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Analisis Tipe HOTS dan LOTS dalam Asesmen Madrasah Matematika Tingkat Lanjut di MAN 1 Trenggalek Sudarmanto, Sudarmanto; Mubarok, Moch Yazid; Munir, Ulfa Saikhul; Sutopo, Sutopo; Musrikah, Musrikah
Polinomial : Jurnal Pendidikan Matematika Vol. 5 No. 1 (2026)
Publisher : Papanda

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56916/jp.v5i1.3341

Abstract

The demands of 21st century learning emphasize the importance of developing higher-order thinking skills through assessments aligned with the revised Bloom’s Taxonomy. However, mathematics assessment practices at the secondary education level are still predominantly focused on measuring lower-order thinking skills. This study aims to describe the characteristics and proportion of Higher Order Thinking Skills (HOTS) and Lower Order Thinking Skills (LOTS) items in the Advanced Mathematics Madrasah Assessment at Senior High School 1 Trenggalek. The study employed a descriptive approach using a combination of qualitative and quantitative methods through document analysis of 40 assessment items consisting of multiple-choice, complex multiple-choice, and short-answer questions. Each item was analyzed based on the cognitive levels of the revised Bloom’s Taxonomy (C1–C6) and classified into HOTS and LOTS categories. The results indicate that the assessment is still dominated by LOTS items, particularly at the C3 (applying) level, which emphasizes procedural skills. HOTS items were identified at the C4 and C5 levels, while the C6 (creating) level has not yet been accommodated. In terms of item format, multiple-choice questions tend to measure LOTS, whereas complex multiple-choice and short-answer questions are more likely to assess HOTS. These findings suggest that the assessment remains in a transitional phase toward strengthening higher order reasoning.
Manajemen Pengelolaan Pendingin Ruangan Berbasis QR Code Terintegrasi Wasono, Mardi; Fakhrudin, Aris; Sudarmanto, Sudarmanto; Julian Nggebu, David; Laksono Wijaya, Wibi
Jurnal Pengelolaan Laboratorium Pendidikan Vol.8, No.1, Januari 2026
Publisher : UPT Laboratorium Terpadu, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/jplp.8.1.73-84

Abstract

The management of air conditioning (AC) infrastructure in the Faculty of Mathematics and Natural Sciences, Gadjah Mada University (FMIPA UGM) is still carried out separately by each unit, resulting in problems such as lack of maintenance, lack of asset data integration, and low human resource work efficiency. This study aims to design and implement an air conditioning management information system based on Quick Response (QR) Code technology that is integrated with smartphone devices and internet networks. The research methods include needs analysis, web-based system design, implementation using a MySQL database and the CodeIgniter 4 framework, and system functional testing. The results of the study indicate that the developed system is able to integrate air conditioning asset data, facilitate real-time damage reporting through QR Code scanning, and provide automatic notifications to admins and those in charge of related units. The implementation of this system is proven to increase the efficiency of asset management and accelerate the process of handling air conditioning problems in the FMIPA UGM environment. Keywords: Management, Air Conditioning, Human Resource Efficiency, QR Code.
Content-Based Filtering untuk Sistem Rekomendasi Produk E-Commerce Pratama, Fiki; Prastio, Sabib; Seniwati, Erni; Hartanti, Ninik Tri; Sudarmanto, Sudarmanto
The Indonesian Journal of Computer Science Research Vol. 5 No. 1 (2026): Januari
Publisher : Hemispheres Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59095/ijcsr.v5i1.254

Abstract

Perkembangan pesat platform e-commerce mendorong kebutuhan akan sistem rekomendasi yang mampu membantu pengguna menemukan produk yang relevan secara efektif. Meskipun sistem rekomendasi telah banyak dikembangkan, implementasi yang teruji secara empiris pada dataset produk e-commerce Indonesia masih terbatas. Penelitian ini bertujuan untuk mengembangkan sistem rekomendasi produk e-commerce berbasis content-based filtering dengan memanfaatkan kemiripan konten antar produk. Dataset yang digunakan bersumber dari Tokopedia Products 2025 yang diperoleh melalui Kaggle, berisi informasi produk seperti nama, kategori, dan deskripsi. Representasi fitur teks dilakukan menggunakan metode Term Frequency–Inverse Document Frequency (TF-IDF) untuk mengekstraksi karakteristik penting dari setiap produk. Selanjutnya, tingkat kemiripan antar produk dihitung menggunakan Cosine Similarity guna menghasilkan rekomendasi produk yang paling relevan berdasarkan preferensi pengguna. Implementasi dilakukan menggunakan bahasa pemrograman Python dengan pustaka pandas, NumPy, dan scikit-learn. Hasil evaluasi menggunakan berbagai ukuran sampel query dan nilai K yang terdiri dari sample size 50 query yang paling representatif, sistem mencapai Precision@10 sebesar 88.40%, Recall@10 sebesar 46.81%, dan F1-Score@10 sebesar 50.88%, dengan nilai F1-Score optimal dicapai pada K=30 sebesar 66.79%. Hasil penelitian menunjukkan bahwa kombinasi TF-IDF dan Cosine Similarity mampu memberikan rekomendasi produk yang relevan dengan tingkat akurasi yang baik, sehingga metode ini layak diterapkan sebagai solusi sistem rekomendasi pada platform e-commerce berbasis konten