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All Journal SMARTICS Journal International Journal of Applied Sciences in Tourism and Events Journal of Information Systems and Informatics Algoritme Jurnal Mahasiswa Teknik Informatika
Muhammad Taufik Roseno
Universitas Sumatera Selatan
Arts Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering

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The Community Participation in Tourism Sustainability and Tourist Satisfaction Sari Lestari Zainal Ridho; Paisal Paisal; Dina Mellita; Muhammad Taufik Roseno
Jurnal Internasional Ilmu Pengetahuan Terapan bidang Pariwisata dan Events Vol 5 No 1 (2021): June 2021
Publisher : Pusat Penelitian dan Pengabdian kepada Masyarakat (P3M) Politeknik Negeri Bali

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31940/ijaste.v5i1.2115

Abstract

Purpose: The purpose of this study was to evaluate the role of community in economic sustainability towards post-visit behavior to cultural tourism destinations in the city of Palembang. Research methods: This research is a quantitative study, which examines the relationship between the variables of the role of community, in this case, the community, in the economy sustainability of tourism and several other variables, which is the role of the community in ecological, social, cultural sustainability toward post-visit behavior, in the form of tourist satisfaction. The technique analysis used was multiple regression analysis techniques and the data of this study were collected through a survey in 2019, using a questionnaire of 107 respondents who were tourists who had visited one of the cultural tourism destinations in Palembang. Result and Discussion: The results of this study indicate that the role or participation of community in the sustainability of tourism has a positive and significant effect on visitor or tourist satisfaction Conclusion: the community participation in tourism sustainability is significantly important in order to create tourist satisfaction; hence it’s important to focus on community empowerment.
Stability-XAI: Analisis Stabilitas Fitur dan Interpretabilitas Model Machine Learning pada Prediksi Penyakit Jantung Ubaidillah Ubaidillah; Muhammad Taufik Roseno; Serly Oktarina
Jurnal Algoritme Vol 7 No 1 (2026): October 2026 || Algoritme Jurnal Mahasiswa Teknik Informatika
Publisher : Program Studi Teknik Informatika Universitas Multi Data Palembang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35957/algoritme.v7i1.17538

Abstract

Heart disease requires early detection with high accuracy. This study proposes a Stability-XAI approach integrating feature stability analysis and Explainable Artificial Intelligence (XAI) to enhance the transparency of heart disease prediction models. Utilizing four UCI Machine Learning Repository datasets (Cleveland, Hungarian, Switzerland, Statlog) totaling 990 samples, the workflow includes preprocessing, feature selection (ANOVA F-score, Mutual Information, RFE), Optuna hyperparameter optimization across Logistic Regression, Random Forest, and XGBoost models, and SHAP interpretation. Results demonstrate that Optimized Random Forest achieves peak performance with 92.63% accuracy and 96.80% ROC-AUC. Features cp, ca, and exang exhibited the highest consistency (stability score 1.000), while cp, ca, and thal contributed most significantly according to SHAP analysis. The Stability-XAI approach effectively produces a heart disease prediction model that is high-performing, transparent, and interpretable.
Comparing CNN Models for Rice Disease Detection: ResNet50, VGG16, and MobileNetV3-Small Muhammad Taufik Roseno; Serly Oktarina; Yuwinti Nearti; Hadi Syaputra; Nirmala Jayanti
Journal of Information System and Informatics Vol 6 No 3 (2024): September
Publisher : Asosiasi Doktor Sistem Informasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51519/journalisi.v6i3.865

Abstract

The Oryza sativa (rice) plant is an important staple food source, especially in the Asian region. Rice production is often disrupted by diseases such as Brown Spot, Leaf Scald, Rice Blast, Rice Tungro, and Sheath Blight, which can reduce yield and crop quality. This research aims to classify rice plant diseases using a deep learning approach with Convolutional Neural Networks (CNN) architecture, namely ResNet50, VGG16, and MobileNetV3-Small. The dataset used is Rice Leaf Disease Classification which consists of 1305 images with five disease labels. The data is divided into training, validation, and testing sets with proportions of 70%, 15%, and 15%. The results showed that the MobileNetV3-Small model provided the best accuracy on the test data of 79%, while VGG16 achieved the validation accuracy of 78.84%. Based on these results, MobileNetV3-Small is considered the most superior model for rice disease classification. This research shows the great potential of applying deep learning in automatic rice disease detection.
Development of a Digital Village Concept based on Information Technology Infrastructure and Strategy Management to Facilitate SPBE Ogan Ilir Regency Serly Oktarina; Muhammad Taufik Roseno; Ubaidillah Ubaidillah; Darius Antoni; Lailatuz Zahro; Hadi Syaputra
Journal of Information System and Informatics Vol 6 No 4 (2024): December
Publisher : Asosiasi Doktor Sistem Informasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51519/journalisi.v6i4.875

