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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
Agriculture, Biological Sciences & Forestry Humanities Astronomy Biochemistry, Genetics & Molecular Biology Chemistry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Immunology & microbiology Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Medicine & Pharmacology Neuroscience Physics Public Health Social Sciences Transportation Veterinary Other

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KLASIFIKASI TINGKAT PENGANGGURAN TERBUKA DI PULAU JAWA MENGGUNAKAN REGRESI LOGISTIK ORDINAL Indah, Yunna Mentari; Fitrianto, Anwar; Erfiani, Erfiani; Indahwati, Indahwati; Aliu, Muftih Alwi
Jurnal Lebesgue : Jurnal Ilmiah Pendidikan Matematika, Matematika dan Statistika Vol. 5 No. 2 (2024): Jurnal Lebesgue : Jurnal Ilmiah Pendidikan Matematika, Matematika dan Statistik
Publisher : LPPM Universitas Bina Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46306/lb.v5i2.629

Abstract

Unemployment is one of the indicators for measuring the economic conditions of a region. It is also a social and economic problem in many countries, including Indonesia, especially in areas with a density of economic activity, such as Java Island. The purpose of this study was to classify and analyze the factors that affect the open unemployment rate in cities and regions on Java Island, which are categorized as low, medium, and high. The research method used in this study was ordinal logistic regression analysis. The data source comes from the BPS website in 2023 with four predictor variables: population size, labor force participation rate, average years of schooling, and gross regional domestic product at constant prices. The research results show that the variables population size and labor force participation rate had a significant effect on the open unemployment rate, while the variables average years of schooling and gross regional domestic product at constant prices did not have a significant effect on the open unemployment rate with the accuracy of the ordinal logistic model is 77.27%.
ANALISIS FAKTOR YANG MEMPENGARUHI INDEKS PEMBANGUNAN MANUSIA DI INDONESIA MENGGUNAKAN MODEL REGRESI LOGISTIK BINER Vitona, Desi; Erfiani, Erfiani; Indahwati, Indahwati; Fitrianto, Anwar; Aliu, Mufthi Alwi
Jurnal Lebesgue : Jurnal Ilmiah Pendidikan Matematika, Matematika dan Statistika Vol. 5 No. 2 (2024): Jurnal Lebesgue : Jurnal Ilmiah Pendidikan Matematika, Matematika dan Statistik
Publisher : LPPM Universitas Bina Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46306/lb.v5i2.634

Abstract

The primary tool for assessing the extent of human development progress in a country is the Human Development Index (HDI). There are three components of Indonesia's Human Development Index (HDI). The method used to characterize the quality of human existence is based on these foundational aspects of HDI. The three elements include the role of economic advancement in human progress, as well as health, knowledge, and a decent standard of living. The objective of this research is to conduct binary logistic regression modeling to identify the key aspects that influence the Human Development Index of Regencies and Cities in Indonesia. If the response variable is binary and the predictor factors consist of one or more continuous or categorical variables, binary logistic regression is the statistical technique used to model the categorical response variable. The research results indicate that the percentage of Life Expectancy (X1), Average Length of Schooling (X2), Expected Years of Schooling (X3), and Per Capita Expenditure (X4), both partially and simultaneously, are independent variables that have the most significant impact on HDI at a real level of α = 5%. A balanced accuracy rating of 91.83% was achieved from the model evaluation, indicating that the model is useful
MODEL KLASIFIKASI REGRESI LOGISTIK BINER UNTUK LAPORAN MASYARAKAT DI OMBUDSMAN REPUBLIK INDONESIA Daswati, Oktaviyani; Indahwati, Indahwati; Erfiani, Erfiani; Fitrianto, Anwar; Aliu, Muftih Alwi
Jurnal Lebesgue : Jurnal Ilmiah Pendidikan Matematika, Matematika dan Statistika Vol. 5 No. 2 (2024): Jurnal Lebesgue : Jurnal Ilmiah Pendidikan Matematika, Matematika dan Statistik
Publisher : LPPM Universitas Bina Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46306/lb.v5i2.702

