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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Pythagoras: Jurnal Matematika dan Pendidikan Matematika CAUCHY: Jurnal Matematika Murni dan Aplikasi Jurnal Sains Dan Teknologi (SAINTEKBU) BAREKENG: Jurnal Ilmu Matematika dan Terapan Civil and Environmental Science Journal (CIVENSE) Journal of Electronics, Electromedical Engineering, and Medical Informatics
Sa'adah, Umu
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Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Library & Information Science Mathematics Mechanical Engineering Physics Transportation

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Seleksi Nilai Fuzziness Exponent Optimal pada Algoritma Fuzzy c-Means untuk Mengelompokkan Provinsi di Indonesia Berdasarkan Indikator Pembangunan Ekonomi Sa'adah, Umu; Handamari, Endang Wahyu; Andawaningtyas, Kwardiniya; Setyowati, Nur Fitriana
PYTHAGORAS Jurnal Pendidikan Matematika Vol 17, No 2: December 2022
Publisher : Department of Mathematics Education, Faculty of Mathematics and Natural Sciences, UNY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21831/pythagoras.v17i2.54897

Abstract

Pada tahun 2015, PBB merancang 17 Tujuan Pembangunan Berkelanjutan (SDGs) untuk mencapai kesejahteraan manusia pada tahun 2030 dengan mengintegrasikan tiga dimensi pembangunan berkelanjutan: ekonomi, sosial, dan lingkungan. Salah satu faktor yang digunakan untuk menilai keberhasilan sebuah wilayah atau pemerintahan dalam mengelola kesejahteraan dan kemakmuran masyarakat adalah tingkat perekonomian. Untuk mewujudkan kondisi tersebut diperlukan strategi dalam pembangunan pada sektor ekonomi. Penelitian ini bertujuan untuk mengelompokkan Provinsi di Indonesia menjadi 3 klaster berdasarkan indikator pembangunan ekonomi menggunakan algoritma fuzzy c-means. Penentuan 3 klaster dimaksudkan untuk klaster provinsi dengan tingkat pembangunan ekonomi rendah, sedang dan tinggi. Data yang digunakan dalam penelitian ini merupakan data sekunder yang diperoleh dari laman resmi Badan Pusat Statistika. Dengan mengetahui karakteristik provinsi berdasarkan indikator pembangunan ekonomi (IPE), maka pengambil keputusan dapat menyusun strategi perencanaan program pembangunan ekonomi berdasarkan skala prioritas pada masing-masing provinsi. Hasil pengelompokan menunjukkan bahwa Provinsi Papua sangat membutuhkan prioritas pembangunan khususnya dalam sektor ekonomi guna peningkatan indeks pembangunan manusia, angka partisipasi sekolah berusia 7 sampai 12 tahun, angka partisipasi sekolah berusia 13 sampai 15 tahun, angka partisipasi sekolah berusia 16 sampai 18 tahun, sumber air minum yang layak, sumber penerangan listrik, dan sanitasi yang layak, karena indikator-indikator tersebut memiliki nilai rendah.
Hybrid methods to identify ovarian cancer from imbalanced high-dimensional microarray data Sapitri, Ni Kadek Emik; Sa'adah, Umu; Shofianah, Nur
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 2: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i2.pp1173-1182

Abstract

Scientists have used microarray data to identify healthy people and patients with various types of cancer, including ovarian cancer. Ovarian cancer is the most dangerous of all types of cancer that attacks the female reproductive organ. The right combination of methods is needed to identify ovarian cancer from microarray data because that type of data is high-dimensional and imbalanced. This research aims to propose two hybrid methods which are a combination of infinite feature selection (IFS) as features selector with classification and regression tree (CART) as a classifier. IFS can work with two separate scenarios, namely supervised infinite feature selection (SIFS) and unsupervised infinite feature selection (UIFS). This research also compares the performance of the two hybrid methods proposed (SIFS-CART and UIFS-CART) with CART without IFS. The data used is OVA_ovary that has 10937 columns and 1545 rows. The results shows that SIFS-CART achieves maximum performance using 1000 features and UIFS-CART 5000 features. CART without IFS uses all 10935 features. The balanced accuracy results show SIFS-CART can outperform CART without IFS and UIFS-CART. Using less features to get highest balanced accuracy results, SIFS is more effective in performing feature selection on the OVA_ovary dataset compared to UIFS.
Analysis of Binary Logistic Regression Model on Passenger Transportation Mode Selection Between Train and Bus on Malang-Blitar Route Nabila, Nuzulul Laili; Abusini, Sobri; Sa'adah, Umu
Civil and Environmental Science Journal (CIVENSE) Vol. 8 No. 1 (2025)
Publisher : Fakultas Teknik Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21776/ub.civense.2024.008.01.6

