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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Jurnal Ilmu Pertanian Indonesia FORUM STATISTIKA DAN KOMPUTASI Media Statistika CAUCHY: Jurnal Matematika Murni dan Aplikasi Disease Prevention and Public Health Journal JURNAL PETERNAKAN Sosioinforma JUITA : Jurnal Informatika International Journal of Advances in Intelligent Informatics BAREKENG: Jurnal Ilmu Matematika dan Terapan JTAM (Jurnal Teori dan Aplikasi Matematika) JURNAL ILMIAH PETERNAKAN TERPADU J Statistika : Jurnal Ilmiah Teori Dan Aplikasi Statistika Zero : Jurnal Sains, Matematika, dan Terapan Jambura Journal of Mathematics ComTech: Computer, Mathematics and Engineering Applications J-ADIMAS (Jurnal Pengabdian kepada Masyarakat) JURNAL AKUNTANSI KEUANGAN DAN MANAJEMEN Inferensi Jurnal Pendidikan dan Kebudayaan Jurnal Lebesgue : Jurnal Ilmiah Pendidikan Matematika, Matematika dan Statistika Jurnal Statistika dan Aplikasinya JURNAL ILMIAH GLOBAL EDUCATION Jurnal Indonesia Sosial Sains Jurnal Natural Xplore: Journal of Statistics Jurnal Pengabdian Masyarakat dan Lingkungan Parameter: Jurnal Matematika, Statistika dan Terapannya PRAGMATIS Jurnal Manajemen dan Bisnis Jurnal Medika Veterinaria Scientific Journal of Informatics Journal of Mathematics, Computation and Statistics (JMATHCOS) Indonesian Journal of Statistics and Its Applications Jurnal Info Kesehatan Limits: Journal of Mathematics and Its Applications
Indahwati
Department Of Statistics, School Of Data Science, Mathematics And Informatics, IPB University, Campus IPB University, Bogor 16680, Indonesia
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National Milk Production Dynamics: Interactions Among Dairy Cattle Population, Milk Imports and Exports in Indonesia 2020–2024 Kefi Amtiran, Chandraone Putra; Alahmad, Ali Omar; Notodiputro, Khairil Anwar; Mualifah, Laily Nissa Atul; Indahwati, Indahwati
JURNAL ILMIAH PETERNAKAN TERPADU Vol. 13 No. 3 (2025)
Publisher : DEPARTMENT OF ANIMAL HUSBANDRY, FACULTY OF AGRICULTURE, UNIVERSITY OF LAMPUNG

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/jipt.v13i3.p789-801

Abstract

The dairy cattle sub-sector plays a vital role in fulfilling the national demand for animal protein; however, domestic milk production has yet to meet the increasing demand. This study analyzes the impact of dairy cattle population, as well as milk import and export, on milk production in Indonesia from 2020 to 2024. Panel data from various provinces were analyzed using a fixed effects model to identify significant variables. Results indicate that the dairy cattle population has a positive and significant effect on national milk production, with variations across island regions. Conversely, milk import and export showed no significant impact on domestic production. These findings emphasize the importance of region-based development strategies, increasing dairy cattle productivity, and implementing appropriate import protection and substitution policies to enhance national milk production self-sufficiency. This study is expected to provide a basis for policymaking and strategic interventions aimed at sustainable development of the dairy industry.
The Influence of Women’s Empowerment on The Preference for Contraceptive Methods in Indonesia: A Multinomial Logistic Regression Modelling Fulazzaky, Tahira; Indahwati, Indahwati; Fitrianto , Anwar; Erfiani, Erfiani; Khikmah, Khusnia Nurul
JURNAL INFO KESEHATAN Vol 22 No 3 (2024): JURNAL INFO KESEHATAN
Publisher : Research and Community Service Unit, Poltekkes Kemenkes Kupang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31965/infokes.Vol22.Iss3.1213

