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All Journal FORUM STATISTIKA DAN KOMPUTASI Media Statistika Statistika JURNAL MATEMATIKA STATISTIKA DAN KOMPUTASI Jurnal Sains dan Teknologi Techno.Com: Jurnal Teknologi Informasi Kubik KEUNIS Sinkron : Jurnal dan Penelitian Teknik Informatika Syntax Literate: Jurnal Ilmiah Indonesia BAREKENG: Jurnal Ilmu Matematika dan Terapan JOURNAL OF APPLIED INFORMATICS AND COMPUTING JTAM (Jurnal Teori dan Aplikasi Matematika) Teorema: Teori dan Riset Matematika Krea-TIF: Jurnal Teknik Informatika Sainmatika: Jurnal Ilmiah Matematika dan Ilmu Pengetahuan Alam Saintifik : Jurnal Matematika, Sains, dan Pembelajarannya Jambura Journal of Mathematics JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Inferensi International Journal of Advances in Data and Information Systems InPrime: Indonesian Journal Of Pure And Applied Mathematics Jurnal Lebesgue : Jurnal Ilmiah Pendidikan Matematika, Matematika dan Statistika KUBIK: Jurnal Publikasi Ilmiah Matematika Euler : Jurnal Ilmiah Matematika, Sains dan Teknologi Jurnal Riset dan Inovasi Pembelajaran PROSIDING SEMINAR NASIONAL FISIKA (E-JOURNAL) VARIANSI: Journal of Statistics and Its Application on Teaching and Research Xplore: Journal of Statistics Journal of Mathematics, Computation and Statistics (JMATHCOS) Indonesian Journal of Statistics and Its Applications Jurnal Info Kesehatan
Erfiani Erfiani
Pogram Studi Statistika Dan Sains Data, Fakultas Matematika Dan Ilmu Pengetahuan Alam, Institut Pertanian Bogor, Indonesia
Agriculture, Biological Sciences & Forestry Humanities Biochemistry, Genetics & Molecular Biology Chemistry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Immunology & microbiology Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Mechanical Engineering Physics Social Sciences Transportation Other

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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
Forecasting Indonesia's Non-Oil and Gas Exports Using Facebook Prophet: A Seasonal and Trend Analysis Erfiani, Erfiani; Wijaya, Ferdian Bangkit
Inferensi Vol 8, No 3 (2025)
Publisher : Department of Statistics ITS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j27213862.v8i3.23337

Abstract

This study aims to analyze and predict the trend of Indonesia's non-oil and gas exports using the Facebook Prophet model, focusing on identifying seasonal patterns, trends, and volatility present in the export data. Monthly export data from 2015 to 2025, sourced from the Statistics Indonesia (BPS), were used as the basis for analysis. The dataset revealed notable seasonal patterns and substantial volatility, particularly in the period following 2020. To model these dynamics, three Prophet model configurations were tested: one considering only annual seasonality, combining both annual and monthly seasonality, and another incorporating only monthly seasonality. The evaluation of these models showed with an initial Mean Absolute Percentage Error (MAPE) of 8.70%. This model was then optimized through hyperparameter tuning. The optimal parameter configuration (changepoint_prior_scale = 0.5, seasonality_prior_scale = 0.01, fourier_order = 3) resulted in a significant improvement, reducing the MAPE to 4.73%. This optimized model demonstrated its capacity to more precisely capture the complex patterns. Furthermore, the study projected Indonesia’s non-oil and gas exports for the period from April 2025 to December 2026. The projections indicate a relatively stable export trend within the range of 20,000 to 22,000 million USD per month, with consistent seasonal patterns.
MIXED-EFFECTS MODELS WITH GENERALIZED RANDOM FOREST: IMPROVED FOOD INSECURITY ANALYSIS Fransiska, Herlin; Soleh, Agus Mohamad; Notodiputro, Khairil Anwar; Erfiani, Erfiani
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 20 No 2 (2026): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol20iss2pp1111-1124

