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All Journal MUST: Journal of Mathematics Education, Science and Technology JOURNAL OF APPLIED INFORMATICS AND COMPUTING Jurnal Informatika Universitas Pamulang Journal of Information System, Applied, Management, Accounting and Research Jurnal Lebesgue : Jurnal Ilmiah Pendidikan Matematika, Matematika dan Statistika
Yudhianto, Rachmat Bintang
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Computer Science & IT Decision Sciences, Operations Research & Management Education Mathematics

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STUDI KOMPARASI METODE SVM-SMOTE DAN SMOTE-TOMEK DALAM MENGATASI IMBALANCE CLASS MENGGUNAKAN MODEL XGBOOST PADA KLASIFIKASI RUMAH TANGGA PENERIMA KUR Yanuari, Eka Dicky Darmawan; Yudhianto, Rachmat Bintang; Ulfia, Ratu Risha; Sartono, Bagus; Firdawanti, Aulia Rizki
Jurnal Lebesgue : Jurnal Ilmiah Pendidikan Matematika, Matematika dan Statistika Vol. 5 No. 3 (2024): Jurnal Lebesgue : Jurnal Ilmiah Pendidikan Matematika, Matematika dan Statistik
Publisher : LPPM Universitas Bina Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46306/lb.v5i3.857

Abstract

This study aims to compare the SMOTE, SVM-SMOTE, and SMOTE-Tomek methods using the XGBoost model in overcoming the problem of class imbalance and to determine the factors that affect the status of KUR recipients in West Java Province. Three XGBoost models with class balancing techniques SMOTE, SVM-SMOTE and SMOTE-Tomek were applied to SUSENAS data of West Java Province in 2023 consisting of 1 response variable and 19 predictor variables. The results showed that the XGBoost model with the SMOTE balancing method produced better accuracy in overall data classification, but was less effective in classifying minority classes as reflected by low sensitivity and F1-Score values. The XGBoost model with the SMOTE-Tomek balancing method showed better performance in capturing minority classes with higher sensitivity and F1-Score values. The most influential variables in this model in order are per capita expenditure, urban/rural classification, motorcycle ownership, dwelling wall materials and land ownership. Per capita expenditure has the largest influence on the classification of KUR recipients, indicating that household financial management is a major factor in lending decisions. Urban/rural classification and motorcycle ownership also contributed significantly, reflecting differences in social and economic access between regions. Overall, economic factors, infrastructure and social accessibility are the main considerations in determining KUR recipient households in West Java Province.
Analisis Visual dan Karakteristik Klub Sepakbola Liga Inggris Berdasarkan Pola Permainan Menggunakan K-Means Clustering Yudhianto, Rachmat Bintang; Yusuf, Fajar Athallah; Fitrianto, Anwar; Jumansyah, L.M. Risman Dwi
Jurnal Informatika Universitas Pamulang Vol 9 No 3 (2024): JURNAL INFORMATIKA UNIVERSITAS PAMULANG
Publisher : Teknik Informatika Universitas Pamulang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32493/informatika.v9i3.44640

Abstract

This research aimed to analyze and cluster football teams in the English Premier League (EPL) for the 2023/2024 season based on their playing characteristics using K-Means clustering. Understanding the playing styles is essential for optimizing strategies and enhancing team performance. Preprocessing steps included data cleaning, feature engineering, and visualization of key features such as goals, shots, and attacking attempts. Four clusters were identified using the Elbow method, representing teams with varying levels of attacking and defensive capabilities. Evaluation of the clustering results was conducted using Davies-Bouldin (score: 0.47), Calinski-Harabasz (score: 275.89), and Silhouette (score: 0.53) metrics, indicating moderate clustering quality. The findings suggest that EPL teams tend to be attack-oriented, while defensive strength varies across clusters. Limitations in the dataset, such as the number of observations and features, impacted the analysis, and future studies may benefit from incorporating additional features and advanced dimensionality reduction techniques.
Analisis Komparatif Lasagna Plots dan Spaghetti Plots untuk Visualisasi Big Data Longitudinal Kesehatan Pekerja Tangke, Nabillah Rahmatiah; Angelia, Riza Rahmah; Ramadhan, Syaifullah Yusuf; Fitrianto, Anwar; Yudhianto, Rachmat Bintang
Journal of Information System, Applied, Management, Accounting and Research Vol 9 No 4 (2025): JISAMAR (Journal of Information System, Applied, Management, Accounting and Resea
Publisher : Sekolah Tinggi Manajemen Informatika dan Komputer Jayakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52362/jisamar.v9i4.2108

