Article Per Year (5 Year)
p-Index From 2021 - 2026
5.446
P-Index
This Author published in this journals
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 MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer 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 Journal of Mathematics Education and Science International Journal Of Computer, Network Security and Information System (IJCONSIST) Al-Hilal: Journal of Islamic Astronomy Jurnal Matematika Integratif STATISTIKA Scientica: Jurnal Ilmiah Sains dan Teknologi NERO (Networking Engineering Research Operation)
Nurissaidah Ulinnuha
Institut Teknologi Sepuluh Nopember
Religion Agriculture, Biological Sciences & Forestry Arts Humanities Astronomy Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Languange, Linguistic, Communication & Media Library & Information Science Mathematics Mechanical Engineering Medicine & Pharmacology Physics Social Sciences Transportation Other

Published : 43 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 43 Documents
Search

 1   2   3   4   5 
Implementation of The Extreme Gradient Boosting Algorithm with Hyperparameter Tuning in Celiac Disease Classification Alfirdausy, Roudlotul Jannah; Ulinnuha, Nurissaidah; Utami, Wika Dianita
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol 24 No 1 (2024)
Publisher : LPPM Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v24i1.4031

Abstract

Celiac Disease (CeD) is an autoimmune disorder triggered by gluten consumption and involves the immune system and HLA in the intestine. The global incidence ranges from 0.5%-1%, with only 30% correctly diagnosed. Diagnosis remains challenging, requiring complex tests like blood tests, small bowel biopsy, and elimination of gluten from the diet. Therefore, a faster and more efficient alternative is needed. Extreme Gradient Boosting (XGBoost), an ensemble machine learning technique that utilizes decision trees to aid in the classification of Celiac disease, was used. The aim of this study was to classify patients into six classes, namely potential, atypical, silent, typical, latent and none disease, based on attributes such as blood test results, clinical symptoms and medical history. This research method employs 5-fold cross-validation to optimize parameters that are max depth, n estimator, gamma, and learning rate. Experiments were conducted 96 times to get the best combination of parameters. The results of this research are highlighted by an improvement of 0.45% above the accuracy value with the default XGBoost parameter of 98.19%. The best model was obtained in the trial with parameters max depth of 3, n estimator of 100, gamma of 0, and learning rate of 0.3 and 0.5 after modifying the parameters, yielding an accuracy rate of 98.64%, a sensitivity rate of 98.43%, and a specificity rate of 99.72%. This research shows that tuning the XGBoost parameters for Celiac
Implementasi Support Vector Machine untuk Analisis Sentimen Aplikasi Deepseek Prilindaputra, Brilian; Putri, Dinda Rima Rachcita; Ulinnuha, Nurissaidah
INTEGER: Journal of Information Technology Vol 10, No 1: April 2025
Publisher : Fakultas Teknologi Informasi Institut Teknologi Adhi Tama Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31284/j.integer.2024.v10i1.7541

Abstract

Kemunculan DeepSeek, AI canggih yang dikembangkan di China, telah memberikan dampak yang signifikan terhadap lanskap teknologi global. Namun, pengadopsiannya telah mendapat reaksi beragam, dengan beberapa negara memilih untuk memblokir aksesnya karena masalah keamanan data. Penelitian ini bertujuan untuk menganalisis sentimen pengguna terhadap aplikasi DeepSeek di Google Play Store, secara khusus menargetkan ulasan pengguna dari Amerika Serikat. Dengan menggunakan metode klasifikasi Support Vector Machine (SVM), analisis sentimen dilakukan untuk mengkategorikan opini pengguna ke dalam sentimen positif, netral, dan negatif. Dataset yang terdiri dari 10.000 ulasan yang dikumpulkan melalui web scraping, telah dipreproses menggunakan teknik pembersihan teks, pembobotan TF-IDF, dan lemmatization. Model SVM dilatih dan divalidasi menggunakan k-fold cross validation (k-fold = 10), mencapai akurasi terbaik pada parameter C = 100 dan kernel RBF. Hasil evaluasi menunjukkan akurasi rata-rata 90,33%, dengan akurasi puncak 92,20% pada fold 10. Temuan ini menunjukkan polaritas sentimen yang kuat di antara para pengguna. Penelitian ini penyebaran kata dari analisis wordcloud memberikan wawasan bagi para pengembang dan pemangku kepentingan dalam meningkatkan aplikasi AI dengan mengatasi kekhawatiran pengguna dan meningkatkan kepuasan pengguna secara keseluruhan.
GRID SEARCH AND RANDOM SEARCH HYPERPARAMETER TUNING OPTIMIZATION IN XGBOOST ALGORITHM FOR PARKINSON’S DISEASE CLASSIFICATION Aqilah Khansa, Shafa Fitria; Ulinnuha, Nurissaidah; Utami, Wika Dianita
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 19 No 3 (2025): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol19iss3pp1609-1624