Abstract

The concept of women's empowerment encompasses enabling women to take control of their own lives, independently make choices, and fulfill their complete capabilities. Numerous research studies examined the correlation between the empowerment of women and their reproductive health. In Indonesia, female labor force participation is relatively low. As a result, research on the influence of empowering women on contraceptive method preference in Indonesia makes sense. This research aims to find the multinomial logistic regression model in choosing contraceptive methods for married women in Indonesia and to identify the women’s empowerment traits that most impact contraceptive method choice.  For this study, the researchers utilized secondary data obtained from the 2017 Indonesian Demographic and Health Survey (IDHS). The participants consisted of women between the ages of 15 and 49 who were married. The total number of respondents sampled was 49,216. Variables that significantly affect contraceptive method use include the respondent's current employment, the respondent has bank account or other financial institution accounts, the cumulative count of offspring previously born and beating justified if the wife argues with her husband. The analysis is obtained using the multinomial logistic regression test, independency, multicollinearity, and parameter test, and the selection is made by considering either the smallest value of Akaike's information criterion or the option that achieves the highest level of accuracy. Findings highlight four significant variables: Firstly, employed women are more likely to use contraceptives than the unemployed. Secondly, access to banking services correlates with a higher likelihood of contraceptive use. Thirdly, women with more children tend to prefer long-acting reversible contraceptives. Lastly, endorsement of spousal violence justifiability is linked to conventional contraceptive selection. These results emphasize the roles of employment, financial access, family size, and gender-based violence perceptions in shaping contraceptive choices in Indonesia. Model 3 emerges as the most accurate predictor of preferences after eliminating six variables based on rigorous testing and multicollinearity considerations. These findings underscore the importance of addressing economic empowerment and gender-related issues in Indonesian reproductive health programs and policies. Such a comprehensive approach can enhance women's autonomy, enabling them to make crucial life choices and ultimately improving their overall well-being.         
The Effect of Profitability, Liquidity, and Leverage on Financial Distress with Company Size as a Moderating Variable in Infrastructure, Utility, and Transportation Companies Listed on the Indonesia Stock Exchange from 2019 to 2023 Kristorio, Kevin; Wany, Eva; Indahwati, Indahwati
Jurnal Indonesia Sosial Sains Vol. 6 No. 3 (2025): Jurnal Indonesia Sosial Sains
Publisher : CV. Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59141/jiss.v6i3.1589

Abstract

Financial distress is a critical issue affecting companies, particularly in the infrastructure, utility, and transportation sectors, which require substantial capital investment. Various factors, including profitability, liquidity, and leverage, influence financial distress, while company size may play a moderating role. Understanding these factors is crucial for corporate decision-makers and investors to mitigate risks and enhance financial stability. This reseach aims to analyze the effect of profitability, liquidity, and leverage on financial distress, with company size as a moderating variable in infrastructure, utility, and transportation firms listed on the Indonesia Stock Exchange (IDX) from 2019 to 2023. The research adopts a quantitative approach, utilizing secondary data obtained from audited financial statements of 30 selected companies over five years (2019–2023), resulting in 150 observations. The study employs SmartPLS version 4 for data analysis, including descriptive statistical tests, measurement model evaluations, and hypothesis testing through bootstrapping. The findings reveal that profitability and liquidity have a significant positive effect on financial distress, while leverage has a significant negative effect. Furthermore, company size moderates the relationship between liquidity and financial distress but does not moderate the effects of profitability and leverage on financial distress. The research concludes that effective financial management, particularly in maintaining profitability and liquidity, is essential in reducing financial distress. Additionally, company size plays a critical role in strengthening liquidity's impact on financial distress. These findings provide theoretical contributions to financial literature and practical implications for corporate financial management and investment decision-making.
Performance of LAD-LASSO and WLAD-LASSO on High Dimensional Regression in Handling Data Containing Outliers Cahya, Septa Dwi; Sartono, Bagus; Indahwati, Indahwati; Purnaningrum, Evita
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.v6i4.8968

