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All Journal Jurnal Kedokteran Brawijaya CAUCHY: Jurnal Matematika Murni dan Aplikasi El-QUDWAH Jurnal Ilmiah Biosaintropis (Bioscience-Tropic) Indonesian Journal of Artificial Intelligence and Data Mining Bubungan Tinggi: Jurnal Pengabdian Masyarakat Jurnal Cahaya Mandalika Jurnal Syntax Fusion : Jurnal Nasional Indonesia Jurnal Riset Mahasiswa Matematika
Usman Pagalay
Universitas Islam Negeri Maulana Malik Ibrahim Malang
Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Education Engineering Environmental Science Languange, Linguistic, Communication & Media Mathematics Medicine & Pharmacology Social Sciences Other

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Dinamika Model Matematika Reaksi T-Helper Tribhuana, Chilvia; Pagalay, Usman; Susanti, Elly
Jurnal Riset Mahasiswa Matematika Vol 1, No 5 (2022): Jurnal Riset Mahasiswa Matematika
Publisher : Mathematics Department, Maulana Malik Ibrahim State Islamic University of Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18860/jrmm.v1i5.14477

Abstract

T cells are a major component of the human immune system. These T cells have a number that varies depending on the body's immune response when fighting bacteria or viruses. However, the condition of excess immune cells in the body can also be dangerous. Theoretical studies on the dynamics of T-Helper cells in the body are needed to get the right simulation in treating patients without conducting medical tests on every patient on a daily basis. This study discusses the dynamics of the mathematical model of the T-Helper reaction with the influence of antigen and IL-2. From this study, two equilibrium points were obtained, namely disease-free equilibrium and endemic equilibrium. The use of parameter values from the experimental results shows that the disease-free equilibrium point is locally unstable, while the endemic equilibrium point is locally stable. The numerical simulation showed that the antigen increased from 1st day to the highest value at 0.926 on the 11th day until on the 20th day it started to be constant towards at the value  which is the antigen could be activate the resting T-Helper. The process of activating T-Helper, create IL-2 which can stimulating the proliferation and activity of T-Helper cells, so they can divide the activated cell of T-Helper into two memory cells.
Analisis Dinamik Model Penyebaran Tumor Otak dengan Respon Sel Imun Anggraini, Resti Dwi; Pagalay, Usman; Nashichuddin, Achmad
Jurnal Riset Mahasiswa Matematika Vol 1, No 3 (2022): Jurnal Riset Mahasiswa Matematika
Publisher : Mathematics Department, Maulana Malik Ibrahim State Islamic University of Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18860/jrmm.v1i3.14339

Abstract

The brain tumor distribution model with immune cell response is in the form of a non-linear system of ordinary differential equations with five equations. Each equation describes how immune cells in the brain, namely macrophages ( ), CD8+ T cells ( ), TGF-  cytokines ( ) and IFN-  ( ) cytokines interact with tumor cells, namely glioma cells ( ). From the calculation of the equilibrium point, the tumor cell-free conditions (DFE) and the endemic conditions (END) were obtained, in which tumor cells in long-term conditions were always present in the patient's brain. By using certain parameter values, it can be illustrated that the END condition is locally asymptotically stable while the DFE condition is locally unstable. This indicates that brain tumor cells, namely glioma cells ( ) will increase to their maximum value of 882650 cells and remain at that number from day 1000 onwards.
Analisis Dinamik Model Sel Kanker Prostat dengan Terapi Vaksin Kuratif Mawaddah, Siti Sakinah; Pagalay, Usman; Nasichuddin, Achmad
Jurnal Riset Mahasiswa Matematika Vol 3, No 2 (2023): Jurnal Riset Mahasiswa Matematika
Publisher : Mathematics Department, Maulana Malik Ibrahim State Islamic University of Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18860/jrmm.v3i2.22456

Abstract

                Prostate cancer is a type of cancer that occurs in men and requires an effective therapeutic approach. Treatment of prostate cancer depends on the stage at diagnosis. In advanced stages of prostate cancer can be treated with hormone therapy such as chemotherapy which is then followed by vaccine therapy which aims to help increase the body's immune system response to prostate cancer cells. This model consists of a system of ordinary differential equations with five variables used, including androgen-dependent prostate cancer cells, androgen-independent prostate cancer cells, dendritic cells, effector cells, and curative vaccines. Then two equilibrium point conditions are produced, when there is no vaccine  for disease free conditions  and endemic conditions , then when the vaccine  there are three equilibrium conditions namely disease free , side effects  and local existence between prostate cancer cells with vaccine . The results of the stability analysis for each equilibrium point show that when , the condition  is global asymptotic, while the condition  is stable because the eigenvalue is negative. When  for the condition  it is unstable because the two roots are positive, then for the condition  it is global asymptotic and for the condition  it is asymptotically local because all the eigenvalues are negative. The numerical simulations of equilibrium points obtained using the fourth order runge-kutta method according to different q parameter values show that the larger the dendritic cells and effector cells activated, the greater the vaccine that enters the body, resulting in immune cells that will fight prostate cancer cells.
Analisis Perbandingan Metode K-Nn Dan Decision Tree Dalam Klasifikasi Kenyamanan Thermal Bangunan Yusuf Akbar, Ilham; Faisal, Muhammad; Pagalay, Usman
Jurnal Syntax Fusion Vol 2 No 06 (2022): Jurnal Syntax Fusion: Jurnal Nasional Indonesia
Publisher : CV RIFAINSTITUT

