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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 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
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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
Analysis of Factors Influencing Traffic Accidents in Sidoarjo Regency Using the Geographically Weighted Regression Method Aprilianti, Inggrit Delima; Ulinnuha, Nurissaidah; Intan, Putroue Keumala
Statistika Vol. 25 No. 2 (2025): Statistika
Publisher : Department of Statistics, Faculty of Mathematics and Natural Sciences, Universitas Islam Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29313/statistika.v25i2.7772

Abstract

Abstract. Traffic accidents are incidents that may result in trauma, injury, disability, or even death. One of the regencies in East Java Province experiencing an annual increase in traffic accident cases is Sidoarjo Regency. Geographically Weighted Regression (GWR) is a statistical approach that analyses the relationship between independent and dependent variables, taking into account spatial variation in each region. This study applies the GWR method to identify significant factors influencing the number of traffic accidents and to classify sub-regions within Sidoarjo Regency based on those factors. This study uses variables such as accident count, population density, vehicle types, gender ratio, and geographic coordinates to capture spatial differences across Sidoarjo's districts. The results indicate that the adaptive tricube kernel in GWR is the most suitable model, achieving a coefficient of determination (R²) of 99.96%. This performance indicates that the GWR model yields a slightly better fit than the multiple linear regression model, which obtained an R² of 99.86%. The types of vehicles, specifically trucks, cars, and motorcycles, are identified as significant variables in almost all districts. In Sidoarjo Regency, the districts are classified into two clusters based on the independent variables that significantly influence traffic accidents: Cluster 1, the density–vehicle accident cluster, and Cluster 2, the vehicle-only accident cluster. This classification provides a foundation for more targeted government interventions to reduce regional traffic accidents. Policy recommendations include controlling population density and improving road infrastructure in the first cluster, while focusing on vehicle safety, monitoring goods transportation, and implementing road safety campaigns in the second cluster.
COMPARISON OF SPHERICAL TRIGONOMETRY METHOD, JEAN MEEUS ALGORITHM AND GOOGLE QIBLA FINDER IN DETERMINING OF THE QIBLA DIRECTION OF ISLAMIC HOSPITAL Sari, Firda Yunita; Yusuf Ababil, Achmad Fachril; Nafis, Urwatun; Ardelia, Nita; Khasanah, Rofina Muti'atun; Ulinnuha, Nurissaidah; Hamid, Abdulloh
Al-Hilal: Journal of Islamic Astronomy Vol 5, No 2, 2023
Publisher : Fakultas Syari'ah dan Hukum UIN Walisongo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21580/al-hilal.2023.5.2.17192

Abstract

Accuracy in facing the Qibla is an essential part of performing prayers. This vital value is evident when many mosques are built in public places. This article is qualitative with field data sources, namely coordinate points at the Jemursari Islamic Hospital mosque, Surabaya Islamic Hospital, and Al-Irsyad Hospital Surabaya. Once collected, the data was analyzed using three methods for calculating Qibla direction, namely Spherical Trigonometry, Jean Meeus, and Google Qibla Finder. This article found that the three methods obtained the same results at the Jemursari Islamic Hospital at 294°3'5", at the Surabaya Islamic Hospital at 294° 3'6", and at the Al-Irsyad Surabaya Hospital at 294°3'5 ". However, there is a difference between calculations and field measurements of 2°–7°, including within the Qibla deviation tolerance. It can be concluded that these three methods can accurately determine the Qibla direction in various locations. However, re-checking is required if the measurements exceed the tolerance limits.
Klasifikasi Alzheimer Berdasarkan Data Citra MRI Otak Menggunakan Fcm Dan Anfis Almumtazah, Nilna; Kiromi, Muhammad Sahrul; Ulinnuha, Nurissaidah
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 10 No 3: Juni 2023
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jtiik.2023106826

