Articles
<![CDATA[VOIP TECHNOLOGY SIMULATION BASED ON HYBRID FIBER COAXIAL CABLE ]]>
<![CDATA[Suryaputra Paramita, Adi]]>
<![CDATA[ Proceedings of KNASTIK 2012 ]]>
Publisher : <![CDATA[Duta Wacana Christian University ]]>
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<![CDATA[This Research aims to determine how the implementation of the Hybrid FiberCoaxial (HFC) network for Voice Over Internet Protocol (VOIP), as is well-knownthat these days VOIP networks increasingly widespread use as an alternative in thefield of communication, in building a VOIP network would need an infrastructure Agood network and reliable, one important factor is the network speed andbandwidth required, in this study will be tested how the implementation of the use ofHFCs for VOIP networks, and the results of existing trials indicate that HFCprovides the smallest delay compared to another medium transmission, from theresult can be seen that at present one of the medium best transmission forimplementing a VOIP network is a Hybrid Fiber Coaxial]]>
<![CDATA[PERANCANGAN INTEGRASI TEKNOLOGI OPEN SOURCE PADA SISTEM INFORMASI PROGRAM STUDI TEKNIK INFORMATIKA UNIVERSITAS CIPUTRA ]]>
<![CDATA[Paramita, Adi Suryaputra]]>
<![CDATA[ Proceedings of KNASTIK 2009 ]]>
Publisher : <![CDATA[Duta Wacana Christian University ]]>
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<![CDATA[Sistem Informasi pada saat ini adalah suatu kebutuhan wajib bagi tiap institusi, karena hal inilah sebuah SistemInformasi harus dirancang dengan baik agar informasi yang masuk dan keluar bisa dikelola dengan baik dan efektif. Salahsatu upaya untuk membuat informasi yang dikelola bisa efektif adalah dengan melakukan integrasi sistem yang ada. ProgramTeknik Informatika Universitas Ciputra saat ini memiliki 3 aplikasi untuk Sistem Informasi yaitu WikiIFT sebagai saranapenampung aspirasi dari dosen dan mahasiswa, PortalIFT sebagai website Program Studi dan E-Learning sebagai saranabelajar mengajar secara onlineKetiga aplikasi tersebut menggunakan teknologi opensource dan masih berdiri secara sendiri-sendiri, dengansemakin bertambahnya waktu dan semakin banyaknya informasi yang beredar saat ini, dirasa perlu adanya integrasi ketigateknologi tersebut agar tidak terjadi adanya duplikasi informasi yang ada pada ketiga sistem tersebut, salah satu bagian yangdimungkinkan untuk diintegrasikan adalah username dan password untuk tiap user pada aplikasi-aplikasi tersebut, integrasiyang dimungkinkan adalah dengan meracang sistem single sign on dalam integrasi ketiga aplikasi tersebut]]>
<![CDATA[Improving K-NN Internet Traffic Classification Using Clustering and Principle Component Analysis ]]>
<![CDATA[Adi Suryaputra Paramita]]>
<![CDATA[ Bulletin of Electrical Engineering and Informatics Vol 6, No 2: June 2017 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/eei.v6i2.608
<![CDATA[K-Nearest Neighbour (K-NN) is one of the popular classification algorithm, in this research K-NN use to classify internet traffic, the K-NN is appropriate for huge amounts of data and have more accurate classification, K-NN algorithm has a disadvantages in computation process because K-NN algorithm calculate the distance of all existing data in dataset. Clustering is one of the solution to conquer the K-NN weaknesses, clustering process should be done before the K-NN classification process, the clustering process does not need high computing time to conqest the data which have same characteristic, Fuzzy C-Mean is the clustering algorithm used in this research. The Fuzzy C-Mean algorithm no need to determine the first number of clusters to be formed, clusters that form on this algorithm will be formed naturally based datasets be entered. The Fuzzy C-Mean has weakness in clustering results obtained are frequently not same even though the input of dataset was same because the initial dataset that of the Fuzzy C-Mean is less optimal, to optimize the initial datasets needs feature selection algorithm. Feature selection is a method to produce an optimum initial dataset Fuzzy C-Means. Feature selection algorithm in this research is Principal Component Analysis (PCA). PCA can reduce non significant attribute or feature to create optimal dataset and can improve performance for clustering and classification algorithm. The resultsof this research is the combination method of classification, clustering and feature selection of internet traffic dataset was successfully modeled internet traffic classification method that higher accuracy and faster performance.]]>
