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All Journal Jurnal Pengabdian Masyarakat Nusantara Advance Sustainable Science, Engineering and Technology (ASSET)
Rahul Bhandari
Jindal Global University
Chemistry Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Health Professions Industrial & Manufacturing Engineering Social Sciences Other

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New SMEs Products Development and Improvement of Competitiveness Compliance Towards Food Standards in Sijeruk Village Banjarmangu Districts Banjarnegara Regency Astohar Astohar; Andi Kartika; Sari Rahmadhani; Muhamad Saufi Che Rusuli; Rahul Bhandari; Nico Irawan; Chanwut Thongkamkaew; Mulyani Mulyani; Paskah A Sinaga
Jurnal Pengabdian Masyarakat Nusantara Vol. 3 No. 4 (2021): Desember : Jurnal Pengabmas Nusantara
Publisher : Universitas Muhammadiyah Manado

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57214/pengabmas.v3i4.509

Abstract

Micro, Small, and Medium Enterprises (MSMEs) in Sijeruk village, Banjarmangu district, Banjarnegara regency have shown significant development. However, there remain several challenges faced by UMKM entrepreneurs, including limited marketing outreach, financial reports intertwined with household finances, and inadequate innovation in product development and packaging. This community service initiative aims to address these issues effectively.
Licensing Socialization, Digital Marketing and MSME Bookkeeping Workshop for Retired Migrant Workers In Pagak Village Purwareja Klampok District Banjarnegara Regency Mirna Dyah Praptitorini; Anis Turmudhi; Noor Salim; Rahul Bhandari; Muhamad Saufi Che Rusuli; Marlon Rael Astillero; Benediktus Ardie Reho; Siti Shobandiyah
Jurnal Pengabdian Masyarakat Nusantara Vol. 4 No. 4 (2022): Desember : Jurnal Pengabmas Nusantara
Publisher : Universitas Muhammadiyah Manado

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57214/pengabmas.v4i4.510

Abstract

MSMEs need to provide opportunities for local entrepreneurs and utilize local resources to help stimulate economic activity and increase the competitiveness of a region as a whole. Based on the problems mentioned above, it is necessary to hold training on the use of digital marketing tools and socialize the processing of business permits for micro, small and medium enterprises (MSMEs) in Pagak Village, Banjarnegara Regency as a solution. In the socialization process, an MSME association was also formed whose members were active in the business world in Pagak Village. All presenters have tried to provide all the knowledge about the digital marketing and bookkeeping process for MSMEs and various interesting things regarding business licensing, so it is hoped that the entrepreneurial knowledge provided can be directly applied by the MSME community, especially in Pagak Village.
Comparative Evaluation of Automatic Labeling and Modeling Strategies for Indonesian Sentiment Analysis: Methodology and Performance Evaluation Khoiriya Latifa; Agung Handayanto; Nur Latifah Dwi M.S; Rahul Bhandari; Ton Nguyen Trong Hien; Doston Pirnazarov
Advance Sustainable Science Engineering and Technology Vol. 8 No. 3 (2026): May - July
Publisher : Science and Technology Research Centre Universitas PGRI Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26877/asset.v8i3.2862

Abstract

Sentiment analysis is vital for understanding consumer perception, yet Indonesian sentiment classification faces challenges due to labeled data scarcity and computational constraints. This study advances automatic labeling techniques and establishes performance benchmarks for Indonesian text. The research compares two labeling approaches InSet Lexicon and IndoBERT based Hugging Face pipeline on 8,447 Tapera-related opinions. Results show InSet Lexicon produced a highly skewed distribution (89.66% neutral), while the IndoBERT pipeline achieved a more balanced distribution (47.66% neutral, 38.43% positive, 13.91% negative).. Evaluation of various modeling strategies revealed that combining InSet Lexicon + TF-IDF with Naïve Bayes or Random Forest achieved scores above 85%. While RNN-LSTM reached >90% accuracy, it required significant resources. Notably, fine-tuning IndoBERT with optimal hyperparameters yielded the most robust performance, achieving 80–90% accuracy with a low validation loss of 0.1. The study concludes that for small datasets (<12,000 samples), the most effective strategies for Indonesian sentiment analysis are either the InSet Lexicon paired with traditional Machine Learning or automatic labeling using pre-trained models followed by rigorous fine-tuning.
Co-Authors Agung Handayanto Andi Kartika Astohar Astohar Benediktus Ardie Reho Chanwut Thongkamkaew Doston Pirnazarov Khoiriya Latifa Marlon Rael Astillero Mirna Dyah Praptitorini Muhamad Saufi Che Rusuli Mulyani Mulyani Nico Irawan Noor Salim Nur Latifah Dwi M.S Paskah A Sinaga Sari Rahmadhani Siti Shobandiyah Ton Nguyen Trong Hien Turmudhi, Anis