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Made Adi Paramartha Putra
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Smart Techno (Smart Technology, Informatic and Technopreneurship)
Published by Universitas Primakara
ISSN : -     EISSN : 25410679     DOI : 10.59356
Core Subject : Science,
Computer Science & IT
Jurnal Smart-Techno merupakan jurnal ilmiah dan bersifat terbuka untuk menampung hasil penelitian ilmiah. Jurnal ini bersifat elektronik dengan harapan memungkinkan penyebaran informasi ilmiah tanpa batas ke seluruh wilayan Indonesia. Secara garis besar, Jurnal Smart-Techno menampung hasil karya ilmiah yang berasal dari penelitian di bidang Smart Technology, Informatics and Technopreneurship. Jurnal online ini terbit 2 (dua) kali dalam setahun yaitu pada bulan Februari dan September secara berkala. Adapun topik-topik yang dapat diterbitkan melalui karya ilmiah ini meliputi bidang-bidang (namun tidak terbatas pada): Technopreneurship Digital Start-up Technology Innovation Virtual Reality Data Mining Data Warehousing Matematika Diskrit Teori Graph Artificial Intelligence Natural Language Processing Robotic Image Processing Microcontroller User Experience (UX) Mobile Computing Distributed/Parallel Computing Communication System Network Security Wireless Communication Internet of Things Smart Home Smart City Smart Village Smart System E government E learning
Arjuna Subject : Ilmu Komputer (semua kategori) - Aplikasi Ilmu Komputer
Articles 84 Documents
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Implementation of DeepFace for Gender Prediction Based on Facial Images Wijaya, Aditya; Dwi Langit, Sadam; Musyaffa, Abdurrozzaq
Smart Techno (Smart Technology, Informatics and Technopreneurship) Article in Press
Publisher : Primakara University

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Abstract

This study evaluates the performance of a pretrained DeepFace model for gender classification based on facial images using the UTKFace dataset. A total of 100 facial images were employed as test data, consisting of 50 male and 50 female samples selected through controlled random sampling to maintain class balance. Image preprocessing was conducted automatically using the DeepFace.analyze() function, which includes face detection, alignment, size normalization, and facial cropping. The study did not involve model retraining and relied solely on the inference capability of the pretrained DeepFace model. The experimental results show that the model correctly classified 45 male and 44 female images, achieving accuracies of 90% and 88% for the male and female classes, respectively, with an overall accuracy of 89%. Confusion matrix analysis indicates that misclassifications were primarily influenced by image quality factors such as lighting variations, camera angles, and facial expressions. Overall, the findings demonstrate that DeepFace is effective for gender classification without retraining; however, further improvements in preprocessing techniques and dataset diversity may enhance classification performance in future research.
How Interface Design Nudges Instagram Users Toward Posting Less Permanent Content Driya, Putu Dhanu; Sumerta, Ni Putu Abigail Firsta; Diputra, I Gusti Nyoman Anton Surya
Smart Techno (Smart Technology, Informatics and Technopreneurship) Article in Press
Publisher : Primakara University

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Abstract

This study examines the effect of Instagram interface design nudges on Gen Z users’ preference for ephemeral content over permanent feed posts, and the mediating roles of cognitive biases and self-presentation concerns. A survey of 347 Gen Z users was analyzed using parallel mediation (PROCESS Model 4). Results indicated that interface nudges significantly predicted cognitive biases (b = 0.903, p = 0.047) and self-presentation concerns (b = 0.807, p = 0.039), but neither mediator significantly influenced ephemeral posting (indirect effect M1 = 0.0021, 95% CI [-0.0244, 0.0192]; M2 = 0.0003, 95% CI [-0.0165, 0.0236]). The direct effect of nudges on ephemeral posting was significant (b = 0.060, p = 0.031), indicating that UI design directly encourages temporary content sharing. These findings highlight the dominant role of interface design in guiding user behavior, suggesting that nudges influence ephemeral posting primarily through direct behavioral effects rather than mediated psychological mechanisms.
Implementation of Latent Dirichlet Allocation in a Cookie-Based Final Project Topic Recommendation System Putri, Fiddar Tahwifa; Yanuarti, Rosita; Warisaji, Taufiq Timur
Smart Techno (Smart Technology, Informatics and Technopreneurship) Article in Press
Publisher : Primakara University

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Abstract

The selection of a final project topic is a crucial stage in the academic journey of students, as it determines the direction of research while serving as a means to apply the knowledge acquired during their studies. However, in practice, many students experience difficulties in choosing a topic that aligns with their interests and areas of expertise. This challenge is largely attributed to the absence of systems capable of providing personalized recommendations. To address this issue, this study develops a final project topic recommendation system by integrating the Latent Dirichlet Allocation (LDA) algorithm with a cookie-based approach to accommodate user preferences. The dataset used consists of 200 final project documents from the Informatics Engineering program, with titles and abstracts serving as the primary features for topic modeling during model training and perplexity evaluation. In addition, users’ search histories and relevance feedback stored in cookie sessions are utilized as personalization features to generate more tailored recommendations. FastText is employed to produce more contextual word vector representations, while cosine similarity is applied to measure the closeness between search keywords and final project topic documents. Evaluation results based on perplexity indicate that the model with 22 topics yields the most statistically optimal performance. Furthermore, testing using Click-Through Rate (CTR) demonstrates that the combination of topic modeling and user preference personalization produces the highest relevance, achieving a CTR of 15.6%, which is significantly higher than the baseline CTR of 2.2%. These findings confirm that the proposed system is capable of delivering more relevant, adaptive, and user-oriented final project topic recommendations.
Optimization of the Payment Process at Toko Pertanian Kurnia Manokwari Using Business Process Reengineering and Throughput Efficiency Faizal Qadri Trianto; Suharso, Wildan
Smart Techno (Smart Technology, Informatics and Technopreneurship) Article in Press
Publisher : Primakara University

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Abstract

Rapid advances in digital technology within the agribusiness sector require Micro, Small, and Medium Enterprises (MSMEs) to adapt their operational strategies to remain competitive. This study presents a single-case study conducted at Toko Pertanian Kurnia Manokwari, an agribusiness MSME in West Papua, which experiences inefficiencies in its payment and order fulfillment processes due to reliance on manual bank transfers and centralized owner-based verification. The study aims to optimize the payment process through the application of Business Process Reengineering (BPR) by modeling the existing (As-Is) and redesigned (To-Be) processes using Business Process Model and Notation (BPMN) and evaluating process performance with the ASME Standard Process Chart through throughput efficiency measurement. The analysis identifies centralized verification as a single point of failure that prolongs transaction cycle times. The proposed solution integrates an API-based automated payment gateway to replace manual verification. The results indicate that the As-Is process achieves a throughput efficiency of 35.48% with a total cycle time of 186 minutes, whereas the evaluation of the redesigned To-Be process model indicates a potential increase in throughput efficiency to 100% and a reduction in cycle time to 23 minutes. These findings demonstrate that BPR supported by digital payment system integration, based on To-Be process modeling, can significantly improve transaction efficiency and operational scalability in agribusiness MSMEs.

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