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All Journal Al Ishlah Jurnal Pendidikan AXIOM : Jurnal Pendidikan dan Matematika
Rizki Habibi
Universitas Negeri Medan
Education Languange, Linguistic, Communication & Media Mathematics Other

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Indonesian Elementary Students’ Perceptions of Teachers’ Affective Support: A Cluster Analysis Using National Literacy and Numeracy Assessment Data Rizki Habibi; Muliawan Firdaus; Ichwanul Muslim Karo Karo
AL-ISHLAH: Jurnal Pendidikan Vol 17, No 4 (2025): DECEMBER 2025
Publisher : STAI Hubbulwathan Duri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35445/alishlah.v17i4.7754

Abstract

Affective support from teachers—such as academic expectations, attention and care, and constructive feedback—plays a critical role in students’ learning outcomes but is often underexplored in large-scale educational assessments, particularly in developing countries. This study examines how Indonesian elementary students perceive teacher affective support and how these perceptions relate to their literacy and numeracy performance. Using data from the 2023 Indonesian National Assessment involving 214,481 fifth-grade students, we employed K-Means clustering to identify latent student profiles based on their literacy, numeracy, and self-reported perceptions of teacher support. Variables were normalized, and the optimal number of clusters was determined using the Elbow, Silhouette, and Davies-Bouldin methods. Five distinct student clusters emerged, each characterized by unique combinations of academic achievement and affective perceptions. High-achieving students consistently reported more positive perceptions of teacher support, particularly in terms of feedback and expectations. ANOVA tests confirmed significant differences (p 0.001) across clusters in all affective and academic variables, with moderate to large effect sizes. The findings highlight the alignment between academic success and perceived teacher affective support. This clustering approach reveals nuanced student profiles that traditional methods may overlook, offering a data-driven foundation for differentiated teaching, teacher training, and policy interventions. Clustering national assessment data provides actionable insights for enhancing affective support in classrooms. The methodology is scalable and adaptable for use in other educational systems seeking to personalize instruction and promote equity.
Optimization of operational cost planning in integrated farming systems using a mixed-integer linear programming approach Lasker Pangarapan Sinaga; Rizki Habibi; Suvriadi Panggabean
AXIOM : Jurnal Pendidikan dan Matematika Vol 15, No 1 (2026)
Publisher : State Islamic University of North Sumatra

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30821/axiom.v15i1.26491

Abstract

This study addresses the challenge of optimizing small-scale integrated farming systems (IFS) by minimizing operational costs while ensuring sustainable land use across agricultural, livestock, and aquaculture components. The main objective is to develop a Mixed-Integer Linear Programming (MILP) model that incorporates deterministic parameters such as land availability, labor allocation, and internal-external input flows. The model integrates multiple interrelated subsystems using production coefficients, resource constraints, and cost structures derived from actual smallholder scenarios. A two-period simulation was conducted to evaluate the model’s effectiveness using fixed input values, reflecting rural farming conditions. The results demonstrate that the system achieved consistent outputs without requiring external purchases of manure, feed, or irrigation water. The total operational cost reached IDR 96,770,000, with optimized land and labor allocation across periods. This research contributes a novel MILP formulation tailored to integrated farming, providing practical insights for policymakers and practitioners. Its implications extend to the development of decision-support systems for rural agricultural planning. However, the model's deterministic assumption limits its adaptability to dynamic environments. Future work should explore stochastic variants and real-time input adjustments to improve model flexibility and realism.
Co-Authors Ichwanul Muslim Karo Karo Lasker Pangarapan Sinaga Suvriadi Panggabean