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All Journal Kontribusi: Jurnal Penelitian dan Pengabdian Kepada Masyarakat Brilliance: Research of Artificial Intelligence Journal of Multidisciplinary Science: MIKAILALSYS
Nashihin, Durrotun
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Agriculture, Biological Sciences & Forestry Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Decision Sciences, Operations Research & Management Education Engineering Environmental Science Health Professions Law, Crime, Criminology & Criminal Justice Mathematics Physics Social Sciences Other

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Optimization of Distance Learning Systems Using Artificial Intelligence and the Internet of Things in Improving the Quality of Education in the Post-Pandemic Era Yahya, Saifudin; Islam, Khoirul; Nashihin, Durrotun
Journal of Multidisciplinary Science: MIKAILALSYS Vol 2 No 3 (2024): Journal of Multidisciplinary Science: MIKAILALSYS
Publisher : Darul Yasin Al Sys

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58578/mikailalsys.v2i3.3953

Abstract

The COVID-19 pandemic forced a sudden change from face-to-face learning to Distance Learning (PJJ), posing challenges in the quality of learning and student interaction. This study examines the application of Artificial Intelligence (AI) and the Internet of Things (IoT) to optimize the PJJ system in the post-pandemic era. The goal is to evaluate the impact of AI in personalizing learning and IoT in improving student interactivity and collaboration, as well as identify the challenges of implementing this technology. The research uses a mixed-methods approach by combining qualitative and quantitative analysis. Data were obtained from 50 educators, 100 students, and 20 administrators through questionnaires, in-depth interviews, and participatory observations. Correlation tests and logistic regression are used to assess the influence of AI and IoT on learning quality. The results show that 85% of students and 78% of educators agree that AI helps in customizing learning materials. IoT also increases student engagement, with 76% of students feeling more engaged and the likelihood of student engagement increasing 2.35 times greater with IoT. Key challenges include limited technological infrastructure and lack of training for educators. In conclusion, AI and IoT have great potential in improving the quality of education, but support is needed for infrastructure and educator training so that the implementation of these technologies is optimal.
Peningkatan Promosi Desa Wisata Soco Melalui Digitalisasi Objek Wisata dan Edukasi Komunitas Sisephaputra, Bonda; Suroni, Azis; Islam, Khoirul; Yahya, Saifudin; Nashihin, Durrotun; Al Rosyid, Harun; Orvala, Fathan; Putra, Dicky Sanjaya; Pratama, Tora Rizal
Kontribusi: Jurnal Penelitian dan Pengabdian Kepada Masyarakat Vol. 6 No. 1 (2025): November 2025
Publisher : Cipta Media Harmoni

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53624/kontribusi.v6i1.747

Abstract

Latar Belakang: Promosi desa wisata berbasis digital merupakan strategi penting dalam mengembangkan potensi lokal dan memperluas jangkauan wisatawan. Namun, banyak desa, termasuk Desa Soco di Kabupaten Magetan, belum memanfaatkan teknologi secara optimal. Rendahnya literasi digital masyarakat menjadi kendala utama dalam promosi mandiri. Tujuan: Mengetahui sejauh mana digitalisasi objek wisata dan edukasi komunitas dapat meningkatkan kapasitas promosi wisata di Desa Soco. Metode: Menggunakan pendekatan kualitatif deskriptif dalam bentuk studi kasus. Data dikumpulkan melalui observasi, wawancara, dokumentasi, dan evaluasi pelatihan partisipatif. Hasil: Pelatihan fotografi, videografi, editing video, pengelolaan website, dan media sosial meningkatkan pemahaman peserta terhadap strategi promosi digital. Media berupa video profil, website, dan kanal YouTube disajikan sebagai model pengelolaan konten. Peserta menunjukkan motivasi dan kesiapan mengelola media digital secara mandiri. Kesimpulan: Digitalisasi objek wisata yang disertai pelatihan teknis efektif meningkatkan kapasitas promosi desa wisata. Pendampingan lanjutan dibutuhkan untuk pengembangan konten yang berkelanjutan.  
Disagreement Analysis of Sentiment Predictions on Student Satisfaction Surveys Using Two IndoBERT Models Nashihin, Durrotun; Lisnani, Lisnani; Hanafi, Imam
Brilliance: Research of Artificial Intelligence Vol. 5 No. 2 (2025): Brilliance: Research of Artificial Intelligence, Article Research November 2025
Publisher : Yayasan Cita Cendekiawan Al Khwarizmi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/brilliance.v5i2.7093

Abstract

Understanding student satisfaction survey presents both opportunities and challenges in a higher education. While sentiment analysis offers an efficient means of interpreting large volumes of textual data, inconsistencies between models can affect the reliability of resulting insights. This study aims to compare two IndoBERT sentiment models by analyzing their disagreement patterns and deriving insights to enhance institutional understanding of student satisfaction. The methodology involves two pretrained models (IndoBERT base finetuned SMSA and IndoBERT lite finetuned SMSA GooglePlay) applied to 657 student survey responses without additional fine-tuning. Evaluation focuses specifically on disagreement cases between the two models, using precision, recall, F1-score, accuracy and weighted average to assess model consistency. The results indicate that IndoBERT base demonstrates stronger contextual reliability, achieving a weighted average F1-score of 0.60 compared to 0.24 for IndoBERT lite on disagreement cases. IndoBERT lite tends to overestimate positive sentiment, particularly for short or ambiguous text inputs, whereas IndoBERT base maintains a more balanced interpretation across sentiment categories. The result from IndoBERT base also shows that positive sentiment gives the highest percentage at 53.4%, followed by neural at 34.6% and negative at 12.0% respectively. The negative sentiment is most likely related to campus facilities. These findings highlight that the disagreement analysis is valuable for identifying model biases and can provide insights to support institutional improvement from student satisfaction survey. For future research, more robust models can be developed by fining-tuned directly on student survey data, along with developing user-friendly application to assist universities in extracting the student survey data.
Co-Authors Al Rosyid, Harun Bonda Sisephaputra Imam Hanafi Islam, Khoirul Lisnani Lisnani Orvala, Fathan Pratama, Tora Rizal Putra, Dicky Sanjaya Suroni, Azis Yahya, Saifudin