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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 355 Documents
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Rancang Bangun Sistem Deteksi Manipulasi Citra Digital Berbasis Error Level Analysis (ELA) dan Analisis Metadata Exif Windi Prasetya Wibowo; Sigit Sugiyanto; Harjono Harjono; Ermadi Satria Wijaya
Jurnal Teknologi Informasi dan Multimedia Vol. 8 No. 4 (2026): November
Publisher : Sekawan Institut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35746/jtim.v8i4.1142

Abstract

Rapid advancements in digital technology have made image-processing applications increasingly ac-cessible, leading to a rise in digital image manipulation cases and casting doubt on image authenticity in forensic investigations, criminal cases, and journalism. Manipulated images are difficult to distin-guish visually, while existing analysis approaches rely on disparate tools, making the identification process inefficient. This research aims to design a system that integrates Error Level Analysis (ELA) and EXIF metadata analysis to automatically detect signs of digital image manipulation. The system was developed using the Waterfall model, comprising analysis, design, implementation, testing, and maintenance phases. Testing involved black-box testing across nine functional scenarios and manual validation using nine photo samples from three different devices (SONY ILCE-6400, Apple iPhone 14, and Infinix X6725). Test results indicate that all functional scenarios operated according to specifica-tions and the system successfully detected all manipulated samples (9 out of 9) through the combina-tion of ELA visualization and EXIF metadata analysis. The integration of these two methods proved complementary, thereby enhancing the effectiveness of digital image authenticity verification. While the developed system serves as a tool for the preliminary detection of digital image manipulation, it is not intended to provide conclusive forensic evidence.
Pengenalan Instrumen Bedah Menggunakan Deskriptor Geometris Berbasis Kontur Fica Aida Nadhifatul Aini; Siti Duratun Nasiqiati Rosady; Talifatim Machfuroh; Zakiyah Amalia; Tri Luhur Indayanti Sugata
Jurnal Teknologi Informasi dan Multimedia Vol. 8 No. 3 (2026): August
Publisher : Sekawan Institut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35746/jtim.v8i3.1182

Abstract

Surgical instrument recognition is a critical component in robot-assisted surgery for precisely identifying the types of instruments being utilized. This study investigates the application of contour-based geometric descriptors to recognize surgical instruments without requiring a model training process (training-free). The proposed approach extracts instrument contours and calculates three geometric descriptors: Aspect Ratio, Solidity, and Contour Ratio. The descriptor values of the input objects are compared against an offline reference descriptor matrix using the Euclidean distance measure. The main contributions of this study lie in proposing a lightweight and training-free surgical instrument recognition scheme utilizing a combination of three contour-based geometric descriptors, as well as providing a transparent and traceable (explainable) classification decision mechanism based on Euclidean distance similarity comparison across classes. Evaluation was performed using a primary laboratory dataset acquired with a Logitech Brio 4K camera (1080p, 60 fps) at a distance of 30 cm, an illumination of 300 lux, and a plain green cloth background. The testing dataset comprised 10 images containing 30 controlled instrument instances (10 instances each for Tissue Forceps, Dressing Forceps, and Mayo-Hegar Needle Holder). On this specific testing dataset, the proposed method achieved a recognition accuracy of 100% with an average processing time of 59.31 ms per image. Although obtaining perfect accuracy under controlled laboratory conditions, these results are interpreted strictly within the limits of the experimental environment and do not yet reflect generalization to complex real-world surgical images.
Improving Obesity Classification Performance through SMOTE and Feature Selection in Machine Learning Models Tsanaiya Rafa Zuhura; Vania Rapita Augesta; Septian Eko Prasetyo
Jurnal Teknologi Informasi dan Multimedia Vol. 8 No. 4 (2026): November
Publisher : Sekawan Institut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35746/jtim.v8i4.1033

