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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 30 Documents
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Search results for , issue "Vol 8 No 2 (2023)" : 30 Documents clear
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.
User Experience Design and Prototypes of Mobile-based Learning Media for Children with Special Needs in the Dyslexia Category Rian Andrian; Aldi Yasin; Rizki Hikmawan; Ahmad Fauzi; Muhamad Irwan Ramadhan
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.v8i2.959

Abstract

Education is the right of all living things regardless of social status, gender, or physical condition. Persons with disabilities have the same rights and obligations as citizens. Based on the 1945 Constitution Article 31 Paragraph 1 and Law Number 20 of 2003 concerning the National Education System, it can be concluded that the state provides full guarantees for children with special needs to obtain quality education services. Children with special needs are divided into several categories, in this study the research team will focus on solving learning problems for children with disabilities in the dyslexia category. Dyslexia also known as reading disorder, is a disorder characterized by reading below the expected level for one's age. This study aims to find learning solutions by developing user experience designs and prototypes of mobile-based learning media for children with special needs in the dyslexia category. This research applies design thinking methodology to understand users, challenge assumptions, redefine problems, and create innovative solutions to prototype and test.
Implementation of Dynamic Topic Modeling to Discover Topic Evolution on Customer Reviews Valentinus Roby Hananto
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.v8i2.963

Abstract

Annotation and analysis of online customer reviews were identified as significant problems in various domains, including business intelligence, marketing, and e-governance. In the last decade, various approaches based on topic modeling have been developed to solve this problem. The known solutions, however, often only work well on content with static topics. As a result, it is challenging to analyze customer reviews that include dynamic and constantly expanding collections of short and noisy texts. A method was proposed to handle such dynamic content. The proposed system applied a dynamic topic model using BERTopic to monitor topics and word evolution over time. It would help decide when the topic model needs to be retrained to capture emerging topics. Several experiments were conducted to test the practicality and effectiveness of the proposed framework. It demonstrated how a dynamic topic model could handle the emergence of new and over-time-correlated topics in customer review data. As a result, improved performance was achieved compared to the baseline static topic model, with 25% of new segmented texts discovered using the dynamic topic model. Experimental results have, therefore, convincingly demonstrated that the proposed framework can be used in practice to develop automatic review annotation tools.
Artificial Neural Network for Classification Task in Tabular Datasets and Image Processing: A Systematic Literature Review Adi Zaenul Mustaqim; Nurdana Ahmad Fadil; Dyah Aruming Tyas
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.v8i2.1002

Abstract

Artificial Neural Network (ANN) is one of the machine learning algorithms that is widely used for classification cases. Some examples of classification cases that can be handled with ANN include classifications in the health sector, banking, and classification in image processing. This study presents a systematic literature review (SLR) of the ANN algorithm to find a research gap that can be used in future research. There are 3 phases used in preparing the SLR. Those are planning, conducting, and reporting. Formulation of research questions and establishing a review protocol is carried out in the planning phase. The second phase is conducted. In this phase, searching for relevant articles is carried out, determining the quality of the literature found and selecting particles according to what has been formulated in the planning phase. The selected literature is then carried out by the process of extracting data and information and then synthesizing the data. Writing SLR articles based on existing findings is carried out in the last phase, namely reporting. The results of data and information extraction from the 13 reviewed articles show that the ANN algorithm is powerful enough with satisfactory results to handle classification cases that use tabular datasets or image datasets. The challenges faced are the need for extensive training data so that ANN performance can be better, the use of appropriate evaluation measures based on the cases studied does not only rely on accuracy scores, and the determination of the correct hyperparameters to get better performance in the case of image processing.
Designing a Virtual Campus Tour using Image Stitching Techniques to Provide Information on College Entrance Test Ferzha Putra Utama; Andang Wijanarko; Jemmi Alfarobi
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.v8i2.1030

Abstract

The University of Bengkulu administers college entrance exams, however some test takers still require assistance in locating the correct room, despite the building being marked. It is crucial to avoid errors in finding the right test room, as it can cause potential students to waste valuable time. Therefore, a more precise and practical solution is necessary to provide information on test locations. This study designs a location-based virtual tour that offers a 360-degree view, providing information on the location of each building and the conditions inside and outside each test room. The virtual tour encompasses 81 buildings, including test rooms, with 28 to 32 images captured at each location, then stitched together using image stitching techniques. The goal of the virtual tour is to create a comprehensive view of the test location and provide more detailed information on the room's condition. Furthermore, the usability of this virtual tour was tested on 140 high school students as potential test participants, utilizing the System Usability Scale (SUS) to evaluate its effectiveness, resulting in a score of 72.19. In other words, the virtual tour was found to be an effective tool in helping users understand the test location.
The Impact of Data Augmentation Techniques on the Recognition of Script Images in Deep Learning Models Wulan Sapitri; Yesi Novaria Kunang; Ilman Zuhri Yadi; Mahmud Mahmud
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.v8i2.1073