Abstract

Technology in the digital era is currently progressing very rapidly. This is marked by the increasingly massive number of social media users in everyday life. Survey results from the Indonesian Internet Service Providers Association (APJII) in 2022 recorded that the number of internet users in Indonesia reached 196.7 million people. This number increased by 23.5 million or 8.9% compared to 2018. With information technology and the internet, information is now becoming more easily spread and can be accessed by all levels of society thanks to the internet, not just people in urban areas, but people living there. in rural areas too. The Ogan Ilir Regency Government initiated information technology infrastructure including village internet or Digital Village to solve the problem of inequality in digitalization of society in rural and urban areas. Development and implementation of a digital village is a program that implements electronic-based government system (SPBE) services to the community and empowers the community based on the use of technology. This research aims to conduct a survey of information technology infrastructure to identify village potential, marketing and accelerating access and public services. Apart from that, this research also identifies digital-based life patterns of people in rural and urban areas, as well as to advance economic development in rural areas to improve SPBE services in Ogan Ilir Regency. The method used is a quantitative method for surveying and mapping the use of information technology in villages and ultimately producing the concept of an independent digital village. Research data was obtained from surveys and FGDs with the Ogan Ilir district government, village heads, village communities and micro, small and medium enterprises. Meanwhile, secondary data will be obtained through the results of MSME and village profiles from the Central Statistics Agency.
Performance Analysis of Convolutional Neural Network in Pempek Food Image Classification with MobileNetV2 and GoogLeNet Architecture Yudha Pratomo; Muhammad Taufik Roseno; Hadi Syaputra; Darius Antoni
Journal of Information System and Informatics Vol 7 No 1 (2025): March
Publisher : Asosiasi Doktor Sistem Informasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51519/journalisi.v7i1.1026

Abstract

This research develops a pempek food image classification system using two Deep Learning architectures, namely MobileNetV2 and GoogLeNet. The dataset consists of five types of pempek with a total of 446 images, which are divided for training (70%), validation (15%), and testing (15%). The model was evaluated based on accuracy, precision, recall, and F1-score. The results showed that GoogLeNet achieved a validation accuracy of 96.21%, higher than MobileNetV2 which was only 70.58%. GoogLeNet is also more stable in convergence and more accurate in recognizing different types of pempek. This research shows that GoogLeNet is more optimal for pempek classification. In the future, this research can be extended by adding more datasets, exploring more sophisticated models, and developing mobile or web-based applications.
Pengaruh ANOVA F-score terhadap Performa dan Interpretabilitas XGBoost untuk Prediksi Penyakit Jantung Ubaidillah Ubaidillah; Muhammad Taufik Roseno; Hadi Syaputra
SMARTICS Journal Vol 12 No 2 (2026): SMARTICS Journal (Oktober 2026)
Publisher : Universitas PGRI Kanjuruhan Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21067/smartics.v12i2.14756

Abstract

Heart disease remains a major health problem and has encouraged the use of machine learning for data-driven prediction. This study investigates the effect of ANOVA F-score feature selection on the predictive performance and interpretability of XGBoost using the UCI Heart Disease Cleveland subset with 303 observations and 13 predictor features. Two scenarios were evaluated: XGBoost using all features and XGBoost using features selected by ANOVA F-score. Model evaluation employed nested stratified cross-validation with 10 outer folds and 5 inner folds, while hyperparameter optimization was performed using Optuna with 100 trials. XGBoost using all features achieved an accuracy of 0.8346 ± 0.0684, an F1-score of 0.8149 ± 0.0759, and a ROC-AUC of 0.9020 ± 0.0447. XGBoost with ANOVA F-score achieved an accuracy of 0.8149 ± 0.0532, an F1-score of 0.7948 ± 0.0527, and a ROC-AUC of 0.9033 ± 0.0381. Feature selection reduced the transformed input space from 22 to an average of 17.8 features, equivalent to a 19.09% reduction. The Wilcoxon signed-rank test showed no statistically significant performance differences at the 0.05 significance level. SHAP analysis identified cp, ca, thal, slope, and oldpeak as the dominant features in both models. Overall, ANOVA F-score produced a more compact feature representation while preserving the main interpretability pattern, but did not significantly improve XGBoost predictive performance.
Co-Authors Darius Antoni Dina Mellita Hadi Syaputra Lailatuz Zahro Nirmala Jayanti Paisal Paisal Sari Lestari Zainal Ridho Serly Oktarina Ubaidillah Ubaidillah Ubaidillah Ubaidillah Yudha Pratomo Yuwinti Nearti