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All Journal G-Tech : Jurnal Teknologi Terapan Indonesian Community Journal
I Nyoman Yudi Anggara Wijaya
Universitas Primakara, Indonesia
Agriculture, Biological Sciences & Forestry Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Energy Engineering Environmental Science Public Health Social Sciences Other

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Digitalisasi Pengelolaan Keuangan Usaha Bagi Wirausaha Muda di SMA Negeri 1 Petang I Gst. Agung Pramesti Dwi Putri; A.A. Gede Adi Mega Putra; A. A. Istri Ita Paramitha; I Nyoman Yudi Anggara Wijaya
I-Com: Indonesian Community Journal Vol 4 No 3 (2024): I-Com: Indonesian Community Journal (September 2024)
Publisher : Fakultas Sains Dan Teknologi, Universitas Raden Rahmat Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33379/icom.v4i3.5112

Abstract

Building an entrepreneurial spirit is one of the goals intended to be achieved in high school education. This entrepreneurial spirit is essential for creating students who are productive, creative, and innovative. One of the skills that must be mastered by young entrepreneurs is business financial management. Digitalization in financial management can be a solution to improve efficiency and transparency in business financial management. Therefore, it is important for students at SMAN 1 Petang to complement their entrepreneurship knowledge with business financial management skills. This community service aims to equip young entrepreneurs at SMAN 1 Petang with financial management skills by utilizing the latest technology. The community service activity was conducted in April 2024, involving 37 students who are members of the entrepreneurship extracurricular activity. The outcome of this community service is that young entrepreneurs have gained an understanding of product cost calculation and financial recording and management using the BukuWarung. With the financial management skills acquired, young entrepreneurs will be better equipped to manage and develop their businesses
Sentiment Analysis of Student Perceptions of Generative AI using Data Augmentation and Machine Learning Models Ni Made Satvika Iswari; I Nyoman Yudi Anggara Wijaya
G-Tech: Jurnal Teknologi Terapan Vol 10 No 3 (2026): G-Tech, Vol. 10 No. 3 July 2026
Publisher : Universitas Islam Raden Rahmat, Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70609/g-tech.v10i3.10613

Abstract

The development of Generative AI has significantly changed how students access information, understand learning materials, and generate ideas in higher education. Although Generative AI supports independent learning, it also raises concerns regarding overdependence, declining critical thinking skills, and academic integrity. This study evaluates the performance of sentiment analysis models using a processing pipeline that incorporates lexical, semantic, and generative data augmentation. The main challenge addressed in this study is class imbalance, particularly the limited number of negative sentiment samples compared to positive and neutral classes. This study applies an experimental quantitative approach consisting of dataset preparation, text preprocessing, data augmentation, feature extraction using TF-IDF, model training, and evaluation using Stratified K-Fold Cross Validation. The machine learning models evaluated include Multinomial Naive Bayes, Logistic Regression, Random Forest, and Linear Support Vector Machine. The experimental results show that Linear SVM achieved the best performance, with an average accuracy of 79.07% and a weighted F1-score of 73.90%. Compared descriptively with the non-augmented baseline, Linear SVM showed an observed increase in accuracy from 65.00% to 79.07% and in weighted F1-score from 63.03% to 73.90%. Data augmentation also enabled partial recognition of minority-class sentiment, although a substantial proportion of negative and positive samples were still misclassified as neutral. These findings indicate that hybrid data augmentation can support the performance of classical machine learning models on small and imbalanced educational text datasets, particularly when combined with TF-IDF and Linear SVM. However, a post-hoc audit identified a discrepancy between the class distribution of the original dataset and that of the final processed dataset. Therefore, the observed model performance should be interpreted as the result of the overall processing pipeline rather than as the isolated effect of data augmentation.
Peningkatan Keterampilan Digital Siswa SMKN 1 Sukawati melalui Pelatihan Pengembangan Aplikasi Web Terintegrasi Artificial Intelligence Ni Made Satvika Iswari; I Nyoman Yudi Anggara Wijaya
I-Com: Indonesian Community Journal Vol 6 No 3 (2026): I-Com: Indonesian Community Journal (September 2026)
Publisher : Fakultas Sains Dan Teknologi, Universitas Raden Rahmat Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70609/i-com.v6i3.10611

Abstract

Kemajuan teknologi web dan Artificial Intelligence (AI) menuntut siswa untuk memiliki keterampilan digital yang semakin baik, terutama pada jenjang Sekolah Menengah Kejuruan (SMK). Akan tetapi, kesempatan siswa untuk memperoleh pengalaman praktik menggunakan teknologi terbaru masih relatif terbatas. Kegiatan pengabdian kepada masyarakat ini bertujuan untuk mengembangkan keterampilan digital siswa SMKN 1 Sukawati melalui pelatihan pembuatan aplikasi web yang dikombinasikan dengan teknologi AI. Pelaksanaan kegiatan dilakukan melalui pelatihan praktik langsung dengan pendekatan pembelajaran berbasis proyek, yang mencakup materi HTML, CSS, JavaScript, serta penerapan API AI pada aplikasi sederhana. Evaluasi kegiatan dilakukan melalui pre-test, post-test, penilaian proyek, dan observasi. Hasil kegiatan menunjukkan adanya peningkatan pemahaman dan keterampilan praktis siswa dalam mengembangkan aplikasi web berbasis AI, meskipun masih ditemukan kendala dalam proses adaptasi terhadap materi baru. Kegiatan ini menunjukkan bahwa pelatihan yang berorientasi pada praktik mampu mendukung peningkatan kompetensi digital siswa dan relevan diterapkan dalam pengembangan pembelajaran berbasis teknologi di SMK.
Co-Authors A. A. Istri Ita Paramitha A.A. Gede Adi Mega Putra I Gst. Agung Pramesti Dwi Putri Ni Made Satvika Iswari