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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Jurnal Manajemen dan Organisasi FORUM STATISTIKA DAN KOMPUTASI Pythagoras: Jurnal Matematika dan Pendidikan Matematika Media Statistika Jurnal Ilmu Dasar Jurnal Manajemen Teknologi CAUCHY: Jurnal Matematika Murni dan Aplikasi 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) JURNAL AGRONIDA Saintifik : Jurnal Matematika, Sains, dan Pembelajarannya ComTech: Computer, Mathematics and Engineering Applications Jurnal Manajemen Inferensi Jurnal Agro Ekonomi International Journal of Advances in Data and Information Systems Journal of Data Science and Its Applications Jurnal Teknik Informatika (JUTIF) JURNAL ILMIAH GLOBAL EDUCATION Eduvest - Journal of Universal Studies Xplore: Journal of Statistics STATISTIKA Asian Journal of Social and Humanities 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
Aerospace Engineering Agriculture, Biological Sciences & Forestry Biochemistry, Genetics & Molecular Biology Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management 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 Materials Science & Nanotechnology Mathematics Mechanical Engineering Neuroscience Physics Social Sciences Transportation Other

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Scenario Planning: Enhancing Employee Engagement In Tax Institutions Humairoh, Andi Zahira Al; Maarif, Mohammad Syamsul; Sumertajaya, I Made
Eduvest - Journal of Universal Studies Vol. 5 No. 11 (2025): Eduvest - Journal of Universal Studies
Publisher : Green Publisher Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59188/eduvest.v5i11.51869

Abstract

This research explores the intersection of employee engagement and scenario planning within tax institutions, emphasizing the importance of a motivated workforce for enhancing organizational effectiveness. The study employs a mixed-methods approach, integrating qualitative and quantitative data collection techniques, including surveys and semi-structured interviews, to assess current levels of employee engagement and the factors influencing them among approximately 360 employees from various tax institutions across Indonesia. The findings reveal that work-life balance contribute significantly to employee disengagement. By implementing scenario planning, tax institutions can proactively address these challenges, fostering a culture of adaptability and resilience. The research identifies key drivers of engagement and develops actionable recommendations for enhancing employee engagement through targeted strategies, such as improving work-life balance and creating a supportive organizational culture. Ultimately, this study aims to develop actionable recommendations for enhancing employee engagement through targeted strategies, ultimately contributing to improved organizational performance and employee satisfaction. The implications of this study extend to public administration, offering valuable insights for policy and practice in the realm of employee engagement within the public sector.
Dimensionality Reduction Evaluation of Multivariate Time Series of Consumer Price Index in Indonesia Valentika, Nina; Sumertajaya, I Made; Wigena, Aji Hamim; Afendi, Farit Mochamad
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.v10i1.34151

Abstract

Multivariate time series (MTS) analysis of the Consumer Price Index (CPI) in Indonesia often encounters challenges such as outliers, missing data, and inter-variable correlations. Principal Component Analysis (PCA) is a practical approach for dimensionality reduction; however, its performance may vary depending on the data characteristics. This study is a quantitative comparative study that integrates empirical analysis and Monte Carlo simulation based on a first-order Vector Autoregressive (VAR(1)) model to evaluate three PCA approaches: Classical PCA, Robust PCA (RPCA), and PCA of MTS. These methods were applied to weekly price data of eight strategic food commodities across 70 districts and cities in Indonesia. The evaluation employed three criteria: (1) dimensionality reduction efficiency (empirical and simulation), (2) reconstruction accuracy measured using Root Mean Square Error (RMSE) (empirical), and (3) robustness to outliers and inter-variable correlations (simulation). Empirical results indicate that Classical PCA (lag 1) and RPCA (lag 1) are both efficient and effective in reducing dimensionality with minimal information loss. Using the first three principal components, all three methods were able to explain at least 85% of the total variance, with lag 1 identified as optimal. Simulation results reveal that RPCA (lag 1) provides the most stable and consistent performance in the presence of outliers, while Classical PCA (lag 2) performs better under conditions of high inter-variable correlation and a low proportion of outliers. These findings suggest that robust covariance estimation can improve the accuracy of dimensionality reduction and enhance the stability of multivariate time-series analysis for food price data in Indonesia.
Development of generalized principal component analysis using multiple imputation genetic algorithm Zubedi, Fahrezal; Sumertajaya, I Made; Notodiputro, Khairil Anwar; Syafitri, Utami Dyah
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 1: February 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i1.pp454-468