Abstract

Parkinson's disease is a neurodegenerative disorder affecting motor abilities, with a prevalence of 329 cases per 100,000 individuals. Early diagnosis is crucial to prevent complications. This study classifies Parkinson's disease using the Extreme Gradient Boosting (XGBoost) algorithm with hyperparameter tuning via Grid Search and Random Search. The dataset from Kaggle consists of 2105 records from 2024 and includes 32 clinical and demographic features such as age, gender, BMI, medical history, and Parkinson's symptoms. The XGBoost method effectively manages large and complex data and reduces. Tuning was performed with 5-fold cross-validation for result validity. After tuning with Grid Search, the model achieved 93.35% accuracy in 44 minutes 51 seconds, with optimal parameters gamma=5, max depth=3, learning rate=0.3, n estimators=100, and subsample=0.7. Meanwhile, Random Search with 50 iterations achieved 93.97% accuracy in 3 minutes 4 seconds with optimal parameters gamma=5, max depth=3, learning rate=0.262, n estimators=58, and subsample=0.631. Random Search also shows better time efficiency than Grid Search, although with relatively similar accuracy. The results of this study confirm that hyperparameter tuning using Random Search not only produces competitive accuracy performance but also minimizes computation time, making it a more optimal choice for Parkinson's disease classification.
Classification of Wood Types Based on Wood Fiber Texture Using GLCM - ANN Septiani, Intan Karunia Septiani; Wika Dianita Utami; Nurissaidah Ulinnuha; Dino Ramadhan
Jurnal Fourier Vol. 14 No. 1 (2025)
Publisher : Program Studi Matematika Fakultas Sains dan Teknologi UIN Sunan Kalijaga Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14421/fourier.2025.141.9-20

Abstract

In Indonesia, various types of wood grow and develop with various characteristics and benefits. Each type of wood has differences in texture and fiber, to classify it must have sufficient knowledge about the texture and fiber of wood. A wood species identification system is needed to help the classification process. The purpose of this research is to classify Teak Wood, Sengon Wood, Mahogany Wood, and Gmelina Wood which are often sold in Indonesia. The classification method used in this research is Artificial Neural Network with Gray Level Co- occurrence Matrix (GLCM) extraction. Pre-processing stages include Histogram Equalization, filtering, converting images into grayscale form, and data augmentation. Feature extraction of pre-processing results using GLCM is taken, namely contrast, correlation, energy, homogeneity, and entropy. From the research results, classification using Artificial Neural Network was obtained with 46% accuracy, 43% precision, 42.5% recall, and 42% F1-Score with a GLCM inclination angle of 90°. So, this method can be used to classify the types of wood, but it is less accurate because there are still deficiencies in the model.
BASIC PYTHON PROGRAMMING TRAINING TO ENHANCE DIGITAL LITERACY AMONG STUDENTS AT SMA WACHID HASYIM 2 SIDOARJO Yuliati, Dian; Ulinnuha, Nurissaidah
Jurnal Pemberdayaan Masyarakat Madani (JPMM) Vol. 9 No. 1 (2025): Jurnal Pemberdayaan Masyarakat Madani (JPMM) (DOAJ & SINTA 4 Indexed)

Publisher : Fakultas Ekonomi dan Bisnis Universitas Negeri Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21009/JPMM.009.1.06

Abstract

Improving digital literacy among students has become one of the main focuses of the era of the Fourth Industrial Revolution. Programming skills must be mastered to prepare young generations to face global challenges. This community service aims to enhance the digital literacy of students at SMA Wachid Hasyim 2 Sidoarjo by introducing and applying basic Python programming. The method used is Asset-Based Community Development (ABCD), which leverages existing school assets and involves active student participation in the learning process. This training was attended by twelfth-grade students interested in technology and digital skill development. Evaluation results indicate a significant improvement in students' understanding of programming, with the average pretest score increasing from 64.86 to 96.22 after the training. To ensure the continuity of learning, the service team established a coding community as a platform for students who wish to explore programming further. This community aims to develop collaboration and communication skills among students. The program successfully empowered students created a positive learning environment, and demonstrated promising potential for their skill development in programming.
Implementation of BiLSTM to Predict World Crude Oil Prices Sari, Firda Yunita; Ulinnuha, Nurissaidah
KUBIK Vol 10 No 1 (2025): IN PRESS
Publisher : Jurusan Matematika, Fakultas Sains dan Teknologi, UIN Sunan Gunung Djati Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

