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UPAYA DAN KENDALA PENGEMBANGAN MULTIMEDIA DALAM PEMBELAJARAN MATEMATIKA DI JURUSAN MATEMATIKA UNESA Ismail, ; Atik Wintarti, ; Yuni Yamasari, ; Asma Johan,
Jurnal Penelitian Pendidikan Matematika dan Sains Vol 15, No 1 (2008)
Publisher : Jurnal Penelitian Pendidikan Matematika dan Sains

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In learning and teaching mathematics, the use of multimedia is important in order to help students to understand mathematics topics. There are several competencies needed to develop multimedia in teaching mathematics, i.e. competencies in pedagogy, in mathematics content, in programming, and creativity. Therefore, the Mathematics Department of Unesa prepares its students by providing subjects such as: Teaching-Learning Process I-IV, School Mathematics I-II, Media in Learning teaching Mathematics, Computer Aplication and Visual Programming. Seminar of  Mathematics education and Skripsi are used to implement those subjects in the form of study report. In the second semester of year 2007/2008 some researches on development of multimedia in learning teaching mathematics have been held. The implementation results show that there are still a number of problems during the implementation of the multimedia.

Profil Berpikir Kritis Siswa Laki-Laki Maskulin dan Perempuan Feminin dalam Menyelesaikan Masalah Literasi Numerasi pada Asesmen Kompetensi Minimum Reni Rachmawati; Dwi Juniati; Atik Wintarti
EDUKASIA Jurnal Pendidikan dan Pembelajaran Vol. 3 No. 3 (2022): Edukasia: Jurnal Pendidikan dan Pembelajaran
Publisher : LP. Ma'arif Janggan Magetan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62775/edukasia.v3i3.218

The purpose of this research was to describe the critical thinking of masculine male and feminine female students in solving numeracy literacy problems in the minimum competency assessment. This research is a descriptive research with a qualitative approach. The data collection method used the Bem Sex Role Inventory test to determine gender tendencies, giving math ability tests, giving problem solving assignments and interviews to determine students' critical thinking profiles. The research subjects consisted of two grade VIII junior high school students with masculine male and feminine female gender with moderate math ability. The results showed that masculine male students could fulfill all critical thinking indicators, while feminine female students could not fulfill one of the critical thinking indicators, namely analyzing problems with mathematical concepts. In addition, masculine male and feminine female students have different ways or solutions in solving numeracy literacy problems in the minimum competency assessment.

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

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

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)

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