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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 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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COMPARATIVE ANALYSIS OF BCBIMAX AND PLAID BICLUSTERING ALGORITHM FOR PATTERN RECOGNITION IN INDONESIA FOOD SECURITY Sumertajaya, I Made; Hikmah, Nur; Afendi, Farit Mochamad
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 20 No 1 (2026): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol20iss1pp0335-0346

Abstract

Biclustering is an unsupervised learning algorithm that simultaneously groups rows and columns in a data matrix. Unlike conventional clustering, which evaluates objects across all variables independently, biclustering identifies subsets of objects and variables that share similar patterns—revealing localized structures within complex datasets. This study applies the BCBimax and Plaid algorithms to examine food security patterns across 34 Indonesian provinces. The indicators cover three key dimensions: availability, accessibility, and utilization of food. The algorithms are evaluated using the Jaccard Index, Mean Squared Residue (MSR), and the number of provinces effectively clustered. Results show that BCBimax, using a binarization threshold based on the median value, generates eight biclusters covering 58.8% of provinces. Meanwhile, the Plaid algorithm, applying constant column model parameters, produces six biclusters with 55.88% coverage, including overlapping memberships. Overall, BCBimax demonstrates superior performance, as indicated by a lower average MSR value (0.035) compared to Plaid (0.209). The Jaccard Index similarity score of 14.61% suggests that the biclusters formed by each method are significantly distinct. Both approaches indicate that the majority of Indonesian regions exhibit low to moderate food security characteristics.
PERFORMANCE EVALUATION OF SEASONAL ARIMA-SVR AND SEASONAL ARIMAX-SVR HYBRID METHODS ON FORECASTING PADDY PRODUCTION Risnawati, I'lmisukma; Afendi, Farit Mochamad; Sumertajaya, I Made
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 20 No 1 (2026): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol20iss1pp0367-0380

Abstract

This study explores advances in forecasting time series data by combining linear and non-linear models. Traditional methods such as ARIMA and its variant ARIMAX are effective for linear data but have limitations when dealing with non-linearity. Support Vector Regression (SVR), a non-linear method, complements these weaknesses. Hybrid models such as ARIMA-SVR and ARIMAX-SVR synergize ARIMA or ARIMAX for linear components and SVR for non-linear components, improving accuracy. The purpose of this study is to evaluate the performance of hybrid ARIMA-SVR and ARIMAX-SVR methods on Indonesian paddy production data. The data analyzed is national-level data per sub-round (i.e., three sub-rounds per year) from sub-round 1 (January-April) of 1992 to sub-round 3 (September-December) of 2024, obtained from the Indonesian Central Statistics Agency and the Indonesian Ministry of Agriculture.Forecasting accuracy is measured using Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE). The results show that the best model is the Seasonal ARIMAX (1,1,1)(0,1,1)[3]-SVR ( 0.05) hybrid model, with the smallest RMSE and MAPE values of 0.304 and 1.473%. The addition of the harvested area variable and the ASF dummy improved the accuracy of the ARIMAX model prediction, while the application of SVR to ARIMAX residuals successfully captured previously undetected linear patterns. Based on these considerations, the Seasonal ARIMAX(1,1,1)(0,1,1)[3]-SVR ( 0.05) hybrid model was selected as the model with the best performance.
Spatiotemporal Clustering of Key Food Commodity Prices Using Multivariate Time Series Tsabitah, Dhiya Ulayya; Angraini, Yenni; Sumertajaya, I Made
International Journal of Advances in Data and Information Systems Vol. 6 No. 3 (2025): December 2025 - International Journal of Advances in Data and Information Syste
Publisher : Indonesian Scientific Journal

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59395/ijadis.v6i3.1422

Abstract

Food price stabilization remains a critical challenge in economic development planning and food security, particularly in developing countries like Indonesia, which exhibit high spatial and temporal diversity. To develop an efficient and adaptive predictive approach for understanding food commodity price dynamics, this study integrates multivariate time series clustering using a Dynamic Time Warping-based K-Means algorithm with a hybrid forecasting model that combines Vector Error Correction Model with Exogenous Variables and Long Short-Term Memory. The clustering evaluation results indicate reasonably cohesive group structures, with a silhouette score of 0.45 and a Davies-Bouldin Index of 0.67. Each cluster profile reveals significant differences in price trends, volatility, and anomaly patterns. Model validation using the Wilcoxon signed-rank test shows that the differences between cluster-level forecasts and individual-level actual values are generally statistically insignificant. These findings suggest that the proposed integrative approach can accurately capture regional price patterns and serve as a foundation for more data-driven and responsive policymaking in food price stabilization efforts. The 30-period forecasts for rice, eggs, and red onions reflected dynamic variations aligned with spatial characteristics: rice shows relatively stable behavior, eggs exhibit strong seasonal patterns, and red onions display the highest price volatility.
Pemetaan Penelitian Resiliensi Organisasi pada Pendidikan Tinggi: Sebuah Analisis Bibliometrik Mulya, Diki Akhwan; Sumertajaya, I Made; Sukmawati, Anggraini
Jurnal Manajemen dan Organisasi Vol. 16 No. 3 (2025): Jurnal Manajemen dan Organisasi
Publisher : IPB University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29244/jmo.v16i3.67868

Abstract

Organizations are confronted with multiple uncertainties and unexpected changes, requiring them to survive and adapt in order to achieve long-term success. One strategic approach that has increasingly attracted scholarly attention is the strengthening of organizational resilienceNevertheless, academic studies on organizational resilience within the higher education sector continue to exhibit diverse developmental patterns and therefore require comprehensive mapping. This study adopts a bibliometric approach using data retrieved from the Scopus database. Articles were collected using the keyword string “organizational_resilience_AND_higher_education” within the subject areas of business, management, and accounting. All data were analyzed using VOSviewer software to map publication trends, author networks, institutional affiliations, and keyword interrelationships. The results reveal that the highest number of publications occurred in 2021, with a total of 50 articles. The most productive institutional affiliation was Technische Universität Dresden, with five publications. Furthermore, keywords with the highest total link strength included industry, COVID, organizational resilience, and technology. Author network analysis identified 14 interconnected authors within collaborative research clusters. These findings indicate a growing academic focus on organizational resilience in the higher education sector, particularly in response to global challenges such as the COVID-19 pandemic and technological transformation.
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.
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 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 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 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 Fadilah, Anggita Rizky FAHREZAL ZUBEDI Faqih Udin dan Jono M. Munandar Meivita Amelia 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 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. 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 Muradi, Hengki 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 Pratiwi, Windy Ayu Pudji Muljono Purwaningsih, Siti Samsiyah Puspasari, Novia Rahardiantoro, Septian Rahma Anisa Rahma Anisa Rhesa Adisty, Mohamad Risnawati, I'lmisukma 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 Tsabitah, Dhiya Ulayya Ulfah Sulistyowati 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 Zulkarnain, Rizky