Abstract:Abstract: This research is motivated by the problem of building material inventory management at Jaqfar Building Store, which is still done manually and based on subjective estimates. This often results in inaccuracies in…
n determining stock levels, either in the form of overstock or understock, which hinders operational effectiveness. The purpose of this study is to apply the Multiple Linear Regression method to analyze the relationship between incoming stock (X1) and outgoing stock (X2) variables with the ending stock variable (Y) to produce an optimal inventory prediction model. The research methodology used includes collecting historical transaction data for building materials such as cement, ceramics, zinc, plywood, and iron. This web-based prediction system was developed using the PHP programming language and a MySQL database. The analysis results show that the resulting regression model can provide a mathematical picture of future inventory patterns based on historical data. Implementation of this system is expected to assist the management of Jaqfar Building Materials Store in making strategic decisions regarding purchasing and sales in a more measured and efficient manner.
Keyword: building materials; data mining; inventory; multiple linear regression
Abstrak: Penelitian ini dilatarbelakangi oleh permasalahan pengelolaan persediaan bahan bangunan di Toko Bangunan Jaqfar yang masih dilakukan secara manual dan berdasarkan perkiraan subjektif. Hal ini menyebabkan sering terjadinya ketidaktepatan dalam menentukan jumlah stok, baik berupa kelebihan barang (overstock) maupun kekurangan barang (understock) yang menghambat efektivitas operasional. Tujuan dari penelitian ini adalah menerapkan metode Multiple Linear Regression (Regresi Linear Berganda) untuk menganalisis hubungan antara variabel stok masuk (X1) dan stok keluar (X2) terhadap variabel stok akhir (Y) guna menghasilkan model prediksi persediaan yang optimal. Metodologi penelitian yang digunakan mencakup pengumpulan data historis transaksi bahan bangunan seperti semen, keramik, seng, triplek, dan besi. Sistem prediksi ini dikembangkan berbasis web menggunakan bahasa pemrograman PHP dan basis data MySQL. Hasil analisis menunjukkan bahwa model regresi yang dihasilkan mampu memberikan gambaran matematis mengenai pola persediaan di masa mendatang berdasarkan data historis. Implementasi sistem ini diharapkan dapat membantu manajemen Toko Bangunan Jaqfar dalam mengambil keputusan strategis terkait pembelian dan penjualan secara lebih terukur serta efisien.
Kata kunci: bahan bangunan; data mining; persediaan; regresi linear berganda
Abstract:YouTube has become a major platform for public discourse in Indonesia, yet large-scale sentiment analysis of its comments remains challenging due to dynamic content, informal language, and limited labeled data. This study…
y proposes a Selenium–IndoBERT pipeline for sentiment analysis of Indonesian YouTube comments using a pseudo-labeling approach. Data were collected from ten YouTube videos discussing the One Piece flag phenomenon, yielding 10,842 comments after preprocessing. Selenium was employed to extract comments from dynamic pages, while IndoBERT was fine-tuned on a small manually labeled dataset and used to generate pseudo-labels for unlabeled data. Model performance was evaluated using probabilistic metrics, including Coverage, Expected Calibration Error (ECE), and Brier Score. At a confidence threshold of 0.75, 78.5% of comments received pseudo-labels, with an ECE of 0.095 and a Brier Score of 0.174. Manual validation showed substantial agreement with human annotations (Fleiss’ kappa = 0.72). The results indicate that the proposed pipeline enables scalable and reliable sentiment analysis with minimal manual annotation.
Abstract:Abstract: Human metapneumovirus (HMPV) poses a global health threat, but its detection remains challenging due to limited environmental monitoring. This study aims to develop a portable diagnostic tool for rapid HMPV detection…
ection by integrating cutting-edge biotechnology (CRISPR-Cas system and immunoassay) with air quality sensors on an Internet of Things (IoT)-based microfluidic platform controlled by an ESP32 microcontroller. The system is supported by a companion application and data analysis using Vertex AI, and is capable of providing results in less than fifteen minutes. The development results demonstrate the potential for improving detection accuracy and reliability, particularly with further development of virus-specific biosensors, sensor optimization, and algorithms. This technology is effective as a complementary tool for early screening and environment-based risk management in areas with limited laboratory facilities, although it does not completely replace molecular diagnostic methods such as PCR. A rapid diagnostic approach based on environmental sensors, IoT, and artificial intelligence is a promising strategy to improve early HMPV detection, accelerate public health responses, and strengthen respiratory infection prevention through integrated environmental monitoring and education functions.