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54543/fusion.v2i06.197

Abstract

Thermal comfort and good air quality can have a positive influence on a person's health and activities. Human knowledge will determine the right quality for a building or room that can only be used at a minimum first if it is based on the value of temperature and humidity standard measuring instruments that display raw data so that a system is needed that makes it easy for humans to determine thermal building comfort. Most systems that discuss thermal comfort revolve around temperature and humidity monitoring still do not use a machine learning model to maximize data analysis. In this study, a comparison of machine learning models is designed that is able to provide a classification to predict the category or label given to a data set consisting of the thermal comfort variable. The output produced in this comparison is the result of labeling predictions from 3 temperature comfort levels by testing 30% praise data and 70% training data. From the results of the segmentation accuracy level using K-Nearest Neighbor, the accuracy reaches 100% with the highest accuracy at a value of K = 1, while the Decision tree comparison method also gets an accuracy value that reaches 100%, this shows that thermal comfort can be applied in the classification machine learning method
Solusi Eksak Model Linier Injeksi Insulin Dalam Tubuh Putri Dianwati, Diajeng Maharani; Pagalay, Usman
Jurnal Riset Mahasiswa Matematika Vol 4, No 1 (2024): Jurnal Riset Mahasiswa Matematika
Publisher : Mathematics Department, Maulana Malik Ibrahim State Islamic University of Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18860/jrmm.v4i2.30081

Abstract

This study the insulin injection model in the body has three parts, namely the number of insulin concentrations in the non-monomeric state , the number of h concentrations in the monomeric state , and the number of concentrations in plasma . This study aims to find the exact solution of the insulin injection model in the body and to find out the amount of insulin concentration injected. So that it can provide an understanding of the mechanism of insulin injection absorption in the body. The steps to find the exact solution of the model can be done by the variable separation method and then finding the integration factor and direct integration until the exact solution of the model is found. The results of this study indicate that the linear model of insulin injection in the body is influenced by the amount of insulin dose injected, then this will result in the absorption of insulin contained in the monomeric, non-monomeric and plasma states. The absorption of insulin concentration is also influenced by the magnitude of the factor on the absorption rate, the transfer rate from the subcutaneous tissue to the peripheral compartment and the rate of elimination from the body
Random Forest Classification of Infant Mortality Rate in Indonesia: A Gini-Based Analysis Karisma, Ria Dhea Layla Nur; Pagalay, Usman; Khudzaifah, Muhammad
CAUCHY: Jurnal Matematika Murni dan Aplikasi Vol 10, No 2 (2025): CAUCHY: JURNAL MATEMATIKA MURNI DAN APLIKASI
Publisher : Mathematics Department, Universitas Islam Negeri Maulana Malik Ibrahim Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18860/cauchy.v10i2.29508

Abstract

One of the indicators used to measure the success of development programs in Indonesia is the Infant Mortality Rate (IMR). IMR is a sensitive indicator and represents maternal and child health problems in a country. Random forest is an ensemble machine learning method that combines multiple decision trees using bootstrap aggregation. It aims to improve the prediction accuracy and robustness of the model. In addition, it can be applied to both case classification and regression because it can handle high-dimensional and complex cases and non-linear relationships. In this study, Random Forest is used to solve the classification of IMR cases in Indonesia, making them easy to interpret and related to policy relevance. The aim of this study is to predict infant mortality factors using the Gini Index to determine which variables need to be improved. The Gini Index is used to identify key factors, enabling targeted policy interventions. It highlights the most influential variables, helping policymakers focus on areas that require improvement for more effective outcomes. The evaluation model in this study uses out-of-bag estimation and k-fold validation. The model achieves an overall accuracy of 99.97%, with a sensitivity of 99.87% and specificity of 100\%, indicating excellent performance. The most important variables in this study are breastfeeding, type of birth (single and twin), and birth weight of the baby. The parent node in IMR is breastfeeding, where live IMRs that are breastfed have a greater chance of survival than dead IMRs that are not breastfed.
Cross-Dataset Evaluation of Support Vector Machines: A Reproducible, Calibration-Aware Baseline for Tabular Classification Syafi'ah, Nurus; Jamhuri, Mohammad; Pranata, Farahnas Imaniyah; Kusumastuti, Ari; Juhari, Juhari; Pagalay, Usman; Khudzaifah, Muhammad
Jurnal Riset Mahasiswa Matematika Vol 4, No 6 (2025): Jurnal Riset Mahasiswa Matematika
Publisher : Mathematics Department, Maulana Malik Ibrahim State Islamic University of Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18860/jrmm.v4i6.33438

