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Gedung Fakultas Sains dan Teknologi Lt. 4 Jurusan Teknik Informatika Jl. A.H. Nasution No. 105 Cibiru Bandung 40614 Telp. (022) 7800525 / Fax (022) 7803936 Email : jurnal@if.uinsgd.ac.id
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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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Knowledge Management System for Railway Supply Chain Perspective Jayakrishnan, Mailasan; Mohamad, Abdul Karim; Yusof, Mokhtar Mohd
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.
The Importance of Computational Thinking to Train Structured Thinking in Problem Solving Andrian, Rian; Hikmawan, Rizki
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.v6i1.677

Abstract

Ability to do problem solving will be greatly influenced by how the flow of thinking in decomposing a problem until it finds the root of the problem so that it can determine the best solution. There is currently a growing recognition around the world that all fields require a prerequisite ability, namely to think logically, in a structured manner, and use computational tools to rapidly model and visualize data. This ability is known as Computational Thinking (CT). In this study, the author applied the computational thinking key concept in a case study to train structured thinking in problem solving. Computational thinking key concept includes Decomposition, Pattern recognition, Abstraction, and lastly use algorithms when they design simple steps to solve problems. Based on our case study that has been model, the result shows us that Computational Thinking can be used to train structured thinking in problem solving in everyday life
Organization Cybernetics for Railway Supplier Selection Jayakrishnan, Mailasan; Mohamad, Abdul Karim; Mohd Yusof, Mokhtar
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.v6i1.689

Abstract

The comprehensive stimulation for this research arises from the necessity to continually understand and investigate the Information System (IS) discipline body of knowledge from organizational practice. Specifically, in this study, we focus on comparing a few available excellence frameworks, data analytics, and cybernetics approaches. Such knowledge and skill practice in the IS field is predominant for both IS research and teaching. On the other hand, to propose a relevant performance reporting model using data analytics and cybernetics that entail a body of knowledge and skill is crucial for the development and transformation of organizational excellence. Yet, it helps to design an online real-time organizational dashboard that produces knowledge for its application and decision-making within an organizational practice. IS discipline in an organization is comparatively young and its specification in academia as well as in practice is rapidly changing, we focus on the practical design, and IS structure for organizational excellence through employing information technologies.
A Fast Dynamic Assignment Algorithm for Solving Resource Allocation Problems Amalia, Ivanda Zevi; Saikhu, Ahmad; Soelaiman, Rully
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.
Usability of Jawara Sains Mobile Learning Application Using System Usability Scale (SUS) Suharsih, Ririn; Febriani, Rinanda; Triputra, Sutadi
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.v6i1.700

Abstract

The application of interactive learning multimedia is one of the factors that impact the learning process for achieving learning outcomes. In developing a mobile application, the main principle is usability. This research goal is to presents a usability evaluation of the Jawara Sains, which is a mobile application to learn science subjects. This research used a System Usability Scale (SUS) questionnaire to measure the level of users’ perceived usability. This score can indicate the usability performance of effectiveness, efficiency, and ease of use. Jawara Sains's SUS score achieved 75.45, which indicates a B grade, acceptable, and categorized in the good range. This score can also indicate whether a user is a promoter or not. The analysis showed that the Jawara Sains is categorized into the passive Net Promoter Score (NPS), which means users will not influence other people. Therefore, recommendations are needed for its usability improvements.
Prediction System for Problem Students using k-Nearest Neighbor and Strength and Difficulties Questionnaire Kurniadi, Dede; Mulyani, Asri; Muliana, Inda
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.
The Comparison of Audio Analysis Using Audio Forensic Technique and Mel Frequency Cepstral Coefficient Method (MFCC) as the Requirement of Digital Evidence Dzulfikar, Helmy; Adinandra, Sisdarmanto; Ramadhani, Erika
JOIN (Jurnal Online Informatika) Vol 6 No 2 (2021)
Publisher : Department of Informatics, UIN Sunan Gunung Djati Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15575/join.v6i2.702

