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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Afeksi: Jurnal Penelitian dan Evaluasi Pendidikan
Asma’ Khoirunnisa’
Universitas Negeri Yogyakarta
Computer Science & IT Education Engineering Languange, Linguistic, Communication & Media Mathematics Social Sciences Other

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MEDIA POSTER SEBAGAI AKTUALISASI POTENSI CIPTAKAN KARYA GEMILANG MELALUI KOMUNIKASI VISUAL PENDIDIKAN INKLUSI Hanifah Mar’atush Shalihah; Yunita Ambarwati; Asma’ Khoirunnisa’; Heru Sukoco
Afeksi: Jurnal Penelitian dan Evaluasi Pendidikan Vol 4, No 4 (2023)
Publisher : Pusat Studi Penelitian dan Evaluasi Pembelajaran

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59698/afeksi.v4i4.134

Abstract

Mahasiswa sebagai representasi universitas dan agen perubahan, seharusnya memainkan peran aktif dalam memajukan pendidikan berkelanjutan di Indonesia (Education for Sustainable Development/ESD) melalui penerapan teknologi digital untuk menghasilkan berbagai inovasi di bidang pendidikan dan pembelajaran sehingga literasi digital dapat tumbuh dan berkembang dalam dunia pendidikan. Sejalan dengan kondisi ini, memperoleh pendidikan sesuai harapan tentunya merupakan hak setiap orang, termasuk bagi orang-orang dengan kebutuhan khusus, yaitu pendidikan yang kondusif dan inklusif. Penelitian ini mengkaji poster sebagai aktualisasi potensi dan penciptaan karya yang brilian melalui pendidikan inklusif menggunakan analisis komunikasi visual. Penelitian ini menggunakan metode design thinking yang terdiri dari tahap-tahap empathize, define, ideate, prototype, dan test. Hasil penelitian menunjukkan bahwa poster yang disajikan dapat mendukung peran Komisi Nasional Disabilitas dalam mewujudkan pendidikan inklusif bagi semua orang, terutama bagi orang-orang dengan disabilitas.
Data-driven analysis of growth factors in oyster mushroom cultivation: a case study from Indonesia’s market Yosef Budiman; Gilang Adi Prasetyo; Asma’ Khoirunnisa’; Hanifah Mar’atush Shalihah; Muhamad Riyan Maulana; Yanuar Agung Fadlullah; Sugiri Sugiri
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 4: August 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i4.pp3568-3580

Abstract

The oyster mushroom is one of the potential agricultural products that can be developed as an alternative to other agricultural products, to maintain Indonesia's economic condition. However, the production of oyster mushrooms remains low and falls short of the minimum amount of market demand. This study employs a machine learning (ML)–based approach to identify the key parameters influencing oyster mushroom production rates. Recursive feature elimination (RFE) was applied to reduce the initial 19 features to nine, enabling faster processing while maintaining high predictive accuracy. The results showed that agricultural features showed a high contribution rather than environmental, economic, and demographic features. Furthermore, these parameters were related to the train-test analysis to visualize the statistical analysis shown by the best method, adaptive boosting (AdaBoost), with coefficient of determination (R2), mean squared error (MSE), and mean absolute error (MAE) values of 0.997575, 0.009841, and 0.085884, respectively. Related research relevant to the research findings was analyzed to validate that agricultural product features affect the decline of oyster mushroom production. Other supported research conducted by integrating real-time analysis and twin digital models, which can enhance substrate quality.
Co-Authors Gilang Adi Prasetyo Hanifah Mar’atush Shalihah Heru Sukoco Muhamad Riyan Maulana Sugiri Sugiri Yanuar Agung Fadlullah Yosef Budiman Yunita Ambarwati