\documentclass{bjaids}

%% Recommended packages
\usepackage{amsmath,amssymb,booktabs,multirow,array}
\usepackage[numbers]{natbib}
\usepackage{graphicx}
\usepackage{caption}
\usepackage[ruled,vlined,linesnumbered]{algorithm2e}
\usepackage[hidelinks]{hyperref}
\captionsetup[figure]{name=Fig.,justification=centering,singlelinecheck=true}
\captionsetup[table]{name=Table,justification=centering,singlelinecheck=true}

\BjaidsSet{
  title = {Title of the Manuscript for Bayanika Journal of Artificial Intelligence and Data Science},
  shorttitle = {Short Running Title of the Manuscript},
  article-type = {Research Article},
  volume = {1},
  issue = {1},
  year = {2026},
  published = {Month Day, 2026},
  startpage = {1},
  endpage = {10},
  doi = {10.xxxx/bjaids.v1i1.1},
  issn = {XXXX-XXXX},
  eissn = {XXXX-XXXX},
  abstract = {
    This template is prepared for manuscripts submitted to the
    \textit{Bayanika Journal of Artificial Intelligence and Data Science}
    (BJAIDS). The abstract should summarize the research background,
    objective, method, main results, and contribution in a concise paragraph.
    Authors are encouraged to state the methodological novelty and empirical
    findings clearly. The recommended abstract length is approximately
    150--250 words.
  },
  keywords = {Artificial intelligence, Data science, Machine learning, Explainable AI, Computational modelling},
  received = {Received: Date Month Year; Revised: Date Month Year; Accepted: Date Month Year; Published: Date Month Year}
}

\author{First A. Author}{Department, Faculty, University, City, Postal Code, Country}{first.author@email.com}{https://orcid.org/0000-0000-0000-0000}

\author*{Second B. Author}{Department, Faculty, University, City, Postal Code, Country}{second.author@email.com}{https://orcid.org/0000-0000-0000-0000}

\thispagestyle{thefirstpage}

\begin{document}

\maketitle

\section{Introduction}

The introduction should present the research background, practical or theoretical motivation, recent developments in the field, research gap, and the specific contribution of the manuscript \cite{smith2025adaptiveAI}. For BJAIDS, authors should clearly position their work within artificial intelligence, data science, machine learning, computational intelligence, intelligent systems, or closely related areas \cite{garcia2024datascience,ahmad2023xaiReview}. Standard journal articles may be cited using \texttt{natbib}, for example recent studies on adaptive artificial intelligence, scalable data science pipelines, explainable artificial intelligence, and robust deep learning can be cited as \cite{nguyen2025onlinefirst,bishop2006pattern,goodfellow2016deep}.

The theoretical foundation of a manuscript may also rely on books, edited volumes, and specific book chapters. For example, classical machine learning theory, deep learning foundations, edited handbooks, fuzzy explainability chapters, and probabilistic machine learning chapters can be cited as \cite{jones2024handbookAI,kim2024chapterXAI,murphy2012chapter}. This paragraph is intentionally included in the template to demonstrate how different reference categories can be cited naturally in the body of an article.

The final paragraph of the introduction is recommended to state the main contributions explicitly. For example, the contributions of this study are: (1) the development of a new computational framework, (2) the empirical evaluation of the proposed method using appropriate datasets, and (3) the interpretation of the results in relation to artificial intelligence and data science applications.

\section{Related Work}

This section reviews relevant studies and positions the manuscript against the existing literature. Authors should avoid a purely descriptive review and instead synthesize methodological trends, limitations of previous studies, and the specific research gap addressed by the present work.

\subsection{Recent Development in the Research Area}

Recent developments may include conference papers, workshop contributions, and proceedings that report emerging computational approaches before they appear as full journal articles. Examples include lightweight transformer classification models, graph-based anomaly detection, complete conference proceedings, and workshop-based evaluation of explainable artificial intelligence systems \cite{rahman2024conferenceAI,lee2023conferenceAlternative,icads2024proceedings,martinez2025workshop}. Authors may also cite preprints, working papers, and technical reports when the cited material is relevant and traceable, as illustrated by uncertainty-aware neural networks, foundation model benchmarking, and machine learning infrastructure reports \cite{zhou2025arxiv,williams2024workingpaper,kumar2023technicalReport}.

\subsection{Research Gap}

A strong research gap should clarify the limitation of prior work and justify why the proposed method is necessary. In fields related to AI governance, public-sector data use, and responsible innovation, authors may cite government reports, policy reports, white papers, and institutional documents to support contextual motivation \cite{ministry2024AIReport,oecd2024aiPolicy,company2025whitepaper}. When the manuscript uses external data or computational tools, the corresponding dataset, software, repository, and documentation should be cited explicitly to support transparency and reproducibility \cite{anderson2024dataset,rahman2025kaggleDataset,patel2025software,smith2024zenodoSoftware,tensorflow2025manual,scikitlearn2025manual}.

\section{Methodology}

This section explains the proposed method with sufficient mathematical, computational, and procedural detail. Important variables, assumptions, and algorithmic steps should be clearly defined. Authors should also cite official author guidelines, institutional webpages, applicable regulations, standards, or legal documents whenever these sources shape the research protocol, publication policy, data governance, or ethical framework \cite{bjaids2026authorGuidelines,organization2025webpage,indonesia2024lawAI,ministerial2025regulationData,dsnmui2024fatwa,iso2024standardAI,ieee2024standard}.