Abstract

This study investigates the performance of machine learning models for seven-class obesity classification using Synthetic Minority Over-sampling Technique for Nominal and Continuous features (SMOTE-NC) and model-based feature selection. The UCI obesity dataset initially contained 2,111 records from Colombia, Peru, and Mexico. After removing 24 exact duplicates, 2,087 unique records were retained and divided using a stratified 80:20 training-test split. To prevent data leakage, SMOTE-NC was applied exclusively to the training folds within the cross-validation pipeline. Feature selection was performed using Random Forest-based SelectFromModel with a median importance threshold, reducing 31 encoded features to 16 selected features. Logistic Regression, Support Vector Machine, and Random Forest were evaluated using repeated stratified five-fold cross-validation with ten repetitions. The ablation analysis produced mean macro F1-scores of 93.21\% for the baseline, 93.27\% for SMOTE-NC only, 93.69\% for feature selection only, and 93.89\% for the combined SMOTE-NC and feature-selection pipeline. The combined pipeline significantly outperformed the baseline after Holm correction (adjusted $p < 0.001$), whereas SMOTE-NC alone did not produce a significant improvement (adjusted p = 0.527). After GridSearchCV optimization, Random Forest achieved 94.98\% accuracy and a macro F1-score of 94.84\% on the untouched test set. These findings demonstrate that the combined pipeline improves multiclass obesity classification, with feature selection providing the primary performance contribution and SMOTE-NC serving as a complementary class-balancing technique.
Klasifikasi Sentimen Ulasan Game Roblox di Google Play Store Menggunakan TF-IDF Weighted Word2Vec dan XGBoost Sahrul Bilalea; Dewi Rahmawaty Isa; Siti Nurmardia Abdussamad
Jurnal Teknologi Informasi dan Multimedia Vol. 8 No. 4 (2026): November
Publisher : Sekawan Institut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35746/jtim.v8i4.1100

Abstract

User reviews on the Google Play Store contain a wide range of opinions regarding the experience of using the Roblox game, both positive and negative. The large number of reviews makes manual sentiment identification inefficient, necessitating a sentiment analysis method capable of processing reviews systematically. This study aims to classify the sentiment of Roblox game reviews using TF-IDF Weighted Word2Vec as the feature representation and Extreme Gradient Boosting (XGBoost) as the classification algorithm. A total of 1,179 reviews were collected via web scraping during November 2025, then manually labeled and validated by an Indonesian language expert. The research stages included preprocessing—such as case folding, cleaning, tokenization, stopword removal, and stemming—followed by feature representation using TF-IDF Weighted Word2Vec with 200-dimensional vectors. The dataset was split in an 80:20 ratio for training and testing, while model optimization was performed using Grid Search with 5-fold Cross Validation. The labeling results consisted of 786 positive reviews and 393 negative reviews. The best configuration yielded an average validation accuracy of 89.5%, while the test data achieved an accuracy of 0.89. The positive class achieved precision, recall, and F1-score of 0.91, 0.92, and 0.91, respectively, while the negative class achieved 0.83, 0.82, and 0.83. These results indicate that the combination of TF-IDF Weighted Word2Vec and XGBoost is capable of effectively classifying the sentiment of Roblox game reviews.
Perancangan dan Implementasi Sistem Booking Suku Cadang Bengkel Motor Berbasis Web dengan Integrasi Chatbot Pelayanan Pelanggan Muhammad Rofiqul Minan; Nur Widjiyati
Jurnal Teknologi Informasi dan Multimedia Vol. 8 No. 4 (2026): November
Publisher : Sekawan Institut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35746/jtim.v8i4.1103

