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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 325 Documents
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Sistem Rekomendasi Pekerjaan Berbasis Kompetensi Mahasiswa Menggunakan Pendekatan Content-Based Filtering Intan Sulistyaningrum Sakkinah; Muhammad Ainul Fikri; Rosyida Arian Rahma
Jurnal Teknologi Informasi dan Multimedia Vol. 8 No. 2 (2026): May
Publisher : Sekawan Institut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35746/jtim.v8i2.997

Abstract

Students often experience difficulties in obtaining job recommendations that match their academic competencies and personal interests. This problem is caused by the suboptimal utilization of stu-dents' academic data in the job matching process, resulting in recommendations that are often less relevant to students' profiles and abilities. Therefore, a job recommendation system is needed that can process academic data and user preferences to produce more appropriate job recommenda-tions that meet students' needs. This study aims to develop a web-based job recommendation sys-tem that utilizes Grade Point Average (GPA) with a Content-Based Filtering approach. The devel-oped system matches student profiles—including GPA, major, skills, and interests—with job characteristics in the information technology field. The level of match between student profiles and job profiles is calculated using the Cosine Similarity algorithm. Evaluation of system performance is carried out by implementing a 5-fold cross-validation scheme and using Top-3 Accuracy and Exact Accuracy metrics. The test results show that the system obtained an average Top-3 Accuracy score of 85.0% and Exact Accuracy of 54.7%. These results demonstrate that the developed system is capable of generating relevant job recommendations with a high level of consistency. Therefore, this system is expected to contribute to supporting students' decision-making process in planning career paths that align with their academic achievements and individual preferences.
Evaluasi Sistem Retrieval-Augmented Generation Berbasis Low-Code dalam Meningkatkan Akurasi Konseptual Pembelajaran Sosiologi Rusmanto Rusmanto; Jasrino Maulana Putra
Jurnal Teknologi Informasi dan Multimedia Vol. 8 No. 2 (2026): May
Publisher : Sekawan Institut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35746/jtim.v8i2.1016

Abstract

The utilization of Large Language Models (LLMs) in higher education offers significant efficiency, yet it introduces critical risks of information hallucination and conceptual bias, particularly in the discipline of Sociology. This study aims to evaluate the performance of a Retrieval-Augmented Generation (RAG) system based on the low-code platform n8n as a robust solution for hallucina-tion mitigation. The system integrates semantic search using Supabase as a vector database and the Gemini 2.5 Flash model to restrict response generation exclusively to verified academic litera-ture. The research employed an Experimental Single-System Evaluation method with a du-al-evaluation approach (quantitative and qualitative) across 50 test instruments. Quantitative testing using the ROUGE-L metric recorded a mean score of 0.354, indicating adequate structural similarity despite variations inherent to the paraphrasing nature of LLMs in analytical tasks. Thematic qualitative analysis of evaluator comments revealed 98.2% positive sentiment, with the dominant theme being “Conceptually Accurate”. Ultimately, 100% of the expert panel declared the system suitable for implementation as a reliable supplementary learning medium.
Penerapan Teknik Steganografi dalam Gambar pada Aplikasi Telegram Menggunakan Metode Least Significant Bit (LSB) Lu’lu’ Atik Fitriyani; Fahmi Fachri
Jurnal Teknologi Informasi dan Multimedia Vol. 8 No. 2 (2026): May
Publisher : Sekawan Institut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35746/jtim.v8i2.1021

