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Proceeding of International Conference Health, Science And Technology (ICOHETECH)
Published by Universitas Duta Bangsa Surakarta
ISSN : -     EISSN : 2962634X     DOI : https://doi.org/10.47701/icohetech.v3i1
Core Subject : Health, Science, Engineering,
Computer Science & IT Decision Sciences, Operations Research & Management Health Professions Industrial & Manufacturing Engineering Medicine & Pharmacology
- Health and Medical - Health Information Technology - Science - Engineering - Information Technology
Arjuna Subject : Profesi Kesehatan - Menejmen Informasi Kesehatan
Articles 530 Documents
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ANDROID-BASED GUARD MONITORING AND SITE SURVEILLANCE SYSTEM Ondos, Mariel U.; Jr, Domingo V. Origines
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: 10.47701/4r140f21

Abstract

The System is designed to enhance security operations and ensure accountability among security personnel by integrating mobile technology with real-time data collection, the system improves the efficiency, transparency, and reliability of monitoring activities across designated locations. Developed using the Agile Development Model, the system incorporates key features such as monitoring via QR codes and a timestamp camera that enables guards to take and submit pictures. The software also makes it easier for guards to report incidents, enabling them to document any anomalies they come across while on patrol. Evaluation of the system was conducted using the ISO 25010 Software Quality Framework, with fourteen (14) evaluators including administrators, advisory committee members, and IT experts. The results indicated strong performance: the QR code generation feature scored 4.7, while the user module for real-time incident capture and visited site reporting earned 4.8. Overall, the system achieved a 4.5 rating for functional suitability, 4.3 for both performance efficiency and compatibility, and 4.2 for usability and reliability. Aligned with Sustainable Development Goal (SDG) 9: Industry, Innovation, and Infrastructure, the system promotes innovation in security management and strengthens institutional infrastructure. Upon implementation at the Davao del Sur State College General Services Office (GSO), it is expected to significantly improve patrol compliance, incident documentation, and overall operational performance.
APPLICATION OF SMART FARMING IN THE USE OF FRESHWATER FISH CULTIVATION WASTEWATER FOR IOT-BASED SANSEVIERIA PLANT IRRIGATION Iskandar, Dwi; Putri, Frestiany Regina; Anggraheni, Norhisma Zulfani
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/q1r5ca07

Abstract

Modern technology has brought significant changes to various sectors, including agriculture and environmental management. Challenges faced in plant cultivation include limited clean water sources and waste generated from various agricultural and aquaculture systems. The ornamental plant Sansevieria is one type of plant widely used in interior design due to its ability to improve air quality and its attractive aesthetics. By utilizing wastewater from fish ponds as an irrigation medium, it is hoped that these plants will not only be able to grow well and obtain adequate nutritional intake, but can also provide additional benefits in the form of reusing resources previously considered waste. The technology to be developed is based on the integration of the Internet of Things, which utilizes wastewater from freshwater fish aquaculture as an irrigation medium for ornamental Sansevieria plants. The method used is the Research and Development (R&D) method, a systematic approach that aims to develop, test, and refine a product through a series of gradual steps. One of the results of this research is a system that is able to demonstrate its potential in reducing dependence on clean water and providing new uses for aquaculture waste to maintain plant health efficiently.
COMPUTATIONAL MATHEMATICAL MODELING FOR LUNG CANCER DISEASE PREDICTION USING MULTIPLE LINEAR REGRESSION Farida, Anisatul; Indah, Ratna Puspita; Hartanti, Dwi; Silva, Adão Manuel da
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/twy84j24