Abstract

In several research areas, it is common to have a dataset with more explanatory variables than the number of observations, called high-dimensional data. This condition can lead to multicollinearity problem. The least absolute shrinkage and selection operator (LASSO) solves the problem by shrinking the estimated coefficient to zero so that it can simultaneously carry on the variable selection and the parameter estimation.  But LASSO performs poorly when the data contains some outliers in the response or explanatory variables. Robust methods have addressed this problem based on the least-absolute-deviation approach, such as LAD-LASSO and WLAD-LASSO. This current research aims to evaluate the performance of the LAD-LASSO and WLAD-LASSO methods on high-dimensional and low-dimensional data containing outliers. To evaluate the performance of these methods, the simulation study was conducted. The simulation study used three scenarios (without outliers, outliers on the response variable (5%, 10%, 15%), outliers both on the response and explanatory variables (5%, 10%, 15%)). We also used the Minimum Regularized Covariance Determinant (MRCD) estimator in calculating the weights on the WLAD-LASSO. The best method from this simulation then will be applied to sembung leaf extract data to identify antioxidant marker compounds in sembung leaf extract. The simulation results show that LAD-LASSO tends to be very tight in selecting, while LASSO tends to be too loose.  Meanwhile, WLAD-LASSO is in the middle of those two techniques and performs the best in identifying the important variables correctly. Even the existence of weights cause WLAD-LASSO more robust against the presence of outliers in the response and explanatory variables compared to LAD-LASSO. Furthermore, performance of these methods on high-dimensional data decrease compared to low-dimensional data. The performance of these methods also tends to decrease when the rate of outlier increases. The WLAD-LASSO was then implemented in actual data to find the compound of antioxidant markers in the sembung leaf extract. The compounds/formulas obtained are Umbelliferone, 12-Hydroxyjasmonic Acid, C22H14N8O2, and Acetyleugenol (with a prediction error is 0.133050). These compounds/formulas can be developed as natural antioxidants and have the potential to be developed as medicinal ingredients.
Performance Evaluation of ARIMA and LSTM Models to Handle Multi-Interventions in Automobile Production Forecasting Maghfiroh, Firda Aulia; Indahwati, Indahwati; Saefuddin, Asep
Jurnal Ilmiah Global Education Vol. 6 No. 4 (2025): JURNAL ILMIAH GLOBAL EDUCATION
Publisher : LPPM Institut Pendidikan Nusantara Global

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55681/jige.v6i4.4694

Abstract

Intervention refers to disturbances caused by internal or external variables, such as market changes, international events, or policy shifts. The dataset used in this study contains three intervention events, referred to as a multi-input intervention. The data consist of car production figures from PT Astra Daihatsu Motor obtained from the official GAIKINDO website. The forecasting task focuses on predicting PT Astra Daihatsu Motor’s production, which was influenced by three major interventions: policy changes in 2013, the impact of the COVID-19 pandemic in 2020, and the increase in SUV production in 2022. This study compares ARIMA and LSTM models for car production forecasting. The dataset covers monthly production data from January 2010 to June 2024, with a total of 174 observations. RMSE, MAPE, and SMAPE are employed as accuracy measures. Based on the testing data (May 2023–June 2024), the results show that the LSTM model outperforms ARIMA in capturing trend patterns, with lower error values of RMSE (4587.65), MAPE (10.37), and SMAPE (10.39), compared to ARIMA with RMSE (5059.48), MAPE (11.59), and SMAPE (10.50). Accordingly, LSTM represents a relevant and robust modeling alternative for production forecasting in operational decision-making, owing to its flexibility and strong capability in capturing complex data patterns.
PERFORMANCE ANALYSIS OF MODIFIED-ODBOT AND SMOTE FOR TREE-BASED CLASSIFICATION OF IMBALANCED HUMAN DEVELOPMENT INDEX DATA Yunna Mentari Indah; Anwar Fitrianto; Indahwati Indahwati
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 20 No 3 (2026): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol20iss3pp2311-2326