Abstract

Spare parts information services at motorcycle repair shops still face challenges in providing customers with fast and accessible information. Information regarding prices, specifications, and stock availability is generally provided by administrators or mechanics through instant messaging applications, which can delay responses when workshop activities are busy. An artificial intelligence-based chatbot can serve as an alternative by providing automated information services 24 hours a day. This study aims to develop an artificial intelligence-based chatbot as a spare parts information service integrated with a web-based ordering information system. The chatbot uses the Gemini API as a Large Language Model (LLM) to understand user queries and provide information based on spare parts data stored in the database, including products, prices, specifications, and stock availability. The system was developed using the System Development Life Cycle (SDLC) with the Waterfall model, using Laravel and MySQL, and is equipped with ordering and reservation features. The results of Black Box Testing showed a 100\% success rate across all chatbot and information system functions. The chatbot is capable of providing spare parts information automatically 24 hours a day, while the system supports the ordering and reservation processes.
Model Pergerakan Unit Autentik dalam Video Game Strategi Berbasis Estimasi Realita Ibnu Athaillah; Heri Waspada
Jurnal Teknologi Informasi dan Multimedia Vol. 8 No. 4 (2026): November
Publisher : Sekawan Institut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35746/jtim.v8i4.1110

Abstract

The authenticity of game mechanics in historically themed video games affects player immersion and experience, yet research on unit movement speed models adapted from reality remains scarce. This study designs and implements a unit movement model for a medieval-themed strategy game, in which unit speed is affected by load (armor and weapons), transportation, ground slope, and surface type, based on estimation formulas adapted from real-world biomechanics studies. The model is implemented in C\# on the Unity game engine using a data-oriented design paradigm for efficient parallel computation. Terrain effects are precomputed into a grid map so that lookups during simulation run in $O(1)$ complexity. Performance testing shows the model simulates up to 4.096 units at an average frame time of 13,8 ms (above 60 FPS), while a user perspective test with 30 respondents shows increased authenticity (3,12 to 4,43) and immersion (3,27 to 4,21) scores without a significant decrease in enjoyment. These results indicate that a reality-estimation-based movement model can be implemented efficiently while balancing authenticity and fun.
Prototipe Sistem Penyimpanan Berkas Berbasis Blockchain Menggunakan IPFS dan Non-Fungible Token Salman El Farisi; Alfian Nur Usyaid; Fatiah Al Zahra; Akhmam Fahmi
Jurnal Teknologi Informasi dan Multimedia Vol. 8 No. 4 (2026): November
Publisher : Sekawan Institut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35746/jtim.v8i4.1132

Abstract

The shift in people's behavior from paper to digital documents has led to an increasing need for flexible and easily accessible storage. Cloud-based services such as iCloud, Google Drive, and OneDrive have become the primary choice. However, dependence on third-party cloud providers has the potential to create its own challenges. One of these is data ownership. This research focuses on addressing data ownership issues in cloud-based file storage through a non-fungible token (NFT) protocol approach on a blockchain network. The purpose of this research is to investigate the potential application of the Non-Fungible Token (NFT) protocol on a blockchain network as a novel approach to ensuring ownership of digital files in cloud storage services. Analysis was conducted by testing the performance of the prototype system in terms of file upload time and the efficiency of gas fee usage in the smart contract function. The test results show average upload times of 13,1 seconds, 16,7 seconds, and 18,8 seconds for file sizes of 1 MB, 10 MB, and 25 MB, respectively, at a network speed of 1024 Kbps, with an average gas fee of 150,38 Gwei or 1,504 x10^{-7} ETH (Rp. 11,49). These findings suggest that the NFT approach is feasible to implement to guarantee document ownership in cloud-based file storage systems.
Perancangan dan Implementasi Website Deteksi Dini Kesehatan Mental Menggunakan Self-Reporting Questionnaire-20 M. Ervan Putra Alfian; M. Dermawan Mulyadiputro; Sirajunnasihin Sirajunnasihin; Hendra Setiawan
Jurnal Teknologi Informasi dan Multimedia Vol. 8 No. 4 (2026): November
Publisher : Sekawan Institut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35746/jtim.v8i4.1135

