Data Science: Journal of Computing and Applied Informatics
Data Science: Journal of Computing and Applied Informatics (JoCAI) is a peer-reviewed biannual journal (January and July) published by TALENTA Publisher and organized by Faculty of Computer Science and Information Technology, Universitas Sumatera Utara (USU) as an open access journal. It welcomes full research articles in the field of Computing and Applied Informatics related to Data Science from the following subject area: Analytics, Artificial Intelligence, Bioinformatics, Big Data, Computational Linguistics, Cryptography, Data Mining, Data Warehouse, E-Commerce, E-Government, E-Health, Internet of Things, Information Theory, Information Security, Machine Learning, Multimedia & Image Processing, Software Engineering, Socio Informatics, and Wireless & Mobile Computing. ISSN (Print) : 2580-6769 ISSN (Online) : 2580-829X Each publication will contain 5 (five) manuscripts published online and printed. JoCAI strives to be a means of periodic, accredited, national scientific publications or reputable international publications through printed and online publications.
Articles
91 Documents
<![CDATA[Predicting Fraudulence Transaction under Data Imbalance using Neural Network (Deep Learning) ]]>
<![CDATA[Patria, Harry]]>
<![CDATA[ Data Science: Journal of Computing and Applied Informatics Vol. 6 No. 2 (2022): Data Science: Journal of Computing and Applied Informatics (JoCAI) ]]>
Publisher : <![CDATA[Talenta Publisher ]]>
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DOI: 10.32734/jocai.v6.i2-8309
<![CDATA[The number of financial transactions has the potential to cause many violations of the law (fraud). Conventional machine learning has been widely used, including logistic regression, random forest, and gradient boosted. However, the machine learning can work as long as the dataset contains fraud. Many new financial technology companies need to anticipate the potential for fraud, which they have not experienced much. This potential for a crime can also be experienced by old service providers with a low frequency of previous fraud. With the data imbalance, traditional machine learningis likely to produce false negatives so that they do not accurately predict potential fraud. This study optimizes the machine learning approach based on Neural Networks to improve model accuracy through the integration of KNIME and Python Programming with KERAS and TensorFlow models. The study also conducts a comparative analysis to scrutinize the performance of Adam and Adamax Optimizer. Using data from European cardholders in 2013, this study proves that workflows and neural network algorithms can detect with up to 95% accuracy even with a very small fraud sample of only 0.17% or 492 of 284,807 transactions. In addition, the Adam optimizer performs higher accuracy than the Adamax optimizer. The implication is that this supervisory technology innovation can be developed to minimize transaction crimes in the financial services sector.]]>
<![CDATA[Application of Nonlinear Autoregressive Neural Network Model to Forecast Local Mean Sea Level ]]>
<![CDATA[Chi, Yeong Nain]]>
<![CDATA[ Data Science: Journal of Computing and Applied Informatics Vol. 6 No. 2 (2022): Data Science: Journal of Computing and Applied Informatics (JoCAI) ]]>
Publisher : <![CDATA[Talenta Publisher ]]>
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DOI: 10.32734/jocai.v6.i2-8975
<![CDATA[The primary purpose of this study was to apply the nonlinear autoregressive neural network to model the long-term records of the monthly mean sea level from January 1978 to October 2020 at Grand Isle, Louisiana, as extracted from the National Oceanic and Atmospheric Administration Tides and Currents database. In this study, the empirical results revealed that the Bayesian Regularization algorithm was the best-suit training algorithm for its high regression R-value and low mean square error compared to the Levenberg-Marquardt and Scaled Conjugate Gradient algorithms for the nonlinear autoregressive neural network. Understanding past sea levels is important for the analysis of current and future sea level changes. In order to sustain these observations, research programs utilizing the existing data should be able to improve our understanding and significantly narrow our projections of ffuture sea-level changes.]]>
<![CDATA[Decision Support System for Election of OSIS Chair for Muhammadiyah Schools Using the Simple Multi Attribute Rating Technique Exploiting Rank (SMARTER) Method ]]>
<![CDATA[Winda Suci Lestari Nasution]]>;
<![CDATA[Patriot Nusa]]>