Abstract

A classification model is needed to predict data into the right class according to the pattern of previous data. Binary Logistic Regression can be used in building classification models, even though the independent variables are categorical scale data. Through binary logistic regression, it can also be seen which category of independent variables influences the response variable. Public complaint reports at the Ombudsman of the Republic of Indonesia are classified into reports that found maladministration and not. The Binary Logistic Regression model with several categorical independent variables related to the public complaint reports data applied resulted in a classification model with an overall classification accuracy of 66.08% and a sensitivity of 75.31% in estimating the presence of maladministration findings in the submitted public complaint reports. Based on the 95% confidence level of the model, it is known that the factors that influence the occurrence of maladministration are the Group of Reportees, the Substance of the Report, the Method of Submission, the Request for Confidentiality, and the Location of the Inspection Office. This model can be used as a reference to reduce the incidence of maladministration cases in public service providers by focusing socialization and education on categories that have a real influence on each of these factors
Prevalence and Risk Factors of Scabies in Cats at Koiverde Petcare Clinic during 2022 Kamil, Farid Ikram; Narindria, Yasmin Nadhiva; Notodiputro, Khairil Anwar; Indahwati, Indahwati; Mualifah, Laily Nissa; Putra, Stefanus Morgan Setyadi Perdana
Jurnal Medika Veterinaria Vol 18, No 2 (2024): J. Med.Vet
Publisher : Universitas Syiah Kuala

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21157/j.med.vet..v18i2.38993

Abstract

Scabies is a skin disease caused by Sarcoptes scabiei or Notoedres cati mites in the corneum layer of the skin. This case study aimed to determine the prevalence and risk factors affecting the incidence of scabies in cats. Medical record data were obtained from Koiverde Petcare clinic from January to December 2022. The data obtained were then processed with binary logistic regression analysis and Odds Ratio (OR) using Minitab 19 for Windows software. OR value of the risk factors of breeds, sex and age were evaluated. Based on the results of the study, the prevalence rate of scabies in January-December 2022 period at the Koiverde Petcare clinic was 2.84%. The breeds most at risk of being infected with scabies was the Himalayan breed (X16), the sex most at risk of being infected was male (X21), and the age of the cat most at risk of being infected with scabies was the young age of the cat.
Future Prospect Versus Past Performance Menjelang Pemilihan Umum tahun 2024 Indahwati, Indahwati; Agustini , Ni Ketut Yulia
Jurnal Akuntansi, Keuangan, dan Manajemen Vol. 6 No. 1 (2024): Desember
Publisher : Penerbit Goodwood

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35912/jakman.v6i1.3351

Abstract

Purpose: This study aims to determine whether what investors actually expect between future prospects or past performance is related to the certainty and policy of market rules and the upcoming government to provide an overview of decision-making for management and investors. Research methodology: The research variables in this study were operational decisions, strategic decisions, financial performance, and company value as proxies for future prospects and past performance. Factor analysis and multiple linear regression were used for analysis. The population in this study includes companies listed on the Indonesia Stock Exchange. The sample was obtained using the purposive sampling method with the criteria of companies in the industry that were directly affected by the election on a quarterly basis, namely the 3rd and 4th quarters of 2023, and as many as 49 companies with 69 analysis units were obtained. Results: The results show that the three variables–operational decisions (DSO), strategic decisions (growth), and financial performance (ROA)–have a significant effect on the company's value (P/E) ahead of the 2024 elections. Research implications: Investors look more at future prospects by examining operational decisions, strategic decisions, and the company's financial performance. Limitations: The lack of data and the analysis techniques used were very simple. Contributions: This study reveals how operational, strategic, and financial performance decisions affect the value of companies in Indonesia ahead of the 2024 elections. Using regression analysis, this study identifies key factors such as Days Sales Outstanding (DSO), growth (growth), and Return on Assets (ROA) as determinants of company value. These findings provide practical insights for managers and investors, as well as theoretical contributions, by adding the political economy context to the analysis of company values.
Membangun Minat dan Jiwa Kewirausahaan Pada Siswa SMK Kartini Surabaya Agustini, Ni Ketut Yulia; Indahwati, Indahwati; Kholidiah, Kholidiah
Jurnal Pengabdian Masyarakat dan Lingkungan (JPML) Vol 3 No 2 (2025): Jurnal Pengabdian Masyarakat dan Lingkungan (JPML)
Publisher : Universitas Muhammadiyah Gresik

