cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
-
Contact Email
-
Phone
-
Journal Mail Official
jurnal@if.uinsgd.ac.id
Editorial Address
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
Location
Kota bandung,
Jawa barat
INDONESIA
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
 18   19   20   21   22 
Multi-Step Vector Output Prediction of Time Series Using EMA LSTM Mohammad Diqi; Ahmad Sahal; Farida Nur Aini
JOIN (Jurnal Online Informatika) Vol 8 No 1 (2023)
Publisher : Department of Informatics, UIN Sunan Gunung Djati Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15575/join.v8i1.1037

Abstract

This research paper proposes a novel method, Exponential Moving Average Long Short-Term Memory (EMA LSTM), for multi-step vector output prediction of time series data using deep learning. The method combines the LSTM with the exponential moving average (EMA) technique to reduce noise in the data and improve the accuracy of prediction. The research compares the performance of EMA LSTM to other commonly used deep learning models, including LSTM, GRU, RNN, and CNN, and evaluates the results using statistical tests. The dataset used in this study contains daily stock market prices for several years, with inputs of 60, 90, and 120 previous days, and predictions for the next 20 and 30 days. The results show that the EMA LSTM method outperforms other models in terms of accuracy, with lower RMSE and MAPE values. This study has important implications for real-world applications, such as stock market forecasting and climate prediction, and highlights the importance of careful preprocessing of the data to improve the performance of deep learning models.
Malware Image Classification Using Deep Learning InceptionResNet-V2 and VGG-16 Method Didih Rizki Chandranegara; Jafar Shodiq Djawas; Faiq Azmi Nurfaizi; Zamah Sari
JOIN (Jurnal Online Informatika) Vol 8 No 1 (2023)
Publisher : Department of Informatics, UIN Sunan Gunung Djati Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15575/join.v8i1.1051

Abstract

Malware is intentionally designed to damage computers, servers, clients or computer networks. Malware is a general term used to describe any program designed to harm a computer or server. The goal is to commit a crime, such as gaining unauthorized access to a particular system, so as to compromise user security. Most malware still uses the same code to produce another different form of malware variants. Therefore, the ability to classify similar malware variant characteristics into malware families is a good strategy to stop malware. The research is useful for classifying malware on malware samples presented as bytemap grayscale images. The malware classification research focused on 25 malware classes with a total of 9,029 images from the Malimg dataset. This research implements the VGG-16 and InceptionResNet-V2 architectures by running 2 different scenarios, scenario 1 uses the original dataset and the other scenario uses the undersampled dataset. After building the model, each scenario will get an evaluation form such as accuracy, precision, recall, and f1-score. The highest score was obtained in scenario 2 on the VGG-16 method with a score of 94.8% and the lowest in scenario 2 on the InceptionResNet-V2 method with a score of 85.1%.
Implementation of Ant Colony Optimization – Artificial Neural Network in Predicting the Activity of Indenopyrazole Derivative as Anti-Cancer Agent Isman Kurniawan; Nabilla Kamil; Annisa Aditsania; Erwin Budi Setiawan
JOIN (Jurnal Online Informatika) Vol 8 No 1 (2023)
Publisher : Department of Informatics, UIN Sunan Gunung Djati Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15575/join.v8i1.1055

Abstract

Cancer is a disease induced by the abnormal growth of cells in body tissues. This disease is commonly treated by chemotherapy. However, at first, cancer cells can respond to the activity of chemotherapy over time, but over time, resistance to cancer cells appears. Therefore, it is required to develop new anti-cancer drugs. Indenopyrazole and its derivative have been investigated to be a potential drug to treat cancer. This study aims to predict indenopyrazole derivative compounds as anti-cancer drugs by using Ant Colony Optimization (ACO) and Artificial Neural Network (ANN) methods. We used 93 compounds of indenopyrazole derivative with a total of 1876 descriptors. Then, the descriptors were reduced by using the Pearson Correlation Coefficient (PCC) and followed by the ACO algorithm to get the most relevant features. We found that the best number of descriptors obtained from ACO is ten descriptors. The ANN prediction model was developed with three architectures, which are different in hidden layer number, i.e., 1, 2, and 3 hidden layers. Based on the results, we found that the model with three hidden layers gives the best performance, with the value of the R2 test, R2 train, and Q2 train being 0.8822, 0.8495, and 0.8472, respectively.
Classification of Stunting in Children Using the C4.5 Algorithm Muhajir Yunus; Muhammad Kunta Biddinika; Abdul Fadlil
JOIN (Jurnal Online Informatika) Vol 8 No 1 (2023)
Publisher : Department of Informatics, UIN Sunan Gunung Djati Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15575/join.v8i1.1062

