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Jurnal Teknimedia: Teknologi Informasi dan Multimedia
Published by STMIK Syaikh Zainuddin Nahdlatul Wathan Anjani
ISSN : 27226263     EISSN : 27226271     DOI : -
Core Subject : Science, Engineering,
Computer Science & IT Control & Systems Engineering Engineering
JURNAL TEKNIMEDIA : Teknologi Informasi dan Multimedia terbitan berkala ilmiah nasional diterbitkan oleh STMIK Syaikh Zainuddin NW Anjani. Tujuan diterbitkannya Jurnal TEKNIMEDIA adalah untuk memfasilitasi publikasi ilmiah dari hasil penelitian-penelitian di Indonesia serta ikut mendorong peningkatan kualitas dan hasil penelitian untuk akademisi dan peneliti. Jurnal TEKNIMEDIA terbit 2 (dua) kali dalam satu tahun (lima bulan sekali) pada bulan Januari-Mei dan Juni-Desember dengan ruang lingkup bidang ilmu Informatika, Telekomunikasi dan rumpun Komputer Sains.
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 202 Documents
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RANCANG BANGUN SISTEM PERPUSTAKAAN DIGITAL CERDAS DENGAN DETEKSI LOKASI DAN MANAJEMEN POIN TERINTREGRASI Tegar Priyadi; Ahmat Josi; M. Hizbul Wathan
TEKNIMEDIA: Teknologi Informasi dan Multimedia Vol. 7 No. 1 (2026): June 2026
Publisher : Badan Penelitian dan Pengabdian Masyarakat (BP2M) STMIK Syaikh Zainuddin NW Anjani

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46764/teknimedia.v7i1.335

Abstract

This study designed and developed a web-based intelligent digital library system aimed at overcoming low reading interest and library visits. This system allows users to access digital book collections for free within the library environment, as well as regulate access to digital books outside the library through a point system mechanism as an incentive and control mechanism. The Prototype method was used in the development of this system, which involved the stages of requirements gathering, rapid design, and prototype evaluation. Functional testing of the system was carried out using the Blackbox Testing method. The results of the implementation show that this system has succeeded in providing easy access to digital reading materials, motivating interest in reading through a point system obtained from attendance and reading activities, and facilitating data management for librarians. The location detection feature via WiFi IP ensures that free digital book access is only available within the library zone, while access outside the zone requires point redemption. It is hoped that this system can increase users' interest in reading by combining easy access to digital information and optimising library technology.
PENGEMBANGAN PLATFORM DIGITAL EKONOMI KREATIF DAERAH BERBASIS USER-CENTERED DESIGN (STUDI KASUS: EKRAFMAGELANG.ID) Muhammad Ichwandar Akrianto; Muhamad Maksum Hidayat; Ahmad Nugroho
TEKNIMEDIA: Teknologi Informasi dan Multimedia Vol. 7 No. 1 (2026): June 2026
Publisher : Badan Penelitian dan Pengabdian Masyarakat (BP2M) STMIK Syaikh Zainuddin NW Anjani

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46764/teknimedia.v7i1.336

Abstract

The development of digital technology has driven significant transformations in the management and promotion of regional creative economies. However, the use of digital platforms by creative economy actors in the region still faces various obstacles, such as limited information presentation, unintegrated data management, and a less than optimal user experience. This study aims to develop a regional digital creative economy platform in Magelang Regency by applying the User-Centered Design (UCD) method so that the resulting system is in line with user needs and characteristics. The UCD method is implemented through several stages, including user-oriented planning, understanding the context of use, identifying user needs, designing solutions, and evaluating predetermined needs. Data collection was conducted through interviews with platform managers, creative economy actors, and users, followed by designing user flows and system mock-ups. The evaluation system was conducted using a usability test method to measure ease of use and interface comfort. The results of the study indicate that the developed digital platform is able to present product information and MSME profiles in a structured, easily accessible manner, and provides a good user experience. Thus, the application of the UCD method has proven effective in supporting the development of a regional digital creative economy platform and is expected to increase the promotion and visibility of creative economy actors in a sustainable manner.
PERANCANGAN FLIPBOOK DIGITAL INTERAKTIF DENGAN CHATBOT EDUKATIF PADA MATERI RANTAI MAKANAN UNTUK SISWA SEKOLAH DASAR Dina Rahmadani; Sarini Vita Dewi
TEKNIMEDIA: Teknologi Informasi dan Multimedia Vol. 7 No. 1 (2026): June 2026
Publisher : Badan Penelitian dan Pengabdian Masyarakat (BP2M) STMIK Syaikh Zainuddin NW Anjani

