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All Journal Jurnal Manajemen dan Organisasi FORUM STATISTIKA DAN KOMPUTASI Pythagoras: Jurnal Matematika dan Pendidikan Matematika Media Statistika Jurnal Ilmu Dasar Jurnal Manajemen Teknologi Jurnal Agro Ekonomi JAM : Jurnal Aplikasi Manajemen Indonesian Journal of Business and Entrepreneurship (IJBE) JUITA : Jurnal Informatika Indonesian Journal of Biotechnology Jurnal Aplikasi Bisnis dan Manajemen (JABM) E-Journal Jurnal Manajemen. AL ISHLAH Jurnal Pendidikan International Research Journal of Business Studies (E-Journal) Jurnal Penelitian Pertanian Tanaman Pangan BAREKENG: Jurnal Ilmu Matematika dan Terapan Jurnal Ekonomi Integra JTAM (Jurnal Teori dan Aplikasi Matematika) Limits: Journal of Mathematics and Its Applications JURNAL AGRONIDA Saintifik : Jurnal Matematika, Sains, dan Pembelajarannya ComTech: Computer, Mathematics and Engineering Applications Jurnal Manajemen Inferensi Jurnal Agro Ekonomi Journal of Data Science and Its Applications Jurnal Teknik Informatika (JUTIF) Xplore: Journal of Statistics STATISTIKA Scientific Journal of Informatics Journal of Mathematics, Computation and Statistics (JMATHCOS) International Research Journal of Business Studies Indonesian Journal of Statistics and Its Applications Limits: Journal of Mathematics and Its Applications
I Made Sumertajaya
Departemen Statistika, Fakultas Matematika Dan Ilmu Pengetahuan Alam, Institut Pertanian Bogor Kampus Darmaga, Bogor 16680
Agriculture, Biological Sciences & Forestry Biochemistry, Genetics & Molecular Biology Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Immunology & microbiology Languange, Linguistic, Communication & Media Materials Science & Nanotechnology Mathematics Mechanical Engineering Physics Social Sciences Transportation Other

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Factors Influencing Informal Workers’ Registration for Social Security: A Comparative Analysis Between Indonesia and Taiwan Mohamad Rhesa Adisty; Jono Mintarto Mundandar; I Made Sumertajaya
Jurnal Aplikasi Bisnis dan Manajemen (JABM) Vol. 9 No. 2 (2023): JABM Vol. 9 No. 2, Mei 2023
Publisher : School of Business, Bogor Agricultural University (SB-IPB)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.17358/jabm.9.2.523

Abstract

Social security should be mandatory for all members of society to protect them from social risks, including the informal workers who are particularly vulnerable to such risks. However, the coverage of social security for informal workers in Indonesia remains very low. Therefore, the study aims to identify the factors that drive informal workers' desire to enroll in social security programs. The Theory of Planned Behavior will be utilized as a tool to uncover these factors. The study will compare the findings with the policies implemented in Indonesia and Taiwan as a comparison for countries with extensive social security coverage. The research sample is determined by using purposive sampling method with 100 respondents participated in this study. Data are examined by using structural equation model - partial least square (SEM-PLS). The results show that Attitude Toward Behavior and Perceived Behavioral Control have a significant impact on the intention of informal workers to join social security programs, while subjective norms have not been proven to have a significant impact. In conclusion, Indonesia needs to review its current policies, which primarily focus on subjective norms, and learn from Taiwan's successful implementation of broad social security coverage. Transforming informal labor into formal employment can be an effective strategy for achieving this goal. Keywords: social security, informal worker, sem-pls, theory of planned behavior
Peran Mediasi Budaya Organisasi dalam Memperkuat Resiliensi Manajemen Perguruan Tinggi Diki Akhwan Mulya; I Made Sumertajaya; Anggraini Sukmawati
Jurnal Manajemen dan Organisasi Vol. 14 No. 2 (2023): Jurnal Manajemen dan Organisasi
Publisher : IPB University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29244/jmo.v14i2.42332