<![CDATA[Analisis Sentimen Pada Maskapai Penerbangan di Platform Twitter Menggunakan Algoritma Support Vector Machine (SVM) ]]>
<![CDATA[Hendry Cipta Husada]]>;
<![CDATA[Adi Suryaputra Paramita]]>
<![CDATA[ Teknika Vol 10 No 1 (2021): Maret 2021 ]]>
Publisher : <![CDATA[Center for Research and Community Service, Institut Informatika Indonesia (IKADO) Surabaya ]]>
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DOI: 10.34148/teknika.v10i1.311
<![CDATA[Perkembangan teknologi saat ini telah memberikan kemudahan bagi banyak orang dalam mendapatkan dan menyebarkan informasi di berbagai social media platform. Twitter merupakan salah satu media yang kerap digunakan untuk menyampaikan opini sebagai bentuk reaksi seseorang atas suatu hal. Opini yang terdapat di Twitter dapat digunakan perusahaan maskapai penerbangan sebagai parameter kunci untuk mengetahui tingkat kepuasan publik sekaligus bahan evaluasi bagi perusahaan. Berdasarkan hal tersebut, diperlukan sebuah metode yang dapat secara otomatis melakukan klasifikasi opini ke dalam kategori positif, negatif, atau netral melalui proses analisis sentimen. Proses analisis sentimen dilakukan dengan proses data preprocessing, pembobotan kata menggunakan metode TF-IDF, penerapan algoritma, dan pembahasan atas hasil klasifikasi. Klasifikasi opini dilakukan dengan machine learning approach memanfaatkan algoritma multi-class Support Vector Machine (SVM). Data yang digunakan dalam penelitian ini adalah opini dalam bahasa Inggris dari para pengguna Twitter terhadap maskapai penerbangan. Berdasarkan pengujian yang telah dilakukan, hasil klasifikasi terbaik diperoleh menggunakan SVM kernel RBF pada nilai parameter ð¶(complexity) = 10 dan ð›¾(gamma) = 1, dengan nilai accuracy sebesar 84,37% dan 80,41% ketika menggunakan 10-fold cross validation.]]>
<![CDATA[Faktor-faktor Penting Dalam Penyampaian Pelatihan Atau Workshop Pemrograman Secara Daring ]]>
<![CDATA[Laura Mahendratta Tjahjono]]>;
<![CDATA[Adi Suryaputra Paramita]]>
<![CDATA[ Teknika Vol 10 No 3 (2021): November 2021 ]]>
Publisher : <![CDATA[Center for Research and Community Service, Institut Informatika Indonesia (IKADO) Surabaya ]]>
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DOI: 10.34148/teknika.v10i3.392
<![CDATA[Pelatihan dalam bentuk pembelajaran melalui daring kini semakin sering dilakukan, terutama di bidang pemrograman yang merupakan dasar utama di rumpun ilmu komputer. Salah satu hal yang mendorong pelaksanaan pembelajaran daring ini adalah situasi global yang mengharuskan pembatasan jarak akibat adanya pandemi Covid-19. Kemajuan teknologi berupa penyebaran jaringan internet dan ketersediaan berbagai aplikasi yang menjadi penunjang pembelajaran daring ini. Hal ini juga dibantu dengan tersedianya berbagai aplikasi lain yang bisa membantu untuk pengajaran dalam bentuk presentasi maupun untuk penilaian ujian yang siap digunakan untuk pembelajaran daring. Kemajuan teknologi ini tentunya merupakan hal yang baik, namun ternyata masih banyak kendala yang dihadapi bagi para peserta pembelajaran daring untuk mengadopsi teknologi tersebut. Berdasarkan permasalahan tersebut dirumuskan sebuah rumusan masalah untuk penelitian ini yaitu bagaimana melakukan identifikasi faktor-faktor yang mempengaruhi kesuksesan proses pelatihan pemrograman yang dilakukan secara daring? Dan Faktor-faktor apa yang berdampak pada pemahaman materi bagi peserta pelatihan? Proses identifikasi faktor-faktor yang dapat mendorong kesuksesan pelatihan berbasis online merupakan fokus dari penelitian ini. Langkah awal adalah dengan melakukan proses pengumpulan data menggunakan survei dan kuesioner yang sudah disusun berdasarkan pada metode Technology Acceptance Model (TAM), kemudian data dianalisis berdasarkan statistika deskriptif. Penelitian ini berhasil melakukan identifikasi bagaimana seharusnya pelatihan berbasis daring dilakukan dengan efektif berdasarkan faktor-faktor penting yang sudah teridentifikasi.]]>
<![CDATA[Improving K-NN Internet Traffic Classification Using Clustering and Principle Component Analysis ]]>
<![CDATA[Adi Suryaputra Paramita]]>
<![CDATA[ Bulletin of Electrical Engineering and Informatics Vol 6, No 2: June 2017 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/eei.v6i2.608
