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JOIN (Jurnal Online Informatika)
Published by Universitas Islam Negeri Sunan Gunung Djati Bandung
ISSN : 25281682     EISSN : 25279165     DOI : 10.15575/join
Core Subject : Science,
Computer Science & IT
JOIN (Jurnal Online Informatika) is a scientific journal published by the Department of Informatics UIN Sunan Gunung Djati Bandung. This journal contains scientific papers from Academics, Researchers, and Practitioners about research on informatics. JOIN (Jurnal Online Informatika) is published twice a year in June and December. The paper is an original script and has a research base on Informatics.
Arjuna Subject : -
Articles 490 Documents
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Decision Support System for Employee Recruitment Using El Chinix Traduisant La Realite (Electre) And Weighted Product (WP) Mohamad Irfan; Undang Syaripudin; Cecep Nurul Alam; Muhammad Hamdani
JOIN (Jurnal Online Informatika) Vol 5 No 1 (2020)
Publisher : Department of Informatics, UIN Sunan Gunung Djati Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15575/join.v5i1.606

Abstract

Management of human resources (HR) is important to achieve company goals. One of the activities in HR management is recruitment, selection, and training. Recruitment and selection are usually done not using a system so that the calculations are still done manually. But by processing data using the system can produce a decision in recommending prospective employees that can have a positive impact on the company. The company selection process is carried out through two stages: administrative selection and final selection in the form of psychological test assessment, interviews, ability tests and communication. The use of the Elimination Et Choix Traduisant La Realite (ELECTRE) method in the administrative selection stage and the Weighted Product (WP) method in the final selection stage is a new discovery made to get the best decision in accordance with the required criteria. By using this method the final results will be obtained namely the recommendation of several prospective employees who are fit to work in the company. The performance results of this system reach one hundred percent, the data from the system is in accordance with the expected calculation.
Identification of White Blood Cells Using Machine Learning Classification Based on Feature Extraction Anwar Siswanto Musliman; Abdul Fadlil; Anton Yudhana
JOIN (Jurnal Online Informatika) Vol 6 No 1 (2021)
Publisher : Department of Informatics, UIN Sunan Gunung Djati Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15575/join.v6i1.704

Abstract

In various disease diagnoses, one of the parameters is white blood cells, consisting of eosinophils, basophils, neutrophils, lymphocytes, and monocytes. Manual identification takes a long time and tends to be subjective depending on the staff's experience, so the automatic identification of white blood cells will be faster and more accurate. White blood cells are identified by examining a colored blood smear (SADT) and examined under a digital microscope to obtain a cell image. Image identification of white blood cells is determined through HSV color space segmentation (Hue, Saturation Value) and feature extraction of the Gray Level Cooccurrence Matrix (GLCM) method using the Angular Second Moment (ASM), Contrast, Entropy, and Inverse Different Moment (IDM) features. The purpose of this study was to identify white blood cells by comparing the classification accuracy of the K-nearest neighbor (KNN), Naïve Bayes Classification (NBC), and Multilayer Perceptron (MLP) methods. The classification results of 100 training data and 50 white blood cell image testing data. Tests on the KNN, NBC, and MLP methods yielded an accuracy of 82%, 80%, and 94%, respectively. Therefore, MLP was chosen as the best classification model in the identification of white blood cells.
Knowledge Management System for Railway Supply Chain Perspective Mailasan Jayakrishnan; Abdul Karim Mohamad; Mokhtar Mohd Yusof
JOIN (Jurnal Online Informatika) Vol. 5 No 2 (2020)
Publisher : Department of Informatics, UIN Sunan Gunung Djati Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15575/join.v5i2.675

Abstract

Knowledge Management System (KMS) is a monitoring system that emphasizes the desired and actual performance of an industry. Aligning KMS to viably execute the Railway Industry methodology and supply chain operations utilizing legitimate knowledge management capabilities. Yet KMS controls the planning and priorities through action controls that emphasize on operational control level, result controls toward the strategic planning level, personnel controls on retaining the right operation with the right skills, and transaction control on the accurate and complete legal transactions for ensuring strategic management. Therefore, we have come up with a dynamic KMS for the Railway Supply Chain context that focuses on operational, tactical, and strategic perspectives on the information sources, value, analytics, and requirement for current and future drivers of an industry perspective. Moreover, this KMS aims to redesign the Information System by promoting a reductionist approach to problem-solving and best decision-making practices within an industry context.
Product Review Ranking in e-Commerce using Urgency Level Classification Approach Hamdi Ahmad Zuhri; Nur Ulfa Maulidevi
JOIN (Jurnal Online Informatika) Vol. 5 No 2 (2020)
Publisher : Department of Informatics, UIN Sunan Gunung Djati Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15575/join.v5i2.612

