Article Per Year (5 Year)
p-Index From 2021 - 2026
2.383
P-Index
This Author published in this journals
All Journal CAUCHY: Jurnal Matematika Murni dan Aplikasi Jurnal Teknologi Informasi dan Ilmu Komputer BAREKENG: Jurnal Ilmu Matematika dan Terapan JTAM (Jurnal Teori dan Aplikasi Matematika) Jurnal Teknik Informatika (JUTIF) Pakem : Jurnal Pengabdian Kepada Masyarakat Economics Development Analysis Journal Journal of Statistics and Data Science
Ni Wayan Surya Wardhani
brawijaya university
Agriculture, Biological Sciences & Forestry Arts Humanities Chemistry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Energy Engineering Environmental Science Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mathematics Mechanical Engineering Physics Public Health Social Sciences Transportation Other

Published : 18 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 18 Documents
Search

 1   2 
Nonparametric Smoothing Spline Approach in Examining Investor Interest Factors Pratama, Yossy Maynaldi; Fernandes, Adji Achmad Rinaldo; Wardhani, Ni Wayan Surya; Hamdan, Rosita
JTAM (Jurnal Teori dan Aplikasi Matematika) Vol 8, No 2 (2024): April
Publisher : Universitas Muhammadiyah Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31764/jtam.v8i2.20192

Abstract

The nonparametric approach is an appropriate approach for patterns of relationships between predictor variables and response variables that are not or have not been known in form. In other words, there is no complete information about the pattern of relationships between variables. Curve estimation is determined based on relationship patterns in existing data. The nonparametric approach has great flexibility for estimating regression curves. This study aims to form a model on investor interest factors in improving tourism investment decisions with a nonparametric approach. The nonparametric method used is the smoothing spline regression method. The smoothing spline method is used because the modeling results from the smoothing spline approach can follow the relationship model between variables contained in the data. Thus, this method really helps researchers to model relationships between variables that are not linear and whose linear form is unknown. The results of the analysis showed that the nonparametric smoothing spline regression analysis method could model data by 94.63%, indicates that data variance can be explained by 94.63% with models, while other variance outside the study explain the remaining 5.37%. That is, investment motivation is one of the most important factors to improve investment decisions. 
PENDAMPINGAN PENATAAN SISTEM ADMINISTRASI DESA DENGAN MENGEMBANGKAN APLIKASI ADMINISTRASI DESA TERPADU DI KELURAHAN ARJOSARI Pramoedyo, Henny; Ngabu, Wigbertus; Wardhani, Ni Wayan Surya; Iriany, Atiek; Chairunissa, Abela
PAKEM : Jurnal Pengabdian Kepada Masyarakat Vol 5 No 2 (2025): Pakem : Jurnal Pengabdian Kepada Masyarakat
Publisher : Universitas Pattimura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/pakem.5.2.131-142

Abstract

This community service activity aims to assist Kelurahan Arjosari in restructuring its administrative system more effectively and efficiently through the development of a village administration application built with Microsoft Excel and Visual Basic for Applications (VBA). Based on initial observations, the administrative processes in the kelurahan were still conducted manually, leading to slow public service delivery and a high risk of errors. The developed application is designed to integrate with the village's population database, automate the generation of documents based on National Identification Numbers (NIK), and provide automatic data validation features. Through Focus Group Discussions (FGD) and the official launching of the application, local officials were actively involved in the planning and training phases. Evaluation results show that the application is easy to use, accelerates the document service process, and improves administrative data accuracy. This initiative has had a positive impact on the quality of public services at the local level and serves as a model for applying simple but effective technology to support digital transformation in village governance
Development of Accuracy for the Weighted Fuzzy Time Series Forecasting Model Using Lagrange Quadratic Programming Rozy, Agus Fachrur; Solimun, Solimun; Wardhani, Ni Wayan Surya
JTAM (Jurnal Teori dan Aplikasi Matematika) Vol 7, No 4 (2023): October
Publisher : Universitas Muhammadiyah Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31764/jtam.v7i4.16783

Abstract

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..
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.
Peningkatan Akurasi Metode Weighted Fuzzy Time Series Forecasting Menggunakan Algoritma Evolusi Differensial dan Fuzzy C-Means Rozy, Agus Fachrur; Solimun, Solimun; Wardhani, Ni Wayan Surya
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 10 No 5: Oktober 2023
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jtiik.2023107505

Abstract

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.
Simulation Study and Development of Semiparametric Multiresponse Multigroup Truncated Spline Regression for Rice Pest Control Azizah, Laila Nur; Fernandes, Adji Achmad Rinaldo; Wardhani, Ni Wayan Surya
CAUCHY: Jurnal Matematika Murni dan Aplikasi Vol 10, No 1 (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.v10i1.29773