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46764/teknimedia.v7i1.337

Abstract

The advancement of digital technology demands the presence of innovative learning media that can support interactive and engaging learning processes, especially at the elementary school level. One of the materials that requires visualization support and good conceptual understanding is the food chain material in the Natural and Social Sciences (IPAS) subject. This study aims to design and develop learning media in the form of an interactive digital flipbook equipped with a simple educational chatbot on the food chain material for elementary school students. The research method used is the Research and Development (R&D) method by applying the ADDIE model which is limited to the analysis, design, and development stages. The developed media was then validated by two media experts and two material experts using an assessment instrument in the form of a questionnaire. The validation results showed that the flipbook learning media with an educational chatbot obtained a feasibility percentage of 93.3% from the media experts and 97.3% from the material experts, which is categorized as very feasible. Based on these results, the developed learning media is considered suitable for use as an alternative media in learning the food chain. It is hoped that this research can be a reference or reference material in the development of interactive digital learning media in elementary schools.
Universitas Internasional Batam ANALISIS KEAMANAN SISTEM INFORMASI MENGGUNAKAN ALGORITMA SUPPORT VECTOR MACHINE TERHADAP PENGGUNA SHOPEE Muhamad Dody Firmansyah; Christopher Christopher; Mangapul Siahaan
TEKNIMEDIA: Teknologi Informasi dan Multimedia Vol. 7 No. 1 (2026): June 2026
Publisher : Badan Penelitian dan Pengabdian Masyarakat (BP2M) STMIK Syaikh Zainuddin NW Anjani

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46764/teknimedia.v7i1.344

Abstract

The expansion of e-commerce in Indonesia has made information system security a crucial concern, especially on sites like Shopee that see a lot of user activity and transaction volumes. Potential security hazards, such as account misuse, unauthorized access, and suspicious activity, are increased by the volume of online transactions. Therefore, in order to comprehend the elements linked to security threats based on user characteristics and behavioral patterns, an analytical approach is necessary. The purpose of this study is to apply machine learning to examine security risk tendencies among Shopee users. A standardized questionnaire addressing demographic factors, usage frequency, security awareness levels, and experiences with questionable activity was used to gather data from 101 active users. Data cleaning, label encoding, Min–Max normalization, and feature selection were among the steps in the data processing procedure. The classification model used was the Support Vector Machine (SVM) technique with a Radial Basis Function (RBF) kernel. The creation of a security risk analysis model based on user perceptions and behavioral aspects rather than system log or transactional data is what makes this study unique. By using non-technical indications as predictive factors in e-commerce platforms, this method provides an alternate viewpoint for spotting possible security threats.
DEVELOPMENT OF AUGMENTED REALITY BOOKS AS A LEARNING MEDIUM FOR THE STRUCTURE AND FUNCTION OF FLOWERING PLANT PARTS USING THE MARKER-BASED TRACKING METHOD Tanta Fahira; Sarini Vita Dewi
TEKNIMEDIA: Teknologi Informasi dan Multimedia Vol. 7 No. 1 (2026): June 2026
Publisher : Badan Penelitian dan Pengabdian Masyarakat (BP2M) STMIK Syaikh Zainuddin NW Anjani

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46764/teknimedia.v7i1.354

Abstract

Science learning plays an important role in developing students’ understanding of natural phenomena through conceptual mastery and application in everyday life. One science topic that requires strong conceptual and visual understanding is the structure and function of plant parts, particularly complete flowers. However, science learning on this topic still largely relies on textbooks and two-dimensional images as instructional media. These media are not able to clearly present the shape, position, and function of flower parts, causing students to have difficulty visualizing abstract concepts. As a result, students’ understanding of the material is not yet optimal. Therefore, this study aims to develop and implement an Augmented Reality Book (AR Book) using a marker-based tracking method as a learning medium, as well as to determine its feasibility and effectiveness in the learning process. This study employed a Research and Development (R&D) method using the ADDIE model, which consists of analysis, design, development, implementation, and evaluation stages. The research subjects were fourth-grade students of MIN 8 Aceh Besar. Data were collected through validation by media experts and subject-matter experts, student response questionnaires, and analysis of learning media implementation results. The results showed that the developed AR Book obtained a feasibility score of 90% from media experts and 91% from subject-matter experts, both of which fall into the “very good” category. In addition, the results of its implementation with students showed a percentage of 94%, also categorized as very good. Based on these findings, it can be concluded that the marker-based tracking AR Book is feasible and effective as a science learning medium and is able to improve students’ understanding, interest, and engagement in learning the structure and function of complete flower plant parts
MODEL TERBARUKAN HYBIRD CNN DAN LEXICON-BASED UNTUK MENDETEKSI SENTIMEN PUBLIK TERHADAP DANANTARA Andri Wijaya; Thomas Filikano
TEKNIMEDIA: Teknologi Informasi dan Multimedia Vol. 7 No. 1 (2026): June 2026
Publisher : Badan Penelitian dan Pengabdian Masyarakat (BP2M) STMIK Syaikh Zainuddin NW Anjani

