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All Journal Jurnal Sistem Cerdas Jurnal Abdi: Media Pengabdian Kepada Masyarakat Journal of Current Studies in SDGs Journal of Current Studies in SDGs
Fadhilah Qalbi Annisa
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Humanities Automotive Engineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Environmental Science Languange, Linguistic, Communication & Media Social Sciences Other

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Pelatihan Pengolahan Dan Visualisasi Data Sebagai Penunjang Peningkatan Pendidikan Menggunakan Microsoft Excel Hasanuddin Al-Habib; Yuliani Puji Astuti; Fadhilah Qalbi Annisa; Riskyana Dewi Intan Puspitasari; Harmon Prayogi; Ulfa Siti Nuraini
Jurnal ABDI: Media Pengabdian Kepada Masyarakat Vol. 10 No. 2 (2025): JURNAL ABDI : Media Pengabdian Kepada masyarakat
Publisher : Universitas Negeri Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26740/abdi.v10i2.37764

Abstract

The aim of the community service to provide training and mentoring in educational data processing using Microsoft Excel. The community service activity involves teachers as participant from Sekolah Indonesia Bangkok, Thailand. The activities are carried out in phases which include planning, coordination with partner school, obtaining necessary permits, developing training module, conducting the training, and evaluating the outcomes. The training consisted of four stages which are a pre-test, training material presentation, a question-and-answer session, and a post-test. The output of the community service was a Microsoft Excel training module that cover both the technical aspects of the software and simple practical application. The effectiveness of the training was evaluated based on the results of the pre-test and post-test. The result showed a significant improvement regarding to participants’ understanding, as evidenced by the increase in the number of correct answers and the average percentage between the pre-test and post-test. The average percentage of correct answers of the participants before the training was around 34% and after the training about 52%. Meanwhile, the average percentage of the pre-test is 62,27% and post-test is 87,91%.
Anxiety Anxiety Detection Based on EEG Signal using 1-D Convolutional Neural Network Classifier Fadhilah Qalbi Annisa
Jurnal Sistem Cerdas Vol. 8 No. 2 (2025)
Publisher : APIC

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37396/jsc.v8i3.506

Abstract

Anxiety is defined as fear and symptoms of somatic tension experienced when a threat or danger is anticipated. In recent years, biological markers have been explored to detect anxiety noninvasively, one method is Electroencephalography (EEG). Detecting state anxiety using EEG is an intriguing area of research. This study detects the state of anxiety based on an EEG signal using a 1-Dimensional Convolutional Neural Network (1-D CNN). The dataset is provided by the Database for Anxious States based on Psychological Stimulation (DASPS). DASPS is an EEG recording obtained from twenty-three participants for this investigation. The data were analyzed for statistical features, and then a 1-D CNN was employed to classify anxiety levels. The results show that 95.1% of mild and severe anxious conditions can be accurately detected. Furthermore, 94.8% of detection accuracy is achieved when anxiety is classified as normal, mild, moderate, or severe. Overall, this study provides a solid foundation for multi-level anxiety detection by improving the accuracy and selecting better features.
Data-Driven Seismic Hazard Zonation of Indonesia to Support SDG 11 Using DBSCAN and K-Means Clustering Atik Wintarti; Fadhilah Qalbi Annisa; Harmon Prayogi; Yuliani Puji Astuti; Ibnu Febry Kurniawan
Journal of Current Studies in SDGs Vol. 2 No. 2 (2026): June
Publisher : Sekolah Tinggi Agama Islam Sabilul Muttaqin Mojokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63230/jocsis.2.2.224

Abstract

Objective: To examine ten years of earthquake data recorded across Indonesia drawing on 5,364 events with magnitudes above M 5.0 between 2016 and 2025. Method: DBSCAN algorithm was run after the optimal neighborhood radius was determined objectively from a k-distance plot. An elbow at about 65 km was identified and the value yielded 16 spatially distinct clusters alongside 460 noise events. K-means algorithm identified four seismic regimes. Results: Of the four regimes, one cluster (Cluster 1) concentrated every major earthquake in the catalog (64 events with M >= 7.0), even though it accounted for fewer than one event in ten. The three remaining clusters captured background seismicity at near-identical mean magnitudes of approximately from 5.33 to 5.35. At the conventional zonal level, Maluku-Sulawesi generated the most events about 40.8% from total events, while Sumatra registered the highest seismic energy output. A Gutenberg-Richter b-value of 0.98 was estimated for the full catalog. Novelty: Introducing earthquake zonation methods based on machine learning for earthquake catalog of Indonesia. These findings support multiple Sustainable Development Goals including the identification of underestimated high-energy rupture corridors informs evidence-based urban risk reduction (SDG 11), strengthens the scientific foundation for earthquake disaster preparedness (SDG 13), introduces an innovative and reproducible machine learning methodology applicable to infrastructure (SDG 9), and contributes a freely transferable workflow that adopt data-driven zonation methods (SDG 17)
Data-Driven Seismic Hazard Zonation of Indonesia to Support SDG 11 Using DBSCAN and K-Means Clustering Atik Wintarti; Fadhilah Qalbi Annisa; Harmon Prayogi; Yuliani Puji Astuti; Ibnu Febry Kurniawan
Journal of Current Studies in SDGs Vol. 2 No. 2 (2026): June
Publisher : Sekolah Tinggi Agama Islam Sabilul Muttaqin Mojokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63230/jocsis.2.2.224

Abstract

Objective: To examine ten years of earthquake data recorded across Indonesia drawing on 5,364 events with magnitudes above M 5.0 between 2016 and 2025. Method: DBSCAN algorithm was run after the optimal neighborhood radius was determined objectively from a k-distance plot. An elbow at about 65 km was identified and the value yielded 16 spatially distinct clusters alongside 460 noise events. K-means algorithm identified four seismic regimes. Results: Of the four regimes, one cluster (Cluster 1) concentrated every major earthquake in the catalog (64 events with M >= 7.0), even though it accounted for fewer than one event in ten. The three remaining clusters captured background seismicity at near-identical mean magnitudes of approximately from 5.33 to 5.35. At the conventional zonal level, Maluku-Sulawesi generated the most events about 40.8% from total events, while Sumatra registered the highest seismic energy output. A Gutenberg-Richter b-value of 0.98 was estimated for the full catalog. Novelty: Introducing earthquake zonation methods based on machine learning for earthquake catalog of Indonesia. These findings support multiple Sustainable Development Goals including the identification of underestimated high-energy rupture corridors informs evidence-based urban risk reduction (SDG 11), strengthens the scientific foundation for earthquake disaster preparedness (SDG 13), introduces an innovative and reproducible machine learning methodology applicable to infrastructure (SDG 9), and contributes a freely transferable workflow that adopt data-driven zonation methods (SDG 17)
Co-Authors Atik Wintarti Harmon Prayogi Harmon Prayogi Hasanuddin Al-Habib Ibnu Febry Kurniawan Riskyana Dewi Intan Puspitasari Ulfa Siti Nuraini Yuliani Puji Astuti YULIANI PUJI ASTUTI