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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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PSO based Hyperparameter tuning of CNN Multivariate Time- Series Analysis Putra Utama, Agung Bella; Wibawa, Aji Prasetya; Muladi, Muladi; Nafalski, Andrew
JOIN (Jurnal Online Informatika) Vol 7 No 2 (2022)
Publisher : Department of Informatics, UIN Sunan Gunung Djati Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15575/join.v7i2.858

Abstract

Convolutional Neural Network (CNN) is an effective Deep Learning (DL) algorithm that solves various image identification problems. The use of CNN for time-series data analysis is emerging. CNN learns filters, representations of repeated patterns in the series, and uses them to forecast future values. The network performance may depend on hyperparameter settings. This study optimizes the CNN architecture based on hyperparameter tuning using Particle Swarm Optimization (PSO), PSO-CNN. The proposed method was evaluated using multivariate time-series data of electronic journal visitor datasets. The CNN equation in image and time-series problems is the input given to the model for processing numbers. The proposed method generated the lowest RMSE (1.386) with 178 neurons in the fully connected and 2 hidden layers. The experimental results show that the PSO-CNN generates an architecture with better performance than ordinary CNN.
The Measurement and Evaluation of Information System Success Based on Organizational Hierarchical Culture Haerani, Reni; Abdul Rahman, Titik Khawa; Kamelia, Lia
JOIN (Jurnal Online Informatika) Vol 7 No 2 (2022)
Publisher : Department of Informatics, UIN Sunan Gunung Djati Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

In this study, the adoption of the Delone & McLean information system success model and its adaptation using the organizational hierarchy culture theory is used to explore the state of information system success and examine the factors that suggest success. This research was conducted at universities in Banten Province, which currently rely on information systems in many ways, especially those related to university management. By measuring the evaluation of the success of information systems and the hierarchical culture in organizations using a model that the researcher built according to the integration of 2 models. The results the measurement of the success of information systems were obtained from distributing questionnaires, there were still 85 (63%) respondents, and 84 (61.3%) were satisfied with the performance of the information system success model. The least squares structural equation modeling analysis (PLS-SEM) was then applied due to the sample size. The previous stage consisted of evaluating the reflective measurement model in evaluating the reliability of internal consistency using Composite Reliability, Reliability indicators, Convergent Validity and Discriminant Validity, finally it was concluded that the success of information system by hierarchical culture integration model in the organization on could be passed on the more complex research terms, especially using samples, and different questionnaires.
Performance Analysis of Cache Replacement Algorithm using Virtual Named Data Network Nodes Yovita, Leanna Vidya; Wibowo, Tody Ariefianto; Ramadha, Ade Aditya; Satriawan, Gregorius Pradana; Raniprima, Sevierda
JOIN (Jurnal Online Informatika) Vol 7 No 2 (2022)
Publisher : Department of Informatics, UIN Sunan Gunung Djati Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15575/join.v7i2.875

Abstract

As a future internet candidate, named Data Network (NDN) provides more efficient communication than TCP/IP network. Unlike TCP/IP, consumer requests in NDN are sent based on content, not the address. The previous study evaluated the NDN performance using a simulator. In this research, we modeled the system using virtual NDN nodes, making the model more relevant to the real NDN. As an essential component in every NDN router, the content store (CS) has a function to keep the data. We use First In First Out (FIFO) and Least Recetly Used (LRU) in our nodes as cache replacement algorithms. The in-depth exploration is done using various scenarios. The result shows that the cache hit ratio (CHR) increases if the size of the CS, the number of interests, and the number of consumers increases. CHR decreases as the number of producers and the number of prefixes increase. As CHR increases, round trip time (RTT) decreases. LRU provides better performance for all cases: higher CHR of 5-15% and lower RTT of 1-10% than FIFO.
Automatic Detection of Hijaiyah Letters Pronunciation using Convolutional Neural Network Algorithm Gerhana, Yana Aditia; Azis, Aaz Muhammad Hafidz; Ramdania, Diena Rauda; Dzulfikar, Wildan Budiawan; Atmadja, Aldy Rialdy; Suparman, Deden; Rahayu, Ayu Puji
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.v7i1.882

Abstract

Abstract— Speech recognition technology is used in learning to read letters in the Qur'an. This study aims to implement the CNN algorithm in recognizing the results of introducing the pronunciation of the hijaiyah letters. The pronunciation sound is extracted using the Mel-frequency cepstral coefficients (MFCC) model and then classified using a deep learning model with the CNN algorithm. This system was developed using the CRISP-DM model. Based on the results of testing 616 voice data of 28 hijaiyah letters, the best value was obtained for accuracy of 62.45%, precision of 75%, recall of 50% and f1-score of 58%.
Improving Indonesian Named Entity Recognition for Domain Zakat Using Conditional Random Fields Widiyanti, Nur Febriana; Sukmana, Husni Teja; Hulliyah, Khodijah; Khairani, Dewi; Oh, Lee Kyung
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.
Sentiment Analysis for the Brazilian Anesthesiologist Using Multi-Layer Perceptron Classifier and Random Forest Methods Asian, Jelita; Dholah Rosita, Moneyta; Mantoro, Teddy
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.v7i1.900