Abstract

Resilience is the ability to survive, adapt, rise, and recover from unexpected conditions. The Covid-19 pandemic is unpredictable for almost all organizations, including Bogor Agricultural University (IPB), one of the state universities in Indonesia with legal status (PTN-BH). The study analyzes the effect of strategic human resource management (SHRM) on the resilience of university management in facing the COVID-19 pandemic, mediated by organizational culture. Respondents in this study were lecturers and education personnel at Bogor Agricultural University (IPB). The sampling technique used a stratified random sampling technique, obtaining 358 respondents. The research data were obtained by survey method using an online questionnaire. Data were analyzed using the Structural Equation Modeling-Partial Least Square (SEM-PLS) method. The results showed that organizational culture successfully mediated the effect of SHRM on organizational resilience positively and significantly. Strategic HRM latent variables have a positive and significant impact on organizational culture, and organizational culture has a positive and significant impact on organizational resilience. The direct effect of SHRM on organizational resilience is positive and effective according to lecturers but not substantial according to education employees. This result means that the strategic HR policies implemented can support lecturers in adapting to changing conditions to continue to carry out their duties and achieve their performance targets. Meanwhile, for education employees, existing policies that have yet to help tend to contribute significantly to efforts to achieve organizational resilience. Education employees requires strategic HRM policy support that is different from lecturers to be able to contribute significantly to the achievement of organizational resilience.
Analisis Clustering Time Series untuk Pengelompokan Provinsi di Indonesia Berdasarkan Indeks Pembangunan Manusia Jenis Kelamin Perempuan Dwi Agustin Nuriani Sirodj; I Made Sumertajaya; Anang Kurnia
Statistika Vol. 23 No. 1 (2023): Statistika
Publisher : Department of Statistics, Faculty of Mathematics and Natural Sciences, Universitas Islam Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29313/statistika.v23i1.2181

Abstract

ABSTRAK Indeks Pembangunan Manusia (IPM) mencerminkan bagaimana kualitas dari pembangunan suatu wilayah tertentu. Selain adanya ketimpangan nilai IPM antar wilayah provinsi di Indonesia, jika dilihat dari sudut pandang gender, maka kesenjangan IPM laki-laki dan perempuan pun tidak bisa dihindari. Peningkatan pertumbuhan pembangunan di setiap wilayah tentu harus mendorong peningkatan kesetaraan gender pula, dalam hal ini kesenjangan pembangunan antara laki-laki dan perempuan harus mampu diminimalisir sehingga penting untuk melihat bagaimana kondisi IPM perempuan perwilayah provinsi di Indonesia agar dapat dilakukan langkah-langkah intervensi untuk meminimalisir isu ketimpangan yang harapannya dapat mendorong indeks pembangunan di wilayah tersebut. Metode analisis yang digunakan untuk mengelompokkan daerah berdasarkan nilai IPM perempuan adalah Clustering time series. Hasil analisis memperlihatkan metode clustering time series dengan menggunakan jarak dynamic time-warping (DTW) menghasilkan dua kelompok yaitu kelompok 1 (daerah dengan IPM perempuan rendah): daerah Papua dan kelompok 2 (daerah dengan IPM perempuan tinggi): daerah lain selain Papua. Pengelompokan yang dibentuk menghasilkan nilai koefisien Silhouette sebesar 0,74. Nilai tersebut menandakan bahwa kelompok yang dibentuk berada dalam kategori kuat dalam artian bahwa dua kelompok tersebut mempunyai karakteristik yang jelas berbeda sehingga metode pengelompokan dengan jarak DTW dapat digunakan dalam pengelompokan provinsi-provinsi di Indonesia berdasarkan nilai IPM Perempuan. ABSTRACT The Human Development Index (HDI) reflects the quality of development in a particular region. In addition to the inequality of HDI values between provinces in Indonesia, when viewed from a gender perspective, the gap between the HDI of men and women is inevitable. Increased development growth in each region must certainly encourage an increase in gender equality as well; in this case, the development gap between men and women must be able to be minimized, so it is important to see how the condition of the women's HDI per region in Indonesia so that intervention steps can be taken to minimize the issue of inequality. The analysis method used in this paper is Time Series Clustering. The analysis results show that the time series clustering method using dynamic time-warping (DTW) distance produces two groups: group 1 (regions with low female HDI): Papua region and group (2 regions with high female HDI): all provinces except Papua. The grouping formed produced a Silhouette coefficient value of 0.74. This value indicates that the groups formed are in a strong category, so the clustering method with DTW distance can be used in grouping provinces in Indonesia based on the value of Women's HDI.
Bicluster Analysis of Cheng and Church's Algorithm to Identify Patterns of People's Welfare in Indonesia Laradea Marifni; I Made Sumertajaya; Utami Dyah Syafitri
JUITA: Jurnal Informatika JUITA Vol. 11 No. 2, November 2023
Publisher : Department of Informatics Engineering, Universitas Muhammadiyah Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30595/juita.v11i2.17446

