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All Journal Jurnal Sistem Informasi Mahakarya (JSIM) Green Engineering: Journal of Engineering and Applied Science Technema: Journal of Intelligent Engineering and Computing
Tuti Susilawati
Universitas Mahakarya Asia
Agriculture, Biological Sciences & Forestry Civil Engineering, Building, Construction & Architecture Computer Science & IT Electrical & Electronics Engineering Engineering

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RANCANG BANGUN APLIKASI PESEDIAAN BARANG BERBASIS DESKTOP PADA PBF BINTANG SEMESTA FARMA-JAKARTA Tuti Susilawati; Dian Afriady; Genta Ramaputra
JSIM : Jurnal Sistem Informasi Mahakarya Vol 4 No 1 (2021): Jurnal Sistem Informasi Mahakarya (JSIM)
Publisher : LPPM universitas Mahakarya Asia

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Abstract

Permasalahan yang ada di Pbf Bintang Semesta Farma adalah sistem persediaan barang obat sering kali menimbulkan masalah, dimana pencatatan daftar dan harga obat bersifat manual, sehingga pegawai harus selalu melihat daftar dan persediaan barang (dalam lembaran kertas catatan) kemudian mencatatnya ke dalam persediaan secara manual. Suplayer pun sering mengeluh pelayanan yang lama dari suplayer ketika pegawai menginformasikan persediaan barang serta mengecek ada stok tidak barangnya. Pbf juga suka untuk mengetahui kondisi persediaan barang secara real time. Untuk itu dibutuhkannya suatu sistem aplikasi persediaan barang yang menggunakan program Visual Basic 2010 dapat membantu kegiatan proses kerja yang berjalan pada saat ini sehingga kesalahan yang ada dapat diminimalisir, Metode yang digunakan adalah analisis ini penulis menggunakan metode SWOT (Strengths Weaknesses Oportunities Threats) untuk menganalisa sistem yang belum ada. Setelah metode analisis dilakukan maka proses metode yang selanjutnya dilakukan adalah metode perancangan sistem, perancangan sistem yaitu dengan menggunakan notasi UML (Unifield modelaling Language) untuk menggambarkan rancangan sistem yang ingin diusulkan.
SISTEM INFORMASI PEMBAYARAN ADMINISTRASI PADA SMK EKA SAKTI JAKARTA BERBASIS DESKTOP Tuti Susilawati; Ahmad furqon; Mirza Ardian Prasetya
JSIM : Jurnal Sistem Informasi Mahakarya Vol 4 No 2 (2021): Jurnal Sistem Informasi Mahakarya (JSIM)
Publisher : LPPM universitas Mahakarya Asia

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Sistem pembayaran administrasi di SMK EKA SAKTI JAKARTA masih dilakukan secara manual, baik dalam hal transaksi maupun rekap data. Sehingga memperlambat di dalam proses pembayaran, pencatatan dan rekap pembayaran.Hal ini menyebabkan proses-proses yang terkait dengan pembayaran administrasion belum berjalan secara optimal. Untuk mengatasi masalah itu, maka penulis mengusulkan untuk merancang dan membangun aplikasi yang mendukung sistem basis data agar pengolahan pembayaran administrasi lebih efektif dan efisien. Sistem ini dirancang dan dibangun menggunakan Delphi XE2. Tujuan sistem ini adalah mempermudah pengarsipan data pembayaran administrasi, diharapkan dengan adanya system yang berbasis desktop ini memaksimalkan pekerjaan admin agar target penyampaian informasi dan volume pekerjaan dapat berjalan lebih efisien dan efektif.
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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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.  
Edge Intelligence in Smart Manufacturing Ecosystems Irwandi Rizki Putra; Anjela Karunia Amalia; Krisna Widi Nugraha; Tuti Susilawati; Hadi Jayusman
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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The rapid evolution of smart manufacturing ecosystems has intensified the need for intelligent architectures capable of supporting real-time decision-making, operational resilience, and sustainable industrial performance. This study investigates the effectiveness of Edge Intelligence within smart manufacturing environments through an empirical system-design and experimental validation approach. A three-layer architecture consisting of the Industrial Internet of Things layer, the edge intelligence layer, and the cloud orchestration layer was developed and evaluated under predictive maintenance, production scheduling, and anomaly detection scenarios. Performance assessment employed metrics including inference latency, response time, bandwidth consumption, prediction accuracy, throughput, resource utilization, reliability, resilience, and energy efficiency. The experimental results demonstrate that edge-enabled intelligence significantly improves manufacturing performance by reducing latency and communication overhead while increasing operational responsiveness, decision consistency, throughput, and system reliability. The architecture also enhances adaptive decision-making capabilities, strengthens human-machine collaboration, improves cybersecurity resilience, and contributes to environmental sustainability through more efficient resource utilization and reduced carbon emissions. The findings establish Edge Intelligence as a strategic ecosystem capability that enables resilient, adaptive, human-centric, and sustainable manufacturing systems aligned with the emerging objectives of Industry 5.0.  
Development of a Digital Twin Based Smart Green Building Energy Management Model Integrating IoT Sensors and Predictive Sustainability Analytics Asro Asro; Solihin Solihin; John Chaidir; Febri Adi Prasetya; Tuti Susilawati; Muhamad Furqon; Bentar Priyopradono
Green Engineering: International Journal of Engineering and Applied Science Vol. 2 No. 2 (2025): April : Green Engineering: International Journal of Engineering and Applied Sci
Publisher : International Forum of Researchers and Lecturers

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70062/greenengineering.v2i2.287

Abstract

Introduction: The integration of Digital Twin (DT) technology and the Internet of Things (IoT) into Building Energy Management Systems (BEMS) offers a transformative approach to optimizing energy consumption in buildings. This study explores the development of a Digital Twin based BEMS prototype, which leverages real time data collection, predictive analytics, and machine learning to enhance energy efficiency, reduce costs, and support sustainability goals in modern buildings. The research also addresses key gaps in current energy management systems, including real time adaptive control and integration with smart grid platforms. Literature Review: Previous research highlights the limitations of traditional BEMS, which often rely on static control strategies and lack real time adaptability. Recent advancements, including predictive maintenance and machine learning integration, have improved energy optimization. However, challenges such as data interoperability, scalability, and cybersecurity remain. This review consolidates current approaches and identifies opportunities for enhancing BEMS through the integration of DT technology, IoT, and machine learning. Materials and Method: The methodology employed involves the design of a Digital Twin based BEMS prototype, incorporating IoT sensors for real time data collection on variables such as HVAC load, occupancy, and environmental factors. The system uses time series forecasting and adaptive control strategies to optimize energy consumption. A case study building is used for validation, with performance metrics such as energy savings, CO₂ footprint reduction, and peak load reduction assessed to evaluate the system's effectiveness. Results and Discussion: The results demonstrate a significant reduction in energy consumption (up to 50%) compared to traditional BEMS, along with improved forecasting accuracy and sustainability performance. The prototype achieved a high R² score in predicting energy usage, validated through real world application in the case study building. The economic feasibility analysis showed substantial cost savings and a strong return on investment, making the system a financially viable solution for energy efficient building management.
Co-Authors Ahmad furqon Anjela Karunia Amalia Asro Asro Bentar Priyopradono Dian Afriady Erlita Sulistiati Febri Adi Prasetya Genta Ramaputra Hadi Jayusman Irwandi Rizki Putra John Chaidir Krisna Widi Nugraha Mirza Ardian Prasetya Muhamad Furqon Solihin Solihin