Abstract

Sexual harassment is defined as giving sexual attention both verbally, either in speech or writing, and physically to victims who are predominantly women, On July 13, 2022, there was a tweet featuring a video of sexual harassment that made it trend in various countries. The video irritated Twitter users and made various comments resulting in various sentiments that can be analyzed using sentiment analysis. The purpose of this study is to see what the public thinks about the sexual harassment case of Brazilian anesthesiologist. Besides the sentiment analysis, another aim of this study is to see how objective are those sentiments based on their polarity. This study uses a comparison of two methods in sentiment analysis, namely Multi-Layer Perceptron Classifier and Random Forest, and labeling automatically using TextBlob.  This results in 94.44% accuracy, 94.44% precision, 92% recall and 93% f1_score. For MLP Classifier and accuracy 96.42%, precision 94.44%, recall 96.66% and f1_score 95.56% for Random Forest. Sentiment polarity score from the TextBlob is -0.5 and subjectivity is 0.4 which indicates that most statements are negative and subjective score is 0.4, which means those sentiments are subjective in nature.
Social Network Analysis: Identification of Communication and Information Dissemination (Case Study of Holywings) Aditiawarman, Umar; Lumbia, Mega; Mantoro, Teddy; Ibrahim, Adamu Abubakar
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.v8i1.911

Abstract

Social media especially Twitter has been used by corporation or organization as an effective tool to interact and communicate with the consumers. Holywings is one of the popular restaurants in Indonesia that use social media as a tool to promote and disseminate information regarding their products and services. However, one of their promotional items has gone viral and invited public protests which turned into a trending topic on Twitter for a couple of weeks. Holywings allegedly improperly promoted their products by using the most honorable names, “Muhammad” and “Maria”. Social network analysis of Twitter data is conducted to identify and examine information circulating among the users, which leads to wider public attention and law enforcement. In this study, we focused on the conversation about Holywings on Twitter from 24 June to 31 July 2022. The analysis was carried out using Python to retrieve data and Gephi software to visualize the interactions and the intensity of the network group in viewing the spread of information. The findings reveal the centrality account that caused the news to go viral are the CNN Indonesia (@CNNIndonesia) news media account and Haris Pertama (@knpiharis), with a centrality of 0.161 and 0.282, respectively. There are also 121 groups involved in the conversation with modularity of 0.821.
Data Analysis of Social Media's Impact on COVID19 Pandemic Users' Mental Health Dewi, Deshinta Arrova
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.v7i1.912

Abstract

Social media has a significant impact on people's daily lives and spread widely. Unrestrained usage of social media could have worsening consequences on mental health. The majority of COVID-19 users who were exposed to social media learned numerous facts, which made their anxiety and depression-related mental health disorders worse. This study aims to determine how social media usage affects users' mental health during the COVID19 pandemic. Through surveys and expert interviews, this study collects both quantitative and qualitative data. The total number of respondents involved was 106 with the average age group of 18-41-year-old. Using reliability testing (Cronbach alpha test) and inferential statistic (Pearson Correlation and Chi-Square), results show that during the COVID19 pandemic, there is a significant link between social media use and mental health. Anxiety and depression brought on by social media are more common among young adults, predominantly female, between the ages of 18 and 24 than in men. Additionally, correlation plot analysis with a variety of queries reveals the mental health issues and activities on social media.
The Implementation of Restricted Boltzmann Machine in Choosing a Specialization for Informatics Students Nastiti, Vinna Rahmayanti Setyaning; Sari, Zamah; Chintia Eka Merita, Bella
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.v8i1.917

Abstract

Choosing a specialization was not an easy task for some students, especially for those who lacked confidence in their skill and ability. Specialization in tertiary education became the benchmark and key to success for students’ future careers. This study was conducted to provide the learning outcomes record, which showed the specialization classification for the Informatics students by using the data from the students of 2013-2015 who had graduated. The total data was 319 students. The classification method used for this study was the Restricted Boltzmann Machine (RBM). However, the data showed imbalanced class distribution because the number of each field differed greatly. Therefore, SMOTE was added to classify the imbalanced class. The accuracy obtained from the combination of RBM and SMOTE was 70% with a 0.4 mean squared error.
Comparative Analysis of Naive Bayes, K-Nearest Neighbors (KNN), and Support Vector Machine (SVM) Algorithms for Classification of Heart Disease Patients Damayunita, Aina; Fuadi, Rifqi Syamsul; Juliane, Christina
JOIN (Jurnal Online Informatika) Vol 7 No 2 (2022)
Publisher : Department of Informatics, UIN Sunan Gunung Djati Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15575/join.v7i2.919

Abstract

Heart disease is still the leading cause of death. In this study, we tried to test several factors that can identify patients with heart disease using 3 classification algorithms: Naive Bayes, K-Nearest Neighbors (KNN), and Support Vector Machine (SVM).  The purpose of this study is to find out which algorithm can produce the highest accuracy in classifying, analyzing, and obtaining confusion matrix values along with the accuracy of predicting heart disease based on several factors or other comorbidities that the patient has, ranging from BMI to the patient's skin cancer status.  From the results of trials conducted by the SVM algorithm, it has the highest accuracy value, which is 92% while the Naive Bayes algorithm is the lowest with an accuracy value of 88%.

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