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All Journal FORUM STATISTIKA DAN KOMPUTASI Media Statistika JURNAL MATEMATIKA STATISTIKA DAN KOMPUTASI Jurnal Gaussian IPTEK The Journal for Technology and Science CAUCHY: Jurnal Matematika Murni dan Aplikasi Sosioinforma JUITA : Jurnal Informatika Jurnal Pengelolaan Sumberdaya Alam dan Lingkungan (Journal of Natural Resources and Environmental Management) International Journal of Advances in Intelligent Informatics Scientific Journal of Informatics JOIN (Jurnal Online Informatika) Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Indonesian Journal of Applied Statistics Jurnal Penelitian Pertanian Tanaman Pangan BAREKENG: Jurnal Ilmu Matematika dan Terapan JOURNAL OF APPLIED INFORMATICS AND COMPUTING SINTECH (Science and Information Technology) Journal Zero : Jurnal Sains, Matematika, dan Terapan MIND (Multimedia Artificial Intelligent Networking Database) Journal JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Jurnal Aplikasi Statistika & Komputasi Statistik FIBONACCI: Jurnal Pendidikan Matematika dan Matematika Inferensi International Journal of Advances in Data and Information Systems InPrime: Indonesian Journal Of Pure And Applied Mathematics ESTIMASI: Journal of Statistics and Its Application Majalah Ilmiah Matematika dan Statistika (MIMS) Jurnal Lebesgue : Jurnal Ilmiah Pendidikan Matematika, Matematika dan Statistika Journal of Applied Data Sciences Euler : Jurnal Ilmiah Matematika, Sains dan Teknologi Enthusiastic : International Journal of Applied Statistics and Data Science Prosiding Seminar Nasional Official Statistics Jurnal Natural Eduvest - Journal of Universal Studies Xplore: Journal of Statistics PROCEEDINGS OF THE INTERNATIONAL CONFERENCE ON DATA SCIENCE AND OFFICIAL STATISTICS Parameter: Jurnal Matematika, Statistika dan Terapannya Scientific Journal of Informatics Journal of Mathematics, Computation and Statistics (JMATHCOS) Advance Sustainable Science, Engineering and Technology (ASSET) Indonesian Journal of Statistics and Its Applications Journal on Mathematics Education
Kusman Sadik
Program In Statistics And Data Science, School Of Data Science, Mathematics, And Informatics, IPB University, Indonesia
Aerospace Engineering Agriculture, Biological Sciences & Forestry Humanities Astronomy Biochemistry, Genetics & Molecular Biology Chemistry 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 Industrial & Manufacturing Engineering Mathematics Mechanical Engineering Neuroscience Physics Social Sciences Transportation Other

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The Impact of the L1/L2 Ratio on Selection Stability and Solution Sparsity along the Elastic Net Regularization Path in High-Dimensional Genomic Data Fahira, Fani; Sadik, Kusman; Suhaeni, Cici; M Soleh, Agus
Journal of Applied Informatics and Computing Vol. 10 No. 1 (2026): February 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v10i1.12059

Abstract

High-dimensional genomic datasets (p>n) pose persistent challenges for predictive modeling and biomarker-oriented feature selection due to multicollinearity and instability of selected feature sets under resampling. Although Elastic Net is widely used to address correlated predictors via combined L1/L2 regularization, the practical role of the L1/L2 mixing ratio (α) is often treated as a secondary tuning choice driven primarily by predictive accuracy. This study investigates how varying α shapes the trade-off among selection stability, solution sparsity, and predictive performance along the Elastic Net regularization path. Experiments were conducted using the publicly available METABRIC breast cancer cohort (n = 1,964) with 21,113 gene expression features and a binary overall survival status outcome. Logistic regression with Elastic Net penalty was fitted across a grid of α values, with the regularization strength (λ) selected by cross-validation. Feature selection stability was evaluated under repeated resampling using the Jaccard index, Dice coefficient, and Adjusted Rand Index (ARI), while sparsity was summarized by the average number of non-zero coefficients; predictive performance was assessed using AUC, accuracy, and F1-score. Results show a monotonic decline in stability as α increases: α = 0.2 yields the highest stability (Jaccard 0.324, Dice 0.487, ARI 0.434), whereas LASSO (α = 1.0) produces the lowest stability (Jaccard 0.278, Dice 0.431, ARI 0.400). In contrast, predictive performance varies only marginally across α (AUC 0.696–0.704; accuracy 0.666–0.671; F1-score 0.738–0.742), while sparsity changes substantially (average selected features 110–204). Coefficient path analyses further illustrate abrupt shrinkage under LASSO versus smoother, group-preserving shrinkage under Elastic Net, consistent with improved reproducibility under lower-to-moderate α. Frequency-of-selection analysis highlights genes repeatedly selected across resampling, supporting interpretability of stable configurations without claiming causal biomarker validity. Overall, the findings demonstrate that α is a substantive modeling choice that materially affects stability and sparsity even when accuracy is similar, motivating stability-aware tuning for high-dimensional genomic prediction and reproducible feature discovery.
Household Climate Resilience Index and Its Determinants: An Empirical Study in DKI Jakarta Sundari, Marta; Sadik, Kusman; Wigena, Aji Hamim; Fitrianto, Anwar; Boer, Rizaldi
Jurnal Pengelolaan Sumberdaya Alam dan Lingkungan (Journal of Natural Resources and Environmental Management) Vol 16 No 2 (2026): Jurnal Pengelolaan Sumberdaya Alam dan Lingkungan (JPSL)
Publisher : Pusat Penelitian Lingkungan Hidup, IPB (PPLH-IPB) dan Program Studi Pengelolaan Sumberdaya Alam dan Lingkungan, IPB (PS. PSL, SPs. IPB)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29244/jpsl.16.2.162