<![CDATA[ Data Science: Journal of Computing and Applied Informatics Vol. 6 No. 2 (2022): Data Science: Journal of Computing and Applied Informatics (JoCAI) ]]>
Publisher : <![CDATA[Talenta Publisher ]]>
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DOI: 10.32734/jocai.v6.i2-9071
<![CDATA[OSIS (Organisasi Intra Sekolah) is an official Student Council, which found in every school in Indonesia. The problem to be solved in a school is the need for an OSIS as a forum for students in schools to achieve the goals of coaching and developing students in accordance with the school's vision and mission. The main task of OSIS task is to achieve the goals in accordance with the school's vision and mission, therefore OSIS chair should have competencies and skills. The right decisions are needed for the implementation of the school's vision and mission. This first stage of research is performed by doing interview and survey to determine the criteria of OSIS chair. Based on interview and questionnaire has indicate that the student council member elects the chair based on several criteria consist of managerial ability, responsibility, communication and cooperation as well as discipline. The method proposed in the selection of the OSIS chairperson using simple multi attribute rating technique exploiting rank (SMARTER) approach and using Rank Order Centroid (ROC) weighting. The result of this study indicates that 75% of OSIS coaches and members need a decision-making system that can assist OSIS in making computerized decisions in determining the next OSIS chair candidate.]]>
<![CDATA[The Development of an Android-Based “LaporKPS†Application to Support the Service Center for Reports of Sexual Violence and Harassment Cases ]]>
<![CDATA[Adhim Bagas Wisnu Aji]]>;
<![CDATA[Eko Supraptono]]>
<![CDATA[ Data Science: Journal of Computing and Applied Informatics Vol. 6 No. 2 (2022): Data Science: Journal of Computing and Applied Informatics (JoCAI) ]]>
Publisher : <![CDATA[Talenta Publisher ]]>
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DOI: 10.32734/jocai.v6.i2-9092
<![CDATA[Cases of sexual violence and harassment have recently occurred among the public. Many print and non-print media report on issues of sexual violence and harassment. The PPP case is an abuse of the relationship between women and men which harms one of the parties because they are harassed or degraded in dignity, either verbally or non-verbally. Semarang was the highest rank in cases of sexual violence in Central Java in 2018. The PPP case reporting mechanism requires victims to go to a service center. Many risks make victims reluctant to report the incident that occurred because they are embarrassed in society and afraid that perpetrators will intimidate them. This paper aims to develop an android-based application to summarize the reporting mechanism by reporting PPP incidents experienced by victims using gadgets. Several menus provided include reporting menu, counseling menu, article menu, and Robo menu to identify categories of forms of harassment based on user input. The application is developed by using a customized waterfall method. Several testing has been developed to evaluate the application, such as Blackbox testing (achieves 100%), usability testing (achieves 79%), and media content testing (achieves 85.45%).]]>
<![CDATA[Detection of the Use of Mask to Prevent the Spread of COVID-19 Using SVM, Haar Cascade Classifier, and Robot Arm ]]>
<![CDATA[Pratiwi, Andini]]>;
<![CDATA[Nababan, Erna Budhiarti]]>;
<![CDATA[Amalia]]>
<![CDATA[ Data Science: Journal of Computing and Applied Informatics Vol. 6 No. 2 (2022): Data Science: Journal of Computing and Applied Informatics (JoCAI) ]]>
Publisher : <![CDATA[Talenta Publisher ]]>
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DOI: 10.32734/jocai.v6.i2-9289
<![CDATA[In the effort to hold up the case spread of COVID-19’s growth rate by implementing health protocols such as the use of masks, supervision is needed especially for the people who have not or still have problems to wearing masks. In this research, the system utilizes the robotic power to identify visitors whether they are wearing masks or not, and automatically distribute masks if the user is detected as not wearing a mask. The user face detection process uses the Haar Cascade Classifier algorithm and SVM (Support Vector Machine) to classify users who wear masks or not. For the user who is detected as not wearing masks, myCobot-Pi with the support of suction pump will distribute masks to users. The use of myCobot-Pi as a raspberry pi based robotic arm allows the application of the system on devices that are minimal in terms of specifications and size. Through trials by taking 41 examples of detection cases, 29 cases were found that managed to detect the correct use of masks. In addition, in this study we use PP sheet plastic protector to replace the packaging of the mask because it can be carried by the suction pump properly.]]>