Abstract

The transportation dynamics between Malang and Blitar, characterized by significant student and worker mobility, present a complex decision-making landscape for public transportation mode selection. This study employed binary logistic regression to analyze factors influencing passenger choices between trains and buses, utilizing a comprehensive survey of 100 respondents. The research revealed convenience as the most statistically significant factor in transportation mode selection, transcending traditional considerations such as ticket pricing. Despite 80 participants initially expressing a preference for trains, the predictive model suggested a potential scenario where 74% might ultimately choose buses. This counterintuitive finding highlights accessibility, service frequency, boarding ease, and overall travel comfort in transportation decision-making. By quantifying the probabilistic relationships between various variables, the study provides transportation planners with a sophisticated analytical tool for understanding passenger behavior. The findings underscore passengers' willingness to pay a premium for transportation modes offering greater flexibility and comfort, challenging conventional assumptions about cost-driven travel choices. The binary logistic regression model's insights provide valuable guidance for infrastructure development and service optimization in the Malang-Blitar transportation corridor, emphasizing the critical role of convenience in shaping transportation preferences.
Ensemble Bagging in Binary Logistic Regression for Transportation Mode Selection Nabila, Nuzulul Laili; Abusini, Sobri; Sa'adah, Umu
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.v10i2.32241

Abstract

This study examines train versus bus transportation mode choice on the Malang–Blitar route using binary logistic regression combined with ensemble bagging. Data from 100 respondents were analyzed using 80% for training and 20% for testing with k-fold cross-validation. Variables included travel cost differences, time, safety, comfort, and ease of access. Bagging was selected over other ensemble methods due to its effectiveness in reducing variance and overfitting with small datasets. Results showed the standard logistic regression achieved 85% accuracy on test data, while ensemble bagging with 200 replications improved accuracy to 90.83% (confidence interval: 90.379%–91.187%). McNemar’s test confirmed a statistically significant improvement (p 0.01). Under equivalent conditions, 20.6% of respondents preferred trains while 79.4% chose buses. Ease of access emerged as the primary decision factor, outweighing cost and time considerations. The optimal replication number was 200; exceeding 300 replications decreased model performance. This research contributes an optimized ensemble methodology for transportation mode prediction in developing countries, demonstrating that accessibility infrastructure significantly influences passenger preferences over traditional economic factors.
Heart Disease Classification Using Random Forest and Fox Algorithm as Hyperparameter Tuning Masbakhah, Afidatul; Sa'adah, Umu; Muslikh, Mohamad
Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol 7 No 4 (2025): October
Publisher : Department of Electromedical Engineering, POLTEKKES KEMENKES SURABAYA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/jeeemi.v7i4.932

Abstract

Heart disease remains the leading cause of death worldwide, making early and accurate diagnosis crucial for reducing mortality and improving patient outcomes. Traditional diagnostic approaches often suffer from subjectivity, delay, and high costs. Therefore, an effective and automated classification system is necessary to assist medical professionals in making more accurate and timely decisions. This study aims to develop a heart disease classification model using Random Forest, optimized through the FOX algorithm for hyperparameter tuning, to improve predictive performance and reliability. The main contribution of this research lies in the integration of the FOX metaheuristic optimization algorithm with the RF classifier. FOX, inspired by fox hunting behavior, balances exploration and exploitation in searching for the optimal hyperparameters. The proposed RF-FOX model is evaluated on the UCI Heart Disease dataset consisting of 303 instances and 13 features. Several preprocessing steps were conducted, including label encoding, outlier removal, missing value imputation, normalization, and class balancing using SMOTE-NC. FOX was used to optimize six RF hyperparameters across a defined search space. The experimental results demonstrate that the RF-FOX model achieved superior performance compared to standard RF and other hybrid optimization methods. With a training accuracy of 100% and testing accuracy of 97.83%, the model also attained precision (97.83%), recall (97.88%), and F1-score (97.89%). It significantly outperformed RF-GS, RF-RS, RF-PSO, RF-BA, and RF-FA models in all evaluation metrics. In conclusion, the RF-FOX model proves highly effective for heart disease classification, providing enhanced accuracy, reduced misclassification, and clinical applicability. This approach not only optimizes classifier performance but also supports medical decision-making with interpretable and reliable outcomes. Future work may involve validating the model on more diverse datasets to further ensure its generalizability and robustness.
The Classification of Insurance Claim Risk Using the Multilayer Perceptron Method Handamari, Endang Wahyu; Sa'adah, Umu; Arifin, Maulana Muhamad
SAINTEKBU Vol. 17 No. 01 (2025): Vol. 17 (01) January 2025
Publisher : KH. A. Wahab Hasbullah University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32764/saintekbu.v17i01.5178

Abstract

Policyholders purchase insurance policies to protect themselves or their assets from potential financial risks in the future. Insurance guarantees that if an event covered by the policy occurs, the insurance company will provide compensation according to the agreed terms. Insurance companies conduct risk assessments for each policyholder to determine the premium that must be paid, making it essential to classify risk categories accurately. The Multilayer Perceptron (MLP) is one method used for classification problems. It is a machine learning algorithm belonging to the family of artificial neural networks. MLP is a flexible algorithm that can solve various classification problems, including those with complex features and non-linear relationships between input and output variables. The result of this research is the development and implementation of a Multilayer Perceptron method to classify risk categories. The evaluation of the Multilayer Perceptron model for risk classification shows satisfactory performance. Based on the classification report from training and test data, the model does not exhibit overfitting or underfitting.
MILP Model Solution Steps: Implementation of Big M Simplex and Branch and Bound in the Coffee Supply Chain Islamiyah, Ananda Hans; Sa'adah, Umu; Karim, Corina
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.v10i2.35380