Keywords: air quality; CRISPR-Cas; Human Metapneumovirus (HMPV); Internet of Things, portable diagnostic; public health; rapid detection; sensors.
Abstrak: Human metapneumovirus (HMPV) merupakan ancaman bagi kesehatan global, namun pendeteksiannya masih sulit akibat keterbatasan pemantauan lingkungan. Studi ini bertujuan mengembangkan alat diagnostik portabel untuk deteksi cepat HMPV melalui integrasi bioteknologi mutakhir (sistem CRISPR-Cas dan immunoassay) dengan sensor kualitas udara pada platform mikrofluida berbasis Internet of Things (IoT) yang dikendalikan mikrokontroler ESP32. Sistem ini didukung aplikasi pendamping dan analisis data menggunakan Vertex AI, serta mampu memberikan hasil dalam waktu kurang dari lima belas menit. Hasil pengembangan menunjukkan potensi peningkatan akurasi dan keandalan deteksi, terutama dengan pengembangan lanjutan berupa biosensor spesifik virus, optimalisasi sensor, dan algoritma. Teknologi ini efektif sebagai alat pelengkap untuk skrining awal dan manajemen risiko berbasis lingkungan di wilayah dengan keterbatasan fasilitas laboratorium, meskipun tidak sepenuhnya menggantikan metode diagnostik molekuler seperti PCR. Pendekatan diagnostik cepat berbasis sensor lingkungan, IoT, dan kecerdasan buatan menjadi strategi menjanjikan untuk meningkatkan deteksi dini HMPV, mempercepat respons kesehatan masyarakat, serta memperkuat pencegahan infeksi saluran pernapasan melalui fungsi pemantauan dan edukasi lingkungan yang terintegrasi.
Kata kunci: CRISPR-Cas; diagnostik portabel; deteksi cepat; Human Metapneumovirus (HMPV); kesehatan masyarakat; IoT (Internet of Things); sensor kualitas udara.
Abstract:Abstract: Malaria remains a major global health problem, particularly in tropical countries such as Indonesia. Accurate early diagnosis is essential for reducing malaria-related morbidity and mortality. Conventional microscopic…
oscopic examination is time-consuming, highly dependent on expert personnel, and prone to human error. This study compares the performance of two Convolutional Neural Network (CNN) architectures, ResNet-50 and DenseNet-121, for malaria image classification. The Cell Images for Malaria dataset provided by the National Institutes of Health (NIH) through Kaggle was used, consisting of 27,558 microscopic blood cell images categorized into Parasitized and Uninfected classes. The dataset was divided into 80% training data and 20% testing data. Image preprocessing included resizing to 224 × 224 pixels, normalization, labeling, and data augmentation using RandomFlip, RandomRotation, RandomZoom, and RandomContrast. Experimental results showed that the ResNet-50 model trained for 100 epochs achieved the highest performance, with an accuracy of 95.54% and precision, recall, and F1-score of 0.96. The confusion matrix indicated 5,272 correctly classified images out of 5,510 testing samples. These findings demonstrate that ResNet-50 outperformed DenseNet-121 and has strong potential for supporting accurate, reliable, and efficient computer-aided malaria diagnosis based on microscopic blood smear images.
Keywords: computer-aided diagnosis; convolutional neural network (CNN); densenet-121; early detection; image classification; malaria; microscopic blood smear images; resnet-50;
Abstrak : Malaria masih menjadi masalah kesehatan global yang serius, terutama di negara tropis seperti Indonesia. Diagnosis dini yang akurat sangat penting untuk menurunkan angka morbiditas dan mortalitas. Metode konvensional berupa pemeriksaan mikroskopis memiliki keterbatasan karena memerlukan waktu yang relatif lama, bergantung pada tenaga ahli, dan berpotensi menimbulkan kesalahan manusia. Penelitian ini bertujuan membandingkan kinerja arsitektur Convolutional Neural Network (CNN) yaitu ResNet-50 dan DenseNet-121 dalam klasifikasi citra malaria. Dataset yang digunakan berasal dari Cell Images for Malaria yang disediakan oleh National Institutes of Health (NIH) melalui platform Kaggle, terdiri dari 27.558 citra dengan pembagian 80% data latih, 20% data validasi. Tahap praproses meliputi cleaning, resizing citra menjadi 224×224 piksel, normalisasi, labeling, serta data augmentasi menggunakan RandomFlip, RandomRotation, RandomZoom, dan RandomContrast. Hasil pengujian menunjukkan bahwa model ResNet-50 pada epoch 100 memperoleh akurasi sebesar 95,54% dengan nilai precision, recall, dan F1-score masing-masing sebesar 0,96. Confusion matrix menunjukkan jumlah prediksi benar sebanyak 5.272 dari total 5.510 data uji. Hasil ini menunjukkan bahwa arsitektur CNN mampu mengklasifikasikan citra malaria dengan tingkat akurasi yang tinggi dan memiliki kemampuan generalisasi yang baik terhadap data baru. Penelitian ini memberikan kontribusi dalam evaluasi performa arsitektur CNN untuk mendukung pengembangan sistem diagnosis malaria berbasis citra mikroskopis yang lebih cepat dan akurat.
