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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.
Arjuna Subject : -
Articles 296 Documents
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Implementasi Algoritma FP-Growth untuk Sistem Rekomendasi Produk Kebutuhan Pokok pada E-Commerce Herlambang, Mahjid; Susanto, Susanto
Jurnal Teknologi Informasi dan Multimedia Vol. 8 No. 1 (2026): February
Publisher : Sekawan Institut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35746/jtim.v8i1.900

Abstract

The rapid development of e-commerce in Indonesia necessitates recommendation systems that can capture user purchasing patterns accurately, adaptively, and in a data-driven manner. This study implements the FP-Growth algorithm to analyze transaction data from a self-developed essential-goods e-commerce platform. The research dataset consists of 60 user accounts with a total of 600 completed transactions, processed using a Python-based analytical module and au-tomatically integrated into a Laravel backend through a dedicated execution script. The FP-Growth algorithm is applied to generate frequent itemsets and association rules using a min-imum support of 0.01, a minimum confidence of 0.1, and a minimum lift of 1.0. The results indi-cate that the most dominant associative patterns occur among kitchen staple products such as in-stant noodles, chicken eggs, and wheat flour, as well as household cleaning products such as de-tergents and fabric softeners. Several rules exhibit confidence values as high as 0.9615 and lift values up to 4.451, indicating strong and statistically significant relationships between products. System performance evaluation using a Top-4 recommendation scheme shows a Hit Rate of 54.35% and a Recall of 54.35%, demonstrating that the system is able to provide relevant recom-mendations for the majority of transactions. This implementation is shown to improve recom-mendation accuracy while strengthening personalization and cross-selling strategies on essen-tial-goods e-commerce platforms. These findings confirm that FP-Growth is an effective and effi-cient method for identifying empirical purchasing patterns and supporting the development of recommendation systems in small- to medium-scale e-commerce environments.
Utilizing Natural Language Processing for the Analysis of BMKG Decadal Atmospheric Dynamics Reports in 2025 Ramadhan, Ardiansyah; Azis, Aaz Muhmmad Hafidz
Jurnal Teknologi Informasi dan Multimedia Vol. 8 No. 1 (2026): February
Publisher : Sekawan Institut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35746/jtim.v8i1.946

Abstract

The Meteorology, Climatology, and Geophysics Agency (BMKG) publish decennial reports that provide valuable insights into Indonesia's meteorological conditions and their temporal fluctuations. However, due to their narrative structure, conducting direct quantitative analysis is problematic. This study seeks to address this issue by using a transparent, repeatable natural language processing (NLP) method to identify temporal trends in climatic conditions favourable to the formation of acid rain. The collection contains 36 BMKG decadal atmospheric dynamics studies for 2025. The proposed approach entails gathering textual input, performing basic preprocessing (case normalization, character sanitization, space-based tokenization, and stop-word removal), and subsequently employing predefined keyword dictionaries for analysis. These dictionaries delineate weather conditions that either facilitate or inhibit the formation of acid rain. The scores for acid rain conditions are determined by the frequency of specific keywords, adjusted for the document's length. Subsequently, they are categorized into groups utilizing statistical thresholds derived from the mean and standard deviation of the adjusted scores. Non-parametric statistical tests are employed to examine temporal patterns with greater specificity. The findings indicate that normalized acid rain scores are elevated in the initial years of the decade, specifically 2025, before gradually declining until year-end. The Spearman rank correlation test reveals a statistically significant negative correlation between normalized scores and time (\rho = -0.494, p = 0.0022). The Mann–Kendall test indicates a significant downward trend (Z = -2.902). These results demonstrate that the climatic conditions responsible for acid rain occurred only temporarily, rather than year-round. The core element of this work is a straightforward, lexicon-based NLP approach that is easily understood, replicable, and applicable, and can transform narrative atmospheric reports into structured quantitative metrics. This is beneficial for research on atmospheric dynamics and environmental analysis with official written data.
Pengembangan Deteksi Pesan Spam pada Website Inti Everspring Indonesia Menggunakan Algoritma Support Vector Machine Akbar, Syafaat; Hani'ah, Mamluatul; Rozi, Imam Fahrur
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.v8i2.872

