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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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The Hybrid of Jaro-Winkler and Rabin-Karp Algorithm in Detecting Indonesian Text Similarity Yulianto, Muhamad Arief; Nurhasanah, Nurhasanah
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.v6i1.640

Abstract

The String-matching technique is part of the similarity technique. This technique can detect the similarity level of the text. The Rabin-Karp is an algorithm of string-matching type. The Rabin-Karp is capable of multiple patterns searching but does not match a single pattern. The Jaro-Winkler Distance algorithm can find strings within approximate string matching. This algorithm is very suitable and gives the best results on the matching of two short strings. This study aims to overcome the shortcomings of the Rabin-Karp algorithm in the single pattern search process by combining the Jaro-Winkler and Rabin-Karp algorithm methods. The merging process started from pre-processing and forming the K-Gram data. Then, it was followed by the calculation of the hash value for each K-Gram by the Rabin-Karp algorithm. The process of finding the same hash score and calculating the percentage level of data similarity used the Jaro-Winkler algorithm. The test was done by comparing words, sentences, and journal abstracts that have been rearranged. The average percentage of the test results for the similarity level of words in the combination algorithm has increased. In contrast, the results of the percentage test for the level of similarity of sentences and journal abstracts have decreased. The experimental results showed that the combination of the Jaro-Winkler algorithm on the Rabin-Karp algorithm can improve the similarity of text accuracy.
Detection of Fraudulent Financial Statement based on Ratio Analysis in Indonesia Banking using Support Vector Machine Sibaroni, Yuliant; Ekaputra, Muhammad Novario; Prasetiyowati, Sri Suryani
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. 
Application of VGG Architecture to Detect Korean Syllables Based on Image Text Dewi, Irma Amelia; Shaneva, Amelia
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.v6i2.653

Abstract

Korean culture began to spread widely throughout the world, ranging from lifestyle, music, food, and drinks, and there are still many exciting things from this Korean culture. One of the interesting things to learn is to know Korean letters (Hangul), which are non-Latin characters. If the Hangul letters have been learned, the next thing that lay people must learn is the Korean syllables, which are different from the Indonesian syllables. Because of the difficulty of learning Korean syllables, understanding a sentence needed a system to recognize Korean syllables. Therefore, in this study designing a system to acknowledge Korean syllables, the method used is Convolutional Neural Network with VGG architecture. The system performs the process of detecting Korean syllables based on models that have been trained using 72 syllable classes. The tests on 72 Korean syllable classes obtain an average accuracy of 96%, an average precision value of 96%, an average recall value of 100%, and an average F1 score of 98%.
Genetic Algorithm to Optimize k-Nearest Neighbor Parameter for Benchmarked Medical Datasets Classification Prasetio, Rizki Tri
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.v5i2.656

Abstract

Computer assisted medical diagnosis is a major machine learning problem being researched recently. General classifiers learn from the data itself through training process, due to the inexperience of an expert in determining parameters. This research proposes a methodology based on machine learning paradigm. Integrates the search heuristic that is inspired by natural evolution called genetic algorithm with the simplest and the most used learning algorithm, k-nearest Neighbor. The genetic algorithm were used for feature selection and parameter optimization while k-nearest Neighbor were used as a classifier. The proposed method is experimented on five benchmarked medical datasets from University California Irvine Machine Learning Repository and compared with original k-NN and other feature selection algorithm i.e., forward selection, backward elimination and greedy feature selection.  Experiment results show that the proposed method is able to achieve good performance with significant improvement with p value of t-Test is 0.0011.
Location Selection Query in Google Maps using Voronoi-based Spatial Skyline (VS2) Algorithm Annisa, Annisa; Angraeni, Leni
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.
Multiscale Retinex Application to Analyze Face Recognition Supriyanto, Supriyanto; Harika, Maisevli; Ramadiani, Maya Sri; Ramdania, Diena Rauda
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.v5i2.668