Abstract

Mental health problems require accessible early screening, particularly when awareness, stigma, and access to professional services remain barriers. This study designs and implements the MentalCare website using the Self-Reporting Questionnaire-20 (SRQ-20) through a Research and Development (R&D) approach with a Prototyping model. The system was developed using Laravel, PHP, and MySQL. Evaluation covered functional testing, usability, and expert review of the SRQ-20 implementation. Black Box Testing showed that all main functional scenarios and boundary-value classifications operated as expected. The System Usability Scale (SUS) involving 38 respondents produced an average score of 70.59 (Acceptable). Evaluation by a clinical psychologist produced an overall score of 3.87 out of 4 (Very Appropriate); all six sample scoring cases were also consistent with the expert assessment. These results indicate that MentalCare can support accessible and automated early mental health screening, while its outputs remain screening information and not a clinical diagnosis.
Pengembangan Game Edukasi Rambu Lalu Lintas Berbasis Android Untuk Siswa Sekolah Menengah Pertama Samarinda Muhamad Zafar Sofyan; Celine Aloyshima Haris
Jurnal Teknologi Informasi dan Multimedia Vol. 8 No. 4 (2026): November
Publisher : Sekawan Institut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35746/jtim.v8i4.1137

Abstract

Traffic accidents in Indonesia are largely caused by human error (85%), with adolescents under 17 involved in 14% of cases—partly due to poor traffic sign literacy and monotonous conventional learning media. This study developed and evaluated the feasibility of an Android-based Traffic Sign Educational Game for students of SMP Negeri 27 Samarinda. Using the Research and Development (R&D) method with the 4D model (Define, Design, Develop, Disseminate), materials were adapted from Regulation of the Minister of Transportation No. PM 13/2014 on Traffic Signs. Data were collected from three media experts, one material expert, and ninth-grade students via observation, interviews, and questionnaires, then analyzed descriptively. Based on descriptive analysis using a 1–5 Likert scale, media and material expert validations yielded average scores of 4.39 and 4.75, respectively (both categorized as highly feasible), while student responses averaged 4.18. The grand mean derived from the three respondent groups was 4.20, equivalent to a feasibility percentage of 84% (highly feasible category), indicating that the developed educational game is effective as a learning medium for teaching traffic sign materials to junior high school students.
Penerapan Metode Closest Facility Analysis untuk Penentuan Rute Ambulans berbasis Sistem Informasi Geografis Maghriza Azzanna; Nurdin Nurdin; Fajriana Fajriana
Jurnal Teknologi Informasi dan Multimedia Vol. 8 No. 4 (2026): November
Publisher : Sekawan Institut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35746/jtim.v8i4.1138

Abstract

Flooding in Banda Aceh may disrupt access from evacuation shelters to referral hospitals, making ambulance route planning essential to integrated emergency response. This study applies GIS-based Closest Facility Analysis (CFA) to determine minimum-cost routes from 18 evacuation shelters to six referral hospitals. Road network, shelter, hospital, flood inundation, and administrative boundary data were standardized to WGS 1984 UTM Zone 46N, checked for topology and connectivity, and built into a network dataset in ArcGIS Pro. Shelters were defined as incident facilities and hospitals as destination facilities, with theoretical distance and travel time as impedance attributes (assuming an ambulance speed of 60 km/h). CFA produced 18 routes with distances of 0.66–4.49 km and travel times of 40 seconds to 4 minutes 29 seconds; the mean distance was 2.10 km and the mean travel time 2 minutes 7 seconds, with 11 routes (61.11%) no longer than 2 km. The shortest route connects SMP IT Nurul Islah with RSUD dr. Zainoel Abidin, while the longest connects Escape Building Ulee Lheue with RS Harapan Bunda. CFA can identify the spatial accessibility of shelters to healthcare facilities through the actual road network and provides route, distance, and travel time information as a basis for preliminary ambulance referral planning. Operational implementation, however, still requires integrating dynamic information such as traffic conditions, affected road segments, and hospital service capacity.

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