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UTILIZATION OF AI IN MARKETING CREATIVE PRODUCTS MADE FROM WOOD WASTE IN THE CRAFTSMEN COMMUNITY OF NGEMPLAK DISTRICT, BOYOLALI REGENCY Siswanto, Siswanto; Nughthoh Arfawi Kurdhi; Nugroho Arif Sudibyo; Supriyadi Wibowo; Putranto Hadi Utomo
J-ABDI: Jurnal Pengabdian kepada Masyarakat Vol. 5 No. 5 (2025): Oktober 2025
Publisher : Bajang Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53625/jabdi.v5i5.11546

Abstract

This study explores the application of Artificial Intelligence (AI) to enhance the aesthetic quality and design innovation of creative products made from wood waste in Ngemplak District, Boyolali Regency, Indonesia. Local craftsmen in this area often face challenges in improving product appearance due to limited access to design tools and technological resources. By integrating AI-based design systems, such as generative design algorithms, image enhancement models, and style transfer techniques, artisans can develop more appealing, modern, and market-responsive product designs while maintaining the authenticity of local craftsmanship. The use of AI not only supports creativity and efficiency in the product development process but also contributes to sustainability by increasing the added value of upcycled wood materials. This study highlights how AI-driven visual enhancement can bridge traditional craftsmanship with digital innovation, promoting competitiveness and sustainable growth in rural creative industries.
Analisis Hasil Belajar, Gender dan Self Regulated Learning (SRL) Mahasiswa Menggunakan Algoritma K-Means Clustering Muhammad Wahid Syaifuddin; Septiana Wijayanti; Nugroho Arif Sudibyo
Khatulistiwa: Jurnal Pendidikan dan Sosial Humaniora Vol. 2 No. 2 (2022): Juni : Jurnal Pendidikan dan Sosial Humaniora
Publisher : Pusat Riset dan Inovasi Nasional

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55606/khatulistiwa.v2i2.540

Abstract

The purpose of this research is to use a clustering algorithm on learning outcomes, gender, and SRL. In this study, the K-Means clustering method was used. In this study, the calculation process is assisted by Orange software. Determination of the number of clusters with Orange software based on the Silhouette Score. The results show that there are seven clusters with three variables, namely learning outcomes, gender, and self-regulated learning. From these clusters, it can be used for group formation and student mapping.
Hubungan antara Internet Self-Efficacy dengan Prestasi Belajar Siswa Sekolah Menengah Atas (Fokus: Analisis Cluster dengan Metode K-Means dan Louvain) Syaifuddin , M Wahid; Sudibyo, Nugroho Arif; Irfan, Muhammad
Jurnal Studi Guru dan Pembelajaran Vol. 6 No. 3 (2023): September - Desember 2023
Publisher : Universitas Cokroaminoto Palopo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30605/jsgp.6.3.2023.2654

Abstract

Internet self-efficacy (ISE) was initiated by Albert Bandura, self-efficacy (SE) is a widely accepted concept that refers to an individual's belief in his ability to complete certain behaviors or tasks in certain situations. The purpose of this study was to determine the relationship between ISE and the learning achievement of 12th-grade high school students. The sample of this study was 12th-grade MIPA 7 SMA Unggulan CT ARSA Foundation Sukoharjo as many as 21 students. ISE data is data obtained from questionnaires distributed to students who have been tested for validity and reliability. On the other hand, learning achievement tests were conducted on students with three-dimensional material. The analysis conducted to determine the relationship between the two variables is bivariate analysis. The results showed that between the ISE variable and Student Learning Achievement, a correlation coefficient value of 0.670 was obtained with a significant relationship between the ISE variable and Student Achievement. The degree of bond correlation value shows a strong/sturdy correlation.
Sentiment Analysis Towards the KitaLulus Application Using the Naive Bayes Method from Google Play Store Reviews Nadia Amalia Putri; Agustina Srirahayu; Nugroho Arif Sudibyo
Jurnal Indonesia Sosial Teknologi Vol. 5 No. 10 (2024): Jurnal Indonesia Sosial Teknologi
Publisher : Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59141/jist.v5i10.1244

Abstract

Job search apps like KitaLulus are essential in helping graduates find jobs based on their skills and interests. Sentiment analysis is needed to understand user opinions about the KitaLulus application. The Naive Bayes method is used in this analysis because of its high efficiency and accuracy. This research used 597 data and achieved an accuracy rate of 91%. The eval_uation results show positive sentiment values for precision, recall, and f1-score of 0.99, 0.94, and 0.97 respectively. On the other hand, the model performance is low for negative and neutral sentiments. The aim of this research is to increase user understanding of the KitaLulus application and provide valuable assistance to developers in their efforts to improve the quality of the application.