Abstract

Lung cancer remains one of the most prevalent and deadly types of cancer worldwide, especially in developing countries with high smoking rates and limited early detection resources. This study aims to develop a computational mathematical model for predicting lung cancer risk using multiple linear regression. The model focuses on two primary factors: genetic predisposition and exposure to passive smoking, which are among the most significant determinants of lung cancer. An observational analytic design was employed using secondary data obtained from cancer registries, hospital records, and national health survey datasets. Computational data preprocessing techniques, including data cleaning, missing value imputation, and variable normalization, were applied to ensure model accuracy and reliability. The regression analysis revealed that both genetic predisposition and passive smoking significantly increased the lung cancer risk score, with regression coefficients of 0.24 and 0.48, respectively. The findings indicate that passive smoking has a greater impact on lung cancer risk compared to genetic factors. The final model demonstrated a coefficient of determination (R²) 0.72 indicates that 72% of the variation in risk can be explained by the combination of these two variables. This finding suggests that environmental factors have a more dominant influence than lifestyle factors on increasing lung cancer risk. This computational model provides a practical tool for early detection and risk stratification, supporting public health policies aimed at tobacco control and targeted screening programs to reduce lung cancer incidence and mortality
Design and Development of SmartKuliner: A Multimedia-Enhanced Web Platform for Culinary Product Promotion Muhammad, Nibras Faiq; Pramono; Andhini, Asmara
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/yy2kvz97

Abstract

This paper presents the design and development of SmartKuliner, a web-based culinary marketplace enhanced with multimedia features to support culinary product promotion. The platform integrates visual design, user-centered interface, and multimedia content to improve user engagement and trust. The development process adopts a multimedia development life cycle (MDLC) and emphasizes usability testing. Results indicate that SmartKuliner provides an effective medium for culinary businesses to promote products and for users to discover trusted local culinary items.
Development of an Image-Based Calorie Detection Model in Indonesian Food for Stunting Prevention Sari, Devi Pramita; Widodo, Sri; Mustofa, Khoirul
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: 10.47701/6d407123

Abstract

Stunting is a global health problem, especially in developing countries including Indonesia. One of the main causes of stunting is malnutrition, especially in children aged 0-23 months. Therefore, this study aims to develop an AI-based model to detect calories in Indonesian food images for stunting prevention, using the Transfer Learning method with AlexNet. In this article, we propose a new deep learning-based food image calorie detection model called, Alexnet Interactive Transfer Learning (AITL). AITL is built based on Alexnet's Convolution Neural Network architecture, and further modified at the last Convolution layer and classification layer. AITL was evaluated using a dataset from the Indonesian food database. Experiments were conducted on the dataset to detect food types and their calorific content. There are ten classes of authentic Indonesian food types, which include: Rendang, Bika Ambon, Pempek, Sate Ayam, Gado-gado, Ayam Pop, Kerak Telor, Rawon, Lemang, and Ayam Betutu. The accuracy of the developed AITL model reached 95.33%. The results of the tests conducted show that Alexnet-based AITL outperforms other CNNs in terms of accuracy and efficiency.
ENHANCING PHOTOVOLTAIC PERFORMANCE USING TITANIUM DIOXIDE BASED NANO-ENHANCED PCM FOR PASSIVE COOLING APPLICATIONS Seto, Dewandono Bayu; Sudiro; Restu, Tuhu
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/gjjms246

Abstract

The performance of photovoltaic (PV) modules is significantly affected by the increase in operating temperature, which leads to reduced power output and overall efficiency. Therefore, passive cooling strategies are required to maintain stable energy conversion in solar systems. One widely investigated approach is the use of phase change materials (PCM) due to their ability to absorb and release latent heat, although their low thermal conductivity remains a major limitation. This study aims to evaluate the effectiveness of incorporating titanium dioxide nanoparticles into PCM, known as nano-enhanced PCM (NePCM), in improving the cooling performance of PV modules. The experimental method was conducted on a 50 Wp polycrystalline PV module under three conditions: without cooling, with pure PCM, and with titanium dioxide-based NePCM. The preparation of NePCM was carried out using a two-step method involving mechanical stirring and ultrasonic sonication to achieve a homogeneous nanoparticle dispersion. The results demonstrated that the addition of titanium dioxide improved the thermal conductivity of PCM, leading to lower operating temperatures, more stable voltage and power output, and higher energy conversion efficiency compared to both pure PCM and the uncontrolled condition. These findings highlight the potential of titanium dioxide-based NePCM as an effective, economical, and sustainable material for passive cooling applications in photovoltaic systems.
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.
Lung Cancer Disease Prediction Model with Multiple Linear Regression Indah, Ratna Puspita; Farida, Anisatul; Masidayu, Sharifah Noor
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/qpmszf57