Kata kunci: convolutional neural network (CNN); citra mikroskopis hapusan darah; densenet-121; diagnosis berbantuan komputer; deteksi dini; klasifikasi citra; malaria; resnet-50
Abstract:This study aims to apply the Analytic Network Process (ANP) method as a decision support tool in determining the eligibility of education grant recipients in North Sumatra Province. The background of this research arises…
from the large number of grant applicants compared to the available budget, as well as the absence of clear and objective evaluation standards. The ANP method was chosen because it allows the interdependence between assessment criteria such as institutional feasibility, performance and achievement, social and educational impact, and accountability and transparency to be analyzed comprehensively. Data were obtained through interviews, documentation, and observation at the North Sumatra Provincial Education Office. The results of the ANP model show that the criterion with the highest weight is accountability and transparency (0.44), followed by social and educational impact (0.31). Among the three alternatives, community-based education foundations (A2) obtained the highest total weight (0.30), indicating that they are the most eligible recipients of education grants. The implementation of the ANP-based decision support system produces valid and consistent ranking results (CR < 0.1), enabling faster, fairer, and more transparent decision-making. Therefore, the ANP method contributes significantly to improving governance, objectivity, and accountability in the distribution of education grants in North Sumatra Province.
Abstract:Abstract: Accurate bone age estimation is essential for monitoring pediatric growth, diagnosing endocrine disorders, and supporting clinical decision-making. Although deep learning has improved prediction accuracy, limited…
ed studies have systematically examined how increasing model depth affects performance and reliability. This study evaluates the effectiveness of progressively deeper convolutional neural networks, specifically EfficientNet variants B0 to B5, for bone age estimation from hand radiographs. Experiments were conducted using 12,611 hand X-ray images from the RSNA Pediatric Bone Age Challenge dataset on Kaggle. To ensure fair comparison, all models were trained using a unified and consistent training pipeline. Model performance was evaluated using Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), Concordance Correlation Coefficient (CCC), and Pearson correlation coefficient. The results show a consistent improvement in prediction accuracy as model depth increases. Among the evaluated models, EfficientNet-B5 achieved the best performance, with an MAE of 21.5 months, MAPE of 6.23%, CCC of 0.9148, and Pearson’s r of 0.9203. These findings confirm that model scaling plays a critical role in enhancing prediction robustness and clinical reliability. Future work should emphasize external validation across diverse populations and incorporate interpretability techniques, such as Grad-CAM, to improve clinical transparency and trust.
Keywords: bone age prediction; deep learning; model evaluation; clinical validation
Abstrak: Estimasi usia tulang yang akurat sangat penting untuk memantau pertumbuhan anak, mendiagnosis gangguan endokrin, dan mendukung pengambilan keputusan klinis. Meskipun pembelajaran mendalam telah meningkatkan akurasi prediksi, studi yang secara sistematis meneliti bagaimana peningkatan kedalaman model memengaruhi kinerja dan keandalan masih terbatas. Studi ini mengevaluasi efektivitas jaringan saraf konvolusional yang semakin dalam, khususnya varian EfficientNet B0 hingga B5, untuk estimasi usia tulang dari radiografi tangan. Eksperimen dilakukan menggunakan 12.611 gambar sinar-X tangan dari dataset RSNA Pediatric Bone Age Challenge di Kaggle. Untuk memastikan perbandingan yang adil, semua model dilatih menggunakan alur pelatihan yang terpadu dan konsisten. Kinerja model dievaluasi menggunakan Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), Concordance Correlation Coefficient (CCC), dan koefisien korelasi Pearson. Hasil menunjukkan peningkatan yang konsisten dalam akurasi prediksi seiring dengan peningkatan kedalaman model. Di antara model yang dievaluasi, EfficientNet-B5 mencapai kinerja terbaik, dengan MAE sebesar 21,5 bulan, MAPE sebesar 6,23%, CCC sebesar 0,9148, dan Pearson’s r sebesar 0,9203. Temuan ini menegaskan bahwa penskalaan model memainkan peran penting dalam meningkatkan optimasi prediksi dan keandalan klinis. Penelitian selanjutnya dapat menekankan validasi eksternal di berbagai populasi dan menggabungkan teknik interpretasi, seperti Grad-CAM, untuk meningkatkan transparansi dan kepercayaan klinis.