Abstract

This research aims to develop a Mixed-Integer Linear Programming (MILP) model to optimize the distribution of coffee from producing sub-districts to storage warehouses, and subsequently to destination markets in Malang Regency during the 2020–2024 period. This model minimizes total logistics costs, which include distribution, shipping, and warehouse operating costs. The Big M Simplex method is used to handle logical constraints in the model, while the Branch and Bound algorithm is used to determine the operational state of the warehouse as a binary variable. The optimization results show that the warehouse is actively operated every year, with a distribution flow capable of meeting all market demands. The optimal purpose function value obtained is IDR 43,265,867,761,500,-. for five years. This shows that the combination of MILP, Big M, and Branch and Bound is effective as a decision-making framework in the optimization of the agribusiness sector's supply chain. This model considers temporal, spatial, and operational cost aspects, so it can be applied practically to data-driven distribution planning. This research contributes to the development of a relevant structured optimization approach for multi-period supply chain systems and discrete decisions
VALUE AT RISK ESTIMATION FOR STOCK PORTFOLIO USING THE ARCHIMEDEAN COPULA APPROACH Saifullah, Mohammad Dicky; Sa'adah, Umu; Andawaningtyas, Kwardiniya; Handamari, Endang Wahyu
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 18 No 3 (2024): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol18iss3pp1779-1790

Abstract

Investment is one of the many ways to achieve future profits. One form of investment that is widely made is stocks. The return obtained in investing in stocks is potentially higher than other investment alternatives, but the risks borne are amplified, so it is necessary to analyze these risks that may occur. In this study, the Archimedean copula method is used to estimate the Value at Risk on shares of PT Bank Rakyat Indonesia Tbk (BBRI) and PT Telekomunikasi Indonesia Tbk (TLKM) for the period September 1, 2021, to August 31, 2023. The stock data is used to determine the Archimedean copula model and calculate the estimated value of Value at Risk (VaR) on the stock return portfolio using the Archimedean copula approach. The Archimedean copula models used are the Clayton copula model, Gumbel copula, and Frank copula. Of the three Archimedean copula models, the best model was selected by looking at the largest Maximum Likelihood Estimation (MLE) value. In this study, the log-likelihood value of Clayton copula is 7.958, Gumbel copula is 6.663, and Frank copula is 8.398. Therefore, Frank copula is the best Archimedean copula model with the largest log-likelihood value of 8.398 for the said data. Then the VaR estimation is done with the Frank copula model. The Value at Risk estimation results based on the Frank copula model show maximum loss rates of -0.0277 at the 90% confidence level, -0.0363 at the 95% confidence level, and -0.0516 at the 99% confidence level.
IDENTIFYING IMPORTANT GENES IN OVARIAN CANCER FROM HIGH-DIMENSIONAL MICROARRAY DATA USING SIFS-CART METHOD Sapitri, Ni Kadek Emik; Sa'adah, Umu; Shofianah, Nur
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 18 No 3 (2024): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol18iss3pp1909-1918

Abstract

Ovarian cancer can be identified from microarray data using machine learning. Many studies only focus on improving the machine learning classification algorithms to achieve higher performance. The purpose of classification is not only to obtain high performance but also to seek new knowledge from the results. This research focuses on both. By using a hybrid Supervised Infinite Feature Selection (SIFS) method with Classification and Regression Tree (CART) or SIFS-CART, this research aims to predict ovarian cancer and identify potential genes for ovarian cancer cases. The data used is the OVA_ovary dataset. SIFS in the best SIFS-CART model reduced 10935 genes in the initial OVA_ovary dataset to 1000 genes. Then, CART was built with these 1000 genes. Based on the balanced accuracy (BA) metric for imbalanced microarray data, the best SIFS-CART model achieves 85.7% BA in training and 83.2% in testing. The optimal CART in the best SIFS-CART model only needs four genes from 1000 selected genes to build it. Those genes are STAR, WT1, PEG3, and ASPN. Based on studies of several pieces of literature in the medical field, it can be concluded that STAR, WT1, and PEG3 play an important role in ovarian cancer cases. However, the relationship between ASPN and ovarian cancer in more detail has not been studied by medical researchers.
Co-Authors Arifin, Maulana Muhamad Endang Wahyu Handamari Islamiyah, Ananda Hans Karim, Corina Kwardiniya Andawaningtyas Masbakhah, Afidatul Mohamad Muslikh Nabila, Nuzulul Laili Saifullah, Mohammad Dicky Sapitri, Ni Kadek Emik Setyowati, Nur Fitriana Shofianah, Nur Sobri Abusini