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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 30 Documents
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Search results for , issue "Vol 6 No 1 (2021)" : 30 Documents clear
The Importance of Computational Thinking to Train Structured Thinking in Problem Solving Rian Andrian; Rizki Hikmawan
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
Location Selection Query in Google Maps using Voronoi-based Spatial Skyline (VS2) Algorithm Annisa Annisa; Leni Angraeni
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.v6i1.667

Abstract

Google Maps is one of the popular location selection systems. One of the popular features of Google Maps is nearby search. For example, someone who wants to find the closest restaurants to his location can use the nearby search feature. This feature only considers one specific location in providing the desired place choice. In a real-world situation, there may be a need to consider more than one location in selecting the desired place. Assume someone would like to choose a hotel close to the conference hall, the museum, beach, and souvenir store. In this situation, nearby search feature in Google Maps may not be able to suggest a list of hotels that are interesting for him based on the distance from each destination places. In this paper, we have successfully developed a web-based application of Google Maps search using Voronoi-based Spatial Skyline (VS2) algorithm to choose some Point Of Interest (POI) from Google Maps as their considered locations to select desired place. We used Google Maps API to provide POI information for our web-based application. The experiment result showed that the execution time increases while the number of considered location increases.
Implementation of Fuzzy C-Means for Clustering the Majelis Ulama Indonesia (MUI) Fatwa Documents Fajar Rohman Hariri
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.v6i1.591

Abstract

Since the Indonesian Ulema Council (MUI) was established in 1975 until now, this institution has produced 201 edicts covering various fields. Text mining is one of the techniques used to collect data hidden from data that form text. One method of extracting text is Clustering. The present study implements the Fuzzy C-Means Clustering method in MUI fatwa documents to classify existing fatwas based on the similarity of the issues discussed. Silhouette Coefficient is used to analyze the resulting clusters, with the best value of 0.0982 with 10 clusters grouping. Classify fatwas based on the similarity of the issues discussed can make it easier and faster in the search for an Islamic law in Indonesia.
Interactive Learning Media for Cellular Communication Systems using the Multimedia Development Life Cycle Model Hasanah Putri; Iqbal Shadiq; Gigin Gantini Putri
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.v6i1.544

Abstract

Based on the observations conducted to the students of Diploma of Telecommunications Engineering Telkom University. It revealed that the students have difficulty learning and understanding the chapters of call processing and network optimization in the course of cellular communication systems. It has resulted from the current learning media, which are only in the form of textbooks and Powerpoint slides considered less attractive. Hence, the learning process becomes ineffective and has an impact on low learning outcomes. In this study, an interactive learning media was designed with the Multimedia Development Life Cycle (MDLC) method, Adobe Flash professional CS6 software, using the action script 2.0 programming language. Learning media were designed according to users’ needs and learning outcomes of cellular communication system courses. Based on the testing results, the functionality showed 100% of features function as design specifications. Meanwhile, the user satisfaction testing results obtained an average MOS of 4.73, which means that the learning media is classified great. Furthermore, based on the quantitative testing, the average value of Quiz after using this interactive learning media was 81, which means that the learning media can increase students’ interest so that it affects the increase in learning outcomes by 66% from previous years.
Usability of Jawara Sains Mobile Learning Application Using System Usability Scale (SUS) Ririn Suharsih; Rinanda Febriani; Sutadi Triputra
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.
Computer Networks Optimization using Load Balancing Algorithms on the Citrix ADC Virtual Server Hardiyan Kesuma Ramadhan; Sukma Wardhana
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.v6i1.672

Abstract

In the digital era and the outbreak of the COVID-19 pandemic, all activities are online. If the number of users accessing the server exceeds IT infrastructure, server down occurs. A load balancer device is required to share the traffic request load. This study compares four algorithms on Citrix ADC VPX load balancer: round-robin, least connection, least response time and least packet using GNS3. The results of testing response time and throughput parameters show that the least connection algorithm is superior. There were a 33% reduction in response time and a 53% increase in throughput. In the service hits parameter, the round-robin algorithm has the evenest traffic distribution. While least packet superior in CPU utilization with 76% reduction. So algorithm with the best response time and throughput is the least connection. The algorithm with the best service hits is round-robin. Large scale implementation is recommended using the least connection algorithm regarding response time and throughput. When emphasizing evenest distribution, use a round-robin algorithm.
Comparison of C4.5 Algorithm and Support Vector Machine in Predicting the Student Graduation Timeliness Agus Mailana; Andi Agung Putra; Sarifudlin Hidayat; Arief Wibowo
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.v6i1.608

Abstract

In higher educational institutions, graduation rates are one of the many aspects to assess the quality of the learning process. Al-Hidayah Islamic University in Bogor is one of the established private Islamic universities to create skilled human resources with moral values required by many companies nowadays. Having another institution in Bogor as a competitor with the same direction and objective is a challenge for Al-Hidayah Islamic University. Thus a solution is required to face the competition. One solution is to predict the student graduation timeliness of the students using data mining method with classification function. The implemented methodology in the data mining is Discovery Knowledge of Database (KDD), starting from selecting, preprocessing, transformation, data mining, and evaluation/ interpretation. There were two Algorithm models used in this paper, namely C4.5 and Support Vector Machine (SVM). The classification procedure consists of predictor variables and one of the target variables. Predictor variables are gender, Grade Point Average, marital status, and job status. Rapid Miner software was used to process the data. The final results of both Algorithms show an 81% precision rate and 80% accuracy level for the C4.5 Algorithm, while SVM has an 88% precision rate and 85% accuracy level.
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.
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.

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