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Rapid Ecosystem-Driven Deep Learning: On-Device Grain Type Classification and Authentication using iOS Swift and Core ML Trianggoro Wiradinata
Journal of Applied Data Sciences Vol 7, No 3: September 2026
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v7i3.1414

The 2025 Indonesian rice scandal highlighted major shortfalls in food security and the pressing need for robust, data-based verification of authenticity. The goal of this work is to design a fast, lightweight, and fully offline rice grain classification and verification system that can run directly on consumer mobile hardware. The basic idea to overcome the technical bottleneck of deploying complex computer vision models on edge devices is to use macOS and Apple’s unified ecosystem as a rapid prototyping and deployment platform. The deliberate avoidance of fragmented and high-latency workflows such as external environments (e.g. TensorFlow or PyTorch) and intermediate formats (e.g. ONNX) is mentioned. The study contributes a streamlined pipeline that incorporates an Image Feature Print V1 feature extractor, natively trained with Create ML on a publicly available dataset of 75,000 balanced images of five rice varieties (Arborio, Basmati, Ipsala, Jasmine, and Karacadag), directly into a native iOS application built with SwiftUI. The novelty of this approach is the usage of native tools like VisionKit and Core ML, which enables the complete elimination of third-party bridging code that normally bloats the binary overhead. The results show excellent edge efficiency on an iPhone 15 with a median prediction rate of 2.75 ms, an initial load time of 0.54 ms and a compilation latency of 3.66 ms. Moreover, the results reveal that by employing aggressive data augmentations, including the addition of visual noise, blur, exposure adjustment, flipping and rotation to ensure robustness, and by deliberately not cropping to preserve absolute grain dimensions, the model achieved a remarkable overall accuracy of 97% with an ultra-compact deployment footprint of only 66 KB. These metrics demonstrate that a fast, fully offline and privacy-preserving verification system is well within reach with modern consumer hardware.

LyFy: Enhancing Batik E-Commerce Live Streaming Through Real-Time Chat Filtering and Product Recommendation Yustus Eko Oktian; Eugene Abigail Setiawan; Trianggoro Wiradinata; Indra Maryati; Yosua Setyawan Soekamto
Teknika Vol. 14 No. 1 (2025): March 2025
Publisher : Center for Research and Community Service, Institut Informatika Indonesia (IKADO) Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34148/teknika.v14i1.1104

Live streaming has emerged as an essential tool for e-commerce, allowing sellers to engage with potential customers in real-time. However, the massive influx of comments during these sessions often includes a mix of useful product-related queries and irrelevant or distracting messages, which can overwhelm the presenter and reduce the effectiveness of the stream. In this paper, we propose LyFy, a browser-based extension designed to filter live chat messages and provide personalized product recommendations in real-time, specifically applied in Batik e-commerce to support the preservation and promotion of this unique cultural heritage of Indonesia. Our system uses a combination of natural language processing (NLP) and machine learning models to identify relevant comments, group similar queries, and offer product suggestions based on viewers' interests. We demonstrate the effectiveness of this system through a prototype implementation and evaluate its performance with qualitative feedback from streamers and users. The evaluation results indicate high user satisfaction, with over 51% of respondents rating LyFy as highly effective and 52% as highly efficient, making it a valuable tool for enhancing e-commerce live streaming interactions.

Digital Risk Culture and Cyber Resilience Advantage in Indonesian Data Center Providers: Firm Competitive Performance and the Moderating Effect of Organizational Risk Response Habit Aditya Dyan Permadi; Trianggoro Wiradinata; Cliff Kohardinata
Inkubis : Jurnal Ekonomi dan Bisnis Vol. 8 No. 1 (2026): INKUBIS Jurnal Ekonomi Dan Bisnis
Publisher : Politeknik Siber Cerdika Internasional

