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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) 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 International Journal of Advances in Data and Information Systems Journal of Data Science and Its Applications Jurnal Teknik Informatika (JUTIF) JURNAL ILMIAH GLOBAL EDUCATION 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
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 Immunology & microbiology Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Mathematics Mechanical Engineering Physics Social Sciences Transportation Other

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Evaluasi Kinerja Spectral Biclustering dalam Identifikasi Potensi Produksi Komoditas Hortikultura di Indonesia Lestari P, Merryanty; Sumertajaya, I Made; Erfiani, Erfiani
Limits: Journal of Mathematics and Its Applications Vol 21, No 3 (2024)
Publisher : Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/limits.v21i3.21718

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.
Perbandingan Metode Regresi Multilevel dan Beta Generalized Linear Mixed Models pada Data Longitudinal Capaian IPK Mahasiswa Meilania, Gusti Tasya; Syafitri, Utami Dyah; Sumertajaya, I Made
Limits: Journal of Mathematics and Its Applications Vol 21, No 3 (2024)
Publisher : Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/limits.v21i3.21788

Abstract

Penelitian ini membandingkan kinerja model Beta Generalized Linear Mixed Model (Beta GLMM) dengan Regresi Multilevel pada data Indeks Prestasi Kumulatif (IPK) mahasiswa. Data IPK yang digunakan dalam penelitian ini terlihat miring ke sisi kiri atau memiliki ekor kiri yang lebih panjang yang mencerminkan kecenderungan mahasiswa memperoleh nilai yang lebih besar daripada rata-rata IPK keseluruhan. Hal ini mengindikasikan bahwa data tidak berdistribusi normal, melainkan diduga berdistribusi Beta. Tujuan dari penelitian ini adalah melakukan perbandingan terhadap metode regresi multilevel dan Beta Generalized Linear Mixed Model (GLMM) untuk melihat faktor-faktor yang memengaruhi IPK mahasiswa setiap semester. Data yang digunakan adalah data longitudinal dimana setiap mahasiswa diamati IPK per semester serta beberapa peubah penjelas lainnya. Pendekatan Beta GLMM digunakan karena Beta GLMM menggabungkan antara pendekatan Linear Mixed Model (LMM) dengan Generalized Linear Model (GLM)Berdasarkan analisis yang dilakukan, diperoleh hasil bahwa metode Beta GLMM memiliki nilai Akaike Information Criterion (AIC) yang lebih rendah dibandingkan metode regresi multilevel. Adapun faktor-faktor yang mempengaruhi capaian IPK mahasiswa berdasarkan analisis Beta GLMM diantaranya semester mahasiswa, SKS mahasiswa setiap semester, status perkawinan, jalur masuk kuliah, sumber biaya pendidikan (beasiswa), interaksi semester dengan status perkawinan, interaksi antara semester dengan jalur masuk kuliah, dan interaksi antara semester dengan beasiswa. Selain itu, diketahui bahwa proporsi keragaman IPK yang dapat dijelaskan oleh perbedaan antar mahasiswa adalah sebesar 0.837. Hal ini menunjukkan bahwa 83.7% dari total variasi IPK dapat dijelaskan oleh perbedaan antar mahasiswa (Level 2), sedangkan sisanya 16.3% dijelaskan oleh variasi pada setiap mahasiswa disetiap semester (Level 1).
THE ROLE OF EMPLOYEE ENGAGEMENT ON JOB SATISFACTION AND ITS EFFECT ON ORGANIZATIONAL PERFORMANCE Noercahyo, Unggul Sentanu; Maarif, Mohammad Syamsul; Sumertajaya, I Made
Jurnal Aplikasi Manajemen Vol. 19 No. 2 (2021)
Publisher : Universitas Brawijaya, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21776/ub.jam.2021.019.02.06

