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All Journal Sinkron : Jurnal dan Penelitian Teknik Informatika Computatio : Journal of Computer Science and Information Systems Faktor Exacta JURNAL INSTEK (Informatika Sains dan Teknologi) Jurnal Nasional Komputasi dan Teknologi Informasi The IJICS (International Journal of Informatics and Computer Science) JURIKOM (Jurnal Riset Komputer) STRING (Satuan Tulisan Riset dan Inovasi Teknologi) EDUMATIC: Jurnal Pendidikan Informatika Building of Informatics, Technology and Science JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) TIN: TERAPAN INFORMATIKA NUSANTARA RESOLUSI : REKAYASA TEKNIK INFORMATIKA DAN INFORMASI Journal of Informatics Management and Information Technology KLIK: Kajian Ilmiah Informatika dan Komputer Journal of Academia Perspectives Jurnal Informatika Dan Tekonologi Komputer (JITEK) Prioritas : Jurnal Pengabdian Kepada Masyarakat Journal of Computing and Informatics Research Kapas: Kumpulan Artikel Pengabdian Masyarakat Journal of Informatics, Electrical and Electronics Engineering Bulletin of Informatics and Data Science Jurnal Informatika: Jurnal Pengembangan IT CHAIN: Journal of Computer Technology, Computer Engineering and Informatics Bulletin of Artificial Intelligence Seminar Nasional Riset dan Teknologi (SEMNAS RISTEK) Aksi Kita: Jurnal Pengabdian Kepada Masyarakat International Journal of Informatics and Data Science Jurnal Informatika Dan Tekonologi Komputer Jurnal Ilmiah Teknik Mesin, Elektro dan Komputer Jurnal Publikasi Teknik Informatika
Erlin Windia Ambarsari
Program Studi Teknik Informatika, Universitas Indraprasta PGRI
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Journal : bulletin of informatics and data science

 1 
Applying IROC Method in Patent Submission Evaluation in Indonesia: A Comparison with MAGIQ and AHP Ambarsari, Erlin Windia; Rahman, Vierhan; Cholifah, Wahyu Nur
Bulletin of Informatics and Data Science Vol 2, No 2 (2023): November 2023
Publisher : PDSI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61944/bids.v2i2.75

Abstract

This study applies the Improved Rank Order Centroid (IROC) to the Indonesian patent submission process within a Multi-Criteria Decision Making (MCDM) framework. The study evaluates four primary elements in patent assessment: "Patent Description," "Illustration," "Inventor's Ownership Statement," and "Rights Assignment Declaration." Preliminary findings indicate the importance of "Patent Description," followed by the other elements in descending order of significance. The evaluation also encompasses three applicant alternatives, with the Second Applicant emerging as the most favorable. The study further contrasts IROC outcomes with MAGIQ and AHP methodologies. While rank-based techniques like ROC and IROC generally produce similar weight distributions, the AHP method, which employs pairwise comparisons, often displays variations. The research underscores the potential of IROC in determining criterion weights, its comparison within the MAGIQ framework, and its validation through AHP. These insights aim to deepen our understanding of decision-making processes and analysis. The conclusion from comparing IROC results with MAGIQ and AHP indicates that the applicant rankings remain consistent. Therefore, further research is needed to understand the differences between evaluation methods and their impacts and explore the influence of cultural or regional factors in the patent submission process
Effectiveness of Weighting in Assessing Ranking Criteria on the SWOT-MAGIQ Matrix Ambarsari, Erlin Windia; Subagio, Relo; Mesran, Mesran
Bulletin of Informatics and Data Science Vol 3, No 1 (2024): May 2024
Publisher : PDSI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61944/bids.v3i1.85

Abstract

The Analytical Hierarchy Process (AHP) has been a prominent tool in decision-making, but the Multi-Attribute Global Interference of Quality (MAGIQ) offers an alternative with its unique weighting mechanism. This research delves into the effectiveness of weighting in assessing ranking criteria within the SWOT-MAGIQ matrix. The study contrasts the traditional Rank Order Centroid (ROC) approach with the Improved Rank Order Centroid (IROC), focusing on their application in the SWOT analysis. While ROC provides simplicity, IROC aims for enhanced accuracy by considering variability in rankings. The results indicate nuanced differences, with ROC assigning higher weights to criteria such as "Friendly Staff" (0.3183 vs. IROC’s 0.3125), while IROC prioritizes aspects like "Strong Customer Relationships" more significantly (0.1103 vs. ROC’s 0.1053). The choice between ROC and IROC hinges on the specific needs of the decision-making context, with IROC potentially offering a more detailed perspective in complex scenarios. This research underscores the importance of selecting the appropriate weighting mechanism to ensure informed and strategic decisions within the SWOT-MAGIQ framework
Hybrid Chaos-Isolation Forest Framework for Anomaly Detection in Indonesia’s Public Procurement Ambarsari, Erlin Windia; Desyanti, Desyanti; Fathudin, Dedin
Bulletin of Informatics and Data Science Vol 4, No 2 (2025): November 2025
Publisher : PDSI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61944/bids.v4i2.137