Abstract

Stunting is a disease caused by malnutrition in children, which results in slow growth. Generally, stunting is characterized by a lack of weight and height in young children. This study aims to classify stunting in children aged 0-60 months using the Decision Tree C4.5 method based on z-score calculations with a sample size of 224 records, consisting of 4 attributes and 1 label, namely Gender, Age, Weight, Height, and Nutritional Status. The results of the study obtained a C4.5 decision tree where the Age variable influenced the classification of stunting with the highest Gain Ratio of 0.185016337. Meanwhile, the evaluation of the model using the Confusion matrix resulted in the highest accuracy of 61.82% and AUC of 0.584.
Digital Image Processing Using YCbCr Colour Space and Neuro Fuzzy to Identify Pornography Beki Subaeki; Yana Aditia Gerhana; Meta Barokatul Karomah Rusyana; Khaerul Manaf
JOIN (Jurnal Online Informatika) Vol 8 No 1 (2023)
Publisher : Department of Informatics, UIN Sunan Gunung Djati Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15575/join.v8i1.1070

Abstract

Pornography is a severe problem in Indonesia, apart from drugs. This can be seen based on data from the Ministry of Communication and Informatics in 2021 which found 1.1 million pornographic content online. The increasing number of access to pornographic content sites on the internet can prove this. Several studies have been conducted to produce preventive formulas. However, this research flow has not been effective in solving the problem. This is because the results of the identification value in the output image obtained are not quite right. This study proposes a procedure for identifying pornographic content in digital images as an alternative approach for the early stages of a destructive content access prevention system. The formulation uses the YCbCr color space to analyze human skin on image objects that represent exposed body parts and the classification process with the Neuro Fuzzy approach. The performance of this formula was tested on 100 digital images of random categories of human objects (usually covered, skimpy, and naked) taken from the internet. The test results are at a relatively good level of accuracy, with a weight of 70% for the entire test data.
Texture Analysis of Citrus Leaf Images Using BEMD for Huanglongbing Disease Diagnosis Sumanto; Agus Buono; Karlisa Priandana; Bib Paruhum Silalahi; Elisabeth Sri Hendrastuti
JOIN (Jurnal Online Informatika) Vol 8 No 1 (2023)
Publisher : Department of Informatics, UIN Sunan Gunung Djati Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15575/join.v8i1.1075

Abstract

Plant diseases significantly threaten agricultural productivity, necessitating accurate identification and classification of plant lesions for improved crop quality. Citrus plants, belonging to the Rutaceae family, are highly susceptible to diseases such as citrus canker, black spot, and the devastating Huanglongbing (HLB) disease. Traditional approaches for disease detection rely on expert knowledge and time-consuming laboratory tests, which hinder rapid and effective disease management. Therefore, this study explores an alternative method that combines the Bidimensional Empirical Mode Decomposition (BEMD) algorithm for texture feature extraction and Support Vector Machine (SVM) classification to improve HLB diagnosis. The BEMD algorithm decomposes citrus leaf images into Intrinsic Mode Functions (IMFs) and a residue component. Classification experiments were conducted using SVM on the IMFs and residue features. The results of the classification experiments demonstrate the effectiveness of the proposed method. The achieved classification accuracies, ranging from 61% to 77% for different numbers of classes, the results show that the residue component achieved the highest classification accuracy, outperforming the IMF features. The combination of the BEMD algorithm and SVM classification presents a promising approach for accurate HLB diagnosis, surpassing the performance of previous studies that utilized GLCM-SVM techniques. This research contributes to developing efficient and reliable methods for early detection and classification of HLB-infected plants, essential for effective disease management and maintaining agricultural productivity.
Application of VGG Architecture to Detect Korean Syllables Based on Image Text Irma Amelia Dewi; Amelia Shaneva
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%.
Application of Augmented Reality to Replicate Couples Sit in Wedding Ceremony Achmad Ariq Rivaldhi; Rohman Dijaya
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