Abstract

Deep learning technology is widely used for recognizing character images, including various regional characters and diverse ancient scripts. Deep learning models require large data sets to recognize images accurately. However, creating a dataset has limitations in terms of quantity, including the Komering script dataset used in this study. Data augmentation techniques can be applied to expand the dataset by modifying existing images to increase data diversity. This study aims to investigate the impact of augmentation techniques on the performance of deep learning models in the case of Komering script recognition. The dataset consists of 500 images for five classes of Komering script characters. Three augmentation techniques, namely random rotation, height shift, and width shift, were applied to the five characters, which were then used to test the model trained to recognize characters in the Komering dataset. This research contributes to providing insights into the effect of augmentation techniques on robust confidence prediction of deep learning models for recognizing newly augmented data. The results demonstrate that the deep learning model can recognize modified data using augmentation techniques with an average accuracy of 80.05%.
XGBoost and Convolutional Neural Network Classification Models on Pronunciation of Hijaiyah Letters According to Sanad Aaz Muhammad Hafidz Azis; Dessi Puji Lestari
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.v8i2.1081

Abstract

According to Sanad, the pronunciation of Hijaiyah letters can serve as a benchmark for correct or valid reading based on the makhraj and properties of the letters. However, the limited number of Qur'anic Sanad teachers remains one of the obstacles to learning the Qur'an. This study aims to identify the most practical combination of classification models in constructing a voice recognition system that facilitates learning without requiring direct interaction with a teacher. The methods employed include the XGBoost algorithm and CNN. As a result, out of the 12 letter trait labels, the CNN model was utilized for 10 of them, specifically for traits S1, S2, S4, S5, T1, T2, T3, T4, T5, and T6, on trait labels S3 and T7 applying the XGBoost model. Furthermore, the inclusion of additional data yielded performance results for each property, with an average accuracy of 78.14% for property S (letters with opposing properties), 70.69% for property T (letters without opposing properties), and an overall average of 73.79% per letter.
Analisis Fitur Dinamik Elektrokardiogram Untuk Klasifikasi Aritmia Yusril Ramadhan; Satria Mandala
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.v8i2.1106

Abstract

Arrhythmia is a deviation from the normal heart rate pattern. Arrhythmias are usually harmless, but they can cause heart problems. Some types of arrhythmias include Atrial Fibrillation (AF), Premature Atrial Contractions (PAC), and Premature Ventricular Contractions (PVC). Many studies have been conducted to identify the dynamic characteristics of electrocardiogram (ECG) irregular waves in the detection of arrhythmias. However, the accuracy obtained in these studies is less than optimal. This study aims to solve the problem by evaluating three main features of arrhythmias using ECG signals: RR interval, PR interval, and QRS complex. Experiments were conducted rigorously on these three features. The accuracy achieved was 98.21%, with a specificity of 98.65% and a sensitivity of 97.37%.
Pengembangan Game Augmented Reality Pembelajaran Bahasa Pemrograman Dasar Menggunakan Agile Scrum Ade Bastian; Sarmidi; Dadan Zaliluddin; Mochammad Bagasnanda Firmansyah
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.v8i2.1133

Abstract

The agile scrum methodology for augmented reality development increases project team efficiency. Private campus are frequently confronted with the dilemma of new students with various backgrounds that come not only from vocational high schools but also from high schools. First year students in the informatics study programme come not only from vocational informatics high schools, but also from high schools that specialize in social studies and languages. This is a difficult task in terms of imparting a comprehension of the fundamentals of programming. This study develops augmented reality in order to teach HTML and Javascript. By combining basic principles with gaming, the proposed augmented reality (AR) makes programming interesting. Players must comprehend their programming logic in order to be immersed in a virtual environment by answering coding bug questions. During usability testing, the System Usability Scale (SUS) assesses user happiness and AR knowledge. Participants from various programming backgrounds were tested on their knowledge of programming languages. According to usability research, 59% of people found AR programming languages useful for learning and understanding basic programming languages. AR and Agile Scrum make programming more enjoyable. This study demonstrates how augmented reality can be used to teach programming languages. These findings imply that Agile Scrum and AR methods can improve learning and programming foundations. More research and development could lead to more complete and complicated AR learning environments for programming instruction.
Identifikasi Kesamaan Pertanyaan pada Soal Bahasa Indonesia Menggunakan Metode Recurrent Neural Network (RNN) Muhammad Iqbal; Hasmawati; Ade Romadhony
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.v8i2.1138

Abstract

In a question-and-answer forum, the identification of question similarity is used to determine how similar two questions are. This procedure makes sure that user-submitted questions are compared to the questions in a database for matches to improve system performance on the online Q&A platform. Currently, question similarity is mostly done in foreign languages. The purpose of this research is to identify question similarities and evaluate the effectiveness of the methods used in Indonesian language questions. The data used is a public dataset with labeled pairs of questions as 0 and 1 where label 0 for different pairs of questions and label 1 for the same pairs of questions. The method used is a Recurrent Neural Network (RNN) with the Manhattan Distance approach to calculate the similarity distance between two questions. The question pairs are taken as two inputs with a reference label to identify the similarity distance between the two question inputs. We evaluated the model using three different optimizers namely RMSprop, Adam, and Adagrad. The best results were obtained using the Adam optimizer with 80:20 ratio split-data and overall accuracy is 76%, precision is 74%, recall is 98.8%, and F1-score is 85.1%.

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