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INDONESIA
Jurnal Teknologi Informasi dan Multimedia
Published by Puslitbang Sekawan Institute Nusa Tenggara
ISSN : 27152529     EISSN : 26849151     DOI : https://doi.org/10.35746/jtim.v2i1
Core Subject : Science,
Computer Science & IT
Cakupan dan ruang lingkup JTIM terdiri dari Databases System, Data Mining/Web Mining, Datawarehouse, Artificial Integelence, Business Integelence, Cloud & Grid Computing, Decision Support System, Human Computer & Interaction, Mobile Computing & Application, E-System, Machine Learning, Deep Learning, Information Retrievel (IR), Computer Network & Security, Multimedia System, Sistem Informasi, Sistem Informasi Geografis (GIS), Sistem Informasi Akuntansi, Database Security, Network Security, Fuzzy Logic, Expert System, Image Processing, Computer Graphic, Computer Vision, Semantic Web, Animation dan lainnya yang serumpun dengan Teknologi Informasi dan Multimedia.
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Articles 20 Documents
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Search results for , issue "Vol. 7 No. 3 (2025): August" : 20 Documents clear
Deteksi Malware pada Perangkat Android Menggunakan Ensemble Learning Muhamad Azwar; Lilik Widyawati; Raisul Azhar; Kartarina Kartarina; Tanwir Tanwir; Andi Sofyan Anas
Jurnal Teknologi Informasi dan Multimedia Vol. 7 No. 3 (2025): August
Publisher : Sekawan Institut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35746/jtim.v7i3.573

Abstract

The increasing use of permission-based applications on mobile platforms has raised concerns regarding privacy and security. Android, being one of the most widely used operating systems for interacting with mobile applications, is particularly susceptible to various security risks that must be promptly addressed. Low digital literacy and a lack of user awareness about security risks—especially when installing applications from unofficial sources or without paying attention to access permissions—make users vulnerable to malware attacks. Uninformed users can easily become victims of malware insertion by irresponsible parties, turning them into targets for data manipulation and even data theft, which may then be sold on illegal forums. Attackers exploit the permission system, allowing them to freely access the target smartphone. This lack of awareness among users increases their vulnerability to malware injection and subsequent threats such as data manipulation and the theft of personal information, which can be traded on underground markets. One approach to detecting malicious behavior in mobile applications is the use of machine learning techniques. These techniques can analyze application patterns and behaviors based on features such as requested permissions. Popular algorithms for malware detection include Support Vector Machine (SVM) and Random Forest (RF), both of which have demonstrated strong performance in various studies. However, to further improve accuracy and reduce classification errors, ensemble learning approaches such as Adaptive Boosting (AdaBoost) are increasingly being adopted. Ensemble learning combines multiple predictive models to produce more reliable classification results compared to single models. This study evaluates the performance of several classification algorithms in detecting malicious Android applications. The results show that AdaBoost achieved a high accuracy rate of 91.65% and an AUC value of 95%, effectively distinguishing between safe applications and malware. Therefore, the use of machine learning algorithms—particularly ensemble methods like AdaBoost—can serve as a promising solution to enhance the security and privacy of Android-based mobile application users.
Segmentasi Hotel di Lombok Menggunakan Metode Klasterisasi Berbasis Harga, Fasilitas, dan Jarak Lokasi Eldy Waliyamursida; Dadang Priyanto; Galih Hendro Martono
Jurnal Teknologi Informasi dan Multimedia Vol. 7 No. 3 (2025): August
Publisher : Sekawan Institut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35746/jtim.v7i3.722

