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All Journal International Journal of Electrical and Computer Engineering Jurnal Ilmiah Matrik Jurnal Masyarakat Merdeka (JMM) Journal of Information Systems and Informatics G-Tech : Jurnal Teknologi Terapan Jurnal Dialektika Informatika (Detika) Abdiformatika: Jurnal Pengabdian Masyarakat Informatika The Indonesian Journal of Computer Science Research JAMU : Jurnal Abdi Masyarakat UMUS Fahma : Jurnal Informatika Komputer, Bisnis dan Manajemen Inovasi Teknologi Masyarakat
Andri Syafrianto
Informatika, STMIK El Rahma Yogyakarta
Religion Agriculture, Biological Sciences & Forestry Arts Humanities Automotive Engineering Biochemistry, Genetics & Molecular Biology Civil Engineering, Building, Construction & Architecture Computer Science & IT Decision Sciences, Operations Research & Management Dentistry Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Medicine & Pharmacology Nursing Public Health Social Sciences Transportation Other

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Journal : Journal of Information Systems and Informatics

 1 
Data Quality Analysis on Open Government Data Portals: A Qualitative Study Using ISO/IEC 25012:2008 Standards Emigawaty, Emigawaty; Syafrianto, Andri
Journal of Information System and Informatics Vol 6 No 2 (2024): June
Publisher : Asosiasi Doktor Sistem Informasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51519/journalisi.v6i2.862

Abstract

This study evaluates the data quality on Open Government Data (OGD) portals using the ISO/IEC 25012:2008 standard, which categorizes data quality into two main groups: inherent data quality and system-dependent data quality. This standard encompasses dimensions such as accuracy, completeness, consistency, and relevance. Using a qualitative approach, interviews were conducted with data providers and users from the government, industry, and academia. The findings indicate that while some datasets are adequate, there are issues with semantic consistency, completeness, timeliness, and currency of the data. These findings highlight the importance of strict and continuous application of data quality standards in OGD management. Recommendations for improvement include training for data managers and enhancing validation mechanisms before data is published. This study supports government efforts to improve transparency and accountability by providing high-quality data that can be reliably used by various stakeholders.
Detecting Deceptive Online Reviews Using a Semantic Reliability Index and Hybrid Text Representation Hartatik; Andri Syafrianto
Journal of Information System and Informatics Vol 8 No 2 (2026): April
Publisher : Asosiasi Doktor Sistem Informasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63158/journalisi.v8i2.1576

Abstract

Online review platforms such as Yelp play an important role in consumer decision-making, but the growing prevalence of fake reviews undermines their reliability. This study proposes a hybrid approach for fake review detection by integrating stylometric features, language model signals, and semantic embeddings within a unified classification framework. The proposed method combines linguistic indicators, including GPT-2 perplexity, lexical diversity, sentence burstiness, punctuation ratio, and sentiment intensity, with TF-IDF representations and Sentence-BERT embeddings. A composite feature, namely the Semantic Reliability Index (SRI), is introduced to capture interactions between semantic similarity and linguistic characteristics, serving as an auxiliary feature within the hybrid model rather than a standalone classifier. Experiments on a Yelp hotel review dataset demonstrate that the hybrid model outperforms baseline methods in terms of F1-score and AUC, indicating improved discriminative capability. It should be noted that the classification setting is based on a binary transformation of ordinal labels, which may simplify the underlying label structure and influence performance interpretation. Overall, this work's contribution lies in a systematic feature-integration strategy that enhances fake review detection in the evaluated dataset.
Co-Authors Agus Qomaruddin Munir Amelia, Dara Anindita Septiarini, Anindita Aris Badaruddin Thoha Avianto Aldi Nugroho Budi Rahmani Didit Suprihanto, Didit Diningrum, Maynella Dwi Eko Riswanto Emigawaty Gultom, Josua Sonniwinter Gunawan, Muhammad Riswandi Hadiwijaya, Noor Alam Hamdani Hamdani Hamdani Hamdani Haqiqi, Aprilika Harliana Harliana Hartatik Herry Ramadhani Heru Ismanto Ismail, Afif Isnaini Solikhah Jamil, Muh. Juliyanti Muhammad Zainal Arifin, Muhammad Zainal Pratiwi, Putri Rinda Purwanti, Silviana Putra, Muhammad Habib Dwi Ramadhan, Fakhri Ramadhan, Muhammad Ady Setiyawan Rasidia, Fikri Senina, Alya Aulya Nurullita Sugiyatno Suparyanto Suparyanto Wiwi Widayani Wiwi Widayani Zilfana Falahi