<![CDATA[Bayesian Regression for Predicting Price Empirical Evidence in American Real Estate ]]>
<![CDATA[Patria, Harry]]>
<![CDATA[ Data Science: Journal of Computing and Applied Informatics Vol. 7 No. 1 (2023): Data Science: Journal of Computing and Applied Informatics (JoCAI) ]]>
Publisher : <![CDATA[Talenta Publisher ]]>
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DOI: 10.32734/jocai.v7.i1-10082
<![CDATA[The two foremost aims of classical regression are to assess the structure and magnitude of the relationship between variables. Despite the aforementioned benefits, unlike classical regression, which only offers a point estimate and a confidence interval, Bayesian regression offers the whole spectrum of inferential solutions. The results of this study demonstrate the Bayesian approach's suitability for regression tasks and its advantage in accounting for additional a priori data, which often strengthens studies. Using data from Boston Housing provided by from UCI ML Repository, this study proves that the prior distributions have the benefit of producing analytical, closed-form conclusions, which eliminates the need to use numerical techniques like Markov Chain Monte Carlo (MCMC). Second, software implementations are offered together with formulas for the posterior outcomes that are supplied, clarified, and shown. The assumptions supporting the suggested approach are evaluated in the third step using Bayesian tools. Prior elicitation, posterior calculation, and robustness to prior uncertainty and model sufficiency are the three processes that are essential to Bayesian inference.]]>
<![CDATA[Supporting Clinical Decision Making: Semantics Based Classification of Medical Referral Letters ]]>
<![CDATA[Jones, Laurence R]]>;
<![CDATA[Wilson, Ian]]>
<![CDATA[ Data Science: Journal of Computing and Applied Informatics Vol. 7 No. 1 (2023): Data Science: Journal of Computing and Applied Informatics (JoCAI) ]]>
Publisher : <![CDATA[Talenta Publisher ]]>
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DOI: 10.32734/jocai.v7.i1-10222
<![CDATA[This study aims to develop a Natural Language Processing based decision support system built from a repository of knowledge drawn from referral letters written between primary care doctors and specialist medical consultants. The developed system translates pre-processed referral letters into a semantic matrix of document vectors and a set of vocabulary features, based solely on the words used within each referral letter. The system applies a one-versus-rest heuristic using a Support Vector Machine (SVM) to convert a multinomial classification problem into individual binary classifications. Each document is matched to its probabilistic best fit specialism. The National Health Service Wales sourced 111,700 examples. Accuracy of 91.8% against 29 medical specialities is achieved. Accuracy increases to 97.4% and 99%, respectively, when also including one or two nearest neighbours to the best fit, providing a basis for informing the decision making of a medical professional. The study demonstrates the efficacy of using referral letters to allow or classification into specialisms and subsequent allocation of specialist care. The approach taken in this study does not require added ontologies and is readily extendable. The system offers support to medical professionals, particularly within training scenarios or where access to opinion may be in short supply.]]>
<![CDATA[Analysis of Embedding Locations in the Subband Frequency DCT on Scanned Images ]]>
<![CDATA[Hidayati, Indri]]>;
<![CDATA[Budiman, Mohammad Andri]]>;
<![CDATA[Zarlis, Muhammad]]>
<![CDATA[ Data Science: Journal of Computing and Applied Informatics Vol. 7 No. 1 (2023): Data Science: Journal of Computing and Applied Informatics (JoCAI) ]]>
Publisher : <![CDATA[Talenta Publisher ]]>
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DOI: 10.32734/jocai.v7.i1-10359
