Articles
<![CDATA[CART Classification on Ordinal Scale Data with Unbalanced Proportions using Ensemble Bagging Approach ]]>
<![CDATA[Arini, Luthfia Hanun Yuli]]>;
<![CDATA[Solimun, Solimun]]>;
<![CDATA[Efendi, Achmad]]>;
<![CDATA[Ullah, Mohammad Ohid]]>
<![CDATA[ JTAM (Jurnal Teori dan Aplikasi Matematika) Vol 8, No 2 (2024): April ]]>
Publisher : <![CDATA[Universitas Muhammadiyah Mataram ]]>
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DOI: 10.31764/jtam.v8i2.20201
<![CDATA[CART is one of the algorithms in data exploration techniques with decision tree techniques. Unbalanced class proportions in the classification process can cause classification results of minor data to be incorrect. One way to overcome the problem of data imbalance is to use an ensemble bagging algorithm. The bagging algorithm utilizes the resampling method to carry out classification so that it can reduce bias in imbalanced data. The data used is secondary data from Fernandes and Solimun's 2023 research report. The number of sample are 100 respondents that has been valid and reliable. The sample for this research was mothers with toddlers in Wajak village, Malang Regency. The results showed that the ensemble bagging CART method is better at overcoming the problem of imbalance in the proportion of classes with a performance value of accuracy, sensitivity, specificity, and F1-Score values of 85%, 94.1%, 66.7%, and 78%. This research is limited to the Sumberputih Village area. So, the results of this research are only representative for the Wajak District area. ]]>
<![CDATA[The Application of Truncated Spline Semiparametric Path Analysis on Determining Factors Influencing Cashless Society Development ]]>
<![CDATA[Pramaningrum, Dea Saraswati]]>;
<![CDATA[Fernandes, Adji Achmad Rinaldo]]>;
<![CDATA[Iriany, Atiek]]>;
<![CDATA[Solimun, Solimun]]>
<![CDATA[ JTAM (Jurnal Teori dan Aplikasi Matematika) Vol 8, No 2 (2024): April ]]>
Publisher : <![CDATA[Universitas Muhammadiyah Mataram ]]>
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DOI: 10.31764/jtam.v8i2.19913
<![CDATA[Semiparametric path analysis is a combination of parametric and nonparametric path analysis. Semiparametric path analysis is used when there are partially nonlinear and unknown patterns of relationships. One approach to semiparametric pathways is truncated spline. Truncated spline approach tends to search for their own estimation of regression functions according to the data. This is because in the truncated spline there are knot points, which are intersection points that indicate changes in data behavior patterns. Truncated spline semiparametric path analysis will be applied to this study to determine the variables that have a significant effect on the development of the Cashless Society so that the result can be used as a reference for banks and the government in maximizing non-cash-based community development. The data used is the result of a questionnaire with 100 respondents of mobile banking users in Jakarta and will be analyzed using R Studio. Based on the results, it was found that the optimal knot point in the truncated spline function is 3 with many knots is 1, thus dividing the condition of digitizing electronic money into 2 regimes. It was concluded that the product and digitalization of electronic money had a significant effect on the development of cashless society where the modeling obtained could explain 83.87548% of the data. However, when electronic digitalization increases through the value of knot points, the development of cashless society tends to stagnate. This could be due to people who are not ready when the condition of digitizing electronic money is increasingly sophisticated because the available electronic money features are increasingly complex. Therefore, it is important for banks to pay attention to the sophistication of electronic money features provided to customers and adjust the target market so that customers are more accustomed and comfortable to use electronic money in the future.]]>