Abstract

Climate change has intensified environmental pressures in urban coastal areas, particularly in DKI Jakarta, where recurrent flooding, tidal inundation, and heat extremes threaten urban sustainability. This study developed a Household Climate Resilience Index (HCRI) to assess the resilience of urban households to climate-related hazards using a robust principal analysis (RPCA) framework. The analysis was based on household survey data from 221 respondents across 17 urban villages in Jakarta, encompassing four resilience dimensions: exposure, sensitivity, incremental adaptation, and transformational adaptation. RPCA with a minimum covariance determinant estimator was applied to minimize the influence of outliers and ensure stable component estimation. The results reveal clear spatial heterogeneity in resilience, characterized by a distinct north–south gradient: northern coastal areas such as Kamal, Koja, and Pluit show the lowest resilience due to high flood exposure and land subsidence, whereas central and southern areas exhibit stronger adaptive capacity. The key determinants of resilience include flood frequency, household education levels, per-family expenditure, and proactive adaptation behaviors. The Kendall correlation test (τ = 0.518, p = 0.015) confirmed a significant positive association between flood occurrence and low resilience levels. The developed HCRI provides a robust, data-driven framework to support targeted climate adaptation policies and urban resilience planning in Jakarta, Indonesia. HCRI outputs, together with the identified key determinants (flood frequency, education, per-family expenditure, and proactive adaptation), can guide the prioritization of urban environmental management and adaptation investments in the most vulnerable urban villages, including drainage upgrading, land subsidence control, and coastal protection.
MIXED-EFFECT MODELS WITH RESTRICTED MAXIMUM LIKELIHOOD (REML), BOOT-STRAPPED REML AND BAYESIAN INFERENCE IN APPLICATION OF GAPMINDER DATA Asysta Amalia Pasaribu; Kusman Sadik; Anang Kurnia
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 20 No 3 (2026): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol20iss3pp1985-1998

Abstract

Mixed effects model combines fixed effects and random effects, allowing for the analysis of data with both fixed and random variations. This modeling approach is widely utilized across various fields. In R, the lme4 package is commonly employed to estimate mixed effects models using Restricted Maximum Likelihood (REML). There are several methods for estimating model parameters, including Bayesian inference, which has gained prominence with ongoing research advancements. Bayesian inference using Markov Chain Monte Carlo (MCMC) is among the most widely used Bayesian methods. Bayesian inference leverages probabilistic distributions to estimate parameters.to understand the general overview of life expectancy, serving as an indicator of survival time across different continents in the Gapminder dataset, it's essential to identify relevant variables after computing mixed effects predictions using Maximum Likelihood and REML estimation. This involves predicting life expectancy by integrating both random and fixed effects, determining relevant variables after estimating the Mixed Effects Model using REML Bootstrap estimation, and identifying influential variables after estimating the Mixed Effects Model using Bayesian MCMC inference. The methods employed include REML, Bootstrapped-REML, and Bayesian MCMC. The results indicate that all inference methods can be utilized to estimate parameters, with all predictor variables influencing life expectancy, except for the population variable. Further research is recommended to utilize data with more complex predictor variables.
EFFECTIVENESS OF DIMENSIONALITY REDUCTION METHODS ON DATA WITH NON-LINEAR RELATIONSHIPS Lukmanul Hakim; Asep Saefuddin; Kusman Sadik; Anwar Fitrianto; Bagus Sartono
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 20 No 3 (2026): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol20iss3pp2507-2522

