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INDONESIA
Indonesian Journal on Computing (Indo-JC)
Published by Universitas Telkom
ISSN : 24609056     EISSN : -     DOI : -
Core Subject : Science,
Computer Science & IT
Indonesian Journal on Computing (Indo-JC) is an open access scientific journal intended to bring together researchers and practitioners dealing with the general field of computing. Indo-JC is published by School of Computing, Telkom University (Indonesia).
Arjuna Subject : -
Articles 251 Documents
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Information Technology Governance Audit Using the COBIT 5 Framework (Case Study of PDAM Tirta Patriot Kota Bekasi) Lisda Awalia Aprilianti; Eko Darwiyanto; Yanuar Firdaus Arie
Indonesia Journal on Computing (Indo-JC) Vol. 6 No. 2 (2021): September, 2021
Publisher : School of Computing, Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34818/INDOJC.2021.6.2.563

Abstract

PDAM Tirta Patriot has implemented information systems for its business process but still has deficiencies, including some business processes that do not have documented guidelines and procedures. Furthermore, the human resources in PDAM Tirta Patriot are insufficient for improving IT governance due to the lack of reliable personnel in the IT unit. Given the importance of IT for PDAM, an audit is needed to evaluate the capability of the IT unit in managing IT. COBIT 5 provides a goals cascade to align the company with its business goals. However, the goals cascade does not provide priority to IT-related goals. Therefore, for prioritizing IT-related goals, this research uses Analytical Hierarchy Process (AHP). From the AHP result, the selected domains are APO01, MEA01, and APO07. This research aims to determine the current capability level and analyze the gap between it and the chosen target capability level, precisely level 2. The current capability level of PDAM Tirta Patriot from domain APO01 is 1, MEA01 is 0, and APO07 is 0, which means none of those have reached the target capability level. Recommendations are given based on the unfulfilled work product for domains that do not reach the target capability level.
Forecasting Number of New Cases Daily COVID-19 in Central Java Province Using Exponential Smoothing Holt-Winters Dinda Fitri Irandi; Aniq Atiqi Rohmawati; Putu Harry Gunawan
Indonesia Journal on Computing (Indo-JC) Vol. 6 No. 2 (2021): September, 2021
Publisher : School of Computing, Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34818/INDOJC.2021.6.2.565

Abstract

There is hard to mention how long the COVID-19 pandemic will discontinue. There are some factors, including the public’s efforts to slow spread and researchers’ work to observe more about this outbreak. From the beginning of the health crisis, particularly following the announcement of the first positive case In Indonesia due to the COVID-19 on March 2, 2020. Afterwards, the number of daily cases increase simultaneously in other regions in Indonesia until today. Due to the fact that the significant mobility of the people, Central Java has contributed the 3rd rank of potential number of COVID-19 positive cases in Indonesia. This study aims to forecast the number of COVID-19 daily new cases in Central Java to assist the government in preparing the necessary resources and controlling the spread of the COVID-19 virus in Central Java Province. We proposed Exponential Smoothing Holt-Winters with the Additive model with seasonal addition considering trend and seasonal factors. The dataset during March 14 to April 17, 2021, revealed fluctuation of trend and seasonal patterns. Our simulation studies indicate that Exponential Smoothing Holt-Winters provides sharp and well performance for forecasting daily new cases of COVID-19 in Central Java province with MAPE less than 10%.
Multi Criteria Recommender System for Music using K-Nearest Neighbors and Weighted Product Method Muhamad Hafidh Nofal; zk abdurahman baizal; Ramanti Dharayani
Indonesia Journal on Computing (Indo-JC) Vol. 6 No. 2 (2021): September, 2021
Publisher : School of Computing, Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34818/INDOJC.2021.6.2.575

Abstract

Currently, the music industry has grown rapidly which has led to an information overload that hinders users from finding the music they want, because everyone has their own unique characteristics. In a previous study, the Recommender System converted music lyrics into digital values using Lexicon's Non-Commercial Research (NRC) and K Nearest Neighbors (KNN) to look for similarities between music. However, this system only uses lyrics to recommend music, so it doesn't pay more attention to user preferences. Therefore, in this study adds criteria from users using the Weighted Product Method (WPM) to weight the music criteria with the input criteria from users. In this study uses a music dataset from 2000 to 2019 taken from the Kaggle website. The purpose of this study was to measure user satisfaction using the System Usability Scale (SUS). In this case, the user is free to answer 10 questions regarding the results of the recommendations provided by the system. Based on the results of the questionnaire, the SUS score was 83.65. This score is included in the EXCELLENT category with grade A scale
An Exponential Smoothing Holt-Winters Based-Approach for Estimating Extreme Values of Covid-19 Cases Abi Rafdhi Hernandy; Aniq Atiqi Rohmawati; Putu Harry Gunawan
Indonesia Journal on Computing (Indo-JC) Vol. 6 No. 2 (2021): September, 2021
Publisher : School of Computing, Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34818/INDOJC.2021.6.2.576