Abstract

Biclustering is a method of grouping numerical data where rows and columns are grouped simultaneously. The Cheng and Church (CC) algorithm is one of the bi-clustering algorithms that try to find the maximum bi-cluster with a high similarity value, called MSR (Mean Square Residue). The association of rows and columns is called a bi-cluster if the MSR is lower than a predetermined threshold value (delta). Detection of people's welfare in Indonesia using Bi-Clustering is essential to get an overview of the characteristics of people's interest in each province in Indonesia. Bi-Clustering using the CC algorithm requires a threshold value (delta) determined by finding the MSR value of the actual data. The threshold value (delta) must be smaller than the MSR of the actual data. This study's threshold values are 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, and 0.8. After evaluating the optimum delta by considering the MSR value and the bi-cluster formed, the optimum delta is obtained as 0.1, with the number of bi-cluster included as 4.
Comparison of the Symmetric and Asymmetric Generalized Autoregressive Conditional Heteroscedasticity (GARCH) Models in Forecasting the 2018-2023 Jakarta Composite Index Yenni Angraini; Adelia Putri Pangestika; I Made Sumertajaya
ComTech: Computer, Mathematics and Engineering Applications Vol. 15 No. 1 (2024): ComTech
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/comtech.v15i1.10610

Abstract

The Autoregressive Integrated Moving Average with Exogenous Variables (ARIMAX) method assumes a homogeneous residual variance, but data with high volatility can cause violations of this assumption. Hence, it is interesting to compare the forecasting accuracy of symmetric and asymmetric Autoregressive Conditional Heteroskedasticity (ARCH) models in various data conditions. The research aimed to compare the accuracy of the symmetric ARCH/ Generalized Autoregressive Conditional Heteroscedasticity (GARCH) and asymmetric TGARCH models in forecasting weekly Jakarta Composite Index (JCI) data on January 1st, 2018, to April 24th, 2023, by involving the influence of COVID-19 as a covariate variable and applying several validation scenario models to training and testing data. Based on the best-selected model, forecasting was carried out from May 1st, 2023, to July 3rd, 2023. The data used were weekly JCI opening data from January 1st, 2018, to April 24th, 2023, with the COVID-19 period as a covariate variable. The analysis results show that symmetric and asymmetric methods can handle violations of the heteroscedasticity assumption in the ARIMAX model. The best model produced based on four data validation scenarios is the asymmetric ARIMAX(3,1,3)-TGARCH(1,2) model with an average MAPE value of 3.158%. In this model, the COVID-19 variable significantly influences the JCI movement. Forecasting is done with forecasting results that are stable with confidence intervals that widen in each period.
ANALISIS INTERAKSI GENOTIP x LINGKUNGAN MENGGUNAKAN STRUCTURAL EQUATION MODELING Sumertajaya, I Made; Matjjik, Ahmad Ansori; Mindra Jaya, I Gede Nyoman
PYTHAGORAS Jurnal Pendidikan Matematika Vol 4, No 1: Juni 2008
Publisher : Department of Mathematics Education, Faculty of Mathematics and Natural Sciences, UNY

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (688.75 KB) | DOI: 10.21831/pg.v4i1.684

Abstract

Additive Main Effect and Multiplicative Model (AMMI Model) nowadays is used to asses in plant breeding, especially to asses the Genotype × Environment Interaction (GEI) on multi-environment trial. The presence of genotype × environment interaction (GEI) creates difficulties in modeling complex trait that involve sequence biological process. Coupling Structural equation modeling with AMMI was developed to analyzed genotype × environment interaction (GEI). Structural equation modeling allows us to account for underlying sequential process in plant development by incorporating intermediate variables associated with those processes in the model. With this method we can incorporating genotypic and environmental covariate in the model and explain how those covariates influence grain yield. SEM-AMMI useful when both environments and genotype are fixed and the purpose of the multi-environment trials (MET) is to assess the combined effect genotypic and environmental covariate on yield and yield components  Keywords : AMMI Model, Structural equation modeling 
Faktor-Faktor yang Mempengaruhi Risiko Saham dengan Menggunakan Regresi Logistik Ordinal Valentika, Nina; Sumertajaya, I Made; Azis, Irfani; Nunung Kusdaniyama
Journal of Mathematics, Computations and Statistics Vol. 7 No. 1 (2024): Volume 07 Nomor 01 (April 2024)
Publisher : Jurusan Matematika FMIPA UNM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35580/jmathcos.v7i1.1962

