Article Per Year (5 Year)
p-Index From 2021 - 2026
0.61
P-Index
This Author published in this journals
All Journal International Journal of Electrical and Computer Engineering Seminar Nasional Aplikasi Teknologi Informasi (SNATI) Proceedings of KNASTIK TELKOMNIKA (Telecommunication Computing Electronics and Control) JUTI: Jurnal Ilmiah Teknologi Informasi Journal of ICT Research and Applications Proceeding of the Electrical Engineering Computer Science and Informatics Indonesian Journal of Electrical Engineering and Computer Science Idealis : Indonesia Journal Information System Jurnal Ilmiah Kajian Keimigrasian (JIKK)
Jaka Sembiring
School Of Electrical Engineering And Informatics Institut Teknologi Bandung
Humanities Computer Science & IT Decision Sciences, Operations Research & Management Dentistry Education Electrical & Electronics Engineering Law, Crime, Criminology & Criminal Justice Library & Information Science Social Sciences Other

Published : 14 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 14 Documents
Search

 1   2 
Data and Metadata Exchange Design with SDMX Format using Web Service for Interoperability Statistical Data Jaka Sembiring; Ana Uluwiyah
Indonesian Journal of Electrical Engineering and Computer Science Vol 14, No 2: May 2015
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Today’s concept of Open Government Data (OGD) for openness, transparency and ease of access of data owned by government agencies becomes increasingly important. This initiative emerges from the demand of data users for the data belongs to the government agencies. The data services providing an easy access, cheap, fast, and interoperability are needed by the users and becomes important indicator performance for respective government agencies. Statistical Data and Metadata Exchange (SDMX) is a new standard format in the data dissemination activities particularly in the exchange of statistical data and metadata via Internet. In this respect SDMX support the implementation of OGD project. This paper is on the technical design, development and implementation of data and metadata exchange service of statistical data using SDMX format to support interoperability data through web services. Three results are proposed: (i) framework for standardization of structure of statistical publications data model with SDMX; (ii) design architecture of data sharing model; and (iii) web service implementation of data and metadata exchange service using Service Oriented Analysis and Design (SOAD) method. Implementation at Statistics Indonesia (BPS) is chosen as a case study to prove the design concept. It is shown through quantitative assessment and black box testing that the design achieves its objective. DOI: http://dx.doi.org/10.11591/telkomnika.v14i2.7505
ANALISIS PENUMPANG DENGAN INTEGRASI SISTEM MICROSERVICE KEIMIGRASIAN DAN MACHINE LEARNING: STUDI KASUS KANTOR IMIGRASI KELAS I TPI TANJUNGPINANG Manurung, Raphael Fransiskus; Sembiring, Jaka; Bandung, Yoanes
Jurnal Ilmiah Kajian Keimigrasian Vol 7 No 1 (2024): Jurnal Ilmiah Kajian Keimigrasian
Publisher : Polteknik Imigrasi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52617/jikk.v7i1.577

Abstract

Layanan perbatasan dan paspor yang efisien sangat penting untuk keamanan nasional dan fasilitasi imigrasi. Ancaman yang terjadi seperti pekerja ilegal dan perdagangan manusia dapat membahayakan integritas bangsa. Studi ini menyelidiki bagaimana integrasi sistem, interoperabilitas data, dan pembelajaran mesin dapat meningkatkan proses imigrasi dengan mengekstraksi insight dari sistem layanan mikro perbatasan dan paspor. Dengan menganalisis data real dari tahun 2022 hingga awal tahun 2024, pra-pemrosesan dari 260.000 catatan pergerakan wisatawan, penelitian ini mencapai tingkat akurasi 96% dalam mengidentifikasi risiko, memberdayakan pengambil keputusan untuk memitigasi ancaman secara efektif. Dengan memanfaatkan artefak algoritma pembelajaran mesin, penelitian ini meningkatkan kinerja integrasi sistem, memastikan kepatuhan terhadap standar internasional dan beradaptasi dengan masa berlaku paspor yang lebih lama untuk keamanan perbatasan yang berkelanjutan. Kepatuhan terhadap prinsip-prinsip Arsitektur Berorientasi Layanan dan metodologi penelitian memperkuat kredibilitas penelitian ini, dengan pengujian ekstensif yang memvalidasi fungsinya. Pendekatan ini berkontribusi pada literasi ilmiah mengenai pengelolaan perbatasan dan paspor, menawarkan wawasan atau pengetahuan baru untuk pengambilan keputusan dan alokasi sumber daya. Selain itu, penilaian kuantitatif statistik terhadap prototipe dilakukan, berdasarkan hasil kuisioner dari 85 pengguna, menunjukkan penerimaan, kepuasan, dan kegunaan yang tinggi, serta memvalidasi efektivitasnya. Dengan tingkat kepuasan 97% dan keberhasilan 100% dalam pengujian kinerja, penelitian ini menghasilkan keandalan dan kegunaan solusi yang diusulkan.
Mixed Data Type Analysis: A Systematic Literature Review Pratama, Hasta; Fitriyanti Lubis, Fetty; Sembiring, Jaka
IDEALIS : InDonEsiA journaL Information System Vol. 7 No. 2 (2024): Jurnal IDEALIS Juli 2024
Publisher : Universitas Budi Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36080/idealis.v7i2.3168