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46764/teknimedia.v7i1.357

Abstract

Danantara is a sovereign wealth fund that has received widespread attention on social media, including on "X." Various public opinions about Danantara have emerged, both positive and negative. To understand public perception more accurately, an effective sentiment analysis method is needed. Lexicon-Based methods are often used but have limitations in capturing context, while Convolutional Neural Networks (CNNs) are able to recognize patterns in text but require a lot of data. Therefore, this study aims to develop a hybrid CNN and Lexicon-based model to improve the accuracy of public sentiment analysis towards Danantara. Data will be collected from social media "X" using scraping or API methods, then go through a preprocessing process before being analyzed using a hybrid approach. Model evaluation is carried out by comparing the performance of CNN, Lexicon-based, and hybrid models using metrics such as accuracy, precision, recall, and F1-Score. The results of this study provide a more accurate model in understanding public opinion and provide insights for policymakers in designing more effective communication strategies with an accuracy rate of 86%. In addition, researchers will also contribute to the development of Natural Language Processing (NLP) and deep learning-based sentiment analysis.
PREDIKSI PERSEDIAAN DAN PERMINTAAN PASAR DENGAN MENGGUNAKAN METODE RANDOM FOREST DAN PRINCIPAL COMPONENT ANALYST Fransiscus Aditya Wibowo; Mardiani Mardiani
TEKNIMEDIA: Teknologi Informasi dan Multimedia Vol. 7 No. 1 (2026): June 2026
Publisher : Badan Penelitian dan Pengabdian Masyarakat (BP2M) STMIK Syaikh Zainuddin NW Anjani

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46764/teknimedia.v7i1.358

Abstract

Inaccurate forecasting of stock requirements and market demand is a major challenge faced by PT Bintang Jaya, which may lead to excess inventory (overstock) or stock shortages (stockout). This issue occurs because the company’s stock planning process still relies on manual approaches and historical experience without optimal utilization of data analytics. Therefore, this study aims to apply machine learning–based prediction techniques to estimate stock needs and market demand more accurately. The methods used in this research include the Random Forest algorithm as the baseline model, and Random Forest combined with Principal Component Analysis (PCA) as a hybrid model to evaluate the impact of dimensionality reduction on prediction performance. The dataset consists of historical sales transaction records from PT Bintang Jaya during the 2022–2024 period, which were processed through data preprocessing, monthly aggregation, and time series feature engineering. The results show that the Random Forest model provides more stable demand predictions and is closer to the actual values compared to the hybrid RF+PCA model. The application of PCA did not improve prediction performance due to the characteristics of the dataset, which is relatively low-dimensional and non-linear. Overall, the baseline Random Forest model demonstrates good and stable performance, indicated by consistent MAE and RMSE values and a coefficient of determination (R²) of approximately 0.69, meaning that the model explains around 69% of the demand variation based on the historical features.
ANALISIS KOMPONEN UTAMA - PENYESUAIAN HISTOGRAM ADAPTIF TERBATAS DAN ATT-UNET UNTUK SEGMENTASI RAMBUT Okky Darmawan Kostidjan; Dwi Sunaryono; Yudhi Purwananto
TEKNIMEDIA: Teknologi Informasi dan Multimedia Vol. 7 No. 1 (2026): June 2026
Publisher : Badan Penelitian dan Pengabdian Masyarakat (BP2M) STMIK Syaikh Zainuddin NW Anjani

