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All Journal JURNAL MATEMATIKA STATISTIKA DAN KOMPUTASI Jurnal Simetris CAUCHY: Jurnal Matematika Murni dan Aplikasi TELKOMNIKA (Telecommunication Computing Electronics and Control) JUTI: Jurnal Ilmiah Teknologi Informasi 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 ARBITRER: Jurnal Pendidikan Bahasa dan Sastra Indonesia InPrime: Indonesian Journal Of Pure And Applied Mathematics KUBIK: Jurnal Publikasi Ilmiah Matematika Al-Hilal: Journal of Islamic Astronomy Scientica: Jurnal Ilmiah Sains dan Teknologi NERO (Networking Engineering Research Operation)
Nurissaidah Ulinnuha
Institut Teknologi Sepuluh Nopember
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Comparison of Linkage Methods in Hierarchical Clustering for Grouping Districts/Cities in East Java Based on Stunting Determinants Putri, Dinda Rima Rachcita; Ulinnuha, Nurissaidah; Intan, Putroue Kumala
Journal of Applied Informatics and Computing Vol. 9 No. 5 (2025): October 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i5.10919

Abstract

Stunting is a long-term nutritional problem that generally occurs in children under five years old and is characterized by a shorter body than other children of the same age due to continuous dietary deficiencies. As a result of the Indonesian Health Survey (SKI) conducted in 2023, the stunting rate in East Java decreased to 17.7%. In 2024, the target is to reduce it to 14%. This study aims to group regencies and cities in East Java based on indicators of child nutritional status by using five linkage approaches in the hierarchical clustering method. This study found areas with similar causes of stunting so that intervention programs can be more targeted. The analysis showed that the centroid linkage methods formed two clusters with the highest cophenetic correlation coefficient of 0.8619. The first cluster consists of 37 regencies/cities with a low stunting category, and the second cluster consists of one regency/city with a high stunting category. The model in this clustering has a silhouette value of 0.6155, which indicates that the model is in the good category.
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 : 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
Co-Authors Abdulloh Hamid Abdulloh Hamid Alfirdausy, Roudlotul Jannah Aqilah Khansa, Shafa Fitria Ardelia, Nita Ariestia Ramadhani Aris Fanani Daffa Nur Cholis Dino Ramadhan Elisa Syafaqoh FAJAR SETIAWAN Farmita, Mayandah Indriyani, Jiphie Gilia Intan, Putroue Kumala Isye Arieshanti Khasanah, Rofina Muti'atun Lutfi Hakim Maghfiroh, Wardatul Maulana, Achmad Resnu Moh. Hafiyusholeh Mohamat Ulin Nuha Mu'afa, Sulthan Fikri Nafis, Urwatun Novianti, Fahriza Prilindaputra, Brilian Purwanti, Ida Putri, Dinda Rima Rachcita Putroue Keumala Intan Rafika Veriani Romdloni, Ro’iqotul Fathiyyah Roudlotul Jannah Alfirdausy SA'DYAH, HALIMATUS Safira, Icha Dwi 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 USWATUN KHASANAH Utami, Tri Mar'ati Nur Wika Dianita Utami Wika Dianita Utami Yanuwar Reinaldi Yudhi Purwananto Yuliati, Dian Yuniar Farida, Yuniar Yusuf Ababil, Achmad Fachril