<![CDATA[K-Nearest Neighbour (K-NN) is one of the popular classification algorithm, in this research K-NN use to classify internet traffic, the K-NN is appropriate for huge amounts of data and have more accurate classification, K-NN algorithm has a disadvantages in computation process because K-NN algorithm calculate the distance of all existing data in dataset. Clustering is one of the solution to conquer the K-NN weaknesses, clustering process should be done before the K-NN classification process, the clustering process does not need high computing time to conqest the data which have same characteristic, Fuzzy C-Mean is the clustering algorithm used in this research. The Fuzzy C-Mean algorithm no need to determine the first number of clusters to be formed, clusters that form on this algorithm will be formed naturally based datasets be entered. The Fuzzy C-Mean has weakness in clustering results obtained are frequently not same even though the input of dataset was same because the initial dataset that of the Fuzzy C-Mean is less optimal, to optimize the initial datasets needs feature selection algorithm. Feature selection is a method to produce an optimum initial dataset Fuzzy C-Means. Feature selection algorithm in this research is Principal Component Analysis (PCA). PCA can reduce non significant attribute or feature to create optimal dataset and can improve performance for clustering and classification algorithm. The resultsof this research is the combination method of classification, clustering and feature selection of internet traffic dataset was successfully modeled internet traffic classification method that higher accuracy and faster performance.]]>
<![CDATA[Improving K-NN Internet Traffic Classification Using Clustering and Principle Component Analysis ]]>
<![CDATA[Adi Suryaputra Paramita]]>
<![CDATA[ Bulletin of Electrical Engineering and Informatics Vol 6, No 2: June 2017 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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Full PDF (435.798 KB)
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DOI: 10.11591/eei.v6i2.608
<![CDATA[K-Nearest Neighbour (K-NN) is one of the popular classification algorithm, in this research K-NN use to classify internet traffic, the K-NN is appropriate for huge amounts of data and have more accurate classification, K-NN algorithm has a disadvantages in computation process because K-NN algorithm calculate the distance of all existing data in dataset. Clustering is one of the solution to conquer the K-NN weaknesses, clustering process should be done before the K-NN classification process, the clustering process does not need high computing time to conqest the data which have same characteristic, Fuzzy C-Mean is the clustering algorithm used in this research. The Fuzzy C-Mean algorithm no need to determine the first number of clusters to be formed, clusters that form on this algorithm will be formed naturally based datasets be entered. The Fuzzy C-Mean has weakness in clustering results obtained are frequently not same even though the input of dataset was same because the initial dataset that of the Fuzzy C-Mean is less optimal, to optimize the initial datasets needs feature selection algorithm. Feature selection is a method to produce an optimum initial dataset Fuzzy C-Means. Feature selection algorithm in this research is Principal Component Analysis (PCA). PCA can reduce non significant attribute or feature to create optimal dataset and can improve performance for clustering and classification algorithm. The resultsof this research is the combination method of classification, clustering and feature selection of internet traffic dataset was successfully modeled internet traffic classification method that higher accuracy and faster performance.]]>
<![CDATA[ARSITEKTUR SISTEM INFORMASI TERINTEGRASI UNTUK DATA JEMAAT LINGKUP REGIONAL PADA GEREJA X ]]>
<![CDATA[Adi Suryaputra Paramita]]>
<![CDATA[ JATISI (Jurnal Teknik Informatika dan Sistem Informasi) Vol 9 No 1 (2022): JATISI (Jurnal Teknik Informatika dan Sistem Informasi) ]]>
Publisher : <![CDATA[Lembaga Penelitian dan Pengabdian pada Masyarakat (LPPM) STMIK Global Informatika MDP ]]>
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DOI: 10.35957/jatisi.v9i1.1665
