recall and F-measure rates.

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PENERAPAN DECISION SUPPORT SYSTEM DYNAMIC MENGGUNAKAN SIMPLE ADDICTIVE WEIGHTING DALAM PENENTUAN PEGAWAI TERBAIK

Jurnal Ilmiah Informatika ◽

10.35316/jimi.v5i1.547 ◽

2020 ◽

Vol 5 (1) ◽

pp. 17-24

Author(s):

Tanti Rismawati ◽

Muhammad Aji Pangestu ◽

Agung Teguh Wibowo Almais

Keyword(s):

Decision Support ◽

Decision Support System ◽

Test Data ◽

Support System ◽

Intelligent System ◽

Confusion Matrix ◽

Web Based ◽

Simple Additive Weighting ◽

A Company ◽

F Measure

Lots of applications or programs that are very useful to simplify human work. This includes applications that are made within a company. A company needs an intelligent system or an agent that controls the company's system. In a company has employees who work. This research will discuss the Dynamic Decision Support System in determining the best employees using one of the web-based Multi-Criteria Decision Making methods, which is Simple Additive Weighting (SAW). By using 2 types of data namely pattern data and test data. The data inputted were 15 data consisting of 10 test data and 5 pattern data. Then a confusion matrix can be obtained in the form of an accuracy value of 25%, a precision of 100%, a recall of 14%, and an F Measure of 24.5%.

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Sistem Pendukung Keputusan Penerimaan Karyawan Menggunakan Simple Additive Weighting Studi Kasus PT. Trafoindo Prima Perkasa

JURNAL Al-AZHAR INDONESIA SERI SAINS DAN TEKNOLOGI ◽

10.36722/sst.v4i4.309 ◽

2018 ◽

Vol 4 (4) ◽

pp. 186

Author(s):

Rohmat Taufiq ◽

Angga Aditya Permana

Keyword(s):

Decision Support ◽

Decision Support System ◽

Support System ◽

Selection Process ◽

Employee Selection ◽

Skilled Workers ◽

Recruitment Process ◽

Simple Additive Weighting ◽

The Times ◽

A Company

Abstrak - Sebuah perusahaan tidak akan berkembang tanpa ada dukungan dari karyawan sebagai salah satu aset perusahaan, sehingga perusahaan dituntut untuk merekrut calon karyawan yang berpotensi dan berbakat guna mendukung keberhasilan perusahaan. Tenaga kerja terampil yang dapat membawa perusahaan berkembang dan bersaing dengan perkembangan zaman, dan aspek perekrutan mulai mendapatkan pandangan khusus, karena proses perekrutan yang tidak sesuai dengan kebutuhan perusahaan dapat menghalang laju berkembangnya perusaan itu sendiri. Oleh karena itu, diperlukan sistem pendukung keputusan untuk proses pemilihan karyawan tersebut. Sistem pendukung keputusan ini menggunakan metode Simple Additive Weighting (SAW). Dalam hal ini para calon karyawan dibandingkan satu dengan yang lainnya sehingga memberikan output nilai intensitas prioritas yang menghasilkan suatu sistem yang memberikan penilaian terhadap setiap karyawan. Sistem pendukung keputusan ini membantu melakukan penilaian setiap karyawan, melakukan perubahan kriteria, dan perubahaan nilai bobot. Hal ini berguna untuk memudahkan pengambilan keputusan yang terkait dengan masalah pemilihan karyawan, sehingga akan di dapatkan karyawan yang paling layak diterima di perusahaan. Abstract – A company will not develop without support from employees as one of the company's assets, so that company is required to recruit potential and talented candidates to support the company. The skilled workers who can bring the company to grow and compete with the times, and the recruitment aspect starts to get a special view, because the recruitment process that is not in accordance with the needs of the company can hinder the development of the company itself. Therefore, a decision support system is needed for the employee selection process. This decision support system uses the Simple Additive Weighting (SAW) method. In this case prospective employees are being compared with to provide an output to provide an output of priority intensity values for an employee assessment that produce a system that provides an assessment of each employee. This decision support system helps assess each employee, changes criteria, and changes the weight value. This is useful to facilitate decision making related to employee selection issues, to a company will get the most appropriate employee to be recruited.Keywords - Decision Support System, Company, SAW, Employee.