Kata kunci: prediksi usia tulang; deep learning; evaluasi model; validasi klinis
Abstract:Abstract: This study discusses the optimization of RAG for a FAQ system in the field of information technology product security certification at BSSN. Although LLM generate reliable responses, they often lack up-to-date…
and domain-specific knowledge, which can be addressed through the RAG approach. This research aims to optimize a domain-specific RAG system by improving embedding performance, enhancing prompt robustness, and increasing retrieval accuracy. The research methods consist of three stages. The first stage involves fine-tuning the bge-m3 embedding model and evaluating its performance using MRR, Recall, and AUC. The second stage applies prompt engineering techniques, namely the SRSM and Autodefense, to mitigate direct-injection and escape-character prompt injection attacks. The third stage evaluates the proposed RAG system using Precision, Recall, and F1-Score metrics against four baseline models. The results of research show that the fine-tuned embedding model achieves higher performance than the original model, with MRR@1 and Recall@1 values of 0.80 and an AUC@100 of 0.7023. In addition, the proposed prompt engineering techniques demonstrate robustness against prompt injection attacks, while the overall RAG system attains a perfect Precision, Recall, and F1-Score of 1.00. In conclusion, the proposed approach effectively enhances retrieval accuracy, embedding quality, and system security, resulting in a more reliable RAG-based FAQ system for information technology product security certification.
Keywords: embedding fine-tuning; large language model; prompt engineering; prompt injection mitigation; retrieval-augmented generation
Abstrak: Studi ini membahas optimasi RAG untuk sistem FAQ di bidang sertifikasi keamanan produk teknologi informasi di BSSN. Meskipun LLM menghasilkan respons yang andal, mereka seringkali kurang memiliki pengetahuan terkini dan spesifik domain, yang dapat diatasi melalui pendekatan RAG. Penelitian ini bertujuan untuk mengoptimalkan sistem RAG spesifik domain dengan meningkatkan kinerja embedding, meningkatkan ketahanan prompt dan meningkatkan akurasi pengambilan. Metode penelitian terdiri dari tiga tahap. Tahap pertama melibatkan fine-tuning model embedding bge-m3 dan mengevaluasi kinerjanya menggunakan Mean Reciprocal Rank (MRR), Recall, dan AUC. Tahap kedua menerapkan teknik rekayasa prompt, yaitu Self- SRSM dan Autodefense, untuk mengurangi serangan direct-injection dan escape-character prompt injection. Tahap ketiga mengevaluasi sistem RAG yang diusulkan menggunakan metrik Presisi, Recall, dan F1-Score terhadap empat model dasar. Hasil penelitian menunjukkan bahwa model embedding yang disempurnakan mencapai kinerja yang lebih tinggi daripada model asli, dengan nilai MRR@1 dan Recall@1 sebesar 0,80 dan AUC@100 sebesar 0,7023. Selain itu, teknik rekayasa prompt yang diusulkan menunjukkan ketahanan terhadap serangan injeksi prompt, sementara sistem RAG secara keseluruhan mencapai Presisi, Recall, dan F1-Score sempurna sebesar 1,00. Kesimpulannya, pendekatan yang diusulkan secara efektif meningkatkan akurasi pengambilan, kualitas embedding dan keamanan sistem, menghasilkan sistem FAQ berbasis RAG yang lebih andal untuk sertifikasi keamanan produk teknologi informasi.
