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All Journal Switch: Jurnal Sains dan Teknologi Informasi Jurnal Riset Multidisiplin Edukasi
Didi Sangaji
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Optimalisasi Prediksi Indeks Kualitas Air di Indonesia dengan Menggunakan Machine Learning Melalui Pendekatan Metode Prophet Didi Sangaji; Tata Sutabri
Switch : Jurnal Sains dan Teknologi Informasi Vol. 2 No. 6 (2024): November : Switch: Jurnal Sains dan Teknologi Informasi
Publisher : Asosiasi Riset Teknik Elektro dan Informatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62951/switch.v2i6.277

Abstract

The Water Quality Index (WQI) shows the condition of water quality in an area based on the status of water quality resulting from the measurement of physical, chemical and bacteriological parameters of a water body both rivers and lakes. Several machine learning techniques can be used to predict water quality in an area, one of which is through the prophet model approach which is able to provide fairly accurate predictions for the water quality index in Indonesia. The main objective of this research is to obtain a WQI prediction value as a baseline in the formulation of future environmental control activity policies using the prophet model. The result is that the predicted value of IKA for 2021-2023 generated through machine learning with the prophet model approach shows that the Mean Absolute Error (MAE) value: 7.01, Root Mean Square Error (RMSE): 8.61 and Mean Absolute Percentage Error (MAPE): 13.06%, which means that IKA prediction with the prophet model is effective in capturing annual patterns between historical data and future predictions.
HYBRID BI MODEL: KOLABORASI MACHINE LEARNING DAN VISUAL ANALYTICS UNTUK PENINGKATAN KETEPATAN PREDIKSI BISNIS Didi Sangaji; Dicopran Sisco; Tata Sutabri
Jurnal Riset Multidisiplin Edukasi Vol. 2 No. 10 (2025): Jurnal Riset Multidisiplin Edukasi (Edisi Oktober 2025)
Publisher : PT. Hasba Edukasi Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.71282/jurmie.v2i10.1087

Abstract

Big data complexity demands integration of accurate machine learning (ML) with interpretable visual analytics (VA). Traditional ML models face transparency challenges, while pure VA systems are limited in multidimensional pattern recognition. This study synthesizes 15 peer-reviewed articles (2021-2025) to evaluate ML-VA integration effectiveness in data-driven business decision-making. We identify five primary visualization designs (interactive dashboards, heatmaps, bubble charts, network graphs, counterfactual visualization), three feedback mechanisms (real-time, user refinement, interactive exploration), and human-in-the-loop (HITL) implementation for algorithm transparency. Results demonstrate Model M3 (SHAP/LIME+Network Graphics) achieves ROC-AUC 0.941, F1-Score 0.921, Accuracy 0.924, and Precision 0.931—exceeding traditional baseline by 16.7% on ROC-AUC. Critical improvements occur in model transparency (+170.5%), interpretability (+215.9%), and user engagement (+118.7%), without compromising predictive accuracy. Hybrid BI implementation yields significant business impact: process efficiency +35%, cost reduction -27%, analytical accuracy +44%, data processing capacity +85%. Structured HITL mechanism ensures meaningful human input, complete audit trails, and continuous model improvement. Evaluation framework encompasses confusion matrix, multi-metrics (accuracy, precision, recall, F1, specificity, ROC-AUC), and internal-external validity. The primary contribution is the proposed Hybrid BI Architecture that synergizes automatic ML capabilities with human domain knowledge, creating a responsible AI ecosystem with robust governance, full transparency, and measurable accountability for superior organizational decision-making in the digital transformation era.
Co-Authors Dicopran Sisco Tata Sutabri