Abstract

In the digital era, the threat of data leakage demands secure information exchange mechanisms on instant messaging applications. This study aims to implement and test the robustness of the Least Significant Bit (LSB) steganography method on image media transmitted through the Telegram platform to ensure information confidentiality and create secure cyber communication against eavesdropping. The novelty of this research lies in the comparative analysis of the pure LSB method's robustness against Telegram's compression protocols, which frequently corrupt lower bits in digital media. This study employs an experimental quantitative approach by testing 15 digital image samples in PNG, BMP, and JPG formats sourced from the Google public repository. The experimental process involves embedding text messages using StegOnline tools, transmission via Telegram, extraction, and image quality analysis based on Mean Squared Error (MSE) and Peak Signal-to-Noise Ratio (PSNR) metrics. The test results indicate that the LSB method achieves a 100% extraction success rate and a Bit Error Rate (BER) of 0 when utilizing the "send document" feature due to its lossless transmission nature, with optimal visual quality indicated by PSNR values above 58 dB. Conversely, transmission through the "send image" feature causes extraction failure due to the platform's compression that corrupts the secret bits. This study concludes that the accuracy stability of LSB steganography highly depends on selecting a lossless transmission for-mat to fully guarantee data integrity.
Optimizing Green-Synthesized Chitosan Nanoparticles for Anticancer Drug Delivery Using Artificial Intelligence: A Systematic Literature Review Halil Akhyar; Ahmad Taufik S; Susi Rahayu
Jurnal Teknologi Informasi dan Multimedia Vol. 8 No. 2 (2026): May
Publisher : Sekawan Institut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35746/jtim.v8i2.1024

Abstract

Chitosan nanoparticles (CNPs) are promising anticancer drug-delivery carriers because of their biodegradability, mucoadhesive behavior, drug-loading capacity, and surface modifiability. However, previous studies have often examined green synthesis, CNP formulation, artificial intel-ligence (AI), and anticancer delivery as separate research domains, leaving limited synthesis of how AI-assisted optimization can improve environmentally sustainable CNP systems for cancer therapy. This systematic literature review evaluated studies published from 2016 to 2025 on green-synthesized CNPs optimized through AI, machine learning, or statistical optimization approaches for anticancer applications. A Scopus search using the Boolean string "chitosan AND anticancer AND optimization" identified 95 records. After eligibility screening, 42 records were excluded because they were older than 2016, non-article publications, non-English records, or out-side the oncology/green-CNP scope, leaving 53 studies for review. The evidence was synthesized into four domains: optimization and predictive modeling, green synthesis and material innova-tion, targeted and multifunctional nanocarriers, and preclinical efficacy and translational readiness. Across the included studies, optimized CNPs showed particle sizes ranging from approxi-mately 5 to 473 nm, encapsulation efficiency up to 98%, and zeta potentials from -31 to +98 mV. Reported therapeutic improvements included enhanced cytotoxicity, reduced IC50 values, sus-tained release up to 72 h, and inhibition rates up to 82% in selected cancer models. Nevertheless, cross-study comparison was limited by inconsistent model-validation metrics, incomplete toxicity reporting, limited in vivo validation, and insufficient scalability assessment. Integrating AI-guided optimization with green CNP synthesis can accelerate sustainable nanomedicine design, but future studies should prioritize standardized reporting, risk-of-bias control, life-cycle assess-ment, and regulatory translation.
Analisis Komparatif Firefly Algorithm dan Particle Swarm Optimization dalam Optimasi K-Means untuk Pengelompokan Ketimpangan Pendapatan Antarprovinsi di Indonesia Adinda Adinda; Fahrezal Zubedi; Nisky Imansyah Yahya
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.v8i3.1026

Abstract

Income inequality among provinces in Indonesia reflects differences in welfare levels influenced by the social and economic characteristics of each region. This study aims to compare the performance of Par-ticle Swarm Optimization (PSO) and Firefly Algorithm (FA) in optimizing the number of clusters in K-Means clustering to group Indonesian provinces based on seven factors related to income inequality, namely Human Development Index (HDI), Number of Poor Population, Open Unemployment Rate, In-flation, Provincial Minimum Wage, GDP per Capita, and Labor Force Participation Rate. The data used are secondary data from 2024 covering 38 provinces. The analytical methods include data standardi-zation using Z-Score, K-Means clustering, and cluster number optimization using PSO and FA evalu-ated by Silhouette Coefficient (SC). The results show that both optimization methods improved clus-tering quality compared to K-Means without optimization, which yielded an SC of 0.23. PSO produced an SC of 0.28 with an optimal cluster number of 5, while FA produced an SC of 0.39 with an optimal cluster number of 3. FA proved superior in generating a more optimal and representative clustering structure. The clustering results reveal distinct characteristics among clusters that can serve as a ba-sis for formulating more targeted income inequality reduction policies in accordance with the charac-teristics of each regional group.
Perbandingan Metode Biclustering untuk Pengelompokan Wilayah Berdasarkan Faktor Penyebab Kusta di Sulawesi Revo Asiki; Fahrezal Zubedi; Armayani Arsal
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.v8i3.1028

