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All Journal JURNAL ILMIAH SAINS TEKNOLOGI DAN INFORMASI Advance Sustainable Science, Engineering and Technology (ASSET)
Khoiriya Latifa
Universitas PGRI Semarang
Chemistry Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Energy Environmental Science Industrial & Manufacturing Engineering

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Analisis Sebaran Sapi Potong Hasil Kawin Suntik di Kabupaten Grobogan Menggunakan Sistem Informasi Geografis (SIG) Irfan Maiyola; Bambang Agus Herlambang; Khoiriya Latifa
JURNAL ILMIAH SAINS TEKNOLOGI DAN INFORMASI Vol. 2 No. 1 (2024): Januari : Jurnal Ilmiah Sains Teknologi dan Informasi
Publisher : CV. ALIM'SPUBLISHING

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59024/jiti.v2i1.591

Abstract

This research analyzes the distribution of artificially inseminated beef cattle across various districts in Grobogan Regency through WebGIS. The data reveals variations in the number of beef cattle per district, with Wirosari District having the highest population reaching 8,277 heads. The utilization of WebGIS provides advantages in information presentation, particularly with pop-up features when placing the cursor on specific regions, facilitating a profound understanding of the traders' distribution at the local level. This advantage offers significant benefits to stakeholders, including local governments and farmers in each district of Grobogan Regency. This analysis not only presents information visually but also opens opportunities for exploration and more accurate decision-making through the WebGIS platform, supporting efforts in sustainable management and development of the livestock sector.
Sistem Informasi Geografis Tentang Pemetaan Jumlah Rumah Sakit Dan Poliklinik Dikota Semarang Tarisa Ramadhani; Bambang Agus Herlambang; Khoiriya Latifa
JURNAL ILMIAH SAINS TEKNOLOGI DAN INFORMASI Vol. 1 No. 4 (2023): Oktober : Jurnal Ilmiah Sains Teknologi dan Informasi
Publisher : CV. ALIM'SPUBLISHING

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59024/jiti.v1i4.598

Abstract

Hospitals and polyclinics are professional health care institutions whose services are provided by doctors, nurses and other health experts. As the function of hospitals becomes increasingly vital, every community member is expected to know the locations of the hospitals and polyclinics closest to where they live to anticipate undesirable events. Geographic Information Systems (GIS) is a technology that is currently a very essential tool in storing, manipulating, analyzing and displaying natural conditions with the help of attribute data and spatial data. GIS can represent the real world on a computer monitor just as a map sheet can represent the real world on paper. Under these conditions, this system is very useful in making it easier for the health service to manage and review the location of hospitals in the city of Semarang, so that in the future it can be in line with programs from other agencies involved in managing city spatial planning. The aim of this research is to build a geographic information system with an integrated database, especially to find the locations of hospitals and polyclinics in the city of Semarang. So it can provide information to users who want to find hospital locations in the city of Semarang.
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 Bambang Agus Herlambang, Bambang Doston Pirnazarov Irfan Maiyola Nur Latifah Dwi M.S Rahul Bhandari Tarisa Ramadhani Ton Nguyen Trong Hien