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All Journal JURNAL MATEMATIKA STATISTIKA DAN KOMPUTASI Techno.Com: Jurnal Teknologi Informasi Jurnal Simetris CAUCHY: Jurnal Matematika Murni dan Aplikasi TELKOMNIKA (Telecommunication Computing Electronics and Control) JUTI: Jurnal Ilmiah Teknologi Informasi Jurnal Teknologi Informasi dan Ilmu Komputer Scientific Journal of Informatics Kubik Jurnal Matematika dan Statistika serta Aplikasinya (Jurnal MSA) Jurnal Fourier Jurnal Sains Matematika dan Statistika InfoTekJar : Jurnal Nasional Informatika dan Teknologi Jaringan Sistemasi: Jurnal Sistem Informasi Jurnal Pemberdayaan Masyarakat Madani (JPMM) INTEGER: Journal of Information Technology Indonesian Journal of Artificial Intelligence and Data Mining BAREKENG: Jurnal Ilmu Matematika dan Terapan JOURNAL OF APPLIED INFORMATICS AND COMPUTING JTAM (Jurnal Teori dan Aplikasi Matematika) Jurnal Telematika TELKA - Telekomunikasi, Elektronika, Komputasi dan Kontrol Infotekmesin ARBITRER: Jurnal Pendidikan Bahasa dan Sastra Indonesia InPrime: Indonesian Journal Of Pure And Applied Mathematics KUBIK: Jurnal Publikasi Ilmiah Matematika JAMBURA JOURNAL OF PROBABILITY AND STATISTICS Journal of Mathematics Education and Science Al-Hilal: Journal of Islamic Astronomy Jurnal Matematika Integratif STATISTIKA Scientica: Jurnal Ilmiah Sains dan Teknologi NERO (Networking Engineering Research Operation) Lontar Komputer: Jurnal Ilmiah Teknologi Informasi
Nurissaidah Ulinnuha
Department Of Mathematics, Faculty Of Sains And Technology, UIN Sunan Ampel Surabaya
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Implementation of Random Forest Method with Information Gain Selection and Hyperparameter Tuning for Alzheimer’s Disease Classification Riska Nuril Fadhila; Nurissaidah Ulinnuha; Dian Yuliati
Lontar Komputer : Jurnal Ilmiah Teknologi Informasi Vol. 16 No. 01 (2025): Vol.16, No. 01 April 2025
Publisher : Institute for Research and Community Services, Udayana University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24843/LKJITI.2025.v16.i01.p01

Abstract

Alzheimer's disease is one of the leading causes of decreased quality of life in the elderly aged 65 years and above. One of the problems facing Alzheimer's cases is the difficulty of making an early diagnosis to prevent disease progression, as early symptoms are often mistaken for senile dementia. Using the Random Forest method with information gain feature selection and hyperparameter tuning optimization, this study aims to determine the results of optimization with feature selection and hyperparameter tuning using Random Search and Grid Search to classify Alzheimer's medical record data consisting of 32 variables, including lifestyle factors, clinical measurements, cognitive and functional assessments, as well as symptoms that indicate Alzheimer's. The results showed that applying Information Gain and parameter optimization with the Grid Search method achieved the highest accuracy among all tested experiments. Random Forest with Information Gain and Grid Search gave an accuracy of 95.57%, sensitivity of 92.93%, and specificity of 96.99%, which showed better performance than the Random Search method. This indicates that parameter optimization has a vital role in improving model performance. This research contributes to assisting paramedics in determining whether a patient has Alzheimer's disease based on the characteristics derived from the data.
Model Regresi Linier Berganda Dalam Menganalisis Faktor-Faktor Urbanisasi Di Jawa Timur Anggraini, Octavia Putri; Ulinnuha, Nurissaidah; Hafiyusholeh, Moh
Jambura Journal of Probability and Statistics Vol 6, No 2 (2025): Jambura Journal of Probability and Statistics
Publisher : Department of Mathematics, Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37905/jjps.v6i2.28446

