Article Per Year (5 Year)
p-Index From 2021 - 2026
3.625
P-Index
This Author published in this journals
All Journal Jurnal Math Educator Nusantara: Wahana Publikasi Karya Tulis Ilmiah di Bidang Pendidikan Matematika JURNAL SILOGISME : Kajian Ilmu Matematika dan Pembelajarannya Aptisi Transactions on Management Integral : Pendidikan Matematika Jurnal Riset Pendidikan dan Inovasi Pembelajaran Matematika (JRPIPM) Mosharafa: Jurnal Pendidikan Matematika International Journal of Social Service and Research Journal of Mathematics Instruction, Social Research and Opinion Journal of General Education and Humanities Prosiding Seminar Nasional Pascasarjana Proceeding of International Conference on Science, Education, and Technology Noumerico: Journal of Technology in Mathematics Education Educational Insights Jurnal Pengembangan Pembelajaran Matematika Journal of Computer Science and Technology Application ITEJ (Information Technology Engineering Journals) Eduma : Mathematics Education Learning and Teaching
HANDOKO, HENDRI
Universitas Islam Negeri Siber Syekh Nurjati Cirebon
Humanities Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Environmental Science Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mathematics Social Sciences Other

Published : 32 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 32 Documents
Search

 1   2   3   4 
Analysis of Junior High School Students’ Mathematical Literacy Processes (Formulating, Employing, and Interpreting) in PISA Space and Shape Abirrotun Nabilah; Mauliddiyah Istiqomah; Hendri Handoko
Noumerico: Journal of Technology in Mathematics Education Vol. 4 No. 1 (2026): Noumerico, March 2026
Publisher : Universitas Islam Tribakti Lirboyo Kediri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33367/jtme.v4i1.8663

Abstract

This study aims to describe students’ mathematical literacy processes when solving PISA problems in the Space and Shape content domain, based on the three PISA literacy processes: Formulating, Employing, and Interpreting. This research uses a descriptive qualitative approach with three ninth-grade students as subjects, selected through purposive sampling: S1 (moderate ability), S2 (low ability), and S3 (high ability). Data were collected through a mathematical literacy test consisting of three PISA Space and Shape problems (Problem 1, Problem 2, and Problem 3) and semi-structured interviews. Data analysis was conducted through data reduction, coding based on literacy process indicators, data display, and conclusion. The results show that S1, in Problem 1, was able to fulfil all mathematical literacy processes (Formulating, Employing, and Interpreting). S2, in Problem 2, was only partially able to achieve the Formulating stage and experienced difficulties with the Employing and Interpreting stages. Meanwhile, S3, in Problem 3, also only partially achieved the Formulating stage and failed to achieve the Employing and Interpreting stages due to an inaccurate mathematical model and inappropriate procedures. These findings indicate that the level of mathematical literacy achievement is not always aligned with students’ ability categories but is influenced by the characteristics of the problem and the accuracy of mathematical modelling. Therefore, learning should emphasise modelling activities, meaningful application of procedures, and interpretation of results in real-world contexts.
Machine Learning Enabled Social Media Competitive Intelligence System Hendri Handoko; Yulina Ismiyanti; Omar Arif Al-Kamari
CORISINTA Vol 3 No 1 (2026): February
Publisher : Pandawan Sejahtera Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/480er062

Abstract

Social media platforms generate massive volumes of publicly accessible digital data that reflect organizational competitive strategies, yet most existing competitor analyses remain manual, descriptive, and limited to surface-level engagement metrics, resulting in low scalability and weak strategic intelligence. This study proposes a Machine Learning Enabled Social Media Competitive Intelligence System designed to automate competitor strategy extraction through artificial intelligence and big data analytics. The objective is to develop a computational framework capable of identifying strategic content patterns, communication objectives, audience positioning, and paid advertising behaviors using data-driven techniques. Large-scale public data from social media posts, engagement indicators, and advertising transparency libraries are collected and processed through data preprocessing pipelines, including text normalization, tokenization, and feature extraction using TF-IDF and word embedding representations. Supervised machine learning algorithms are implemented to classify content themes, detect strategic clusters, and model competitive positioning patterns, while performance evaluation is conducted using accuracy, precision, recall, and F1-score metrics to ensure robustness and reliability. Experimental findings demonstrate that the proposed system significantly enhances analytical consistency, scalability, and strategic insight generation compared to traditional mixed method approaches. This research contributes to the advancement of AI-driven social media analytics and establishes a computational foundation for scalable big data-based competitive intelligence systems aligned with Artificial Intelligence and Big Data domains.
Co-Authors Abirrotun Nabilah Adelia Azzahra Alif Ringga Persada, Alif Ringga Anike Anike Anita Syafianti Arif Abdul Haqq Asep Robiana Bayyinah Rismawati Budi Waluya, Stevanus darwan darwan, darwan Darwan, Darwan Dian Lestari Farizi, Reo Al Hadi Kusmanto Hariyani, Rizky Ijah Khodijah Ikman Nurhakim Rahadi Jamali Sahrodi Mauliddiyah Istiqomah Mubarikah, Anti Muhamad Ali Misri Muhammad Ali Misri Mumun Munawaroh Muslihudin Muslihudin Ni’matul Fauziah Nuke Apriyanti Nur Saebah Nurma Izzati Nurul Fauziah Omar Arif Al-Kamari Putri Pertiwi Rahadi, Ikman Nurhakim Reo Al Farizi Reo Al Farizi Riska Rahayu Robiana, Asep Rochmad - Saluky Shahrul Khan, Mohamad Ilham Sirojudin Wahid St. Budi Waluya Sugiman Sugiman Sugiman Syafianti, Anita Syifa Fizri Fauziah Tia Utami Toheri Toheri, Toheri Tuti Isnani Utami, Tia Vaher, Kristina Wardono Wardono Wardono WINARNO Winarno Winarno Winarno Winarno Winarno, Winarno Yulina Ismiyanti