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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 67 Documents
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SmartENose: Environmental Health Monitoring System for Cattle Sheds Using Fuzzy System Method Pandu Satya Ramadhani; Andi Susilo
Journal TIFDA (Technology Information and Data Analytic) Vol 2 No 2 (2025)
Publisher : Prodi Teknologi Informasi Universitas Darma Persada

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70491/tifda.v2i2.110

Abstract

The air quality in livestock barns significantly impacts the health and productivity of animals. This study designs an air quality monitoring system based on the Internet of Things (IoT) utilizing the ESP32 microcontroller and three gas sensors: MQ-135 (ammonia), MQ-4 (methane), and MQ-7 (carbon monoxide). The collected data is processed using the Fuzzy Sugeno logic method, which involves fuzzification, rule base, and defuzzification stages to classify the air conditions into Safe, Alert, or Dangerous categories. The classification results are displayed in real-time through an I2C LCD, the Blynk application, and the SmartENose website developed with PHP and MySQL. Additionally, the system is equipped with LED indicators and a buzzer for early warning notifications. Testing results indicate that the system can detect gas concentrations responsively and accurately, providing air status classifications that align with the actual conditions in the cattle barn. This research demonstrates that the application of IoT technology, supported by Fuzzy Sugeno logic, can be effectively utilized for monitoring and early warning of air quality in agricultural environments.
Behavioral Biometric-Driven Educational Data Mining: CNN-Based Prediction of Students’ On-Time Graduation from Handwritten Signatures Herianto; Khoirul Mustaan; Yahya; Nur Syamsiyah
Journal TIFDA (Technology Information and Data Analytic) Vol 2 No 2 (2025)
Publisher : Prodi Teknologi Informasi Universitas Darma Persada

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70491/tifda.v2i2.112

Abstract

Timely graduation is a fundamental metric in higher-education accreditation and a key indicator of institutional efficiency. Conventional prediction models largely rely on longitudinal academic records, which are lagging indicators and often fail to detect risks during the early stages of study. This research proposes a paradigm shift by leveraging behavioral biometrics—specifically, the analysis of handwritten signatures using Deep Learning—to predict students’ graduation timelines and academic motivation profiles. Using a dataset from the Undergraduate Information Technology Program at Universitas Darma Persada, the study adopts the Cross-Industry Standard Process for Data Mining (CRISP-DM) framework. A Convolutional Neural Network (CNN) model based on the ResNet-50 architecture was developed, employing transfer learning to extract complex graphological features from signature images. Through rigorous data augmentation and statistical normalization, the model addresses the limitations of a small dataset. Empirical evaluation reports a graduation-prediction accuracy of 65% (Recall: 65%, F1-Score: 64%) and an academic-personality prediction accuracy of 70% (Precision: 74%, F1-Score: 69%). Although its absolute performance remains below transcript-based models, the findings validate the potential of signatures as early leading biometric indicators capable of capturing latent discipline and intrinsic motivation. This approach offers a non-invasive decision-support tool for academic advisors within intelligent education ecosystems.
Development of a Decision Support System for Motorcycle Credit Eligibility Using the TOPSIS Method Endang Ayu Susilawati; Eva Novianti; Avida Awitia; Yahya
Journal TIFDA (Technology Information and Data Analytic) Vol 2 No 2 (2025)
Publisher : Prodi Teknologi Informasi Universitas Darma Persada

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70491/tifda.v2i2.113

Abstract

The growing need for motorcycle financing in Indonesia has encouraged financial institutions to improve the accuracy and consistency of their credit evaluation processes. At PT FIFGROUP, the current assessment procedure still relies heavily on manual surveys and subjective judgments, which often leads to variations in decision outcomes and longer processing times. This study aims to design and develop a Decision Support System (DSS) that facilitates a more objective and efficient assessment of motorcycle credit eligibility by applying the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). Recent advancements in decision-making research highlight TOPSIS as one of the most effective multi-criteria decision-making (MCDM) methods due to its structured approach in comparing alternatives against ideal benchmarks. Building on this body of work, the proposed system incorporates organizational criteria—such as residential status, income stability, expenditure levels, and educational background—into a standardized evaluation model.The research methodology includes system requirement analysis, conceptual and database design, and the integration of the TOPSIS algorithm into the application workflow. Through normalization, weighting, and distance calculations, the system generates a final ranking score that reflects each applicant’s eligibility. The results of the study show that the DSS significantly improves the consistency of credit evaluations, reduces subjective bias, and accelerates the decision-making process. Overall, the implementation of a TOPSIS-based DSS provides a practical and reliable solution for PT FIFGROUP to enhance the quality and efficiency of motorcycle credit assessments
Development of a Digital Signature Classification Information System for Documents Yahya Yahya; Eva Novianti; Anand Fiardhi Ramadhan
Journal TIFDA (Technology Information and Data Analytic) Vol 2 No 2 (2025)
Publisher : Prodi Teknologi Informasi Universitas Darma Persada

