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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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Delineation of The Early 2024 Election Map: Sentiment Analysis Approach to Twitter Data Rahmanulloh, Nur Ulum; Santoso, Ibnu
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.v7i2.925

Abstract

As a democratic country, the people hold an important role in determining power in Indonesia. The closest political agenda in Indonesia is the 2024 Election. A survey has been conducted by several private survey agencies regarding the 2024 political map which has revealed the top five names, namely Prabowo Subianto, Ganjar Pranowo, Anies Baswedan, Sandiaga Uno, and Ridwan Kamil. This study aims to describe the initial map of the 2024 Election through a sentiment analysis approach to Twitter data. This study uses tweet data that mentions five political figures during 2021. In general, the demographic condition of Twitter users that pros or cons to five political figures, among them: located on the Java, in the age group 19–29 years old, and male.  The sentiment analysis method used is supervised learning with different methods for each figure. The difference in methods adjusts the best evaluation value given in each figure. The results showed that the highest positive sentimental tweets and the highest number of pro accounts was about Ganjar Pranowo. On the other hand, the highest negative sentiment and the highest number of contra accounts was about Prabowo Subianto. Many words that often appear on a figure's positive sentiment are expressions of hope, prayer, and support. On negative tweets, the word that comes up a lot relating to the work field or work region of the figures. 
Internet of Things (IoT) for Soil Moisture Detection Using Time Series Model Setiawan, Iman; Junaidi, Junaidi; Fadjryani, Fadjryani; Amaliah, Fika Reski
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.v7i2.951

Abstract

Technology in agriculture has been widely and massively applied. One of them is automation technology and the use of big data through the Internet of Things (IoT). The use of IoT allows a process to run automatically without human intervention. Extreme weather changes and narrow land use are one of the main problems in agriculture. The development of IoT devices has been widely developed regarding this subject. One of them is a soil moisture detection system. This study aims to build an IoT soil moisture detection system. The system will use a sensor as input which is then processed in a microcontroller device and the prediction results are sent to the IoT cloud platform. Prediction results are obtained using a time series model and then its performance is evaluated using RMSE. This model was chosen because the structure of the observed soil moisture data is based on time. The results of this study indicate that the soil moisture IoT system can work well. This is supported by the results of the prediction evaluation value of the RMSE = 1.175682x10-5 model which is very small.
User Experience Design and Prototypes of Mobile-based Learning Media for Children with Special Needs in the Dyslexia Category Andrian, Rian; Yasin, Aldi; Hikmawan, Rizki; Fauzi, Ahmad; Ramadhan, Muhamad Irwan
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 Hananto, Valentinus Roby
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.
Diabetes Risk Prediction Using Extreme Gradient Boosting (XGBoost) Wardhani, Kartina Diah Kusuma; Akbar, Memen
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.v7i2.970

Abstract

One of the uses of medical data from diabetes patients is to produce models that can be used by medical personnel to predict and identify diabetes in patients. Various techniques are used to be able to provide a diabetes model as early as possible based on the symptoms experienced by diabetic patients, including using machine learning. The machine learning technique used to predict diabetes in this study is extreme gradient boosting (XGBoost). XGBoost is an advanced implementation of gradient boosting along with multiple regularization factors to accurately predict target variables by combining simpler and weaker model set estimations. Errors made by the previous model are tried to be corrected by the next model by adding some weight to the model. The diabetes prediction model using XGBoost is shown in the form of a tree, with the accuracy of the model produced in this study of 98.71%
Scalability Testing of Land Forest Fire Patrol Information Systems Khusaeri, Ahmad; Sitanggang, Imas Sukaesih; Rahmawan, Hendra
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.v8i1.977