Abstract

The development of information technology has driven the growth of email-based communica-tion in business environments, including at Inti Everspring Indonesia. However, the high volume of incoming emails increases the potential for spam messages that may disrupt work effectiveness and data security. This study develops a spam detection system on the company’s website by ap-plying the Support Vector Machine (SVM) algorithm. SVM was selected because of its ability to perform text classification efficiently. The dataset used in this research comes from the company’s internal emails, consisting of labeled spam and non-spam messages. Since the dataset is imbal-anced, an oversampling process was applied, followed by text preprocessing steps including case folding, tokenization, removal of stop words, symbols, numbers, and stemming. The model was then trained using the SVM algorithm, and its performance was evaluated using several metrics: accuracy, recall, precision, and F1-score. Based on the experiments, the SVM-based spam detec-tion model achieved 100% precision, 100% recall, and a 100% F1-score. To validate the reliabil-ity of the algorithm, SVM performance was compared with BERT and Naïve Bayes. BERT achieved 96% accuracy, and Naïve Bayes achieved 97% accuracy. These results indicate that SVM is capable of classifying messages accurately, and SVM outperforms both algorithms.
Pengembangan Aplikasi Media Pembelajaran Matematika Berbasis Android untuk Siswa Kelas V di SD Negeri 3 Panggisari Menggunakan Metode MDLC Al Ayubi, Sholakhudin; Sugiyanto, Sigit
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.v8i2.902

Abstract

This study aims to develop Android-based mathematics learning media for 5th-grade students of SD 3 Panggisari as a solution to the low interest in learning and the limitations of conventional learning media. The development method used is the Multimedia Development Life Cycle (MDLC) which includes the stages of concept, design, material collection, assembly, testing, and distribution. The application is designed to contain mathematics materials according to the cur-riculum, equipped with visual illustrations, practice questions, and multiple-choice-based evalu-ations. Functional testing using the black box method shows that all application features run well. The implementation results show an increase in student learning interest, indicated by the results of the questionnaire testing using a Likert scale which obtained an average value of 4.39, thus in-cluded in the agree category. With these results, this Android-based mathematics learning media can be accepted in elementary school environments and is suitable for use as a learning support medium.
Development of an AR-Based Solar System Flashcard Learning Media for Elementary Students Using the MDLC Method Rassy, Regania Pasca; Nurrahmadayeni, Nurrahmadayeni; Raihan, Muhammad Dzulhi; Agustini, Latifa Zahra; Mahdi, Anandi Neina Aeyska; Octariana, Ghina Briliana Fatin; Az Zahro, Luthfiyyah
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.v8i2.917

Abstract

Understanding the solar system remains challenging for elementary school students due to the abstract, spatial, and three-dimensional nature of astronomical concepts, which are often difficult to convey through conventional learning media. In response to this challenge, this study aims to develop and evaluate an Augmented Reality (AR)–based solar system flashcard learning media to support interactive and meaningful science learning at the elementary level. The learning media was developed using the Multimedia Development Life Cycle (MDLC) method, which consists of concept formulation, design, material collection, assembly, testing, and distribution stages. The AR application integrates three-dimensional planetary visualizations with flashcards to facili-tate concrete representation of abstract concepts and enhance student engagement. The evaluation focused on usability and learning support through User Acceptance Testing (UAT), involving 10 elementary school students and employing a 3-point Likert scale questionnaire. The results indi-cate that the AR-based flashcard media is easy to use, functions reliably, and effectively supports students’ understanding of solar system concepts. Students reported positive experiences in in-teracting with the learning media, suggesting its potential to improve motivation and conceptual comprehension in science learning. This study contributes to the development of innovative digi-tal learning media that promotes inclusive and quality education by integrating emerging tech-nologies into early science instruction. In alignment with Sustainable Development Goal (SDG) 4, the proposed AR-based learning media supports equitable access to engaging educational re-sources and enhances learning quality through interactive, technology-enabled instruction for elementary students.
Analisis Perbandingan Load Balancing Menggunakan Algoritma Round Robin dan Weighted Round Robin pada Mikrotik Karim, Muhammad Lailul; Rosadi, Muhammad Imron
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.v8i2.844

Abstract

This study analyzes and compares the performance of Round Robin (RR) and Weighted Round Robin (WRR) load balancing algorithms in a multi-WAN network environment using MikroTik devices. The research aims to evaluate the effectiveness of both algorithms in distributing network traffic across internet links with unequal bandwidth capacities. The experimental setup consisted of two ISP connections with bandwidths of 10 Mbps and 20 Mbps, configured on a MikroTik RB750Gr3 router. Performance evaluation was conducted using key network parameters, in-cluding packet loss, Latency, bandwidth utilization, and CPU load. The results show that the Round Robin algorithm, which distributes traffic evenly without considering link capacity, leads to high packet loss and unstable Latency, particularly on the lower-bandwidth link. In contrast, the Weighted Round Robin algorithm, which allocates traffic proportionally based on bandwidth capacity using a 1:2 ratio, demonstrates significantly better performance. WRR reduces packet loss by up to 4–6 times, provides more stable Latency, optimizes bandwidth utilization, and re-sults in lower CPU load on the router. Although WRR requires more complex configuration, the findings indicate that it is more suitable and efficient for multi-WAN networks with significant bandwidth differences between ISP links.

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