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70491/tifda.v2i2.116

Abstract

A signature is a unique identity attached to a document, playing an essential role in verification and legal authentication. Legally, a signature represents the true authorization of the document’s owner. With the advancement of digital technology, signature pattern identification can now be performed not only manually but also with the support of computer-based systems. Recent studies in digital forensics and pattern recognition highlight the growing importance of automated signature verification to prevent falsification and ensure document integrity, especially within academic and administrative environments. This research aims to develop a web-based Digital Signature Classification Information System designed for use by administrative staff at the Faculty of Engineering, Universitas Darma Persada. The objective is to provide an accessible tool capable of distinguishing between genuine and forged digital signatures with higher accuracy and reliability. The system applies image processing techniques combined with automated classification methods to analyze signature characteristics and determine authenticity. Through this approach, the system reduces the dependency on manual inspection, which is often time-consuming and prone to human error. The results of the implementation show that the system is able to classify digital signatures effectively and can be operated easily by administrative personnel. Its deployment is expected to improve the security, validity, and accuracy of signed documents within the faculty’s administrative workflow. Overall, the system offers a practical solution for enhancing document verification processes and reducing risks associated with signature forgery
IoT Prototype System Design for Monitoring Green Mustard Microgreen Cultivation at Sayur Mini Microgreen Jakarta Dimas Gilang Rhomadhon; Yan Sofyan
Journal TIFDA (Technology Information and Data Analytic) Vol 2 No 2 (2025)
Publisher : Prodi Teknologi Informasi Universitas Darma Persada

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70491/tifda.v2i2.117

Abstract

Urban agriculture, particularly microgreen cultivation, has emerged as a strategic solution to address food needs in limited land areas. This study aims to design and evaluate an Internet of Things (IoT) prototype for monitoring the cultivation of green mustard microgreens (Brassica juncea L.) in Jakarta. The system integrates soil moisture sensors, a DHT22 sensor (temperature and humidity), a TDS (Total Dissolved Solids) sensor, and an NPK soil analyzer, enabling automated and real-time monitoring of environmental conditions. The findings indicate that the implementation of the IoT system improves water-use efficiency by up to 35% and enhances plant growth, achieving an average height of 7.2 cm with stronger root development compared to manual cultivation methods. This research provides a significant contribution to the advancement of precision agriculture in urban environments and serves as a guideline for urban farming practitioners in improving crop yield sustainably.
Implementation of Internet of Things in a Smart Trash Bin System for Organic and Inorganic Waste Sorting using Solar Cells at SMP Al Wathoniyah 9 Jakarta Dwi Prasetyo; Suzuki Syofian
Journal TIFDA (Technology Information and Data Analytic) Vol 2 No 2 (2025)
Publisher : Prodi Teknologi Informasi Universitas Darma Persada

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70491/tifda.v2i2.120

Abstract

Ineffective waste management in schools, characterized by unsegregated waste collection, hinders recycling efforts and harms the local environment. This study addresses this issue by designing, implementing, and evaluating an Internet of Things (IoT)-based smart trash bin system with automatic organic and inorganic waste sorting, powered by solar energy for operation at SMP Al Wathoniyah 9 Jakarta. The system utilizes an ESP32 microcontroller with a multi-sensor detection logic: an inductive proximity sensor for metals, a capacitive proximity sensor for plastics, and an infrared sensor for organic waste, complemented by ultrasonic sensors for fill-level monitoring. Deployed and tested in the school environment, the system demonstrated high reliability, achieving a sorting accuracy of 96.67% across 2,598 trials. Data on waste volume and type are transmitted and displayed in real-time via a dedicated web-based monitoring dashboard. The results confirm the system's effectiveness as a practical tool for automated waste segregation. Furthermore, its solar-powered operation ensures energy independence, while the real-time monitoring capability supports smarter waste management. This implementation serves as a concrete model for integrating environmental technology into education, promoting sustainability awareness and supporting the development of smart schools
Decision Tree Regression Approach to Modeling Dengue, Tuberculosis, and Diarrhea Case Numbers Muhammad Dzaki Zahirsyah; Timor Setiyaningsih
Journal TIFDA (Technology Information and Data Analytic) Vol 2 No 2 (2025)
Publisher : Prodi Teknologi Informasi Universitas Darma Persada

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70491/tifda.v2i2.121

Abstract

The increasing incidence of Dengue Hemorrhagic Fever (DHF), Tuberculosis (TB), and Diarrhea in a district area highlights the urgent need for a data-driven prediction system to support public health policy. This study develops a predictive model of case numbers at the sub-district level using the Decision Tree Regression algorithm within the CRISP-DM methodology. Secondary data from 2020-2023 were utilized, including disease case records (Health Office), demographic data (BPS), and environmental data (BMKG). The system was implemented as a web-based application built with PHP and Python/Flask, enabling dataset management, model retraining, and interactive visualization of predictions, complemented by risk classification and recommended interventions. Experimental results demonstrate high predictive accuracy, with R² values of 0.9130 for TB, 0.8805 for DHF, and 0.8228 for Diarrhea. Overall, the proposed system serves as an objective and measurable decision-support tool, assisting the District Health Office in formulating preventive policies more rapidly and effectively.

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