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59261/inkubis.v8i1.149

Background: The rapid digitalization of critical infrastructure has increased exposure to cyber threats. While research on cybersecurity governance is growing, the mechanisms linking behavioral cybersecurity culture to sustained resilience, particularly in high-availability digital infrastructure like data centers, are underexplored. This study addresses this gap by exploring how Digital Risk Culture (DRC) drives Sustainable Cybersecurity Transformation (SCT) and generates Cyber Resilience Advantage (CRA), with Organizational Risk Response Habit (ORRH) as a boundary condition. Objective: The study investigates how DRC impacts CRA through SCT and examines the moderating role of ORRH. It uses Resource Advantage Theory to conceptualize DRC as behavioral capital, SCT as an orchestration mechanism aligned with the NIST Cybersecurity Framework, and CRA as a resilience-based outcome. Methods: A quantitative approach using Partial Least Squares Structural Equation Modeling (PLS-SEM) tested the hypotheses with data from 125 cybersecurity decision-makers in Indonesian data centers. PLS-SEM was chosen for its predictive modeling capabilities and ability to handle interaction effects. Results: Findings show DRC significantly influences SCT (beta = 0.499, p < 0.001), and SCT strongly enhances CRA (beta = 0.735, p < 0.001). ORRH negatively moderates the DRC-SCT relationship (beta = -0.120), indicating that excessive routinization can weaken adaptive transformation. The model explains 30.5 percent of the variance in SCT and 54.0 percent in CRA. Conclusion: This study highlights that DRC strengthens SCT, which enhances CRA in Indonesian data centers. The non-significant moderating effect of ORRH suggests formal governance mechanisms may counter routine reactivity, offering insights for CIOs and risk managers in fostering resilience-oriented transformation.

Pengaruh Strategi Marketing Mix Pada Keputusan Pembelian Konsumen Emy Spa Bali dengan e-WOM Sebagai Moderasi Lintang Harianti; Endi Sarwoko; Trianggoro Wiradinata
Journal of Accounting and Finance Management Vol. 6 No. 5 (2025): Journal of Accounting and Finance Management (November - December 2025)
Publisher : DINASTI RESEARCH

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.38035/jafm.v6i5.2815

Penelitian ini bertujuan untuk menganalisis pengaruh strategi marketing mix terhadap keputusan pembelian konsumen Emy Spa Bali, dengan electronic word of mouth (e-WOM) sebagai variabel moderasi. Emy Spa merupakan salah satu usaha spa di kawasan Kuta, Bali yang mengalami penurunan jumlah pelanggan secara signifikan meskipun telah melakukan berbagai strategi pemasaran, termasuk relokasi ke area wisata dan promosi digital. Penelitian ini menggunakan pendekatan kuantitatif dengan metode survei dan penyebaran kuesioner kepada responden yang terdiri dari pelanggan Emy Spa. Variabel bebas dalam penelitian ini terdiri dari tujuh elemen marketing mix yaitu produk, harga, tempat, promosi, orang, proses, bukti fisik. Variabel terikatnya adalah keputusan pembelian, sedangkan e-WOM berperan sebagai variabel moderasi. Hasil penelitian diharapkan mampu memberikan pemahaman mengenai pengaruh elemen-elemen bauran pemasaran terhadap keputusan pembelian serta bagaimana ulasan digital dapat memperkuat pengaruh tersebut. Penelitian ini memberikan kontribusi dalam pengembangan strategi pemasaran berbasis digital khususnya pada industri spa di Bali.

Increasing Purchase Intention in Digital Marketing for the Premium Real Estate Industry: The Role of Brand Image, Product Quality, Service Quality, and Customer Satisfaction Sausan Nabilla Malich; Tony Antonio; Trianggoro Wiradinata
Journal Research of Social Science, Economics, and Management Vol. 5 No. 12 (2026): Journal Research of Social Science, Economics, and Management
Publisher : Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59141/jrssem.v5i12.1609

This study analyzes the influence of digital marketing on purchase intention in the premium property industry, examining the mediating role of brand image, perceived product quality, perceived service quality, and customer satisfaction. Using an explanatory quantitative approach, data were collected via an online questionnaire from 110 prospective Developer C consumers categorized as qualified leads who had completed a site visit. Saturated (total) sampling was applied, and data were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) in SmartPLS 4.0. Results show that digital marketing has a positive and significant effect on brand image, perceived product quality, and perceived service quality in the pre-purchase phase. However, in shaping customer satisfaction, only perceived product quality emerges as the dominant functional determinant and the sole significant mediator; brand image and perceived service quality fail to mediate the relationship, as premium property consumers evaluate them merely as minimum hygiene factors. Customer satisfaction, in turn, significantly mediates the relationship between perceived product quality and purchase intention, but not the paths from brand image or service quality. These findings confirm that in high-involvement industries, purchase intention is driven by functional satisfaction with the product's physical quality rather than emotional preference for the developer's reputation. Developer C management is therefore advised to prioritize product quality control and maintain consistency between digital information visualization and physical reality during site visits to minimize gaps in consumer expectations.

