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Aji Setiawan
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Journal Technology Information and Data Analytic
Published by Universitas Darma Persada
ISSN : -     EISSN : 30640660     DOI : https://doi.org/10.70491/tifda.v1i2.43
Core Subject : Science, Education, Agriculture, Engineering,
Agriculture, Biological Sciences & Forestry Computer Science & IT Decision Sciences, Operations Research & Management Engineering Library & Information Science Other
Journal of Technology Information and Data Analytic is a scientific journal managed by the Faculty of Engineering, Darma Persada University. TIFDA is an open access journal that provides free access to the full text of all published articles without charging access fees from readers or their institutions. Readers are entitled to read, download, copy, distribute, print, search, or link to the full text of all articles in the TIFDA Journal. This journal provides immediate open access to its content on the principle that making research freely available to the public supports a greater global exchange of knowledge. Focus & Scope Informatics: Software Engineering, Information Technology, Information System, Data Mining, Multimedia, Mobile Programming, Artificial Intelligence, Computer Graphic, Computer Vision, Augmented/Virtual Reality, Games Programming, Privacy and Data Security, Security, Machine learning, Database Internet of Things Information System : Software Management, Life Cycle Development Tools.
Arjuna Subject : Ilmu Komputer (semua kategori) - Kecerdasan Buatan
Articles 76 Documents
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IoT-Based Air Pollution Trend Analysis: A Case Study in Residential Areas of Karawang Industrial Estate Anindya Tara Danendra Alamsyah; Andi Susilo
Journal TIFDA (Technology Information and Data Analytic) Vol 3 No 1 (2026): Journal Technology Information and Data Analytic
Publisher : Prodi Teknologi Informasi Universitas Darma Persada

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70491/tifda.v3i1.140

Abstract

Air quality degradation in industrial zones poses a significant health risk to surrounding residential communities. Karawang, as a major manufacturing hub in West Java, faces challenges in monitoring particulate matter and gas emissions due to the high cost and limited coverage of standard monitoring stations. This study aims to design a low-cost Internet of Things (IoT) system to monitor and analyze air pollution trends in the residential buffer zones of the Karawang Industrial Estate. The proposed system integrates an ESP32 microcontroller with multispectral sensors, including MQ-135 for hazardous gases, PMS5003T for particulate matter (PM2.5/PM10), and DHT22 for meteorological variables. Data is transmitted in real-time to a web-based server using a non-blocking transmission algorithm to ensure data integrity. The results demonstrate that the system reliably captures diurnal fluctuations in air quality. Trend analysis reveals a consistent pattern of increased pollutant concentration during morning (06:00–09:00) and late-night (19:00–22:00) periods, correlated with anthropogenic activities and meteorological phenomena such as temperature inversion. While the daily average Air Quality Index (AQI) predominantly falls within the "Moderate" category, episodic spikes reaching "Unhealthy for Sensitive Groups" were observed, highlighting the necessity for granular, real-time monitoring for public health mitigation.
IoT-Based Smart Poultry Management System for Layer Hens: Design, Implementation, and Performance Evaluation Harsya Febriansyah; Aji Setiawan
Journal TIFDA (Technology Information and Data Analytic) Vol 3 No 1 (2026): Journal Technology Information and Data Analytic
Publisher : Prodi Teknologi Informasi Universitas Darma Persada

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70491/tifda.v3i1.141

Abstract

Abstract — Traditional small-to-medium scale layer hen farms in Indonesia frequently rely on manual management practices, resulting in environmental instability, inconsistent productivity, and excessive labor demands. This paper presents the design, implementation, and evaluation of an Internet of Things (IoT)-based smart farming prototype for layer chicken coops, incorporating real-time environmental monitoring and automation of critical husbandry processes. The system is built around an ESP32 microcontroller interfaced with a suite of sensors: DHT22 for temperature and humidity, MQ-135 for ammonia concentration, LDR for light intensity, and an IR sensor for automated egg counting. Actuation is achieved via servo motors for scheduled feeding, relay modules controlling ventilation fans and lighting, and a water pump for automated cleaning. A custom web-based monitoring and control interface, developed using PHP and MySQL, delivers real-time dashboards, historical data logging, and manual override capabilities, offering improved stability over third-party MQTT solutions. Laboratory unit testing and a seven-day field deployment at Serayu Farm, Purbalingga, demonstrated that the prototype maintained temperature variation below 2 °C, achieved an average 15 % reduction in ammonia levels, attained 94 % accuracy in egg counting, and reduced manual interventions by approximately 70 %. With a component cost under Rp 800 000 per unit, the system provides a practical, scalable solution for enhancing operational efficiency, animal welfare, and egg productivity in resource-constrained layer hen farming operations.
Design and Implementation of IoT-Based Smart Aquarium using ESP32 for Water Quality Monitoring and Automatic Feeding Yan Sofyan; Kuncoro Banu
Journal TIFDA (Technology Information and Data Analytic) Vol 3 No 1 (2026): Journal Technology Information and Data Analytic
Publisher : Prodi Teknologi Informasi Universitas Darma Persada