<![CDATA[Uploading an identity card as an image for the account verification process or transactions online can be a threat to application users. Identity card theft can be carried out by irresponsible persons if the application can be hacked. Therefore, protection of the image is required for authentication. In this study, the proposed technique is watermarking. A watermark in the form of a binary image will be embedded into the image as ownership using a Discrete Cosine Transform. The Discrete Cosine Transform works in the frequency domain. The location of the embedding of different watermarks was analysed in each 8×8 DCT block. The results of the analysis to assess the imperceptibility of original images and watermarked images using PSNR (Peak Signal to Noise Ratio) and SSIM (Structural Similarity Index Measure), while assessing the watermark robustness embedded using NCC (Normalized Cross Correlation). The results show PSNR (Peak Signal to Noise Ratio) ≥ 54 dB with a watermark strength of 0,1 and an average SSIM (Structural Similarity Index Measure) ≥ 0,9 on 4 scanned images in BMP format with a resolution of 100 DPI. A good watermark embedding is done on the green component at middle frequencies to maintain a balance between imperceptibility and robustness. In contrast, the red component at low frequency is vulnerable to attacks in the form of brightness +20 and contrast +50 with an average NCC (Normalized Cross Correlation) ≤ 0,85.]]>
<![CDATA[Comparative Analysis of Ciphertext Enlargement on Generalization of the ElGamal and Multi-factor RSA ]]>
<![CDATA[Zega, Imanuel]]>;
<![CDATA[Mohammad Andri Budiman]]>;
<![CDATA[Syahril Efendi]]>
<![CDATA[ Data Science: Journal of Computing and Applied Informatics Vol. 7 No. 1 (2023): Data Science: Journal of Computing and Applied Informatics (JoCAI) ]]>
Publisher : <![CDATA[Talenta Publisher ]]>
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DOI: 10.32734/jocai.v7.i1-10360
<![CDATA[Information and communication security has become more crucial and has become a new problem in relation to security, accessibility, data management, and other information policy challenges as a result of how easy it is for all users to use communication media. One of the fields of science that has a technique or art for disguising the data sent by the sender to the recipient with the aim of maintaining the confidentiality of the data is called cryptography. In determining better cryptographic algorithms for data security systems, in addition to considering strength, key length and ciphertext enlargement are also important factors to consider. Therefore, in this study, we attempted to compare the ciphertext magnification of the generalization of the ElGamal and multi-factor RSA algorithms by utilizing the same key length. Generalization of the ElGamal and Multi-factor RSA are both asymmetric algorithms that have public and private key pairs for encryption and decryption. However, at the level of security, the RSA algorithm is based on the difficulty of finding large integer factors into two prime factors. In contrast to the ElGamal algorithm, security is based on the difficulty of calculating the discrete logarithm of a large prime modulus. The results of the comparison algorithm carried out are represented in the form of a table containing the plaintext, key length, and size of the data.]]>
<![CDATA[Performance Analysis of Hybrid Cryptographic Algorithms Rabbit Stream and Enhanced Dual RSA ]]>
<![CDATA[Sarumaha, Demonius]]>;
<![CDATA[Mohammad Andri Budiman]]>;
<![CDATA[Muhammad Zarlis]]>
<![CDATA[ Data Science: Journal of Computing and Applied Informatics Vol. 7 No. 1 (2023): Data Science: Journal of Computing and Applied Informatics (JoCAI) ]]>
Publisher : <![CDATA[Talenta Publisher ]]>
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DOI: 10.32734/jocai.v7.i1-10483
<![CDATA[Cryptography is a technique for encoding data by encrypting plaintext into an unreadable (meaningless) form. Cryptographic methods have good and bad performance depending on the type of algorithm we use. Therefore, the purpose of this study is to measure speed by combining the two algorithms used. The Rabbit Stream algorithm is a stream cipher algorithm whose system security depends on the generation of a key bit stream (keystream), which only guarantees 128-bit key security but has the advantage of being fast in the encryption and decryption process, while the Enhanced Dual RSA algorithm is an asymmetric algorithm to increase data protection from the Dual RSA algorithm by utilizing the Pells equation as a substitute for public key exponents. On the other hand, the algorithm in question requires a significant amount of time to encrypt messages with a large capacity when compared to the Rabbit Stream algorithm. Nonetheless, the study's findings suggest that using a hybrid method is comparatively faster for processing substantial amounts of data.]]>