The main source of energy worldwide is crude oil, which is used by almost all countries as an energy source. Crude oil plays a key role in driving the global economy, especially in the industrial and transportation sectors. Along with technological developments, crude oil price predictions can be made more sophisticated using artificial intelligence-based methods, one of which is the Bidirectional Long Short-Term Memory (BiLSTM) method which is a development of the Long Short-Term Memory (LSTM) method by combining past and future information when processing sequential data, BiLSTM uses forward and backward LSTM simultaneously to increase accuracy. The study used world crude oil price data for 1 year. There are 57 tests with several parameters such as data division, number of neurons, batch size, and activation function. After testing with the BiLSTM method for 57 scenarios, there is the smallest MAPE value of 0.09% at a data division of 90:10, number of neurons 100, batch size of value 4, and ReLu activation function. The resulting prediction model is highly accurate based on the MAPE criterion value.
Implementation of BiLSTM to Predict World Crude Oil Prices Sari, Firda Yunita; Ulinnuha, Nurissaidah
KUBIK Vol 10 No 1 (2025): IN PRESS
Publisher : Jurusan Matematika, Fakultas Sains dan Teknologi, UIN Sunan Gunung Djati Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

The main source of energy worldwide is crude oil, which is used by almost all countries as an energy source. Crude oil plays a key role in driving the global economy, especially in the industrial and transportation sectors. Along with technological developments, crude oil price predictions can be made more sophisticated using artificial intelligence-based methods, one of which is the Bidirectional Long Short-Term Memory (BiLSTM) method which is a development of the Long Short-Term Memory (LSTM) method by combining past and future information when processing sequential data, BiLSTM uses forward and backward LSTM simultaneously to increase accuracy. The study used world crude oil price data for 1 year. There are 57 tests with several parameters such as data division, number of neurons, batch size, and activation function. After testing with the BiLSTM method for 57 scenarios, there is the smallest MAPE value of 0.09% at a data division of 90:10, number of neurons 100, batch size of value 4, and ReLu activation function. The resulting prediction model is highly accurate based on the MAPE criterion value.
Penerapan Model Long Short Term Memory Pada Jumlah Produksi Pupuk Di PT. Pelindo Gresik Romdloni, Ro’iqotul Fathiyyah; Yuliati, Dian; Ulinnuha, Nurissaidah
Jurnal Sains Matematika dan Statistika Vol 11, No 2 (2025): JSMS Juli 2025
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/jsms.v11i2.33034

Abstract

PT Pelindo Multi Terminal adalah subholding dari PT Pelabuhan Indonesia (Persero) yang mengelola berbagai terminal multipurpose di Indonesia, termasuk terminal petikemas. Cabang Gresik merupakan salah satu dari banyak cabang yang dikelola oleh PT Pelindo Multi Terminal, yang berfokus pada operasi terminal petikemas dan layanan bongkar muat. Penelitian ini bertujuan untuk mengevaluasi model Long Short-Term Memory (LSTM) dalam memprediksi jumlah produksi pupuk pada kegiatan bongkar muat di PT Pelindo Multi Terminal Petikemas Branch Gresik. LSTM dipilih karena kemampuannya dalam mengolah data berurutan dan memprediksi pola jangka panjang secara akurat. Data yang digunakan dalam penelitian ini adalah jumlah produksi pupuk dari tahun 2018 hingga 2023. Hasil penelitian menunjukkan bahwa model LSTM mampu memberikan prediksi yang cukup akurat, dengan Mean Squared Error (MSE) sebesar 258141463,92 dan Mean Absolute Percentage Error (MAPE) sebesar 18,60% pada epoch 300 dan menunjukkan bahwa LSTM efektif dalam memproses dan memprediksi jumlah produksi pupuk, serta berpotensi meningkatkan efisiensi operasional bongkar muat pupuk. 
COMPARING GAUSSIAN AND EPANECHNIKOV KERNEL OF NONPARAMETRIC REGRESSION IN FORECASTING ISSI (INDONESIA SHARIA STOCK INDEX) Farida, Yuniar; Purwanti, Ida; Ulinnuha, Nurissaidah
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 16 No 1 (2022): BAREKENG: Jurnal Ilmu Matematika dan Terapan
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (715.853 KB) | DOI: 10.30598/barekengvol16iss1pp321-330

