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All Journal Jurnal Sains dan Pendidikan Fisika Artificial Intelligence in Educational Decision Sciences
Ayu Safitri
Universitas Negeri Makassar
Computer Science & IT Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Physics Social Sciences

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PENGEMBANGAN MEDIA PEMBELAJARAN INTERAKTIF PADA MATERI EFEK DOPPLER KELAS XI SMA NEGERI 3 BARRU Ayu Safitri; Abdul Haris; Ahmad Yani; Pariabti Palloan; Mutahharah Hasyim
Jurnal Sains dan Pendidikan Fisika Vol 19, No 2 (2023): JURNAL SAINS DAN PENDIDIKAN FISIKA
Publisher : Universitas Negeri Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35580/jspf.v19i2.35398

Abstract

Penelitian ini merupakan jenis penelitian Research & Development (R&D) bertujuan untuk menghasilkan media pembelajaran yang valid, praktis dan efektif. Penelitian ini dilaksanakan dengan melalui tahap Penelitian dan Pengembangan dengan model pengembangan ADDIE. Adapun persentase hasil menunjukkan tingkat kelayakan media dari isi maupun kegrafikan media berdasarkan penilaian validator dengan nilai koefisien internal yaitu  1 atau 100%, untuk  tingkat kepraktisan dari media berdasarkan setiap aspek peniliaian angket respon peserta didik memiliki nilai skala persentase skor 84%-93% dan penilaian angket respon guru memiliki nilai sklala persentase skor 93%-100%, untuk tingkat keefektifan dari media berdasarkan persentase skor    N-Gain peserta didik yaitu 74%. Berdasarkan hasil penelitian disimpulkan bahwa media pembelajaran interaktif pada materi efek doppler yang dikembangkan sudah valid, praktis dan efektif digunakan sebagai media pembelajaran fisika
Data-Driven Clustering of Stunting Prevention Services for Pregnant Women and Infants Using Fuzzy C-Means Hanum Zalsabilah Idham; Ayu Safitri; Andi Akram Nur Risal; Dewi Fatmarani Surianto; Firdaus
Artificial Intelligence in Educational Decision Sciences Vol 1 No 2 (2026): Artificial Intelligence in Educational Decision Sciences
Publisher : PT. Academic Bright Collaboration

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.66053/aieds.v1i2.22

Abstract

Purpose – This study addresses persistently high stunting rates in South Sulawesi, Indonesia, which remain above national targets despite declining trends. We developed a clustering model to overcome limitations of traditional methods in handling complex health data with overlapping characteristics, aiming to identify priority regions requiring targeted interventions.Methods – Using 2,267 structured records from Satu Data Indonesia covering maternal and child health indicators, we implemented Fuzzy C-Means (FCM) algorithm with systematic preprocessing, optimal cluster determination via Elbow Method, and quality validation using Silhouette Coefficient.Findings – Analysis revealed three distinct clusters for pregnant women (representing good, moderate, and low service coverage areas) and three corresponding clusters for infants. Validation showed Silhouette values ranging from 0.204 to 0.645, indicating variable cluster separation quality with Cluster 0 pregnant women achieving highest cohesion (0.638) and Cluster 2 infants showing strongest separation (0.645).Research limitations – Data quality limitations affected cluster cohesion in some areas, particularly Cluster 1 infants (0.204 Silhouette value), constraining generalizability. The FCM approach accommodates real-world data complexity better than rigid clustering methods but requires high-quality input data.Originality – This research contributes an adaptive framework for evidence-based stunting prevention through sophisticated data-driven segmentation. Findings offer immediate practical value for health policymakers in resource allocation and intervention planning, with potential adaptation to other regional contexts facing similar public health challenges.
Co-Authors Abdul Haris Ahmad Yani Andi Akram Nur Risal Dewi Fatmarani Surianto Firdaus Hanum Zalsabilah Idham Mutahharah Hasyim, Mutahharah Pariabti Palloan