Abstract

Urbanization occurs when population increases rapidly, encouraging individuals to migrate from villages to big cities. This phenomenon is triggered by the availability of wider employment opportunities and easier access to resources and technology. However, urbanization also has several negative impacts on the environment, such as reducing the ability to create a comfortable and healthy environment for city residents. This study aims to analyze the factors that influence urbanization in East Java Province using multiple linear regression. The data used is quantitative and was obtained from the East Java Provincial Statistics Agency in 2024. The variables analyzed include poverty levels, security levels, health, education, and unemployment rates. The partial analysis results indicate that the Education Ratio variable has a significant influence on urbanization in East Java, with a coefficient of determination value of 54.1\%. These findings are expected to contribute to the formulation of more targeted development policies in managing the pace of urbanization. 
Implementasi Extreme Learning Machine dengan Seleksi Fitur Particle Swarm Optimization untuk Klasifikasi Sindrom Ovarium Polikistik Mukti, Audyra Dewi Puspa; Ulinnuha, Nurissaidah; Asyhar, Ahmad Hanif
Jurnal Matematika Integratif Vol 21, No 2: Oktober 2025
Publisher : Department of Matematics, Universitas Padjadjaran

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24198/jmi.v21.n2.63988.131-142

Abstract

Sindrom Ovarium Polikistik (SOPK) adalah gangguan hormonal yang sering terjadi pada wanita usia reproduktif dan menjadi salah satu penyebab utama masalah kesuburan. Sekitar 3–15% wanita di seluruh dunia mengalami kondisi ini, yang juga dapat memicu berbagai masalah kesehatan lainnya. Penelitian ini bertujuan untuk mengembangkan metode diagnosis SOPK yang lebih efisien dan akurat dengan memanfaatkan algoritma Extreme Learning Machine (ELM) yang dikombinasikan dengan seleksi fitur menggunakan Particle Swarm Optimization (PSO). ELM dipilih karena kemampuannya dalam melakukan klasifikasi secara cepat, sedangkan PSO digunakan untuk memilih fitur-fitur yang paling relevan. Hasil seleksi fitur menghasilkan 18 fitur terpilih dari total 40 fitur. Pencarian parameter terbaik dilakukan dengan pendekatan random search dan grid search. Hasil menunjukkan bahwa random search memberikan performa terbaik, dengan akurasi 95.35%, sensitivitas 96.67%, dan spesifisitas 92.65%. Tanpa seleksi fitur, ELM hanya menghasilkan akurasi 84.20%, sensitivitas 90.10%, dan spesifisitas 70.62%. Temuan ini menunjukkan bahwa seleksi fitur menggunakan PSO mampu meningkatkan performa klasifikasi ELM secara signifikan.
Clustering Data Kecelakaan Lalu Lintas melalui Algoritma K-Means dengan Seleksi Fitur Chi-Square Margaretha, Adellia Putri; Ulinnuha, Nurissaidah; Intan, Putroue Keumala
INTEGER: Journal of Information Technology Vol 10, No 2 (2025): September
Publisher : Fakultas Teknologi Informasi Institut Teknologi Adhi Tama Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31284/j.integer.0.v10i2.7529

Abstract

Kecelakaan lalu lintas merupakan permasalahan signifikan di Indonesia, dengan dampak fatal dan kerugian ekonomi yang besar. Penelitian ini bertujuan untuk menerapkan algoritma K-means untuk mengelompokkan data kecelakaan lalu lintas dengan menggunakan seleksi fitur. Data kecelakaan lalu lintas yang digunakan diperoleh dari sebuah perusahaan asuransi kecelakaan di Sidoarjo dan diproses untuk menghasilkan fitur-fitur yang relevan. Proses seleksi fitur dilakukan untuk menentukan fitur-fitur yang memiliki kepentingan dan informasi yang paling relevan dalam proses pengelompokkan. Metode seleksi fitur yang digunakan dalam penelitian ini adalah seleksi fitur Chi-Square, yang bertujuan untuk memilih fitur-fitur yang memiliki hubungan signifikan dengan variabel target kecelakaan. Hasil penelitian menunjukkan bahwa data terbagi menjadi 2 cluster dengan seleksi fitur maupun tanpa seleksi fitur, yaitu wilayah dengan tingkat kecelakaan tinggi dan rendah. Nilai koefisien sillhouette cluster sebelum dilakukan seleksi fitur adalah sebesar 0,57. Sedangkan setelah diterapkan seleksi fitur dengan Chi-Square, diperoleh hasil yang lebih baik yaitu sebesar 0,72. Penelitian ini menunjukkan bahwa dengan menerapkan metode seleksi fitur dapat meningkatkan performa pengelompokkan data kecelakaan lalu lintas dengan algoritma K-means.
Analysis Comparison of BiLSTM and BiGRU Models for Aircraft Visibility Prediction Saidah, Nayla Fitriyatus; Ulinnuha, Nurissaidah; Farida, Yuniar
JTAM (Jurnal Teori dan Aplikasi Matematika) Vol 10, No 1 (2026): January
Publisher : Universitas Muhammadiyah Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31764/jtam.v10i1.34698