Abstract

Lombok is one of Indonesia's premier tourist destinations, experiencing significant growth in the tourism sector. The increasing number of visitors has directly impacted the hospitality industry, resulting in a wide variety of hotels with diverse characteristics based on price, rating, and customer reviews. This diversity poses a challenge in effectively understanding hotel market segmentation. This study aims to cluster hotels in Lombok using clustering techniques to gain deeper insights into hotel segmentation patterns. The research employs the K-Means Clustering algorithm within the CRISP-DM framework, which includes the phases of Business Understanding, Data Understanding, Data Preparation, Modeling, Evaluation, and Deployment. The dataset comprises attributes such as nightly price, hotel rating, and the number of reviews, all collected from online platforms. The effectiveness of the clustering process is evaluated using the Silhouette Score metric. The results show that the K-Means algorithm delivers the best performance, with a Silhouette Score of 0.9042 (90%), indicating well-defined and distinct clusters. Therefore, K-Means Clustering is recommended as the most effective method for grouping hotels based on the attributes used in this study. This research provides valuable insights into hotel segmentation patterns in Lombok and can serve as a reference for hospitality industry stakeholders in formulating more targeted marketing strategies and business decisions. Future research may consider incorporating additional attributes such as geographic location and tourist seasons to enhance the clustering quality.
Prediksi Cuaca Berdasarkan Variasi miliVolt Xylem Lannea coromandelica Menggunakan Model Artificial Neural Network Backpropagation Iskandar Iskandar; Stella Elizabeth Warokka
Jurnal Teknologi Informasi dan Multimedia Vol. 7 No. 3 (2025): August
Publisher : Sekawan Institut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35746/jtim.v7i3.727

Abstract

The rate of fluid flow in tree xylem generates an electrical potential difference (mV), which serves as a physiological indicator for monitoring plant conditions and predicting weather. This study aimed to develop a regression model based on Artificial Neural Network Backpropagation (ANN-BP) to estimate weather parameters from mV data of Lannea coromandelica. Electrical potential data were collected continuously for seven days using xylem-mounted sensors and synchronized with actual weather data, including air temperature, relative humidity, and light intensity. ANN-BP models employing three training algorithms (traingdx, traincgb, and traingd) were compared using mean squared error (MSE) as the evaluation metric. The traincgb algorithm achieved the best performance with an MSE of 3.29 × 10??. These findings demonstrate that variations in xylem electrical potential can reliably predict weather conditions in real time, supporting the development of an energy-efficient, biologically based weather monitoring system for precision agriculture and climate change mitigation.
Comparison of Social Media Between Tiktok and Instagram to Detect Negative Content Using Natural Language Processing Method Tri Antaka Jagad Laga; Nur Widjiyati
Jurnal Teknologi Informasi dan Multimedia Vol. 7 No. 3 (2025): August
Publisher : Sekawan Institut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35746/jtim.v7i3.730

Abstract

In the digital era, social media platforms have become essential tools for communication, content creation, and information dissemination. However, with the increasing volume of user-generated content, the spread of negative or harmful content has emerged as a major challenge for platform administrators and users alike. This study aims to compare TikTok and Instagram in their capacity to detect and manage negative content using Natural Language Processing (NLP) techniques. A dataset of 2,000 user comments was collected—1,000 from each platform—through web scraping. These comments were analyzed using a variety of NLP methods, including sentiment analysis tools (VADER and TextBlob), text classification algorithms (Support Vector Machine and Random Forest), and Named Entity Recognition (NER) using the spaCy library. The comparison was conducted based on the classification performance of each NLP technique in detecting negative content, considering metrics such as accuracy, precision, recall, and F1-score. The results showed that while both SVM and Random Forest performed well in classification tasks, SVM outperformed the others in terms of overall accuracy and consistency across platforms. Sentiment analysis provided a general overview of content polarity, but it was less effective in detecting nuanced or sarcastic language. NER contributed to identifying specific entities that may be associated with negative expressions, enriching the contextual understanding of comments. This study highlights the potential of combining multiple NLP methods to improve automated content moderation systems. It also underlines the importance of platform-specific characteristics, such as user behavior and engagement style, which influence the nature and frequency of negative content. Future work should focus on improving the handling of contextual ambiguity and sarcasm to ensure more robust and adaptive moderation technologies across different social media platforms.
Prediksi Beban Kerja Server Secara Real-Time pada Pusat Data Cloud dengan Pendekatan Gabungan Long Short-Term Memory (LSTM) dan Fuzzy Logic Naufal Hanif; Dadang Priyanto; Neny Sulistianingsih
Jurnal Teknologi Informasi dan Multimedia Vol. 7 No. 3 (2025): August
Publisher : Sekawan Institut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35746/jtim.v7i3.731

