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bit-Tech

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Published by Komunitas Dosen Indonesia

ISSN : 2622271X EISSN : 26222728 DOI : https://doi.org/10.32877/bt

Core Subject : Science,

Computer Science & IT

The bit-Tech journal was developed with the aim of accommodating the scientific work of Lecturers and Students, both the results of scientific papers and research in the form of literature study results. It is hoped that this journal will increase the knowledge and exchange of scientific information, especially scientific papers and research that will be useful as a reference for the progress of the State together.

Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)

Articles 6 Documents

Search results for , issue "Vol. 1 No. 2 (2018): Data and Information Quality" : 6 Documents clear

Design of Acceptance Decision Support System for New Employees in the Technician Position Using AHP and TOPSIS Methods at CV. Techindo Global Solution dominic adello setiawan; Riki Riki; Yo Ceng Giap
bit-Tech Vol. 1 No. 2 (2018): Data and Information Quality
Publisher : Komunitas Dosen Indonesia

An agency cannot be separated from the role of human resources (HR) working in it. The quality of human resources is one factor to improve the productivity of an institution's performance. Therefore, an assessment in employee selection is an important part of providing qualified employees for the company. Problems that occur in CV Techindo Global Solutions is the process of receiving employees who are still using the manual way and based on subjective assessment results so that the process of acceptance of employees to be slow and inaccurate. The absence of an application program in support of decision making for employee recruitment. Based on this, the author designed the decision support system of employee appraisal on CV. Techindo Global Solutions using AHP (Analytic Hierarchy Process) and TOPSIS (Technique For Order Preference by Similarity to Ideal Solution) methods. Employee acceptance system is done by using Analytic Hierarchy Process method to determine the weight of each criterion and the use of Technique For Order Performance by Similarity to Ideal Solution to conduct ranking alternatives in the form of employee data. This system is built with PHP and MySQL programming language as database. With the program using AHP and TOPSIS method, the new employee's assessment is better than the individual assessment and with the decision support information system, the process of receiving the employee can be helped from the evaluation side.

Model Design of Performance Improvement Strategy of Private Higher Education Using Analytic Hierarchy Process (AHP) Method and Mutivariate Data Analysis (MDA) Sri Hartati; Kenny Puspita Sari; Satria Abadi
bit-Tech Vol. 1 No. 2 (2018): Data and Information Quality
Publisher : Komunitas Dosen Indonesia

The development of the number of Private Universities in Indonesia requires Private Universities to have good Performance and Quality. Private Universities must apply a new angle of thinking that contains elements of flexibility, speed, innovation, and integration. Flexibility, speed, innovation and integration really need human resources full of creativity. Creativity can arise from human resources who have excellence in science. Thus, Private Universities is expected to not only be able to produce the best graduates, but also be able to develop two things contained in the Tri Dharma of higher education, namely researching the results of high-quality research and developing technology for community service. For that Private Universities must always be able to adapt, develop and make improvements through organizational learning. This study aims to determine the strategy model for improving performance using a combination of AHP (Analytical Hierarchy Process) and Multivariate Data Analysis (MDA) methods. The sample of this research is management that manages Private Universities (Private Universities leaders) from several Private Universities in the Province of Lamping. AHP (Analytical Hierarchy Process) method is an analytical tool that can be used to make decisions on conditions with complex factors, especially if the decision is subjective. While the Multivariate Data Analysis (MDA) method refers to the statistical technique used to analyze data that appears from more than one variable. The results of this study indicate that there is a significant effect. Organizational Learning Factors, External Environment, Reputation, Competence, Professionalism, and Performance have a significant effect on Private Private Universities Performance. These criteria and sub-criteria are used as references for Private Universities management as a strategy to improve Private Universities performance in Lampung Province. This is basically a model of reality where each decision involves more than one single variable. This research is important to determine the best model in Private Universities Performance Improvement strategies. The results of this study contributed to the decision maker or management of Private Universities as a reference material in the member policies in improving the Performance of Private Universities

Selection of the Best Lecturers using the AHP (Analytical Hierarchy Process) and TOPSIS (The Technique for Order of Preference by Similarity to Ideal Solution): Case Study of STMIK Insan Pembangunan Winny Purbaratri; Moedjiono Moedjiono; Moch. Fajar Purnomo Alam
bit-Tech Vol. 1 No. 2 (2018): Data and Information Quality
Publisher : Komunitas Dosen Indonesia