Abstract

Abstrak. Tujuan dalam penelitian ini adalah untuk mengetahui kategori risiko pada saham dengan menggunakan beta saham, serta membandingkan model regresi logistik ordinal tanpa efek interaksi dan dengan efek interaksi untuk mengetahui faktor-faktor yang mempengaruhi risiko saham. Hasil penelitian yang diperoleh adalah model terbaik yang diperoleh adalah Untuk rendah|sama Untuk sama|tinggi Ukuran dari koefisien determinasi (R-Square) yang digunakan dalam pengujian kebaikan model adalah Negelkerke yaitu sebesar 66,80%. Variabel yang berpengaruh terhadap risiko saham adalah return pada taraf nyata 10%.
Penentuan Faktor Kemiskinan Indonesia Menggunakan Regresi Logistik Azis, Irfani; Sumertajaya, I Made; Purwaningsih, Siti Samsiyah; Tjahjawati, Sri Surjani
Journal of Mathematics, Computations and Statistics Vol. 6 No. 1 (2023): Volume 06 Nomor 01 (April 2023)
Publisher : Jurusan Matematika FMIPA UNM

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Poverty is a global problem faced by various countries, including Indonesia. This study aims to determine the factors that influence the level of poverty in Indonesia by looking at the poverty classification itself. The data used is data on the website of the Central Statistics Agency and Bappenas in 2021 with the model used is an ordinal logistic regression model. The backward elimination method isused to select the best model with the lowest information criterion akaike value. The results of this study are that the gross domestic product factor and the unemployment rate have a significant positive effect, while population size and the provincial minimum wage have a significant negative effect on the poverty rate in Indonesia.
Perbandingan Regresi Data Panel Variabel Perdagangan berdasarkan Periode Data Selama Pandemi Covid-19 Valentika, Nina; Sumertajaya, I Made; Kusdaniyama, Nunung; Sunardi
Journal of Mathematics, Computations and Statistics Vol. 6 No. 1 (2023): Volume 06 Nomor 01 (April 2023)
Publisher : Jurusan Matematika FMIPA UNM

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

The purpose of this study is to model the effect of returns and trading volume on the bid-ask spread using monthly and annual data during the Covid-19 Pandemic, as well as compare the panel data regression model to see the effect of returns and trading volume on the bid-ask spread based on monthly data periods and annually during the Covid-19 Pandemic. The results showed that the best model for monthly and annual data during the Covid-19 Pandemic was a random effect data model with individual effects or cross sections. In the random effect data model with individual effects or cross-section for monthly data, it is found that volume has a significant effect on the bid-ask spread at a significant level of 5%. Meanwhile, for annual data, it is found that returns have a significant effect on the bid-ask spread at a significant level of 5%. The best model based on monthly and annual data periods is the random effects data model with individual effects or cross sections using annual data.
Implementation of Gamma Regression and Gamma Geographically Weighted Regression on Case Poverty in Bengkulu Province Ilham Alifa Azagi; I Made Sumertajaya; Asep Saefuddin
JTAM (Jurnal Teori dan Aplikasi Matematika) Vol 8, No 3 (2024): July
Publisher : Universitas Muhammadiyah Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31764/jtam.v8i3.22930