Abstract

In this study, we propose an innovative method called the integrated GPCA MIGA, which integrates the multiple imputation genetic algorithm (MIGA) and generalized principal component analysis (GPCA) to perform missing value imputation and data dimensionality reduction simultaneously. The approximated original data produced by GPCA serves as the basis for MIGA to update missing values in the next iteration. At the same time, GPCA refines the low-dimensional representation using the latest imputation results from MIGA, thereby balancing the accuracy of missing value imputation and the stability of dimensionality reduction. The objective of this study is to evaluate the performance of the integrated GPCA-MIGA and analyze trends in human development at the district/city level in Indonesia. The findings of this study show that the integrated GPCA-MIGA effectively reduces the dimensionality of data containing missing values compared to other methods. The integrated GPCA-MIGA method was applied to human development data. The results were then visualized using a biplot, which revealed that human development trends in Jayawijaya from 2019 to 2022 indicate progress in school enrollment rates for ages 16–18 years.
Perbandingan Metode GWR, MGWR, dan MGWR-SAR pada Data Persentase Penduduk Miskin di Pulau Jawa Andina Fahriya; Budi Susetyo; I Made Sumertajaya
Limits: Journal of Mathematics and Its Applications Vol. 22 No. 2 (2025): Limits: Journal of Mathematics and Its Applications Volume 22 Nomor 2 Edisi Ju
Publisher : Pusat Publikasi Ilmiah LPPM Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/limits.v22i2.3057

Abstract

The primary goal of Sustainable Development Goals (SDGs) is to end poverty everywhere in all its forms. Poverty is defined as the inability to meet basic needs, such as food, clothing, shelter, education, and healthcare. In Indonesia, the poor population has reached 26.36 million people, with half of them residing on Java Island. Extensive research has been conducted on poverty, particularly using a spatial approach. Spatial regression is a statistical method that explicitly incorporates geographical aspects into a model framework. In spatial regression, two main challenges arise: spatial dependence and heterogeneity. These two effects are inherently interconnected and must be considered simultaneously. Mixed Geographically Weighted Regression with Spatial Autoregressive (MGWR-SAR) is a combination of Mixed Geographically Weighted Regression (MGWR) and Spatial Autoregressive (SAR). MGWR-SAR effectively addresses both spatial dependence and spatial heterogeneity simultaneously. This study aims to determine the best method for modeling the percentage of poor population on Java. The variables used included PPM, BPJSPBI, PPKM, PLSMP, PPTB, BPNT, NCPR, and IPM. The kernel function was selected based on the smallest cross-validation (CV) value, which was a Fixed Gaussian with a CV of 603.8268. Based on the GWR model, the global variables identified were PPTB, BPNT, and IPM, whereas the remaining variables were local. The MGWR-SAR method was found to be the best model for predicting the percentage of poor population, with an AIC = 448.9645, RMSE = 1.9075, and  = 75.23%.
Evaluasi Kinerja Spectral Biclustering dalam Identifikasi Potensi Produksi Komoditas Hortikultura di Indonesia Merryanty Lestari P; I Made Sumertajaya; Erfiani
Limits: Journal of Mathematics and Its Applications Vol. 21 No. 3 (2024): Limits: Journal of Mathematics and Its Applications Volume 21 Nomor 3 Edisi No
Publisher : Pusat Publikasi Ilmiah LPPM Institut Teknologi Sepuluh Nopember

