PENGEMBANGAN PLATFORM ASESMEN DIAGNOSTIK MELALUI STREAMLIT UNTUK CLUSTERING GAYA BELAJAR PESERTA DIDIK

Abstract. This study aims to develop a technology-based diagnostic assessment platform designed to cluster students’ learning styles using the K-Means clustering algorithm. The development process follows the Richey & Klein model, which includes three stages: planning, production, and evaluation. Learning style data were collected through an online questionnaire via Google Forms and processed using Python. The clustering results were then visualized using the Streamlit platform. The main objective is to assist teachers in identifying students’ learning preferences at the beginning of instruction, thereby enabling more effective and personalized learning experiences. The trial implementation was conducted with eighth-grade students at SMPN 6 Malang, involving both field supervisors and mentoring teachers. The results indicate that the Streamlit-based system improves the efficiency of diagnostic assessments and supports adaptive learning by accommodating diverse learning styles. Keywords: Diagnostic assessment; Learning styles; Streamlit; Personalized learning. Abstrak. Penelitian ini bertujuan untuk mengembangkan sebuah platform asesmen diagnostik berbasis teknologi guna mengelompokkan gaya belajar peserta didik menggunakan algoritma K-Means Clustering. Proses pengembangan menggunakan model Richey & Klein yang melibatkan tiga tahap, yaitu perencanaan, produksi, dan evaluasi. Data karakteristik belajar dikumpulkan melalui angket Google Form dan diolah menggunakan Python, dengan hasil akhirnya divisualisasikan dalam platform Streamlit. Tujuan utama pengembangan ini adalah mempermudah guru dalam memetakan preferensi belajar peserta didik di awal pembelajaran agar proses belajar lebih efektif dan sesuai kebutuhan individu. Uji coba dilakukan pada siswa kelas VIII di SMPN 6 Malang, serta melibatkan dosen pembimbing dan guru pamong. Hasil pengembangan menunjukkan bahwa penggunaan teknologi berbasis Streamlit dapat meningkatkan efisiensi dalam pelaksanaan asesmen diagnostik dan mendukung pembelajaran yang adaptif terhadap perbedaan gaya belajar. Kata Kunci: Asesmen diagnostik; Gaya Belajar; Streamlit; Personalisasi Pembelajaran.

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Publication Details

Journal
HINEF Jurnal Rumpun Ilmu Pendidikan
Published
2025-07-24
DOI
https://doi.org/10.37792/hinef.v4i2.1574
Primary Topic
Edcuational Technology Systems
Type
article
Field-Weighted Citation Impact
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article

PENGEMBANGAN PLATFORM ASESMEN DIAGNOSTIK MELALUI STREAMLIT UNTUK CLUSTERING GAYA BELAJAR PESERTA DIDIK

Muhammad Hidayatullah, Amalia Dieni Tsabita, Fatiya Rosyida
HINEF Jurnal Rumpun Ilmu Pendidikan
Edcuational Technology Systems
article

PENGEMBANGAN PLATFORM ASESMEN DIAGNOSTIK MELALUI STREAMLIT UNTUK CLUSTERING GAYA BELAJAR PESERTA DIDIK

Muhammad Hidayatullah, Amalia Dieni Tsabita, Fatiya Rosyida
article en

Abstract

Abstract. This study aims to develop a technology-based diagnostic assessment platform designed to cluster students’ learning styles using the K-Means clustering algorithm. The development process follows the Richey & Klein model, which includes three stages: planning, production, and evaluation. Learning style data were collected through an online questionnaire via Google Forms and processed using Python. The clustering results were then visualized using the Streamlit platform. The main objective is to assist teachers in identifying students’ learning preferences at the beginning of instruction, thereby enabling more effective and personalized learning experiences. The trial implementation was conducted with eighth-grade students at SMPN 6 Malang, involving both field supervisors and mentoring teachers. The results indicate that the Streamlit-based system improves the efficiency of diagnostic assessments and supports adaptive learning by accommodating diverse learning styles. Keywords: Diagnostic assessment; Learning styles; Streamlit; Personalized learning. Abstrak. Penelitian ini bertujuan untuk mengembangkan sebuah platform asesmen diagnostik berbasis teknologi guna mengelompokkan gaya belajar peserta didik menggunakan algoritma K-Means Clustering. Proses pengembangan menggunakan model Richey & Klein yang melibatkan tiga tahap, yaitu perencanaan, produksi, dan evaluasi. Data karakteristik belajar dikumpulkan melalui angket Google Form dan diolah menggunakan Python, dengan hasil akhirnya divisualisasikan dalam platform Streamlit. Tujuan utama pengembangan ini adalah mempermudah guru dalam memetakan preferensi belajar peserta didik di awal pembelajaran agar proses belajar lebih efektif dan sesuai kebutuhan individu. Uji coba dilakukan pada siswa kelas VIII di SMPN 6 Malang, serta melibatkan dosen pembimbing dan guru pamong. Hasil pengembangan menunjukkan bahwa penggunaan teknologi berbasis Streamlit dapat meningkatkan efisiensi dalam pelaksanaan asesmen diagnostik dan mendukung pembelajaran yang adaptif terhadap perbedaan gaya belajar. Kata Kunci: Asesmen diagnostik; Gaya Belajar; Streamlit; Personalisasi Pembelajaran.

HINEF Jurnal Rumpun Ilmu PendidikanVol. 4(2)
Openalex Percentile: Top 91%
Edcuational Technology Systems
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PENGEMBANGAN PLATFORM ASESMEN DIAGNOSTIK MELALUI STREAMLIT UNTUK CLUSTERING GAYA BELAJAR PESERTA DIDIK — Muhammad Hidayatullah, Amalia Dieni Tsabita, et al. · HINEF Jurnal Rumpun Ilmu Pendidikan (2025) | TGRS Research Map | TGRS