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All Journal KADIKMA Jurnal Informatika dan Teknik Elektro Terapan SMATIKA Jurnal Elemen AKSIOLOGIYA : Jurnal Pengabdian Kepada Masyarakat PROCESSOR Jurnal Ilmiah Sistem Informasi, Teknologi Informasi dan Sistem Komputer Jurnal Nasional Komputasi dan Teknologi Informasi Jurnal Tarbiyah : Jurnal Ilmiah Kependidikan Saintifik : Jurnal Matematika, Sains, dan Pembelajarannya JURNAL MATHEMATIC PAEDAGOGIC Jurnal PTK dan Pendidikan Jurnal Ilmu Komputer dan Desain Komunikasi Visual J-PiMat : Jurnal Pendidikan Matematika Jurnal Mnemonic Jurnal Teknika Jurnal Abdimas Berdaya : Jurnal Pembelajaran, Pemberdayaan dan Pengabdian Masyarakat Reforma: Jurnal Pendidikan dan Pembelajaran Jurnal Teknik Informatika (JUTIF) Jurnal Mandala Pengabdian Masyarakat Griya Journal of Mathematics Education and Application JAPI: Jurnal Akses Pengabdian Indonesia Smatika Jurnal : STIKI Informatika Jurnal
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: Jurnal Matematika, Sains, dan Pembelajarannya 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.
PENINGKATAN KINERJA METODE LEXICON-BASED DENGAN KOREKSI EJAAN JARO-WINKLER PADA ANALISIS SENTIMEN MEDIA SOSIAL Virgiawan Asegaf; Kemal Farouq Mauladi; Nur Qomariyah Nawafilah
Jurnal Informatika dan Teknik Elektro Terapan Vol. 14 No. 3 (2026)
Publisher : Universitas Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/jitet.v14i3.10585

Abstract

Opini publik di media sosial mengenai isu sensitif seperti dugaan ijazah palsu tokoh publik memiliki dampak besar terhadap dinamika kepercayaan masyarakat. Namun, analisis sentimen berbasis kamus pada teks komentar Instagram kerap terhambat oleh tingginya ketidakteraturan bahasa dan kesalahan pengetikan (typo), yang menurunkan sensitivitas deteksi sistem konvensional. Penelitian ini menerapkan pendekatan analisis sentimen berbasis leksikon yang dioptimasi dengan algoritma jarak string Jaro-Winkler untuk mengevaluasi 5.485 komentar bersih dari akun Instagram @tvonenews. Hasil komputasi menunjukkan bahwa integrasi Jaro-Winkler dengan batas panjang kata ≥ 4 karakter dan ambang batas kemiripan ≥ 0,90 berhasil mendeteksi dan menyelamatkan 570 komentar bermuatan opini yang sebelumnya keliru diklasifikasikan sebagai opini netral oleh leksikon murni. Klasifikasi akhir menghasilkan distribusi sentimen yang didominasi oleh kelas Negatif sebesar 43,8% (2.405 komentar), disusul oleh sentimen Netral sebesar 36,9% (2.025 komentar), dan Positif sebesar 19,2% (1.055 komentar). Penelitian ini membuktikan bahwa kombinasi Leksikon dan Jaro-Winkler sangat efisien meningkatkan kinerja deteksi pada kosakata tidak baku tanpa memerlukan proses pelatihan model data, sekaligus merepresentasikan tingginya kritik dan tuntutan masyarakat akan transparansi hukum. Public opinion on social media regarding sensitive issues, such as alleged fake diplomas of public figures, significantly impacts the dynamics of societal trust. However, dictionary-based sentiment analysis on Instagram comments is frequently impeded by high linguistic irregularity and typographical errors (typos), which degrade the detection sensitivity of conventional systems. This study applies a lexicon-based sentiment analysis approach optimized with the Jaro-Winkler string distance algorithm to evaluate 5,485 clean comments from the @tvonenews Instagram account. The computational results demonstrate that the Jaro-Winkler integration with a word length limit of ≥ 4 characters and a similarity threshold of ≥ 0.90 successfully detected and recovered 570 opinionated comments that were previously misclassified as neutral by the pure lexicon method. The final classification yielded a distribution dominated by Negative sentiment at 43.8% (2,405 comments), followed by Neutral at 36.9% (2,025 comments), and Positive at 19.2% (1,055 comments). This research proves that combining Lexicon and Jaro-Winkler efficiently enhances detection performance on non-standard vocabulary without requiring model training data, while also highlighting the public's strong criticism and demand for legal transparency.
Development of a diagnostic assessment for measuring students' conceptual understanding of real numbers Nur Qomariyah Nawafilah; Rizky Oktaviana Eko Putri
Jurnal Elemen Vol 12 No 3 (2026): July
Publisher : Universitas Hamzanwadi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29408/jel.v12i3.34250

Abstract

This research was motivated by the limitations of assessment instruments capable of measuring students' initial conceptual understanding of mathematics related to real numbers, which is the basis of calculus. This study aimed to develop a valid, practical, and effective diagnostic assessment to measure students' conceptual understanding of mathematics. This research is developmental research with the Plomp model, which includes the stages of preliminary research, development or prototyping, and assessment of the product. Data were collected through interviews, observations, questionnaires, and tests and analyzed using quantitative descriptive analysis. The results of the validator assessment on the validation sheet indicate that the developed diagnostic assessment has a validity level of 91.8% (content aspect), 87.6% (construct aspect), and 92.6% (linguistic aspects). The practicality level was 70.85% (practical), as obtained from the questionnaire scores completed by the students. In terms of effectiveness, the developed assessment met the effectiveness criteria because it could identify areas of conceptual difficulty, differentiate levels of conceptual understanding, and provide meaningful information for decision-making. Its uniqueness lies in the development of a diagnostic assessment item design framework based on specific conceptual understanding indicators for real numbers in higher education and its assessment method.
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 Kemal Farouq Mauladi 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 Rizky Oktaviana Eko Putri Septiana Widi Astuti Siti Mujilahwati Siti Mujilawati Mujilawati Sri Pamungkas, Arif Patriot Virgiawan Asegaf