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Smatika Jurnal : STIKI Informatika Jurnal
Published by Universitas Bhinneka Nusantara
ISSN : 20870256     EISSN : 25806939     DOI : https://doi.org/10.32664/smatika
Core Subject : Science,
Computer Science & IT
SMATIKA: STIKI Informatika Jurnal is a journal published by Lembaga Penelitian & Pengabdian kepada Masyarakat (LPPM) of Universitas Bhinneka Nusantara Malang. The scope of this journal in the field of Computer Science, Information Systems, and Information Management.
Arjuna Subject : Ilmu Komputer (semua kategori) - Sistem Informasi
Articles 258 Documents
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Implementasi Internet of Things untuk Memonitoring Pengisian dan Penggunaan Air secara Otomatis Yeris Ari Sandi; Heni Sulistiani
SMATIKA JURNAL : STIKI Informatika Jurnal Vol 15 No 01 (2025): SMATIKA Jurnal : STIKI Informatika Jurnal
Publisher : LPPM UBHINUS MALANG

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32664/smatika.v15i01.1477

Abstract

This research aims to address efficiency issues in household water management through the implementation of the Internet of Things (IoT) on an automated water reservoir system. The problem identified is the lack of efficient control and monitoring of water replenishment and usage, which often leads to wastage and mismatch with homeowners' needs. This research design adopts an IoT-based approach that enables real-time monitoring and remote control via a web application. The system is also equipped with sensors to detect pipe leaks and monitor water pressure. Analysis of the developed system shows that the device is able to provide accurate and responsive data regarding the condition of the water reservoir, and allows users to control water replenishment as needed. This finding shows a significant improvement in water usage efficiency, as well as reducing potential waste due to leaks and inaccuracies in filling the reservoir. The conclusion of this research is that the designed IoT system can be an effective solution for household water management, providing convenience and better control for users, and has the potential to be widely applied in water management at the home scale.
Aplikasi Edukasi Jenis Narkoba Berbasis Augmented Reality Sebagai Media Pembelajaran Bagi Generasi Muda Achmad Fauzi Makarim; Cindy Taurusta
SMATIKA JURNAL : STIKI Informatika Jurnal Vol 15 No 01 (2025): SMATIKA Jurnal : STIKI Informatika Jurnal
Publisher : LPPM UBHINUS MALANG

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32664/smatika.v15i01.1497

Abstract

Drugs are a serious threat to public health, especially the younger generation. Drug education has been carried out by the national narcotics agency but the reduction is not optimal, so a technology known as Augmented Reality (AR) is needed. The aim of research on educational applications on types of drugs for the young generation based on Augmented Reality is to make it easier for the young generation to understand about drugs and make users more interactive and interesting. This research was developed using the Multimedia Development Life Cycle research method. This method has six stages starting from the concept to the distribution results of the applications that have been created. From the research stages that have been carried out, the results of the Augmented Reality Education application on the types and side effects of drugs were obtained. This application still has limitations on Samsung Android devices. It doesn't support the Easy AR plugin. This application was created using the Markless Bassed Tracking method and uses black box testing with a very good category. This application can be used for education for the younger generation and the wider community.
Augmented Reality Application for Human Five Senses Recognition Ilham Wisnu Bachtiar; Cindy Taurusta; Azmuri Wahyu Azinar
SMATIKA JURNAL : STIKI Informatika Jurnal Vol 15 No 01 (2025): SMATIKA Jurnal : STIKI Informatika Jurnal
Publisher : LPPM UBHINUS MALANG

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32664/smatika.v15i01.1503

Abstract

This study aims to design a maekerless Augmented Reality based application as a interactive learning media for the introduction of the five human senses for elementary school students. This application uses the Multimedia Development Life Cycle (MDLC) method because it is in accordance with the manufacturing stage. Features in This application displays 3D visualization of the organs of the five senses that can be manipulated by the user, complete with additional information in the form of text and audio. Feasibility testing conducted on elementary school students and teacher showed that the app had an excellent succes rate, with an average score of 98,1% based on a Likert Scale. This result shows that the application succesfully increasses student interest and effectively improves students understanding of the aterial of the five senses.
Membangun RT/RW Net Dengan Titik Pointing Antar Wilayah Adam Laxmana Mufti; Azmuri Wahyu Azinar; Hindarto Hindarto; Yunianita Rahmawati
SMATIKA JURNAL : STIKI Informatika Jurnal Vol 15 No 01 (2025): SMATIKA Jurnal : STIKI Informatika Jurnal
Publisher : LPPM UBHINUS MALANG