Abstract

Classification of Human Development Index (HDI) data presents significant challenges due to severe class imbalance, where low-development regions are substantially underrepresented. This imbalance reduces classification performance because machine learning models tend to be biased toward the majority classes, making it challenging to accurately identify minority classes. This study proposes a modified ODBOT that replaces Euclidean distance with Mahalanobis distance within the oversampling mechanism (Mahalanobis-based ODBOT) and compares its performance with Euclidean-based ODBOT with and without Principal Component Analysis (PCA), as well as the conventional SMOTE technique. Four tree-based classifications were used, namely Random Forest, Double Random Forest, XGBoost, and LightGBM. The Human Development Index (HDI) data set from the Central Statistics Agency, consisting of 514 observations and four features, with an imbalance ratio (IR) of 19.0, was divided into training and testing sets (ratio 80:20) with 30 repetitions and evaluated using F1-Measure (F1-M), Geometric Mean (G-M), Area Under the Curve (AUC), and computation time. The results show that Mahalanobis-based ODBOT achieved the highest performance on the AUC evaluation metric across all classification models and the highest on the G-M evaluation metric in three of the four classification models, but required significantly longer computation time (2545.66 seconds). In contrast, the Euclidean-based ODBOT with PCA improved F1-M while reducing computation time (7.21 seconds) compared to the original ODBOT (68.23 seconds), while SMOTE consistently improved G-M and AUC across all experiments. These findings suggest that oversampling techniques should be selected based on practical application needs. Specifically, the Mahalanobis-based ODBOT can be recommended when improving prediction performance is a priority, while the Euclidean-based ODBOT with PCA or SMOTE is preferable for real-world implementations that require faster execution and lower computational cost.
Evaluasi Kinerja Metode CLARA dan FCM dalam Analisis Gerombol untuk Data Berjumlah Besar dengan Pencilan Indahwati Indahwati; Intan Juliana Panjaitan; Farit Mochamad Afendi
Limits: Journal of Mathematics and Its Applications Vol. 22 No. 3 (2025): Limits: Journal of Mathematics and Its Applications Volume 22 Nomor 3 Edisi No
Publisher : Pusat Publikasi Ilmiah LPPM Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/limits.v22i3.3118

Abstract

Analisis gerombol adalah suatu metode statistika yang mengidentifikasi gerombol objek berdasarkan karakteristik serupa. Masalah yang sering terjadi dalam analisis gerombol adalah keberadaan data pencilan. Keberadaan pencilan dapat mengakibatkan output yang tidak sesuai dengan gambaran yang sebenarnya, sehingga gerombol yang dihasilkan tidak merepresentasikan objek dengan tepat. Masalah lain yang dapat muncul dalam analisis gerombol adalah besarnya jumlah amatan, sehingga diperlukan metode analisis yang efisien dalam penggerombolan. Penelitian ini juga memperdalam tentang kinerja keduanya terhadap jarak antara pusat gerombol dan kondisi penggerombolan melalui kajian simulasi, dimana masing-masing faktor terdiri dari tiga level yang diobservasi.Metode Clustering Large Applications (CLARA) dan Fuzzy C-Means (FCM) adalah metode yang kekar terhadap pencilan dan mampu menganalisis dataset besar. Metode FCM menggunakan nilai pembobot (w) yang optimal untuk mencapai kekar terhadap pencilan. Metode CLARA memiliki sifat kekar dikarenakan menggunakan medoid sebagai pusat gerombol dan penggunaan jarak Manhattan dalam perhitungan jarak antara objek dan pusat gerombol. Metode tersebut akan dievaluasi menggunakan beberapa kriteria evaluasi kebaikan yaitu berdasarkan rasio simpangan baku dalam gerombol dan antar gerombol. Hasil analisis menunjukkan pengaruh signifikan pada masing-masing faktor dan interaksi antar faktor. Visualisasi menunjukkan bahwa peningkatan persentase pencilan mengurangi akurasi penggerombolan, sementara jumlah data yang lebih besar meningkatkan akurasi. Jarak yang lebih besar antara pusat gerombol dan kondisi gerombol yang terpisah menghasilkan rasio simpangan baku gerombol yang lebih kecil. Hasil penelitian menunjukkan bahwa metode FCM lebih efektif dalam menangani data dengan variasi yang signifikan.
Comparative Performance of Spatial Robust Small Area Estimation Methods: A Simulation Study Baiq Dian Arianingsih; Kusman Sadik; Indahwati Indahwati
ZERO: Jurnal Sains, Matematika dan Terapan Vol 9, No 3 (2025): Zero: Jurnal Sains Matematika dan Terapan
Publisher : UIN Sumatera Utara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30829/zero.v9i3.26477

