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All Journal Journal of Innovation and Applied Technology Jurnal Teknologi Informasi dan Ilmu Komputer Science and Technology Indonesia BAREKENG: Jurnal Ilmu Matematika dan Terapan JTAM (Jurnal Teori dan Aplikasi Matematika) Journal of Multidisciplinary Applied Natural Science Jurnal Sains, Nalar, dan Aplikasi Teknologi Informasi
Eni Sumarminingsih
brawijaya university
Agriculture, Biological Sciences & Forestry Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Energy Engineering Environmental Science Immunology & microbiology Materials Science & Nanotechnology Mathematics Mechanical Engineering Physics Transportation

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Sicad: Smart Information System For Village Administration As An Empowering Ngadiluwih Village Kediri In Improving Community Services Astuti, Ani Budi; Nugroho, Waego Hadi; Sumarminingsih, Eni; Rotchildi, Gusti Ayu Putu Rawi; Sa'diyah, Nur Kamilah; Kalangi, Olyvia Maria; Ibnu, Muhammad
Journal of Innovation and Applied Technology Vol 9, No 2 (2023)
Publisher : Lembaga Penelitian dan Pengabdian Kepada Masyarakat Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21776/ub.jiat.2023.9.2.01

Abstract

The Ngadiluwih Village Government, Kediri Regency, East Java Province also really needs an Android-based application so that the reach of access is wide in an effort to digitize villages to improve village administration services online. The purpose of this community service activity is to build and develop the Ngadiluwih Village Government Smart Information System application, which is abbreviated as Ngadiluwih SICAD in the context of empowering Ngadiluwih Village in an effort to improve online village administration services to the community. The socialization, implementation, and assistance were also carried out to village officials and the community regarding the Ngadiluwih SICAD application product. The results of this activity show that the Ngadiluwih SICAD that has been built is in accordance with the expectations and needs of Ngadiluwih Village and the community with 14 types of letter facilities.
Development of Ramsey RESET to Identify the Polynomials Order of Smoothing Spline with Simulation Study Nurdin, Muhammad Rafi Hasan; Fernandes, Adji Achmad Rinaldo; Sumarminingsih, Eni; Ullah, Muhammad Ohid
JTAM (Jurnal Teori dan Aplikasi Matematika) Vol 9, No 1 (2025): January
Publisher : Universitas Muhammadiyah Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31764/jtam.v9i1.26785

Abstract

Path  analysis is used to determine the effect of exogenous variables on endogenous variables. One of the assumptions in path analysis is the linearity assumption. The linearity assumption can be tested using Ramsey RESET. If the Ramsey RESET results show that all variables are non-linear then one of the alternative models that can be used is nonparametric smoothing spline. The smoothing spline method requires a smoothing spline polynomial order in estimating the nonparametric path analysis function. This polynomial order results in the smoothing spline method having good flexibility in data adjustment. The selection of the smoothing spline polynomial order becomes an obstacle because there is no test to determine the best order. Therefore, the purpose of this study is to find out how the value of V for order 3 and 4, develop Ramsey RESET to identify the best spline polynomial order, and evaluate the Ramsey RESET algorithm through simulation studies on various errors. The results of V values of order 3 and 4 can be obtained through the integral process and it is found that the higher the order, the value of V has a higher rank. Ramsey RESET development is done by modifying the second regression using nonparametric regression functions of order 2, 3, and 4. The simulation study results show that the classical Ramsey RESET can be used to detect linear shapes well because it is not affected by the value of the error variance. However, the classical Ramsey RESET has limitations in detecting non-linear forms other than quadratic and cubic forms so that other forms such as smoothing spline are needed. In testing non-linear models, the lowest p value is obtained in the form that matches the actual conditions, this can be interpreted that the modified Ramsey RESET can detect non-linear forms with spline polynomial orders well. The contribution of this research is to provide a test to identify the best smoothing spline polynomial order using Ramsey RESET modification
Multigroup Analysis on Partial Least Square-Structural Equation Modeling in Modeling College Students' Saving Behavior Asaliontin, Lisa; Sumarminingsih, Eni; Solimun, Solimun; Sepriadi, Hanifa; Iriany, Atiek; Hamdan, Rosita
JTAM (Jurnal Teori dan Aplikasi Matematika) Vol 9, No 1 (2025): January
Publisher : Universitas Muhammadiyah Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31764/jtam.v9i1.27692

