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All Journal Musawa : Jurnal Studi Gender dan Islam Jurnal Obsesi: Jurnal Pendidikan Anak Usia Dini Sisfo: Jurnal Ilmiah Sistem Informasi
Sukasih, Sukasih
Sistem Dan Teknologi Iinformasi, Institut Pendidikan Dan Teknologi Aisyiyah Riau
Religion Arts Humanities Computer Science & IT Decision Sciences, Operations Research & Management Education Engineering Library & Information Science Mechanical Engineering Social Sciences

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Kesadaran Sosial Dari Sekolah Menuju Kebangkitan Kembali Wanita Indonesia Sukasih, Sukasih
Musawa Jurnal Studi Gender dan Islam Vol. 9 No. 2 (2010)
Publisher : Sunan Kalijaga State Islamic University & The Asia Foundation

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14421/musawa.2010.92.219-235

Abstract

Social consciousness can be from the social science learning by using ways portraying the consciousness of society individuals, culture experience and the development of student personality. By using the consciousness of the personality the students of social science education get values and democracy attitude so that it can develop good citizens. Good citizens have their own responsibility, human rights and the same opportunity between men and women. The social science education to examine social change begins from the accepted postulate that changes constitute inseparable parts in the human Zife constantly. The direct and indirect changes in thought, student attitude towards gender equality constitutes changing process developed through the social science education. The social science education develops the social consciousness of students at schools to the reawakening of Indonesian women in the changing process constantly since the school gets the materials of equality and human rights.
Diabetes Risk Prediction Using XGBoost-Based Machine Learning and Explainable Artificial Intelligence Sukasih Sukasih; Budi Arham; Nelti Rizka; Muhammad Badar
Sisfo: Jurnal Ilmiah Sistem Informasi Vol. 10 No. 1 (2026): Sisfo: Jurnal Ilmiah Sistem Informasi, Mei 2026
Publisher : Universitas Malikussaleh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29103/sisfo.v10i1.27124

Abstract

Diabetes mellitus is a widespread chronic disease that affects populations worldwide and is often only identified after complications have occurred. Machine learning techniques can be applied to estimate the likelihood of diabetes based on clinical patient data. This research focuses on developing a predictive model using the Extreme Gradient Boosting (XGBoost) algorithm, along with the application of Explainable Artificial Intelligence (XAI) to interpret the model’s outcomes. The dataset used in this study consists of 768 patient records with eight clinical attributes, namely pregnancies, glucose level, blood pressure, skin thickness, insulin level, body mass index (BMI), diabetes pedigree function, and age. The research process includes data preprocessing, exploratory data analysis, model development, performance evaluation, and interpretation using SHAP. The findings indicate that the XGBoost model achieves high predictive performance and is capable of identifying key factors associated with diabetes risk. Based on SHAP interpretation, glucose level, BMI, age, and insulin are the most influential variables in the prediction process. The integration of machine learning and explainable AI improves model interpretability while maintaining reliable prediction performance.
Tantangan Integrasi Media Pembelajaran Berbasis Teknologi Digital dan Artificial Intelligence (AI) pada Implementasi Deep Learning di Pendidikan Anak Usia Dini Nelti Rizka; Sukasih Sukasih; Hafiko Andresni; Siti Fadilah
Jurnal Obsesi : Jurnal Pendidikan Anak Usia Dini Vol. 10 No. 4 (2026)
Publisher : LPPM Universitas Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/obsesi.v10i4.8527

Abstract

Penelitian ini bertujuan untuk mengetahui tantangan integrasi media pembelajaran berbasis teknologi digital dan Artificial Intelligence (AI) dalam implementasi deep learning di satuan Pendidikan Anak Usia Dini (PAUD). Penelitian menggunakan pendekatan kualitatif dengan metode studi kasus. Pengumpulan data dilakukan melalui wawancara, observasi, survei, dan dokumentasi terhadap kepala sekolah dan guru PAUD. Analisis data dilakukan menggunakan teknik coding melalui tahapan member checking, rich text description, dan audit trail. Hasil penelitian menunjukkan bahwa bahwa tantangan utama integrasi media pembelajaran berbasis teknologi digital dan AI di PAUD meliputi rendahnya kompetensi digital guru, akses internet, serta minimnya pelatihan pemanfaatan AI. Satuan PAUD sudah mulai menggunakan video pembelajaran, smartphone, Interactive Flat Panel (IFP), Smart TV, dan audio edukatif, namun penggunaannya masih bersifat sederhana dan belum terintegrasi secara optimal dalam implementasi deep learning. Pemanfaatan Artificial Intelligence (AI) juga masih sangat terbatas karena rendahnya kompetensi digital guru dan minimnya pelatihan. Faktor pendukung meliputi tersedianya perangkat teknologi, akses sumber belajar digital, motivasi guru, serta dukungan kebijakan pendidikan. Adapun faktor penghambat meliputi keterbatasan sarana prasarana, akses internet, kompetensi digital guru, serta kekhawatiran terhadap dampak penggunaan gadget pada anak usia dini.
Co-Authors Budi Arham Hafiko Andresni Muhammad Badar Nelti Rizka Nelti Rizka Siti Fadilah