Abstract

The purpose of this research was to analyze the role of employee engagement from the perspective of job engagement and organization engagement on job satisfaction and its effect on organizational performance. This research was conducted at a multinational manufacturing chemicals company located in Tangerang and Cilegon. The population was approximately 121 employees consisting of three job levels: Manager, White Collar, and Blue Collar. A target sample determined approximately 93 employees. Sampling using a non-probability sampling approach with a quota sampling method. The questionnaire was distributed to the population, but only 86 respondents filled out and returned the questionnaire. Method of hypothesis testing using Partial Least Square of Structural Equation Modeling (PLS-SEM) approach. The results suggested that job engagement has a positive and significant effect on job satisfaction but does not significantly affect organizational performance. Next, organization engagement has a positive and significant effect on job satisfaction but does not significantly affect organizational performance. Furthermore, job satisfaction has a positive effect and significantly influences organizational performance. Future research is advisable to examine the relations of other variables such as workload, work-life balance, and implementation of an integrated management system, which, believed, can provide a comprehensive view of employee engagement, job satisfaction, and organizational performance.
ANALYSIS OF THE EFFECT OF KNOWLEDGE MANAGEMENT, COMPETENCY, AND INNOVATION ON EMPLOYEE PERFORMANCE Wibowo, Dwi Yoga Ari; Muljono, Pudji; Sumertajaya, I Made
Jurnal Aplikasi Manajemen Vol. 19 No. 4 (2021)
Publisher : Universitas Brawijaya, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21776/ub.jam.2021.019.04.09

Abstract

The conformity of employee competencies with their positions and innovation in work implementation is required by the presence of the Civil Servants (PNS) professional demands. Knowledge management is necessary to fulfill the knowledge needs of employees so that competence, innovation, and employee performance can be improved. This study aims to analyze the effect of knowledge management, competence, and innovation on employee performance. All employees in the Finance and Equipment Bureau of the Ministry of Agriculture were involved as the research object. The data were obtained using the census method of 103 Finance and Equipment Bureau employees, interviews with appraisal officials and personnel managers, and employee performance appraisals in 2019. The data processing uses Structural Equation Modeling - Partial Least Square (SEM PLS) analysis. This study indicates that knowledge management has a significant effect on innovation and competence, whereas innovation significantly affects performance. However, knowledge management and competence do not seem to have a significant effect on employee performance. Knowledge management indirectly has a significant effect on performance through innovation. Nevertheless, knowledge management does not seem to have a significant effect on performance through competence. The suggestion for further research is it shall be conducted in a work unit that requires special skills so that the employee competence is taken into consideration.
The Continuum Regression Analysis with Preprocessed Variable Selection LASSO and SIR-LASSO Suruddin, Adzkar Adlu Hasyr; Erfiani, Erfiani; Sumertajaya, I Made
Inferensi Vol 8, No 1 (2025)
Publisher : Department of Statistics ITS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j27213862.v8i1.21658

Abstract

Analyzing high-dimensional data is a considerable challenge in statistics and data science. Issues like multicollinearity and outliers often arise, leading to unstable coefficients and diminished model effectiveness. Continuum regression is a useful method for calibration models because it effectively handles multicollinearity and reduces the number of dimensions in the data. This method condenses data into autonomous latent variables, resulting in a more stable, precise, and reliable model. It is possible to use the dimensionality reduction method without losing any important information from the original data. This makes it a useful tool for making calibration models work better. In the initial phase, minimizing dimensions via variable selection is crucial. The study aims to build and test the Continuum Regression calibration model using LASSO and SIR-LASSO variable selection preprocessing methods. SIR-LASSO is a method that integrates SIR with the variable selection capabilities of LASSO. This technique aims to handle high-dimensional data by identifying relevant low-dimensional structures. LASSO improves variable selection by applying a penalty to regression coefficients, reducing the impact of less significant or redundant variables. The integration improves SIR's efficacy in assessing high-dimensional data while also enhancing model stability and interpretability. This approach seeks to address the issues of multicollinearity and model instability. We conducted simulations using both low-dimensional and high-dimensional datasets to assess the efficacy of CR LASSO and CR SIR-LASSO. RStudio version 4.1.3 was used for the analysis. The "MASS" package was used to create data with a multivariate normal distribution. The "glmnet" package was used for LASSO variable selection, and the "LassoSIR" package was used for SIR-LASSO variable selection. In the simulation itself, LASSO surpasses SIR-LASSO in variable selection by yielding the lowest RMSEP value in every scenario. On the other hand, SIR-LASSO becomes less stable as the number of dimensions increases, which suggests that it is sensitive to large changes in variables. As shown by lower median RMSEP values across a range of sample sizes and situations, CR LASSO is usually better at making predictions than SIR-LASSO. The RMSEP distributions for LASSO are consistently tighter, which means that its performance is more stable and reliable compared to SIR-LASSO, whose data has more outliers and more variation. Even with a growing sample size, LASSO maintains its advantage, particularly when setting the value at 0.5. SIR-LASSO, although occasionally competitive, generally yields more variable results, particularly with larger sample sizes. Overall, LASSO appears to be a more reliable option for CR model with pre-processed variable selection.
Implementation of Clustering Time Series with DTW to Clustering and Forecasting Rice Prices Each Provinces in Indonesia Tsabitah, Dhiya; Angraini, Yenni; Sumertajaya, I Made
Inferensi Vol 8, No 1 (2025)
Publisher : Department of Statistics ITS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j27213862.v8i1.21952