Abstract

This study aims to determine the factors that influence college students' saving behavior, with gender as a moderating variable. The analysis used is Partial Least Square-Structural Equation Modeling (PLS-SEM) with Multigroup Analysis. This study was conducted on 200 college students in City X who were selected by purposive sampling. Data collection was carried out using a structured questionnaire that measures Perceived Benefits, Perceived Ease of Use, Saving Intentions, and Saving Behavior. Confirmatory Factor Analysis (CFA) and Bootstrapping were used to validate the measurement model and structural relationships. The results showed that Perceived Benefits and Perceived Ease had a significant effect on Saving Intentions and Saving Behavior. In addition, Saving Intentions had a significant effect on Saving Behavior. This relationship applies to both male and female groups, with a determination coefficient of 86.2% for males and 86.7% for females. Moderation analysis shows that gender moderates the relationship between Perceived Benefits and Saving Behavior, as well as between Perceived Ease and Saving Behavior. These findings highlight the importance of considering gender differences in efforts to improve students' savings behavior. 
Perbandingan Metode Jaringan Saraf Tiruan, Fuzzy, Dan Anfis Pada Peramalan Data Inflasi Indonesia Lusia, Dwi Ayu; Semathea, Karen; Sumarminingsih, Eni; Efendi, Achmad
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 12 No 3: Juni 2025
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jtiik.2025128613

Abstract

Peramalan adalah teknik penting untuk mengestimasi nilai masa depan berdasarkan data historis. Namun, metode peramalan sering menghadapi tantangan dalam memilih model dengan tingkat akurasi terbaik. Penelitian ini bertujuan membandingkan kinerja metode Jaringan Syaraf Tiruan (JST) dan Fuzzy Metode Sugeno serta gabungan kedua metode yang disebut Adaptive Neuro Fuzzy Inference System (ANFIS). Ketiga metode digunakan untuk meramalkan inflasi bulanan Indonesia. Penerapan ketiga metode membutuhkan penentuan input yang berdasarkan stasioner dan PACF. Data tidak stasioner lag 2 sehingga Differencing lag 2 kemudian tidak ada lag yang keluar pada PACF. Berdasarkan kedua hal tersebut ditentukan inputnya ialah  dan . Hasil menunjukkan bahwa metode JST dengan 3 lapisan tersembunyi dengan banyak neuron (2,1,1) memberikan kinerja terbaik (nilai RMSE terkecil sebesar 1,16127 pada data testing). Metode terbaik tersebut digunakan untuk meramalkan Inflasi bulan September 2023 hingga Desember 2024 cenderung konstan antara 2,68879% hingga 2,68887%. Kontribusi riset ini adalah metode advance (ANFIS) dengan menggabungankan dua metode (JST dan Fuzzy) belum tentu lebih baik daripada metode tanpa penggabungan (JST atau Fuzzy).   Abstract Forecasting is an important technique for estimating future values ​​based on historical data. However, forecasting methods often face challenges in choosing a model with the best level of accuracy. This study aims to compare the performance of the Artificial Neural Network (ANN) and Fuzzy Sugeno Method methods and a combination of the two methods called the Adaptive Neuro Fuzzy Inference System (ANFIS). The third method is used to predict Indonesia's monthly inflation. The application of the third method requires input determination based on stationary and PACF. The data is not stationary lag 2 so that Differencing lag 2 then there is no lag that comes out in PACF. Based on these two things, the input is determined to be Y_(t-1) and Y_(t-2). The results show that the ANN method with 3 hidden layers with many neurons (2,1,1) gives the best performance (the smallest RMSE value is 1.16127 on the test data). The best method used to predict inflation from September 2023 to December 2024 tends to be constant between 2.68879% to 2.68887%. The contribution of this research is that the advanced method (ANFIS) by combining two methods (ANN and Fuzzy) is not necessarily better than the method without combining (ANN or Fuzzy).
ESTIMATION OF MAXIMUM LIKELIHOOD WEIGHTED LOGISTIC REGRESSION USING GENETIC ALGORITHM (CASE STUDY: INDIVIDUAL WORK STATUS IN MALANG CITY) Menufandu, Dahlia Gladiola Rurina; Fitriani, Rahma; Sumarminingsih, Eni
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 17 No 1 (2023): BAREKENG: Journal of Mathematics and Its Applications
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (399.767 KB) | DOI: 10.30598/barekengvol17iss1pp0487-0494

