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All Journal Jurnal Ilmiah Universitas Batanghari Jambi Jurnal Cendekia : Jurnal Pendidikan Matematika To Maega: Jurnal Pengabdian Masyarakat Majalah Ilmiah Warta Dharmawangsa Jurnal Ilmiah Pendidikan Matematika Al Qalasadi Community Development Journal: Jurnal Pengabdian Masyarakat Reswara: Jurnal Pengabdian Kepada Masyarakat Madaniya Airlangga Journal of Innovation Management Innovative: Journal Of Social Science Research
Budi Antoro
Management Study Program, Faculty Of Economics And Business, Universitas Dharmawangsa,
Religion Agriculture, Biological Sciences & Forestry Humanities Biochemistry, Genetics & Molecular Biology Civil Engineering, Building, Construction & Architecture Computer Science & IT Economics, Econometrics & Finance Education Health Professions Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Public Health Social Sciences Other

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Eksplorasi Pola Berpikir Kreatif Siswa dalam Pemecahan Masalah Matematika Berbasis Computational Thinking Wahyuni, Dewi; Budi Antoro
Jurnal Ilmiah Pendidikan Matematika Al Qalasadi Vol 9 No 1 (2025): JURNAL ILMIAH PENDIDIKAN MATEMATIKA AL QALASADI
Publisher : Prodi Pendidikan Matematika, Fakultas Tarbiyah dan Ilmu Keguruan IAIN Langsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32505/qalasadi.v9i1.11129

Abstract

The abstract should describe clearly the content of paper, and should provide a brief introduction to the problem, objective of paper, followed by a statement regarding the methodology and a brief summary of results. Abstracts are written in a single paragraph, 10pt Book Antiqua, no more than 200 words. This study aims to explore students' creative thinking patterns in solving mathematics problems based on Computational Thinking (CT). CT is a thinking approach that involves decomposition, pattern recognition, abstraction, and algorithmic thinking, enabling students to solve problems systematically and efficiently. This research employs an exploratory qualitative method with purposive sampling of five twelfth-grade students at SMK Samudera Indonesia. Data were collected through problem-solving tests, observations, interviews, and worksheet analysis. The findings indicate that students with a high level of creative thinking can effectively apply CT concepts, particularly in decomposition and abstraction. Meanwhile, students with a moderate level of creative thinking still require examples and guidance in recognizing patterns and structuring solutions systematically. The main challenges identified include difficulties in understanding the fundamental concepts of sequences and series, reliance on formulas without deep comprehension, and a lack of confidence in trying different approaches. These findings suggest that incorporating Computational Thinking in mathematics learning can enhance students' creativity in problem-solving while also developing a more logical and systematic mindset.
Probabilistic Markov Chain Modeling for Predicting User Behavior Patterns in Digital Systems Using Data Mining Hevlie Winda Nazry; Budi Antoro; Fatma Sari Hutagalung
Airlangga Journal of Innovation Management Vol. 7 No. 1 (2026): Airlangga Journal of Innovation Management
Publisher : Universitas Airlangga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20473/ajim.v7i1.87129

Abstract

This study addresses the challenge of transforming sequential clickstream data into accurate yet interpretable behavioral predictions for operational decision-making in digital systems. While complex machine learning models often achieve high accuracy, their limited transparency hinders practical adoption. Therefore, this research aims to develop and evaluate a probabilistic Markov-based framework for predicting users’ next actions while maintaining interpretability. A quantitative data mining approach is applied to e-commerce clickstream data collected in January 2026. User interactions are sessionized and mapped into eight discrete behavioral states. The study compares a frequency-based baseline with first-order, second-order, and variable-order Markov models using back-off and Laplace/Dirichlet smoothing. Model evaluation employs a time-based train–test split with Accuracy@1, Mean Reciprocal Rank (MRR), and log-loss as performance metrics. Results indicate that the variable-order Markov model achieves the best performance, improving Accuracy@1 from 0.231 to 0.331, increasing MRR from 0.318 to 0.437, and reducing log-loss from 1.74 to 1.39. The findings demonstrate that Markov-based models offer an effective balance between predictive accuracy and interpretability, enabling the identification of dominant transitions, drop-off points, and conversion bottlenecks. Future research may extend this framework with time-aware or hidden-state models to capture latent user intent, while managerial implications include data-driven system optimization, recommendation enhancement, and user retention strategies.
Co-Authors Dewi Wahyuni Ega Evinda Putri Fachrul Rozi Fatma Sari Hutagalung Hevlie Winda Nazry Indra Fauzi Lukman Hakim Marviana, Ratna Dina Mekar Meilisa Amalia Ngatno Sahputra Ngatno Sahputra Ngatno Sahputra Septriawan, Muhammad Reza Suhariyanti Syauqi, Teuku Muhammad Teuku Fahmi Yani, Farida Yasir, Amru Zulham Zulham Zulkifli Taib