<![CDATA[Basic Statistics for Farm Data Processing at PT. Kembang Joyo Sriwijaya ]]>
<![CDATA[Nurjannah, Nurjannah]]>;
<![CDATA[Solimun, Solimun]]>;
<![CDATA[Amaliana, Luthfatul]]>;
<![CDATA[Mudjiono, Mudjiono]]>
<![CDATA[ Journal of Innovation and Applied Technology Vol 11, No 1 (2025) ]]>
Publisher : <![CDATA[Lembaga Penelitian dan Pengabdian Kepada Masyarakat Universitas Brawijaya ]]>
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DOI: 10.21776/ub.jiat.2024.010.002.04
<![CDATA[Basic statistical analysis plays an important role in data processing in the farm industry including production, animal health and feed efficiency. PT Kembang Joyo, a bee and sheep farming company, needs to use statistical analysis to increase productivity and maintain product quality. The “Basic Statistics Training Activity in Farm Data Processing” at PT Kembang Joyo Sriwijaya includes material on data processing and analysis methods, including basic concepts of data analysis, descriptive statistics and inferential statistics, and practice in using MS Excel to process data using pivot table functions and data analysis add-ons. The evaluation of this community service activity is carried out on two aspects, the understanding of the learning materials and the participant satisfaction. The pre-post test analysis show that in general, the participants' understanding of the learning material increased significantly after the training. The satisfaction index also shows "very good" results with a service quality of "A".]]>
<![CDATA[PERFORMANCE OF NEURAL NETWORK IN PREDICTING MENTAL HEALTH STATUS OF PATIENTS WITH PULMONARY TUBERCULOSIS: A LONGITUDINAL STUDY ]]>
<![CDATA[Rahmanda, Lalu Ramzy]]>;
<![CDATA[Fernandes, Adji Achmad Rinaldo]]>;
<![CDATA[Solimun, Solimun]]>;
<![CDATA[Ramifidiosa, Lucius]]>;
<![CDATA[Zamelina, Armando Jacquis Federal]]>
<![CDATA[ MEDIA STATISTIKA Vol 16, No 2 (2023): Media Statistika ]]>
Publisher : <![CDATA[Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro ]]>
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DOI: 10.14710/medstat.16.2.124-135
<![CDATA[Comorbidity between pulmonary tuberculosis and mental health status requires effective psychiatric treatment. This study aims to predict anxiety and depression levels in patients with pulmonary tuberculosis and consider future mental health treatment for patients. A sample of 60 pulmonary tuberculosis patients in Malang were involved and evaluated longitudinally every two weeks over 13 periods. In this study, we use the Generalized Neural Network Mixed Model (GNMM) to obtain better results in predicting anxiety and depression levels in patients with pulmonary tuberculosis and compare the results with the Generalized Linear Mixed Model (GLMM). The flexibility of GLMM in modeling longitudinal data, and the power of neural network in performing a prediction makes GNMM a powerful tool for predicting longitudinal data. The result shows that neural network's prediction performance is better than the classical GLMM with a smaller MSPE and fairly accurate prediction. The MSPEs of the three compared models: 1-Layer GNMM, 2-Layer, and GLMM, respectively are 0.0067, 0.0075, 0.0321 for the anxiety levels, and 0.0071, 0.0002, and 0.0775 for the depression levels. Furthermore, future research needs to investigate the data with a larger sample size or high dimensional data with large network architectures to prove the robustness of GNMM.]]>
<![CDATA[Development of Accuracy for the Weighted Fuzzy Time Series Forecasting Model Using Lagrange Quadratic Programming ]]>
<![CDATA[Rozy, Agus Fachrur]]>;
<![CDATA[Solimun, Solimun]]>;
<![CDATA[Wardhani, Ni Wayan Surya]]>
<![CDATA[ JTAM (Jurnal Teori dan Aplikasi Matematika) Vol 7, No 4 (2023): October ]]>
Publisher : <![CDATA[Universitas Muhammadiyah Mataram ]]>
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DOI: 10.31764/jtam.v7i4.16783
