Hermanto | Data Science | Innovative Research Award

Published on by Scientist Day

Global Scientist Day Awards

Hermanto

Universitas Bakti Indonesia

Hermanto
Affiliation Universitas Bakti Indonesia
Country Indonesia
Google Scholar ID pKj9u8oAAAAJ
Documents 46
Citations 6
h-index 4
Subject Area Data Science
Event Global Scientist Day Awards

Hermanto is affiliated with Universitas Bakti Indonesia in Banyuwangi, Indonesia. His Google Scholar profile identifies educational statistics and mathematics education as research interests, while a coauthored publication addresses rainfall forecasting using extreme gradient boosting. This article presents his research profile in the context of the Global Scientist Day Awards without asserting nomination, selection, or receipt of an award.[1][2]

Abstract

This article examines the academic profile of Hermanto, affiliated with Universitas Bakti Indonesia, within the context of the Global Scientist Day Awards. Available sources connect his work with mathematics education, educational statistics, and applied computational research. A verified publication investigates monthly rainfall forecasting in Banyuwangi using extreme gradient boosting. Supplied publication and citation figures are retained as unverified information because they differ from the retrieved Google Scholar record.[1][2]

Keywords

The principal indexing terms are Hermanto, Universitas Bakti Indonesia, Indonesia, data science, mathematics education, educational statistics, extreme gradient boosting, rainfall forecasting, Google Scholar, and Global Scientist Day Awards. These terms describe the supplied institutional context, documented publication themes, and recognition setting; they do not establish award status or verified rankings.

Introduction

Hermanto’s academic record combines educational interests with an identifiable application of machine learning. His affiliation appears on both the supplied Scholar profile and the rainfall forecasting publication. The awards website provides the event context, but the reviewed material does not establish an individual award decision concerning this researcher or nomination.[1]

Research Profile

The linked Google Scholar profile identifies Hermanto with Universitas Bakti Indonesia Banyuwangi and lists educational statistics and mathematics education as interests. Its publication entries include learning technologies, mathematical problem solving, and statistical analysis. Data science is therefore presented here as the supplied classification, not the profile’s stated primary disciplinary designation.[1][2]

Research Contributions

A documented contribution is Hermanto’s coauthorship of research applying extreme gradient boosting to monthly rainfall forecasting in Banyuwangi. This study connects computational prediction with a geographically specific environmental problem. The available evidence establishes participation in that publication, but does not isolate individual author responsibilities or demonstrate broader operational adoption independently. [3]

Publications

Fauziah, Hermanto, and Sukmarini published “Extreme Gradient Boosting pada Peramalan Pola Curah Hujan Bulanan Kabupaten Banyuwangi” in Jurnal Kridatama Sains dan Teknologi, volume six, issue two, pages 430–440, in 2024. This verified example does not independently substantiate the supplied total of forty-six documents attributed here.[1] [3]

Research Impact

The supplied figures report 46 documents, six citations, and an h-index of four. The retrieved Scholar profile instead displays two citations and an h-index of one. These records cannot be treated as equivalent measurements. Without a dated source for the supplied figures, quantitative impact remains unresolved rather than independently verified. [3]

Award Suitability

The organizer describes nomination procedures and several award categories on its website. Hermanto’s documented publication could support a research portfolio submitted for assessment, but suitability cannot be established from publication counts alone. No reviewed source confirms his nomination, eligibility determination, selection, or award receipt; those outcomes require direct documentary evidence. [3][1][2]

Conclusion

Available evidence supports Hermanto’s institutional affiliation, educational research interests, and coauthorship of an applied forecasting study. It does not resolve the supplied bibliometric discrepancies or establish award status. Accordingly, this article serves as a sourced academic profile, preserving a clear distinction between documented scholarship, supplied information, and prospective recognition considerations.

References

  1. Google Scholar. (2026.). Hermanto: Universitas Bakti Indonesia Banyuwangi. Public author profile, ID pKj9u8oAAAAJ.
    https://scholar.google.co.id/citations?user=pKj9u8oAAAAJ&hl=id
  2. Etnomatematika dalam pembelajaran matematika di SMA.
    https://doi.org/10.30605/pedagogy.v10i4.7045
  3. Global Scientist Day Awards. (2026.). Global Scientist Day Awards:
    https://scientistday.org/

Bassant Elkalzah | Data Science | Best Researcher Award

Published on by Scientist Day

Global Scientist Day Awards

Bassant Elkalzah
Imam Mohammad Ibn Saud Islamic University

Bassant Elkalzah
Affiliation Imam Mohammad Ibn Saud Islamic University
Country Saudi Arabia
Google Scholar ID n8GWOIAAAAAJ&hl
Documents 33
Citations 84
h-index 6
Subject Area Data Science
Event Global Scientist Day Awards
ORCID 0000-0003-3218-6228

Global Scientist Day Awards is an international recognition initiative intended to highlight scientific contributions and encourage continued engagement in research. This profile presents the researcher information supplied for Bassant Elkalzah, affiliated with Imam Mohammad Ibn Saud Islamic University in Saudi Arabia, with a stated subject area of Data Science. The profile summarizes reported bibliometric indicators and links to external researcher records for further verification. The inclusion of these details provides a structured overview of the researcher’s academic profile; it does not independently verify publication records, establish award selection, or imply endorsement by the listed institution. [1] [2]

Abstract

This article presents an academic profile of Bassant Elkalzah, whose stated institutional affiliation is Imam Mohammad Ibn Saud Islamic University in Saudi Arabia and whose subject area is Data Science. The supplied profile records 33 documents, 84 citations, and an h-index of 6. These indicators provide a quantitative snapshot of reported scholarly activity, although their interpretation depends on database coverage, publication types, citation dates, and author identification. Links to Google Scholar, Scopus, and ORCID enable readers to consult external records. [1][2]

Keywords

Bassant Elkalzah; Data Science; academic research; research metrics; scholarly publications; citation analysis; Imam Mohammad Ibn Saud Islamic University; Saudi Arabia; Global Scientist Day Awards; ORCID; Google Scholar; Scopus.

