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All Journal Jurnal Layanan Masyarakat (Journal of Public Service) Jurnal Komputer Teknologi Informasi Sistem Komputer (JUKTISI)
Binti Kholifah
Univeristas Negeri Suarabaya
Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Engineering Environmental Science Social Sciences Other

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ENHANCING MOTHERS’ PARENTING SKILLS THROUGH EMOTION REGULATION EDUCATION IN JATIREJO VILLAGE, NGANJUK Nicky Dwi Kurnia; Febi Warta Nur Ani; Binti Kholifah
Jurnal Layanan Masyarakat (Journal of Public Services) Vol. 10 No. 1 (2026): JURNAL LAYANAN MASYARAKAT
Publisher : Universitas Airlangga

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Abstract

Parenting in rural communities still faces various challenges, particularly related to parents’ ability to regulate emotions when dealing with children’s behavior. Limited access to parenting information and socio-economic pressures often affect the quality of parent–child interactions. This community service program aimed to improve mothers’ understanding and skills in applying emotion regulation–based parenting in Jatirejo Village, Rejoso District, Nganjuk Regency. The program involved 40 mothers with children under the age of 10 and was conducted on August 20–22, 2025, at the Jatirejo Village Hall with the support of lecturers and students participating in the Community Service Program (KKN) from Institut Teknologi Mojosari. The program employed a mixed approach through participatory education, group discussions, and parenting simulations. Program evaluation was conducted using participatory observation, reflective interviews, and descriptive measurements based on rating scales. The novelty of this program lies in the integration of emotion regulation education with a reflective approach based on mothers’ real parenting experiences within the village community, enabling participants not only to receive knowledge but also to reflect on their daily parenting practices. The results showed an improvement in mothers’ emotion regulation skills, indicated by an increase in observation scores from 2.15 to 3.12 and reflective interview scores from 2.05 to 3.20. These changes were reflected in mothers becoming calmer, more patient, and more responsive in dealing with children’s behavior. In addition to improving mother–child interactions, the program also strengthened social support among mothers within the community. Therefore, emotion regulation–based parenting education is considered an effective and contextual approach to improving parenting quality in rural communities. Future programs are recommended to be implemented sustainably by integrating similar initiatives into family empowerment activities at the village level.
Analisis Sentimen Multi-Kelas untuk Menilai Kepuasan Mahasiswa terhadap Aplikasi Manajemen Akademik Berbasis Web di Lingkungan Perguruan Tinggi Nicky Dwi Kurnia; Binti Kholifah; Febi Warta Nur Ani; Ayu Fernanda Nurun Nafii’
Jurnal Komputer Teknologi Informasi Sistem Komputer (JUKTISI) Vol. 4 No. 1 (2025): Juni 2025
Publisher : LKP KARYA PRIMA KURSUS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62712/juktisi.v4i1.363

Abstract

Abstrak Penelitian ini bertujuan untuk menganalisis sentimen multi-kelas guna menilai tingkat kepuasan mahasiswa terhadap penggunaan aplikasi manajemen akademik berbasis web di lingkungan perguruan tinggi. Dengan menggunakan metode pemrosesan bahasa alami (Natural Language Processing, NLP) dan algoritma machine learning seperti Random Forest, Support Vector Machine (SVM), dan Neural Network, penelitian ini mengklasifikasikan komentar mahasiswa ke dalam beberapa kelas sentimen: positif, netral, dan negatif. Data dikumpulkan melalui survei online dan ulasan aplikasi yang tersedia selama semester genap tahun akademik 2023/2024. Hasil penelitian menunjukkan bahwa metode SVM memberikan akurasi terbaik sebesar 87,5% dalam klasifikasi sentimen multi-kelas. Temuan ini memberikan gambaran empiris mengenai persepsi mahasiswa dan dapat menjadi acuan bagi pengembang aplikasi akademik untuk meningkatkan kualitas layanan. Penelitian ini juga mengkaji faktor-faktor yang mempengaruhi kepuasan mahasiswa serta rekomendasi pengembangan aplikasi ke depan. Abstract This study aims to analyze multi-class sentiment to assess student satisfaction with web-based academic management applications in higher education institutions. Utilizing natural language processing (NLP) techniques and machine learning algorithms such as Random Forest, Support Vector Machine (SVM), and Neural Networks, this research classifies student comments into several sentiment categories: positive, neutral, and negative. Data were collected via online surveys and application reviews during the even semester of the 2023/2024 academic year. Results indicate that the SVM method achieved the highest accuracy of 87.5% in multi-class sentiment classification. These findings provide empirical insights into student perceptions and serve as a reference for academic application developers to improve service quality. The study also examines factors influencing student satisfaction and offers recommendations for future application development.
Co-Authors Ayu Fernanda Nurun Nafii’ Febi Warta Nur Ani Nicky Dwi Kurnia