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All Journal Jurnal Ilmiah Betrik : Besemah Teknologi Informasi dan Komputer Jurnal Informatika
Alfis Arif
Institut Teknologi Pagar Alam
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Engineering Library & Information Science

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OPTIMALISASI STRATEGI WISATA DI KOTA PAGAR ALAM MENGGUNAKAN ALGORITMA K-MEANS CLUSTERING Monicka Seftia; Efan Efan; Alfis Arif
Jurnal Informatika Vol. 1 No. 02 (2024): Jurnal Informatika (Juri)
Publisher : Al Ihsan Smart Cendekia

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

The aim of this research is to assist the Pagar Alam City Tourism Office in optimizing tourism strategies, especially cultural tourism. The large number of tours managed by the Pagar Alam City Tourism Office causes random data collection so that it is less complete in making the best decisions regarding tourism visit strategies and by optimizing tourism strategies to find out what strategies or steps are the highest based on the category and making it easier for the Tourism Office to make strategies. . This research uses the K-Means Clustering method as a calculation medium for strategy optimization. The development method used, namely CRIPS-DM, consists of six phases, namely the business understanding process, data understanding, data preparation, modeling, evaluation and deployment. The results obtained from this research produced 2 Clustering Patterns for the Number of Megalithic Tourist Visits in Pagaralam City with cluster_0 totaling 13 items with the highest number of visits, cluster_1 totaling 30 items with the lowest number of visits.
Optimasi Model Deep Learning EfficientNet Berbasis Citra Digital untuk Deteksi Penyakit Padi Nurmaleni; Alfis Arif
BETRIK Vol. 16 No. 03 (2025): Jurnal Ilmiah BETRIK : Besemah Teknologi Informasi dan Komputer
Publisher : PPPM Institut Teknologi Pagar Alam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36050/tp7s2t87

Abstract

This study optimized the EfficientNet deep learning model based on digital images for rice disease detection, focusing on two main classes: Brown Spot and Leaf Scald, which are constraints to farmer productivity in Pagar Alam City. The dataset consisted of 780 images (476 Brown Spot, 304 Leaf Scald) processed through 224×224 resizing, normalization, data cleaning, and augmentation (rotation, flip, shear, shift, zoom) to improve generalization and reduce overfitting. The model was initialized with transfer learning from ImageNet, trained and fine-tuned at the final layer, and then evaluated using accuracy, precision, recall, and F1-score metrics. EfficientNet B0 showed a high training accuracy of up to 95% with a validation accuracy of around 80%, indicating good detection performance although there are still symptoms of overfitting that need further optimization. The model was then integrated into a web-based expert system for automatic diagnosis from leaf images and presentation of knowledge-based treatment recommendations, thereby accelerating early identification and supporting decision-making in the field. These results confirm EfficientNet's potential as the foundation for a practical, accurate, and applicable rice disease diagnosis system for local agriculture.
Integrasi Explainable AI (SHAP) Pada Model Machine Learning Untuk Analisis Faktor Penentu Kualitas Fasilitas Kesehatan Berbasis Web Riduan Syahri; Fitria Rahmadayanti; Alfis Arif
BETRIK Vol. 17 No. 02 (2026): Jurnal Ilmiah BETRIK : Besemah Teknologi Informasi dan Komputer
Publisher : PPPM Institut Teknologi Pagar Alam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36050/ndmny220

Abstract

The quality and equitable distribution of healthcare facilities are vital indicators of regional public service development. Although modern Machine Learning models such as Extreme Gradient Boosting (XGBoost) achieve exceptionally high predictive performance in classifying medical facility quality levels, their black-box nature often limits decision-making transparency for policymakers. This study aims to integrate an Explainable Artificial Intelligence (XAI) approach using SHapley Additive exPlanations (SHAP) into an XGBoost model developed in Google Colab and deployed as an interactive web dashboard via Streamlit. The dataset comprises aggregated BPJS Healthcare Facility data across Indonesian regencies/cities supplemented with operational indicators. The experimental results demonstrate superior modeling performance, achieving an accuracy of 95.15% and an F1-Score of 97.30%. Through SHAP Summary Plot analysis, Skor_Fasilitas_UGD and Total_Faskes were identified as the primary dominant factors driving high-quality facility classifications. The Streamlit web application successfully visualizes individual feature contributions (SHAP Waterfall Plot) in real-time, providing intuitive clinical and managerial transparency for public health planning
Co-Authors Efan, Efan Fitria Rahmadayanti Monicka Seftia Nurmaleni Syahri, Riduan