Abstract

Visualisasi data longitudinal skala besar menghadapi tantangan over-plotting dan kesulitan interpretasi ketika menggunakan spaghetti plots tradisional. Penelitian ini bertujuan membandingkan efektivitas lasagna plots sebagai alternatif visualisasi untuk big data longitudinal kesehatan pekerja. Metode penelitian menggunakan pendekatan komparatif dengan dataset 8270 observasi dari 3792 pekerja industri Indonesia periode 2024-2025, mencakup komponen pemeriksaan kesehatan berkala dan paparan okupasional. Data divisualisasikan menggunakan spaghetti plots dan lasagna plots dengan berbagai strategi dynamic sorting (entire-row dan cluster sorting). Hasil analisis menunjukkan distribusi risiko 84.8% kategori rendah-sedang dan 15.2% sedang-tinggi. Lasagna plots dengan entire-row sorting berhasil mendelineasi stratifikasi risiko tanpa overlapping, berbeda dengan spaghetti plots yang sulit diinterpretasi pada populasi besar. Faceted lasagna plots efektif mengidentifikasi pola co-occurrence paparan dan missing data patterns yang mendukung evaluasi kualitas data. Lasagna plots dengan dynamic sorting menawarkan pendekatan visualisasi yang lebih scalable dan informatif dibanding spaghetti plots untuk mendeteksi pola perubahan, cohort effects, dan missing data patterns dalam big data longitudinal kesehatan pekerja.
Household Clustering in West Java Based on Stunting Risk Factors Using K-Modes and K-Prototypes Algorithms Yusran, Muhammad; Nuradilla, Siti; Putri, Mega Ramatika; Fitrianto, Anwar; Yudhianto, Rachmat Bintang
Journal of Applied Informatics and Computing Vol. 9 No. 6 (2025): December 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i6.11508

Abstract

Stunting remains one of Indonesia’s most persistent public health challenges, with West Java contributing the highest number of cases due to its large population and regional disparities in household welfare. Identifying household groups vulnerable to stunting is essential for designing targeted interventions that integrate nutrition, sanitation, and socio-economic development. This study introduces a data-driven clustering framework using the K-Modes and K-Prototypes algorithms to classify 22,161 households in West Java based on 26 indicators from the March 2024 National Socioeconomic Survey (SUSENAS), encompassing food security, sanitation, drinking water access, economic conditions, social assistance, and demographics. The K-Modes algorithm was applied to categorical data, while K-Prototypes integrated numerical and categorical variables, with parameter optimization performed using a grid search and the Elbow method. Clustering performance was evaluated through the Silhouette Score, Calinski–Harabasz Index, and Davies–Bouldin Index, followed by a bootstrapped stability analysis employing the Adjusted Rand Index (ARI) and Normalized Mutual Information (NMI). Results show that K-Prototypes outperformed K-Modes, yielding a higher Silhouette Score (0.6681 compared to 0.2922), higher CH Index (13,890.6 compared to 3,976.1), and lower DBI (0.4607 compared to 1.5274), indicating superior compactness and separation. Stability testing confirmed strong robustness, with mean ARI = 0.959 and mean NMI = 0.932 across 50 bootstrap replications. The optimal five-cluster structure identified distinct socioeconomic groups, with the highest stunting risk found among households with low income, limited housing space, inadequate sanitation, and more children under five. The findings highlight the effectiveness of K-Prototypes in modeling mixed-type data and support the design of evidence-based, regionally adaptive stunting reduction strategies aligned with Presidential Regulation No. 72/2021 on the Acceleration of Stunting Reduction.
PERBANDINGAN K-MEANS DAN K-MEDOIDS DALAM PENGELOMPOKKAN KOMODITAS EKSPOR INDUSTRI DI INDONESIA Arifa, Panji Lokajaya; Rahmasari, Hazelita Dwi; Aimandiga, Carlya Agmis; Fitrianto, Anwar; Yudhianto, Rachmat Bintang
MUST: Journal of Mathematics Education, Science and Technology Vol 10 No 2 (2025): DECEMBER
Publisher : Universitas Muhammadiyah Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30651/must.v10i2.28661

Abstract

International trade plays a crucial role in Indonesia's economic growth, particularly through industrial commodity exports. However, its heavy dependence on a few key commodities makes it vulnerable to global market fluctuations. This study aims to explore trends in industrial commodity export values ​​and compare the performance of cluster methods in grouping commodities based on their value patterns. The research data used are monthly export values ​​from 2022 to mid-2025, sourced from the Central Statistics Agency (BPS). The analytical methods used include trend exploration and cluster analysis with K-Means and K-Medoids using Dynamic Time Warping (DTW) distance. The results of the export value trend exploration indicate that palm oil dominates industrial export value, while other commodities tend to have stable patterns at medium to low values. Evaluation of clustering results using K-Means and K-Medoids each obtained 3 clusters indicating that K-Medoids provided the best performance by obtaining a Silhouette Score of 0.1577 and a Davies-Bouldin Index (DBI) of 1.7990. This value is better than K-Means which obtained a Silhouette Score of 0.1493 and a DBI of 2.3037 indicating that the method is less than optimal in separating clusters. This finding explains that K-Medoids is more robust against outliers and is able to provide more representative groupings. So it can provide a deeper understanding of commodity grouping patterns and contribute to providing export policy recommendations to reduce dependence on primary commodities and increase the export competitiveness of Indonesian industrial products.
Co-Authors Aimandiga, Carlya Agmis Angelia, Riza Rahmah Arifa, Panji Lokajaya Aulia Rizki Firdawanti Bagus Sartono Fitrianto, Anwar Jumansyah, L.M. Risman Dwi Muhammad Yusran NURADILLA, SITI Putri, Mega Ramatika Rahmasari, Hazelita Dwi Ramadhan, Syaifullah Yusuf Tangke, Nabillah Rahmatiah Ulfia, Ratu Risha Yanuari, Eka Dicky Darmawan Yusuf, Fajar Athallah