Abstract

Food insecurity is a complex issue that requires a deep understanding of its influencing factors. Accurate predictions are crucial for effective interventions. Machine learning is well-suited to the large and complex data available in the big data era. However, machine learning generally does not accommodate hierarchical or clustered data structures, making them challenging for machine learning modeling. One model that accommodates hierarchical data structures is the mixed-effects model. This study introduces a novel approach to predict food insecurity by integrating mixed-effects models and a generalized random forest. Mixed-effects models capture variations in hierarchical or clustered data, such as differences between regions, and the generalized random forest, as extended and developed from the traditional random forest, is integrated to model fixed effects and improve prediction accuracy. The empirical data used were the food insecurity data from 2021 in West Java, Indonesia. The results show that mixed-effects models with a generalized random forest significantly improve the prediction accuracy compared to other models. The average performance measure shows GMEGRF is a good model and has a balanced accuracy value of 0.6789709, which is the highest result compared to other methods. This methodological advancement offers a new robust model for understanding and potentially mitigating food insecurity, ultimately informing efforts towards SDG 2 (Zero Hunger).
SPADE-LSTM: An Integrated Sequential Pattern Mining and Deep Learning for Badminton Next-Stroke Prediction Sari, Jefita Resti; Oktarina, Sachnaz Desta; Erfiani, Erfiani
International Journal of Advances in Data and Information Systems Vol. 7 No. 1 (2026): April 2026 - International Journal of Advances in Data and Information Systems
Publisher : Indonesian Scientific Journal

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59395/ijadis.v7i1.1510

Abstract

Badminton rallies consist of complex and rapid stroke transitions that reflect players’ tactical decision-making. While prior studies have examined stroke patterns descriptively or applied standalone predictive models, limited research integrates interpretable sequential pattern mining with deep learning for next-stroke prediction. This study proposes an integrated SPADE–LSTM framework to analyze and predict badminton stroke sequences using a 10-class scheme (drive, dropshot, lob, netting, and smash for two athletes). Match data were transformed into structured stroke sequences and contextual features, then divided into training, validation, and test sets using a match–set–rally grouping strategy to prevent information leakage. Sequential patterns were first extracted using the Sequential Pattern Discovery using Equivalent Classes (SPADE) algorithm to capture frequent tactical transitions. These pattern-based features were subsequently used to train a Long Short-Term Memory (LSTM) model for multi-class classification. The proposed model achieved an accuracy of 88.68%, with weighted precision, recall, and F1-score of 0.9075, 0.8868, and 0.8851, respectively. Misclassifications were mainly observed in tactically similar stroke transitions and minority classes. The results indicate that integrating interpretable sequential pattern mining with deep learning provides both strong predictive performance and meaningful tactical insights for badminton performance analysis.
Clustering of Central Java Districts Based on Educational Indicators: A Comparison of K-Means and Hierarchical Methods Muhammad Syafiq; Nabila Fida Millati; Muh Akbar Idris; Anwar Fitrianto; Kevin Alifviansyah; Erfiani Erfiani
Journal of Mathematics, Computations and Statistics Vol. 9 No. 1 (2026): Volume 09 Issue 01 (March 2026)
Publisher : Jurusan Matematika FMIPA UNM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35580/xen35m31

Abstract

This study aims to cluster districts and municipalities in Central Java based on educational indicators and to compare the clustering performance of K-Means and Hierarchical methods. The analysis uses secondary data from the Statistical Publication of Education in Central Java Province 2024, covering eight indicators related to educational facilities, participation, and attainment. The data were standardized, explored using descriptive statistics, and analyzed using K-Means and Hierarchical clustering methods. The evaluation results show that both methods produced broadly comparable clustering structures. However, Hierarchical Clustering demonstrated slightly stronger performance in terms of cluster separation and compactness, with a higher Silhouette Index (0,591) and Dunn Index (0,320) and a lower Davies–Bouldin Index (0,501) compared with K-Means (SI 0,584, Dunn 0,225, DBI 0,562). Meanwhile, K-Means produced a more balanced partition and a higher Calinski–Harabasz Index (48,63) than Hierarchical Clustering (44,30). The clustering results reveal a clear pattern of educational disparities across the region. A small group consisting of Sukoharjo Regency and the cities of Semarang, Surakarta, Salatiga, and Magelang forms a higher-performing cluster characterized by stronger educational indicators, while most rural districts belong to a lower-performing group. These findings indicate that educational disparities in Central Java remain spatially concentrated and highlight the need for targeted policies to strengthen educational investment and improve progression to higher levels of education in less developed districts.
CLASSIFICATION OF CARDIOVASCULAR AND CHRONIC RESPIRATORY DISEASES UTILIZING ENSEMBLE MODELS WITH DATA EXPLORATION TECHNIQUES I Gusti Ngurah Sentana Putra; Amri Luthfi Najih; Unique DA Resiloy; Rachmat Bintang Yudhianto; Erfiani Erfiani; Anwar Fitrianto
JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Vol 10, No 4 (2025)
Publisher : STKIP PGRI Tulungagung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29100/jipi.v10i4.9311

