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All Journal Jurnal Ilmu Pertanian Indonesia FORUM STATISTIKA DAN KOMPUTASI Seminar Nasional Aplikasi Teknologi Informasi (SNATI) Media Statistika Jurnal Ilmu Dasar Statistika Jurnal Ilmiah Aplikasi Isotop Radiasi Jurnal Edukasi dan Penelitian Informatika (JEPIN) Jurnal Pengelolaan Sumberdaya Alam dan Lingkungan (Journal of Natural Resources and Environmental Management) Scientific Journal of Informatics Jurnal Pendidikan Geografi: Kajian, Teori, dan Praktik dalam Bidang Pendidikan dan Ilmu Geografi MAJALAH ILMIAH GLOBE Syntax Literate: Jurnal Ilmiah Indonesia BAREKENG: Jurnal Ilmu Matematika dan Terapan Indonesian Journal of Chemistry JTAM (Jurnal Teori dan Aplikasi Matematika) Zero : Jurnal Sains, Matematika, dan Terapan JURNAL METEOROLOGI DAN GEOFISIKA InPrime: Indonesian Journal Of Pure And Applied Mathematics ESTIMASI: Journal of Statistics and Its Application SRIWIJAYA JOURNAL OF ENVIRONMENT Xplore: Journal of Statistics Scientific Journal of Informatics Indonesian Journal of Statistics and Its Applications
Aji Hamim Wigena
Department Of Statistics, IPB University, Jalan Meranti Wing 22 Level 4, Dramaga, Babakan, Kec. Dramaga, Kabupaten Bogor, Jawa Barat 16680
Agriculture, Biological Sciences & Forestry Humanities Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering 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 Immunology & microbiology Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Mathematics Mechanical Engineering Physics Public Health Social Sciences Transportation Other

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Perbandingan Metode Particle Swarm Optimization dan Artificial Bee Colony pada Support Vector Machine Hasibuan, Rafika Aufa; Afendi, Farit Mochamad; Wigena, Aji Hamim
JEPIN (Jurnal Edukasi dan Penelitian Informatika) Vol 11, No 1 (2025): Volume 11 No 1
Publisher : Program Studi Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26418/jp.v11i1.91235

Abstract

Optimasi metode klasifikasi merupakan aspek krusial dalam meningkatkan akurasi model, terutama dalam analisis data medis yang kompleks dan memiliki karakteristik peubah yang beragam. Penelitian ini membandingkan performa klasifikasi dari Support Vector Machine (SVM) konvensional dengan dua metode optimasi berbasis metaheuristik yaitu, PSO-SVM dan ABC-SVM. Evaluasi dilakukan pada empat dataset medis, yaitu Breast Cancer, AIDS Disease, Darwin Disease, dan Parkinson Disease, dengan variasi seleksi peubah berbasis proporsi sebesar 30%, 50%, 70% dan 100% dari total peubah pada masing-masing dataset. Hasil penelitian menunjukkan bahwa metode PSO-SVM dan ABC-SVM secara konsisten mampu meningkatkan akurasi klasifikasi dibandingkan SVM standar. Pada beberapa dataset seperti Breast Cancer dan Parkinson Disease, akurasi meningkat dari 96,22% dan 85,53% (SVM) menjadi 100% dengan metode PSO-SVM dan ABC-SVM. Pada dataset AIDS Disease, akurasi meningkat dari 87,36% menjadi 100%. Sementara itu, pada dataset Darwin Disease yang memiliki tingkat overlap tertinggi (OV = 0,99727), peningkatan akurasi lebih terbatas, dari 83,76% (SVM) menjadi 91,65% (ABC-SVM). Proporsi terbaik yang ditemukan bervariasi antar dataset. Namun secara umum proporsi 70% dan 100% menunjukkan hasil akurasi yang paling stabil dengan waktu komputasi yang efisien pada PSO-SVM. Sedangkan pada ABC-SVM, peningkatan akurasi yang tinggi disertai waktu eksekusi yang jauh lebih besar, terutama pada dataset berdimensi tinggi. Analisis lebih lanjut juga menunjukkan bahwa metode optimasi efektif dalam mengatasi tantangan overlapping dan ketidakseimbangan kelas secara moderat, namun efektivitasnya menurun pada kondisi yang lebih kompleks. Dengan demikian, penggunaan metode optimasi PSO-SVM dan ABC-SVM dapat menjadi pendekatan yang efisien untuk meningkatkan akurasi klasifikasi data medis, selama disesuaikan dengan karakteristik data dan sumber daya komputasi yang tersedia.
Performance Analysis of Robust Functional Continuum Regression to Handle Outliers Ismah, Ismah; Erfiani, Erfiani; Wigena, Aji Hamim; Sartono, Bagus
InPrime: Indonesian Journal of Pure and Applied Mathematics Vol. 6 No. 1 (2024)
Publisher : Department of Mathematics, Faculty of Sciences and Technology, UIN Syarif Hidayatullah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15408/inprime.v6i1.38928