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Decision Support System for Selection of Assembly Using Profile Matching Method and Simple Additive Weighting Method (Case Study: GKIN Diaspora Church)

bit-Tech ◽

10.32877/bt.v2i1.100 ◽

2019 ◽

Vol 2 (1) ◽

pp. 43-52

Author(s):

Amesanggeng Pataropura ◽

Riki Riki ◽

Joshua Geraldo Manu

Keyword(s):

Decision Support ◽

Selection Process ◽

Matching Method ◽

Decision Matrix ◽

Weighting Method ◽

Profile Matching ◽

Operational Activities ◽

Simple Additive Weighting ◽

The Church

The church is a place of worship for Christians who are worshipers, in the church there are many organizers called the assemblies whose task is to regulate and manage all kinds of operational activities related to the worship held on Sundays, the duties and authorities of an assembly are in each position he has. To be a church administrator or assembly can be chosen by the congregation by conducting a selection process from the assemblies in the church and to be appointed by the pastor. The process of selecting the assembly can be an error to choose the assembly that can be subjective. Then a system for decision-making decision support is made using the Profile Matching Method and Simple Additive Weighting (SAW) Method. Where Profile matching is a decision support method using calculation of weight and weighting by dividing the main factors and supporting factors. And using the Simple Additive Weighting (SAW) method is a weighted housing method, by normalizing the decision decision matrix (x) to a scale that can be compared with all available alternative ratings

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SISTEM PENDUKUNG KEPUTUSAN SELEKSI TENAGA KERJA UNTUK SECURITY SERVICE MENGGUNAKAN METODE ARAS

JURNAL MEDIA INFORMATIKA BUDIDARMA ◽

10.30865/mib.v2i1.812 ◽

2018 ◽

Vol 2 (1) ◽

Author(s):

Tetty Rosmaria Sitompul ◽

Nelly Astuti Hasibuan

Keyword(s):

Decision Support ◽

Process Modeling ◽

Process Model ◽

Selection Process ◽

Employee Selection ◽

Candidate Selection ◽

Modeling Data ◽

Definition Of ◽

Index Value ◽

A Company

PT. ISS INDONESIA is a company that already has employees, which is very big. With seven branches spread across Indonesia, PT. ISS INDONESIA has more than 500 workers. Recruitment will continue as the opening of supermarkets or PT. ISS INDONESIA requires additional employees to develop or fill in empty formations. Subjects in this study are the application of decision support systems used to assist employee selection process in accordance with the criteria set by the management PT. ISS INDONESIA. System design with modified waterfall process model includes the definition of requirements required by the user in order to design the system for process modeling, data modeling, and user interface. In this research, it is expected to produce Decision Support Candidate Selection Software System With ARAS Method that can be used to manage employee candidate data and computerized criteria ranging from weighting, calculation of dominance value, arithmetic preference, calculation of index value and ARAS calculation.

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RANCANG BANGUN SISTEM PENDUKUNG KEPUTUSAN PENERIMAAN KARYAWAN BARU DENGAN MENGGUNAKAN METODE SIMPLE ADDITIVE WEIGHTING (SAW) BERBASIS WEB DI LOSE STORE

JIKA (Jurnal Informatika) ◽

10.31000/.v2i2.1516 ◽

2019 ◽

Vol 2 (2) ◽

Author(s):

Hengki Rusdianto

Keyword(s):

Human Resources ◽

Selection Process ◽

Test Results ◽

Recruitment Process ◽

Systematic Method ◽

New Employees ◽

New Employee ◽

Speed Up ◽

Simple Additive Weighting ◽

A Company

Recruitment is an important thing for the company, in acquiring prospective new employees to occupy a position. Most companies, recruitment process is still not done by a professional. This happens because there is no systematic method to assess the feasibility of prospective new employees. Recruitment decision support system applications are built using the Simple Additive Weighting (SAW) method. This method was chosen because it can determine the weight values for each attribute, and then proceed with the ranking process that will select the best alternative from several alternatives. In this case, the alternative is entitled to be accepted as a new employee in accordance with the criteria specified. Based on test results, a system built to simplify and speed up the selection process for recruitment, and assist Human Resources Department (HRD) managers in decision-making to determine a new employee at a company.

