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<![CDATA[DEVELOPMENT OF NONPARAMETRIC PATH FUNCTION USING HYBRID TRUNCATED SPLINE AND KERNEL FOR MODELING WASTE-TO-ECONOMIC VALUE BEHAVIOR ]]>
<![CDATA[Rohma, Usriatur]]>;
<![CDATA[Fernandes, Adji Achmad Rinaldo]]>;
<![CDATA[Astutik, Suci]]>;
<![CDATA[Solimun, Solimun]]>
<![CDATA[ BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 19 No 1 (2025): BAREKENG: Journal of Mathematics and Its Application ]]>
Publisher : <![CDATA[PATTIMURA UNIVERSITY ]]>
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DOI: 10.30598/barekengvol19iss1pp331-344
<![CDATA[Waste management remains a challenge, including in Batu City, East Java, Indonesia. Rapid population growth and economic activities in the city have resulted in a substantial increase in waste volume. One of the key factors in solving waste problems is the mindset of the community towards waste management. The application of statistical analysis methods can be an effective approach to solving problems related to waste management from an economic point of view. Nonparametric path analysis is a statistical method that does not rely on the assumption that the curve is known. Nonparametric path analysis is performed if the data does not fulfill the linearity assumption. This study aims to determine the best nonparametric path function with a hybrid truncated spline and kernel approach among EV values of 0.5; 0.8; and 1. In addition, this study also aims to test the significance of the best path function obtained. The data used in this study are timer data obtained from the Featured Basic Research Grant. The results showed that the best model of hybrid truncated spline and kernel nonparametric path analysis is a hybrid model of truncated spline nonparametric path of linear polynomial degree 1 knot and kernel triangle nonparametric path at EV 0.5. In addition, the significance of the best nonparametric truncated spline and kernel hybrid path function estimation using jackknife resampling shows that all exogenous variables have a significant effect on endogenous variables as evidenced by a p-value smaller than (0.05).]]>
<![CDATA[Integrating Path Analysis and Kendall’s Tau-based Principal Component Analysis to Identify Determinants of Child Health ]]>
<![CDATA[Alim, Viky Iqbal Azizul]]>;
<![CDATA[Iriany, Atiek]]>;
<![CDATA[Fernandes, Adji Achmad Rinaldo]]>;
<![CDATA[Solimun, Solimun]]>;
<![CDATA[Utomo, Candra Rezzining Wulat Sariro Weni]]>
<![CDATA[ CAUCHY: Jurnal Matematika Murni dan Aplikasi Vol 10, No 2 (2025): CAUCHY: JURNAL MATEMATIKA MURNI DAN APLIKASI ]]>
Publisher : <![CDATA[Mathematics Department, Universitas Islam Negeri Maulana Malik Ibrahim Malang ]]>
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DOI: 10.18860/cauchy.v10i2.31156
<![CDATA[This study develops a latent variable path analysis model using a Mixed-Scale Principal Component Analysis (PCA) approach based on Kendall’s Tau correlation to identify key determinants of child health in Batu City, Indonesia. Primary data were collected from 100 mothers with children under five years old through questionnaires. The variables examined include Family Demographics, Nutritional Consumption, and Child Health Condition, each measured using mixed-scale indicators (ordinal and numerical). Kendall’s Tau-based PCA was applied to reduce data dimensionality and construct latent variables, which were then integrated into a path analysis model. The results show that maternal age is the most dominant indicator in shaping the Family Demographics construct, while balanced nutritional food is the strongest indicator forming the Nutritional Consumption construct. Path analysis further reveals that Family Demographics significantly affect Child Health Condition both directly and indirectly through Nutritional Consumption, with a coefficient of determination of 77.62\%. These findings underscore the critical role of demographic and nutritional factors in determining child health outcomes and highlight the methodological advantage of Kendall’s Tau-based mixed-scale PCA for analyzing heterogeneous indicator data within a structural path framework.]]>
<![CDATA[Nonlinear Principal Component Analysis with Mixed Data Formative Indicator Models in Path Analysis ]]>
<![CDATA[Hardianti, Rindu]]>;
<![CDATA[Solimun, Solimun]]>;
<![CDATA[Nurjannah, Nurjannah]]>;
<![CDATA[Hamdan, Rosita]]>
<![CDATA[ JTAM (Jurnal Teori dan Aplikasi Matematika) Vol 8, No 1 (2024): January ]]>
Publisher : <![CDATA[Universitas Muhammadiyah Mataram ]]>
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DOI: 10.31764/jtam.v8i1.17559
<![CDATA[This research aims to obtain the main component score of the latent variable ability to pay, determine the strongest indicators forming the ability to pay on a mixed scale based on predetermined indicators, and model the ability to pay on time as mediated by fear of paying using path analysis. The data used is secondary data obtained through distributing questionnaires with a mixed data scale. The sampling technique used in the research was purposive sampling. The number of samples used in the research was 100 customers. The method used is nonlinear principal component analysis with path analysis modeling. The results of this research show that of the five indicators formed by the Principal Component, 74.8% of diversity or information is able to be stored, while 25.20% of diversity or other information is not stored (wasted). Credit term is the strongest indicator that forms the ability to pay variable. The variable ability to pay mortgage has a significant effect on payments by mediating the fear of being late in paying with a coefficient of determination of 73.63%. ]]>
<![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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<![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[THE PERCEPTION OF INDIVIDUAL AND ORGANIZATIONAL CAREERS IN INCREASING THE ORGANIZATIONAL COMMITMENT ]]>
<![CDATA[Widyanti, Rahmi]]>;
<![CDATA[Thoyib, Armanu]]>;
<![CDATA[Setiawan, Margono]]>;
<![CDATA[Solimun, Solimun]]>
<![CDATA[ Journal of Economics, Business, and Accountancy Ventura Vol. 15 No. 2 (2012): August 2012 ]]>
Publisher : <![CDATA[Universitas Hayam Wuruk Perbanas ]]>
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DOI: 10.14414/jebav.v15i2.77
<![CDATA[Organization commitment is very important for individuals working in any organization.Therefore, considering individuals and the perception toward the careers is really important.This study determines the direction of influence of basic individual careers and career developmentprograms on job satisfaction and organizational commitment. This research conducteda survey on Private Higher Education teaching staff of Kopertis (private higher educationcoordinator) Borneo in Banjarmasin. The data from 60 respondents were analyzedusing the Partial Least Square (PLS) to examine the relationship among variables basic individualcareers and career development programs that have a significant and positive impacton job satisfaction and organizational commitment. The results showed that the basic individualcareers and career development programs affect organizational commitment and jobsatisfaction. In addition, it is also proved that job satisfaction mediate the increasing organizationalcommitment.]]>