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

Abstract

Biclustering merupakan metode penggerombolan dua arah untuk menemukan subset baris dan kolom dari suatu matriks data. Spectral biclustering merupakan salah satu algoritma dari biclustering. Algoritma spectral mempunyai tiga metode normalisasi matriks antara lain independent rescaling of rows and columns , bistochastization , dan log . Penerapan spectral biclustering bertujuan untuk mengidentifikasi potensi produksi komoditas hortikultura jenis sayuran di Indonesia. Metode normalisasi bistochastization menghasilkan bicluster optimal dengan nilai rataan mean squared residue terkecil sebesar 0,079593. Bicluster yang dihasilkan sebanyak 5 bicluster. Bicluster 1 dan 2 terdiri dari wilayah Papua dan Sulawesi Tenggara memiliki potensi produksi jenis tanaman sayuran mayoritas kategori rendah di antaranya kentang, bawang merah, bawang putih, dan bawang daun. Bicluster 3 dan 4 terdiri dari sebagian besar wilayah Kalimantan, Riau, Sumatera Selatan, Nusa Tenggara Timur, dan Maluku dengan potensi produksi mayoritas terkategori sedang di antaranya cabai rawit, tomat, buncis, labu siam, dan melinjo. Bicluster 5 merupakan wilayah Jawa, Bali, Nusa Tenggara Barat, sebagian besar wilayah Sumatera dan Sulawesi, serta Kalimantan Selatan. Bicluster 5 memiliki potensi produksi terkategori tinggi pada jenis sayuran sawi, kacang panjang, terung, ketimun, dan jengkol.
Latent Household Food Security in Raja Ampat Marine Protected Areas: A Binary CFA Approach Anggriyani, Indah Ratih; Sumertajaya, I Made; Notodiputro, Khairil Anwar; Angraini, Yenni
CAUCHY: Jurnal Matematika Murni dan Aplikasi Vol 11, No 1 (2026): 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.v11i1.40979

Abstract

This study examines household food security in four marine protected areas in Raja Ampat using repeated cross-sectional household survey data. Data were collected between 2010 to 2024, grouped into five monitoring periods. This study aims to provide a measurement framework for household food security as a latent construct based on binary indicators representing dimensions of food access and to estimate latent household food security scores in the four analyzed areas. In addition to applying confirmatory factor analysis to new empirical data, this study also presents a systematic estimation framework for measuring the latent construct using binary household indicators in repeated cross-sectoral survey data. The framework includes indicator threshold estimation, tetrachoric correlation estimation, parameter estimation using the robust diagonally weighted least squares method, and derivation of latent scores based on posterior expectations using the Gauss–Hermite quadrature approach. The analysis results indicate that the one-factor model provides acceptable fit and adequate construct reliability across the analyzed area-period groups. Estimates of factor loadings and thresholds provide information on the relative contribution and severity of each indicator in representing variations in household food access conditions. Overall, the goodness-of-fit indices indicate that the one-factor structure provides a reasonable representation of the relationships among the observed indicators under the fitted measurement model.
EVALUASI KEPUASAN PENGGUNA JASA LABORATORIUM KIMIA PT KRAKATAU STEEL (PERSERO) TBK TAHUN 2012-2013 Zaikarina, Hilda; Erfiani, .; Sumertajaya, I Made
Indonesian Journal of Statistics and Applications Vol 1 No 1 (2017)
Publisher : Statistics and Data Science Program Study, SSMI, IPB University, in collaboration with the Forum Pendidikan Tinggi Statistika Indonesia (FORSTAT) and the Ikatan Statistisi Indonesia (ISI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29244/ijsa.v1i1.50

Abstract

One of the services contained in PT Krakatau Steel (Persero) Tbk is the chemical composition analysis services in the chemistry lab. Management system that will create a well-managed laboratoryperformance is optimal. Manage standard chemistry laboratory is SNI ISO/IEC 17025. Discussed in this standard laboratory management such as through customer feedback. Laboratory customers selected through stratified random sampling with customer categories as strata, like suppliers, derived from plant and internal processes are not routine. In the research lab result that the customer will be satisfied, including services rendered for Customer Satisfaction Index (CSI) is greater than 70% with the overall characteristics of the respondents subscription in the laboratory was 11.6 years. Overall the indicators included in the priority importance performance analysis (IPA) and has a value kesenjangan beyond the maximum tolerance through kesenjangan analysis approach is the completeness of laboratory equipment (F) and speed of service (K). Keywords : customer satisfaction index (CSI), gap analysis, importance performance analysis (IPA)
KAJIAN SIMULASI PENDUGAAN SELANG KEPERCAYAAN BOOTSTRAP BAGI ARAH MEDIAN DATA SIRKULAR Suhaeni, Cici; Sumertajaya, I Made; Djuraidah, Anik
Indonesian Journal of Statistics and Applications Vol 2 No 1 (2018)
Publisher : Statistics and Data Science Program Study, SSMI, IPB University, in collaboration with the Forum Pendidikan Tinggi Statistika Indonesia (FORSTAT) and the Ikatan Statistisi Indonesia (ISI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29244/ijsa.v2i1.64