Abstract

Review ranking is useful to give users a better experience. Review ranking studies commonly use upvote value, which does not represent urgency, and it causes problems in prediction. In contrast, manual labeling as wide as the upvote value range provides a high bias and inconsistency. The proposed solution is to use a classification approach to rank the review where the labels are ordinal urgency class. The experiment involved shallow learning models (Logistic Regression, Naïve Bayesian, Support Vector Machine, and Random Forest), and deep learning models (LSTM and CNN). In constructing a classification model, the problem is broken down into several binary classifications that predict tendencies of urgency depending on the separation of classes. The result shows that deep learning models outperform other models in classification dan ranking evaluation. In addition, the review data used tend to contain vocabulary of certain product domains, so further research is needed on data with more diverse vocabulary.
A Fast Dynamic Assignment Algorithm for Solving Resource Allocation Problems Ivanda Zevi Amalia; Ahmad Saikhu; Rully Soelaiman
JOIN (Jurnal Online Informatika) Vol 6 No 1 (2021)
Publisher : Department of Informatics, UIN Sunan Gunung Djati Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15575/join.v6i1.692

Abstract

The assignment problem is one of the fundamental problems in the field of combinatorial optimization. The Hungarian algorithm can be developed to solve various assignment problems according to each criterion. The assignment problem that is solved in this paper is a dynamic assignment to find the maximum weight on the resource allocation problems. The dynamic characteristic lies in the weight change that can occur after the optimal solution is obtained. The Hungarian algorithm can be used directly, but the initialization process must be done from the beginning every time a change occurs. The solution becomes ineffective because it takes up a lot of time and memory. This paper proposed a fast dynamic assignment algorithm based on the Hungarian algorithm. The proposed algorithm is able to obtain an optimal solution without performing the initialization process from the beginning. Based on the test results, the proposed algorithm has an average time of 0.146 s and an average memory of 4.62 M. While the Hungarian algorithm has an average time of 2.806 s and an average memory of 4.65 M. The fast dynamic assignment algorithm is influenced linearly by the number of change operations and quadratically by the number of vertices.
Prediction System for Problem Students using k-Nearest Neighbor and Strength and Difficulties Questionnaire Dede Kurniadi; Asri Mulyani; Inda Muliana
JOIN (Jurnal Online Informatika) Vol 6 No 1 (2021)
Publisher : Department of Informatics, UIN Sunan Gunung Djati Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15575/join.v6i1.701

Abstract

The student counseling process is the spearhead of character development proclaimed by the government through education regulation number 20 of 2018 concerning strengthening character education. Counseling at the secondary school level carries out to attend to these problems that might resolve with a decision support system. So that makes research challenging to measure completion on target because it is not doing based on data. The counseling teacher does not know about student's mental and emotional health conditions, so it is often wrong to handle them. Therefore, we need a system that can recognize conditions and provide recommendations for managing problems and predicting students who have potential issues. The Algorithm used to predict problem students is K-Nearest Neighbor with a dataset of 100 students. The stages of predictive calculation are data collection, data cleaning, simulation, and accuracy evaluation. Meanwhile, building the system is done using the rapid application development methodology where the instrument used to map the student's condition is the Strenght and Difficulties Questionaire instrument. This research is a system to predict problem students with an accuracy rate of 83%. The level of user experience based on the User Experience Questionnaire (UEQ) results in the conclusion that the system reaches "Above Average.". This system is expecting to help counseling teachers implement an early warning system, help students know learning modalities, and help parents recognize the child's personality better.
Detection of Fraudulent Financial Statement based on Ratio Analysis in Indonesia Banking using Support Vector Machine Yuliant Sibaroni; Muhammad Novario Ekaputra; Sri Suryani Prasetiyowati
JOIN (Jurnal Online Informatika) Vol. 5 No 2 (2020)
Publisher : Department of Informatics, UIN Sunan Gunung Djati Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15575/join.v5i2.646

