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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) Zero : Jurnal Sains, Matematika, dan Terapan 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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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 Hilda Zaikarina; . Erfiani; I Made Sumertajaya
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 Zaima Nurrusydah; Erfiani Erfiani; Bagus Sartono
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 Umam Hidayaturrohman; Erfiani Erfiani; Farit M Afendi
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.
ROBUST SPATIAL REGRESSION MODEL ON ORIGINAL LOCAL GOVERNMENT REVENUE IN JAVA 2017 Winda Chairani Mastuti; Anik Djuraidah; Erfiani Erfiani
Indonesian Journal of Statistics and Applications Vol 4 No 1 (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.v4i1.573

Abstract

Spatial regression measures the relationship between response and explanatory variables in the regression model considering spatial effects. Detecting and accommodating outliers is an important step in the regression analysis. Several methods can detect outliers in spatial regression. One of these methods is generating a score test statistics to identify outliers in the spatial autoregressive (SAR) model. This research applies a robust spatial autoregressive (RSAR) model with S- estimator to the Original Local Government Revenue (OLGR) data. The RSAR model with the 4-nearest neighbor weighting matrix is the best model produced in this study. The coefficient of the RSAR model gives a more relevant result. Median absolute deviation (MdAD) and median absolute percentage error (MdAPE) values ​​in the RSAR model with 4-nearest neighbor give smaller results than the SAR model.
Comparison of Functional Regression and Functional Principal Component Regression for Estimating Non-Invasive Blood Glucose Level: Perbandingan Metode Regresi Fungsional dan Regresi Komponen Utama Fungsional untuk Menduga Kadar Glukosa Darah pada Alat Non-Invasif Nurul Fadhilah; Erfiani Erfiani; Indahwati Indahwati
Indonesian Journal of Statistics and Applications Vol 5 No 1 (2021)
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.v5i1p14-25

Abstract

The calibration method is an alternative method that can be used to analyze the relationship between invasive and non-invasive blood glucose levels. Calibration modeling generally has a large dimension and contains multicolinearities because usually in functional data the number of independent variables (p) is greater than the number of observations (p>n). Both problems can be overcome using Functional Regression (FR) and Functional Principal Component Regression (FPCR). FPCR is based on Principal Component Analysis (PCA). In FPCR, the data is transformed using a polynomial basis before data reduction. This research tried to model the equations of spectral calibration of voltage value excreted by non-invasive blood glucose level monitoring devices to predict blood glucose using FR and FPCR. This study aimed to determine the best calibration model for measuring non-invasive blood glucose levels with the FR and FPCR. The results of this research showed that the FR model had a bigger coefficient determination (R2) value and lower Root Mean Square Error (RMSE) and Root Mean Square Error Prediction (RMSEP) value than the FPCR model, which was 12.9%, 5.417, and 5.727 respectively. Overall, the calibration modeling with the FR model is the best model for estimate blood glucose level compared to the FPCR model.
Loopy Orthogonal Signal Correction Scatter Correction in Non-Invasive Blood Glucose: Koreksi Pencaran Loopy Orthogonal Signal Correction pada Glukosa Darah Non-Invasif Dahlia Misrika; Erfiani Erfiani; Aji Wigena
Indonesian Journal of Statistics and Applications Vol 7 No 2 (2023)
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.v7i2p105-113

Abstract

Spectroscopy is the study of matter based on light, sound, or particles emitted, absorbed, or reflected as well as the study of methods for generating and analyzing spectra. The spectrum has systematic diversity, namely the presence of light scattering and differences in the size of objects. The spectroscopic output allows for scattering shifts, because the same object measured several times does not exactly produce the same spectrum. Problems found in the spectrum can be overcome by pre-processing the data, namely the scatter correction method. Scatter correction is used to reduce the physical properties in the spectrum so that the information obtained is relatively the same for each spectrum, produces good estimates, and can be interpreted well. One of the spectroscopic tools that utilize infrared light is a non-invasive blood glucose level measuring device. The output of the tool is the time domain and intensity spectrum. Each object from the resulting spectrum still has noise, so scatter correction can be applied to this data. The purpose of this study was to perform a loopy Orthogonal Signal Correction (OSC) scatter correction method on time domain spectrum data on intensity on a non-invasive blood glucose level measuring device. The OSC method uses the concept of orthogonality to the mean by drawing the intensity value, weighting it, calculating the vector loading and then making corrections to the initial intensity. Based on the analysis, the loopy OSC method is better than OSC because the convergence is more accurate, the mean difference is smaller, the variance is smaller and the value converges on all the values tested. Based on exploration and the average difference, the loopy OSC method is better able to form the same pattern for each replication. This also shows that an object that is measured repeatedly has been able to be identified as the same object.
Co-Authors . Aunuddin A. A., Muftih Abd. Rahman Abqorunnisa, Farah Agus M Soleh Agus Mohamad Soleh Ahmad Khairul Reza Ahmad Nur Rohman Ahmad Syauqi Aji Hamim Wigena Aji 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 Pratiwi, Asri Assyifa Lala Pratiwi Hamid Aunuddin . Aunuddin Aunuddin Az-Zahra, Putri Nisrina Azis, Tukhfatur Rizmah Bagus Sartono Bartho Sihombing Bimawan Sudarmoko Budi Susetyo Dahlia Misrika 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 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 L.M. Risman Dwi Jumansyah Lestari, Nila Made Agung Prebawa Parama Artha Mahfuz Hudori Marshelle, Sean Megawati Megawati 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 Nindya Wulandari Novitri Novitri Nugraha, Adhiyatma Nur Khamidah 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 Tukhfatur Rizmah Azis Umam Hidayaturrohman Unique DA Resiloy Uswatun Hasanah Utami Dyah Syafitri Utomo, Agung Tri Vitona, Desi Waode, Yully Sofyah Wati, Wahyuni Kencana Weisha, Ghea Wijaya, Ferdian Bangkit Winda Chairani Mastuti Windi D.Y Putri Yulia Christina Yuniar Istiqomah Zaima Nurrusydah