Abstract

Weighted Logistic Regression (WLR) is a method used to overcome imbalanced data or rare events by using weighting and is part of the development of a simple logistic regression model. Parameter estimation of the WLR model uses Maximum Likelihood estimation. The maximum likelihood parameter estimator value is obtained using an optimization approach. The Genetic algorithm is an optimization computational algorithm that is used to optimize the estimation of model parameters. This study aims to estimate the Maximum Likelihood Weighted Logistic Regression with the applied genetic algorithm and determine the significant variables that affect the working status of individuals in Malang City. The data used is the result of data collection from the National Labor Force Survey of Malang City in 2020. The results of the analysis show that the variable education completed and the number of household members has a significant effect on individual work status in Malang City.
Model Hibrida CNN Berbobot dan Model Adaboost Decision Tree untuk Klasifikasi Penyakit Kubis pada Dataset Tidak Seimbang Iriany , Atiek; Sovia, Nabila Ayunda; Wardhani, Ni Wayan Surya; Sumarminingsih, Eni
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 12 No 5: Oktober 2025
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jtiik.2025125

Abstract

Metode klasifikasi berbasis gambar banyak digunakan dalam bidang pertanian untuk mendeteksi penyakit pada berbagai tanaman, termasuk jenis yang rentan terhadap infeksi seperti kubis. Namun, performa optimal dalam klasifikasi gambar sangat dipengaruhi oleh jumlah dan keseimbangan data. Ketidakseimbangan data dalam klasifikasi penyakit tanaman kubis dapat menyebabkan model lebih memfokuskan perhatian pada kelas mayoritas, sehingga mengabaikan kelas minoritas, terutama pada klasifikasi multi-kelas. CNN sering digunakan dalam klasifikasi gambar, tetapi memiliki kelemahan dalam menangani data tidak seimbang karena cenderung lebih fokus pada kelas mayoritas. Untuk mengatasi permasalahan ini, dikembangkan model Hybrid yang mengombinasikan metode pembobotan pada CNN untuk ekstraksi fitur, model estimasi menggunakan Decision Tree, serta teknik ensemble Adaboost pada tahap klasifikasi. Pendekatan ini dirancang untuk meningkatkan kemampuan generalisasi terhadap kelas minoritas serta menghasilkan distribusi prediksi yang lebih seimbang. Hasil penelitian menunjukkan bahwa model Hybrid yang diusulkan mampu meningkatkan performa klasifikasi sebagaimana terlihat dari peningkatan Weighted Average F1-score menjadi 97%, dibandingkan model CNN tunggal dengan pembobotan yang hanya mencapai 63%. Peningkatan ini menunjukkan bahwa model Hybrid tidak hanya lebih efektif dalam menangani ketidakseimbangan data, tetapi juga mampu melakukan generalisasi yang lebih baik.   Abstract Image-based classification methods are widely used in agriculture to detect diseases in various plants, including those susceptible to infections, such as cabbage. However, achieving optimal performance in image classification is highly influenced by the quantity and balance of the data. Data imbalance in cabbage disease classification can cause the model to focus more on the majority class while neglecting the minority class, especially in multi-class classification. CNN is commonly used for image classification but struggles with imbalanced data, as it tends to prioritize the majority class. To address this issue, a hybrid model has been developed by combining weighting techniques in CNN for feature extraction, a Decision Tree for estimation, and the Adaboost ensemble technique for classification. This approach is designed to enhance generalization for minority classes and produce a more balanced prediction distribution. The results of the study indicate that the proposed Hybrid model is capable of improving classification performance, as evidenced by an increase in the Weighted Average F1-score to 97%, compared to the weighted CNN model, which achieved only 63%. This improvement demonstrates that the Hybrid model is not only more effective in addressing data imbalance but also better at generalizing the data.
Transformasi Kota Cerdas dalam Mitigasi Banjir: Pemodelan Curah Hujan DKI Jakarta dengan Pendekatan Spatial Vector Autoregressive (SpVAR) dan Pemetaan Bobot Queen Contiguity Melanwati, Rinda Lolita; Sumarminingsih, Eni; Pramoedyo, Henny
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 10 No 6: Desember 2023
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jtiik.2023107537