Abstract

Penyakit Alzheimer adalah kondisi neurologis yang secara bertahap membunuh sel-sel otak dan dapat membahayakan otak secara permanen. Sekitar 50 juta orang di seluruh dunia menderita penyakit Alzheimer atau demensia jenis lain. Jumlah pasien Alzheimer yang banyak mengindikasikan bahwa penting untuk melakukan deteksi dini dengan menggunakan pencitraan MRI otak. Penelitian ini bertujuan untuk mencegah terjadinya alzheimer dengan melakukan deteksi dini sehingga menurunkan kemungkinan meninggalnya pasien alzheimer. Adaptive Neuro-Fuzzy Inference System (ANFIS) adalah metode untuk mengklasifikasikan penyakit Alzheimer. ANFIS menggabungkan ANN dengan FIS sehingga keduanya dapat bekerja sama untuk memberikan hasil yang berarti. Fuzzy C-Means (FCM) 3 cluster pertama-tama akan mensegmentasi data citra MRI untuk menghasilkan citra WM, GM, dan CSF. Citra GM juga akan digunakan untuk metode ekstraksi fitur GLCM. Nilai sensitivitas rata-rata terbaik dicapai pada uji coba k-fold 5 dengan type of membership function trapezoidal, 50 epoch, dan sudut 90°, dengan sensitivitas 90,27%, sesuai dengan hasil uji berganda yang telah dijalankan. Sementara k-fold 10 ditemukan memiliki sudut dan jenis fungsi keanggotaan yang sama pada saat percobaan epoch 150, diperoleh nilai 89,94%. AbstractAlzheimer's disease is a neurological condition that gradually kills brain cells and can harm the brain permanently. About 50 million people worldwide have Alzheimer's disease or another kind of dementia. Given many Alzheimer's patients, it is essential to identify it using brain MRI imaging. This study intends to prevent Alzheimer's instances by performing early detection, lowering the likelihood that Alzheimer's patients would pass away. The Adaptive Neuro-Fuzzy Inference System (ANFIS) is a method for classifying Alzheimer's disease. ANFIS combines ANN with FIS such that the two can work together to provide meaningful outcomes. Fuzzy C-Means (FCM) 3 clusters will first segment the MRI image data to produce the WM, GM, and CSF pictures. The GM image will also be used for the GLCM method of feature extraction. The best average sensitivity value was reached during the k-fold 5 trial with the type of membership function trapezoidal, 50 epoch, and 90° angle, with a sensitivity of 90.27%, according to the results of multiple tests that have been run. While k-fold 10 was found to have the same angle and kind of membership function at the time of the epoch 150 trial, a value of 89.94% was attained.
Implementasi Algoritma Ant Tree Miner Untuk Klasifikasi Jenis Fauna Ardilla, Yunita; Imama Sabilla, Wilda; Ulinnuha, Nurissaidah
Infotekmesin Vol 12 No 2 (2021): Infotekmesin: Juli 2021
Publisher : P3M Politeknik Negeri Cilacap

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35970/infotekmesin.v12i2.616

Abstract

Classification is a field of data mining that has many methods, one of them is decision tree. Decision tree is proven to be able to classify many kinds of data such as image data and time series data. However, there are several obstacles that are often encountered in the decision tree method. Running time required for the execution of this algorithm is quite long, so this study proposed to use the ant tree miner algorithm which is a development algorithm from the C4.5 decision tree. Ant tree miner works by utilizing ant colony optimization in the process of building its tree structure. Use ant colony optimization expected can optimize the tree that will be formed. From the testing that have been carried out, an accuracy of about 95% is obtained in the process of classifying Zoo dataset with the number of ants between 60 - 90.
Analysis of Regency/City Human Development Index Data in East Java Through Grouping Using Hierarchical Agglomerative Clustering Method Alfirdausy, Roudlotul Jannah; Ulinnuha, Nurissaidah; Hafiyusholeh, Moh.
Sistemasi: Jurnal Sistem Informasi Vol 12, No 3 (2023): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v12i3.2959

Abstract

The evaluation of human development is typically done using the Human Development Index (HDI), which measures the level of development in terms of various essential aspects of quality of life. In the case of East Java, the HDI is categorized as high. However. the distribution of HDI among the Regencies/Cities in East Java is still uneven. Therefore, it becomes necessary to cluster the districts/cities based on their HDI and the achievement of each indicator contributing to the HDI. Clustering is a data analysis technique used to group similar data together. Hierarchical agglomerative clustering is one of the methods used for this purpose. The aim of this study is to provide a reference for the government to understand the distribution of characteristic groupings among the districts/cities based on their HDI profiles in East Java. The analysis of East Java's HDI data for 2021 revealed that the best method and cluster was obtained using Average Linkage, with a Cophenetic coefficient value of 0.8105891, resulting in two clusters. The cluster with the highest Silhouette coefficient value of 0.6196077 comprised 34 districts/cities, classified as the low cluster, while the high cluster consisted of four cities/regencies.
Pengandalian Efek Moving Holiday dengan RegARIMA dalam Proses Peramalan Nilai Tukar Rupiah Terhadap US Dollar Tussholikhah, Anissa Nurul Farida; Ulinnuha, Nurissaidah; Utami, Wika Dianita; Intan, Putroue Keumala
Jurnal Matematika Integratif Vol 20, No 1: April 2024
Publisher : Department of Matematics, Universitas Padjadjaran

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24198/jmi.v20.n1.54416.63-80