Abstract

Covid-19 is an ongoing outbreak across the world infecting millions, having significant fatality rate, and triggering economic disruption on a large scale. The demand of healthcare facility has been significantly affected by the increased Covid-19 cases. Many countries have been forced to do lockdown and physical distancing to avoid a crucial peak of novel Covid-19 pandemic that potentially overwhelms healthcare services. Central Java is the province with the third highest population density in Indonesia and predicted to be affected significantly over a particular period of this outbreak. Our paper aims to provide a modelling to estimate extreme values of daily Covid-19 cases in Central Java, between March and April 2021. We particularly capture seasonality during this period using Exponential Smoothing Holt-Winters. We employ that Value at Risk and mean excess function based-approaches for extreme value estimation. Our simulation studies indicate that Exponential Smoothing Holt-Winters and Value at Risk provide sharp and well prediction for extreme value with zero violation. Since a number of positive cases has resulted unprecedented volatility, estimating the extreme value of daily Covid-19 cases become a crucial matter to support maintain essential health services.
Tourism Recommender System using Weighted Parallel Hybrid Method with Singular Value Decomposition Yoan Amri Akbar; zk abdurahman baizal; Agung Toto Wibowo
Indonesia Journal on Computing (Indo-JC) Vol. 6 No. 2 (2021): September, 2021
Publisher : School of Computing, Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34818/INDOJC.2021.6.2.579

Abstract

Presently, we often get suggestions for recommendations for tourist attractions from various sources such as the internet, magazines, newspapers, or travel agencies. Because there is numerous information, tourists become difficult to determine the tourism destination that suits their wishes. We created a tourism recommender system that can provide information in the form of recommendations for tourist attractions by the preference of tourists. The method used is a hybrid method that combines several recommendation methods, which are Content-Based Filtering (CB) and Collaborative Filtering (CF). We use tourism data of Lombok Island, West Nusa Tenggara, which will be taken from the TripAdvisor site. We apply the Singular Value Decomposition algorithm on CF and CB. The Hybrid Weighted Parallel Technique is used for Hybrid Method. The results of the experiment show that the weighting technique hybrid method provides higher prediction accuracy than when undergoing the recommender system method separately. The average results of Mean Square Error were obtained 0.7275 (CF), 0 .4583 (CB), and 0.2548 (Hybrid Method). The result indicates that the Hybrid Method with the Weighting Technique has the highest accuracy of another method.
Application of User-centred Design Method in Laundry Management Application Development Rizki Pambudi; Gita Fadila Fitriana; Rifki Adhitama
Indonesia Journal on Computing (Indo-JC) Vol. 6 No. 3 (2021): December, 2021
Publisher : School of Computing, Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34818/INDOJC.2021.6.3.591

Abstract

On a system from the user side often experience difficulties in its operation. To solve this problem, the laundry management application development process applies the User Centered Design method. The stages taken include Specify the Context of Use and Specify User and Organizational Requirements to identify, determine who the user is and the characteristics of the system user by conducting interviews with laundry business owners. Next, the Produce Design Solution stage is carried out, which is the stage of making a system that can be used by users. The last stage is Evaluate Designs against User Requirements by using Usability Testing to measure the appropriateness and success of the application as well as black box testing to test system functionality. The results of application development and testing using Usability Testing on the aspects of Usefulness, Ease of Use, Ease of Learning and Satisfaction get a feasibility score of 79.8% which means that the development of a laundry management application using the User Centered Design method is feasible because this application is easy to use, easy to learn, provides satisfaction and as expected and is useful for laundry entrepreneurs to manage their transaction data.
Implementation of LSTM-RNN for Bitcoin Prediction Nur Ghaniaviyanto Ramadhan; Nia Annisa Ferani Tanjung; Faisal Dharma Adhinata
Indonesia Journal on Computing (Indo-JC) Vol. 6 No. 3 (2021): December, 2021
Publisher : School of Computing, Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34818/INDOJC.2021.6.3.592