<![CDATA[The development of technology presents challenges for non-profit organizations such as churches. Indirectly churches are required to be more organized and present accurate data and carry out an in-depth analysis of the condition of the church congregation. Church X has 40 local churches in regional areas spread across several East Java and Bali cities such as Surabaya, Malang, Kediri, Tuluagung, Sidoarjo, and Denpasar. In providing effective services to the local congregation, Church X needs to create an integrated information system for congregational data management and the information system used to see the current condition of the congregation in all local churches so that effective and targeted services can be carried out. In this study, an administrative information system model for integrated congregational data management in Church X will be designed, an information system designed to be web-based. It is easy to implement and does not require investment in expensive information and communication technology equipment. The integrated information system model will be built using microservices. Through this model, an Information System model will be obtained that can assist the process of collecting data on congregations in the regional church X while maintaining data privacy for each local church.]]>
<![CDATA[Model Sistem Informasi Penilaian Rekan Satu Kelompok Secara Kolaboratif Untuk Pembelajaran Berbasis Proyek ]]>
<![CDATA[Adi Suryaputra Paramita]]>
<![CDATA[ JATISI (Jurnal Teknik Informatika dan Sistem Informasi) Vol 9 No 2 (2022): JATISI (Jurnal Teknik Informatika dan Sistem Informasi) ]]>
Publisher : <![CDATA[Lembaga Penelitian dan Pengabdian pada Masyarakat (LPPM) STMIK Global Informatika MDP ]]>
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DOI: 10.35957/jatisi.v9i2.1493
<![CDATA[Project-based learning is one of the learning models that is currently considered suitable for Engineering-based study programs, where in project-based learning it is expected that students can produce a product that can be prepared through a collaborative process. The resulting product certainly needs to be assessed at the end of the semester, in this learning model it is hoped that the assessment process will also be carried out collaboratively, so that the research process can be carried out properly and comprehensively, it is necessary to create an integrated information system model that can adopt these needs. The information system is also a solution during the COVID-19 pandemic, which causes not all lecturers to meet and gather in one place, with this information system assessment can be done online without the need to meet physically. This information system is built based on web and mobile applications, making it easier to access without any geographical and location restrictions. The result of this research is an Information System model for a collaborative assessment process for project-based learning that can be accessed from anywhere.]]>
<![CDATA[EXPLORATORY FACTOR ANALYSIS OF TEAM CLIMATE INVENTORY (TCI) ON TECHNOLOGY START-UP ]]>
<![CDATA[Tony Antonio]]>;
<![CDATA[Amanda Teonata]]>;
<![CDATA[Trianggoro Wiradinata]]>;
<![CDATA[Adi Suryaputra]]>;
<![CDATA[Agoes Tinus Lis Indrianto]]>
<![CDATA[ International Journal of Economics, Business and Accounting Research (IJEBAR) Vol 5, No 4 (2021): IJEBAR : Vol. 05, Issue 04, December 2021 ]]>
Publisher : <![CDATA[LPPM ITB AAS INDONESIA (d.h STIE AAS Surakarta) ]]>
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DOI: 10.29040/ijebar.v5i4.3312
<![CDATA[Team climate in organization is an important element to make the innovation process in an organization works. Study on team and its behaviour is done extensively around the world. It shows the importance of a team. Team climate is one of the characteristics of an innovative team. Team Climate Inventory is a measurement scale to examine the climate factors in a team. Earlier TCI was developed by West in 1990 and then extended in 1995 and 1998. Kivinaki and Eloainio made a shorter version of West’s which consists only 14 items. The shorter version is administered to a total five teams of co-working space start-up. The technology start-up has an intensive program every day within a month under a supervision of tutor from international wellknown company and an entrepreneurial-based university. The quantitative survey was followed by interviewing some of the member and leaders of the start-ups. The item analysis shows that all items are accepted with CITC value are above 0.3. And high reliability with Cronbach’s alpha value is above 0.8. The analysis shows that TCI has 3 factors, which consist of vision, participatory safety, and support for innovation. Keywords: Team Climate, Innovation, Technology start-up]]>