Abstract

The phenomenon of big data presents distinct challenges in the analysis process, especially when the data contains a very large number of variables. High complexity, potential redundancy, and the risk of overfitting are major issues that must be addressed through dimensionality reduction techniques. Principal Component Analysis (PCA) is a common method effective for data with linear relationships but has limitations in identifying nonlinear patterns. This research aims to improve performance of classification by introducing autoencoder for dealing with nonlinear relationship, data noise, missing values, outliers, and data with various scales. This study employs a quantitative approach through analysis of simulated and empirical data in the form of the Village Development Index from the Central Statistics Agency, which contains variables with various measurement scales. Both dimensionality reduction methods—PCA and neural network-based autoencoders—are tested across various data scenarios. The evaluation is conducted based on their effectiveness in preserving data structure, as well as the Mean Squared Error (MSE) values in the reconstruction process. The results indicate that PCA excels in computational efficiency and accuracy for data with linear relationships. In contrast, the autoencoder demonstrates superior performance in detecting nonlinear patterns, achieving lower Mean Squared Error (MSE) values with stable MSE standard deviations. Additionally, the autoencoder proves to be more robust in handling missing values and outliers compared to PCA. The selection of dimensionality reduction methods highly depends on the characteristics of the analyzed data. Autoencoders represent a superior alternative for handling complex and nonlinear data, although they require model parameter tuning. Further research is recommended to explore the influence of network architecture and training strategies of autoencoders on dimensionality reduction performance.
Comparative Performance of Spatial Robust Small Area Estimation Methods: A Simulation Study Baiq Dian Arianingsih; Kusman Sadik; Indahwati Indahwati
ZERO: Jurnal Sains, Matematika dan Terapan Vol 9, No 3 (2025): Zero: Jurnal Sains Matematika dan Terapan
Publisher : UIN Sumatera Utara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30829/zero.v9i3.26477

Abstract

Estimating parameters for small areas often faces limitations due to insufficient sample sizes, resulting in low-precision estimates. The Small Area Estimation (SAE) approach is used to address this problem by utilizing auxiliary variables to improve estimation efficiency. This study evaluates four SAE methods, namely EBLUP, REBLUP, SEBLUP, and SREBLUP, through a simulation study based on a nested error model across 18 scenarios that combine two area sizes (16 and 64 areas), levels of outlier contamination in the error component, and degrees of spatial correlation in the area-level random effects. Each scenario is replicated 50 times. Model performance is evaluated using Relative Bias (RB) and Relative Root Mean Square Error (RRMSE). The results show that non-robust methods are sensitive to outliers, whereas robust methods produce more stable estimates. The SREBLUP method demonstrates the best performance under low to moderate spatial correlation. In addition, an ANOVA test is conducted to identify factors that significantly affect the response.
Perbandingan Performa MSGARCH, LSTM, dan Hybrid MSGARCH-LSTM pada Peramalan Data Deret Waktu yang Mengandung Heteroskedastisitas Freya, Wa Ode Rona; Sadik, Kusman; Susetyo, Budi
ESTIMASI: Journal of Statistics and Its Application Vol. 7, No. 1, Januari, 2026 : Estimasi
Publisher : Hasanuddin University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20956/ejsa.v7i1.45934

Abstract

Volatility forecasting is crucial for estimating potential portfolio losses, particularly in cryptocurrency markets like Bitcoin, which exhibit high and irregular price fluctuations. Models from the Generalized Autoregressive Conditional Heteroscedasticity (GARCH) family, including Markov Switching GARCH (MSGARCH), are widely used to handle heteroscedastic data and capture regime changes. Meanwhile, Long Short-Term Memory (LSTM) is effective for modeling nonlinear and complex patterns in financial time series. This study proposes a hybrid MSGARCH-LSTM model by incorporating MSGARCH predictions as additional input to the LSTM. The model is evaluated using simulated data resembling Bitcoin's characteristics, with Heteroscedasticity Mean Absolute Error (HMAE) as the primary metric, and analyzed using ANOVA and Tukey's post-hoc test. The results identify four superior hybrid configurations, all of which significantly outperform the standalone MSGARCH and LSTM models. Based on the characteristics of Bitcoin data, the MSGARCH (2-regime with sged error distribution)-LSTM model is selected for empirical analysis. This model achieved an HMAE of 0.3197 and an HMSE of 0.2088, with accuracy improvements of 61.20% and 83.50% compared to the standalone MSGARCH model. These findings indicate that the hybrid MSGARCH-LSTM model improves volatility forecasting accuracy in highly volatile cryptocurrency markets.
Technical Analysis of the Indonesian Stock Market with Gated Recurrent Unit and Temporal Convolutional Network Siti Aisyah; Yenni Angraini; Kusman Sadik; Bagus Sartono; Gerry Alfa Dito
JUITA: Jurnal Informatika JUITA Vol. 12 No. 2, November 2024
Publisher : Department of Informatics Engineering, Universitas Muhammadiyah Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30595/juita.v12i2.23464