Abstract

Efficient resource management in Cloud Data Centers is essential to reduce energy waste and maintain optimal system performance. This study aims to predict server workload in real time using a hybrid approach that combines Long Short-Term Memory (LSTM) and Fuzzy Logic. CPU and RAM usage data were collected every second from a Proxmox Cluster using its API, then normalized and processed using an LSTM model to forecast future workloads. The predicted results were then classified using Fuzzy Logic into three workload categories: light, medium, and heavy. The model was evaluated using Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE), where the results showed an MAE of 2.48 on the training data and 3.09 on the testing data, as well as RMSE values of 5.15 and 5.57, respectively. Based on these evaluation results, the prediction system achieved an accuracy of 97.52% on the training data and 96.91% on the testing data, indicating that the model can generate accurate and stable predictions. This method enables automated decision-making such as workload-based power management, thereby improving energy efficiency and overall system performance.
Implementasi Arsitektur Deep Convolutional Neural Network (CNN) dengan Transfer Learning untuk Klasifikasi Penyakit Kulit I Putu Agus; Khasnur Hidjah; Neny Sulistianingsih; Galih Hendro; Syahrir Syahrir
Jurnal Teknologi Informasi dan Multimedia Vol. 7 No. 3 (2025): August
Publisher : Sekawan Institut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35746/jtim.v7i3.734

Abstract

Skin diseases are common health problems that require early diagnosis to prevent serious complications. This study aims to develop an automatic skin disease image classification system using a transfer learning approach based on Convolutional Neural Networks (CNN). Image datasets were obtained from Kaggle and underwent preprocessing stages including resizing, normalization, and augmentation. Four CNN architectures were evaluated: VGG16, ResNet50, MobileNetV2, and InceptionV3, implemented using Python and the Keras library on the Google Colab platform. The dataset was split into three training and testing ratios (90:10, 80:20, and 70:30) to assess the impact of data proportion on model performance. Models were trained by modifying the output layer to match the number of classes, and evaluated using accuracy, precision, recall, F1-score, confusion matrix, and ROC curve metrics. The results show that a 70:30 ratio yielded the most optimal training performance. InceptionV3 achieved the highest validation accuracy at 80.04%, but experienced overfitting, while VGG16 demonstrated better generalization to test data. This study proves that transfer learning with CNN is effective in improving the accuracy of automatic skin disease diagnosis and has the potential to become an efficient diagnostic solution, especially in areas with limited medical infrastructure.
Sistem Pelaporan Data Capaian Standar Pelayanan Minimal Bidang Kesehatan Berbasis Website Nugrah Satria Bagassabirin; Acihmah Sidauruk; Erni Seniwati; Wiwi Widayani; Agung Nugroho
Jurnal Teknologi Informasi dan Multimedia Vol. 7 No. 3 (2025): August
Publisher : Sekawan Institut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35746/jtim.v7i3.735

Abstract

Reporting the achievement of Minimum Service Standards in the health sector (SPM BK) plays a crucial role in evaluating and improving healthcare services. the reporting of Minimum Service Standards (SPM BK) achievement data at the Sumbawa District Health Office is not standardized, with inconsistent report formats and submissions via third-party messaging applications (WhatsApp). This leads to delays, data inconsistencies, and calculation errors, which affect the quality of monitoring. This study aims to design and develop a web-based information system to facilitate the reporting and monitoring of SPM BK achievements at the Sumbawa District Health Office. The web-based system enables easier reporting and monitoring, while the processing of SPM BK data can be automated to reduce the potential for human error. The system development followed the Waterfall model, starting from data collection through interview methods, system needs analysis, system design using Entity Relationship Diagram, Use Case Diagram, and Wireframe, followed by the system development process using Laravel 10 and MySQL database, and closed with the testing stages using the black box method, and User Acceptance Testing (UAT). The UAT results showed a success rate of 84.7% with a "Excellent" interpretation, indicating that the system meets user needs both functionally and in terms of interface. This system is expected to improve the speed, consistency, and accuracy of SPM achievement reporting.
Prediksi Gender Berdasarkan Nama Menggunakan Kombinasi Model IndoBERT, Convolutional Neural Network (CNN) dan Bidirectional Long Short-Term Memory (BiLSTM) Abi Mas'ud; Bambang Krismono Triwijoyo; Dadang Priyanto
Jurnal Teknologi Informasi dan Multimedia Vol. 7 No. 3 (2025): August
Publisher : Sekawan Institut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35746/jtim.v7i3.736

