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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 14 Documents
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Search results for , issue "Vol. 8 No. 1 (2026)" : 14 Documents clear
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.

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