Determinants of retired customers' interest in using M-Banking at Bank Mandiri taspen with UTAUT Adelia Tasya Nabila; Trianggoro Wiradinata; Metta Padmalia
Jurnal Mantik Vol. 10 No. 1 (2026): May : Manajemen, Teknologi Informatika dan Komunikasi (Mantik)
Publisher : Institute of Computer Science (IOCS)

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

This study investigates the determinants of retired customers’ intention to use Movin mobile banking at Bank Mandiri Taspen by applying the Unified Theory of Acceptance and Use of Technology (UTAUT) and examining the role of trust as a mediating variable. Using survey data from 100 retired users and analyzing the data with PLS-SEM, the study finds that performance expectancy and facilitating conditions are the primary drivers of usage intention. In contrast, effort expectancy and social influence do not significantly influence intention. Interestingly, trust does not directly affect intention and does not mediate the relationship between UTAUT constructs and intention to use. These findings suggest that, among retirees, adoption decisions are shaped more by tangible benefits and practical support than by psychological trust mechanisms. The study contributes to the refinement of UTAUT in elderly contexts and offers strategic recommendations for designing user-centered digital banking services tailored to retirees

BERT-base, DistilBERT, and BERTweet: A COMPARATIVE STUDY ON APP REVIEW Jesslyn Levana Halim; Trianggoro Wiradinata
JURNAL INFORMATIKA DAN KOMPUTER Vol 10, No 1 (2026): February 2026
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat - Universitas Teknologi Digital Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26798/jiko.v10i1.2217

User reviews for the Instagram application represent a vast and unstructured data source, offering valuable insights into public sentiment. However, their sheer volume and informal linguistic nature necessitate efficient and accurate automated analysis. This study conducts a comprehensive comparative analysis of three prominent Transformer-based models: BERT-base-uncased, DistilBERT, and BERTweet, to determine the most optimal architecture for sentiment classification of these reviews. Utilizing a dataset of 30,000 preprocessed reviews sourced from Kaggle, the models were fine-tuned for a binary classification task (positive/negative). Performance was systematically evaluated using accuracy, precision, recall, and F1-Score. The experimental results reveal a highly competitive performance landscape. BERT-base-uncased achieved the highest F1-Score (0.8475), establishing it as the best choice for balanced performance. Conversely, BERTweet, pre-trained on social media text, excelled in precision (0.8619), making it superior for reliable positive predictions. Meanwhile, DistilBERT demonstrated its value by offering a compelling balance of high performance (F1-Score 0.8373) and significant computational efficiency. This research concludes that the selection of an optimal model is not absolute but is contingent on specific application requirements, such as the priority of balanced accuracy, predictive reliability, or resource efficiency.

Rapid Ecosystem-Driven Deep Learning: On-Device Grain Type Classification and Authentication using iOS Swift and Core ML Trianggoro Wiradinata
Journal of Applied Data Sciences Vol 7, No 3: September 2026
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v7i3.1414

The 2025 Indonesian rice scandal highlighted major shortfalls in food security and the pressing need for robust, data-based verification of authenticity. The goal of this work is to design a fast, lightweight, and fully offline rice grain classification and verification system that can run directly on consumer mobile hardware. The basic idea to overcome the technical bottleneck of deploying complex computer vision models on edge devices is to use macOS and Apple’s unified ecosystem as a rapid prototyping and deployment platform. The deliberate avoidance of fragmented and high-latency workflows such as external environments (e.g. TensorFlow or PyTorch) and intermediate formats (e.g. ONNX) is mentioned. The study contributes a streamlined pipeline that incorporates an Image Feature Print V1 feature extractor, natively trained with Create ML on a publicly available dataset of 75,000 balanced images of five rice varieties (Arborio, Basmati, Ipsala, Jasmine, and Karacadag), directly into a native iOS application built with SwiftUI. The novelty of this approach is the usage of native tools like VisionKit and Core ML, which enables the complete elimination of third-party bridging code that normally bloats the binary overhead. The results show excellent edge efficiency on an iPhone 15 with a median prediction rate of 2.75 ms, an initial load time of 0.54 ms and a compilation latency of 3.66 ms. Moreover, the results reveal that by employing aggressive data augmentations, including the addition of visual noise, blur, exposure adjustment, flipping and rotation to ensure robustness, and by deliberately not cropping to preserve absolute grain dimensions, the model achieved a remarkable overall accuracy of 97% with an ultra-compact deployment footprint of only 66 KB. These metrics demonstrate that a fast, fully offline and privacy-preserving verification system is well within reach with modern consumer hardware.