Abstract

Leprosy is one of the health issues that spread due to various social and environmental factors. It is a public health issue whose spread is influenced by various social and environmental factors. The aim of this study is to group districts/cities on the island of Sulawesi based on the factors that influence the spread of leprosy using a biclustering approach. The methods used were Cheng & Church (CC) and Iterative Signature Algorithm (ISA), which can cluster data simultaneously on the dimensions of area and variables. The data used are secondary data from 2024 covering eight variables and a number of districts/cities as the units of observation. The analysis stages included pre-processing, missing value handling using mean imputation, data standardisation, and application of the two biclustering methods. The performance was evaluated using Mean Squared Residue (MSR), the Liu & Wang index, and variance. The results of the study show that the CC method produces an average MSR value of 0.006015, which is lower than the ISA method's value of 0.015006. The average Liu & Wang index value for the CC method was 0.2905, lower than the ISA method's value of 0.7356. Furthermore, the average variance in the CC method was 0.07737, which was lower than the ISA method at 2.7859. Based on these three evaluation criteria, the Cheng & Church method is more effective in grouping regions based on factors influencing the spread of leprosy in Sulawesi.
Implementasi Ensemble Voting Classifier untuk Analisis Sentimen Publik terhadap Kontroversi Pertandingan Indonesia vs Bahrain pada Platform Youtube Dlovan Ferdiansyah; Susanto Susanto; Ardi Pramono
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.v8i3.1029

Abstract

The qualifying match between the Indonesian National Team and Bahrain sparked diverse public responses that escalated into controversy, particularly in digital spaces such as YouTube comment sections. This phenomenon prompted research to analyze public sentiment using a machine learn-ing approach to understand trends and polarization of public opinion. The research data was ini-tially collected as many as 1,000 raw comments through the YouTube scraping process, which then went through a cleaning stage resulting in 984 valid comments. The research data was ob-tained through scraping YouTube comments, The valid data is then labeled using a combination of lexicon (au-to-suggest) and manual validation approaches into three categories, namely positive, negative and neutral. The preprocessing stage focused on normalizing non-standard language (slang) and handling negations to maintain contextual meaning. Next, feature extraction was per-formed using Feature Union, which combines word- and character-based TF-IDF, as well as nu-meric features such as text length and punctuation proportion. To address data imbalance, the SMOTE method was applied to improve minority class representation. The model used was an Ensemble Voting Classifier with a soft voting approach, which combines a calibrated Support Vector Machine, Logistic Regression, and Random Forest. Model optimization was performed us-ing GridSearchCV to obtain the best parameters. The evaluation results showed that the model performed well with an accuracy of 89.34%, a precision of 89.17%, a recall of 89.34%, and an F1-score of 89.18%. Furthermore, the application of SMOTE and negation handling has been shown to help reduce bias toward the majority class. The application of the SMOTE method has been shown to significantly improve model performance compared to the baseline model without oversampling, which only achieved an accuracy of 88.83%. Overall, this ensemble approach with multidimensional feature engineering is effective in producing an accurate sentiment analysis model for evaluating public response to sporting events.
Assessment Manajemen Risiko Keamanan Sistem Informasi Menggunakan Framework ITIL V3 Domain Service Operation Andi Sofyan Anas; Rifqi Hammad; I Nyoman Switrayana; Muhammad Haris Nasri
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.v8i3.1037

