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[PSO based Hyperparameter tuning of CNN Multivariate Time- Series Analysis ]]>
<![CDATA[Agung Bella Putra Utama]]>;
<![CDATA[Aji Prasetya Wibawa]]>;
<![CDATA[Muladi Muladi]]>;
<![CDATA[Andrew Nafalski]]>
<![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.v7i2.858
<![CDATA[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.]]>
<![CDATA[Diabetes Risk Prediction Using Extreme Gradient Boosting (XGBoost) ]]>
<![CDATA[Kartina Diah Kusuma Wardhani]]>;
<![CDATA[Memen Akbar]]>
<![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.v7i2.970
<![CDATA[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%]]>
<![CDATA[Development of a Digital Platform Prototype, to Facilitate Inclusive Learning for Children with Special Needs ]]>
<![CDATA[Rian Andrian Andrian]]>;
<![CDATA[Aldi Yasin]]>;
<![CDATA[Muhammad Raihan Ijlal Hanan]]>;
<![CDATA[Muhamad Irwan Ramadhan]]>;
<![CDATA[Taufik Ridwan]]>;
<![CDATA[Rizki Hikmawan]]>
<![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.v7i2.835
<![CDATA[Persons with disabilities have the same rights and responsibilities as citizens. Based on the 1945 Constitution Republic of Indonesia, 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. Many of the problems of inclusive learning that occurred during the Covid-19 pandemic, ranging from the unpreparedness of the school to various problems with environmental factors so that innovation was needed to overcome these problems. In this article, the author develops a prototype of a digital-based learning platform as a solution to facilitate inclusive learning for children with special needs.]]>
<![CDATA[Performance Analysis of Cache Replacement Algorithm using Virtual Named Data Network Nodes ]]>
<![CDATA[Leanna Vidya Yovita]]>;
<![CDATA[Tody Ariefianto Wibowo]]>;
<![CDATA[Ade Aditya Ramadha]]>;
<![CDATA[Gregorius Pradana Satriawan]]>;
<![CDATA[Sevierda Raniprima]]>
<![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.v7i2.875
<![CDATA[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.]]>
<![CDATA[A Model-Driven IS 4.0 Development Framework for Railway Supply Chain ]]>
<![CDATA[Jayakrishnan, Mailasan]]>;
<![CDATA[Mohamad, Abdul Karim]]>;
<![CDATA[Yusof, Mokhtar Mohd]]>
<![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[YouTube X-Rating Detection with Bahasa-Slang Title Using Query Expansion and Rule Based Approaches ]]>
<![CDATA[Wardani, Dewi Wisnu]]>;
<![CDATA[Shabihah, Salsabila F]]>
<![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[Development of a Digital Platform Prototype, to Facilitate Inclusive Learning for Children with Special Needs ]]>
<![CDATA[Andrian, Rian Andrian]]>;
<![CDATA[Yasin, Aldi]]>;
<![CDATA[Hanan, Muhammad Raihan Ijlal]]>;
<![CDATA[Ramadhan, Muhamad Irwan]]>;
<![CDATA[Ridwan, Taufik]]>;
<![CDATA[Hikmawan, Rizki]]>
<![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.v7i2.835
<![CDATA[Persons with disabilities have the same rights and responsibilities as citizens. Based on the 1945 Constitution Republic of Indonesia, 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. Many of the problems of inclusive learning that occurred during the Covid-19 pandemic, ranging from the unpreparedness of the school to various problems with environmental factors so that innovation was needed to overcome these problems. In this article, the author develops a prototype of a digital-based learning platform as a solution to facilitate inclusive learning for children with special needs.]]>
<![CDATA[Anti-Corruption Disclosure Prediction Using Deep Learning ]]>
<![CDATA[Utomo, Victor Gayuh]]>;
<![CDATA[Kumkamdhani, Tirta Yurista]]>;
<![CDATA[Setiarso, Galih]]>
<![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[Random Forest Method Approach to Customer Classification Based on Non-Performing Loan in Micro Business ]]>
<![CDATA[Muhajir, Muhammad]]>;
<![CDATA[Widiastuti, Julia]]>
<![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[Multi Rule-based and Corpus-based for Sundanese Stemmer ]]>
<![CDATA[Sutedi, Ade]]>;
<![CDATA[Nasrulloh, Muhammad Rikza]]>;
<![CDATA[Elsen, Rickard]]>
<![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.]]>
