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Nawafilah, Nur Qomariyah
Program Studi Teknik Informatika, Universitas Islam Lamongan
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Algoritma Backpropagation untuk Memprediksi Korban Bencana Alam Nur Nafi'iyah; Ahmad Ahmad Salaffudin1; Nur Qomariyah Nawafilah
SMATIKA JURNAL : STIKI Informatika Jurnal Vol 9 No 02 (2019): SMATIKA Jurnal : STIKI Informatika Jurnal
Publisher : LPPM UBHINUS MALANG

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32664/smatika.v9i02.400

Abstract

Indonesia is a country prone to natural disasters. Because Indonesia is a maritime country and its geographical area is Mount Merapi. In order to reduce victims of natural disasters or other disasters, we conducted research related to predictions of victims of natural disasters. The purpose of this study is to help the team or related parties in preparing themselves to deal with the victims of a growing natural disaster. The algorithm used in predicting victims of natural disasters is backpropagation. The data used in this study is the DIBI dataset taken from the Google dataset. The predicted impact was 5128 lines, 524 missing victims, 2653 injured, 941 lines dead. Each dataset with each category of disaster impacts, missing victims, injured victims, and death victims was made of 2 input variables. Input variables from each category are district code, and year and the output variable is the number of disaster victims. Neural network structure and architecture of this study, namely 2 input layer nodes, 2 hidden layer nodes, and 1 output layer node. From the architecture, training and testing were carried out, where the results of testing disaster impact data were 110 lines of MSE value of 0.0371, testing results of wounded victims data as much as 53 lines of MSE value of 0.0256, results of testing of missing victims as much as the 24 lines of the MSE value are 0.041, and the results of testing of the dead are 41 lines of the MSE value of 0.029.
Pengaruh AI-Enhanced Learning, Literasi AI, dan Motivasi Belajar terhadap Persepsi Pemahaman Konseptual Matematika Mahasiswa Ayu Ismi Hanifah; Nur Qomariyah Nawafilah; Masruroh Masruroh; Mohamad Fahmi Yusuf
SAINTIFIK Vol 12 No 2 (2026): Saintifik: Jurnal Matematika, Sains, dan Pembelajarannya
Publisher : Universitas Sulawesi Barat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31605/saintifik.v12i2.671

Abstract

Perkembangan Artificial Intelligence (AI) dalam pendidikan tinggi membuka peluang untuk meningkatkan kualitas pembelajaran, khususnya pada mata kuliah Aljabar Linier yang memiliki konsep-konsep abstrak dan menuntut pemahaman konseptual yang mendalam. Penelitian ini bertujuan menganalisis pengaruh AI-Enhanced Learning, literasi AI, dan motivasi belajar terhadap persepsi pemahaman konseptual mahasiswa. Penelitian menggunakan pendekatan kuantitatif dengan metode survei terhadap 40 mahasiswa Program Studi Teknik Informatika yang telah menempuh mata kuliah Aljabar Linier. Data dikumpulkan melalui kuesioner skala Likert dan dianalisis menggunakan metode PLS-SEM. Berdasarkan hasil pengujian path coefficient, seluruh hipotesis dinyatakan signifikan karena memiliki nilai T-statistics > 1,96 dan P-values < 0,05. Hasil penelitian menunjukkan bahwa AI-Enhanced Learning, literasi AI, dan motivasi belajar berpengaruh positif dan signifikan terhadap pemahaman konseptual mahasiswa. Di antara ketiga variabel tersebut, AI-Enhanced Learning memberikan pengaruh paling dominan, diikuti oleh motivasi belajar dan literasi AI. Model penelitian juga mampu menjelaskan 64,3% variasi pemahaman konseptual mahasiswa. Temuan ini menunjukkan bahwa integrasi AI yang didukung oleh literasi AI yang memadai dan motivasi belajar yang tinggi dapat meningkatkan kualitas pemahaman konsep pada pembelajaran Aljabar Linier. Oleh karena itu, pengembangan strategi pembelajaran berbasis AI perlu diiringi dengan penguatan kompetensi digital dan motivasi belajar mahasiswa agar pembelajaran menjadi lebih efektif dan bermakna.
Development of Smart Study Web Application for Classifying Student Material Understanding Levels Using Naive Bayes Classifier Susilo, Purnomo Hadi; Mujtahidah, Vita Ihwatin; Nawafilah, Nur Qomariyah; Ramli, Azizul Azhar
Jurnal Teknik Informatika (Jutif) Vol. 7 No. 3 (2026): JUTIF Volume 7, Number 3, June 2026
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2026.7.3.5507

Abstract

The rapid development of information and communication technology requires adaptive digital learning systems that are able to evaluate students’ learning outcomes objectively. However, the Smart Study application previously functioned only as a quiz delivery platform and lacked analytical capabilities to assess students’ levels of material understanding, particularly in practical courses such as Computer Networks. This study aims to design and develop a web-based Smart Study application integrated with the Naive Bayes classification algorithm to determine students’ understanding levels based on quiz performance data. The research methodology includes data collection from Informatics Engineering students at Universitas Islam Lamongan, followed by data preprocessing through cleaning and categorical conversion of features, including final score, average response time, response time variability, and correct incorrect response time ratio. The dataset was divided into 80% training data and 20% testing data. The Naive Bayes model was trained and evaluated using accuracy, precision, recall, F1-score, and a confusion matrix. The results show that the proposed model achieved an accuracy of 75%, correctly classifying 15 out of 20 testing samples. The model demonstrated strong performance in identifying the Comprehended class with an F1-score of 0.83, while performance for the Not Comprehended class was lower with an F1-score of 0.55 due to class imbalance. This study contributes to the fields of learning analytics and educational data mining by demonstrating the integration of a simple machine learning method into an e-learning application to support early detection of learning difficulties and data-driven evaluation of digital learning processes in higher education.
Co-Authors Abid Muhtarom Abid Muhtarom, Abid Ahmad Ahmad Salaffudin1 Ahmad Ahmad Salaffudin1 Aliyudin, Muhammad Anggraini, Erry Ari Avianto Arif Patriot Sri Pamungkas Arinda Leliana Armyta Puspitasari Ayu Ismi Hanifah AZZA ABIDATIN BETTALIYAH Bettaliyah, Azza Abidatin Chayadi Chayadi Chayadi Erry Anggraini Hammam Rofiqi Agustapraja Husen Husen husen husen Husen, Husen Husen Masruroh Masruroh Masruroh MASRUROH Mohamad Fahmi Yusuf Muhammad Busthomi Mujtahidah, Vita Ihwatin Nafi'iyah, Nur Nur Nafi&#039;iyah Nur nafi'iyah Nur Nafi’iyah Nur Nafi’iyah Nurul Fitria Apriliani Pamungkas, Muhammad Duta Ari Purnomo Hadi Susilo, Purnomo Hadi Purnomo, Nanto Ramli, Azizul Azhar Septiana Widi Astuti Siti Mujilahwati Siti Mujilawati Mujilawati Sri Pamungkas, Arif Patriot