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46764/teknimedia.v7i1.361

Abstract

In the field of medical image analysis, artifacts such as dermal hair pose a major challenge to both visual interpretation and automated image processing during dermoscopic examinations. Hair covering the lesion area can obscure the lesion boundaries, reduce the quality of feature extraction, and lead to segmentation and classification errors. Recent studies have shown that dermal hair remains one of the most persistent artifacts affecting automated analysis, even in state-of-the-art segmentation models. These artifacts also degrade the performance of AI-based systems that rely on visual information. This study aims to improve the accuracy of hair segmentation in dermoscopic images through the application of effective and efficient preprocessing techniques. This study applies Principal Component Analysis (PCA) as a grayscale method to reduce the computational burden while preserving essential image features, and Contrast-Limited Adaptive Histogram Equalization (CLAHE) to enhance local contrast and highlight thin or low-contrast hair structures. The combination of PCA and CLAHE serves as a preprocessing stage to improve the quality of input images for deep learning-based segmentation models. The main contribution of this research is the integration of PCA-based grayscale methods with CLAHE in a single preprocessing pipeline before deep learning segmentation and the evaluation of their effects on the performance of the segmentation model. The evaluation is conducted using the AttU-Net architecture with Dice Similarity Coefficient (DSC) and Jaccard Index (JAC) metrics. The proposed PCA–CLAHE preprocessing achieves DSC and JAC values ​​of 75.24% and 61.04%, respectively, outperforming the model without preprocessing. These results indicate that PCA–CLAHE effectively improves image quality and segmentation accuracy while maintaining computational efficiency.
SISTEM DETEKSI KERUSAKAN PANEL PLTS APUNG DI EMBUNG SIDOBANDUNG BERBASIS CONVOLUTIONAL NEURAL NETWORK DENGAN VISUALISASI AUGMENTED REALITY Thomas Brian; Immanuel Freddy Augustino; Parman Parman; Muhamad Sukarno
TEKNIMEDIA: Teknologi Informasi dan Multimedia Vol. 7 No. 1 (2026): June 2026
Publisher : Badan Penelitian dan Pengabdian Masyarakat (BP2M) STMIK Syaikh Zainuddin NW Anjani

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46764/teknimedia.v7i1.364

Abstract

This study aims to develop an Augmented Reality (AR) application integrated with a Convolutional Neural Network (CNN) as an interactive system for detecting damage in floating solar power plant (PLTS) panels at Embung Sidobandung in order to maintain the efficiency of the photovoltaic energy system. Conventional manual inspection methods are considered inefficient and prone to errors due to human factors. Therefore, a deep learning approach is employed to automatically and interactively detect and classify solar panel damage. AR technology is utilized to display panel condition information directly through a mobile device camera, enabling real-time damage monitoring. The dataset consists of 615 solar panel images, including 472 images of physical damage and 143 images of electrical damage. Experimental results show that the system is capable of classifying solar panel damage types in real time, achieving a precision of 93.48%, recall of 89.58%, and an F1-score of 91.49% for physical damage, and a precision of 70.59%, recall of 80.00%, and an F1-score of 75.00% for electrical damage, with an overall accuracy of 87.30%. Although the developed application provides interactive and informative visualization, varying lighting conditions in aquatic environments and differences in image acquisition angles remain challenges that affect system accuracy. Overall, the integration of CNN and AR has the potential to serve as an effective and efficient solution for developing damage detection systems for floating solar power plant (PLTS) panels.
PREDIKSI TARGET PENDAPATAN PAJAK DAERAH DI KABUPATEN SUMBAWA MENGGUNAKAN ALGORITMA EXTREME GRADIENT BOOSTING (XGBOOST) Sukarti Sukarti; Ekastini Ekastini
TEKNIMEDIA: Teknologi Informasi dan Multimedia Vol. 7 No. 1 (2026): June 2026
Publisher : Badan Penelitian dan Pengabdian Masyarakat (BP2M) STMIK Syaikh Zainuddin NW Anjani

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46764/teknimedia.v7i1.366

Abstract

Locally Generated Revenue (PAD) is the primary source of funding for local governments, with local taxes being the largest component supporting revenue. In Sumbawa Regency, tax target determination is still based on historical potential and realization, resulting in suboptimal accuracy in determining tax targets. This study aims to develop a prediction model for local tax revenue targets using the XGBoost algorithm. Secondary data was obtained from the Sumbawa Regency Regional Revenue Agency (Bapenda), covering various types of local taxes for the 2021–2025 period. The research method uses the Cross Industry Standard Process for Data Mining (CRISP-DM) framework with stages of business understanding, data understanding, preprocessing, modeling, evaluation, and implementation. The evaluation results show an RMSE value of 743,314,988.84 or 743 million, MAPE of 5.34%, and R² of 0.9845, indicating low prediction errors and the model's ability to understand data patterns well. The model is then implemented into a Flask-based web system to support the data input process, model performance, and generate more accurate and data-based predictions of regional tax revenue targets, and has the potential to become a strategic tool in making decisions about determining regional tax targets.

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