Abstract

Support Vector Machines (SVMs) remain competitive for small and medium-sized tabular classification problems, yet reported results on benchmark datasets vary widely due to inconsistent preprocessing, validation, and probability calibration. This paper presents a calibration-aware, cross-dataset benchmark that evaluates SVMs against classical baselines—Logistic Regression, Decision Tree, and Random Forest—under leakage-safe pipelines and statistically principled protocols. Using three representative binary datasets (Titanic survival, Pima Indians Diabetes, and UCI Heart Disease), we standardize imputation, encoding, scaling, and nested cross-validation to ensure comparability. Performance is assessed not only on discrimination metrics (accuracy, precision, recall, F1, PR--AUC) but also on probability reliability (Brier score, Expected Calibration Error) and threshold optimization. Results show that tuned RBF--SVMs consistently outperform Logistic Regression and Decision Trees, and perform comparably to Random Forests. Calibration (Platt scaling, isotonic regression) substantially reduces error and improves decision quality, while domain-specific features enhance Titanic prediction. By embedding all steps in a transparent, reproducible protocol and validating across multiple datasets, this study establishes a rigorous methodological baseline for SVMs in tabular binary classification, providing a reference point for future machine learning research.
PREDIKSI TINGKAT KEPERCAYAAN MASYARAKAT TERHADAP PILPRES 2024 MENGGUNAKAN TF-IDF DAN BOW MENGGUNAKAN METODE SVM Mustaqim, Eka Rifut Nur; Pagalay, Usman; Crysdian, Cahyo
Jurnal Cahaya Mandalika ISSN 2721-4796 (online) Vol. 5 No. 1 (2024): Jurnal Cahaya Mandalika
Publisher : Institut Penelitian Dan Pengambangan Mandalika Indonesia (IP2MI)

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

Abstract

Dalam era modern ini, dunia maya telah menjadi salah satu aspek yang tak terpisahkan dari kehidupan sehari-hari kita. Dunia maya, atau internet, adalah hasil dari kemajuan teknologi informasi yang telah merevolusi dunia selama beberapa dekade terakhir. Namun, lebih dari sekadar teknologi, ini telah menjadi sebuah ekosistem yang hidup, dihuni oleh miliaran orang yang terhubung, menciptakan dan mengonsumsi informasiPrediksi pada pemanfaatan big data ini dengan cara kerja mencari dan mengolah data dari segala bentuk ekspresi atau keadaan yang sedang atau telah dialami seseorang user yang diluangkan dalam bentuk teks kedalam media sosial, Prediksi tidak harus memberikan jawaban secara pasti kejadian yang akan terjadi, melainkan berusaha untuk mencari jawaban sedekat mungkin yang akan terjadi.Berdasarkan pada permasalahan yang telah dibahas beberapa teknik yang paling umum dan sering digunakan dalam feature extraction TF-IDF dan BOW, dikarenakan kedua teknik tersebut sangat bersaing serta berperan baik dan sama-sama digunakan untuk merepresentasikan numerik dari data teks serta memiliki kekurangan dan kelebihan masing masing. Pada penelitian kali ini akan membandingkan kedua metode tersebut yan dipadukan dengan menggunakan metode SVM, untuk model penelitian TF-IDF dengan menggunakan metode SVM mendapatkan hasil Accurasi sebesar 85%, hasil nilai precission sebesar 84%, hasil Recall sebesar 83% dan untuk hasil F1-Score sebesar 83%, sedangkan penelitian menggunakan teknik BOW dengan metode SVM mendapatkan hasil Accurasi sebesar 84%, hasil nilai precission sebesar 79%, hasil Recall sebesar 89% dan untuk hasil F1-Score sebesar 83%.
Co-Authors Ach. Nashichuddin Alfi Nur Rochmatin Alvi Milliana Anggraini, Resti Dwi Anita Ambarsari Ari Kusumastuti Arina Nur Laila Arrofiqi, Muhammad Rosyid Badruz Zamanil Charis Cahyo Crysdian Chilvia Tribhuana Dwi Safitri Dwi Safitri, Dwi Elly Susanti Elly Susanti Erfan Ainul Yakin Fitriyani Fitriyani Heni Widayani Icha Zakiyya Nafisah Roza Juhari Juhari, Juhari Junik Rahayu Khusnul Khamidiyah, Khusnul Laila, Arina Nur Luluk Ianatul Afifah, Luluk Ianatul M Abbas Arriziq Mawaddah, Siti Sakinah Miftakhul Rosidah Mohammad Jamhuri Mokhamad Amin Hariyadi Muhammad Faisal Muhammad Farid Wadjdi Muhammad Khudzaifah Mustaqim, Eka Rifut Nur Nasichuddin, Achmad Pranata, Farahnas Imaniyah Putri Dianwati, Diajeng Maharani Rahmi Annisa Resti Dwi Anggraini Ria Dhea Layla Nur Karisma Ririen Kusumawati Roihatul Mutiah Rosidah, Miftakhul Syafi'ah, Nurus Tribhuana, Chilvia Wirda Anggraini Yusuf Akbar, Ilham