Abstract

Information system security is a critical aspect in supporting the continuity of information tech-nology services, particularly in educational institutions that heavily rely on digital systems. This study aims to evaluate the maturity level of information system security risk management using the Information Technology Infrastructure Library (ITIL) V3 framework within the Service Oper-ation domain. This research employs a quantitative approach with total sampling, involving 13 respondents who are directly engaged in information system management at the research site. Data were collected through questionnaires and interviews. The research instrument was devel-oped based on four subdomains of ITIL V3 Service Operation: Event Management, Incident Man-agement, Problem Management, and Access Management. Instrument validity was tested using the Pearson productmoment correlation, and reliability was measured using Cronbach's Alpha. The results indicate that the overall maturity level is 3.44, which falls into the Defined Process category (Level 3), approaching Managed and Measurable (Level 4). Event Management obtained the highest value at 3.88 (Level 4), followed by Access Management at 3.37 (Level 3), Incident Management at 3.32 (Level 3), and Problem Management at 3.21 (Level 3). Gap analysis reveals a discrepancy of 1.56 from the expected optimized condition at Level 5. These findings suggest that although processes have been implemented and documented, improvements are still required par-ticularly in root cause analysis (Problem Management), access control (Access Management), and continuous performance evaluation. This study is expected to serve as a reference for enhancing IT service governance and information system security management based on ITIL practices.
Implementasi Naïve Bayes pada Sistem Informasi Posyandu Digital untuk Klasifikasi Stunting Balita Berbasis Website Mursalin Mursalin; Asmaul Husna RS; Sirajunnasihin Sirajunnasihin; Hendra Setiawan; Ari Kurniawati
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.v8i3.1035

Abstract

Posyandu plays a central role in child growth monitoring and stunting prevention at the commu-nity level; yet Posyandu Lelede 2, Desa Lelede, continues to rely on handwritten registers as its primary recording medium. This dependence generates three recurring operational problems: low data accuracy, difficulty retrieving children's growth histories, and chronic delays in submitting reports to the puskesmas, all of which compromise the effectiveness of early nutritional interven-tion. To address these limitations, the present study designed and implemented a web-based po-syandu information system incorporating the Gaussian Naive Bayes algorithm for automated stunting classification. Development followed the Waterfall methodology through five sequential phases- requirements analysis, design, implementation, testing, and maintenance- with the system constructed in PHP using the Laravel framework and MySQL as the database management system. The anthropometric variables processed included age, body weight, height, and sex of the children. A total of 1,164 records were utilized, comprising 1,000 training records constructed based on WHO anthropometric standards and 164 primary testing records obtained directly from Posyan-du Lelede 2 from December 2025 to February 2026. Evaluation via confusion matrix demonstrated a classification accuracy of 82.31% against WHO anthropometric standards, while Black Box Testing confirmed that all functional modules operated as specified. The system is therefore con-sidered a viable digital model for strengthening posyandu services and child health data manage-ment at the grassroots level.
Implementasi Metode First Expired First Out (FEFO) pada Sistem Manajemen Inventaris Distributor Daging Berbasis Web Laely Syafitri; Susanto Susanto; Titis Handayani
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.v8i3.1040

Abstract

Frozen meat inventory management by distributors requires the implementation of a system that can maintain product quality while reducing the risk of losses due to expired meat. CV. Heimon Anugrah Pangan still experiences several obstacles in manual stock recording which causes data synchronization and accumulation of old products in the warehouse. This study aims to imple-ment FEFO based on expiration dates in a web-based inventory management system. The system development technique used in this study is the waterfall method with steps starting from needs analysis, system design, implementation, testing, and maintenance. The system was created using PHP and MYSQL with in-bound and outbound management features, notification of stock ap-proaching expiration, and automatic reports in PDF format. As a result, the FEFO method can be implemented by the system with product expenditure priorities based on batches and the nearest expiration date. In system trials with black box testing, all features were proven to work accord-ing to user expectations. With this system, inventory management can be carried out effectively, efficiently, and with minimal human error.

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