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All Journal GRADIEN Dharma Raflesia : Jurnal Ilmiah Pengembangan dan Penerapan IPTEKS Journal of Applied Data Sciences LOSARI: Jurnal Pengabdian Kepada Masyarakat TRIBUTE: JOURNAL OF COMMUNITY SERVICES Dikdimas : Jurnal Pengabdian Kepada Masyarakat Riemann : Research of Mathematics and Mathematics Education
Ulfasari Rafflesia
Jurusan Matematika Fakultas MIPA Universitas Bengkulu
Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Engineering Environmental Science Health Professions Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Nursing Physics Public Health Social Sciences Transportation Other

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Analysis of Seismic Data in Sumatra using Robust K-Means Clustering Rafflesia, Ulfasari; Rosadi, Dedi; Sari, Devni Prima; Novianti, Pepi
Journal of Applied Data Sciences Vol 6, No 1: JANUARY 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i1.523

Abstract

Indonesia is located within the Pacific Ring of Fire and frequently experiences significant seismic activities, rendering the region susceptible to hazards. Specifically, Sumatra is an island in the western part of the country, near the Eurasian and Indo-Australian tectonic plates. Over the past five years, an observable uptick in seismic events has been recorded in Sumatra. This research aimed to cluster the Sumatra region’s seismic data using the k-means algorithm and its extensions, including trimmed and robust sparse k-means, to determine the characteristics and patterns of seismic events. The k-means clustering algorithm operates effectively on many data but needs to work better in the presence of outliers. Meanwhile, the data identification reports the presence of outliers in the seismic data. The clustering analysis identified two main clusters, supported by multivariate and spatial outlier detection during preprocessing. The first cluster, encompassing 62% of seismic events, is located offshore near the Mentawai seismic gap, characterized by shallow depths (33–41 km) and magnitudes of 4.5–5.0 Ms. The second cluster, representing 28% of events, includes both mainland and offshore regions, associated with the Sumatran Fault system and slab deformation zones, at moderate depths (54–154 km) with magnitudes of 4.3–4.4 Ms. Rare deep-focus events exceeding depths of 214 km were identified as outliers. Evaluation using Silhouette, Davies-Bouldin, and Dunn indices determined that k=2 was the optimal number of clusters. This study contributes by integrating robust clustering methods to handle outliers, enhancing the reliability of seismic data analysis. This study demonstrates the value of applying trimmed and robust sparse k-means algorithms to improve clustering performance in regions with complex tectonic activity.
GERAKAN ASRI DAN BERSIH (LINGSRIH) SEBAGAI UPAYA MENGURANGI SAMPAH DI KAWASAN WISATA PANTAI PANJANG LINGKUNGAN Agwil, Winalia; Rafflesia, Ulfasari; Rachamawati , Ramya; Damayanti , Septri
LOSARI: Jurnal Pengabdian Kepada Masyarakat Vol. 4 No. 1 (2022): Juni 2022
Publisher : LOSARI DIGITAL

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53860/losari.v4i1.87

Abstract

Kebersihan lingkungan pantai menjadi salah satu hal penting agar pantai menjadi menarik di mata para pengunjung atau wisatawan. Namun, di pantai panjang kota bengkulu masih ditemukan sampah-sampah bertebaran. Sampah-sampah tersebut berasal dari limbah rumah tangga dan juga aktivitas pengunjung di pantai. Untuk menanggulangi hal tersebut salah satu kegiatan yang dapat dilakukan oleh mahasiswa dan dosen adalah dengan melakukan gerakan lingkungan bersih dan asri. Tujuan kegiatan ini adalah untuk mengurangi sampah-sampah yang berserakan dan menimbulkan rasa peduli lingkungan. Berdasarkan kegiatan yang dilakukan dapat dilihat mahasiswa dan beberapa masyarakat setempat sangat antusias berpartisipasi dalam kegiatan.
Towards a Fertilizer-Independent Village: Zero-Waste Management in Tik Kuto Village, Rimbo Pengadang-Lebong, Bengkulu Province Sutrawati, Mimi; Ginting, Sura Menda; Jumiarni, Dewi; Andani, Apri; Sukisno, Sukisno; Rafflesia, Ulfasari; Nadiya, Elva; Windra, Hengki; Dewi, Maylan Ratu; Pratama, Fadhillah Akbar
DIKDIMAS : Jurnal Pengabdian Kepada Masyarakat Vol. 4 No. 3 (2025): DIKDIMAS : JURNAL PENGABDIAN KEPADA MASYARAKAT VOL 4 NO 3 DESEMBER 2025
Publisher : Asosiasi Profesi Multimedia Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58723/dikdimas.v4i3.571

