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All Journal Sebatik Smart Techno (Smart Technology, Informatic and Technopreneurship)
Sanggraloka, Bima
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Computer Science & IT

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Development of Technology-Information-Based Businesses for Borokah Herbal Drink and UD Mela Jaya Fish Crackers in Nogosari Village, Pacet District, Mojokerto Regency Adityo, R Dimas; Syafi’i, Syafi’i; Ekantoro, Julyantoro; D.F , Fernando Yunianda; Sanggraloka, Bima
Sebatik Vol. 29 No. 2 (2025): December 2025
Publisher : STMIK Widya Cipta Dharma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46984/sebatik.v29i2.2693

Abstract

This community service project focuses on empowering two micro, small, and medium enterprises (MSMEs) in Nogosari Village, Pacet District, Mojokerto Regency: Jamu Herbal Barokah and UD Mela Jaya, a traditional fish cracker producer. Both enterprises face significant challenges in management and marketing, primarily due to limited financial literacy, lack of business administration systems, and minimal utilization of digital technology. The project aims to enhance their competitiveness and sustainability through the implementation of information technology-based solutions. These include the development of simple digital applications for financial recording and production management, as well as the adoption of digital marketing strategies using social media, marketplaces, and websites. Training and mentoring sessions are conducted to improve partners’ capacity in business management, financial documentation, and digital branding. The program aligns with Indonesia’s Asta Cita agenda and the Sustainable Development Goals (SDGs), particularly Goal 8—Decent Work and Economic Growth. Through the integration of information technology, the project promotes inclusive local economic growth, increases employment opportunities, and strengthens the independence of local entrepreneurs. Expected outcomes include improved business governance, wider market reach, and enhanced digital literacy among MSME actors, serving as a model for sustainable rural economic development based on local potential.
Sentiment Analysis of Public Opinions on Electric Vehicles on Social Media Using the Support Vector Machine (SVM) Algorithm with Optimized Preprocessing Rahma, Aurora Aulia; Firmansyah, Fernando Yunianda Dwi; Sanggraloka, Bima; Adam, Jovi Satria; Prasetyo, Eko; Tias, Rahmawati Febrifyaning
Smart Techno (Smart Technology, Informatics and Technopreneurship) Vol. 8 No. 2 (2026)
Publisher : Primakara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59356/smart-techno.v8i2.229

Abstract

The development of regulations and trends in electric vehicle adoption in Indonesia has generated diverse public responses and opinions on social media platforms such as YouTube. However, manually evaluating large volumes of unstructured review data tends to be inefficient and less effective. This study applies the Support Vector Machine (SVM) algorithm with a Radial Basis Function (RBF) kernel, utilizing Term Frequency-Inverse Document Frequency (TF-IDF) weighting and optimized preprocessing techniques to automatically classify public opinion sentiment. The dataset consists of 1,517 Indonesian-language YouTube comments that underwent a series of preprocessing stages, including case folding, cleaning, tokenization, stopword removal, normalization, and stemming. The resulting cleaned dataset was then transformed into a numerical matrix containing 1,180 word features and divided into training and testing sets using a 70:30 ratio. The initial labeling results showed that public opinions were dominated by negative sentiment, accounting for 57.3% (869 comments), followed by positive sentiment at 33.2% (504 comments) and neutral sentiment at 9.5% (144 comments). The performance evaluation of the SVM classification model using a confusion matrix achieved an overall accuracy of 78.1%. These findings demonstrate that the application of optimized preprocessing techniques combined with a nonlinear SVM kernel provides reliable performance in accurately identifying and mapping the characteristics of digital public opinion.
Co-Authors Adam, Jovi Satria D.F , Fernando Yunianda Ekantoro, Julyantoro Eko Prasetyo Firmansyah, Fernando Yunianda Dwi R Dimas Adityo Rahma, Aurora Aulia Syafi’i, Syafi’i Tias, Rahmawati Febrifyaning