Abstract

The median direction is one of central tendency of circular data. The estimation process usually requires information about sampling distribution of statistic that want to be used as a parameter estimate. Theoretically, sampling distribution derived from population distribution. But, it is not easy to get sampling distribution of median although the population distribution is known. When the sampling distribution cannot be derived easily from population distribution, the bootstrap method can be an alternative to handle it. This study wants to evaluate the effect of increasing concentration parameter to the performance of bootstrap confidence interval estimation for median direction through simulation study. Three methods were used to estimate the interval which are equal-tailed arc (ETA), symmetric arc (SYMA), and likelihood-based arc (LBA). The most important criterion to evaluate them were true coverage and interval width. The simulation results that in general, the increasing of concentration parameter followed by more narrow interval. For small concentration parameter (k<1), all methods give unstable true coverage and interval width. The authors also identify that those three methods produce intervals with identical width when the parameter concentration is 20 or more. In terms of coverage and interval width, the best method was ETA.
PENGGEROMBOLAN DESA/KELURAHAN BERDASARKAN INDIKATOR KEMISKINAN DENGAN MENERAPKAN ALGORITMA TSC DAN K-PROTOTYPES Munthe, Andrew Donda; Sumertajaya, I Made; Syafitri, Utami Dyah
Indonesian Journal of Statistics and Applications Vol 2 No 2 (2018)
Publisher : Statistics and Data Science Program Study, SSMI, IPB University, in collaboration with the Forum Pendidikan Tinggi Statistika Indonesia (FORSTAT) and the Ikatan Statistisi Indonesia (ISI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29244/ijsa.v2i2.169

Abstract

Statistic Indonesia (BPS) noted that in 2014 there were 3.270 villages in Nusa Tenggara Timur Province. Most of them have a high percentage of poverty. Therefore, the village clustering based on poverty indicators is very important. The clustering algorithm that can be used on large data size and with mixed variables are Two Step Cluster (TSC) and K-Prototypes. The purpose of this research is to compare of TSC and K-Prototypes algorithm for village clustering in Nusa Tenggara Timur Province based on poverty indicators. The data were taken from 2014 village potential data (PODES 2014) collected by BPS. The best selection criteria for the cluster is the minimum ratio between variance within groups and variance between groups. The result showed that the best clustering algorithm was TSC which had the smallest ratio (2.6963). The best clustering showed that villages in Nusa Tenggara Timur Province divided into six groups with different characteristics.
KAJIAN MODEL PERAMALAN KUNJUNGAN WISATAWAN MANCANEGARA DI BANDARA KUALANAMU MEDAN TANPA DAN DENGAN KOVARIAT Rochayati, Isti; Syafitri, Utami Dyah; Sumertajaya, I Made; IJSA, Indonesian Journal of Statistics and Its Applications
Indonesian Journal of Statistics and Applications Vol 3 No 1 (2019)
Publisher : Statistics and Data Science Program Study, SSMI, IPB University, in collaboration with the Forum Pendidikan Tinggi Statistika Indonesia (FORSTAT) and the Ikatan Statistisi Indonesia (ISI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29244/ijsa.v3i1.171