Introduction

Data Science combines statistical reasoning, computational methods, and data-driven analysis to investigate questions across scientific and professional disciplines. Research in this field can support pattern identification, predictive modelling, decision-making, and the interpretation of complex datasets. Academic profiles help readers locate publications and examine selected indicators of scholarly activity. However, metrics should be interpreted alongside research quality, methodological transparency, reproducibility, and disciplinary context rather than as standalone measures of scientific merit. [1]

Research Profile

The information supplied for Bassant Elkalzah identifies Imam Mohammad Ibn Saud Islamic University in Saudi Arabia as the institutional affiliation and Data Science as the subject area. The associated Google Scholar and Scopus links provide starting points for examining indexed scholarly output, while the ORCID record supplies a persistent researcher identifier. The stated profile metrics are 33 documents, 84 citations, and an h-index of 6. [2] [3]

Research Contributions

An assessment of contributions in Data Science ordinarily considers the research questions addressed, the appropriateness of analytical methods, the quality and accessibility of data, and the reproducibility of results. Potential contributions may include developing analytical techniques, applying computational approaches to domain-specific problems, or producing evidence that supports further investigation. The supplied profile does not identify individual publications or specific findings, so particular discoveries and methodological achievements cannot be attributed to the researcher without reviewing the underlying scholarly records. [1]

Publications

The supplied information reports 33 documents associated with the research profile. To establish a reliable publication list, readers should consult the linked Google Scholar and Scopus records and compare titles, author names, publication venues, years, and digital object identifiers where available. Database records can differ because of indexing policies, duplicate entries, author-name variations, and differences in document types. Individual publications and their DOIs have not been enumerated here because a verified bibliography was not included in the supplied information. [2] [3]

Research Impact

The profile reports 84 citations and an h-index of 6. The h-index is a bibliometric measure that reflects a combination of publication productivity and citation frequency: an h-index of 6 generally means that at least six indexed publications have each received six or more citations within the database and time of measurement. Citation counts can indicate scholarly attention, but they do not directly measure accuracy, originality, societal benefit, or research quality. [4]

Award Suitability

Global Scientist Day Awards provides the context for this academic recognition profile. The reported institutional affiliation, subject area, and bibliometric indicators may serve as initial information for a research-related nomination. A fair award assessment should additionally examine the candidate’s verified publications, originality, methodological quality, contribution to the field, research integrity, and relevance to the award category. [2] [3][4]

Conclusion

This profile summarizes the supplied academic information for Bassant Elkalzah, including the stated affiliation with Imam Mohammad Ibn Saud Islamic University, Saudi Arabia, the subject area of Data Science, and the reported metrics of 33 documents, 84 citations, and an h-index of 6. External researcher identifiers offer routes for further examination of the underlying records.

References

  1. Google Scholar. (2026). Bassant Elkalzah: Google Scholar author profile.
    https://scholar.google.com/citations?user=n8GWOIAAAAAJ&hl=en
  2. Elsevier. (2026.). Scopus author details: Bassant Elkalzah, Author ID 60258457200.
    https://www.scopus.com/authid/detail.uri?authorId=60258457200
  3. ORCID. (2026.). Bassant Elkalzah: ORCID researcher record.
    https://orcid.org/0000-0003-3218-6228
  4. Enhanced estimation for distribution function using auxiliary variable: Application with radiation data.
    https://www.sciencedirect.com/science/article/pii/S1687850725003462
  5. Global Scientist Day Awards. (2026.). Official award website.
    https://scientistday.org/

Marco Zanotti | Data Science | Best Researcher Award

Published on by Scientist Day

Mr. Marco Zanotti | Data Science | Best Researcher Award

Mr. Marco Zanotti | University of Milan-Bicocca | Italy

Marco Zanotti is an accomplished Machine Learning Engineer and Data Scientist specializing in time series analysis, forecasting, anomaly detection, and econometrics. With extensive experience across sectors such as e-commerce fashion, tourism, aviation, and digital services, he has played a key role in designing and improving advanced forecasting systems that drive data-informed business decisions. He currently serves as a Data Scientist and Forecasting Specialist at Wanan Luxury (Rome, Italy), following previous positions at Blogmeter, T-Voice, and Uvet Amex GBT, where he contributed to predictive modeling, machine learning optimization, and process automation. In academia, Marco is an Adjunct Professor at the University of Milan and other leading Italian universities, where he teaches modern time series forecasting, machine learning, programming, and statistics at both Bachelor’s and Master’s levels. He holds a Ph.D. in Statistics from the University of Milano-Bicocca, a Post-graduate Diploma in Data Science for Economics, Business and Finance, and an M.Sc. in Economics and Finance from the University of Milan. Fluent in Italian, English, and French, Marco is proficient in R, Python, SQL, Git, Shiny, and Google Cloud Platform. A member of the International Institute of Forecasters, he is passionate about bridging the gap between academic research and industrial applications, mentoring young data scientists, and advancing the science of predictive analytics.

Profile: Orcid

Featured Publications

Zanotti, M. (2025, December). On the retraining frequency of global models in retail demand forecasting. Machine Learning with Applications. https://doi.org/10.1016/j.mlwa.2025.100769

Zanotti, M., & Mazzucchelli, L. (2021). dispositionEffect [Research protocol or software].