Abstract

Audio forensics is the application of science and scientific methods in handling digital evidence in the form of audio. In this regard, the audio supports the disclosure of various criminal cases and reveals the necessary information needed in the trial process. So far, research related to audio forensics is more on human voices that are recorded directly, either by using a voice recorder or voice recordings on smartphones, which are available on Google Play services or iOS Store. This study compares the analysis of live voices (human voices) with artificial voices on Google Voice and other artificial voices. This study implements the audio forensic analysis, which involves pitch, formant, and spectrogram as the parameters. Besides, it also analyses the data by using feature extraction using the Mel Frequency Cepstral Coefficient (MFCC) method, the Dynamic Time Warping (DTW) method, and applying the K-Nearest Neighbor (KNN) algorithm. The previously made live voice recording and artificial voice are then cut into words. Then, it tests the chunk from the voice recording. The testing of audio forensic techniques with the Praat application obtained similar words between live and artificial voices and provided 40,74% accuracy of information. While the testing by using the MFCC, DTW, KNN methods with the built systems by using Matlab, obtained similar word information between live voice and artificial voice with an accuracy of 33.33%.
Spanning Tree Protocol (STP) Based Computer Network Performance Analysis on BPDU Config Attacks and Take Over Root Bridge Using the Linear Regression Method Indrianingsih, Yuliani; Wintolo, Hero; Saputri, Eviana Yulianti
JOIN (Jurnal Online Informatika) Vol 6 No 2 (2021)
Publisher : Department of Informatics, UIN Sunan Gunung Djati Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15575/join.v6i2.703

Abstract

Spanning Tree Protocol (STP) is used in manageable switch devices that apply more than one path to the connection between switches. This study aims to assist engineer staff in improving STP network security. Furthermore, the benefit is to improve the STP network security system by using the Spanning Tree Protocol and Virtual Local Area Network (VLAN) trunking mitigation techniques. The results of testing data before STP is attacked, after STP attacked, and anticipatory data. Then a simple linear regression analysis is carried out by the result of that there is not a significant relationship between time and size in the DoS attack, which  48.6% of the time variable is influenced by the size of  variable, while  the remaining 51.4% by other variables. Root attack is 43.8%-time variable is influenced by size variable, the remaining 56.2% by other variables. Correlation between Karl Pearson DoS and root, there is a significant relationship between time and size, with the DoS correlation coefficient (-0.697) in contrast root (-0.662), and paired sample t-test (paired sample t-test) can be concluded the anticipation which is done by using BPDU guard and root guard mitigation.
Identification of White Blood Cells Using Machine Learning Classification Based on Feature Extraction Musliman, Anwar Siswanto; Fadlil, Abdul; Yudhana, Anton
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.
Prediction Model for Soybean Land Suitability Using C5.0 Algorithm Nurkholis, Andi; Styawati, Styawati
JOIN (Jurnal Online Informatika) Vol 6 No 2 (2021)
Publisher : Department of Informatics, UIN Sunan Gunung Djati Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15575/join.v6i2.711

Abstract

Soybean is one of the protein main sources that can be used for consumption in tempeh, tofu, milk, etc. Based on projection results, soybean production and consumption balance in Indonesia, in 2018-2022, it is estimated that deficit will increase by 6.18% per year. So, it's necessary to guide soybean land suitability, which can be carried out by evaluating existing land suitability to support soybean farming expansion and production. This study conducted an analytical study to evaluate soybean land suitability using C5.0 algorithm based on land and weather characteristics. The C5.0 algorithm is an extension of spatial decision tree, an ID3 decision tree extension. Dataset is divided into two categories: explanatory factors representing seven land characteristics (drainage, land slope, base saturation, cation exchange capacity, soil texture, soil pH, and soil mineral depth) and two weather data (rainfall and temperature), and a target class represent soybean land suitability in two study areas, namely Bogor and Grobogan Regency. The result generated two land suitability models with the best model obtained accuracy for training data 98.58%, while testing data was 97.17%. The best model rules are 69 rules that do not involve three attributes: cation exchange capacity, soil mineral depth, and rainfall.

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