Abstract

Big data is essential in the age of 4.0 industry as it becomes the basis of decision making. Deep learning research in the last few years has been proven effective in understanding complex big data patterns, especially in the finance sector. The rapid growth of the Indonesian stock market in the last 20 years, which was driven by globalization, prompted fluctuation in the Bursa Efek Jakarta (JKSE) which was influenced by stock prices, commodity prices, and exchange rate. This study identifies the main indicators of Indonesian stock market crisis, applies and compares deep learning models, particularly Gated Recurrent Unit (GRU) and Temporal Convolutional Network (TCN), in predicting stock prices. This study identified 20 JKSE crisis points between the 2002-2023 period with average return value at around -6%. All variables correlated positively with JKSE, with SET.BK as the highest correlated variable in lag 0. The American and European stock market, commodity price, and exchange rate tend to show a pattern opposite to the JKSE crisis. Predictor variables such as STI, HIS, KLSE, KS11, SET.BK, PSEI.PS, RUT, and USDIDR are chosen based on significant cross correlation and average return plot. Hyperparameter tuning and cross validation within a 3 years window concluded that the GRU model is accurate and efficient, with RMSE value at 43.35568 and MAE value at 33.66909 in the validation data.
OPTIMASI XGBOOST DALAM PREDIKSI KECEPATAN KENDARAAN SECARA REAL-TIME : PERBANDINGAN METODE TUNING HYPERPARAMETER Panji Lokajaya Arifa; Kusman Sadik; Agus M Soleh; Cici Suhaeni
Jurnal Gaussian Vol 15, No 1 (2026): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/j.gauss.15.1.01-11

Abstract

Real-time vehicle speed prediction plays a vital role in the development of intelligent transportation systems aimed at improving traffic flow and safety. This study investigates the performance of the XGBoost algorithm enhanced with three hyperparameter tuning techniques: Grid Search, Bayesian Optimization, and Genetic Algorithm. A simulated dataset was constructed reflect diverse urban traffic scenarios, incorporating environmental variables such as weather, road conditions, and traffic density. The models were assessed using 5 and 10-fold cross-validation based on prediction metrics (MSE, RMSE, MAE and R²) as well as computational efficiency in terms of training and inference time. The findings reveal that Bayesian Optimization achieves the highest prediction accuracy, while Grid Search offers the fastest training time. Genetic Algorithm demonstrates a balanced trade-off between accuracy and computational efficiency, making it a competitive and practical choice. These results highlight the importance of selecting hyperparameter tuning strategies based on specific system needs in real-time traffic prediction using XGBoost. 
Improving Land Use Classification Accuracy Using Zonal Statistics And Supervised Machine Learning Gede Awantara; Kusman Sadik; Agus Mohamad Soleh; Cici Suhaeni
Parameter: Jurnal Matematika, Statistika dan Terapannya Vol 5 No 1 (2026): Parameter: Jurnal Matematika, Statistika dan Terapannya
Publisher : Jurusan Matematika FMIPA Universitas Pattimura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/parameterv5i1pp181-194

Abstract

This study aims to improve land use classification accuracy by integrating zonal statistics with supervised machine learning using Sentinel-2 imagery. Two classification models were developed: Model A based on single-pixel values and Model B using aggregated zonal statistics derived from polygon shapefile data. Two algorithms, Random Forest and Classification and Regression Trees (CART), were implemented and evaluated through 5-fold cross validation. The results show that Model B consistently outperformed Model A, with the best performance achieved by Random Forest Model B, reaching an overall accuracy of 73.74% and a kappa coefficient of 0.5999. Class-wise evaluation based on F1-score revealed strong performance in dominant classes such as settlement, water bodies, and rice fields, while underrepresented classes like cropland and shrubland were more difficult to classify due to class imbalance. These findings highlight the effectiveness of zonal statistics in producing more representative training features and improving model stability and accuracy in land use classification tasks.
Bayesian Hierarchical Lognormal Modeling of Dengue Incidence with Area-Specific Temporal Effects Erwan Setiawan; Anang Kurnia; Kusman Sadik
Parameter: Jurnal Matematika, Statistika dan Terapannya Vol 5 No 1 (2026): Parameter: Jurnal Matematika, Statistika dan Terapannya
Publisher : Jurusan Matematika FMIPA Universitas Pattimura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/parameterv5i1pp139-152