Abstract

The main challenge that facial recognition introduces is the difficulty of uneven lighting or dark tendencies. The image is poorly lit, which makes it difficult for the system to perform facial recognition. This study aims to normalize the lighting in the image using the Multiscale Retinex method. This method is applied to a face recognition system based on Principal Component Analysis to determine whether this method effectively improves images with uneven lighting. The results showed that the Multiscale Retinex approach to face recognition's correctness was better, from 40% to 76%. Multiscale Retinex has the advantage of dark facial image types because it produces a brighter image output.
Sentiment Analysis from Indonesian Twitter Data Using Support Vector Machine And Query Expansion Ranking Atsqalani, Hasbi; Hayatin, Nur; Aditya, Christian Sri Kusuma
JOIN (Jurnal Online Informatika) Vol 7 No 1 (2022)
Publisher : Department of Informatics, UIN Sunan Gunung Djati Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15575/join.v7i1.669

Abstract

Sentiment analysis is a computational study of a sentiment opinion and an overflow of feelings expressed in textual form. Twitter has become a popular social network among Indonesians. As a public figure running for president of Indonesia, public opinion is very important to see and consider the popularity of a presidential candidate. Media has become one of the important tools used to increase electability. However, it is not easy to analyze sentiments from tweets on Twitter apps, because it contains unstructured text, especially Indonesian text. The purpose of this research is to classify Indonesian twitter data into positive and negative sentiments polarity using Support Vector Machine and Query Expansion Ranking so that the information contained therein can be extracted and from the observed data can provide useful information for those in need. Several stages in the research include Crawling Data, Data Preprocessing, Term Frequency – Inverse Document Frequency (TF-IDF), Feature Selection Query Expansion Ranking, and data classification using the Support Vector Machine (SVM) method. To find out the performance of this classification process, it will be entered into a configuration matrix. By using a discussion matrix, the results show that calcification using the proposed reached accuracy and F-measure score in 77% and 68% respectively.
Sentiment Analysis about Large-Scale Social Restrictions in Social Media Twitter Using Algoritm K-Nearest Neighbor Romli, Ikhsan; Prameswari R, Shanti; Kamalia, Antika Zahrotul
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.v6i1.670

Abstract

Sentiment analysis is a data processing to recognize topics that people talk about and their sentiments toward the topics, one of which in this study is about large-scale social restrictions (PSBB). This study aims to classify negative and positive sentiments by applying the K-Nearest Neighbor algorithm to see the accuracy value of 3 types of distance calculation which are cosine similarity, euclidean, and manhattan distance for Indonesian language tweets about large-scale social restrictions (PSBB) from social media twitter. With the results obtained, the K-Nearest Neighbor accuracy by the Cosine Similarity distance 82% at k = 3, K-Nearest Neighbor by the Euclidean Distance with an accuracy of 81% at k = 11 and K-Nearest Neighbor by Manhattan Distance with an accuracy 80% at k = 5, 7, 9, 11, and 13. So, in this study the K-Nearest Neighbor algorithm with the Cosine Similarity Distance calculation gets the highest point.
Vehicle Tracking to Determine Position in The Parking Lot Utilizing CCTV Camara Suheryadi, Adi; P, Willy Permana; PY, Reza; Sumarudin, A; Firdaus, Firdaus
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.v6i2.671

Abstract

Traveling to a place using a private vehicle is an activity that many people do when visiting an area. The visitors leave their cars in several parking lots within a certain period. The resulted, them having difficulty finding vehicles in the parking lot. This study aims to assist parking service users in finding cars parked at the parking location using CCTV cameras. Apart from being used as a security system, cameras have installed in the parking lot can also be used to track the visitor's cars to the point where they park. The proposed method consists of three large blocks: background subtraction, vehicle recognition, and vehicle tracking. Results this study obtained in the test include the accuracy for the vehicle tracking process of about 91.5%, with a true positive rate of approximately 81.12%, and vehicle recognition about 70%.
Computer Networks Optimization using Load Balancing Algorithms on the Citrix ADC Virtual Server Ramadhan, Hardiyan Kesuma; Wardhana, Sukma
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.

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