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All Journal Seminar Nasional Aplikasi Teknologi Informasi (SNATI) TELKOMNIKA (Telecommunication Computing Electronics and Control) Bulletin of Electrical Engineering and Informatics Seminar Nasional Teknologi Informasi Komunikasi dan Industri Journal Information System Development Jurnal Pengabdian Masyarakat Asia
I Made Murwantara
Universitas Pelita Harapan
Arts Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Mathematics Other

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Journal : telkomnika telecommunication computing electronics and control

 1 
An adaptive IoT architecture using combination of concept-drift and dynamic software product line engineering I Made Murwantara; Pujianto Yugopuspito
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 19, No 4: August 2021
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v19i4.19012

Abstract

Internet of things (IoT) architecture needs to adapt autonomously to the environment and operational to maintain their supreme services. One common problem in the IoT architecture is to manage the reliability of data services, such as sensors’ data, that only sending data to the collector via gateway. If there is a disruption of services, then it is not easy to manage the system reliability. To this, an adaptive environment which is based on software reconfiguration creates a great challenge to provide better services. In this work, the software product line engineering (SPLE) reconfigures the edge devices via rules and software architecture. To identify disruption of data services which can be detected based on anomaly and truncated data. Our work makes use of concept drift to provide a recommendation to the system manager. This is important to avoid misconfiguration in the system We demonstrate our method using an open-source internet of things portal system that integrated to a cluster of sensors which is attached to specific gateway before the data are collected into a cloud storage for further processes. In identifying drifting data, the adaptive sliding window (ADWIN) method outperforms the Page-Hinkley (PH) with more selective identification and sensitive reading.
Cluster-based water level patterns detection Friska Natalia Ferdinand; Yustinus Soelistio; Ferry Vincenttius Ferdinand; I Made Murwantara
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 17, No 3: June 2019
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v17i3.11774

Abstract

Indonesian Disaster Data and Information in 2016 showed that flood has reached a soaring 32.2% overall. In one of the common flood region (2016), Tangerang, the flood had impacted 30,949, and destroys more than 400 residentials. In spite of this dreadful fact, Tangerang has no systematically ways of detecting the flood patterns. Therefore, there is urgency for a system that is able to detect potential flood risks in Tangerang. This study explores a mean to systematically find flood patterns in Tangerang and attempt to visualize the risks based on 11 years of data on four major river stations within Tangerang vicinity. All the data obtained from Ciliwung Cisadane River Basin Center (BBWS) between 2009 until 2017 with total data of 368,184 rows. This study proposes an interactive dashboard based on the water level data covering rivers of Angke, Pesanggrahan, and Cisadane. Three clustering methods are implemented, the K-Medoids, DBScan, and x-means, to segregate the water level data, taken from four stations obtained from Ciliwung Cisadane River Basin Center (BBWS), into meaningfull periodic flood patterns. The output of this research is an interactive dashboard created based on the newly found patterns. The dashboard is designed to be simple and easy to use for non-technical persons. We believe that the output of this research could be implemented into the decision-making process taken by the Ciliwung Cisadane River Basin Center (BBWS) in order to improve countermeasure attempts on the potentially flooded areas.
Comparison of machine learning performance for earthquake prediction in Indonesia using 30 years historical data I Made Murwantara; Pujianto Yugopuspito; Rickhen Hermawan
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 18, No 3: June 2020
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v18i3.14756

Abstract

Indonesia resides on most earthquake region with more than 100 active volcanoes,and high number of seismic activities per year. In order to reduce the casualty, some method to predict earthquake have been developed to estimate the seismic movement. However, most prediction use only short term of historical data to predict the incoming earthquake, which has limitation on model performance. This work uses medium to long term earthquake historical data that were collected from 2 local government bodies and 8 legitimate international sources. We make an estimation of a mediumto-long term prediction via Machine Learning algorithms, which are Multinomial Logistic Regression, Support Vector Machine and Na¨ıve Bayes, and compares their performance. This work shows that the Support Vector Machine outperforms other method. We compare the Root Mean Square Error computation results that lead us into how concentrated data is around the line of best fit, where the Multinomial Logistic Regression is 0.777, Na¨ıve Bayes is 0.922 and Support Vector Machine is 0.751. In predicting future earthquake, Support Vector Machine outperforms other two methods that produce significant distance and magnitude to current earthquake report.
Towards Adaptive Sensor-cloud for Internet of Things I Made Murwantara; Hendra Tjahyadi; Pujianto Yugopuspito; Arnold Aribowo; Irene A. Lazarusli
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 16, No 6: December 2018
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v16i6.11557

Abstract

The emerge of the Internet of Things (IoT) data as a commodity to optimize public services such as Fishing Locator has made sensor-cloud an important object. When sensors that are members of multiple IoT gateways can inter-operate at the same time for more than one application, it will reduce cost to deploy IoT infrastructure. However, reliability has also developed as the most important aspect for real-time data collection that should be streamed constantly. Due to uncertainty factors sensors failure is potentially occurred, then an adaptive approach should be addressed into this as to guarantee the flow of streaming data. This paper proposed an adaptive sensor-cloud mechanism to manage the reliability by using a runtime model approach where a transition model and dynamic software product line engineering will take place to weaving the system. Our technique is comparable to other approaches and can be implemented in many types of Cloud-based services.
Co-Authors Alessandro Luiz Kartika Andriyani Arnold Aribowo Fanny Sinthia Ferdinand, Ferry Vincenttius Friska Natalia Ferdinand Hendra Tjahyadi Irene A. Lazarusli Johan Julianto Roestam Kartika, Alessandro Luiz Rickhen Hermawan Taslim, Deddy Gunawan Yugopsupito, Pujianto Yugopuspito, Pujianto Yustinus Soelistio