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All Journal DEVICE Proceeding of International Conference Health, Science And Technology (ICOHETECH) Journal Of Artificial Intelligence And Software Engineering International Journal of Economics, Business Management and Accounting
Ardiyanto, Marta
Unknown Affiliation
Civil Engineering, Building, Construction & Architecture Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Engineering Health Professions Industrial & Manufacturing Engineering Mechanical Engineering Medicine & Pharmacology

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Development of a Control and Monitoring System for an IoT Rover Based on ESP32 and LoRa in Hazardous Areas Ananda, Naufal Choirul; Maulindar, Joni; Ardiyanto, Marta
Journal of Artificial Intelligence and Software Engineering Vol 5, No 2 (2025): June
Publisher : Politeknik Negeri Lhokseumawe

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30811/jaise.v5i2.6962

Abstract

This study developed a control and monitoring system for an IoT-based rover using ESP32 and LoRa, designed for hazardous area exploration. The system integrates two wireless communication methods: LoRa for long-range sensor data transmission and NRF24L01 for real-time control. The MQ-6 sensor detects LPG gas, while ultrasonic sensors function as an automatic safety system. A web-based interface built with Next.js and Supabase displays real-time sensor data. The system was developed using a prototyping method that includes requirement analysis, system design, hardware and software development, and testing. Test results show that LoRa transmits data reliably up to 15 meters without obstructions, and NRF24L01 supports stable control up to 100 meters. The MQ-6 sensor accurately detects gas presence, and ultrasonic sensors consistently stop the rover when obstacles are detected within 30 cm. The monitoring website successfully presents real-time data for operator decision-making. Overall, the system is effective and responsive for remote operation in high-risk environments, with strong potential for deployment in scenarios such as gas leaks, disaster zones, or other dangerous areas.
Business Sustainability Transformation in Creative Culinary SMEs in Surakarta City Utami, Indah Wahyu; Hastuti, Indra; Ardiyanto, Marta; Saputri, Lisa Nur Savira Dewi; Adirangga, Ihsan Kandung
International Journal of Economics, Business Management and Accounting (IJEBMA) Vol. 7 No. 2 (2025): July 2025
Publisher : MultiTech Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59890/ijebma.v7i2.3194

Abstract

The creative industry is one of the strategic sectors that can significantly drive national economic growth. In this context, Micro, Small, and Medium Enterprises (MSMEs) operating in the creative industry, particularly the culinary sector, demonstrate great potential in creating economic value while also opening up employment opportunities. This study aims to develop a business sustainability model for creative industry MSMEs through business diversification and the implementation of environmentally friendly human resource management practices. The research focuses on culinary sector MSMEs in the city of Surakarta as a representation of the rapidly growing and highly competitive creative industry. Data collection methods include literature review, surveys, and interviews with culinary sector MSMEs in the city of Surakarta. The results of this study are expected to contribute theoretically to the development of a business sustainability model and serve as a practical guide for SMEs in enhancing the resilience and sustainability of their businesses amid the dynamic, competitive, and sustainable business environment
Implementation and Optimization of Saliency Mapping Algorithms in Convolutional Neural Networks (CNN) to Enhance Transparency in Pneumonia Diagnosis Ardiyanto, Marta; Irawan, Ridwan Dwi; Yudhianto, Kresna Agung
Proceeding of the International Conference Health, Science And Technology (ICOHETECH) 2025: Proceeding of the 6th International Conference Health, Science And Technology (ICOHETECH)
Publisher : LPPM Universitas Duta Bangsa Surakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47701/c9jq7074

Abstract

This study aims to develop a transparent and reliable artificial intelligence model for pneumonia diagnosis using chest X-ray images by implementing and optimizing Convolutional Neural Networks (CNN) with Saliency Mapping. The research employed a combination of advanced optimization techniques, including aggressive data augmentation, class weight balancing, L2 regularization, dropout, batch normalization, and adaptive learning rate scheduling to address overfitting challenges. A functional prototype was then deployed in a Streamlit-based application to provide an interactive diagnostic tool. The evaluation results demonstrated that the model achieved strong performance, with high training accuracy and competitive testing accuracy, while visualization through Saliency Mapping provided meaningful interpretability by highlighting critical lung regions, particularly the mid-to-lower lung fields and hilar area. This interpretability ensured that the system not only delivered accurate predictions but also supported clinical reasoning by aligning with radiological characteristics of early-stage pneumonia and bronchopneumonia. The integration into a user-friendly application illustrates the potential for practical adoption in healthcare settings, especially in regions with limited access to radiologists. Overall, the study demonstrates that combining CNN-based classification with explainable AI techniques can bridge the gap between advanced machine learning and clinical applicability, offering a strategic pathway to improve pneumonia diagnosis and patient outcomes.
AUDIT TATA KELOLA SISTEM INFORMASI KERJA PRAKTIK PADA UNIVERSITAS DUTA BANGSA SURAKARTA MENGGUNAKAN FRAMEWORK COBIT 2019 Ardiyanto, Marta; Sopingi, Sopingi
Device Vol 14 No 2 (2024): November
Publisher : Fakultas Teknik dan Ilmu Komputer (FASTIKOM) UNSIQ