Abstract

This study presents the development and validation of a Bayesian hierarchical model to estimate the incidence rate of dengue fever (DF) in West Java, Indonesia. Bayesian hierarchical models offer powerful tools for handling uncertainty and regional heterogeneity, yet their implementation remains challenging—especially in complex datasets with multilevel structures. The proposed model incorporates both random intercepts (for regencies/cities) and random slopes (for year), with various prior distribution scenarios tested to ensure robustness. Among the tested predictors, population density was found to significantly influence DF incidence. Model performance evaluation using Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) yielded values of 31.26 and 48.77, respectively, indicating good predictive accuracy. This research highlights the effectiveness of hierarchical Bayesian modeling for epidemiological analysis and contributes to more targeted public health strategies in dengue-endemic regions
Co-Authors . Erfiani A.Tuti Rumiati Aam Alamudi ACHMAD FAUZAN Adib Roisilmi Abdullah Agus M Soleh Agus M Soleh Agus M. Soleh Agus Mohamad Soleh Agus Muhammad Saleh Ahmad Rifai Nasution Aji Hamim Wigena Aji Hamim Wigena Akbar Rizki Akbar Rizki Akbar Rizki Akmala Firdausi Amalia, Rahmatin Nur Anadra, Rahmi Ananda Shafira Anang Kurnia Andespa, Reyuli Andi Okta Fengki ASEP SAEFUDDIN Astari, Reka Agustia Astari, Reka Agustia Asysta Amalia Pasaribu Aulya Permatasari Azka Ubaidillah Bagus Sartono Baiq Dian Arianingsih Budi Susetyo Budi Susetyo Cici Suhaeni Cici Suhaeni Cici Suhaeni Dewi Zulyani Pomalingo Dhita Elsha Pangestika Dian Handayani Dito, Gerry Alfa Dwi Agustin Nuriani Sirodj Efriwati Efriwati Embay Rohaeti Eriski Isnanda Erwan Setiawan Evita Purnaningrum Fahira, Fani FARDILLA RAHMAWATI Farit Mochamad Afendi Fitrianto, Anwar Fradha Intan Arassah Freya, Wa Ode Rona Gede Awantara Gerry Alfa Dito Haikal, Husnul Aris Hari Wijayanto Hasnataeni, Yunia Hazan Azhari Zainuddin Hermawati, Neni I Gusti Ngurah Sentana Putra I Made Sumertajaya I Wayan Mangku Indahwati Iqbal, Teuku Achmad Jefita Resti Sari Khairi A N Khairil Anwar Notodiputro Khikmah, Khusnia Nurul Khusnul Khotimah Kusni Rohani Rumahorbo Latifah, Leli Lili Puspita Rahayu Logananta Puja Kusuma Logananta Puja Kusuma Lukmanul Hakim M Soleh, Agus Mochamad Ridwan Mochamad Ridwan, Mochamad Mohammad Masjkur Muh Nur Fiqri Adham Muhammad Nur Aidi Muhammad Yusran Mulianto Raharjo Naima Rakhsyanda Naufalia Alfiryal Nisrina Az-Zahra, Putri Nur Khamidah Nurhidayah Nurhidayah Nusar Hajarisman Panji Lokajaya Arifa Parwati Sofan Purnama Sari Rifqi Aulya Rahman Rita Rahmawati Rizaldi Boer Rizki, Akbar Rizqi, Tasya Anisah ROCHYATI ROCHYATI Sabrina Adnin Kamila Sahamony, Nur Fitriyani Satriyo Wibowo Sentana Putra I Gusti Ngurah Siregar, Jodi jhouranda Siti Aisyah SITI NURADILLA Siti Raudlah Sitti Nurhaliza Soleh, Agus M Sri Kustiara Suhaeni, Cici Sundari, Marta Supriatin, Febriyani Eka Tendi Ferdian Diputra Titin Suhartini Titin Suhartini, Titin Tri Wahyuni Uswatun Hasanah Utami Dyah Syafitri Viarti Eminita Widhiyanti Nugraheni Yenni Angraini Yenni Kurniawati Yuli Eka Putri Zafira Fakhriyah