<![CDATA[Limitation within the WFTS model, which relies on midpoints within intervals and linguistic variable relationships for assigning weights. This reliance can result in reduced accuracy, especially when dealing with extreme values during trend to seasonality transformations. This study employs the Weighted Fuzzy Time Series (WFTS) method to adjust predictive values based on actual data. Using Lagrange Quadratic Programming (LQP), estimated weights enhance the WFTS model. MAPE assesses accuracy as the model analyzes monthly IHSG closing prices from January 2017 to January 2023.The MAPE value of 0.61% results from optimizing WFTS with LQP. It utilizes a deterministic approach based on set membership counts in class intervals, continuously adjusting weights during fuzzification, minimizing the deviation between forecasted and actual data values.The Weighted Fuzzy Time Series Forecasting Model with Lagrange Quadratic Programming is effective in forecasting, indicated by a low MAPE value. This method evaluates each data point and adjusts weights, offering reliable investment insights for IHSG strategies..]]>
<![CDATA[Peningkatan Akurasi Metode Weighted Fuzzy Time Series Forecasting Menggunakan Algoritma Evolusi Differensial dan Fuzzy C-Means ]]>
<![CDATA[Rozy, Agus Fachrur]]>;
<![CDATA[Solimun, Solimun]]>;
<![CDATA[Wardhani, Ni Wayan Surya]]>
<![CDATA[ Jurnal Teknologi Informasi dan Ilmu Komputer Vol 10 No 5: Oktober 2023 ]]>
Publisher : <![CDATA[Fakultas Ilmu Komputer, Universitas Brawijaya ]]>
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DOI: 10.25126/jtiik.2023107505
<![CDATA[Prediksi adalah suatu pendekatan yang digunakan untuk mengantisipasi ketidakpastian masa depan. Metode prediksi konfensional terkendala pada penyesuaian data terhadap asumsi yang digunakan sehingga diperlukan Metode Weighted Fuzzy Time Series. Meskipun metode WFTS telah terbukti efektif terdapat tantangan dalam meningkatkan akurasi peramalan yang dihasilkan. Dua teknik yang sering digunakan dalam konteks ini adalah Algoritma Evolusi Differensial (ED) dan Fuzzy C-Means (FCM). Data yang digunakan pada penelitian ini adalah Jakarta Islamic Index (JKII) per bulan dari bulan Agustus 2018 hingga Juli 2023. Data yang digunakan adalah data sekunder yang diperoleh dari situs www.yahoo.finance.com. Analisis dilakukan untuk meningkatkan akurasi dari metode peramalan WFTS dengan klasifikasi FCM dan proses optimalisasi menggunakan hasil forecasting dengan Algoritma Evolusi Diffensial (DE).Hasil klasifikasi dengan Fuzzy C-Means, ditemukan 7 klaster dengan jumlah keanggotaan yang berbeda. Perhitungan nilai peramalan dilkakukan dengan defuzzyfikasi dengan mengubah variabel linguistik menjadi bilangan real. Proses transformasi ini melibatkan perkalian antara bobot yang diperoleh dari estimasi Fuzzy C Means dengan nilai titik tengah pada setiap cluster. Proses optimalisasi hasil dilakukan dengan menggunakan algoritma DE dapat meningkatkan akurasi dari forecasting. Kesimpulan yang didapat yaitu algoritma evolusi differensial dapat meningkatkan akurasi forecasting dari metode weighted fuzzy time series dengan kombinasi pembentukan kelas interval menggunakan metode fuzzy c-means. Hal ini dikarenakan nilai MAPE yang dihasilkan dari algoritma evolusi differensial lebih kecil daripada model weighted fuzzy time series. Abstract Prediction is a form of approach in anticipating future uncertainties. Conventional prediction methods encounter difficulties in adapting data with the assumptions used, necessitating the application of the Weighted Fuzzy Time Series (WFTS) method. Although the WFTS method has proven effective, there are challenges in improving the accuracy of the generated forecasts. There are two commonly applied approaches: the Differential Evolution (DE) algorithm and Fuzzy C-Means (FCM). The data used in this research is the Jakarta Islamic Index (JKII) on a monthly basis from August 2018 to July 2023. The information collected is secondary data obtained from the website www.yahoo.finance.com. The analysis conducted involves performing FCM classification to form interval classes and optimizing the forecasting