Abstract

Severe weather conditions such as fog and heavy precipitation pose significant threats to aviation safety. Accurate prediction of aircraft visibility is therefore essential to support operational decision-making and reduce the likelihood of accidents. This study aims to compare and evaluate the performance of two bidirectional deep learning models, BiLSTM and BiGRU, in predicting aircraft visibility using historical meteorological data from BMKG Juanda Sidoarjo. The novelty of this research lies in applying and comparing bidirectional recurrent architectures for visibility prediction, an approach rarely explored in aviation meteorology, to assess their capability in capturing temporal dependencies within time-series visibility patterns. Both models were trained using hyperparameter tuning, with the best configuration obtained from a 24-hour input window, batch size of 32, 64 neurons, a dropout rate of 0.1, and 100–200 epochs. The dataset was divided into training and testing sets (80:20), and model performance was evaluated using Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and Mean Absolute Percentage Error (MAPE) to assess both predictive accuracy and computational efficiency. The results indicate that while BiLSTM achieved slightly higher accuracy, BiGRU demonstrated superior overall efficiency, obtaining competitive error metrics (MSE = 1.50 × 10⁶, RMSE = 1,223.5, MAPE = 19.35%) compared to BiLSTM (MSE = 1.58 × 10⁶, RMSE = 1,258.1, MAPE = 19.50%). BiGRU’s advantage lies in its simpler structure and faster computation, which reduce training complexity without sacrificing forecast accuracy. Overall, this research contributes to the development of efficient bidirectional time-series models for aviation meteorology, offering a practical framework for real-time visibility forecasting in computationally limited environments. The balance between accuracy, speed, and model simplicity makes BiGRU a more scalable and applicable choice for enhancing flight safety operations.
Co-Authors Abdulloh Hamid Abdulloh Hamid Ahmad Hanif Asyhar Alfirdausy, Roudlotul Jannah Almumtazah, Nilna Anggraini, Octavia Putri Aprilianti, Inggrit Delima Aqilah Khansa, Shafa Fitria Ardelia, Nita Ariestia Ramadhani Aris Fanani Daffa Nur Cholis Dian Yuliati Dino Ramadhan Elisa Syafaqoh Endah Nur Salamah, Endah Nur Fadhila, Riska Nuril FAJAR SETIAWAN Farmita, Mayandah Fery Firmansyah Fransisca, Velicia Hani Khaulasari Hilmi, Moh. Aditya Sirojul Indriyani, Jiphie Gilia Intan, Putroue Kumala Isye Arieshanti Khasanah, Rofina Muti'atun Kiromi, Muhammad Sahrul Lutfi Hakim Maghfiroh, Wardatul Margaretha, Adellia Putri Maulana, Achmad Resnu Maulidya, Rahmania Moh. Hafiyusholeh Mohamat Ulin Nuha Mu'afa, Sulthan Fikri Mukti, Audyra Dewi Puspa Nafis, Urwatun Novianti, Fahriza Prilindaputra, Brilian Purwanti, Ida Putri, Anindya Maya Putri, Dinda Rima Rachcita Putroue Keumala Intan Rafika Veriani Riska Nuril Fadhila Romdloni, Ro’iqotul Fathiyyah SA'DYAH, HALIMATUS Safira, Icha Dwi Saidah, Nayla Fitriyatus Sari, Dian Candra Rini Novita Sari, Firda Yunita Sari, Ghaluh Indah Permata Septiani, Intan Karunia Septiani Sholihah, Siti Azizatus Siti Aisyah Sufriyah, Lailiyatus Sulthan Fikri Mu'afa Tussholikhah, Anissa Nurul Farida USWATUN KHASANAH Utami, Tri Mar'ati Nur Wahyudi, Sharenada Norisdita Wika Dianita Utami Wika Dianita Utami Wilda Imama Sabilla Yanuwar Reinaldi Yudhi Purwananto Yuliati, Dian Yuniar Farida Yunita Ardilla Yusuf Ababil, Achmad Fachril