Enhancing Online Batik Shopping Experience through Live Streaming Commerce and the LYFY Application Trianggoro Wiradinata; Wilbert Bryan Wibowo; Yustus Eko Oktian; Indra Maryati; Yosua Setyawan Soekamto
Journal of Applied Data Sciences Vol 6, No 1: JANUARY 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i1.504

Online batik shopping often results in buyer dissatisfaction due to discrepancies between product descriptions and the actual items received. Static images and text on e-marketplace platforms are insufficient to convey the intricate details of batik designs, leading to mismatches in customer expectations. To mitigate this issue, Live Streaming Commerce (LSC) features, such as those on Shopee Live, allow sellers to showcase products in real-time, providing more accurate representations. However, sellers face challenges in managing overwhelming volume of comments during live streams, making it difficult to prioritize important queries. LYFY, a comment management app developed to streamline these interactions, aims to address this problem by improving the quality of interaction between live streamers and prospective buyers through filtering important comments. This study examines the determinants affecting the adoption of LYFY by online batik vendors. The research integrates the Task-Technology Fit (TTF), Technology Acceptance Model (TAM), and Expectation-Confirmation Model (ECM) frameworks to evaluate LYFY's performance in fulfilling user requirements. Data were collected from 243 respondents with LSC experience, and the research model underwent evaluation through Partial Least Squares Structural Equation Modeling (PLS-SEM). The measurement model exhibited high reliability and validity, with values surpassing the suggested thresholds, thereby providing solid support for subsequent analysis. Key factors such as TTF, confirmation, perceived usefulness, ease of use, and satisfaction were examined to determine their impact on user adoption. The analysis revealed that TTF has the strongest influence on confirmation, perceived usefulness, satisfaction, and individual performance. Additionally, perceived ease of use and confirmation substantially influence continuance intentions and satisfaction. These results suggest that enhancing LYFY's task-technology fit and simplifying its user interface are crucial for improving user satisfaction and adoption. By addressing these areas, LYFY can better support live stream sellers, reduce product expectation discrepancies, and improve overall customer experience, particularly in the online batik market.

Online Measuring Feature for Batik Size Prediction using Mobile Device: A Potential Application for a Novelty Technology Trianggoro Wiradinata; Theresia Ratih Dewi Saputri; Richard Evan Sutanto; Yosua Setyawan Soekamto
Journal of Applied Data Sciences Vol 4, No 3: SEPTEMBER 2023
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v4i3.121

The garment industry, particularly the batik sector, has experienced significant growth in Indonesia, coinciding with a rise in the number of online consumers who purchase batik products through e-Marketplaces. The observed upward trend in growth has seemingly presented certain obstacles, particularly in relation to product alignment and product information dissemination. Typically, batik entrepreneurs originate from micro, small, and medium enterprises (MSMEs) that have not adhered to global norms. Consequently, the sizes of batik products offered for sale sometimes exhibit inconsistencies. The issue of size discrepancies poses challenges for online consumers seeking to purchase batik products through e-commerce platforms. An effective approach to address this issue involves employing a smartphone camera to anticipate body proportions, specifically the length and width of those engaged in online shopping. Subsequently, by the utilization of machine learning techniques, the optimal batik size can be determined. The UKURIN feature was created as a response to a specific requirement. However, it is essential to establish a methodology for investigating the elements that impact the intention to use this feature. This will enable developers to prioritize their feature development strategies more effectively. A total of 179 participants completed an online questionnaire, and subsequent analysis was conducted utilizing the Extended Technology Acceptance Model framework. The findings indicate that Perceived Usefulness emerged as the most influential factor. Consequently, when designing and developing the novelty feature of UKURIN, it is imperative for designers and application developers to prioritize the benefits aspect.

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