Abstract

Robust functional continuum regression (RFCR) is an innovation as a development of functional continuum regression that can be applied to functional data and is resistant to outliers. The resistance of RFCR depends on the applied weighting function. This study aims to evaluate the RFCR performance to handle outliers. We propose the various weighting functions in this evaluation, i.e., Huber, Hampel, Ramsay, and Tukey (Bisquare), which do not eliminate or give zero weight to observed data identified as outliers. This contribution is essential to determining the appropriate RFCR method without eliminating the outlier data. The result shows that the RFCR performance with the Huber weighting function is better than the others, based on the goodness of fit, consisting of the root means square error of prediction (RMSEP), the correlation between the actual data and the model, and the mean absolute error (MAE).Keywords: functional data analysis; Huber weighted function; Hampel weighted function; Ramsay weighted function; Tukey (Bisquare) weighted function. AbstrakRegresi kontinum fungsional kekar (RFCR) merupakan inovasi yang merupakan pengembangan dari regresi kontinum fungsional yang dapat diaplikasikan pada data fungsional dan tahan terhadap outlier. Resistansi RFCR bergantung pada fungsi pembobotan. Penelitian ini bertujuan untuk mengevaluasi kinerja RFCR. Kami mengusulkan beberapa fungsi pembobotan dalam evaluasi tersebut, yaitu Huber, Hampel, Ramsay, dan Tukey (Bisquare), dengan tidak menghilangkan atau memberikan bobot nol pada data observasi yang teridentifikasi sebagai outlier. Kontribusi ini penting untuk menentukan metode RFCR yang tepat tanpa menghilangkan data outlier. Hasil menunjukkan bahwa kinerja RFCR dengan fungsi pembobotan Huber lebih baik dibandingkan fungsi pembobotan lain berdasarkan goodness of fit, yang terdiri dari root mean square error of prediksi (RMSEP), korelasi antara data aktual dan model, dan mean kesalahan absolut (MAE).Kata Kunci: analisis data fungsional; fungsi berbobot Huber; fungsi tertimbang Hampel; fungsi tertimbang Ramsay; fungsi berbobot Tukey (Bisquare). 2020MSC: 62J99, 62R10
Comparison of Discriminant Analysis and Support Vector Machine on Mixed Categorical and Continuous Independent Variables for COVID-19 Patients Data Haikal, Husnul Aris; Wigena, Aji Hamim; Sadik, Kusman; Efriwati, Efriwati
Scientific Journal of Informatics Vol 11, No 1 (2024): February 2024
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v11i1.48565

Abstract

Purpose: Numerous factors can affect the duration of COVID-19 recovery. One method involves utilizing natural herbal medication. This study seeks to determine the variables influencing the duration of COVID-19 recovery and to compare discriminant analysis and support vector machine models using COVID-19 patient data from West Sumatra.Methods: Two data mining methods, Discriminant Analysis and Support Vector Machine with different types of kernels (linear, polynomial, and radial basis function), were employed to categorize the time of COVID-19 recovery in this work. The study utilized 428 data points, with 75% allocated for training data and 25% for testing data. The independent factors were evaluated by determining the selection variables' information value (IV) to gauge their influence on the dependent variable. Data resampling techniques were employed to tackle the problem of data imbalance. This study employs data resampling techniques, including undersampling, oversampling, and SMOTE. The balancing accuracy of Discriminant Analysis and Support Vector Machine was examined.Result: The Discriminant Analysis with SMOTE achieved a balanced accuracy of 66.50%, outperforming the linear kernel Support Vector Machine with SMOTE, which had a balanced accuracy of 63.20% in this dataset.Novelty: This study assessed the novelty, originality, and value by comparing Discriminant Analysis and SVM algorithms with categorical and continuous independent variables. This research explores techniques for managing imbalanced data using undersampling, oversampling, and SMOTE, with variable selection based on information value assessment. 
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.
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.
Co-Authors . Indahwati Aan Kardiana Abdul Kudus Afnan, Irsyifa Mayzela Agus Mohamad Soleh Akhbamah Primadaniyah Febrin Alwani, Nadira Nisa Amanda Permata Dewi Anang Kurnia Anik Djuraidah Anis Dyah Rahmawati Arina, Faula Arista Marlince Tamonob ASEP SAEFUDDIN Aunuddin Aunuddin Bagus Sartono Budi Suhardjo Budi Susetyo Bunawan Sunarlim Citra, Reza Felix Dani Al Mahkya Dedi Sucahyono Dewi Retno Sari Saputro, Dewi Retno Edvin Aldrian Efriwati Efriwati Embay Rohaeti Erfiani Erfiani Erfiani, Erfiani Erwin Eka Syahputra Makmur Fadjrian Imran Farit Mochamad Afendi Fildzah Hanum Syazwina Fitrianto, Anwar Galih Hedy Saputra Geri Zanuar Fadli Haikal, Husnul Aris Hanny Komalig Hari Wijayanto Hasibuan, Rafika Aufa Herlina Hanum Herlina Hanum, Herlina Hidayat Pawitan I Made Sumertajaya I Wayan Mangku Indahwati Ismah . Ismah, Ismah Itasia Dina Sulvianti Kusman Sadik Latifah K. Darusman Made Agung Prebawa Parama Artha Maemonah, Maemonah Mia Sri Listiani Ahmad Muhammad Nur Aidi Novian Tamara Nunung Nurjanah Nunung Nuryartono Nurrahman, Fathu Pigitha, Nindi Pingkan Awalia Rizaldi Boer Santoso, Zein Rizky Sinaga, Enny Keristiana Soeranto Human Soleh, Agus M Sundari, Marta Syafitri, Utami Tiyas Yulita Trinah Wati Urip Haryoko Utami Dyah Syafitri Valentika, Nina Via Sulviana Woro Estiningtyas Wulan Tri Wahyuni Yonny Koesmaryono Yuan Millafanti Yumna Karimah Yuniarty, Titin Yunus Subagyo Swarinoto _ Aunuddin