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All Journal Journal of Humanities and Social Studies Jurnal Ilmiah Akuntansi Kesatuan Journal of Business, Management and Social Studies Journal of Mutidisciplinary Issues Advance Sustainable Science, Engineering and Technology (ASSET)
Princes, Elfindah
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Agriculture, Biological Sciences & Forestry Arts Humanities Chemistry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Social Sciences Other

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Camping Site Recommendation System Using Collaborative Filtering Method on Campsite Indonesia Mobile Application Cakrawala, Emerald Shan; Princes, Elfindah
Jurnal Ilmiah Akuntansi Kesatuan Vol. 13 No. 6 (2025): JIAKES Edisi Desember 2025
Publisher : Institut Bisnis dan Informatika Kesatuan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37641/jiakes.v13i6.4525

Abstract

Information overload in tourism applications poses significant challenges for users selecting relevant destinations from numerous options. This research implements Collaborative Filtering (CF) to address information overload in the Campsite Indonesia mobile application, where users face difficulties choosing from 246 camping locations. Three CF variants are evaluated: User-Based CF, Item-Based CF, and Hybrid Collaborative Filtering. The dataset comprises 746 users, 246 camping locations, 350 explicit feedback interactions (likes), and 7,306 implicit feedback interactions (views) from August 2022 to July 2025, with 94.05% sparsity in the user-item interaction matrix. The research employs CRISP-DM methodology encompassing data preparation, modeling, evaluation, and deployment phases. Experimental results demonstrate that Item-Based CF achieves superior performance with Hit Rate@10 of 0.2222 and NDCG@10 of 0.0743, significantly outperforming User-Based CF (HR@10: 0.0556, NDCG@10: 0.0215) and Hybrid CF (HR@10: 0.0000, NDCG@10: 0.0000). Item-Based CF also exhibits the highest coverage (41.10%) with 60 unique recommended locations. The system is deployed through a Flask-based REST API server with five endpoints for recommendation scenarios. This research contributes domain-specific insights for camping location recommendations in developing countries. 
A Decision Support System Based on Transformer-Driven Sentiment Analysis of Social Media Data Wibisono, Arie Christian; Princes, Elfindah
Advance Sustainable Science Engineering and Technology Vol. 8 No. 2 (2026): February-April
Publisher : Science and Technology Research Centre Universitas PGRI Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26877/asset.v8i2.3123

Abstract

The growing availability of social media data offers new opportunities for decision support systems (DSS) in large-scale human resource screening. This study proposes a technology-driven DSS architecture integrating transformer-based sentiment analysis to support early-stage candidate profiling. Its novelty lies in combining IndoBERT-based sentence embeddings with a structured DSS layer that aggregates tweet-level sentiment into risk-aware recommendations, rather than treating sentiment classification as a standalone output. Using a quantitative experimental design, 5,000 public posts from 100 users were processed through an NLP pipeline incorporating mean-pooled embeddings, feature engineering, principal component analysis, and Support Vector Machine classification. The model achieved 69.1% accuracy, with weighted precision, recall, and F1-score of 0.694, 0.691, and 0.691, outperforming baseline models by 6.5–15.0 percentage points. Sentiment outputs are treated as probabilistic behavioral signals within an advisory DSS framework, not direct indicators of candidate suitability. Preliminary validation on 50 cases showed moderate correlations (ρ = 0.52–0.61) with conventional assessments. The system remains non-automated, incorporating confidence thresholds, uncertainty handling, and mandatory human oversight. Limitations include moderate accuracy, reliance on text-only data, and linguistic ambiguity.
Co-Authors Abdinagoro, Sri Bramantoro Adler Haymans Manurung Amini, Nuzulia Bambang Setiono Cakrawala, Emerald Shan Erlis, Erlis Firmansyah, Fatah Panji Idris Idris Oetama, Raymond Sunardi Pangaribuan, Christian Haposan Romprasert, Suppanunta Setiawan, Johan Silalahi, Wilma Soeryanto, Novianti Wibisono, Arie Christian Yosevine, Prisca