Abstract

Non-communicable diseases, especially cardiovascular and chronic respiratory conditions, contribute significantly to Indonesia’s healthcare burden and BPJS expenditure. Health claim data often suffer from class imbalance, multicollinearity, and outliers that impair model accuracy. This study evaluates the impact of essential data exploration techniques such as winsorizing, correlation and VIF analysis, variable selection, and SMOTE on the performance of ensemble classifiers. The dataset comprises 497,439 BPJS health insurance claims from 2022, including 27 predictors (14 numerical and 13 categorical). Two data pipelines were compared: one without preprocessing and another incorporating systematic data exploration. Five ensemble models were tested, namely Decision Tree, Extra Trees, Random Forest, XGBoost, and LightGBM. Model performance was assessed using F1-score, balanced accuracy, and G-mean across 20 stratified cross-validations. The results show that preprocessing substantially improves classification fairness and accuracy. Bagging models, particularly Random Forest, achieved the highest improvement, with balanced accuracy and G-mean increasing from around 0.93 to 0.99. Boosting models showed modest gains. These findings highlight that rigorous data exploration enhances ensemble classifier performance, enabling more reliable disease classification and supporting fairer, data-driven decision-making in BPJS health management.
KAJIAN EKSPLORASI TENTANG POLA KESEJAHTERAAN MULTIDIMENSI DI JAWA BARAT MENGGUNAKAN ANALISIS GEROMBOL Az-Zahra, Putri Nisrina; Tangdilomban, Claudian Tikulimbong; Mutmainah, Zamrah; Fitrianto, Anwar; Alifviansyah, Kevin; Erfiani, Erfiani
JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Vol 10, No 4 (2025)
Publisher : STKIP PGRI Tulungagung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29100/jipi.v10i4.9307

Abstract

Kesejahteraan multidimensi mencerminkan kualitas hidup yang melampaui indikator tunggal seperti IPM. Penelitian ini berfokus pada eksplorasi dan visualisasi pola kesejahteraan multidimensi di Jawa Barat menggunakan algoritma K-Means dan HDBSCAN. Data Susenas Maret 2024 mencakup 12 variabel dalam empat dimensi: pendidikan, kesehatan, ekonomi, dan fasilitas rumah tangga. Reduksi dimensi dilakukan dengan PCA sebelum clustering. Hasil menunjukkan HDBSCAN lebih optimal dibandingkan K-Means, dengan Silhouette Score 0,558, Calinski-Harabasz Index 41,584, dan Davies-Bouldin Index 0,603. Visualisasi cluster mengungkap ketimpangan antarwilayah, di mana daerah perkotaan cenderung lebih sejahtera, sedangkan pedesaan dan pinggiran menunjukkan variasi yang lebih beragam.
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)
PENERAPAN CYLINDRICAL DAN FLEXIBLE SPACE TIME SCAN STATISTIC DALAM MENGIDENTIFIKASI KANTONG KEMISKINAN DI PULAU JAWA TAHUN 2011-2015 Nurrusydah, Zaima; Erfiani, Erfiani; Sartono, Bagus
Indonesian Journal of Statistics and Applications Vol 3 No 2 (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.v3i2.274

Abstract

The Indonesian government formed the National Team for the Acceleration of Poverty Reduction (TNP2K) to eradicate poverty. TNP2K requires identification of priority areas or poverty hotspots so that the program can be targeted. Scan statistic is one of the most widely used methods to identify poverty hotspots. Cylindrical STSS uses cylindrical scanning windows while most geographical areas are not circular. Flexible STSS is able to detect poverty hotspots in a flexible form. This study aims to identify poverty hotspots using Cylindrical and Flexible STSS then compare the results of both and then determine the best STSS method. Cylindrical STSS tends to have wider hotspots than Flexible STSS. There are a number of districts that are not eligible to be included as poverty Flexible STSS is able to produce better poverty hotspots by not including these districts Poverty hotspots produced by Flexible STSS have higher LLR values. The more suitable STSS method has optimal K values and high suitability with TNP2K priority areas. Cylindrical STSS has an optimal K value when K = 8 and 9. Flexible STSS has a constant LLR value. Flexible STSS has a higher LLR value than Cylindrical STSS at each K value. Flexible STSS with K = 9 has optimal K and high suitability with TNP2K priority areas so that it is the more suitable STSS method to identify poverty hotspots in Java.
IMPLEMENTASI TRANSFORMASI FOURIER UNTUK TRANSFORMASI DOMAIN WAKTU KE DOMAIN FREKUENSI PADA LUARAN PURWARUPA ALAT PENDETEKSIAN GULA DARAH SECARA NON-INVASIF Hidayaturrohman, Umam; Erfiani, Erfiani; Afendi, Farit M
Indonesian Journal of Statistics and Applications Vol 4 No 2 (2020)
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.v4i2.504