Abstract

Perubahan iklim dan cuaca ekstrem menjadi tantangan global, termasuk di Indonesia, dengan peningkatan banjir di DKI Jakarta. Penanggulangan membutuhkan peramalan curah hujan yang akurat. Model VAR digunakan untuk memahami hubungan variabel cuaca. Namun, data deret waktu sering memiliki dimensi spasial. Oleh karena itu, dikembangkan model Spatial Vector Autoregressive (SpVAR) yang mempertimbangkan dimensi spasial dan waktu. Pembobot queen contiguity digunakan untuk representasi yang lebih akurat. Penelitian ini memanfaatkan data BPS DKI Jakarta dari Januari 2017 hingga Desember 2021. Hasilnya menunjukkan pengaruh spasial dalam model SpVAR (1,3) dengan bobot queen contiguity. Curah hujan, suhu, dan kelembaban udara saling mempengaruhi di wilayah diprediksi dan lainnya. Model ini penting dalam strategi mitigasi banjir dan kebijakan kota cerdas untuk mengurangi risiko banjir di DKI Jakarta.   Abstract Climate change and extreme weather pose global challenges, including in Indonesia, leading to increased floods in DKI Jakarta. Addressing this requires accurate rainfall forecasts. The VAR model is used to understand the relationships between weather variables. However, time series data often have spatial dimensions. Therefore, a Spatial Vector Autoregressive (SpVAR) model has been developed considering both spatial and temporal dimensions. Queen contiguity weighting is used for more accurate representation. This study utilizes BPS DKI Jakarta data from January 2017 to December 2021. The results show spatial influence in the SpVAR (1,3) model with queen contiguity weighting. Rainfall, temperature, and humidity mutually influence predicted and other areas. This model is crucial for flood mitigation strategies and smart city policies to reduce flood risks in DKI Jakarta.
COVID-19 Vaccination and PPKM Policy with the Implementation of the Fuzzy Sugeno Method to Income Classification Wahyuni, Djihan; Sumarminingsih, Eni; Astutik, Suci
JTAM (Jurnal Teori dan Aplikasi Matematika) Vol 6, No 4 (2022): October
Publisher : Universitas Muhammadiyah Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31764/jtam.v6i4.10096

Abstract

This study aims to determine the implementation of Fuzzy Sugeno in classifying textual data obtained from Twitter so as to determine the polarity of public opinion regarding PPKM policies and Covid-19 vaccinations. This study uses primary data via Twitter related to COVID-19 vaccination and PPKM policies in Indonesia starting from February 9, 2021 to January 17, 2022. There are several stages carried out, namely data collection, data pre-processing, data labeling, data weighting. , identification of membership functions, determination of fuzzy sets, formation of classification systems, and evaluation of classification results. The results of this study explain that Fuzzy Sugeno's performance in classifying tweets is quite good with an average accuracy of 89.13%. Meanwhile, public opinion regarding PPKM policies and Covid-19 vaccinations tends to be balanced with 36.92% of tweets classified as positive sentiments, 22.85% negative sentiments, and another 40.23% classified as neutral sentiments. In addition, the fuzzy set that is formed based on the data observation method is very well done because it is able to adjust the frequency of the data in each category. This really helps improve the performance of the built classification system. 
Bayesian IGARCH Modeling of Jakarta Composite Index Volatility Using Hamiltonian Monte Carlo Algorithm Maulana, Eka Dani; Sumarminingsih, Eni; Nurjannah; Astuti, Ani Budi; Astutik, Suci
Science and Technology Indonesia Vol. 11 No. 1 (2026): January
Publisher : Research Center of Inorganic Materials and Coordination Complexes, FMIPA Universitas Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26554/sti.2026.11.1.261-279