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Mobile Anomaly Detection Based on Improved Self-Organizing Maps

Mobile Information Systems ◽

10.1155/2017/5674086 ◽

2017 ◽

Vol 2017 ◽

pp. 1-9 ◽

Cited By ~ 3

Author(s):

Chunyong Yin ◽

Sun Zhang ◽

Kwang-jun Kim

Keyword(s):

Anomaly Detection ◽

Mobile Devices ◽

Clustering Algorithms ◽

Recall Rate ◽

Accuracy Rate ◽

Optimal Method ◽

Self Organizing Maps ◽

Different Characteristics ◽

Precision Rate ◽

Self Organizing

Anomaly detection has always been the focus of researchers and especially, the developments of mobile devices raise new challenges of anomaly detection. For example, mobile devices can keep connection with Internet and they are rarely turned off even at night. This means mobile devices can attack nodes or be attacked at night without being perceived by users and they have different characteristics from Internet behaviors. The introduction of data mining has made leaps forward in this field. Self-organizing maps, one of famous clustering algorithms, are affected by initial weight vectors and the clustering result is unstable. The optimal method of selecting initial clustering centers is transplanted from K-means to SOM. To evaluate the performance of improved SOM, we utilize diverse datasets and KDD Cup99 dataset to compare it with traditional one. The experimental results show that improved SOM can get higher accuracy rate for universal datasets. As for KDD Cup99 dataset, it achieves higher recall rate and precision rate.

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Pengembangan Plugin WordPress untuk Sistem Pendukung Keputusan Seleksi Penerimaan Karyawan dengan Metode SAW (Studi Kasus: Kumon Pengasih)

Respati ◽

10.35842/jtir.v12i3.223 ◽

2017 ◽

Vol 12 (3) ◽

Author(s):

Sarji Sarji ◽

Kusrini Kusrini ◽

M Rudyanto Arief

Keyword(s):

Decision Support ◽

Quality Management ◽

Decision Support System ◽

Human Resources ◽

Support System ◽

Selection Process ◽

Employee Selection ◽

Selection System ◽

Employee Recruitment ◽

Simple Additive Weighting

INTISASIDengan Sumber Daya Manusia yang kompeten di bidangnya akan berdampak pada produk atau jasa yang dihasilkan oleh perusahaan menjadi berkualitas, sehingga dapat meningkatkan keunggulan bersaing bagi perusahaan. Untuk itu setiap perusahaan harus memiliki SDM yang mampu mewujudkan manajemen yang kompetitif dan berkualitas. Untuk mendapatkan SDM yang berkualitas dimulai dari proses seleksi karyawan yang dilakukan secara profesional. Oleh karena itu, dalam perekrutan karyawan harus dianalisa dengan cermat dan teliti agar karyawan yang diterima bekerja sesuai dengan kriteria yang dibutuhkan. Penelitian ini bertujuan mengembangkan Sistem Pendukung Keputusan yang dapat digunakan untuk membantu proses seleksi karyawan sesuai dengan kriteria-kriteria yang telah ditentukan berdasarkan kebutuhan perusahaan. Bagi perusahaan yang belum memiliki formulasi perhitungan dalam proses seleksi karyawan nantinya dapat menggunakan sistem ini. Sistem ini dikembangkan menjadi produk massal berupa Plugin WordPress yang dapat membantu pengguna WordPress yang belum memiliki sistem seleksi penerimaan karyawan yang terkomputerisasi. Metode yang digunakan dalam sistem ini adalah Simple Additive Weighting. Pengguna dapat memasukan kriteria dan bobot sendiri sesuai dengan kebutuhan perusahaan. Sistem ini telah di implementasikan di Kumon Pengasih dan berdasarkan perbadingan antara perhitungan seleksi karyawan secara manual di Kumon Pengasih dengan perhitungan yang diproses oleh sistem menghasilkan rekomendasi yang sama.Kata kunci— SPK,Sistem Pendukung Keputusan, Seleksi Karyawan, Rekrutmen Karyawan,Plugin WordPress. ABSTRACTHuman Resources competent in the field will impact on products or services produced by the company to be qualified, so as to increase competitive advantage for the company. Every company must have human resources capable of realizing a competitive and quality management. To get qualified human resources starting from the process of employee selection conducted professionally. Therefore, in the recruitment of employees must be carefully and thoroughly analyzed for employees who received work in accordance with the required criteria. This study aims to develop Decision Support System that can be used to assist employee selection process in accordance with the criteria that have been determined based on the needs of the company. For companies that do not have a calculation formulation in the employee selection process will be able to use this system. This system is developed into a mass product of WordPress Plugin that can help WordPress users who do not have a computerized employee reception selection system. The method used in this system is Simple Additive Weighting. Users can enter their own criteria and weights according to company needs. This system has been implemented in Kumon Pengasih and based on the comparison between the calculation of manpower selection manually in Kumon Pengasih with calculations processed by the system produce the same recommendation.Kata kunci— DSS,Decision Support System,WordPress Plugin, Employee Recruitment.