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32664/smatika.v15i01.1520

Abstract

The digital divide between urban and rural areas in Indonesia remains a significant challenge in the current digital era, with limited internet access in remote regions hindering economic, educational, and social progress. This research aims to develop a wireless LAN-based RT/RW Net system as a solution to enhance efficient and affordable internet access. The system utilizes point-to-point access between regions using specialized routers to overcome geographical and physical barriers, thereby enabling internet access in hard-to-reach areas. By leveraging WiFi 5 technology as a solution for better range and performance, along with ease of operation and affordable construction costs, the system is designed for efficiency. Device configuration is carried out using the built-in application of the TP-Link EAP 110 router, facilitating easier and more efficient management. The PPDIOO (Prepare, Plan, Design, Implement, Operate, Optimize) research methodology is chosen as the approach for developing the RT/RW Net network. The advantages of this method include structured and systematic design capabilities, greater flexibility in meeting design needs, and the ability to optimize the quality of the network once it is operational. This method can also analyze and build networks according to local geographical conditions. Expected outcomes include improved quality and reliability of the RT/RW Net network, provision of cheaper and more convenient internet access for communities, and contributions to a more equitable dissemination of information and communication technology across Indonesia, enabling communities to enjoy better access to information, broader educational opportunities, and enhanced economic prospects.
Analisis Sentimen Hasil Transkripsi Audio Berbahasa Indonesia Menggunakan T5 (Text-to-Text Transfer Transformer) Hilman Suhendar; Cepy Slamet; Undang Syaripudin
SMATIKA JURNAL : STIKI Informatika Jurnal Vol 15 No 01 (2025): SMATIKA Jurnal : STIKI Informatika Jurnal
Publisher : LPPM UBHINUS MALANG

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32664/smatika.v15i01.1521

Abstract

In the digital era, sentiment analysis has become a vital tool for understanding public opinion, particularly from data derived from digital media such as videos. However, voice-based sentiment analysis in the Indonesian language remains uncommon. This research aims to develop the T5 model for sentiment analysis of Indonesian generated from speech using speech-to-text technology. The primary advantages of the T5 model lie in its ability to process lengthy texts, comprehend natural language context, and adapt training for specific tasks such as sentiment analysis. The research dataset was obtained from 20 YouTube videos, segmented into clips of a maximum duration of 15 seconds, resulting in a total of 300 sentences consisting of 150 positive sentiments and 150 negative sentiments. The generated text data was processed using the T5 model, which was specifically trained to detect positive and negative sentiments through the optimization of specific hyperparameters. The results demonstrated that the T5 model achieved an accuracy of 83%, with a precision of 0.85, a recall of 0.83, and an F-measure of 0.83 when tested on datasets different from the training data. This research indicates that the T5 model can be adapted for voice-based sentiment analysis in the Indonesian language with satisfactory results. These findings contribute to the development of voice-based sentiment analysis technology, which can be applied to opinion analysis or product reviews. In the future, improving the pre-processing stage and using more diverse datasets are expected to improve the overall performance of the model.
Analisis Sentimen Tingkat Kepuasan Aplikasi WordPress Menggunakan Metode K-Nearest Neighbor dan Naive Bayes Moch Siddiq Hamid; Ade Eviyanti; Hindarto Hindarto; Novia Ariyanti
SMATIKA JURNAL : STIKI Informatika Jurnal Vol 15 No 01 (2025): SMATIKA Jurnal : STIKI Informatika Jurnal
Publisher : LPPM UBHINUS MALANG

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32664/smatika.v15i01.1522

Abstract

User satisfaction reflects emotions when comparing services received with expectations, so understanding user satisfaction is important for app development. This research aims to evaluate user satisfaction with WordPress apps on the Google Play Store and identify areas for improvement. Sentiment analysis with KNN and Naïve bayes algorithms as the method used to extract information from 5,000 user reviews downloaded from Google Play Store,. The results showed the majority of reviews had positive sentiments, with Naïve Bayes providing better results than KNN, achieving 88% accuracy, 89.45% precision, 88% recall, and 83% F1-Score on a 90:10 data split. The word cloud of positive reviews featured words such as “great”, “good”, “helpful”, “app”, and “good”, reflecting user satisfaction with the ease and benefits of the app, while negative reviews featured words such as “difficult”, “try”, and “fail” indicating technical difficulties and user dissatisfaction. This study concludes that WordPress apps have provided a satisfactory experience for most users, but some technical areas need improvement. The results of this study will provide valuable information for app developers in efforts to improve service quality and the app's reputation
Klasifikasi Fake dan Real Menggunakan Vision Transformer dan EfficientNet-B0 pada Gambar Asli dan Generatif AI M. Syahrul Anwar Aria; Cepy Slamet; Muhammad Deden Firdaus
SMATIKA JURNAL : STIKI Informatika Jurnal Vol 15 No 01 (2025): SMATIKA Jurnal : STIKI Informatika Jurnal
Publisher : LPPM UBHINUS MALANG