Abstract

Indonesia faces a significant imbalance between domestic supply and demand, leading to escalating rice prices and pronounced regional disparities. To elucidate underlying price patterns and forecast future trends, this study employed Hierarchical Clustering Time-Series with DTW and ARIMA modelling at both individual and cluster levels. Comprehensive analysis, incorporating visualization and threshold comparisons, identified Central Kalimantan as an outlier. Individual ARIMA models demonstrated exceptional performance, with MAPE values below 10%. The clustering time-series correlation using Cophenetic coefficient, reached 0.68 for ward linkages. Two clustering approaches were explored: (1) ignoring the outlier province, (2) excluding Central Kalimantan and incorporating it into a separate cluster. Optimal cluster measurement, the Elbow, Silhouette, Calinski-Harabasz, and Davies-Bouldin, yielded 6-7 clusters for the former approach and 3-5 clusters for the latter. Comparative analysis of individual and cluster forecasts, coupled with paired t-tests, revealed that Ward linkage in the second approach produced the most favorable results, with 27/34 provinces exhibiting cluster MAPE values less than or equal totheir individual MAPE. This finding underscores the efficacy of cluster-based modeling in generating accurate and representative estimates for a substantial portion of provinces. A 12-period rice price forecast indicates a prevailing trend of rising prices in most regions of Indonesia.
EVALUASI KEPUASAN PENGGUNA JASA LABORATORIUM KIMIA PT KRAKATAU STEEL (PERSERO) TBK TAHUN 2012-2013 Hilda Zaikarina; . Erfiani; I Made Sumertajaya
Indonesian Journal of Statistics and Applications Vol 1 No 1 (2017)
Publisher : Departemen Statistika, IPB University dengan Forum Perguruan Tinggi Statistika (FORSTAT)

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 Cici Suhaeni; I Made Sumertajaya; Anik Djuraidah
Indonesian Journal of Statistics and Applications Vol 2 No 1 (2018)
Publisher : Departemen Statistika, IPB University dengan Forum Perguruan Tinggi Statistika (FORSTAT)

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 Andrew Donda Munthe; I Made Sumertajaya; Utami Dyah Syafitri
Indonesian Journal of Statistics and Applications Vol 2 No 2 (2018)
Publisher : Departemen Statistika, IPB University dengan Forum Perguruan Tinggi Statistika (FORSTAT)

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 Isti Rochayati; Utami Dyah Syafitri; I Made Sumertajaya; Indonesian Journal of Statistics and Its Applications IJSA
Indonesian Journal of Statistics and Applications Vol 3 No 1 (2019)
Publisher : Departemen Statistika, IPB University dengan Forum Perguruan Tinggi Statistika (FORSTAT)

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 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 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 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 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 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