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30587/jpml.v3i2.9085

Abstract

Building interest and entrepreneurial spirit in the younger generation is not easy, the perception that entrepreneurship is not an attractive or profitable career choice is one of the obstacles in building interest and developing an entrepreneurial spirit among young people, this perception causes young people's interest in entrepreneurship to still be very low. This service aims to provide entrepreneurship training in order to build economic independence for students at Vocational High School SMK Kartini 7 Surabaya. Generation Z is a generation that is expected to be able to contribute to participating in encouraging economic development and improving welfare, improving welfare starts for themselves, family, friends and the surrounding community. Inability to see business opportunities, lack of ideas or concepts, lack of knowledge and skills, fear of failure, inability to manage and face risks, lack of self-confidence, lack of support from people around them, all of these are factors that hinder the growth of interest and entrepreneurial spirit among the younger generation. For this reason, efforts are needed to overcome these obstacles, through entrepreneurship training. Entrepreneurship training aims to foster interest and build an entrepreneurial spirit among vocational school students and build awareness that entrepreneurship is very important to increase potential and self-development, build an independent, creative and innovative spirit, a tough generation that does not give up easily.
Analysis of VAE-LSTM Performance in Detecting Anomalies in Average Daily Temperature Data in Jakarta 2000-2023 Angraini, Yenni; Ramdani, Indri; Indahwati, Indahwati
Jurnal Natural Volume 25 Number 2, June 2025
Publisher : Universitas Syiah Kuala

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24815/jn.v25i2.41856

Abstract

Climate change is happening worldwide, so global climate conditions are a major concern. In densely populated urban areas such as Jakarta, it is impossible to avoid the impacts of climate change, particularly the daily changes in air temperature. Therefore, a sophisticated and efficient approach is needed to find inconsistencies in daily air temperature data to provide critical information for sustainable urban planning and efforts to reduce risks. This research will combine two innovative approaches for hybrid anomaly detection. The method combines generative methods and can extract complex features, such as variational autoencoder (VAE), along with the temporal coding capabilities of long-short-term memory (LSTM), a type of Recurrent Neural Network (RNN). The data used in this study is the average daily air temperature data in Jakarta, obtained from the Kemayoran Meteorological Station and provide by the Meteorology, Climatology, and Geophysics Agency (BMKG). The data used is daily from April 2000 to December 2023. The threshold used to detect anomalies was 229.5, which resulted in excellent performance, namely F1-Score 0.985, Recall 1.000, and Precision 0.971. The VAE-LSTM model identified all dates with significant temperature anomalies, including January 21, 2014, February 22, 2014, November 12, 2014, and February 9, 2015. These dates are significant as they represent extreme weather events that can have severe implications for urban planning and climate change adaptation. The anomalies fall into the categories of point and contextual anomalies. This study contributes to climate research by providing evidence of the effectiveness of deep learning-based hybrid models in detecting complex and context-sensitive temperature anomalies.
Comparison of GMERF and GLMM Tree Models on Poverty Household Data with Imbalanced Categories Bukhari, Ari Shobri; Notodiputro, Khairil Anwar; Indahwati, Indahwati; Fitrianto, Anwar
Inferensi Vol 8, No 2 (2025)
Publisher : Department of Statistics ITS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j27213862.v8i2.21901

