JOIN (Jurnal Online Informatika)
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.
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<![CDATA[AR Make-up Filter for Social Media using the HSV Color Extraction ]]>
<![CDATA[Maisevli Harika]]>;
<![CDATA[Setiadi Rachmat]]>;
<![CDATA[Nurul Dewi Aulia]]>;
<![CDATA[Zulfa Audina Dwi]]>;
<![CDATA[Vandha Pradwiyasma Widartha]]>
<![CDATA[ JOIN (Jurnal Online Informatika) Vol 7 No 2 (2022) ]]>
Publisher : <![CDATA[Department of Informatics, UIN Sunan Gunung Djati Bandung ]]>
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DOI: 10.15575/join.v7i2.994
<![CDATA[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.]]>
<![CDATA[Anti-Corruption Disclosure Prediction Using Deep Learning ]]>
<![CDATA[Victor Gayuh Utomo]]>;
<![CDATA[Tirta Yurista Kumkamdhani]]>;
<![CDATA[Galih Setiarso]]>
<![CDATA[ JOIN (Jurnal Online Informatika) Vol 7 No 2 (2022) ]]>
Publisher : <![CDATA[Department of Informatics, UIN Sunan Gunung Djati Bandung ]]>
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DOI: 10.15575/join.v7i2.840
<![CDATA[Corruption gives major problem to many countries. It gives negative impact to a nation economy. People also realized that corruption comes from two sides, demand from the authority and supply from corporate. On that regard, corporates may have their part in fight against corruption in the form of anti- corruption disclosure (ACD). This study proposes new method of ACD prediction in corporate using deep learning. The data in this study are taken from every companies listed in Indonesia Stock Exchange (IDX) from the year 2017 to 2019. The companies can be categorized in 9 categories and the data set has 8 features. The overall data has 1826 items in which 1032 items are ACD and the other 794 items are non-ACD. In this study, the deep neural network or deep learning is composed from input layer, output layer and 3 hidden layers. The deep neural network uses Adam optimizer with learning rate 0.0010, batch size 16 and epochs 500. The drop out is set to 0.05. The accuracy result from deep learning in predicting ACD is considered good with the average training accuracy is 74.76% and average testing accuracy is 76.37%. However, the loss result isn’t good with average training loss and testing loss are respectively 51.76% and 50.96%. Since the aim of the study to find the possibility of deep learning as alternative of logistic regression in ACD prediction, accuracy comparison from deep learning and logistic regression is held. Deep learning has average prediction accuracy of 76.37% is better than logistic regression with average accuracy of 67.15%. Deep learning also has higher minimum accuracy and maximum accuracy compared to logistic regression. This study concludes that deep learning may give alternatives in ACD prediction compared the more common method of logistic regression.]]>
<![CDATA[The Measurement and Evaluation of Information System Success Based on Organizational Hierarchical Culture ]]>
<![CDATA[Reni Haerani]]>;
<![CDATA[Titik Khawa Abdul Rahman]]>;
<![CDATA[Lia Kamelia]]>
<![CDATA[ JOIN (Jurnal Online Informatika) Vol 7 No 2 (2022) ]]>
Publisher : <![CDATA[Department of Informatics, UIN Sunan Gunung Djati Bandung ]]>
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<![CDATA[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.]]>
<![CDATA[YouTube X-Rating Detection with Bahasa-Slang Title Using Query Expansion and Rule Based Approaches ]]>
<![CDATA[Dewi Wisnu Wardani]]>;
<![CDATA[Salsabila F Shabihah]]>
<![CDATA[ JOIN (Jurnal Online Informatika) Vol 7 No 2 (2022) ]]>
Publisher : <![CDATA[Department of Informatics, UIN Sunan Gunung Djati Bandung ]]>
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DOI: 10.15575/join.v7i2.799
<![CDATA[The detection of X-rating content on the Internet is still rarely done in Indonesia and the performance of the existing work to detect X-rating content, especially in video is still low. The largest video portal, YouTube, does not yet have automatic X-rating content detection through its content either. Some X-rating content prevention service providers in Indonesia, such as the Internet Positive and Nawala Project, detect X-rating content using the keyword detection method of a web page and then block the web page with DNS filtering. However, that method does not pay attention to using Bahasa-Slang. This work developed Metasearch named Safedio. Safedio aims to detect X-rating content on YouTube content through video titles that contain Bahasa-Slang. Safedio utilizes Query Expansion and Rule-Based approaches. The Query Expansion is a technique to get additional rules in search. In the end, Safedio can detect X-rating content through video titles in both Bahasa and Bahasa-Slang. The average results return with precision 71%, recall 46% and accuracy 72%.]]>
<![CDATA[Multi Rule-based and Corpus-based for Sundanese Stemmer ]]>
<![CDATA[Ade Sutedi]]>;
<![CDATA[Muhammad Rikza Nasrulloh]]>;
<![CDATA[Rickard Elsen]]>
<![CDATA[ JOIN (Jurnal Online Informatika) Vol 7 No 2 (2022) ]]>
Publisher : <![CDATA[Department of Informatics, UIN Sunan Gunung Djati Bandung ]]>
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DOI: 10.15575/join.v7i2.846
