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Analysis of the Implementation of Artificial Intelligence (AI)-Based Promotional Media by Micro, Small, and Medium Enterprises (MSMEs) in Enhancing Brand Awareness in the Digital 5.0 Era Wahyu Fitri; Anggi Hadi Wijaya; Zumiarti Zumiarti
Edumaspul: Jurnal Pendidikan Vol 9 No 2 (2025): Edumaspul: Jurnal Pendidikan
Publisher : Universitas Muhammadiyah Enrekang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33487/edumaspul.v9i2.9194

Abstract

With the increasing use of Artificial Intelligence (AI)–based technology today, this development has become a positive opportunity for Micro, Small, and Medium Enterprises (MSMEs) in Padang City. By utilizing AI, MSME actors can significantly reduce marketing costs when promoting their products. This study aims to analyze the implementation of MSME product promotion media using Artificial Intelligence (AI) as an effort to enhance brand awareness in the digital 5.0 era. As of March 2025, there were 47,289 MSME actors recorded in Padang City, spread across 11 districts. Fundamentally, MSMEs are currently the largest contributors to the national economy. However, literacy and understanding of the use of emerging technologies remain limited among MSME actors. This condition is reinforced by data from 2014 to 2018, which shows that only 46.87% of MSMEs utilized promotional media and internet access. Furthermore, from 2019 to the end of 2021, nearly 90% of MSMEs experienced stagnation due to the spread of the COVID-19 pandemic. Therefore, specific research efforts and targeted actions are needed to examine the implementation of AI-based promotional media to improve the literacy and capabilities of MSME actors. This study employs a mixed-method research design (qualitative–quantitative) with a project-based approach. The research process began with the collection of primary and secondary data through in-depth interviews with 30 informants and the distribution of 476 questionnaires, supported by data from social media, online media, mass media, and other relevant sources. This research integrates interdisciplinary expertise (communication studies, social sciences, and economics) to address the identified problems, resulting in findings that describe MSME needs and implement effective strategies for promoting MSME products, as well as providing policy recommendations and practical suggestions for relevant stakeholders. The expected outcome of this research is one mandatory output in the form of a National Journal indexed in SINTA 1–4, aligned with Technology Readiness Level (TRL) 1, with a final target of TRL 3.
Sistem Toko Online Berbasis PHP Menggunakan Framework Bootstrap: Desty’s Pastry Saputra, Muhammad Farhan; Wijaya, Anggi Hadi; ., Ipriadi
Jurnal Sistem Informasi Dan Informatika Vol 4 No 1 (2026): Januari 2026
Publisher : Prodi Sistem Informasi Universitas Dharma Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47233/jiska.v4i1.2493

Abstract

“Desty’s Pastry” previously relied on WhatsApp-based ordering and manual record-keeping, resulting in frequent order errors, limited market reach, and the absence of modern payment and notification features. This study aims to address these challenges by developing a web-based online store to improve operational efficiency and enhance customer experience. The system was built using the Waterfall development model, with requirements collected through interviews and direct observations. PHP 8.2, Bootstrap 5.3.5, and MySQL were used as core technologies, supported by Data Flow Diagrams and Entity Relationship Diagrams for system design. The resulting system provides key features including product catalog management, user authentication, shopping cart functionality, order processing, and an administrative dashboard, along with digital payment integration through QRIS. Black-box testing using Equivalence Partitioning showed that core functionalities such as registration, product selection, cart management, and order processing performed correctly with accurate data handling. Overall, the system successfully resolves initial operational issues and offers a scalable solution for SMEs adopting digital sales platforms.
Digitalization of Rural Water Management: Android-Based Billing for Community Systems using the ADDIE model Nurfiah Nurfiah; Afdhal Dinilhak; Luthfil Khairi; Budy Satria; Anggi Hadi Wijaya; Ajeng Dwi Asti; Arifan Rahman
Journal of Information Systems and Technology Research Vol. 4 No. 2 (2025): May 2025
Publisher : Ali Institute or Research and Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55537/jistr.v4i02.1135