results of the WFTS method with DE. The Fuzzy C-Means classification resulted in finding 7 clusters with different membership counts. Forecasting values are calculated through defuzzification by converting linguistic variables into real numbers. This transformation process involves multiplying the weights obtained from the Fuzzy C-Means estimation with the mid-point values of each cluster.The optimization process is performed using the DE algorithm. The research findings conclude that the use of the differential evolution algorithm improves the accuracy of the forecasting from the Weighted Fuzzy Time Series method with the approach of combining interval class formation through the Fuzzy C-Means method. The DE algorithm works by seeking the best solution in a complex parameter space through iterations and performance evaluations, thereby significantly enhancing the performance of the forecasting model.]]>
<![CDATA[Bootstrap Resampling in Gompertz Growth Model with Levenberg–Marquardt Iteration ]]>
<![CDATA[Gultom, Fandi Rezian Pratama]]>;
<![CDATA[Solimun, Solimun]]>;
<![CDATA[Nurjannah, Nurjannah]]>
<![CDATA[ JTAM (Jurnal Teori dan Aplikasi Matematika) Vol 6, No 4 (2022): October ]]>
Publisher : <![CDATA[Universitas Muhammadiyah Mataram ]]>
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DOI: 10.31764/jtam.v6i4.8617
<![CDATA[Soybean plants have limited growth with a planting period of 12 weeks, which causes the observed sample to be very small. A small sample of soybean plant growth observations can be bias causes in the conclusion of prediction results on soybean plant growth. The purpose this study is to apply the bootstrap resampling technique in Gompertz growth model which overcomes residual distribution with small samples, the research data was taken from soybean plant growth in four varieties with four spacing treatments, five replications and twelve weeks (long planting period). Gompertz growth model uses nonlinear least squares method in estimating parameters with Levenberg–Marquardt iteration. The value of the Gompertz model after resampling bootstrap has no significant difference. The adjusted R2 value of 0.96 is close to 1. This means that the total diversity of plant heights can be explained by the Gompertz model of 96 percent. Judging from the graph of predictions of soybean plant growth before resampling and after resampling coincide with each other it can also be seen in the initial growth values before resampling 14, 05 and 14.18, the maximum growth values are 55.13 and 55.60. Bootsrap resampling technique can overcome residual normality in the Gompertz growth model, but does not change the information in the initial data.]]>
<![CDATA[Principal Component Regression Modelling with Variational Bayesian Approach to Overcome Multicollinearity at Various Levels of Missing Data Proportion ]]>
<![CDATA[Balqis, Nabila Azarin]]>;
<![CDATA[Astutik, Suci]]>;
<![CDATA[Solimun, Solimun]]>
<![CDATA[ JTAM (Jurnal Teori dan Aplikasi Matematika) Vol 6, No 4 (2022): October ]]>
Publisher : <![CDATA[Universitas Muhammadiyah Mataram ]]>
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DOI: 10.31764/jtam.v6i4.10223
<![CDATA[This study aims to model Principal Component Regression (PCR) using Variational Bayesian Principal Component Analysis (VBPCA) with Ordinary Least Square (OLS) as a method of estimating regression parameters to overcome multicollinearity at various levels of the proportion of missing data. The data used in this study are secondary data and simulation data contaminated with collinearity in the predictor variables with various missing data proportions of 1%, 5%, and 10%. The secondary data used is the Human Depth Index in Java in 2021, complete data without missing values. The results indicate that the multicollinearity in secondary and original data can be optimally overcome as indicated by the smaller standard error value of the regression parameter for the PCR using VBPCA method which is smaller and has a relative efficiency value of less than 1. VBPCA can handle the proportion of missing data to less than 10%. The proportion of missing data causes information from the original variable to decrease, as evidenced by immense MAPE value and the parameter estimation bias that gets bigger. Then the cross validation (Q^2 ) value and the coefficient of determination (adjusted R^2 ) are get smaller as the proportion of missing data increases. ]]>