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70491/tifda.v3i1.142

Abstract

Advances in Internet of Things (IoT) technology have enabled the development of smart monitoring and automation systems across various application domains, including aquarium management. Conventional aquarium monitoring often relies on manual observation, which can be time-consuming and less effective for continuous environmental monitoring. This study aims to design, implement, and evaluate an IoT-based Smart Aquarium system utilizing the ESP32 microcontroller as the central controller. The proposed system integrates multiple environmental sensors, including temperature, pH, turbidity, and water level sensors, to acquire real-time aquarium data. Sensor measurements are processed by the ESP32 and transmitted to a cloud-based database, enabling real-time access through a mobile application. The developed platform also incorporates an automatic feeding mechanism using a servo motor, a monitoring camera for visual observation, and a solar panel subsystem as a supplementary energy source. Experimental results demonstrate that all sensor modules successfully acquired and transmitted environmental data to the monitoring platform. The mobile application was able to display real-time sensor readings and monitoring information through a centralized dashboard. Furthermore, the automatic feeding subsystem operated according to predefined schedules, while the camera monitoring and solar power subsystems functioned as intended. The results indicate that the proposed Smart Aquarium platform successfully integrates sensing, communication, monitoring, and automation technologies within a unified IoT ecosystem. The developed system provides an effective solution for real-time monitoring and remote aquarium management through the integration of embedded systems, cloud services, and mobile computing technologies.
Implementation of The Random Forest Algorithm for Early Detection Indications of Autism in Special Needs School (SLB) Students Bagus Tri Mahardika; Duha Nur Pambudi
Journal TIFDA (Technology Information and Data Analytic) Vol 3 No 1 (2026): Journal Technology Information and Data Analytic
Publisher : Prodi Teknologi Informasi Universitas Darma Persada

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70491/tifda.v3i1.143

Abstract

This study aims to develop a system for early detection signs of autism in pupils at Special Needs Schools (SLB) by applying the Random Forest method. The problem addressed is how to provide an accurate and easily accessible tool for the early identification of signs of autism. The solution involves developing a Random Forest-based classification model using data from the Autism Spectrum Quotient (AQ-10) questionnaire, and then integrating it into a web application system built with a PHP frontend and a Flask backend. This system allows users to complete the questionnaire, upload data, and obtain prediction results automatically. Test results show that the model has an average accuracy of 99%, precision of 98%, recall of 100%, and an F1-score of 99%, as well as an AUC value above 0.98 in every fold. Consequently, this system is effective as a tool for initial screening to detect signs of autism in students at special schools in a practical and efficient manner.
Development of Augmented Reality (AR) to Support Learning in The Introduction to Information Technology (PTI) Course: A Case Study at Darma Persada University Ferdi Maulana Akbar; Adam Arif Budiman
Journal TIFDA (Technology Information and Data Analytic) Vol 3 No 1 (2026): Journal Technology Information and Data Analytic
Publisher : Prodi Teknologi Informasi Universitas Darma Persada

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70491/tifda.v3i1.144

Abstract

Advances in Augmented Reality (AR) technology offer significant potential for enhancing learning in the Introduction to Information Technology (PTI) course at Darma Persada University, particularly regarding computer hardware concepts that are difficult to comprehend abstractly through conventional textbooks and slides. This study developed a marker-based AR application aligned with the Cisco IT Essentials curriculum, employing the Multimedia Development Life Cycle (MDLC) methodology. The application was built using Unity3D, Vuforia, Blender, and Visual Studio, presenting interactive 3D models, instructional videos, graded quizzes, volume controls, and marker guides distributed via Google Drive. Black-box testing confirmed successful operation on Android devices, and user testing involving 13 respondents demonstrated positive responses regarding ease of use and improved comprehension of the subject matter. The overall average rating was 3.63 out of 5. This application serves as an effective alternative interactive learning medium for the PTI course.
Implementation of Sentiment Analysis in Internship Evaluation Information System Endang Ayu Susilawati; Eva Novianti; Rahmad Ibnu Vikri
Journal TIFDA (Technology Information and Data Analytic) Vol 3 No 1 (2026): Journal Technology Information and Data Analytic
Publisher : Prodi Teknologi Informasi Universitas Darma Persada

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70491/tifda.v3i1.146

Abstract

Internship is an academic activity that connects students with real-world work environments, making comments and scores from companies critical data sources for evaluating student achievement and curriculum alignment. In the Information System Study Program at Darma Persada University, company feedback from the 2016-2025 period had not been integrated or analyzed comprehensively, particularly regarding textual comments from corporate HR. This study aims to design, build, and evaluate a internship evaluation information system that processes numerical scores, letter grades, and company textual comments using sentiment analysis. The system was developed using the Rapid Application Development (RAD) methodology through requirements planning, design workshops, rapid construction, and evaluative implementation. Sentiment analysis was implemented via the Hugging Face API service to classify comments into positive, neutral, and negative categories along with their confidence scores. System evaluation was conducted using Black-box testing for functional validity, sentiment classification performance metrics (Accuracy, Precision, Recall, F1-Score), and user assessment using the System Usability Scale (SUS) questionnaire. The test results showed a 100% success rate in Black-box functional testing. The sentiment analysis model achieved an accuracy of 85.0%, precision of 84.6%, recall of 85.0%, and an F1-Score of 84.8% on the sample evaluation set. Furthermore, the system usability assessment via the SUS questionnaire yielded an average score of 82.5, placing the system in the 'Excellent' usability category (Grade A). This system effectively assists the study program in interpreting internship quality patterns objectively, systematically, and in a data-driven manner.

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