Kata kunci: penyempurnaan embedding; model bahasa besar; rekayasa prompt; mitigasi injeksi prompt; retrieval-augmented generation
Abstract:Abstract: The determination of thesis topics is a fundamental stage in academic research, yet the evaluation process remains predominantly manual and subjective. This reliance on individual lecturer perception often leads…
s to inconsistent feasibility assessments and fails to systematically measure the topic's alignment with strategic needs. This research aims to develop a Decision Support System (DSS) model based on fuzzy logic to assess the feasibility of thesis topics objectively and systematically, focusing on enhancing the relevance of student research. The research method employed the Fuzzy Inference System (FIS) with the Sugeno method. This model was designed through literature review and FGD to establish four criteria (Topic Relevance, Difficulty Level, Idea Novelty, Reference Availability) and 81 rule bases. The model validation results against expert judgment using 15 test data showed a high accuracy rate of 91.31%, with a Mean Absolute Percentage Error (MAPE) value of 8.69%. In conclusion, this DSS model is proven to be valid and consistent, and it can be relied upon as an objective tool to improve the quality and relevance of thesis topics.
Keywords: academic evaluation; decision support system; fuzzy logic; fuzzy sugeno; thesis feasibility
Abstrak: Penentuan topik skripsi merupakan tahapan fundamental dalam penelitian akademik, namun proses evaluasinya hingga kini masih cenderung manual dan subjektif. Ketergantungan pada persepsi dosen secara individu sering kali menyebabkan penilaian kelayakan yang tidak konsisten serta kegagalan dalam mengukur keselarasan topik dengan kebutuhan strategis secara sistematis. Penelitian ini bertujuan mengembangkan model Sistem Pendukung Keputusan (SPK) berbasis logika fuzzy untuk menilai kelayakan topik skripsi secara objektif dan sistematis, dengan fokus pada peningkatan relevansi penelitian mahasiswa. Metode penelitian yang digunakan adalah Fuzzy Inference System (FIS) dengan metode Sugeno. Model ini dirancang melalui tinjauan pustaka dan Focus Group Discussion (FGD) untuk menetapkan empat kriteria (Relevansi Topik, Tingkat Kesulitan, Kebaruan Ide, Ketersediaan Referensi) serta 81 basis aturan. Hasil validasi model terhadap penilaian pakar menggunakan 15 data uji menunjukkan tingkat akurasi yang tinggi yaitu 91,31%, dengan nilai Mean Absolute Percentage Error (MAPE) sebesar 8,69%. Kesimpulannya, model SPK ini terbukti valid dan konsisten, serta dapat diandalkan sebagai alat objektif untuk meningkatkan kualitas dan relevansi topik skripsi.
Kata kunci: evaluasi akademik; sistem pendukung keputusan; logika fuzzy; fuzzy sugeno; kelayakan skripsi
Abstract:Abstract: The rapid expansion of digital learning environments has increased students’ reliance on Learning Management Systems (LMS), including SPOT UPI. However, limited studies have examined the platform’s overall user…
user experience across all User Experience Questionnaire (UEQ) dimensions. This study aims to evaluate the user experience (UX) of SPOT UPI, identify its strengths and weaknesses, and provide recommendations for system improvement. A quantitative-dominant mixed-method design was applied, involving 81 student respondents for the UEQ survey and two participants for follow-up semi-structured interviews selected through purposive sampling. The UEQ data were analyzed to generate mean scores for six UX dimensions, while interview data were thematically analyzed to support the interpretation of quantitative findings. The results indicate that Perspicuity (1.05) and Efficiency (0.78) achieved the highest scores, reflecting adequate clarity and functionality. In Contrast, Stimulation (0.50) and Novelty (-0.15) were the lowest, indicating limited engagement and innovation. Overall, pragmatic quality (0.84) outperformed hedonic quality (0.17), suggesting that users value functionality more than enjoyment. In conclusion, SPOT UPI is generally usable but lacks aesthetic appeal, emotional engagement, and innovative features, highlighting the need for interface redesign and performance optimization to enhance the overall learning experience.
