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All Journal Jurnal Sistem Informasi Mahakarya (JSIM) Jurnal Ilmiah Betrik : Besemah Teknologi Informasi dan Komputer Global Science: Journal of Information Technology and Computer Science Technema: Journal of Intelligent Engineering and Computing
Erlita Sulistiati
Universitas Mahakarya Asia
Computer Science & IT

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SISTEM INFORMASI PEMESANAN PAKET WISATA PADA MAHIRA TOUR & TRAVEL DENGAN BERBASIS WEB Erlita Sulistiati; Dewi Mulyama; Ahmad Budi Trisnawan
JSIM : Jurnal Sistem Informasi Mahakarya Vol 5 No 2 (2022): Jurnal Sistem Informasi Mahakarya (JSIM)
Publisher : LPPM universitas Mahakarya Asia

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Abstract

Mahira Tour & Travel merupakan suatu perusahaan yang bergerak dibidang Tour and Travel. Sistem pemesanan Paket Wisata yang ada di Mahira Tour& Travel masih bersifat konvensional. Pelanggan datang ke kantor perusahaan atau sebaliknya. Dalam mempromosikan jasanya, media yang digunakan masih kurang efektif, sehingga informasi yang diberikan kurang maksimal. Banyak pelanggan tidak mengetahui spesifik tujuan dan harga tiap paket. Untuk mengatasi permasalahan yang ada, maka dirancang sebuah sistem informasi pemesanan paket wisata secara online dengan tujuan untuk membantu memperluas promosi dan meningkatkan keuntungan perusahaan. Sistem berbasis website ini dibangun menggunakan PHP dan MySQL. Sistem Informasi yang telah dibangun memberikan kemudahan untuk mendapatkan informasi bagi masyarakat khususnya yang ingin mengetahui paket wisata. Hasil dari penelitian ini admin bisa mengelola data-data paket, manajemen website(about us, berita, chatting) dan cetak pemesanan.
Evaluasi Komparatif Ridge, SVR, Extratrees Untuk Prediksi Konsumsi Energi Iot Smarthome Dengan lag-1 Sri Hartati; Rusidi; Erlita Sulistiati
BETRIK Vol. 16 No. 02 (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/hvqebr82

Abstract

This study presents a comparative evaluation of Ridge Regression, Support Vector Regression (SVR, RBF), and ExtraTrees Regressor against a lag-1 (persistence) baseline for short-term load forecasting (STLF) of IoT smart-home energy consumption at an hourly resolution. The research is motivated by the need for accurate forecasts to enable operational efficiency, peak shaving, and residential demand response. The workflow emphasizes reproducibility: hourly resampling, short-gap imputation, minimalist feature engineering (calendar and lag features), and a time-based split of 70%/15%/15% (train/validation/test). Models are compared using MAE, RMSE, and R², prioritizing RMSE due to its sensitivity to spikes. Results show that on validation ExtraTrees performs best (RMSE 1.012, R² 0.854) and surpasses the lag-1 baseline (RMSE 1.507, R² 0.677). However, on the test set the lag-1 baseline is most accurate (RMSE 1.457, R² 0.808, MAE 0.900), while ExtraTrees is the closest yet does not surpass it (RMSE 1.482, R² 0.802, MAE 1.119). SVR and Ridge degrade markedly on test (RMSE > 2.0; R² < 0.65). These findings highlight the strength of persistence at a one-hour horizon and the need for seasonal/exogenous features and hyperparameter tuning to reduce large errors. Our practical contribution is a concise, Colab-based pipeline. Future work includes walk-forward validation, residual modeling over lag-1, adding daily/weekly lags and weather variables, and significance testing of error differences.
Context Sensitive Artificial Intelligence for Dynamic User Behavior Modeling in Next Generation Smart Information Platforms Rusmin Saragih; Enda Ribka Meganta P; Tiwuk Widiastuti; Ahmad Jurnaidi Wahidin; Erlita Sulistiati; Muhamad Furqon
Global Science: Journal of Information Technology and Computer Science Vol. 1 No. 4 (2025): December: Global Science: Journal of Information Technology and Computer Scienc
Publisher : International Forum of Researchers and Lecturers

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70062/globalscience.v1i4.194