\subsection{Problem Formulation}

Let $X = \{x_1,x_2,\ldots,x_n\}$ denote the dataset, where each observation $x_i \in \mathbb{R}^d$ consists of $d$ features. A general predictive or analytical model can be expressed as
\begin{equation}
  \hat{y}_i = f(x_i;\theta),
\end{equation}
where $f(\cdot)$ is the model function and $\theta$ represents the model parameters. If the manuscript is derived from a dissertation, thesis, patent, unpublished manuscript, or institutional profile, these sources should be cited only when they are directly relevant to the proposed formulation or research provenance \cite{brown2024patent,abdullah2024phd,sari2023master,chen2025unpublished,bayanika2026brochure}.

\subsection{Proposed Algorithm}

Algorithm~\ref{alg:proposed} provides an example of algorithm formatting. Non-journal sources such as newspapers, magazines, encyclopedias, lecture notes, presentations, and webinars may be cited when they provide contextual, pedagogical, definitional, or public-facing evidence rather than core methodological validation \cite{newspaper2025AI,magazine2024datascience,encyclopedia2024machinelearning,lecture2025AI,presentation2025DataScience,webinar2025AIethics}.

\begin{algorithm}[htbp]
\caption{Example Proposed Algorithm}\label{alg:proposed}
\KwIn{Dataset $X$, model parameter $\theta$, maximum iteration $T$}
\KwOut{Prediction or analytical output $\hat{Y}$}
Initialize model parameters $\theta$\;
\For{$t=1$ \KwTo $T$}{
  Compute model output $\hat{Y}^{(t)} = f(X;\theta^{(t)})$\;
  Evaluate the objective function $\mathcal{L}(\theta^{(t)})$\;
  Update the model parameter $\theta^{(t+1)}$\;
}
\Return{$\hat{Y}$}\;
\end{algorithm}

\section{Experiments}

This section describes the dataset, preprocessing, experimental design, baseline methods, evaluation metrics, software environment, and statistical testing procedure where applicable. Reports with DOI, publications with ORCID notes, non-English books with translated titles, and repository items can be cited when they support experimental provenance, identity disambiguation, methodological background, or supplementary materials \cite{institute2025doiReport,orcidExample2025,santoso2024indonesianBook,repository2024item}.

\subsection{Dataset and Preprocessing}

Authors should describe the dataset source, number of observations, number of features, target variables, missing values, preprocessing steps, and data partitioning strategy. If an article has been accepted but is not yet published, it may be cited when permitted by the journal policy; forthcoming books may be cited when bibliographic details are sufficiently stable \cite{accepted2026article,forthcoming2026book}.

\subsection{Evaluation Metrics}

The evaluation metrics should match the research objective. For classification, common metrics include accuracy, precision, recall, F1-score, ROC-AUC, and confusion matrix. For regression, common metrics include MAE, RMSE, MAPE, and $R^2$. Retracted articles should be cited only when the retraction status itself is relevant to the discussion, for example in a methodological caution, research integrity analysis, or correction notice \cite{retractedExample2023}.

\section{Results and Discussion}

This section presents the main findings and interprets them in relation to the research problem. Tables and figures should be discussed substantively rather than merely described. The citation examples above are distributed across the manuscript body so that every entry in the accompanying \texttt{refs\_bjaids\_template.bib} file is cited using normal \verb|\cite{}| commands rather than the artificial \verb|\nocite{*}| command.

Figure~\ref{fig:bjaids-logo-example} shows an example of how to insert a figure in the BJAIDS LaTeX template. Authors should place figure files in the same folder as the main \texttt{.tex} file and call them using the \verb|\includegraphics| command.

\begin{figure}[htbp]
  \centering
  \includegraphics[width=0.35\textwidth]{logo_bjaids.png}
  \caption{Example of inserting a figure in the BJAIDS LaTeX template.}
  \label{fig:bjaids-logo-example}
\end{figure}

\begin{table}[htbp]
\caption{Example performance comparison}
\label{tab:performance}
\centering
\begin{tabular}{lccc}
\toprule
Method & Accuracy & F1-score & AUC \\
\midrule
Baseline 1 & 0.850 & 0.842 & 0.901 \\
Baseline 2 & 0.872 & 0.865 & 0.918 \\
\textbf{Proposed Method} & \textbf{0.901} & \textbf{0.896} & \textbf{0.943} \\
\bottomrule
\end{tabular}
\end{table}

\section{Conclusion}

The conclusion should summarize the objective, method, major findings, contribution, limitation, and future research direction in a concise and coherent manner. In the final submitted manuscript, authors should retain only the references actually cited and used in the article.

\section*{Declarations}

\section*{Author Contributions Statement}

Author 1: conceptualization, methodology, writing--original draft. Author 2: supervision, validation, writing--review and editing. Please adjust this statement according to the actual contribution of each author.

\section*{Conflict of Interest Statement}

The authors declare that there is no conflict of interest regarding the publication of this article.

\section*{Funding Declaration}

This research received no external funding. If funding was received, provide the name of the funding agency and grant number.

\section*{Data Availability Statement}

The data that support the findings of this study are available from the corresponding author upon reasonable request. If the dataset is publicly available, provide the repository name and access link.

\section*{Acknowledgment}

The authors may acknowledge individuals, institutions, or organizations that supported the research but did not meet the criteria for authorship.

\section*{Declaration of Generative AI in Scholarly Writing}

During the preparation of this work, the authors used generative AI tools only for language editing and proofreading, where applicable. The authors reviewed and edited the content and take full responsibility for the published article.

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\end{document}