Abstract

Background: In Tik Kuto Village, organic household and agricultural waste remains largely underutilized, contributing to environmental pollution and potentially reducing community health status and productivity. The lack of practical, low-cost, and easily applied waste management technologies at the household and farm levels highlights the need for community-based interventions that support sustainable and environmentally friendly practices.Aims: The specific objective is to enhance community capacity to process organic waste into Local Microorganisms (MOL), Liquid Organic Fertilizer (POC), and ecoenzymes within a Zero-Waste Management framework.Methods: The program was implemented through participatory outreach and counseling, demonstrations, technical training, and mentoring activities involving farmer groups and housewives. Educational support materials included posters and booklets. The resulting MOL served as an activator for POC, which was fermented for four weeks. Ecoenzymes were produced from fruit peels, brown sugar, and water in a 3:1:10 ratio and fermented for a minimum of three months. Program effectiveness was evaluated using pre-tests, post-tests, and direct observation of participant engagement.Results: A total of 18 farmers participated in the program. Although most participants were middle-aged and experienced farmers, their baseline knowledge of organic waste processing was low. Post-training evaluation showed substantial improvements, with understanding of waste sorting and processing reaching 94–100%. Knowledge related to materials, production steps, application dosage, and benefits of MOL, POC, and ecoenzymes increased from 6–28% to 83–94%.Conclusion: The program successfully improved participants’ knowledge and behavioral intentions regarding organic waste management at the household and community levels.
Improving Classification Performance of Imbalanced Data Using SMOTE: empirical studies Ulfasari Rafflesia; Dedi Rosadi
Riemann: Research of Mathematics and Mathematics Education Vol. 8 No. 1 (2026): EDISI APRIL
Publisher : Program Studi Pendidikan Matematika Universitas Katolik Santo Agustinus Hippo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.38114/riemann.v8i1.199

Abstract

Data balancing methods in multi-class settings continue to evolve as the importance of balanced data conditions for classification analysis grows. However, limited studies have provided comprehensive empirical comparisons across both binary and multi-class imbalanced datasets. Data imbalance can affect model predictions, particularly by leading to inaccurate identification of minority classes. Therefore, this study aims to evaluate the effectiveness of the Synthetic Minority Over-sampling Technique (SMOTE) in improving classification performance. Three benchmark datasets from the UCI Machine Learning Repository—Breast Cancer, Ecoli, and Glass—were selected to represent imbalanced classification problems in both binary and multi-class settings. The proposed framework addresses class imbalance during data preprocessing using SMOTE. Each dataset is first divided into training and testing subsets. SMOTE is applied only to the training data to address class imbalance, while the test data is kept unchanged for evaluation. Then, the classification process is applied to the original (imbalanced) data and to the balanced data generated by SMOTE. The classifiers used in this study are SVM, a decision tree, and AdaBoost. The classification results are evaluated based on accuracy, sensitivity, and F1-score. The results show that the decision tree and AdaBoost improve classification performance under imbalanced data conditions. In particular, AdaBoost achieves the best overall performance in terms of prediction accuracy and class balance, demonstrating the effectiveness of combining SMOTE with ensemble methods for handling imbalanced datasets.
Co-Authors Agwil, Winalia Apri Andani, Apri Buyung Keraman Dedi Rosadi Devni Prima Sari Dewi, Maylan Ratu Dyah Setyo Rini Evi Maryanti Idhia Sriliana Indah Wahyuliani Jose Rizal Jumiarni, Dewi Lusiana Lusiana Mimi Sutrawati Mulia Astuti NADIYA, ELVA Nur Afandi Pepi Novianti Pratama, Fadhillah Akbar Rachamawati , Ramya Septri Damayanti Sukisno Sukisno, Sukisno Sura Menda Ginting Windra, Hengki Yulian Fauzi Zulfia Memi Mayasari