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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 89 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) Vol. 8 No. 1 (2026)
Publisher : Primakara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59356/smart-techno.v8i1.182

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) Vol. 8 No. 1 (2026)
Publisher : Primakara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59356/smart-techno.v8i1.184

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) Vol. 8 No. 1 (2026)
Publisher : Primakara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59356/smart-techno.v8i1.190

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) Vol. 8 No. 1 (2026)
Publisher : Primakara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59356/smart-techno.v8i1.195

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.
Design And Development of An Internet of Things-based Smartbell Using ESP32-Cam And Telegram Rakhmawati, Puji Utami; Syahnaryanti, Dinda Mareta; Rizdania; Sumantri
Smart Techno (Smart Technology, Informatics and Technopreneurship) Vol. 8 No. 1 (2026)
Publisher : Primakara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59356/smart-techno.v8i1.191

Abstract

When guests or couriers arrive, homeowners must go to the front door to identify them due to the limitations of conventional doorbells, which only produce sound. If the homeowner is not at home, they may miss the arrival of guests, and couriers might leave packages elsewhere, posing a risk of loss since there is no real-time notification system. To address this issue, a Smartbell based on the Internet of Things (IoT) was designed using the ESP32-CAM module as an image capture device, integrated with Telegram to provide homeowners with real-time visual information. This study applied the prototype method, which consists of stages such as requirements identification, system design, coding, functionality and time testing, as well as system evaluation before implementation. The test results show that the Smartbell successfully performed as expected. In the simulation, when the bell button was pressed, the buzzer sounded, and the ESP32-CAM camera automatically captured an image and sent it to Telegram in real-time. Since the Smartbell was successfully connected to the Telegram bot, it can be operated remotely. Testing with a Wi-Fi network resulted in an average response time of 0.74 seconds, while using a cellular data network achieved 0.54 seconds. With a response time of less than one second from the integration of ESP32-CAM and Telegram, this system supports the homeowner’s needs as a remote and real-time guest monitoring solution.
Analysis of the Effect of Spectral Feature Dimensionality on Audio Classification Performance Fratiwi, Tria Hikmah; Yuningsih, Lilis
Smart Techno (Smart Technology, Informatics and Technopreneurship) Vol. 8 No. 1 (2026)
Publisher : Primakara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59356/smart-techno.v8i1.196

Abstract

This study examines the impact of spectral feature quantity on the classification performance of dangdut music sub-genres, namely classical dangdut, dangdut rock, and dangdut koplo. Previous studies reported relatively low classification accuracy, which is presumed to be influenced by spectral features with small numerical values and dense feature distributions. To address this issue, two feature configurations were evaluated six and five spectral features using the K-Nearest Neighbor (KNN) algorithm and a Genetic Algorithm-optimized KNN (GA- KNN). Model performance was assessed using accuracy, precision, recall, and F1-score, supported by confusion matrix analysis. The results show that the six-feature configuration consistently outperforms the five- feature configuration for both methods. GA-KNN achieved the best performance with six spectral features, yielding an accuracy of 71.53%, precision of 0.7147, recall of 0.7153, and an F1-score of 0.7140, outperforming conventional KNN, which achieved an accuracy of 62.50% and an F1-score of 0.6135. When reduced to five spectral features, performance declined for both methods; GA-KNN reached an accuracy of 66.67% with an F1-score of 0.6611, while conventional KNN dropped to 52.08% accuracy with an F1- score of 0.5121, accompanied by increased misclassification between sub-genres with similar spectral and rhythmic characteristics. These findings indicate that spectral features with small numerical values still contribute meaningful discriminative information and should be carefully evaluated before applying feature reduction in music genre classification tasks.
Sentiment Analysis of YouTube Comments for the Jumbo Movie Trailer Using IndoBERT Zamakhsyari, Fardan; Suhana, Rizka; Ramadhani, Irfan; Priyo Santoso, Dwi
Smart Techno (Smart Technology, Informatics and Technopreneurship) Vol. 8 No. 1 (2026)
Publisher : Primakara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59356/smart-techno.v8i1.198