<![CDATA[Penguatan Prestasi OSN Matematika SMP dan SMA melalui Kolaborasi Guru dan Siswa: Studi pada SMA Sumber Putih Kabupaten Malang ]]>
<![CDATA[Solimun, Solimun]]>;
<![CDATA[Hapsari, Meilina Retno]]>;
<![CDATA[Mudjiono, Mudjiono]]>;
<![CDATA[Lusiana, Evellin Dewi]]>;
<![CDATA[Hidayat, Kamelia]]>;
<![CDATA[Sianipar, Celia]]>;
<![CDATA[Zahra, Septi Nafisa Ulluya]]>
<![CDATA[ Seminar Nasional Penelitian dan Pengabdian Kepada Masyarakat 2025 Prosiding Seminar Nasional Penelitian dan Pengabdian Kepada Masyarakat (SNPPKM 2025) ]]>
Publisher : <![CDATA[Universitas Harapan Bangsa ]]>
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DOI: 10.35960/snppkm.v4i1.1332
<![CDATA[This community service program was designed to enhance the achievement of the National Science Olympiad in Mathematics through collaboration between teachers and students in Malang Regency. The background of this program lies in the limited availability of structured coaching, insufficient intensive mentoring by teachers, and the lack of competition simulations that hinder students’ academic readiness and mental resilience. The objective was to strengthen teacher competence and prepare students more effectively for the demands of Olympiad-level competitions. The program was implemented through seminars for teachers, intensive classes for students, collaborative discussion forums, competition simulations, and continuous evaluation using statistical-based monitoring. The results showed that teachers developed stronger skills in designing innovative and systematic coaching strategies, while students demonstrated improved readiness in facing challenging problems and competition pressure. The program also fostered a collaborative and participatory learning ecosystem that supports sustainable academic development. In conclusion, this initiative contributed to improving the quality of mathematics education in Malang Regency and provided a replicable model for Olympiad coaching in other schools and regions.]]>
<![CDATA[Integrating Clustering and Hybrid Nonparametric Path Modeling for Waste Management Behavior in Batu City ]]>
<![CDATA[Moh Zhafran Hidayatulloh]]>;
<![CDATA[Solimun Solimun]]>;
<![CDATA[Adji Achmad Rinaldo Fernandes]]>;
<![CDATA[Anggun Fadhila Rizqia]]>;
<![CDATA[Fachira Haneinanda Junianto]]>
<![CDATA[ ZERO: Jurnal Sains, Matematika dan Terapan Vol 10, No 1 (2026): Zero: Jurnal Sains Matematika dan Terapan ]]>
Publisher : <![CDATA[UIN Sumatera Utara ]]>
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DOI: 10.30829/zero.v10i1.26671
<![CDATA[This study formulates a hybrid nonparametric path analysis utilizing truncated spline-Fourier series integrated with Fuzzy C-Means clustering methodologies to investigate nonlinear behavioral patterns in community-based waste management. The model was utilized on survey data from 210 respondents in Bumiaji District, Batu City, encompassing Environmental Quality, Use of Waste Banks, Use of the 3R Principles, and Economic Benefits from Waste. Two distinct behavioral groups with varying emphasis on Use of Waste Banks and Environmental Quality were identified by the analysis. The hybrid model obtained a high coefficient of determination (0.891) and captures nonlinear relationships more successfully than the traditional linear approach. These results emphasize how crucial nonlinear behavioral dynamics are in influencing waste management behavior/decisions. The proposed framework helps local governments create more focused and efficient waste-management plans by offering a useful and adaptable analytical for understanding community behavior.]]>
