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The Influence of Social Media on Students' Social Interactions Yanti, Dawi; Ria Kamilah Agustina
Ed-Humanistics : Jurnal Ilmu Pendidikan Vol 10 No 2 (2025): Ed-Humanistics
Publisher : Fakultas Ilmu Pendidikan (FIP) Universitas Hasyim Asy'ari (Unhasy) Tebuireng Jombang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33752/ed-humanistics.v10i2.10794

Abstract

The widespread use of social media has transformed the way students interact with each other, both online and offline. This study explores the influence of social media on students' social interactions, focusing on its impact on their communication patterns, relationships, and social skills. A mixed-methods approach was employed, combining survey data from 200 students from two universities in Indonesia with in-depth interviews from a subset of participants. The results show that social media has both positive and negative effects on students' social interactions. On the one hand, social media enables students to connect with peers globally, access information, and participate in online discussions. On the other hand, excessive social media use can lead to social isolation, decreased face-to-face communication, and reduced empathy. The study highlights the need for educators and parents to be aware of the potential risks and benefits of social media and to promote healthy social media habits among students. Keywords: social media, social interaction, students, communication, relationships, social skills
Peramalan Kelembaban Tanah Berbasis IoT Menggunakan Pendekatan Machine Learning dan Deep Learning Darmiyati, Iin; Oktafiani, Fitri; Sahara, Ain; Yanti, Dawi; Maulana, Ranjiv; Hamsir, Hamsir; Nurjannah, Nurjannah
PETROGAS: Journal of Energy and Technology Vol 8, No 1 (2026): MARCH
Publisher : Sekolah Tinggi Teknologi MIGAS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58267/petrogas.v8i1.212

Abstract

Kelembaban tanah merupakan parameter penting yang mempengaruhi produktivitas tanaman serta efisiensi penggunaan air dalam sistem pertanian dan perkebunan. Pengelolaan irigasi konvensional umumnya menggunakan nilai ambang batas tetap sehingga tidak mampu memprediksi perubahan kondisi kelembaban tanah di masa mendatang. Penelitian ini mengembangkan sistem peramalan kelembaban tanah berbasis Internet of Things (IoT) dengan pendekatan machine learning dan deep learning. Sensor dihubungkan dengan mikrokontroler ESP32 yang mengirimkan data secara real time ke penyimpanan cloud. Dataset yang diperoleh terdiri dari ribuan entri dan diolah menggunakan Python melalui proses pembersihan data, normalisasi serta pelatihan model. Beberapa model prediksi yang digunakan meliputi Linear Regression, Random Forest Regression, Support Vector Regression (SVR), dan Long Short-Term Memory (LSTM). Dataset dibagi menjadi data pelatihan dan data pengujian dengan rasio 80:20. Evaluasi kinerja model dilakukan menggunakan Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), dan koefisien determinasi (R²). Hasil penelitian menunjukkan bahwa model LSTM memberikan performa prediksi terbaik karena mampu menangkap pola temporal pada data deret waktu sensor IoT. Sistem ini berpotensi mendukung pengembangan irigasi cerdas untuk meningkatkan efisiensi penggunaan air dan pengelolaan perkebunan yang berkelanjutan.Kata kunci: Kelembaban tanah; Internet of Things; Machine Learning; Deep Learning; Pertanian presisi.