Abstract

The film industry in Indonesia has experienced significant growth, from cinematography to animation. Along with this growth, public opinion has also varied, from assessments of the storyline to the production process. To assess public sentiment on social media, a system is needed that can accommodate this process. This study aims to analyse public sentiment towards the trailer for the animated film ‘Jumbo,’ which was released on the YouTube platform. Using an NLP approach, two fine-tuned IndoBERT models were compared: ‘Aardiiiiy/indobertweet-base-Indonesian-sentiment-analysis’ and ‘rikidharmawan/finetuning-sentiment-model-indobertweet-v2’. The data to be processed was obtained from 1,468 YouTube comments through a crawling process using the YouTube API. The data was then analysed using both models to classify the comments into positive, neutral, and negative sentiments. Evaluation was conducted using a confusion matrix with accuracy, precision, recall, and F1-score metrics. The evaluation results show that ‘Aardiiiiy/indobertweet-base-Indonesian-sentiment-analysis’ is superior, with an accuracy of 57.2% and a higher average F1-score compared to ‘rikidharmawan/finetuning-sentiment-model-indobertweet-v2,’ which has an accuracy of 51.3%. This research contributes to the selection of sentiment analysis models for Indonesian-language data, particularly in the domains of social media and the film industry.
An Explainable Deep Learning for Malaria Blood Cell Classification Using DenseNet121 and Grad-CAM Octavian; Widjaja, Imelda; Amir, Supri
Smart Techno (Smart Technology, Informatics and Technopreneurship) Vol. 8 No. 1 (2026)
Publisher : Primakara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59356/smart-techno.v8i1.199

Abstract

Malaria diagnosis based on microscopic examination of blood smears is time-consuming and highly dependent on skilled laboratory personnel, which limits its scalability in resource-constrained environments. This study investigated whether an explainable deep learning approach could provide reliable and interpretable malaria blood cell classification using a convolutional neural network based on the DenseNet121 architecture combined with Gradient-weighted Class Activation Mapping to visualize the image regions influencing model predictions. Five-fold cross-validation was applied to ensure a stable and unbiased performance evaluation. The model achieved a mean classification accuracy of 0.8285 with low variation across folds, and the precision, recall, and F1-score values were balanced between the parasitized and uninfected classes. Visual explanations consistently highlighted intracellular regions associated with parasite presence in infected cells and more uniform cytoplasmic regions in uninfected samples, indicating that the network learned the biologically meaningful features of the cells. The results demonstrated that DenseNet121 provided a stable and interpretable solution for malaria blood cell classification when supported by a visual explanation, thereby enabling transparent automated screening. The proposed framework is suitable for integration into smart healthcare and medical informatics systems, where both predictive reliability and interpretability are required.
Implementation of a Web-Based Boarding House and Rental Search and Booking Information System Using the Waterfall Method Peri Irawan, Peri; Sembiring, Falentino; Hidayat, Rahmat
Smart Techno (Smart Technology, Informatics and Technopreneurship) Article in Press
Publisher : Primakara University

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

Abstract

The rapid development of information technology has increased the need for a more efficient and structured housing search system for students. This study aims to develop an integrated web-based information system for searching and booking boarding houses and rental properties in accordance with user requirements. The development method employed is the Waterfall model, which consists of the stages of Requirement Analysis, System Design, Implementation, and Testing. Data were collected through observations of Facebook social media, questionnaires distributed to 81 students, and interviews with 10 boarding house owners. The system was developed using PHP with the Laravel framework and a MySQL database. System testing was conducted using Black-box Testing and User Acceptance Testing (UAT) involving 20 respondents, resulting in a user satisfaction rate of 87.4%. The findings indicate that the developed system enhances the efficiency of housing searches and facilitates the online booking process. With a structured approach grounded in empirical needs, the system is considered effective and feasible as a web-based solution for searching and booking boarding houses.

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