Abstract

Foreign tourist arrivals could be considered as time series data. Modelling these data could make use of internal and external factors. The techniques employed here to model these time series data are SARIMA, SARIMAX, VARIMA, and VARIMAX. SARIMA is a model for seasonal data and VARIMA is a model for multivariate time series data. If some explanatory variables are incorporated and have significant influence on the response, the former two models become SARIMAX and VARIMAX respectively. Three stages of creating the model are model identification, parameter estimation, and model diagnostics. The variables used in this study were foreign tourist visits, international passenger arrivals, inflation rates, currency exchange rates, and Gross Regional Domestic Product (GRDP) over the period of 2010-2017. All four models fulfill their model assumptions and therefore could be applied. The best model of foreign tourist arrivals was VARIMA with the value of MAPE testing data = 6.123.
Co-Authors A Kurnia A. A. Mattjik AA Mattjik Abd. Rasyid Syamsuri Abdu Alifah Abdul Aziz Nurussadad Ade Gusalinda Adelia Putri Pangestika Adiyana, Imam Afendi, Farit M Agus Mohamad Soleh Agustin Faradila Ahmad Anshori Mattjik Ahmad Ansori Matjjik Ahmad Ansori Mattjik Ahmad Ansori Mattjik Aidi, Muhamad Nur Aidi, Muhammad N Aini, Febri Nur Aji Hamim Wigena Akbar Rizki Alfian Futuhul Hadi Alwani, Nadira Nisa Amanda Permata Dewi Anang Kurnia Andi Setiawan Andina Fahriya Andrew Donda Munthe Anggraini Sukmawati Anggriyani, Indah Ratih Anik Djuraidah Arina, Faula Aropah, Vina Da'watul Aropah, Vina Da’watul ASEP SAEFUDDIN Astari, Reka Agustia Azagi, Ilham Alifa Azis, Irfani Bagus Sartono Budi Susetyo Choiriyah, Evita Choirun Nisa CHOIRUN NISA Chrisinta, Debora Cici Suhaeni Cynthia Wulandari Dede Dirgahayu Domiri Dede Dirgahayu Domiri, Dede Dirgahayu 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 Fadhlia, Sarah Fadilah, Anggita Rizky FAHREZAL ZUBEDI Faqih Udin dan Jono M. Munandar Meivita Amelia Faradila, Agustin Farit M Afendi Farit Mochamad Afendi Fitria Hasanah Fitrianto, Anwar Halimatus Sa'diyah Hari Wijayanto Haryastuti, Rizqi Hengki Muradi Hidayat, Agus Sofian Eka Hilda Zaikarina Huda, Usep Firdaus Humairoh, Andi Zahira Al I Gede Nyoman Mindra Jaya IJSA, Indonesian Journal of Statistics and Its Applications Ilma Nabila Ilmani, Erdanisa Aghnia 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 Khairil Anwar Notodiputro Kurnia, A Kusdaniyama, Nunung Kusman Sadik Laradea Marifni Lestari P, Merryanty Linda Sakinah M Afendi, Farit 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 Mintarto Mundandar, Jono Muhamad Nur Aidi Muhammad Amirullah Yusuf Albasia Muhammad N Aidi Muhammad Nur Aidi Muhammad Ulinnuha Mulianto Raharjo Munanda, Jono Mintarto Munthe, Andrew Donda Muradi, Hengki Newton Newton Newton, Newton Nina Valentika Ningsih, Wiwik Andriyani Lestari Noercahyo, Unggul Sentanu Novi Hidayat Pusponegoro Nunung Kusdaniyama Nunung Kusdaniyama Nur Hikmah Nurlia Eka Damayanti Nurpadilah, Winda Nurus Sabani Pasaribu, Sahat M. Pepi Novianti Pika Silvianti Pratiwi, Windy Ayu Pratiwi, Windy Ayu Pudji Muljono Purwaningsih, Siti Samsiyah Puspasari, Novia Rahardiantoro, Septian Rahma Anisa Rahma Anisa Rhesa Adisty, Mohamad Risnawati, I'lmisukma Rita Rahmawati Rizqi Haryastuti Rochayati, Isti 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 Suhaeni, Cici Sunardi Sunardi Sunardi Suruddin, Adzkar Adlu Hasyr Sutomo, Valantino A Syafitri, Utami Syella Sumampouw Tsabitah, Dhiya Tsabitah, Dhiya Ulayya Ulfah Sulistyowati Ulfah Sulistyowati Ulinnuha, Muhammad Utami Dyah Syafitri Valantino A Sutomo Valentika, Nina Wibowo, Dwi Yoga Ari Winda Nurpadilah Windi D.Y Putri Wiwik Andriyani Lestari Ningsih Yenni Angraini Yoga, Ibnu Abi Zaikarina, Hilda Zulkarnain, Rizky