Abstract

Decision tree and forest methods have become popular approaches in data science and continue to evolve. One of these developments is the combination of decision trees with Generalized Linear Mixed Models (GLMM), resulting in the GLMM Tree, which is applicable to multilevel and longitudinal data. Another model, Generalized Mixed Effect Random Forest (GMERF), extends the concept of decision forests with GLMM, effectively handling complex data structures with non-linear interactions. This study compares the performance of GLMM Tree and GMERF models in classifying poor households in South Sulawesi Province, characterized by imbalanced categories. GLMM Tree provides a simple, interpretable classification through tree diagrams, while GMERF highlights variable importance. Initial tests show all three models (GLMM, GLMM Tree, and GMERF) achieve high accuracy and specificity but exhibit low sensitivity. By applying oversampling, sensitivity and AUC are significantly improved, though this is accompanied by a decline in accuracy and specificity, revealing a trade-off. The study concludes that while GLMM, GLMM Tree and GMERF have their strengths, using them together offers a more comprehensive understanding of poverty classification. Handling imbalanced data with oversampling is effective in increasing sensitivity, but careful consideration is needed due to its impact on overall accuracy.
The Implementation of the Fuzzy C-Means Method in Handling Outlier Data in the 2021 Village Potential Data of Bengkulu Province Panjaitan, Intan Juliana; Indahwati, Indahwati; Afendi, Farit Mochamad
ComTech: Computer, Mathematics and Engineering Applications Vol. 16 No. 1 (2025): ComTech
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/comtech.v16i1.12274

Abstract

Clustering groups aims to ensure similarity within clusters and disparity between them. The research evaluated the Fuzzy C-Means method’s effectiveness in clustering large datasets containing outliers, focusing on the 2021 Village Potential data from Bengkulu Province. The dataset, comprising 1,514 observations from villages and urban villages, provided a comprehensive resource for understanding regional development. Outliers, a common challenge in cluster analysis, were detected using univariate and multivariate methods, revealing substantial variability. PCA was applied, improving clustering quality to address multicollinearity among variables. In the results, the fuzzifier (w) parameter in the FCM method plays a crucial role in controlling the degree of membership for data points in clusters, which can potentially reduce the impact of outliers, enhancing clustering robustness and accuracy. The FCM method effectively produces clusters with high intra-cluster homogeneity and inter-cluster heterogeneity. Using the Elbow method, three optimal clusters are identified. Cluster 1, dominated by villages in Bengkulu City, is the most advanced, with superior infrastructure and services, but the fewest villages business units, necessitating economic empowerment. Cluster 2, comprising villages in North Bengkulu Regency, demonstrates moderate development but suffers from poor transportation access, requiring improvements to support socio-economic activities. Cluster 3, dominated by villages in Kaur Regency, is the least developed, with limited basic services and infrastructure, highlighting the need for substantial investments in governance and essential services. These findings provide actionable insights for village development in Bengkulu Province, supporting targeted policies tailored to each cluster’s unique characteristics.
Performance Evaluation of Cheng & Church (CC) and Spectral Biclustering Algorithms under Collinearity and Overlap Conditions Hafsah, Siti; Indahwati, Indahwati; Wijayanto, Hari
Scientific Journal of Informatics Vol. 12 No. 2: May 2025
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v12i2.26413

Abstract

Purpose: This study aims to address methodological challenges in evaluating biclustering algorithms under simultaneous collinearity and overlap, which often co-occur in real world multivariate data but are rarely analyzed simultaneously. This research highlights the importance of understanding how these structural challenges affect local pattern detection in data mining applications. Methods: A simulation study was conducted using synthetic matrices embedded with two constant biclusters under 15 combinations of collinearity levels (ρ = 0.3,0.6,0.9) and overlap degrees (none, small, large). Each scenario was replicated 100 times. Performance was assessed using the Liu and Wang Index (ILW), while a three-way ANOVA tested the effects of algorithm type, collinearity, and overlap. Result: Spectral Biclustering maintained stable ILW scores despite increasing collinearity, while CC performed better in low-overlap scenarios but was more sensitive to collinearity. Under high collinearity and large overlap, both algorithms experienced notable degradation. The ANOVA confirmed all main effects and interactions were significant (p < 0.001). Novelty: This study contributes empirical evidence regarding the influence of interacting structural characteristics on biclustering performance. The results deliver practical insights for selecting suitable algorithms and emphasize the potential advantages of hybrid approaches that integrate the stability of spectral methods with the adaptability of residual-based techniques.
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