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Ipung, Heru Purnomo
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Computer Science & IT

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Hadoop Configuration Tuning for Performance Optimization Christian, Christian; I Eng, Kho; Ipung, Heru Purnomo
Journal of Applied Information, Communication and Technology Vol. 4 No. 1 (2017)
Publisher : Swiss German University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33555/ejaict.v4i1.81

Abstract

Configuration parameter tuning is an essential part of the implementation of Hadoop clusters. Each parameter in a configuration plays a role that impacts the ov erall performance of the cluster. Therefore, we need to learn the characteristics of said parameter and understand the impact in hardware utilization in order to achieve optimal configuration. In this paper, we conducted experiments that includes modifying configuration and performed benchmark to find out if there is any performance gain. TeraSort is the program that runs the benchmark, we measure the time needed to complete the sort of the set of data and the CPU utilization during the benchmark. We conclu de that from our experiments we can see significant performance improvements by tuning with the configurations. However, the results may vary between different cluster configuration.
Knowledge Capturing and Codifying in Knowledge Management Using Alfresco: A Case Study of Swiss German University Bandoso, Charles Sosang; Ipung, Heru Purnomo; Soetomo, M. A. Amin
Journal of Applied Information, Communication and Technology Vol. 2 No. 2 (2015)
Publisher : Swiss German University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33555/ejaict.v2i2.88

Abstract

Knowledge management has become the asset of every organization because every knowledge in each group is unique one with another. Two aspects that important in knowledge management is step to capturing theknowledge and codifying the knowledge. Capturing mean to list or create a real documentation of knowledge while codifying is to create a digital version from capturing process so it can be processed or easily manipulated. One of the most growth software to do knowledge management for codifying is alfresco that being established in 2005. Alfresco provide open source so called Alfresco Community Edition that enable developer to create rules in codifying the knowledge. The output of this process is to create functional ECM in a form of a portal where end user can use it to manage a contents or documents.
Study on Social Media Users and Its Relation to the e-Commerce Activities on Youth in Indonesia Irawan, Krisma Budianto; Ipung, Heru Purnomo; Galinium, Maulahikmah
Journal of Applied Information, Communication and Technology Vol. 3 No. 1 (2016)
Publisher : Swiss German University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33555/ejaict.v3i1.92

Abstract

Internet, Social Media and e-Commerce are three most common thingspeople could find and usually interact with nowadays. Internet has beenpart of our basic daily needs: no one can work professionally withoutbeing connected to the network even once in a day. Those people usuallyalso have at least one Social Media accounts; as well know about theexistence of e-Commerce markets. Also, Social Media and e-CommerceChannel nowadays more likely to interact with each other: peoplespreading the word about their commerce in the Social Networks. Basedon that fact, this research purpose is to find out which factors, based onthe Technology Acceptance Model, is playing the huge role on this socalled relations between the interaction of people in the Social Media andtheir activities in the e-Commerce, especially on Indonesian youth. Itturns out, that Voluntariness has 55% contribution towards this relations,as well as Job Relevance, Result Demonstrability and Computer SelfEfficacy with 63%, 60% and 58% contributions respectively.
Mobile Website User Experience (UX) Evaluation on Indonesia's Online Marketplace Pratama, Khosyi Yoga; Purnama, James; Ipung, Heru Purnomo
Journal of Applied Information, Communication and Technology Vol. 3 No. 2 (2016)
Publisher : Swiss German University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33555/ejaict.v3i2.96

Abstract

Internet economy is growing in Indonesia, and so is the smartphone usage. The popularity of online marketplace is getting bigger by the day yet the market is not utilized as best as it could. A lot of new smartphone user may not be familiar with the concept of mobile application, yet they browse for products to purchase using their mobile web browser. Therefore a mobile website User Experience (UX) evaluation on online marketplace in Indonesia is needed. By using User Experience Questionnaire (UEQ), this research hopes to evaluate which aspect of a marketplace’s mobile website is looked for by its users. An interview is conducted to 30 people to simulate shopping experience on Tokopedia and Bukalapak using a mobile web browser. The result shows that users prefers shopping without having to register, thus making online shopping quick and easy. In addition, user prefers to have all information they need regarding a product in one page rather than having to perform many action to reveal more information.
Developing a Scalable and Accurate Job Recommendation System with Distributed Cluster System using Machine Learning Algorithm Dicky, Timothy; Erwin, Alva; Ipung, Heru Purnomo
Journal of Applied Information, Communication and Technology Vol. 7 No. 2 (2020)
Publisher : Swiss German University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33555/jaict.v7i2.108

Abstract

The purpose of this research is to develop a job recommender system based on the Hadoop MapReduce framework to achieve scalability of the system when it processes big data. Also, a machine learning algorithm is implemented inside the job recommender to produce an accurate job recommendation. The project begins by collecting sample data to build an accurate job recommender system with a centralized program architecture. Then a job recommender with a distributed system program architecture is implemented using Hadoop MapReduce which then deployed to a Hadoop cluster. After the implementation, both systems are tested using a large number of applicants and job data, with the time required for the program to compute the data is recorded to be analyzed. Based on the experiments, we conclude that the recommender produces the most accurate result when the cosine similarity measure is used inside the algorithm. Also, the centralized job recommender system is able to process the data faster compared to the distributed cluster job recommender system. But as the size of the data grows, the centralized system eventually will lack the capacity to process the data, while the distributed cluster job recommender is able to scale according to the size of the data.
Co-Authors Bandoso, Charles Sosang christian christian Dicky, Timothy Erwin, Alva I Eng, Kho Irawan, Krisma Budianto James Purnama Maulahikmah Galinium Pratama, Khosyi Yoga Soetomo, M. A. Amin