STMIK Insan Pembangunan is a College that was established in 1990, located in Tangerang Regency. Supported by 41 Lecturer staff. Lecturers have the position as professional staff at the higher education level who are appointed in accordance with the laws and regulations. Lecturers are educators who provide a number of knowledge to students in universities or universities. The best lecturer selection system is used to support learning and teaching activities in the campus so that students are competent in the field of concentration taken. So it is needed teaching staff or lecturers who are competent in their fields, in this case to meet the criteria of the competent lecturer is needed a system that supports in this case deciding which lecturers are considered the best. The process of selecting the Best Lecturers in the current system is that there is a shortage that takes a long time to process the results of the questionnaire data and only uses one of the criteria of the Tridarma of Higher Education, namely Education and Teaching. So that the resulting decision is not yet valid. In this study a Decision Support System (DSS) will be made where the decision support system can help a person in making accurate and well-targeted decisions. The method used is AHP to calculate the weight of each criterion and TOPSIS to rank each alternative based on each criterion. The results obtained in this study are a system that is able to produce the best rank of lecturers in STMIK Insan Pembangunan.

Evaluation of Lecturer Teaching Performance Using AHP and SAW Methods Benny Daniawan
bit-Tech Vol. 1 No. 2 (2018): Data and Information Quality
Publisher : Komunitas Dosen Indonesia

The lecturer performance evaluation activity is the routine of an university in continuously improving internal quality as an evaluation and development of educational institutions. Buddhi Dharma University Tangerang, every semester evaluates lecturers' performance. But the results obtained are not optimal, this is due to the absence of an effective and efficient method in determining the results, especially in the Faculty of Science and Technology, Information System Departement. The assessment process is carried out by distributing questionnaire papers and filled out by students. This study aims to analyze the results of the questionnaire, calculated by combining the Analytical Hierarchy Process (AHP) method for weighting and combined the Simple Addictive Weighting (SAW) method for ranking. The results obtained were the level of criteria weighting accuracy reached 90.39% with 28 lecturers which teaching 47 subjects in the Information Systems Departement.

Business Intelligent Method For Academic Dashboard Niki Destiandi; Aditiya Hermawan
bit-Tech Vol. 1 No. 2 (2018): Data and Information Quality
Publisher : Komunitas Dosen Indonesia

Business Intelligence Lifecycle is a method for developing effective business intelligence (BI) decision support applications such as the Academic Dashboard. There are six steps in the BI life cycle from the beginning to implementation such as Justification, Planning, Business Analysis, Design, Construction, and Deployment, where each step is developed to be more detailed in accordance with BI's environmental needs (L. T. Moss). Management of tertiary institutions in Indonesia requires appropriate and fast academic reports that make it possible to make strategic decisions and in order to improve the quality of education. Academic evaluations can be presented with the dashboard being easy for decision making. The dashboard is a page that displays graphics as a KPI from an organization and provides everything needed to make key research results [4]. Problems that occur there are a lot of academic data that is stored but when turning it into a report at the time of evaluation academic activities are difficult and require a long time and require monitoring, evaluation and measurement tools that can measure the performance of universities. The Business Intelligence Lifecycle can be used to provide information to produce high resolution by adding KPI components.

Comparison of C4.5 Algorithm, Naive Bayes and Support Vector Machine (SVM) in Predicting Customers that Potentially Open Deposits Yusuf Kurnia; Kuera Kusuma
bit-Tech Vol. 1 No. 2 (2018): Data and Information Quality
Publisher : Komunitas Dosen Indonesia

This research is based on the application of data mining processing to produce information that is useful in helping decision making. In this study aims to determine the superior algorithm between C4.5, Naive Bayes and SVM algorithms in predicting which customers who have high potential to open deposits. The data used in this study is secondary data where its data is obtained from the UCI dataset. The comparison results of the accuracy value of C4.5 Algorithm 90.57%, accuracy of Naive Bayes 87.70% and SVM 89.29%. Based on the results of the comparison of accuracy values, it is found that the C4.5 algorithm has the highest level of accuracy. So that the application of supporting applications to predict customers who have the potential to open deposits uses the rules for establishing C4.5 data processing.

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Home Page

OAI Link

Editorial Team

Contact

Reviewer

Google Scholar

Contact Name Budi Hermawan

Contact Email -

Phone +62081703408296

Journal Mail Official info@kdi.or.id

Editorial Address Jl. Flamboyan 2 Blok B3 No. 26 Griya Sangiang Mas - Tangerang 15132

Location Kab. tangerang, Banten INDONESIA

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Contact Name
Budi Hermawan

Contact Email
-

Phone
+62081703408296

Journal Mail Official
info@kdi.or.id

Editorial Address
Jl. Flamboyan 2 Blok B3 No. 26 Griya Sangiang Mas - Tangerang 15132

Location
Kab. tangerang,

Banten

INDONESIA