Abstract

This study proposes the use of ratio analysis-based features combined with the SVM classifier to identify fraudulent financial statements. The detection method used in this study applies a data mining classification approach. This method is expected to replace the expert in forensic accounting in identifying fraudulent financial statements that are usually done manually. The experimental results show that the proposed classifier model and ratio analysis-based features provide more than 90% accuracy results where the optimal number of features based on ratio analysis is 5 features, namely Capital Adequacy Ratio (CAR), (ANPB) to total earning assets and non-earning assets (ANP), Impairment provision on earning assets (CKPN) to earning assets, Return on Asset (ROA), and Return on Equity (ROE). The contribution of the study is to complement the research of fraudulent financial statements detection where the classifier method used here is different compare to other research. The selection of banking cases in Indonesia is also unique in this research which distinguishes it from other research because the financial reporting standards in each country can be different. 
Customer Loyality Segmentation on Point of Sale System Using Recency-Frequency-Monetary (RFM) and K-Means Bayu Rizki; Nava Gia Ginasta; Muh Akbar Tamrin; Ali Rahman
JOIN (Jurnal Online Informatika) Vol. 5 No 2 (2020)
Publisher : Department of Informatics, UIN Sunan Gunung Djati Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15575/join.v5i2.511

Abstract

It is no doubt that the development of the business world has been progressive. Point of sale is one of the many system used as a means of payment in various existing businesses, especially in heterogeneous markets. The activity of transactions between Point of Sale Systems and Customers occur in the business world. Keep in mind also that one of the main factors of business success, is from customers. There is the need of an attractive strategy and certainly it will be to increase the income and assets of a business. To know that, this research will explore the behavior of customer which is based marketing, through RFM Method (Recency, Frequency and Monetary). The case of this study is in Goldfinger Store. It will do segmentation and also use data mining technique to do clustering by using K-Means with result of loyal and potential customer. The results of segmentation using RFM (Recency, Frequency, Monetary) and K-Means methods have produced multiple clusters by dividing them into groups.
Comparison of Machine Learning Classification Methods in Hepatitis C Virus Lailis Syafa’ah; Zulfatman Zulfatman; Ilham Pakaya; Merinda Lestandy
JOIN (Jurnal Online Informatika) Vol 6 No 1 (2021)
Publisher : Department of Informatics, UIN Sunan Gunung Djati Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15575/join.v6i1.719

Abstract

The hepatitis C virus (HCV) is considered a problem to the health of societies are the main. There are around 120-130 million or 3% of the world's total population infected with HCV. Without treatment, most major infectious acute evolve into chronic, followed by diseases liver, such as cirrhosis and cancer liver. The data parameters used in this study included albumin (ALB), bilirubin (BIL), choline esterase (CHE), -glutamyl-transferase (GGT), aspartate amino-transferase (AST), alanine amino-transferase (ALT), cholesterol (CHOL), creatinine (CREA), protein (PROT), and Alkaline phosphatase (ALP). This research proposes a methodology based on machine learning classification methods including k-nearest neighbors, naïve Bayes, neural network, and random forest. The aim of this study is to assess and evaluate the level of accuracy using the algorithm classification machine learning to detect the disease HCV. The result show that the accuracy of the method NN has a value of accuracy are high, namely at 95.12% compared to the method KNN, naïve Bayes and RF in a row amounted to 89.43%, 90.24%, and 94.31%.
Embedding a Blockchain Technology Pattern Into the QR Code for an Authentication Certificate Qurotul Aini; Untung Rahardja; Melani Rapina Tangkaw; Nuke Puji Lestari Santoso; Alfiah Khoirunisa
JOIN (Jurnal Online Informatika) Vol. 5 No 2 (2020)
Publisher : Department of Informatics, UIN Sunan Gunung Djati Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15575/join.v5i2.583

Abstract

In the disruptive 4.0 era that emphasizes technological sophistication, blockchain is present as a technology that increasingly influences human life, helping humans in all aspects, including education. The role of blockchain technology in the world of education is to test the validity of diplomas, the increasing number of fake diplomas for an interest, both for work and continuing education to a higher level. The purpose of this research with the implementation of blockchain is expected to make it easier for users to verify the authenticity of a diploma. This study uses the SWOT analysis method to identify all possibilities that exist in blockchain technology. The final result of this research, the system will print a physical certificate in the form of paper in general, then the certificate will be printed a QR code. To verify numeric code on QR Code via scanning on smartphone or QR Reader. It is hoped that the blockchain technology applied to digital assets can reduce cases of forgery of diplomas and other important documents.

Page 13 of 49 | Total Record : 490

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