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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. 2 No. 1 (2019): Data Mining and Green Technology" : 6 Documents clear

Optimization of Application of Genetic Algorithm Using C4.5 Method to Predict Breast Cancer Disease Hartana Wijaya
bit-Tech Vol. 2 No. 1 (2019): Data Mining and Green Technology
Publisher : Komunitas Dosen Indonesia

Cancer is a big challenge for humanity. Cancer can affect various parts of the body. This deadly disease can be found in humans of all ages. However, the risk of cancer increases with age. Breast cancer is the most common cancer among women, and is the biggest cause of death for women. Then there are problems in the detection of breast cancer, causing patients to experience unnecessary treatment and huge costs. In a similar study, there were several methods used but there were problems due to the shape of nonlinear cancer cells. The C4.5 method can solve this problem, but C4.5 is weak in terms of determining parameter values, so it needs to be optimized. Genetic Algorithm is one of the good optimization methods, therefore the parameter values ??of C4.5 will be optimized using Genetic Algorithms to get the best parameter values. The results of this study are that C4.5 Algorithm based on genetic algorithm optimization has a higher accuracy value (96%) than only using the C4.5 algorithm (94.99%) and which is optimized with the PSO algorithm (95.71%). This is evident from the increase in the value of accuracy of 1.01% for the C4.5 algorithm model that has been optimized with genetic algorithms. So it can be concluded that the application of genetic algorithm optimization techniques can increase the value of accuracy in the C4.5 algorithm.

The Analysis and Design Marketplace Information Systems Web-Based of Electronic Repair Service Providers with Haversine Method Darmayana Putra; Benny Daniawan; Suwitno Suwitno; Andri Wijaya
bit-Tech Vol. 2 No. 1 (2019): Data Mining and Green Technology
Publisher : Komunitas Dosen Indonesia

This system is designed to help customers find electronic service centers were spread in Tangerang. The distribution of electronic service center makes it difficult for users to determine the right electronic service center. Sometimes customers don’t know the results of repairs from the electronic service center. Search for electronic service center on the system is using the Haversine method. Haversine is used to calculate the distance between the position of the customer and the position of the electronic service centers. In addition to Haversine, the search for electronic service center will be adjusted to the type of goods you want to repair and rating from the results of the performance electronic service center. In this system, the customer can choose the damage diagnosis available and the customer can also make a special order by describing the customer's damage. This system was tested using Black Box Testing with Boundary Value Analysis technique, based on the results of testing the system can use data with a success percentage of 96.66%. The system is tested with User Acceptance Testing to find out how much the level of user acceptance of the system designed, and the result is 75% of users agree with the system.

Extraction Opinion of Social Media in Higher Education Using Sentiment Analysis Thomas Edison Tarigan; Robby C Buwono; Sri Redjeki
bit-Tech Vol. 2 No. 1 (2019): Data Mining and Green Technology
Publisher : Komunitas Dosen Indonesia

The purpose of this research is to extract social media Twitter opinion on a tertiary institution using sentiment analysis. The results of sentiment analysis will provide input to universities as a form of evaluation of management performance in managing institutions. Sentiment analysis generated using the Naïve Bayes Classifier method which is classified into 4 classes: positive, normal, negative and unknown. This study uses 1000 data tweets used for training data needs. The data is classified manually to determine the sentiment of the tweet. Then 20 tweet data is used for testing. The results of this study produce a system that can classify sentiments automatically with 75% test results for sentiment, some obstacles in processing real-time tweets such as duplicate tweets (spam tweets), Indonesian structures that are quite complex and diverse.

Decision Support System for Selection of Assembly Using Profile Matching Method and Simple Additive Weighting Method (Case Study: GKIN Diaspora Church) Amesanggeng Pataropura; Riki Riki; Joshua Geraldo Manu
bit-Tech Vol. 2 No. 1 (2019): Data Mining and Green Technology
Publisher : Komunitas Dosen Indonesia

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

Employee Performance Assessment Decision Support Using Profile Matching Method Compared to Simple Additive Weight Addition at Dharma Buddhi University, Tangerang Muhammad Subhana; Yakub Yakub
bit-Tech Vol. 2 No. 1 (2019): Data Mining and Green Technology
Publisher : Komunitas Dosen Indonesia

An employee performance evaluation of the Buddhist Dharma University is needed to see the potential of its human resources. To get an employee performance appraisal in one year requires a decision support system that is fast and measurable so that the information obtained is accurate. The method used in assessing employee performance uses profile matching and is compared with the SAW (simple additive weight) method so that the results can be properly compared. The purpose of employee appraisal is so that leaders can easily obtain information about employee performance ratings at Buddhii Dharma University. The results of the value using the profile matching method can be recommended for salary increases and positions of 4 employees. Which can be recommended for salary increases there are 17 employees and those who are not eligible for salary increases and positions are valued at 12 employees. And comparing with the Simple Additive Weight (SAW) method, there are 19 employees who are eligible to raise salaries and 14 employees who are not eligible to raise salaries and positions

Drop Out Students Identification Using Knowledge Base Dara Kusumawati; Dini Faktasari
bit-Tech Vol. 2 No. 1 (2019): Data Mining and Green Technology
Publisher : Komunitas Dosen Indonesia

The purpose of this study is modeling for the initial identification of college dropout students. Samples were taken from drop out student data for the past 4 years. Information from this sample will be acquired as a knowledge base in system modeling. The research aims to provide a knowledge-based system approach using Dempster Shafer for the management of student drop outs at universities especially in Yogyakarta. The symptoms of DO students are obtained from knowledge about DO that appears on campus in Yogyakarta. The system output is in the form of 3 groups of classification namely initial potential DO, enough potential and once potential. The results of the study produced a system that could help university managers deal with drop-out problems early.

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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

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