Abstract

Lung cancer remains one of the leading causes of cancer-related mortality worldwide, particularly in developing countries where smoking prevalence is high. This study aims to develop a predictive model for lung cancer risk using multiple linear regression based on two main factors: genetic predisposition and exposure to passive smoking. The research was conducted using an observational analytic design with secondary data derived from cancer registries, hospital medical records, and national health surveys. Data processing included cleaning, imputation of missing values, and standardization of exposure variables. The results of the regression analysis showed that both genetic risk and passive smoking significantly increased the lung cancer risk score, with coefficients of 0.24 and 0.48, respectively. Interestingly, passive smoking demonstrated a stronger impact compared to genetic predisposition, indicating its role as a more dominant determinant of lung cancer risk. The model explained 20.5% of the variation in risk, while the remaining was influenced by other factors such as air pollution, occupational exposure, and lifestyle. These findings highlight the importance of strengthening public health policies, particularly tobacco control in public spaces, and implementing targeted risk-based screening strategies. This predictive model offers a practical tool for early detection, efficient allocation of health resources, and effective cancer prevention strategies.
Optimization of MySQL Database in the Development of Solo Batik Mall Srirahayu, Agustina; Pamekas, Bondan Wahyu; Guterres, Juvinal Ximenes
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/kjy3qe33

Abstract

This study aims to design and optimize a MySQL database for an online batik mall system to support the digitalization of batik micro, small, and medium enterprises (MSMEs). The research employed three stages: analysis, design, and implementation. The analysis phase identified actors (sellers, buyers, and administrators) and business process needs. The design stage focused on database structures and optimization strategies, including indexing, query optimization, caching, normalization, and denormalization. The implementation involved building the database, applying optimization techniques, and evaluating performance. The optimization of MySQL significantly improved query execution speed, reduced system response time, and enhanced resource efficiency. The system was able to manage transactions, product searches, and reporting more effectively, supporting both operational and strategic needs of the online batik mall. The MySQL-based online batik mall system provides an efficient solution for data management, thereby enhancing the competitiveness of batik MSMEs in the digital era.
SMART RECOMMENDATION SYSTEM MODELING FOR BATIK USING THE CONTENT BASED RECOMMENDATION METHOD Atina, Vihi; Purwanto, Eko; Mohd, Farahwahida
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/f0vwwz52

Abstract

Batik was an intangible cultural heritage recognized by UNESCO, with unique variations of motifs, colors, and philosophies in each region, both in Indonesia and Malaysia. The development of the fashion industry and e-commerce brought both opportunities and challenges, since users often had difficulties finding batik that matched their preferences, occasions, or symbolic needs. This research aimed to develop a smart recommendation system model for batik using the content-based recommendation method. The dataset consisted of batik data from Indonesia and Malaysia with attributes such as region of origin, dominant color, main motif, category, and usage. The system development method applied was Prototyping, which included the stages of requirement identification, quick design, and prototype construction. The results showed that the system was able to provide relevant recommendations according to user preferences. For example, when the user selected batik preferences with green color, leaf motif, and casual usage, the system recommended Batik Priangan from Indonesia with the highest similarity value of 0.75. These findings proved that the content-based approach successfully connected batik attributes with user needs. This research was expected not only to simplify the search for batik products in the digital era but also to contribute to the preservation of batik culture through the utilization of information technology.

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