Abstract

This study proposes a name-based gender prediction model in the Indonesian language by combining the architectures of Indonesian Bidirectional Encoder Representations from Transformers (IndoBERT), Convolutional Neural Network (CNN), and Bidirectional Long Short-Term Memory (BiLSTM). The non-standardized and diverse structure of Indonesian names presents a significant challenge for text-based gender classification tasks. To address this, a hybrid approach was developed to leverage the contextual representation power of IndoBERT, the local pattern extraction capability of CNN, and the sequential dependency modeling strength of BiLSTM. The dataset consists of 4,796 student names from Universitas Bumigora, collected between 2018 and 2023. The preprocessing steps include lowercasing, punctuation removal, label encoding, and train-test splitting. Evaluation results based on accuracy, precision, recall, and F1-score indicate that the IndoBERT-CNN-BiLSTM model achieved the best performance, with an accuracy of 90.94%, F1-score of 91.03%, and training stability without signs of overfitting. This model demonstrates high effectiveness in name-based gender classification and holds strong potential for applications such as population information systems, service personalization, and name-based demographic analysis.
Analisis Korelasi Faktor-Faktor Penentu Produktivitas dalam Skema Remote Work Menggunakan Pendekatan Visualisasi dan Statistik Dwi Nur Chasanah; Rosidi Rosidi; M. Abdul Jamal Nasir; Mukhamad Angga Gumilang
Jurnal Teknologi Informasi dan Multimedia Vol. 7 No. 3 (2025): August
Publisher : Sekawan Institut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35746/jtim.v7i3.739

Abstract

The massive shift in work patterns caused by the global pandemic has significantly accelerated the adoption of remote work schemes across various industries and organizations. This condition has created a strong need for data-driven studies to understand the factors that influence employee productivity in flexible work environments. This study aims to analyze the relationships among several key variables, namely employment type (in-office or remote), weekly working hours, and well-being score, in relation to individual productivity scores. The research data were obtained from a publicly available dataset on the Kaggle platform, containing 1,000 entries from respondents with diverse professional backgrounds. The analysis process involved data preprocessing, Pearson correlation analysis, and exploratory data visualization using heatmaps and scatter plots to facilitate result interpretation. The results show that remote work is positively correlated with productivity (r = 0.40), while weekly working hours exhibit a negative correlation (r = -0.25). Meanwhile, the well-being score demonstrates a weak but positive correlation with productivity (r = 0.14). The data visualizations support these numerical findings by presenting consistent patterns among the analyzed variables. These findings offer preliminary insights that are valuable for future studies related to remote work productivity. This study can serve as an initial reference for decision-makers in designing data-driven policies to optimize flexible work arrangements.
Implementasi Sistem Pengadaan Material pada SAC dengan Metode Waterfall Deva Defrina Aldeana; Agustia Hananto; Tukino Tukino; Fitria Nurapriani; Elfina Novalia
Jurnal Teknologi Informasi dan Multimedia Vol. 7 No. 3 (2025): August
Publisher : Sekawan Institut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35746/jtim.v7i3.745

Abstract

Decision support systems in the material procurement process are important solutions to improve operational efficiency and accuracy, especially in retail companies such as SAC (Store Adede Cikampek) which is engaged in the sale of dolls. This study aims to design and build a web-based material procurement system that is able to manage the ordering process, stock recording, verification of incoming goods, and reporting automatically. The system development was carried out using the Waterfall method because its systematic stages are very suitable for handling the material procurement process at SAC which was previously manual and undocumented. With the Waterfall approach, each stage such as needs analysis, design, implementation, testing, to maintenance can be carried out in a structured manner, thus ensuring that the system built is able to overcome problems such as late ordering and errors in recording raw materials. At the implementation stage, this system was developed with various features such as supplier data management, raw material stock management, order history, and periodic report generation. To ensure the effectiveness of the system, testing was carried out using the System Usability Scale (SUS) approach involving twenty respondents from internal operational parties. The evaluation results showed that the developed system succeeded in meeting user needs and increasing the effectiveness of the procurement process by obtaining an average score of 96 which was categorized as "Excellent". This system is also considered easy to use, efficient, and can support the decision-making process in real time. It is expected that the implementation of this system can not only solve the problem of material procurement in SAC, but can also be used as a model for implementing similar systems in similar businesses. This research provides a practical contribution in the development of an integrated information system to support more optimal business processes.

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