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32664/smatika.v15i01.1531

Abstract

Advances in artificial intelligence (AI) technology have enabled the creation of synthetic images that resemble real images, posing challenges in detecting and classifying such images. This study aims to develop an EfficientNet-B0 and Vision Transformer (ViT) based classification model to distinguish between real images and images generated by generative AI. The data used consists of 30,401 original images from the MSCOCO 2017 dataset and 30,401 generative AI-generated images from the SyntheticEye AI-Generated Images Dataset on Kaggle. The results showed that the ViT model achieved 98% accuracy and EfficientNet-B0 achieved 96% accuracy in classifying the images. The conclusion of this research is that both models have great potential in detecting digital media manipulation, with ViT showing superior performance. The practical implication of this research is the development of more advanced technologies for detecting generative images, which can be used in various real applications such as digital security and media verification.
Implementasi Model CNN ResNet50V2 untuk Klasifikasi Pneumonia pada Citra X-Ray Muhammad Afian Anwar; Yana Aditia Gerhana; Undang Syaripudin
SMATIKA JURNAL : STIKI Informatika Jurnal Vol 15 No 01 (2025): SMATIKA Jurnal : STIKI Informatika Jurnal
Publisher : LPPM UBHINUS MALANG

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32664/smatika.v15i01.1538

Abstract

The utilization of technology to build models that can classify pneumonia medical images automatically is needed for early diagnosis. This study aims to implement a Convolutional Neural Network (CNN) model with ResNet50V2 architecture that has been proven to have high accuracy in medical image classification. The model adopts a deep and efficient residual architecture, which facilitates deeper training of the model without suffering from vanishing gradient problem. This study went through four main stages: pneumonia and normal X-ray image data collection, data pre-processing (including set division, transformation, and augmentation), modeling using CNN with hyperparameter tuning, and model evaluation. Evaluation was performed using accuracy, F1-score, and Confusion Matrix metrics. The CNN model with ResNet50V2 as the backbone achieved 97% accuracy, showing excellent performance in differentiating between pneumonia and normal despite a small amount of misclassification. Although this model showed impressive results, challenges such as potential misclassification in cases with unclear or ambiguous images remain. Compared to previous approaches, this model offers advantages in accuracy and processing efficiency thanks to the use of a deeper and more sophisticated ResNet50V2. These advantages are expected to improve the precision of automated diagnosis in future medical applications.
Implementasi YOLOv8 Sebagai Pendeteksi Nominal Uang Rupiah Kertas Berbasis Android Arif Muhamad Iqbal; Jumadi Jumadi; Eva Nurlatifah
SMATIKA JURNAL : STIKI Informatika Jurnal Vol 15 No 02 (2025): SMATIKA Jurnal : STIKI Informatika Jurnal
Publisher : LPPM UBHINUS MALANG

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32664/smatika.v15i02.1545

Abstract

The number of visually impaired individuals in Indonesia reaches 1.5%, or around 4 million people, who often face difficulties in recognizing the denominations of rupiah banknotes. Although Bank Indonesia has added distinguishing features to the banknotes, this method is less effective due to limitations in understanding or the physical condition of the currency. Object detection technology, such as YOLOv8, offers a solution thanks to its advantages in accuracy and speed. This research employs the CRISP-DM approach, which includes six stages: business understanding to understand the needs of visually impaired individuals, data understanding to study the characteristics of the 2022 rupiah banknote dataset, data preparation to prepare 5,435 images of 8 currency denominations (1,000, 2,000, 5,000, 10,000, 20,000, 50,000, 75,000, and 100,000), modeling by training the YOLOv8n model, evaluation to assess model performance using a confusion matrix, and deployment on an Android application capable of real-time currency denomination detection through the camera. The evaluation results show an accuracy of 0.98, a precision of 0.988, a recall of 0.993, an average mAP50 score of 0.994, and an mAP50-95 score of 0.955, indicating that this model is quite effective in helping visually impaired individuals recognize the denominations of rupiah banknotes.
Klasifikasi Ras Kucing Dengan Pendekatan Convolutional Neural Networks Menggunakan Arsitektur Inception V4 Adryan Putra Pratama; Jumadi Jumadi; Eva Nurlatifah
SMATIKA JURNAL : STIKI Informatika Jurnal Vol 15 No 02 (2025): SMATIKA Jurnal : STIKI Informatika Jurnal
Publisher : LPPM UBHINUS MALANG

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32664/smatika.v15i02.1547

Abstract

Classifying cat breeds based on images presents challenges due to subtle differences in appearance among breeds and environmental influences. This study developed an automated classification system utilizing the Inception V4 architecture with a CRISP-DM approach, encompassing business understanding, data preparation, modeling, evaluation, and deployment. The dataset used was derived from the Oxford IIIT Pet Dataset, covering 12 popular cat breeds, and underwent cleaning, augmentation, normalization, and partitioning into training (80%) and validation (20%) datasets. The model was trained over 25 epochs, achieving a highest validation accuracy of 93.31% with average precision, recall, and f1-score of 93%. The system was implemented as a Flask-based web application, enabling real-time classification through image uploads. While overall performance was strong, certain breeds such as Bengal exhibited potential for further improvement.  The findings demonstrate the model's significant potential to support pet health diagnosis and breed conservation efforts. This study contributes substantially to the development of image-based classification technology, with recommendations for performance enhancements through GAN-based data augmentation and testing on more diverse datasets to improve generalizability.

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