<![CDATA[The purpose of this study is to develop a stemming method by involved several methods including morphological (with affix and pro-lexeme removal), syllable (canonical) pattern, and corpus data as a comparison of the final results of stemming. The algorithm checks a number of the string first and removes affixes, then check the syllable pattern according to the stripping result, then compares to the corpus data which determines the final stemming process. In this study, the corpus data was taken from Sundanese dictionary consists of a single word used for the root word and the extracted dataset from the online Sundanese magazine. The results showed that the stripping of affix and pro-lexeme can remove the corresponding affixes and pro-lexeme then compares words that have a syllable pattern then executes the basic words quickly and the use of corpus can improve accuracy and reduce the over-stemming problems that occur in the stemming process.]]>
<![CDATA[Comparative Analysis of Naive Bayes, K-Nearest Neighbors (KNN), and Support Vector Machine (SVM) Algorithms for Classification of Heart Disease Patients ]]>
<![CDATA[Aina Damayunita]]>;
<![CDATA[Rifqi Syamsul Fuadi]]>;
<![CDATA[Christina Juliane]]>
<![CDATA[ JOIN (Jurnal Online Informatika) Vol 7 No 2 (2022) ]]>
Publisher : <![CDATA[Department of Informatics, UIN Sunan Gunung Djati Bandung ]]>
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DOI: 10.15575/join.v7i2.919
<![CDATA[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%.]]>
<![CDATA[A Model-Driven IS 4.0 Development Framework for Railway Supply Chain ]]>
<![CDATA[Mailasan Jayakrishnan]]>;
<![CDATA[Abdul Karim Mohamad]]>;
<![CDATA[Mokhtar Mohd Yusof]]>
<![CDATA[ JOIN (Jurnal Online Informatika) Vol 7 No 2 (2022) ]]>
Publisher : <![CDATA[Department of Informatics, UIN Sunan Gunung Djati Bandung ]]>
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DOI: 10.15575/join.v7i2.794
<![CDATA[Railway Industry (RI) in Malaysia possess below-average Information System (IS) skills and seldom use the IS for decision making at their operation level while they likewise discover digital transformation adaption is crucial and hence RI in Malaysia are in the slow mass of adapter classification. Perceiving the significant task of IS to RI in the economy, the government is resolved to assist and support the improvement of IS to guarantee their sustainability and competitiveness. IS framework being significant because it set up the computerized industry, lively digital, who can structure with simple to utilize and basic dynamic interaction. The present IS model utilized in Malaysia depends on the knowledge and experience of the specialist like system developers and academicians. The maximum of these IS models to identify the visual view of performance in RI are precise and are not strategized toward railway utilize and do not give prescriptive evaluation. The issue is no transition development and the absence of industry capacity to do the transition phases. This research focuses on the technology parameters influencing the adaption of IS to assist decision-makers, administrative bodies, and IS analysis to approach the advantages of its continued and expected improvement in the RI.]]>
<![CDATA[Random Forest Method Approach to Customer Classification Based on Non-Performing Loan in Micro Business ]]>
<![CDATA[Muhammad Muhajir]]>;
<![CDATA[Julia Widiastuti]]>
<![CDATA[ JOIN (Jurnal Online Informatika) Vol 7 No 2 (2022) ]]>
Publisher : <![CDATA[Department of Informatics, UIN Sunan Gunung Djati Bandung ]]>
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DOI: 10.15575/join.v7i2.842
<![CDATA[This study aims to classify potential customers’ characteristics based on non- performing loans through the random forest method. This research uses data obtained from Syariah Mandiri Bank branch in Jambi, which includes data on micro-financing customers in years 2016–2020. The random forest method is used for analysis. The novelty of this work is that, unlike existing researches that used other soft-computing methods, we employ Random Forest method, specifically using an imbalanced class sampling technique. The obtained results show that credit risk can be estimated by taking into account factors such as age, monthly installments, margin, price of insurance, loan principal, occupation, and long installments. The research results indicate that the sensitivity, precision, and G-mean value increase compared to using the original data. Random forest with oversampling technique has the high Area Under the ROC Curve score that is equal to 66.69%.]]>
<![CDATA[Delineation of The Early 2024 Election Map: Sentiment Analysis Approach to Twitter Data ]]>
<![CDATA[Nur Ulum Rahmanulloh]]>;
<![CDATA[Ibnu Santoso]]>
<![CDATA[ JOIN (Jurnal Online Informatika) Vol 7 No 2 (2022) ]]>
Publisher : <![CDATA[Department of Informatics, UIN Sunan Gunung Djati Bandung ]]>
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DOI: 10.15575/join.v7i2.925
<![CDATA[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. ]]>
<![CDATA[Internet of Things (IoT) for Soil Moisture Detection Using Time Series Model ]]>
<![CDATA[Iman Setiawan]]>;
<![CDATA[Junaidi Junaidi]]>;
<![CDATA[Fadjryani Fadjryani]]>;
<![CDATA[Fika Reski Amaliah]]>
<![CDATA[ JOIN (Jurnal Online Informatika) Vol 7 No 2 (2022) ]]>
Publisher : <![CDATA[Department of Informatics, UIN Sunan Gunung Djati Bandung ]]>
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DOI: 10.15575/join.v7i2.951
<![CDATA[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.]]>
