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All Journal Jurnal Penyuluhan Techno.Com: Jurnal Teknologi Informasi Kubik Knowledge Engineering and Data Science Jurnal Informatika Universitas Pamulang Teorema: Teori dan Riset Matematika Jambura Journal of Mathematics KUBIK: Jurnal Publikasi Ilmiah Matematika Euler : Jurnal Ilmiah Matematika, Sains dan Teknologi
Jumansyah, L.M. Risman Dwi
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Agriculture, Biological Sciences & Forestry Computer Science & IT Economics, Econometrics & Finance Education Engineering Mathematics

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Analisis Pola Konvergensi Transpor Kelembapan Udara di Indonesia Bagian Barat Menggunakan K-Means dengan Pembobotan Statistik dan Hierarchical Shape-Based Clustering Pratiwi, Asri; Azis, Tukhfatur Rizmah; Fitrianto, Anwar; Erfiani, Erfiani; Jumansyah, L.M. Risman Dwi
KUBIK Vol 9 No 2 (2024): KUBIK: Jurnal Publikasi Ilmiah Matematika
Publisher : Jurusan Matematika, Fakultas Sains dan Teknologi, UIN Sunan Gunung Djati Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15575/kubik.v9i2.39753

Abstract

This study analyzes the convergence patterns of Vertically Integrated Moisture Transport (VIMT) in the western region of Indonesia using the K-Means method with statistical weighting and Hierarchical Shape-Based Clustering based on Dynamic Time Warping (DTW). Daily data on specific humidity, zonal wind speed, and meridional wind speed from 2020–2023 were used to calculate VIMT. Clustering methods were utilized to identify grouping patterns in moisture transport data. The results showed that moisture convergence significantly increased during the rainy season (November–February). Using the K-Means method, five clusters with clearer separations were obtained compared to the four clusters produced by the Hierarchical Clustering method. Performance evaluation using Silhouette and Calinski-Harabasz scores indicated that the K-Means method was superior, with scores of 0.37 and 104.88 compared to 0.13 and 96.34 for the Hierarchical method. This provides an understanding of the moisture transport patterns, serving as a reference for predicting weather and climate patterns, thereby supporting efforts to mitigate the impacts of extreme weather in Western Indonesia.
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.
Manifold Learning and Undersampling Approaches for Imbalanced Class Sentiment Classification Jumansyah, L.M. Risman Dwi; Soleh, Agus Mohamad; Syafitri, Utami Dyah
Knowledge Engineering and Data Science
Publisher : citeus

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Abstract

Movie reviews are crucial in determining a film's success by influencing audience decisions. Automating sentiment classification is essential for efficient public opinion analysis. However, it faces challenges such as high-dimensional data and imbalanced class distributions. This study addresses these issues by applying manifold learning techniques, Principal Component Analysis (PCA) and Laplacian Eigenmaps (LE) to reduce data complexity and undersampling strategies (Random Undersampling (RUS) and EasyEnsemble) to balance data and improve predictions for both sentiment classes. On reviews of The Raid 2: Berandal, EasyEnsemble achieved the highest average G-Mean of 0.694 using Term Frequency-Inverse Document Frequency (TF IDF) features with a linear kernel without dimensionality reduction. RUS provided balanced but inconsistent results, while Review of Systems (ROS) combined with PCA (85% variance cumulative) improved predictions for negative reviews. Laplacian Eigenmaps were effective for negative reviews with 500 dimensions but less accurate for positive ones. This study highlights EasyEnsemble's superior performance in addressing the class imbalance, though optimization with manifold learning remains challenging.
Co-Authors Agus Mohamad Soleh Aristawidya, Rafika Asri Pratiwi, Asri Azis, Tukhfatur Rizmah Devi Permata Sari Erfiani Erfiani Fauziah, Monica Rahma Fitrianto, Anwar Hasnataeni, Yunia Indah, Yunna Mentari Josua Sahala M.S, Erfiani mutiah, siti Neonnub, Jefrianus Nugraha, Adhiyatma Rizqi, Tasya Anisah Simamora, Ture Siregar, Indra Rivaldi Uswatun Hasanah Utami Dyah Syafitri Veronika Yuneriati Beyleto Yudhianto, Rachmat Bintang Yusuf, Fajar Athallah