Abstract

This research aims to determine the direction of research in the analysis of mixed data types. The world is currently filled with increasingly diverse data, especially in terms of data types, which are not only numerical or categorical but can be both (mixed). In Data Mining, the analysis of mixed data poses significant challenges because numerical and categorical data exhibit different properties. The research methodology employed in this study utilizes the PICOC framework (Population, Intervention, Comparison, Outcome, Context) to collect and review relevant literature. The primary findings from this comprehensive literature survey reveal that a majority of the research related to mixed data is published in reputable journals Q1, indicating sustained interest in the topic of mixed data analysis. Clustering models emerge as the most frequently used models in the field of mixed data analysis. However, it's noteworthy that accuracy metrics remain the predominant evaluation benchmark, often leading to comparisons with the ideal clustered data. The management of mixed data typically involves normalization techniques, specifically normalizing the scale to amalgamate the two types of data. The conclusion drawn from the results of the literature review is the necessity to develop unlabeled mixed data, encompassing both the model and metrics required to assess the outcomes. Additionally, this research emphasizes the significance of a comprehensive development model, ranging from feature selection to evaluation models. Therefore, the analysis of mixed data types remains a field with ample opportunities for exploration and potential innovation. This potential is particularly evident in the areas of dynamic model development and the ability to handle structured and extensive data.
AN INTRODUCTION TO KNOWLEDGE-GROWING SYSTEM: A NOVEL FIELD IN ARTIFICIAL INTELLIGENCE Arwin Datumaya Wahyudi Sumari; Adang Suwandi Ahmad; Aciek Ida Wuryandari; Jaka Sembiring; Farida Agustini Widjajati
JUTI: Jurnal Ilmiah Teknologi Informasi Vol 8, No 2, Juli 2010
Publisher : Department of Informatics, Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (20963.083 KB) | DOI: 10.12962/j24068535.v8i2.a313

Abstract

The essential matter of Artificial Intelligence (AI) is how to build an entity that mimics human intelligence in the way of learning of a phenomenon in a real life to gain knowledge of it and uses the knowledge to solve problems related to it. Based on the findings of intelligenct characteristic displayed by the human brain in growing and generating new knowledge by fusing information perceived by sensory organs, we develop brain-inspired Knowledge-Growing System (KGS) that is, a system that is capable of growing its knowledge along with the accretion of information as the time passes. The essential matter of KGS is knowledge-growing method which is based on a new algorithm called Observation Multi-time A3S (OMA3S) information-inferencing fusion method. In this paper we deliver the development of KGS along with some examples of KGS application to a real-life problem. Based on the state-of-the-art of AI and approaches to construct OMA3S method as KG method as well as validations to assess the system performance, we state that brain-inspired KGS is a novel field in AI.
Co-Authors A.I. Wuryandari A.S. Ahmad Aan Subrata Aciek Ida Wuryandari Adang Suwandi Ahmad Albarda Albarda Ana Uluwiyah Arry A. Arman Arwin Datumaya Wahyudi Sumari Farida Agustini Widjajati Fetty Fitriyanti Lubis Fitasari Wiharni I Made Ariya Sanjaya Johan P. Sipayung Kurniawan, Novianto Budi Kurniawan, Novianto Budi KUSPRIYANTO Manurung, Raphael Fransiskus Nanang Hariyanto Novianto Budi Kurniawan Novianto Budi Kurniawan Nur Iksan Pratama, Hasta Subrata, Aan Suhardi Suhardi Suhardi Suhardi Suhardi, Suhardi Suhardi, Suhardi Suhono Harso Supangkat Widya Liana Aji Yoanes Bandung