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All Journal International Journal of Electrical and Computer Engineering Perfecting a Video Game with Game Metrics Proceeding of the Electrical Engineering Computer Science and Informatics Indonesian Journal of Electrical Engineering and Computer Science
Kridanto Surendro
Bandung Institute of Technology
Computer Science & IT Electrical & Electronics Engineering

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Academic Cloud ERP Quality Assessment Model Kridanto Surendro; Olivia Olivia
International Journal of Electrical and Computer Engineering (IJECE) Vol 6, No 3: June 2016
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (248.427 KB) | DOI: 10.11591/ijece.v6i3.pp1038-1047

Abstract

In the past few decades, educational institutions have been using conventional academic ERP system to integrate and optimize their business process. In this delivery model, each educational institutions are responsible of their own data, installation, and also maintenance. For some institutions, it might cause not only waste of resources, but also problems in management and financial aspects. Cloud-based Academic ERP, a SaaS-based ERP system, begin to come as a solution with is virtualization technology. It allows institutions to use only the needed ERP resources, without any specific installation, integration, or maintenance needs. As the implementation of Cloud ERP increases, problems arise on how to evaluate this system. Current evaluation approaches are either only evaluating the cloud computing aspects or only evaluating the software quality aspects. This paper proposes an assessment model for Cloud ERP system, considering both software quality characteristics and cloud computing attributes to help strategic decision makers evaluate academic Cloud ERP system.
Self-adaptive Software Modeling Based on Contextual Requirements Aradea Aradea; Iping Supriana; Kridanto Surendro
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 16, No 3: June 2018
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v16i3.7032

Abstract

The ability of self-adaptive software in responding to change is determined by contextual requirements, i.e. a requirement in capturing relevant context-atributes and modeling behavior for system adaptation. However, in most cases, modeling for self-adaptive software is does not take into consider the requirements evolution based on contextual requirements. This paper introduces an approach through requirements modeling languages directed to adaptation patterns to support requirements evolution. The model is prepared through contextual requirements approach that is integrated into MAPE-K (monitor, anayze, plan, execute - knowledge) patterns in goal-oriented requirements engineering. As an evaluation, the adaptation process is modeled for cleaner robot. The experimental results show that the requirements modeling process has been able to direct software into self-adaptive capability and meet the requirements evolution.
Designing Game-Based Service Desk towards User Engagement Improvement Kridanto Surendro; Sarifah Putri Raflesia
Indonesian Journal of Electrical Engineering and Computer Science Vol 1, No 2: February 2016
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v1.i2.pp381-389

Abstract

Along the implementation of Information Technology (IT), there are incident, request, problem, and event. According to this, organizations need to implement a function which can be a single point to provide communication between IT service provider and IT users. Information Technology Infrastructure Library (ITIL) mentions service desk as a function to operate solution of  this matter. But, recently organizations find new challenge which is related to service desk staffs’ motivation. The repeated activities which are run by service desk may cause saturation. This situation will affect workplace enviroment and productivity. In this research, we propose a design to help organization build game-like activities as solution to boost service desk’s motivation which can give good impact to service desk’s quality. Our proposed design uses game approach and ITIL practices to ensure that game-based service desk is well designed.
Vocational Higher Education Governance Recommendation Based On Cobit 5 Enabler Generic Model Heru Nugroho; Kridanto Surendro
Indonesian Journal of Electrical Engineering and Computer Science Vol 1, No 3: March 2016
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v1.i3.pp647-655

Abstract

Enterprise governance for higher education can be viewed as arrangements that include a variety of university assets in order to support the strategy in achieving the goals and objectives.  In the implementation governance in university there are factors that affect good governance which are individual or groups. These factors are then called an enabler of enterprise governance. In the previous research obtained the fact that the enabler of enterprise governance that provide significant influence in the governance of vocational higher education is the organization structure and information. Using four common dimensions for enablers in COBIT 5 Enabler Generic Model will give recommendation for governance in vocational higher education. These recommendations are expected help the vocational higher education in preparation of blue print of governance needs by considering the enablers of organizational structure and information.
Data prediction for cases of incorrect data in multi-node electrocardiogram monitoring Sugondo Hadiyoso; Heru Nugroho; Tati Latifah Erawati Rajab; Kridanto Surendro
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 2: April 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i2.pp1540-1547

Abstract

The development of a mesh topology in multi-node electrocardiogram (ECG) monitoring based on the ZigBee protocol still has limitations. When more than one active ECG node sends a data stream, there will be incorrect data or damage due to a failure of synchronization. The incorrect data will affect signal interpretation. Therefore, a mechanism is needed to correct or predict the damaged data. In this study, the method of expectation-maximization (EM) and regression imputation (RI) was proposed to overcome these problems. Real data from previous studies are the main modalities used in this study. The ECG signal data that has been predicted is then compared with the actual ECG data stored in the main controller memory. Root mean square error (RMSE) is calculated to measure system performance. The simulation was performed on 13 ECG waves, each of them has 1000 samples. The simulation results show that the EM method has a lower predictive error value than the RI method. The average RMSE for the EM and RI methods is 4.77 and 6.63, respectively. The proposed method is expected to be used in the case of multi-node ECG monitoring, especially in the ZigBee application to minimize errors.
Feature Expansion for Sentiment Analysis in Twitter Erwin B. Setiawan; Dwi H Widyantoro; Kridanto Surendro
Proceeding of the Electrical Engineering Computer Science and Informatics Vol 5: EECSI 2018
Publisher : IAES Indonesia Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (272.121 KB) | DOI: 10.11591/eecsi.v5.1660

Abstract

The community's need for social media is increasing, since the media can be used to express their opinion, especially the Twitter. Sentiment analysis can be used to understand public opinion a topic where the accuracy can be measured and improved by several methods. In this paper, we introduce a hybrid method that combines: (a) basic features and feature expansion based on Term Frequency-Inverse Document Frequency (TF-IDF) and (b) basic features and feature expansion based on tweet-based features. We train three most common classifiers for this field, i.e., Support Vector Machine (SVM), Logistic Regression (Logit), and Naïve Bayes (NB). From those two feature expansions, we do notice a significant increase in feature expansion with tweet-based features rather than based on TF-IDF, where the highest accuracy of 98.81% is achieved in Logistic Regression Classifier.
Co-Authors Aradea Dwi H Widyantoro Erwin B. Setiawan Heru Nugroho Heru Nugroho Iping Supriana Olivia Olivia Sarifah Putri Raflesia Sugondo Hadiyoso Tati Latifah Erawati Rajab