Abstract

Estimating parameters for small areas often faces limitations due to insufficient sample sizes, resulting in low-precision estimates. The Small Area Estimation (SAE) approach is used to address this problem by utilizing auxiliary variables to improve estimation efficiency. This study evaluates four SAE methods, namely EBLUP, REBLUP, SEBLUP, and SREBLUP, through a simulation study based on a nested error model across 18 scenarios that combine two area sizes (16 and 64 areas), levels of outlier contamination in the error component, and degrees of spatial correlation in the area-level random effects. Each scenario is replicated 50 times. Model performance is evaluated using Relative Bias (RB) and Relative Root Mean Square Error (RRMSE). The results show that non-robust methods are sensitive to outliers, whereas robust methods produce more stable estimates. The SREBLUP method demonstrates the best performance under low to moderate spatial correlation. In addition, an ANOVA test is conducted to identify factors that significantly affect the response.
BCBimax Biclustering Algorithm with Mixed-Type Data Hanifa Izzati; Indahwati Indahwati; Anik Djuraidah
JUITA: Jurnal Informatika JUITA Vol. 12 No. 1, May 2024
Publisher : Department of Informatics Engineering, Universitas Muhammadiyah Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30595/juita.v12i1.21519

Abstract

The application of biclustering analysis to mixed data is still relatively new. Initially, biclustering analysis was primarily used on gene expression data that has an interval scale. In this research, we will transform ordinal categorical variables into interval scales using the Method of Successive Interval (MSI). The BCBimax algorithm will be applied in this study with several binarization experiments that produce the smallest Mean Square Residual (MSR) at the predetermined column and row thresholds. Next, a row and column threshold test will be carried out to find the optimal bicluster threshold. The existence of different interests in the variables for international market potential and the number of Indonesian export destination countries is the reason for the need for identification regarding the mapping of destination countries based on international trade potential. The study's results with the median threshold of all data found that the optimal MSR is at the threshold of row 7 and column 2. The number of biclusters formed is 9 which covers 74.7% of countries. Most countries in the bicluster come from the European Continent and a few countries from the African Continent are included in the bicluster.
Forecasting world sugar contract futures using long short-term memory technique with multi-step ahead forecasting strategy Khairil Anwar Notodiputro; Kayla Fakhriyya Jasmine; Indahwati Indahwati; Wandee Wanishsakpong
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 3: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i3.pp2633-2642