Abstract

ISSI reflects the movement of sharia stock prices as a whole. It is necessary to forecast the share price to help investors determine whether the shares should be sold, bought, or retained. This study aims to predict the value of ISSI using nonparametric kernel regression. The kernel regression method is one of the nonparametric regression methods used to estimate conditional expectations using kernel functions. Kernel functions used in this study are gaussian and Epanechnikov kernel functions. The estimator used is the estimator Nadaraya-Watson. This study aims to compare the two kernel functions to predict the value of ISSI in the period from January 2016 to October 2019. The analysis results obtained the best method in predicting ISSI values, namely nonparametric kernel regression using Nadaraya-Watson estimator and Gaussian kernel function with the MAPE value of 15% and the coefficient of determination of 85%. Independent variables that significantly affect ISSI are interest rates, exchange rates, and inflation. Curve smoothing is done using bandwidth value (h) searched by the Silverman rule. The calculation result with the Silverman rule obtained a bandwidth value of 101832.7431.
Prediksi Besar Daya Listrik dari Gelombang Laut Sawu Menggunakan Bidirectional Long Short-Term Memory (Bi-LSTM) Safira, Icha Dwi; Novitasari, Dian Candra Rini; Ulinnuha, Nurissaidah; Setiawan, Fajar
Jurnal Telematika Vol. 20 No. 1 (2025)
Publisher : Yayasan Petra Harapan Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61769/telematika.v20i1.742

Abstract

Several islands in East Nusa Tenggara Province (NTT) are underdeveloped areas with insufficient electrification. Therefore, renewable energy power plants are needed, namely Oscillating Water Column Technology Ocean Wave Power Plants (PLTGL-OWC). The objective of this study is to determine the performance of the bidirectional long short-term memory (Bi-LSTM) method in predicting the potential power generated from the height, length, and period of the Sawu Sea waves in NTT using PLTGL-OWC. This study utilises Sawu Sea wave data collected every 12 hours over 9 months. Bi-LSTM is used in this study because it can overcome the vanishing Gradient problem by utilising both the forward layer and the backward layer, making it more effective in solving complex issues, such as time series prediction. This study conducted tests on hyperparameter batch size and hidden layer node configurations. The smallest mean absolute percentage error (MAPE) prediction values obtained were 9.1943% for the wave height parameter, 11.3585% for the wave length parameter, and 7.1485% for the wave period parameter. It means that the Bi-LSTM method is suitable for predicting the electrical power generated by the PLTGL-OWC in the Sawu Sea, as the height and period parameters fall within the MAPE < 10% category, and the length parameter falls within the MAPE 10-20% category. The average electrical power generated is 2,639,865.948 watts per day over a 31-day period. The Sawu Sea has the potential to serve as a renewable energy source in the NTT region.
Co-Authors Abdulloh Hamid Abdulloh Hamid Ahmad Hanif Asyhar Alfirdausy, Roudlotul Jannah Almumtazah, Nilna Aprilianti, Inggrit Delima Aqilah Khansa, Shafa Fitria Ardelia, Nita Ariestia Ramadhani Aris Fanani Basuki, Athiyah Fitriyani Burhanudin, Muhamad Aris Daffa Nur Cholis Dino Ramadhan Elisa Syafaqoh Fadhila, Riska Nuril FAJAR SETIAWAN Farmita, Mayandah Fery Firmansyah Fransisca, Velicia Hani Khaulasari Haniefardy, Addien Hilmi, Moh. Aditya Sirojul Indriyani, Jiphie Gilia Intan, Putroue Kumala Isye Arieshanti Khasanah, Rofina Muti'atun Kiromi, Muhammad Sahrul kristanti, beni tiyas Lutfi Hakim Maghfiroh, Wardatul Maulana, Achmad Resnu Maulidya, Rahmania Moh. Hafiyusholeh Mohamat Ulin Nuha Mu'afa, Sulthan Fikri Nafis, Urwatun Novianti, Fahriza Pertiwi, Aisyah Prilindaputra, Brilian Purwanti, Ida Putri, Anindya Maya Putri, Dinda Rima Rachcita Putroue Keumala Intan Rafika Veriani Romdloni, Ro’iqotul Fathiyyah SA'DYAH, HALIMATUS Safira, Icha Dwi Sari, Dian Candra Rini Novita Sari, Firda Yunita Sari, Ghaluh Indah Permata Satria, Vinza Hedi Septiani, Intan Karunia Septiani Sholihah, Siti Azizatus Siti Aisyah Sufriyah, Lailiyatus Sulthan Fikri Mu&#039;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, Yuniar Yunita Ardilla Yusuf Ababil, Achmad Fachril