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All Journal QISTINA: Jurnal Multidisiplin Indonesia Jurnal Riksa Cendikia Nusantara
Rahayu, Fatiha Aini
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Peran Metode Pembuktian dalam Pengembangan Pemikiran Kritis Mahasiswa Matematika Gomes, Beatrix Chatarina Da; Girsang, Aline Theresia; Bukit, Cindy Christina Br; Musyaffa, Fakhirah; Rahayu, Fatiha Aini
QISTINA: Jurnal Multidisiplin Indonesia Vol 4, No 2 (2025): December 2025
Publisher : CV. Rayyan Dwi Bharata

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57235/qistina.v4i2.7283

Abstract

Population growth becomes an essential element in mathematics education, not only strengthening conceptual understanding but also developing students’ critical thinking skills. This study discusses the role of proof methods in enhancing analytical, evaluative, and synthetic abilities among mathematics students. Through literature review and conceptual analysis, it was found that mathematical proof encourages students to question assumptions, identify logical errors, and construct strong arguments. The results show that integrating proof methods into the curriculum can improve critical thinking skills by 30–40% based on empirical studies. Recommendations include implementing problem-based and collaborative teaching approaches to maximize these benefits
Penerapan Metode Least Mean Square (LMS) Untuk Prediksi Harga Saham BBRI (Studi Kasus: Data Harga Penutupan Harian (April 2025 – April 2026)) Bukit, Cindy Christina Br; Rahayu, Fatiha Aini; Anti, Yurni
Jurnal Riksa Cendikia Nusantara Vol. 2 No. 4 (2026): Riksa Cendikia Nusantara - April 2026
Publisher : Jurnal Riksa Cendikia Nusantara

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Abstract

Penelitian ini membahas penerapan algoritma Least Mean Square (LMS) yang dikembangkan oleh Widrow & Hoff (1960) untuk memprediksi harga saham PT Bank Rakyat Indonesia Tbk (BBRI) berdasarkan data harga penutupan harian. LMS merupakan algoritma adaptif berbasis gradient descent yang memperbarui bobot secara iteratif untuk meminimalkan Mean Squared Error (MSE) antara prediksi dan nilai aktual. Dataset yang digunakan terdiri dari 237 data harian dalam rentang April 2025 hingga April 2026. Data dibagi menjadi 80% data latih (188 sampel) dan 20% data uji (48 sampel). Hasil eksperimen menunjukkan bahwa model LMS mampu menghasilkan prediksi dengan Mean Absolute Percentage Error (MAPE) sebesar 1,38% pada data latih dan 1,57% pada data uji, mengindikasikan tingkat akurasi yang baik untuk prediksi satu langkah ke depan (one-step ahead forecasting).
Co-Authors Anti, Yurni Bukit, Cindy Christina Br Girsang, Aline Theresia Gomes, Beatrix Chatarina Da Musyaffa, Fakhirah