Abstract

This study proposes and empirically evaluates a Hybrid Chaos-Isolation Forest (HC-iForest) framework for detecting anomalies in Indonesia’s public procurement datasets. The purpose of this research is to address the difficulty of identifying irregular procurement patterns, as existing assessment mechanisms remain largely descriptive and retrospective. The framework integrates chaos-based temporal descriptors—permutation entropy, turning points, and volatility—with statistical indicators to enhance sensitivity to nonlinear and irregular time series. Using monthly procurement data from the Open Contracting Data Standard (OCDS) covering the period from 2019 to 2024, the model identified anomalous fiscal patterns associated with year-end budget adjustments and procurement surges. Empirical evaluation using correlation, ablation, and statistical validation shows that the hybrid model introduces non-redundant anomaly information, achieving a Spearman rank correlation of approximately 0.75 compared to the baseline Isolation Forest, with reduced overlap at intermediate thresholds (Jaccard similarity of 0.20 at the Top 5%). These results confirm that chaos-driven features improve model stability and interpretability. The findings reveal that anomalies are systemic manifestations of institutional and fiscal behavior rather than random deviations. The HC-iForest framework offers a data-driven early-warning mechanism for oversight agencies such as LKPP and ICW, strengthening transparency and accountability in public spending. Future studies may extend this framework through neural or spatiotemporal hybrid architectures to support intelligent and adaptive fiscal monitoring systems
Comparison of Case-Based Reasoning and Hybrid Case-Based Methods in Expert System for Diagnosing Rice Plant Diseases Roznim, Roznim; Mesran, M.Kom, Mesran; Setiawansyah, Setiawansyah; Ambarsari, Erlin Windia
Bulletin of Informatics and Data Science Vol 4, No 2 (2025): November 2025
Publisher : PDSI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61944/bids.v4i2.132

Abstract

Rice plants are susceptible to various types of diseases that can reduce productivity and quality of the harvest. Therefore, an expert system is needed that can help the disease diagnosis process quickly and accurately. This study compares two approaches in expert systems, namely the Case-Based Reasoning (CBR) method and the Hybrid Case-Based method, to diagnose rice plant diseases based on the symptoms experienced. Data on symptoms and types of diseases were analyzed using both methods to see the level of suitability of the resulting diagnosis. The test results showed that the Hybrid Case-Based method produced a higher level of certainty for all types of diseases compared to the CBR method. For example, Bacterial Leaf Blight disease has a certainty value of 99.5% in the Hybrid method, higher than 83.8% in the CBR method. These findings indicate that the Hybrid method is more effective and accurate in the process of diagnosing rice plant diseases. Thus, an expert system based on the Hybrid Case-Based method is recommended to support decision making in the agricultural sector, especially in early detection of rice diseases
Hybrid Autoencoder and NiaARM Framework for Flash Viral Detection on YouTube Shorts Erlin Windia Ambarsari; Mercy Hermawati; Dedin Fathudin
Bulletin of Informatics and Data Science Vol 5, No 1 (2026): May 2026
Publisher : PDSI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61944/bids.v5i1.162

Abstract

YouTube Shorts has rapidly emerged as a dominant short-form video platform, yet small creator channels often experience an unusual viral phenomenon best described as flash viral—a sudden surge of views that peaks within 24 to 48 hours and then collapses almost as quickly. Detecting and explaining this pattern is challenging because traditional statistical detectors miss multivariate signatures, while classical Association Rule Mining (ARM) such as Apriori loses information through mandatory discretization. This study proposes a hybrid framework that combines a semi-supervised Deep Learning Autoencoder with Nature-Inspired Numerical Association Rule Mining (NiaARM) using Differential Evolution and Particle Swarm Optimization. The framework is validated on six temporal snapshots of the Indonesian Boburu YouTube Shorts channel, comprising 63 unique videos (42 active) collected between February 22 and March 19, 2026. Experimental results show that the Autoencoder achieves an F1-score of 0.667 with 100% recall, matching the best classical baseline (Z-Score) while providing learnable representational capacity for future scaling. NiaARM-PSO discovered 3,115 high-quality numerical association rules with a maximum lift of 63.00, compared to only 43 rules and a maximum lift of 2.52 obtained by Apriori, an improvement of approximately 25 times. Traffic source decomposition further revealed that 99.9% of viral views originated from external platforms rather than YouTube's recommendation system, indicating that flash viral on micro-channels is externally driven. This research contributes a methodological framework that simultaneously detects and explains flash viral phenomena in short-form video analytics
Co-Authors Abdul Karim Ade Irma Agustina Lubis Agi Candra Bramantia Agus Perdana Windarto Andreas Adi Trinoto Annisa Elfina Augustia Aulia Paramita Baehaqi Bramantia, Agi Candra Dedin Fathudin Dedin Fathudin Desyanti Desyanti Desyanti Dodi Siregar Dodi Siregar Dona Katarina Dudi Parulian Dudi Parulian Fathudin, Dedin Fazrie, Mohammad Finata Rastic Andrari Frieyadie Furqoni Yudhistira Gravita Alfiani Herlinda Herlinda Herlinda Herlinda Herlinda Herlinda Herlinda Herlinda, Herlinda Hetty Rohayani Jahril Jayawarsa, A.A. Ketut Jeperson Hutahaean Jumaryadi, Yuwan KUSTIAN, NUNU Laurent Nababan Mercy Hermawati Mesran, Mesran Mohammad Taufan Asri Zaen Noni Selvia Noni Selvia Nunu Kustian Nunu Kustian Nurfidah Dwitiyanti Nurfidah Dwitiyanti Paramita, Aulia Parulian, Dudi Puji Lestari, Fibria Anggraini Purba, Bister Putri Dina Mardika Putri Dina Mardika Rahman, Vierhan Randi Ramliyana Reflan Revife Purba Relo Subagio Rezkiyana Hikmah Ridha Maya Faza Lubis Rizki Ridwan Rizki Ridwan Roswita Daeli Roznim, Roznim RR. Ella Evrita Hestiandari Saidah, Fatiyah Setiawansyah Setiawansyah Siagian, Jaya Sari Anggraini Siti Julaeha, Siti Sofiansyah Fadli Sri Agustiani Br Siburian Sri Melati Sagita Subagio, Relo Sunarmintyastuti, Lies Vierhan Rahman Wahyu Nur Cholifah Wilatiktah, Anatasya Aulya