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All Journal Informatika Journal on Mathematics Education
Alfa Saleh, Alfa
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Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Control & Systems Engineering Education Library & Information Science Mathematics Social Sciences Other

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Integrating traditional food and technology in statistical learning: A learning trajectory Ramadhani, Rahmi; Prahmana, Rully Charitas Indra; Soeharto; Saleh, Alfa
Journal on Mathematics Education Vol. 15 No. 4 (2024): Journal on Mathematics Education
Publisher : Universitas Sriwijaya in collaboration with Indonesian Mathematical Society (IndoMS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22342/jme.v15i4.pp1277-1310

Abstract

In the 21st century, understanding variability and developing statistical investigation skills are crucial for enhancing students' data literacy. However, these essential skills are often overlooked, limiting students' growth in numeracy whereby statistical problems are frequently disconnected from real-world or cultural contexts, reducing student engagement. To address this issue, this study integrates the culturally relevant context of Lemang Batok, which enhances students' ability to understand, apply, and analyze data through appropriate statistical concepts. The research uses an ethno-flipped classroom model that promotes flexible, collaborative learning, aiming to design a learning trajectory for teaching descriptive statistics in this context to improve numeracy skills. Utilizing design research methodology, specifically a validation study, the research followed three phases: preliminary design, experimental design, and retrospective analysis. The subjects were junior high school students from Medan and Binjai Cities, North Sumatera-Indonesia. The results indicated that the learning trajectory developed through tiered discussions significantly improved students' numeracy skills in descriptive statistics, as evidenced by increased critical thinking and enhanced abilities to analyze variability.
PENINGKATAN ALGORITMA C4.5 MENGGUNAKAN ENSEMBLE LEARNING UNTUK MENDETEKSI PENYAKIT GINJAL Agusviyanda, Agusviyanda; Novita, Rita; saleh, Alfa; Jamaris, Muhammad
Jurnal Informatika Vol 12, No 3: INFORMATIKA
Publisher : Fakultas Sains & Teknologi, Universitas Labuhanbatu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36987/informatika.v12i3.7542

Abstract

Deteksi dini penyakit ginjal sangat penting untuk menurunkan risiko komplikasi dan meningkatkan prognosis pasien. Permasalahan utama dalam diagnosis penyakit ginjal adalah adanya gejala yang tidak spesifik dan ketidakseimbangan distribusi data pasien. Penelitian ini mengusulkan peningkatan performa algoritma C4.5 untuk deteksi penyakit ginjal dengan mengintegrasikan beberapa tahapan modern, yaitu pra-pemrosesan menggunakan Label Encoder dan Ordinal Encoder untuk mengolah fitur kategorikal, penyeimbangan data menggunakan metode SMOTE-ENN, serta seleksi fitur dengan LASSO. Selanjutnya, model dasar C4.5 ditingkatkan dengan metode ensemble learning menggunakan AdaBoost. Hasil pengujian menunjukkan bahwa integrasi Adaboost pada algoritma C4.5 secara signifikan meningkatkan akurasi deteksi penyakit ginjal dibandingkan model dasar maupun model-model pada penelitian terdahulu. Model terbaik pada penelitian ini mencapai akurasi 99%, melebihi performa XGBoost maupun stacking ensemble pada kasus serupa. Kontribusi penelitian ini menegaskan efektivitas kombinasi boosting, balancing, dan seleksi fitur dalam membangun sistem pendukung keputusan berbasis machine learning untuk diagnosis penyakit ginjal.
Co-Authors Agusviyanda, Agusviyanda Jamaris, Muhammad Rahmi Ramadhani Rita Novita Rully Charitas Indra Prahmana Soeharto