Abstract

The Patrol Information System for the Prevention of Forest Land Fires (SIPP Karhutla) in Indonesia is a tool for assisting patrol activities for controlling forest and land fires in Indonesia. The addition of Karhutla SIPP users causes the need for system scalability testing. This study aims to perform non-functional testing that focuses on scalability testing. The steps in scalability testing include creating schemas, conducting tests, and analyzing results. There are five schemes with a total sample of 700 samples. Testing was carried out using the JMeter automation testing tool assisted by Blazemeter in creating scripts. The scalability test parameter has three parameters: average CPU usage, memory usage, and network usage. The test results show that the CPU capacity used can handle up to 700 users, while with a memory capacity of 8GB it can handle up to 420 users. All users is the user menu that has the highest value for each test parameter The average value of CPU usage is 44.8%, the average memory usage is 69.48% and the average network usage is 2.8 Mb/s. In minimizing server performance, the tile cache map method can be applied to the system and can increase the memory capacity used.
AR Make-up Filter for Social Media using the HSV Color Extraction Harika, Maisevli; Rachmat, Setiadi; Aulia, Nurul Dewi; Dwi, Zulfa Audina; Widartha, Vandha Pradwiyasma
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.v7i2.994

Abstract

Choosing the appropriate cosmetics is an arduous task. Because cosmetics are tested directly on the skin to ensure each person’s preferences are met. The consumer repeatedly tries a sample and then discards it until he discovers one that meets his tastes. The cosmetics business and consumers are affected by this move. Companies can utilize Augmented Reality (AR) technology as an alternative to mass-producing cosmetic samples. The difficulty of deploying augmented reality is the difficulty of putting cosmetics into camera video streams. Each individual bears the burden of skin color and its effect on light. HSV Color Extraction was the method employed for this study. The application of augmented reality intends to enable consumers to test cosmetics with their chosen color and assist businesses in competing in the industry by promoting items and engaging customers. This work makes it easier to choose cosmetics using augmented reality and social media. AR simulates the usage of the desired color cosmetics, whereas social media allows users to obtain feedback on their color preferences. The outcomes of this study indicate that augmented reality (AR) apps can display filters in bright, dim, and even wholly dark lighting conditions. This research contributes originality that cosmetic firms can utilize to market their products on social media.
Run Length Encoding Compresion on Virtual Tour Campus to Enhance Load Access Performance Bastian, Ade; Mardiana, Ardi; Berliani, Mega; Firmansyah, Mochammad Bagasnanda
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.v8i1.1000

Abstract

Virtual tour is one of the rapidly growing applications of multimedia technology which is used for various purposes, including the dissemination of information in an interesting way. The education sector is also not spared from using virtual tour media for promotional purposes, and campuses are no exception to this rule. Large virtual tour content causes high access speed, ultimately reducing the level of comfort experienced by users. This study aims to compress panoramic images displayed on a campus virtual tour using a lossless compression method and the Run Length Encoding (RLE) algorithm. First, panoramic images are combined into one, then individual images are compressed. When recreating a virtual campus tour, compressed images are used so that the amount of data transferred is smaller. The load access speed index increases from 7,233 seconds to 3,789 seconds when images are compressed from 64 bits to 8 bits, with a compression percentage of 27%. The findings from this research are that the RLE algorithm has not been able to compress large files effectively even though it is quite successful in increasing the load access of the virtual tour website.
Artificial Neural Network for Classification Task in Tabular Datasets and Image Processing: A Systematic Literature Review Mustaqim, Adi Zaenul; Fadil, Nurdana Ahmad; Tyas, Dyah Aruming
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.
Catbreedsnet: An Android Application for Cat Breed Classification Using Convolutional Neural Networks Ramadhan, Anugrah Tri; Setiawan, Abas
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.v8i1.1007

Abstract

There are so many cat races in the world. Ignorance in recognizing cat breeds will be dangerous if the cat being kept is affected by a disease, which allows mishandling of the cat being kept. In addition, many cat breeds have different foods from one race to another. The problem is that a cat caretaker cannot easily recognize the cat breed. Therefore, technology needs to help a cat caretaker to treat cats appropriately. In this study, we proposed a Machine Learning approach to recognize cat breeds. This study aims to identify the cat breed from the cat images then deployed on an Android smartphone. It was tested with data from cat images of 13 races. The classification method applied in this study uses the Convolutional Neural Network (CNN) algorithm using transfer learning. The base models tested are MobilenetV2, VGG16, and InceptionV3. The results tested using several models and through several experimental scenarios produced the best classification model with an accuracy of 82% with MobilenetV2. The model with the best accuracy is then embedded in an application with the Android operating system. Then the application is named Catbreednet.

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