Abstract

This study explores the development and implementation of a context sensitive artificial intelligence (AI) model designed to predict and personalize user behavior in smart information platforms. Traditional user behavior models often fail to adapt to dynamic and evolving user needs, especially in diverse environments where contextual factors such as time of day, location, and device type play a critical role in shaping user preferences. To address these limitations, the proposed context sensitive AI model integrates real time contextual data alongside traditional behavioral data, enabling it to make more accurate predictions and provide personalized, relevant content. The model utilizes advanced machine learning techniques, such as deep learning and reinforcement learning, to continuously update and refine user behavior models based on contextual shifts. Through the integration of contextual parameters, the model demonstrates improved prediction accuracy, system responsiveness, and overall user satisfaction compared to static, context agnostic models. Furthermore, the study discusses the key advantages of context aware AI, such as its ability to dynamically adjust to real time changes in user behavior, providing more adaptive, personalized services. Challenges encountered during the model's development, including issues related to data privacy, scalability, and the integration of multiple contextual data sources, are also addressed. The findings suggest that context sensitive AI can significantly enhance the effectiveness of smart platforms by improving user engagement and content relevance. Finally, the study provides recommendations for further research to explore deep learning methods for context detection and to improve the discoverability and integration of AI driven features in user interfaces.
Credit Card Fraud Detection Using Random Forest and XGBoost on a Public Kaggle Dataset Erlita Sulistiati; Tuti Susilawati
Technema: Journal of Intelligent Engineering and Computing Vol. 1 No. 1 (2026): March: Technema: Journal of Intelligent Engineering and Computing
Publisher : CV SCRIPTA INTELEKTUAL MANDIRI

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Abstract

Credit card fraud detection remains a critical challenge in digital financial ecosystems characterized by extreme class imbalance and evolving attack patterns. This study adopts an empirical experimental design to evaluate and compare Random Forest and XGBoost models using the publicly available Kaggle Credit Card Fraud Detection dataset. A controlled pipeline incorporating stratified data splitting, SMOTE-based imbalance mitigation, and GridSearchCV hyperparameter optimization was implemented to ensure methodological consistency and reproducibility. Performance was assessed through precision, recall, F1-score, ROC-AUC, confusion matrix analysis, and computational efficiency metrics. Results indicate that XGBoost outperformed Random Forest in recall, F1-score, and ROC-AUC, demonstrating enhanced minority-class discrimination and reduced false negatives under optimized conditions. Random Forest exhibited competitive precision and interpretability transparency, though with slightly lower sensitivity. Scalability evaluation confirmed that both models maintained low inference latency suitable for near-real-time deployment. The findings highlight the critical role of imbalance handling and parameter optimization in ensemble-based fraud detection and support boosting-oriented approaches as strategically advantageous for operational financial security systems.  
Digital Twin Technology for Sustainable Industrial Operations Erlita Sulistiati; Bustomi Bustomi; Guslila Sari Nasution; Atina Salamah; Rian Ardianto
Technema: Journal of Intelligent Engineering and Computing Vol. 1 No. 2 (2026): : June: Technema: Journal of Intelligent Engineering and Computing
Publisher : CV SCRIPTA INTELEKTUAL MANDIRI

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Abstract

Digital Twin technology has emerged as a strategic enabler for sustainable industrial transformation by integrating physical operations, virtual representations, predictive analytics, and sustainability-oriented decision support into a unified cyber–physical environment. This study aims to develop and analytically evaluate a comprehensive Digital Twin framework capable of supporting sustainable industrial operations through the integration of operational efficiency, energy performance, resource optimization, and system resilience dimensions. A non-empirical system design approach was employed to construct a multilayer architecture consisting of physical operation, data acquisition, communication and synchronization, digital twin modeling, analytics and optimization, and sustainability decision-support layers. Technical evaluation was conducted through model-based simulation and analytical assessment using standardized sustainability and operational indicators. The findings demonstrate that the proposed framework strengthens operational visibility, predictive maintenance capability, energy efficiency, resource utilization, responsiveness, and resilience through continuous interaction between physical and virtual environments. The analysis further indicates that Digital Twin integration facilitates circularity, sustainability governance, and Industry 5.0 readiness by enabling adaptive and data-driven industrial decision making. The study contributes a holistic conceptual framework that advances the understanding of Digital Twin technology as a sustainability-enabling infrastructure for future industrial systems.  
Co-Authors Ahmad Jurnaidi Wahidin Atina Salamah Bustomi Bustomi Dewi Mulyama Enda Ribka Meganta P Guslila Sari Nasution Muhamad Furqon Rian Ardianto Rusidi Rusmin Saragih, Rusmin sri hartati Tiwuk Widiastuti Trisnawan, Ahmad Budi Tuti Susilawati