Abstract

Rice pest control is a critical challenge in the agricultural sector that requires a deep understanding of rice pest management. Regression analysis is a statistical method capable of describing and predicting cause-and-effect relationships between individuals. In real-life applications, not all relationships exhibit a known curve pattern, and non-identifiable curve forms are often observed. Additionally, a single cause may affect more than one outcome, and the outcomes themselves can have interrelationships. Such relationships can be approached through a multi-response semiparametric regression using a truncated spline multi-group model. This study aims to develop a multi-response semiparametric multi-group regression model using the truncated spline approach to understand the variables influencing rice pest control under light and dark conditions. This model is applied to secondary and simulated data with various scenarios to determine the best model. The study results indicate that the optimal model for secondary data is a semiparametric regression model with a linear order and a single knot point, achieving a determination coefficient of 89.17%. Simulation results show that the scenario 1 model (linear with a single knot point) produces a high determination coefficient. This multi-response regression model proves more optimal when error variance and multicollinearity levels are kept low to moderate.
Modified WLS - Path Analysis In The Control Of Cattle Flies And Skin Defects Zuhdi, Muhammad Rizal; Fernandes, Adji; Wardhani, Ni Wayan Surya; Hamdan, Rosita
CAUCHY: Jurnal Matematika Murni dan Aplikasi Vol 10, No 1 (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.v10i1.30118

Abstract

The cattle industry has an important role in the economy, especially in providing high-quality cowhide for various industries. However, fly infestation, especially from the Chloropidae and Muscidae families, causes many defects in cattle hides that negatively affect their economic value. This study aims to identify factors that influence the extent of defects in cattle hides, focusing on the role of temperature, humidity, and fly infestation. The study was conducted in a Malang cattle farming center by measuring temperature and humidity, catching flies, and calculating the defect area on cattle skin. Data were analyzed using path analysis using modified weighting with the help of R Studio software. The weights used were modified by including correlation in the weight matrix. The results showed that temperature had a significant effect on increasing the defect area in the cheek area of cattle, while humidity had no significant effect. In the abdominal area, neither temperature nor humidity affected the defect area. Infestations of Chloropidae and Muscidae flies were also shown to contribute to increased defect area in the cheek area, but not in the stomach. Preventive strategies for fly control and protection of cattle skin from temperature extremes are needed, especially in the cheek area.
Enhancing Image Classification of Cabbage Plant Diseases Using a Hybrid Model Convolutional Neural Network and XGBoost Sovia, Nabila Ayunda; Wardhani, Ni Wayan Surya; Sumarminingsih, Eni; Shofa, Elvo Ramadhan
CAUCHY: Jurnal Matematika Murni dan Aplikasi Vol 10, No 1 (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.v10i1.30866

Abstract

Classifying imbalanced datasets presents significant challenges, often leading to biased model performance, particularly in multiclass classification. This study addresses these issues by integrating Convolutional Neural Networks (CNN) and XGBoost, leveraging CNN’s exceptional feature extraction capabilities and XGBoost's robust handling of imbalanced data. The Hybrid CNN-XGBoost model was applied to classify cabbage plants affected by pests and diseases, which are categorized into five classes, with a significant imbalance between healthy and affected plants. The dataset, characterized by severe class imbalance, was effectively handled by the proposed model. A comparative analysis demonstrated that the CNN-XGBoost approach, with a Balanced Accuracy of 0.93 compared to 0.53 for the standalone CNN, significantly outperformed the standalone model, particularly for minority class predictions. This approach not only enhances the accuracy of plant disease and pest diagnosis but also provides a practical solution for farmers to efficiently identify and classify cabbage plants, contributing to more effective agricultural management.
Co-Authors Abdila, Naufal Shela Achmad Efendi Al Jauhar, Hafizh Syihabuddin Ali Djamhuri Angga Dwi Mulyanto Atiek Iriany Azizah, Laila Nur Chairunissa, Abela Dewi Kurnia Sari Eni Sumarminingsih Fernandes, Adji Fernandes, Adji Achmad Rinaldo Hamdan, Rosita Henny Pramoedyo Iriany , Atiek Maghfiro, Maulidya Marchamah, Mamlu’atul Nurjannah Nurjannah Pratama, Yossy Maynaldi Putri, Henida Ratna Ayu Rahma Fitriani Rohma, Usriatur Rozy, Agus Fachrur Shofa, Elvo Ramadhan Solimun Solimun, Solimun Sovia, Nabila Ayunda Suci Astutik Weni Utomo, Candra R. W. S Wigbertus Ngabu Zuhdi, Muhammad Rizal