Abstract

Bitcoin is a cryptocurrency that is used worldwide for digital payments or simply for investment purposes. Bitcoin is a new technology so there are currently very few prices prediction models available. Problems arise when someone uses bitcoin without understanding strong fundamentals. This can result in a lot of loss for the person. These problems certainly need to be overcome by predicting bitcoin prices using a machine learning approach. The purpose of this research is to predict the bitcoin USD price using the Long Short-Term Memory Recurrent Neural Network (LSTM-RNN) model. The LSTM-RNN model was chosen because it is better than the traditional neural network model. Measurement of the results in this study using the Root Mean Square Error (RMSE). The RMSE results obtained on the application of the LSTM-RNN model 6461.14.
Comparative Analysis of K-Nearest Neighbor and Modified K-Nearest Neighbor Algorithm for Financial Well-Being Data Classification Ichwanul Muslim Karo Karo
Indonesia Journal on Computing (Indo-JC) Vol. 6 No. 3 (2021): December, 2021
Publisher : School of Computing, Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34818/INDOJC.2021.6.3.593

Abstract

Financial Well-Being is the condition that a person has been able to meet current and future financial obligations. There are many parameters in determining people who have obtained financial well-being. Classification is a data mining task that can be used to identify someone with financial well-being. One of the most popular classification algorithms is K Nearest Neighbor (KNN). However, there is also a Modified K Nearest Neighbor (MKNN) classification algorithm which is an extended KNN. In this paper, we will analyze a comparison of KNN and MKNN algorithms to classify financial well-being datasets. Comparative analysis is based on the accuracy and running time of both algorithms. Prior to the classification process, K-Fold Cross Validation was performed to find the optimal data modeling. The results of the K Fold Cross Validation modeling will be a model for the sample of training data and data testing. Evaluation of classification results based on precision, recall, and F-1. The test resulted in a higher KKN performance compared to MKNN in all test parameters, with an average gap of 25 percent. In addition, it was also found that the execution time of the KNN algorithm was faster than that of the MKNN
Trials and Progress Prediction of Covid-19 Vaccine Using Linear Regression and SIR Parameters Ananda Aulia Rizky; Novi Rahmawati; Adil El-Faruqi; Faisal Dharma Adhinata; Nur Ghaniaviyanto Ramadhan
Indonesia Journal on Computing (Indo-JC) Vol. 6 No. 3 (2021): December, 2021
Publisher : School of Computing, Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34818/INDOJC.2021.6.3.594

Abstract

This study aims to elucidate the worldwide effectiveness of the COVID-19 vaccine to reduce the number of COVID-19 patients. Currently, almost all countries in the world are trying to overcome COVID-19 by imposing a lockdown system. The government is also looking for a solution to suppress the spread of COVID-19 by administering a vaccine. Vaccination is one of the efforts that are considered effective in overcoming COVID-19 in affected countries. At least 85 types of vaccines are still in the development stage, while the vaccines that have been agreed upon are Pfizer-Biotech messenger RNA vaccines (bnt162b2) and Moderna (mRNA-1273). The hope is that the COVID-19 outbreak can be handled immediately to restore the residents' economy with vaccination. The methodology used in this study uses data mining with linear regression and SIR techniques to evaluate whether circulating vaccines can effectively suppress the spread of COVID-19.
Design of API Gateway as Middleware on Platform as a Service Dita Oktaria; Joel Andrew M. K. Ginting; Maman Abdurohman; Rahmat Yasirandi
Indonesia Journal on Computing (Indo-JC) Vol. 6 No. 3 (2021): December, 2021
Publisher : School of Computing, Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34818/INDOJC.2021.6.3.597

Abstract

The process of building a platform is a process that consists of various stages, there is a focus of work and requires different preparation, but until now there has been no awareness in utilizing data and information sources that have been available to be used as a basis for developing or creating a platform that is able to improve quality a system of integrity. For this reason, a Platform as a Service (PaaS) architecture was built which provides application development services to process data and information obtained from practicum activities during the lecture period based on cloud computing using the Service Oriented Architectural (SOA) method. The API gateway is used as middleware system. The results of the implementation and analysis carried out prove that the architecture using the API gateway as a built-in middleware can be considered to develop the Telkom University lab service system. Although there are adjustments to resources and needs, but the purpose of this architectural development has generally been realized. From the results of tests performed on a platform architecture that uses a gateway API, it produces RTT 2.081 seconds, 45 MB memory, and 8% CPU for each user in 100 users.

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