Abstract

The integration of information technology in everyday life has changed the way people work, learn, socialize, make transactions, and make decisions. The use of Android-based smartphones is a real example of the use of technology. Android, which is open-source, has encouraged the development of applications widely according to need. Access to water is a fundamental human right. PAMSIMAS is a flagship program of the regional and central governments that seeks to meet water needs through the provision of clean water services in line with the Sustainable Development Goals (SDGs). In Durian Seribu Village, PAMSIMAS is a service to meet the water needs of the community and become a solution for rural communities to get clean water at low cost, but its management is still manual, such as recording water usage and billing, so it is inefficient, time-consuming, and prone to errors. From these problems, this study proposes the development and implementation of an Android application designed to simplify the recording and billing process for the PAMSIMAS program in Durian Seribu Village. This application aims to simplify management, increase data transparency, and simplify reporting. The results of tests that have been carried out using the black box method show that this application can facilitate officers in recording and billing payments for PAMSIMAS water usage. Officers only need to enter the total water usage, and the application will automatically calculate and print a receipt as proof of payment. Officers also do not need to calculate manually when reporting the total payment to the administrator. For administrators, this application makes it easier to monitor and evaluate the performance of recording officers. After the application was used for recording and billing, PAMSIMAS's revenue increased by around 30% from the revenue before using the application.
Analisis Ketahanan Fitur MFCC dan Log-Mel Spektrogram untuk Speech Emotion Recognition Berbasis CNN pada Berbagai Kondisi Signal-to-Noise Ratio Rifki Yuliandra; Arifan Rahman; Afdhal Dinilhak; Luthfil Khairi; Anggi Hadi Wijaya
Jurnal Sains Dan Teknologi | E-ISSN : 3063-9980 Vol. 2 No. 4 (2026): April - Juni
Publisher : GLOBAL SCIENTS PUBLISHER

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Speech Emotion Recognition (SER) has achieved remarkable performance under controlled, clean-audio conditions; however, its robustness in noise-laden, real-world environments remains insufficiently characterized. This study investigates the performance degradation of a Convolutional Neural Network (CNN)-based SER system on the RAVDESS dataset when subjected to synthetic noise at various Signal-to-Noise Ratio (SNR) levels (−5, 0, 5, 10, and 15 dB). We compare two widely used feature representations: Mel-Frequency Cepstral Coefficients (MFCC) and Log-Mel Spectrogram. Both models were trained exclusively on clean audio and evaluated under twelve noise conditions using Additive White Gaussian Noise (AWGN) and babble noise. Experimental results on the RAVDESS dataset (2,880 samples, 8 emotion classes) reveal a distinct asymmetry: the MFCC-based CNN achieves a 48.84% clean-audio accuracy with a maximum degradation of 27.08 percentage points (pp). Conversely, the Log-Mel Spectrogram model achieves a higher clean baseline of 66.67% but suffers a severe drop of up to 47.69 pp under noise, approaching the random baseline of 12.5%. These findings demonstrate that MFCC features offer superior robustness to additive noise due to implicit spectral smoothing via mel filterbanks and Discrete Cosine Transform (DCT), despite exhibiting lower clean-audio discriminability. This research highlights a fundamental trade-off between feature discriminability and noise robustness in uncontrolled acoustic environments.
YOLOv11-based detection and classification of diseases in Siamese orange fruit using digital images Rehan Khairuno; Anggi Hadi Wijaya
Science, Technology, and Communication Journal Vol. 6 No. 3 (2026): SINTECHCOM Journal (June 2026)
Publisher : Lembaga Studi Pendidikan dan Rekayasa Alam Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59190/stc.v6i3.375

Abstract

Diseases in Siamese orange fruit are one of the factors that can reduce the quality and yield of agricultural production. Manual disease identification requires considerable time and depends heavily on human observation skills; therefore, an automated system capable of detecting diseases quickly and accurately is needed. This study aims to implement the YOLOv11 model for detecting and classifying diseases in Siamese orange fruit based on digital images. The dataset used consisted of four classes, namely anthracnose, citrus canker, scab, and healthy, with a total of 627 images divided into training, validation, and testing datasets. The study utilized 20 variations of data augmentation, and DataV18 produced the best performance. The training process was conducted using the YOLOv11s architecture with 200 epochs and various data augmentation techniques. Based on the testing results, the model achieved a precision of 70.5%, recall of 61.2%, F1-score of 65.5%, mAP@0.5 of 61.8%, and mAP@0.5:0.95 of 44.8%. The results indicate that the YOLOv11 model has a fairly good capability in detecting diseases in Siamese orange fruit based on digital images and has the potential to be applied in the development of artificial intelligence-based plant disease detection systems.