Abstract

Time series models that model volatility in financial data, especially in stock market indices such as the Jakarta Composite Index (JCI), are Generalized Autoregressive Conditional Heteroskedasticity (GARCH) models. Following the ratification of the revised Armed Forces Law in March 2025, the JCI experienced increasing volatility, indicating persistent volatility. The problems in the JCI data require a time series model that can capture persistent volatility, namely the Integrated Generalized Autoregressive Conditional Heteroskedasticity (IGARCH) model. Parameter estimation for IGARCH models generally uses the Maximum Likelihood Estimation (MLE) method, which has limitations in handling parameter uncertainty. The Bayesian approach can address parameter uncertainty through the Markov Chain Monte Carlo (MCMC) methods. Among these, Hamiltonian Monte Carlo (HMC) is more efficient than Metropolis-Hastings and Gibbs Sampling, particularly in exploring complex posterior distributions. This study utilizes daily closing price data of the Jakarta Composite Index (JCI) as the main observation variable, observed from April 3, 2023, to April 9, 2025. This study aims to construct a volatility model for the Jakarta Composite Index (JCI) using a Bayesian IGARCH model with an HMC algorithm. This research only uses the IGARCH(1,1) model. The model has a strong ability to capture the JCI’s volatility structure, and its point forecasts are stable. However, credible intervals reveal the uncertainty level, so the volatility of JCI may decrease or increase.
Modeling Zero-Inflated Poisson Invers Gaussian Regression Bayesian Approach Jannah, Berliana; Wardhani, Ni Wayan Surya; Sumarminingsih, Eni
JTAM (Jurnal Teori dan Aplikasi Matematika) Vol 10, No 1 (2026): January
Publisher : Universitas Muhammadiyah Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31764/jtam.v10i1.34068

Abstract

Deaths due to dengue hemorrhagic fever (DHF) remains one of the most pressing public health issues in Indonesia, especially in urban areas such as Semarang City, which has a high population density and diverse environmental conditions that potentially increase the risk of transmission and death from DHF. This study aims to model the number of DHF in Semarang City using a Bayesian-based Zero-Inflated Poisson Inverse Gaussian Regression (ZIPIGR) approach. The research data was obtained from the Semarang City Health Office and the Central Statistics Agency (BPS) in 2024, with the response variable being the number of DHF deaths and five predictor variables. The data showed overdispersion and a high proportion of zeros (around 50%), indicating the presence of excess zeros in count data with a small sample size. The Bayesian ZIPIGR method was chosen because it can produce more stable parameter estimates than classical methods such as Maximum Likelihood Estimation (MLE), especially for data with complex likelihood functions, small sample sizes, and many zero values. Parameter estimation was performed using Gibbs Sampling simulation in the Markov Chain Monte Carlo (MCMC) framework. The results show that the Bayesian ZIPIGR model performs better than the MLE ZIPIGR model based on the Root Mean Square Error (RMSE) value. Factors that significantly influence DHF mortality are population density, slum area, and number of health workers. These results confirm that regional density and health worker capacity play an important role in increasing the risk of DHF mortality in urban areas. The developed model has been proven to be highly accurate in modeling count data with excess zero characteristics and makes an important contribution to health policy formulation. In practical terms, this model can be used to improve early warning systems and DHF control strategies in densely populated urban areas such as the city of Semarang.
Co-Authors Achmad Efendi Ani Budi Astuti Asaliontin, Lisa Atiek Iriany Aulia, Natasha Darmanto Elfrida Ratnawati Fernandes, Adji Achmad Rinaldo Hamdan, Rosita Henny Pramoedyo Herman Cahyo Diartho Iriany , Atiek Jannah, Berliana Kalangi, Olyvia Maria Lusia, Dwi Ayu Luthfatul Amaliana, Luthfatul Maulana, Eka Dani Melanwati, Rinda Lolita Menufandu, Dahlia Gladiola Rurina Mudjiono Ni Wayan Surya Wardhani Nurdin, Muhammad Rafi Hasan Nurjannah Rahma Fitriani Rotchildi, Gusti Ayu Putu Rawi Ruszardi, Luzar Dwain Sa'diyah, Nur Kamilah Semathea, Karen Sepriadi, Hanifa Solimun Solimun, Solimun Sovia, Nabila Ayunda Suci Astutik Ullah, Muhammad Ohid Waego Hadi Nugroho Wahyuni, Djihan