Abstract

Naik turunnya nilai tukar rupiah merupakan salah satu elemen yang mempengaruhi keadaan ekonomi atau tingkat inflasi suatu negara. Fluktuasi nilai tukar mata uang juga dapat dipengaruhi oleh beberapa hari besar nasional, seperti Hari Raya Idul Fitri, yang memiliki periode yang tidak dapat diprediksi setiap tahunnya. Sehingga perlu dilakukan penelitian ini untuk mengetahui prediksi nilai tukar mata uang dengan mempertimbangkan efek moving holiday dan hasil akan dibandingkan dengan metode prediksi tanpa mempertimbangkan efek moving holiday. Dari banyaknya proses prediksi yang dapat dilakukan, penelitian ini menggunakan metode RegARIMA yang merupakan salah satu perkembangan dari ARIMA dengan pengendalian efek moving holiday. Perbandingan hasil diperoleh dari evaluasi ARIMA dengan RegARIMA, untuk mengetahui sebaik apa model menangani efek moving holiday. Berdasarkan nilai MAPE yang diperoleh, model RegARIMA lebih unggul dari ARIMA. MAPE dari RegARIMA bernilai lebih kecil, yakni sebesar 1.82% dibandingkan ARIMA yang memperoleh MAPE sebesar 2.43%. Sehingga model RegARIMA berhasil dalam menangani efek moving holiday dalam proses prediksi.
Pemodelan Matematika Pada Penyebaran Penyakit Tuberculosis di Provinsi Jawa Timur Sari, Firda Yunita; Maulidya, Rahmania; Hilmi, Moh. Aditya Sirojul; Wahyudi, Sharenada Norisdita; Fransisca, Velicia; Putri, Anindya Maya; Asyhar, Ahmad Hanif; Ulinnuha, Nurissaidah
Journal of Mathematics Education and Science Vol. 7 No. 2 (2024): Journal of Mathematics Education and Science
Publisher : Universitas Nahdlatul Ulama Sunan Giri Bojonegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32665/james.v7i2.2733

Abstract

Tuberculosis yang banyak dikenal dengan sebutan TBC ialah suatu penyakit pernapasan yang menular, dipicu karena adanya Mycobacterium Harituberculosis. TBC menempati peringkat ke-2 setelah COVID-19 sebagai penyakit menular dengan tingkat kematian tertinggi di seluruh dunia. Pada tahun 2020 Indonesia menempati urutan ke-3 dalam kasus TBC tertinggi dibawah India dan Tiongkok. Pada tahun 2021 Provinsi Jawa Timur menjadi peringkat tertinggi ketiga dengan kasus TBC sebesar 466.297 jiwa. Penelitian ini bertujuan untuk mengetahui hasil analisis kestabilan model matematis dan simulasi dari dinamika penyebaran penyakit TBC pada tahun 2021 di Jawa Timur dengan keterbaruan yaitu perbandingan parameter uji coba menggunakan metode runge-kutta orde 4 dan model matematis SITR. Model tersebut merupakan pengembangan dari model SIR dengan menambahkan kompartemen T (treatment). Dalam penelitian didapatkan hasil dari model matematika SITR pada penyakit tuberculosis memperoleh kestabilan titik kesetimbangan endemik dan ketidakstabilan titik kesetimbangan bebas penyakit, hal ini disebabkan bilangan reproduksi dasar kedua parameter , yang menunjukkan bahwasanya Tuberculosis di Provinsi Jawa Timur berpotensi mewabah. Maka diperlukan upaya dalam mencegah dan mengendalikan penyebaran penyakit ini supaya mengurangi dampaknya terhadap kesehatan masyarakat.
Sentiment Analysis of User Reviews for the LinkedIn Application Using Support Vector Machine and Naïve Bayes Algorithm Ulinnuha, Nurissaidah; Pertiwi, Aisyah; Basuki, Athiyah Fitriyani; Kristanti, Beni Tiyas; Haniefardy, Addien; Burhanudin, Muhamad Aris; Satria, Vinza Hedi
IJCONSIST JOURNALS Vol 7 No 1 (2025): September
Publisher : International Journal of Computer, Network Security and Information System

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33005/ijconsist.v7i1.159

Abstract

Social Networking Sites (SNS) have become integral communication platforms for knowledge sharing and professional connections. LinkedIn, a leading professional network, is widely utilized in today's digital era, primarily by professionals and the business community. This research focuses on analyzing user sentiment on LinkedIn through the application of the Support Vector Machine (SVM) and Naive Bayes methods. Understanding user opinions and satisfaction is important, and sentiment analysis serves as a key tool for this purpose. This study is a comparative analysis of Support Vector Machine (SVM) and Naïve Bayes algorithm for classifying user reviews of the LinkedIn application. Drawing on data from Google Play reviews, this research explores a range of user sentiment towards the LinkedIn platform, including positive, negative and neutral reviews. The application of SVM and Naive Bayes algorithms successfully classifies reviews into relevant sentiment categories. Analyzing 2000 review datasets with an 80% training and 20% testing data split, Support Vector Machines demonstrate an 80% accuracy rate, while Naïve Bayes achieves a 70% accuracy rate. The Support Vector Machines (SVM) algorithm has better accuracy than the Naïve Bayes algorithm based on the test scenarios that have been carried out.
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'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