Abstract

Spatial analysis involves leveraging spatial references inherent in the data being analyzed. The method to be used in spatial analysis is the Geographically Weighted Regression (GWR) method. GWR is an extension of the linear regression model at each location by adding a weighting function to the model. Generally, the GWR model uses residuals with a normal distribution in its analysis. One distribution that can be used is the gamma distribution. With the development of methods in statistics, when a response variable follows a gamma distribution, analysis is performed using Gamma Regression (GR). GR analysis is conducted because the response variable meets the gamma distribution assumption. One method used for spatial effects with a gamma-distributed response variable is the Gamma Geographically Weighted Regression (GGWR) method. In 2022, Bengkulu Province was among the ten poorest provinces in Indonesia. Therefore, the main objective is to compare the GR and GGWR models and analyze the factors affecting poverty in Bengkulu Province using these models. The results of this study show that the GR model has an R² accuracy of 87.93%, while the GGWR model has an R² accuracy of 95.87%. This indicates that the best model for the analysis is the GGWR. An example of the GGWR model equation for poverty in Bengkulu Province is Y=exp⁡(-6.039+3.15×〖10〗^(-6) X_1-0.055X_2+0.156X_4-0.00021X_5+0.004X_7-0.021X_8-0.006X_9+4.794×〖10〗^(-5) X_10). The factors influencing the GGWR model in Bengkulu Province are Population, Life Expectancy, Average Years of Schooling, Adjusted Per Capita Expenditure, School Participation Rate, Per Capita Expenditure on Food, Households Receiving Rice for the Poor, and Gross Regional Domestic Product. The benefit of this research is to serve as a reference for the provincial government of Bengkulu regarding the variables that influence poverty. It is expected that this will help the government reduce the poverty rate in Bengkulu Province. 
Co-Authors A Kurnia A. A. Mattjik AA Mattjik Abd. Rasyid Syamsuri Abdu Alifah Abdul Aziz Nurussadad Ade Gusalinda Adelia Putri Pangestika Agus Mohamad Soleh Agustin Faradila Ahmad Anshori Mattjik Ahmad Ansori Matjjik Ahmad Ansori Mattjik Ahmad Ansori Mattjik Aidi, Muhammad N Aji Hamim Wigena Akbar Rizki Alfian Futuhul Hadi Alwani, Nadira Nisa Amanda Permata Dewi Anang Kurnia Andi Setiawan Andrew Donda Munthe Anggraini Sukmawati Anik Djuraidah Arina, Faula Aropah, Vina Da'watul Aropah, Vina Da’watul ASEP SAEFUDDIN Azis, Irfani Bagus Sartono Budi Susetyo Budi Susetyo Choirun Nisa Chrisinta, Debora Cici Suhaeni Cynthia Wulandari Dede Dirgahayu Domiri Dian Kusumaningrum Dian Kusumaningrum Diki Akhwan Mulya Doni Suhartono Dwi Agustin Nuriani Sirodj Dwi Yulianti Embay Rohaeti Emeylia Safitri Erfiani Erfiani Erfiani Erfiani, Erfiani Erwina Erwina Evita Choiriyah Fadilah, Anggita Rizky FAHREZAL ZUBEDI Fahriya, Andina Faqih Udin dan Jono M. Munandar Meivita Amelia Farit M Afendi Farit Mochamad Afendi Fitria Hasanah Fitrianto, Anwar Gusti Tasya Meilania Halimatus Sa'diyah Hari Wijayanto Haryastuti, Rizqi Hengki Muradi Hidayat, Agus Sofian Eka Hilda Zaikarina Huda, Usep Firdaus I Gede Nyoman Mindra Jaya Ilham Alifa Azagi Ilma Nabila Imam Adiyana Indahwati Indonesian Journal of Statistics and Its Applications IJSA Iqbal, Teuku Achmad Irfani Azis Irfani Azis Ismah, Ismah Isti Rochayati Itasia Dina Sulvianti Jamaluddin Rabbani Harahap Jasiulewicz, Anna Jono Mintarto Mundandar Khairil Anwar Notodiputro Kurnia, A Kusdaniyama, Nunung Kusman Sadik Laradea Marifni Lestari P, Merryanty Linda Sakinah M. Syamsul Maarif Ma'mun Sarma Manuel Leonard Sirait Manuel Leonard Sirait Manuel Leonard Sirait Mattjik, AA Maulida, Annisaturrahmah Mega Pradita Pangestika Meilania, Gusti Tasya Merryanty Lestari P Mohamad Rhesa Adisty Muhamad Nur Aidi Muhammad Amirullah Yusuf Albasia Muhammad N Aidi Muhammad Nur Aidi Muhammad Ulinnuha Mulianto Raharjo Newton Newton Nina Valentika Ningsih, Wiwik Andriyani Lestari Noercahyo, Unggul Sentanu Novi Hidayat Pusponegoro Nunung Kusdaniyama Nunung Kusdaniyama Nur Hikmah Nurlia Eka Damayanti Nurus Sabani Pasaribu, Sahat M. Pepi Novianti Pika Silvianti Pratiwi, Windy Ayu Pudji Muljono Purwaningsih, Siti Samsiyah Puspasari, Novia Rahardiantoro, Septian Rahma Anisa Rahma Anisa Rizqi Haryastuti Sahat M. Pasaribu Sarah Fadhlia Sarma, Ma’mun Satria Yudha Herawan SATRIYAS ILYAS Setyono Setyono Setyono Sirait, Manuel Leonard Siti Samsiyah Purwaningsih Sri Surjani Tjahjawati Sunardi Sunardi Sunardi Suruddin, Adzkar Adlu Hasyr Sutomo, Valantino A Syafitri, Utami Syella Sumampouw Tsabitah, Dhiya Ulfah Sulistyowati Utami Dyah Syafitri Valantino A Sutomo Valentika, Nina Wibowo, Dwi Yoga Ari Winda Nurpadilah Windi D.Y Putri Wiwik Andriyani Lestari Ningsih Wiwik Andriyani Lestari Ningsih Yenni Angraini Zulkarnain, Rizky