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All Journal Al Ishlah Jurnal Pendidikan Jurnal Penelitian Pendidikan IPA (JPPIPA) JISIP: Jurnal Ilmu Sosial dan Pendidikan Jurnal Pendidikan Ilmu Pengetahuan Alam (JP-IPA) DIKMAT: Jurnal Pendidikan Matematika Pendikdas: Jurnal Pendidikan Dasar JPK: Jurnal Pendidikan dan Kebudayaan
Lutfin Haryanto
STKIP HARAPAN BIMA
Agriculture, Biological Sciences & Forestry Arts Humanities Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Education Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Mathematics Physics Social Sciences Other

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Penerapan Data Mining dalam Analisis Kejadian Banjir di Indonesia dengan Menggunakan Metode Association Rule Algoritma Apriori Sanjaya, Fifi; Salahuddin, Muhammad; Haryanto, Lutfin; Sarnita, Fitria
Jurnal Pendidikan Ilmu Pengetahuan Alam (JP-IPA) Vol 5, No 1 (2024): Mei 2024
Publisher : STKIP HARAPAN BIMA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56842/jp-ipa.v5i1.312

Abstract

Floods are a natural disaster that frequently occurs in Indonesia. Disaster prevention measures and flood simulation and guidance are still less applied in other cities except for major cities, resulting in a low level of human safety. From the data obtained, many casualties and significant material losses were suffered by flood victims. Floods usually occur during the rainy season, but no one knows when and where they will happen. This study applies data mining techniques with association rules using the apriori algorithm to understand the patterns and association rules of flood events in Indonesia. The data were taken from the official website of the National Disaster Management Agency (BNPB) from 2014-2016 and analyzed using the R program. The association analysis results showed that "if there is flooding due to all survival factors, then there is a possibility of flooding in Cileunang" with support 38.7%, confidence 64.4%, and lift 1.0347319. Meanwhile, "if there is flooding in Cileunang, then there is a possibility of all surviving" with support 38.7%, confidence 64.4%, and lift 1.0347319.
Implementing the Merdeka Belajar Policy through Deep Learning: A Case Study in Social Studies Education in Bima Regency Muhammad Yamin; Abdul Wahab; Lutfin Haryanto; Faidin Faidin
AL-ISHLAH: Jurnal Pendidikan Vol 17, No 3 (2025): SEPTEMBER 2025
Publisher : STAI Hubbulwathan Duri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35445/alishlah.v17i3.7946

Abstract

This study examines the effectiveness of Indonesia’s Merdeka Belajar (Freedom to Learn) policy through the implementation of deep learning strategies in Social Studies education within Bima Regency, a socio-economically constrained region. The policy promotes student-centered learning, autonomy, and contextual relevance—principles that align closely with deep learning pedagogy. A qualitative multiple case study design was employed in two junior high schools. Data were collected through in-depth interviews with ten teachers and thirty students, twenty-four classroom observations, and analysis of planning documents and student work samples. Thematic analysis was conducted using an inductive-deductive coding approach to identify patterns related to student engagement, critical thinking, and pedagogical transformation. Findings revealed a substantial increase in student engagement, with 90% actively participating in group discussions and 70% successfully formulating solutions to social problems. Student feedback indicated strong preference for project-based learning (73%) and the Merdeka Belajar model (83%). Teachers reported increased student motivation, though only 70% demonstrated full understanding of the curriculum's core principles. Document analysis showed partial integration of local context and 21st-century competencies. Key barriers included inadequate digital infrastructure and limited teacher training. These results suggest that the integration of Merdeka Belajar and deep learning can effectively foster critical thinking and participatory learning in under-resourced settings. However, successful and sustainable implementation requires systemic support through infrastructure investment and targeted professional development.
Co-Authors A Haris Abas Oya Abdul Wahab E. Ewisahrani Eva Nursaban F Fathurrahmaniah Faidin Faidin Fitria Sarnita Ilyas Ilyas Jessy Parmawati Atmaja Muhammad Salahuddin Muhammad Suhardi Muhammad Yamin Mujitahid Mujitahid Nita Rahmaniya Prayogi, Saiful Rizcky Juliawan Rostati Rostati Sanjaya, Fifi Sasmita Susilawati Suhirman Suhirman W Widia