Keywords: learning management system; user experience; user experience questionnaire
Abstrak: Perkembangan pembelajaran digital membuat mahasiswa semakin bergantung pada Learning Management System (LMS), termasuk SPOT UPI. Meski digunakan secara luas, evaluasi pengalaman pengguna secara komprehensif berdasarkan seluruh dimensi User Experience Questionnaire (UEQ) masih belum banyak dilakukan. Penelitian ini bertujuan untuk mengevaluasi user experience (UX) pada SPOT UPI, mengidentifikasi keunggulan dan kelemahannya, serta memberikan rekomendasi perbaikan sistem. Penelitian menggunakan desain penelitian mixed-method dominan kuantitatif, melibatkan 81 responden pada survei UEQ dan dua partisipan pada wawancara semi-terstruktur yang dipilih melalui purposive sampling. Data UEQ dianalisis untuk memperoleh nilai rata-rata pada enam dimensi UX, sedangkan data wawancara dianalisis secara tematik untuk memperkaya interpretasi temuan kuantitatif. Hasil menunjukkan bahwa Perspicuity (1,05) dan Efficiency (0,78) menjadi dimensi dengan skor tertinggi, mencerminkan bahwa SPOT UPI mudah dipahami dan cukup membantu dalam menyelesaikan tugas. Sebaliknya, Stimulation (0,50) dan Novelty (-0,15) memperoleh skor terendah, menandakan rendahnya tingkat keterlibatan dan inovasi yang dirasakan pengguna. Secara keseluruhan, pragmatic quality (0,84) lebih tinggi dibandingkan hedonic quality (0,17), menunjukkan bahwa pengguna lebih mengutamakan aspek fungsional daripada kenyamanan emosional. Temuan tersebut mengindikasikan bahwa SPOT UPI sudah layak digunakan secara fungsional, tetapi masih memerlukan peningkatan pada interface, pengalaman visual, dan fitur inovatif agar dapat memberikan pengalaman belajar digital yang lebih menarik dan optimal.
Kata kunci: learning management system; pengalaman pengguna; user experience questionnaire
Abstract:Abstract: Digital banking has brought various innovations in financial services, one of which is Blu Deposito by BCA Digital. However, the adoption rate of digital deposit services is still relatively low compared to digital…
ital payment services. This study aims to identify and analyze the factors that influence customers' intentions and actual behavior in using Blu Deposito with reference to the Technology Acceptance Model (TAM). This study aims to analyze the factors that influence customers' intentions and actual behavior in adopting Blu Deposito using the Technology Acceptance Model (TAM) framework. Data was collected through a Google Form questionnaire from 54 customers at one BCA branch and analyzed using SPSS through validity and reliability tests, descriptive analysis, and multiple regression. The results show that Behavioral Intention (BI)is significantly influenced by Perceived Ease of Use (PEOU), Perceived Usefulness (PU), and Attitude Toward Using (ATU), with PEOU as the most dominant factor. In addition, BI has a significant effect on Actual System Use (AU), which confirms the relevance of applying the TAM model in the context of digital deposit products. These findings indicate that ease of use plays a greater role than financial benefits in encouraging users to adopt Blu Deposits. This study contributes to the understanding of digital deposit adoption and provides managerial insights to improve the usability and user engagement of digital banking services.
Keywords: actual system use; attitude toward using; behavioral intention; perceived ease of use; perceived usefulness; technology acceptance model
Abstrak: Perbankan digital telah menghadirkan berbagai inovasi dalam layanan keuangan, salah satunya Blu Deposito oleh BCA Digital. Meskipun demikian, tingkat adopsi terhadap layanan deposito digital masih relatif rendah dibandingkan dengan layanan pembayaran digital. Penelitian ini bertujuan untuk mengidentifikasi dan menganalisis faktor-faktor yang memengaruhi niat serta perilaku aktual nasabah dalam menggunakan Blu Deposito dengan mengacu pada kerangka Technology Acceptance Model (TAM). Penelitian ini bertujuan untuk menganalisis faktor-faktor yang memengaruhi niat dan perilaku aktual nasabah dalam mengadopsi Blu Deposito dengan menggunakan kerangka Technology Acceptance Model (TAM). Data dikumpulkan melalui kuesioner Google Form dari 54 nasabah di satu cabang BCA dan dianalisis menggunakan SPSS melalui uji validitas, reliabilitas, analisis deskriptif, dan regresi berganda. Hasil penelitian menunjukkan bahwa Behavioral Intention (BI) dipengaruhi secara signifikan oleh Perceived Ease of Use (PEOU), Perceived Usefulness (PU), dan Attitude Toward Using (ATU), dengan PEOU sebagai faktor paling dominan. Selain itu, BI berpengaruh signifikan terhadap Actual System Use (AU), yang menegaskan relevansi penerapan model TAM pada konteks produk deposito digital. Temuan ini menunjukkan bahwa kemudahan penggunaan memiliki peran lebih besar dibandingkan manfaat finansial dalam mendorong pengguna untuk mengadopsi Blu Deposito. Penelitian ini berkontribusi terhadap pemahaman adopsi deposito digital serta memberikan wawasan manajerial untuk meningkatkan kegunaan dan keterlibatan pengguna pada layanan perbankan digital.
Kata kunci: actual system use; attitude toward using; behavioral intention; perceived ease of use; perceived usefulness; technology acceptance model