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32699/device.v14i2.7779

Abstract

Universitas Duta Bangsa Surakarta telah mengimplementasikan sistem terkomputerisasi untuk meningkatkan penyelenggaraan layanan pendidikan, baik akademik maupun non-akademik, kepada para mahasiswa. Salah satu inisiatif tersebut adalah pengenalan Sistem Informasi Kerja Praktik (SIKAP), yang dirancang untuk mendukung proses penyusunan laporan kerja praktik pada mata kuliah yang dilaksanakan di semester tujuh. Sistem SIKAP menawarkan berbagai fitur penting, termasuk menu biodata, menu tempat kerja praktik, serta menu bimbingan dan seminar. Dalam hal audit tata kelola teknologi informasi di Universitas Duta Bangsa Surakarta menggunakan framework COBIT 2019, proses dimulai dengan pemahaman mendalam tentang strategi dan tujuan perusahaan. Setelah menetapkan tujuan tersebut, langkah selanjutnya melibatkan analisis dan observasi berdasarkan Kerangka Kerja Komprehensif serta rekapitulasi audit pada subdomain yang relevan saat menentukan komponen-kontrol TI. Desain COBIT 2019 dianggap sebagai faktor risiko utama dalam evaluasi ini.Penilaian terhadap kondisi saat ini menunjukkan tingkat kapabilitas sebesar 0.75 dalam beberapa area kontrol TI; terdapat juga penilaian dengan tingkat kapabilitas lainnya yaitu 1 dan 0.67 sesuai dengan temuan audit. Berdasarkan hasil audit tata kelola menggunakan framework COBIT 2019, dapat disimpulkan bahwa fokus strategis Universitas Duta Bangsa Surakarta adalah menyelesaikan keluhan terkait sistem informasi melalui fasilitas chat online; mengidentifikasi permasalahan mahasiswa serta memberikan solusi secara cepat; dan menyampaikan buku pedoman baru kepada seluruh pemangku kepentingan dengan efektif.
EPIDEMIC PROGNOSIS: COMPARATIVE PERFORMANCE OF MACHINE LEARNING AND DEEP LEARNING MODELS FOR PREDICTING VIRUS TRANSMISSION DYNAMICS Ely Nastiti, Faulinda; Musa, Shahrulniza; Yafi, Eiad; Ardiyanto, Marta
Proceeding of the International Conference Health, Science And Technology (ICOHETECH) 2023: Proceeding of the 4th International Conference Health, Science And Technology (ICOHETECH)
Publisher : LPPM Universitas Duta Bangsa Surakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47701/icohetech.v4i1.3401

Abstract

The transmission of viral diseases, such as COVID-19, influenza, and other viral strains, poses a substantial worldwide challenge. In the context of health, it is necessary to possess a comprehensive comprehension, meticulous examination, and precise anticipation of the dissemination of this infectious disease. Nonetheless, the presence of diverse data characteristics among different nations poses a considerable obstacle in the development of prediction models for assessing the transmission, mortality, and recovery rates in Indonesia. Understanding the intricacies of viral transmission poses a significant hurdle because to the fluctuating nature of the generalization rate, which is contingent upon country-specific data.The research entailed a comparison of different predictive models, including Random Forest, Simple Linear Regression (SLR), Gaussian Naive Bayes, Multi-Layer Perceptron (MLP), H2O, and Long Short-Term Memory (LSTM), with the purpose of predicting viral transmission. The evaluation metrics encompass MAE, RMSE, and MAPE. The outcomes of the examination of comparison models will aid in identifying the most suitable model for forecasting the transmission of the virus, encompassing the rates of recovery, death, and positive cases, within the specific setting of Indonesia. This work has significance in elucidating the inherent trade-off between efficiency and accuracy within the realm of dynamic data modeling, specifically in the context of COVID-19 viral data.
Co-Authors Adirangga, Ihsan Kandung Ananda, Naufal Choirul Ely Nastiti, Faulinda Indra Hastuti, Indra Irawan, Ridwan Dwi Kresna Agung Yudhianto Maulindar, Joni Musa, Shahrulniza Saputri, Lisa Nur Savira Dewi Sopingi Sopingi, Sopingi Utami, Indah Wahyu Yafi, Eiad