Abstract

Diabetes mellitus is the result of changes in the body caused by a decrease of insulin performance which is characterized by an increase of blood sugar level. Detection of blood sugar can be done with Invasive methods or non-invasive methods. However, non-invasive methods are considered better because they can check early, faster and accurate. The prototype output is values of intensity in the time domain, thus fourier transformation is very much needed to transform into the frequency domain. In this study, Fourier transformation methods used are Discrete Fourier Transform (DFT), Fast Fourier Transform Radix-2, and Fast Fourier Transform Radix-4. Evaluation for the best method is done by comparing the processing speed of each method. The FFT Radix-4 method is more effective to perform the transformation into the frequency domain. The average processing speed with the FFT Radix-4 method reaches 2.67×105 nanoseconds, and this is much faster 5.06×106 nanoseconds than the FFT Radix-2 method and 2.40×107 nanoseconds faster than the DFT method.
Co-Authors . Aunuddin A. A., Muftih Abd. Rahman Abqorunnisa, Farah Afendi, Farit M Agus Mohamad Soleh Ahmad Khairul Reza Ahmad Nur Rohman Ahmad Syauqi Aji Hamim Wigena Alamanda, Dinda Aprilia Alfa Nugraha Pradana Alfa Nugraha Pradana Alfa Nugraha Pradana Alifviansyah, Kevin Aliu, Mufthi Alwi ALIU, MUFTIH ALWI Amatullah, Fida Fariha Amelia, Reni Aminah Aminah Amri Luthfi Najih Anadra, Rahmi Anang Kurnia Andi Harismahyanti A. Anik Djuraidah Anissa Tsalsabila Ardhani, Rizky Arini Annisa Adi Aristawidya, Rafika ASEP SAEFUDDIN Asri Pratiwi, Asri Assyifa Lala Pratiwi Hamid Aunuddin . Aunuddin Aunuddin Az-Zahra, Putri Nisrina Azis, Tukhfatur Rizmah Bagus Sartono Bartho Sihombing Bimawan Sudarmoko Budi Susetyo Daswati, Oktaviyani Daulay, Nurmai Syaroh Deti Anggraeni Ekawati Dian Kusumaningrum Dini Ramadhani Dwi Jumansyah, L.M. Risman Dwi Putri Kurniasari Fanny Amalia Farit M Afendi Farly Shabahul Khairi Fatimah Fatimah Fauziah, Monica Rahma Fitrianto, Anwar Freza Riana Fulazzaky, Tahira Hamim Wigena, Aji Hari Wijayanto Hasnataeni, Yunia Herlin Fransiska Hilda Zaikarina I Gusti Ngurah Sentana Putra I Made Sumertajaya Ihsan, Muhammad Taufik Ilmani, Erdanisa Aghnia Indah, Yunna Mentari Indahwati Irzaman, Irzaman Ismah, Ismah Julianti, Elisa D Jumansyah, L. M. Risman Dwi Jumansyah, L.M. Risman Dwi Kevin Alifviansyah Khikmah, Khusnia Nurul Khusnia Nurul Khikmah Lestari, Nila Made Agung Prebawa Parama Artha Mahfuz Hudori Marshelle, Sean Mastuti, Winda Chairani Megawati Megawati Misrika, Dahlia Mohammad Masjkur Muggy David Cristian Ginzel Muh Akbar Idris Muhammad Nur Aidi Muhammad Syafiq mutiah, siti Mutmainah, Zamrah Nabila Fida Millati Nadira Nisa Alwani Nenden Rahayu Puspitasari Novitri Novitri Nugraha, Adhiyatma Nur Khamidah nurrusydah, zaima Nurul Fadhilah Nurul Fadhilah Pardomuan Robinson Sihombing Qalbi, Asyifah R, Arifuddin Rachmat Bintang Yudhianto Rahmatun Nisa, Rahmatun Ratih Dwi Septiani Reka Agustia Astari Reni Amelia Retno Dwi Jayanti Rika Rachmawati Riska Asri Pertiwi Sachnaz Desta Oktarina Sari, Jefita Resti Siregar, Indra Rivaldi Sofia Octaviana Tangdilomban, Claudian Tikulimbong Tetinia Gulo Tiara, Yesan Umam Hidayaturrohman Unique DA Resiloy Uswatun Hasanah Utami Dyah Syafitri Utomo, Agung Tri Vitona, Desi Waode, Yully Sofyah Wati, Wahyuni Kencana Weisha, Ghea Wigena, Aji Wijaya, Ferdian Bangkit Winda Chairani Mastuti Windi D.Y Putri Yulia Christina Yuniar Istiqomah Zaikarina, Hilda Zaima Nurrusydah