Abstract

Time series analysis using stochastic and dynamic models for data forecasting is a key in assisting planning and decision-making processes in various sectors. Long short-term memory (LSTM), with its advantage in understanding patterns and non-linearity in sequential data, is applied in a multi-step ahead forecasting strategy on world sugar futures prices. Fluctuations in sugar prices have a significant impact on the agriculture, trade, and food industry sectors. Forecasting sugar prices becomes a crucial tool for industries, investors, and traders to anticipate changes and make informed decisions. The objectives of this study are to identify the best strategy for forecasting the world sugar contract price and to perform forecasting using the best model. The research results indicate that hyperparameter tuning in LSTM models produces varied combinations and effects. Furthermore, the recursive strategy is suitable for long-term forecasting, while the direct strategy is appropriate for short-term forecasting. Forecasting values for long-term periods remains challenging in achieving high accuracy.
Co-Authors A. A., Muftih Aam Alamudi Aditya Ramadhan Agus Mohamad Soleh Agustini , Ni Ketut Yulia Agustini, Ni Ketut Yulia Aji Hamim Wigena Akbar Rizki Alahmad, Ali Omar Alfi Indah Nurrizqi Aliu, Mufthi Alwi ALIU, MUFTIH ALWI Amelia, Reni Amin, Yudi Fathul Anang Kurnia Andi Harismahyanti A. Anik Djuraidah annida marsa salsabila Antonius Benny Setyawan Ardi Surya Ari Handayani Arie Anggreyani Aristawidya, Rafika ASEP SAEFUDDIN Assyifa Lala Pratiwi Hamid Aunuddin . Bagus Sartono Baiq Dian Arianingsih Budi Susetyo Bukhari, Ari Shobri Cahya, Septa Dwi Cahyani Oktarina Chrisinta, Debora Dairul Fuhron Daswati, Oktaviyani Dea Fisyahri Akhilah Putri Dian Kusumaningrum Erfiani Erfiani Erfiani Erfiani Etis Sunandi Eva Wany, Eva Evita Purnaningrum Farit Mochamad Afendi Farit Mohamad Afendi Fatimah Fatimah Fira Nurahmah Al Aminy Fitrianto, Anwar Fulazzaky, Tahira Ghina Fauziah Hanifa Izzati Hanifa Izzati Hari Wijayanto Hasanah, Lailatul I Gusti Putu Purnaba I Made Sumertajaya Iin Maena Ilham Azagi Indah, Yunna Mentari Intan Juliana Panjaitan Irawan Irawan Jasmin Nur Hanifa Jaya, Eddy Santosa Julianti, Elisa D Kamil, Farid Ikram Karunia, Nia Kayla Fakhriyya Jasmine Kefi Amtiran, Chandraone Putra Khairil Anwar Notodiputro Khikmah, Khusnia Nurul Kholidiah, Kholidiah Khusnia N. K. Khusnia Nurul Khikmah Kristorio, Kevin Kusman Sadik Laily Nissa Atul Mualifah Latifah, Leli Lestari, Nila Lili Puspita Rahayu Logananta Puja Kusuma Made Sumertajaya Maghfiroh, Firda Aulia Mega Maulina Miranti, Ita Miranti, Ita Mohammad Masjkur Mualifah, Laily Nissa Muftih Alwi Aliu Muhammad Nur Aidi Naima Rakhsyanda Narindria, Yasmin Nadhiva Nurul Fadhilah Panjaitan, Intan Juliana Pika Silvianti Puput Cahya Ambarwati Putra, Stefanus Morgan Setyadi Perdana Putri, Christiana Anggraeni Ramdani, Indri Rasyid, Baharun Ray Sastri Regan, Regan Reni Amelia Reni Amelia Reza, Charolina Therezia Rifki Hamdani Rindy Anggun Pertiwi Rizka Annisa Mingka Salvina Salvina Setyo Wahyudi Silmi Annisa Rizki Manaf Siti Hafsah Siwi Haryu Pramesti Tina Aris Perhati Titin Agustina Titin Suhartini Titin Suhartini, Titin Titin Yuniarty Yuniarty Tri Wahyuni Utami Dyah Syafitri Vera Maya Santi Via Sulviana Vitona, Desi Wahyudi Setyo Wandee Wanishsakpong Widya Putri Nurmawati Yani Prihantini Hiola Yenni Angraini Yuniarty, Titin Yunna Mentari Indah Zulkarnain, Rizky _ Aunuddin