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TapChain: A Rule Chain Recognition Model Based on Multiple Features

Security and Communication Networks ◽

10.1155/2021/6568602 ◽

2021 ◽

Vol 2021 ◽

pp. 1-11

Author(s):

Keyu Jiang ◽

Hanyi Zhang ◽

Weiting Zhang ◽

Liming Fang ◽

Chunpeng Ge ◽

...

Keyword(s):

Recall Rate ◽

Security Model ◽

Correction Algorithm ◽

Accuracy Rate ◽

Accurate Identification ◽

Multiple Features ◽

Recognition Model ◽

Model Based ◽

Iot Devices ◽

Precision Rate

Trigger-action programming (TAP) is an intelligent tool, which makes it easy for users to make intelligent rules for IoT devices and applications. Unfortunately, with the popularization of TAP and more and more rules, the rule chain from multiple rules appears gradually and brings more and more threats. Previous work pays more attention to the construction of the security model, but few people focus on how to accurately identify the rule chain from multiple rules. Inaccurate identification of rule chains will lead to the omission of rule chains with threats. This paper proposes a rule chain recognition model based on multiple features, TapChain, which can more accurately identify the rule chain without source code. We design a correction algorithm for TapChain to help us get the correct NLP analysis results. We extract 12 features from 5 aspects of the rules to make the recognition of the rule chain more accurate. According to the evaluation, compared with the previous work, the accuracy rate of TapChain is increased by 3.1%, the recall rate is increased by 1.4%, and the precision rate can reach 88.2%. More accurate identification of the rule chain can help to better implement the security policies and better balance security and availability. What’s more, according to the rule chain that TapChain can recognize, there is a new kind of rule chain with threats. We give the relevant case studies in the evaluation.

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Sensor Fusion Basketball Shooting Posture Recognition System Based on CNN

Journal of Sensors ◽

10.1155/2021/6664776 ◽

2021 ◽

Vol 2021 ◽

pp. 1-16

Author(s):

Jingjin Fan ◽

Shuoben Bi ◽

Guojie Wang ◽

Li Zhang ◽

Shilei Sun

Keyword(s):

Neural Networks ◽

Sensor Fusion ◽

Recognition System ◽

Recall Rate ◽

Accuracy Rate ◽

Average Precision ◽

Posture Recognition ◽

Deep Learning Model ◽

Precision Rate ◽

Basketball Shooting

In recent years, with the development of wearable sensor devices, research on sports monitoring using inertial measurement units has received increasing attention; however, a specific system for identifying various basketball shooting postures does not exist thus far. In this study, we designed a sensor fusion basketball shooting posture recognition system based on convolutional neural networks. The system, using the sensor fusion framework, collected the basketball shooting posture data of the players’ main force hand and main force foot for sensor fusion and used a deep learning model based on convolutional neural networks for recognition. We collected 12,177 sensor fusion basketball shooting posture data entries of 13 Chinese adult male subjects aged 18–40 years and with at least 2 years of basketball experience without professional training. We then trained and tested the shooting posture data using the classic visual geometry group network 16 deep learning model. The intratest achieved a 98.6% average recall rate, 98.6% average precision rate, and 98.6% accuracy rate. The intertest achieved an average recall rate of 89.8%, an average precision rate of 91.1%, and an accuracy rate of 89.9%.

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Multi-Documents Summarization Based on TextRank and its Application in Online Argumentation Platform

International Journal of Data Warehousing and Mining ◽

10.4018/ijdwm.2018070104 ◽

2018 ◽

Vol 14 (3) ◽

pp. 69-89 ◽

Cited By ~ 2

Author(s):

Caiquan Xiong ◽

Xuan Li ◽

Yuan Li ◽

Gang Liu

Keyword(s):

Text Analysis ◽

Clustering Algorithm ◽

Recall Rate ◽

Consensus Building ◽

Prototype System ◽

Accuracy Rate ◽

Heuristic Clustering ◽

Evaluation Parameters ◽

F Measure ◽

Similar Text

In an Online Argumentation Platform, a great deal of speech messages are produced. To find similar speech texts and extract their common summary is of great significance for improving the efficiency of argumentation and promoting consensus building. In this article, a method of speech text analysis is proposed. Firstly, a heuristic clustering algorithm is used to cluster the speech texts and obtain similar text sets. Then, an improved TextRank algorithm is used to extract a multi-document summary, and the results of the summary are fed back to experts (i.e. participants). The method of multi-document summarization is based on TextRank, which takes into account the position of sentences in paragraphs, the weight of the key sentence, and the length of the sentence. Finally, a prototype system is developed to verify the validity of the method using the four evaluation parameters of recall rate, accuracy rate, F-measure, and user feedback. The experimental results show that the method has a good performance in the system.

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