Abstract

In Indonesia, the bride and groom do pre-wedding photos like a tradition before marriage with couple sit in wedding ceremony. The splendor of the wedding ceremony made many vendors interested in running a wedding party business. These vendors provide services and goods related to wedding parties, such as wedding organizers, bridal, party decorations (couples sit in of wedding ceremony), as well as photo and video documentation. To give more virtual wedding nuance experience to customer, augmented reality was applied. This research develop application of augmented applying to replicate couples sit in of wedding ceremony. Augmented reality (AR) technology adopted to promote couples sit in of wedding ceremony. Moreover, combines wedding ceremony nuance and augmented reality make consumers who want to find 3D wedding background desired.
Model TangselPay Receipts Using the UTAUT 2 Method Aolia Ikhwanudin; Kusrini Kusrini; Agung Budi Prasetio
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.v6i2.803

Abstract

The South Tangerang City Government launched a digital financial service called TangselPay. This payment instrument will function as a means of paying levies and other transactions paid by taxpayers, TangselPay is basically a service from the South Tangerang City Government which is accessed via cellular phones (cell phones/smartphones) with the main aim of providing convenience to taxpayers in making levy payments. . . , so that taxpayers do not need to pay cash to the officer. This study aims to determine what factors influence people's interest in using TangselPay services in South Tangerang. The research model used is a modified model of Unified Theory of Acceptance and Use of Technology 2 (UTAUT 2). Data collection using purposive sampling method with the number of respondents in this study as many as 116 people in our market Pamulang. The data analysis technique in this study used Structural Equation Modeling (SEM) with SmartPLS version 3.3.3 software. The results of the analysis illustrate that the variables of Performance Expectations (PE) and Facilitation Condition (FC) have a positive effect on Use behavior and interest in use have a positive effect on usage behavior. While the variables of business expectations, social influence and hedonic motivation do not have a direct effect.
Improving Indonesian Named Entity Recognition for Domain Zakat Using Conditional Random Fields Nur Febriana Widiyanti; Husni Teja Sukmana; Khodijah Hulliyah; Dewi Khairani; Lee Kyung Oh
JOIN (Jurnal Online Informatika) Vol 8 No 2 (2023)
Publisher : Department of Informatics, UIN Sunan Gunung Djati Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15575/join.v8i2.898

Abstract

In Indonesia, where the majority of the population is Muslim, one of the obligations of a Muslim is zakat. To reduce illiteracy about zakat among Muslims, they need to have access to basic information about it. In order to facilitate the acquisition of this information, this study utilized named entity recognition (NER) and defined 12 named entity classes for the zakat domain, including the pillars of Islam, various types of zakat, and zakat management institutions. The Conditional Random Fields method was used for testing Indonesian-NER in three scenarios. In the specific context of the Zakat domain, NER can extract information about organizations, individuals, and locations involved in collecting and distributing Zakat funds. This information can improve the Zakat system’s efficiency and transparency and support research and analysis on Zakat-related topics. The average performance evaluation of the Indonesian-NER model showed a precision of 0.902, recall of 0.834, and an F1-score of 0.867.

Page 20 of 49 | Total Record : 490

 18   19   20   21   22 

Filter by Year

2016 2025


Reset
Filter By Issues
All Issue Vol 10 No 2 (2025) Vol 10 No 1 (2025) Vol 9 No 2 (2024) Vol 9 No 1 (2024) Vol 8 No 2 (2023) Vol 8 No 1 (2023) Vol 7 No 2 (2022) Vol 7 No 1 (2022) Vol 6 No 2 (2021) Vol 6 No 1 (2021) Vol. 5 No 2 (2020) Vol 5, No 1 (2020) Vol 5 No 1 (2020) Vol 4 No 2 (2019) Vol 4 No 1 (2019) Vol 3 No 2 (2018) Vol 3 No 1 (2018) Vol 2 No 2 (2017) Vol 2 No 1 (2017) Vol 1 No 2 (2016) Vol 1 No 1 (2016) More Issue