cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
Hairani
Contact Email
matrik@universitasbumigora.ac.id
Phone
+6285933083240
Journal Mail Official
matrik@universitasbumigora.ac.id
Editorial Address
Jl. Ismail Marzuki-Cilinaya-Cakranegara-Mataram 83127
Location
Kota mataram,
Nusa tenggara barat
INDONESIA
MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer
Published by Universitas Bumigora
ISSN : 18584144     EISSN : 24769843     DOI : 10.30812/matrik
Core Subject : Science,
Computer Science & IT
MATRIK adalah salah satu Jurnal Ilmiah yang terdapat di Universitas Bumigora Mataram (eks STMIK Bumigora Mataram) yang dikelola dibawah Lembaga Penelitian dan Pengabadian kepada Masyarakat (LPPM). Jurnal ini bertujuan untuk memberikan wadah atau sarana publikasi bagi para dosen, peneliti dan praktisi baik di lingkungan internal maupun eksternal Universitas Bumigora Mataram. Jurnal MATRIK terbit 2 (dua) kali dalam 1 tahun pada periode Genap (Mei) dan Ganjil (Nopember).
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 15 Documents
 1   2 
Search results for , issue "Vol. 24 No. 3 (2025)" : 15 Documents clear
Implementation of Conversational Artificial Intelligence in a3-Dimensional Game onWaste Impact Faisal Reza Pradhana; Ilham Mufandi; Aziz Musthafa; Dian Afif Arifah; Khairul Munzilin Al Kahfi
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol. 24 No. 3 (2025)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v24i3.4755

Abstract

The escalating volume of waste in Indonesia presents significant environmental challenges, primarilydue to insufficient public awareness and engagement. This study aimed to develop a dynamic threedimensionalsimulation game to enhance young people’s understanding of the environmental impactsof waste. The game integrates conversational artificial intelligence technology to create non-playablecharacters that engage users in natural text and voice dialogues. The research employed a research anddevelopment approach following the Software Development Life Cycle waterfall method, encompassingstages of analysis, design, implementation, testing, and maintenance. The game design adopted theMechanical, Dynamic, and Aesthetic framework method. It implemented a first-person perspective tocreate an immersive learning experience: evaluation involved functionality tests, expert reviews, anduser trials. The functionality testing achieved a perfect score of 100 percent, while evaluations by educationaltechnology experts yielded an average score of 94 percent for content quality and interfacedesign. User trials, conducted with individuals aged 10 to 18, indicated a high level of satisfactionwith an average score of 86 percent. These results conclude and demonstrate that integrating conversationalartificial intelligence into a simulation game provides an engaging and effective educationaltool to raise environmental awareness. Nonetheless, the study highlights the need for ongoing supportfrom parents and educators to cultivate sustainable waste management practices among young people.Future research should focus on expanding the game’s scope and evaluating its long-term impact onusers’ environmental literacy.
Artificial Intelligence Enhanced Direct Current Fast ChargingIntegration for Electric Vehicles on 20 kV Grids: A Technical andOntological Study Samsurizal Samsurizal; Arif Nur Afandi; Mohamad Rodhi Faiz
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol. 24 No. 3 (2025)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v24i3.4923

Abstract

Ontological philosophy offers a conceptual foundation to reflect on the existence and evolution of electric vehicles (EVs) as intelligent energy entities. The transition to electric vehicles has attracted global attention, particularly regarding sustainability and energy efficiency. This paper presents a novel approach to integrating artificial intelligence (AI) with DC fast charging on a 20 kV grid, highlighting both ontological and engineering perspectives. It introduces a framework where electric vehicles are no longer passive tools but active energy entities optimized through AI for real-time energy distribution, improving efficiency and grid stability. The ontological investigation explores the essence of electric vehicles as entities that interact with electrical infrastructure while questioning their role in modern transportation systems and environmental paradigms. The study investigates the application of artificial intelligence in optimizing the performance and efficiency of direct current fast charging systems, addressing challenges associated with load balancing, network stability, and real-time data processing. Artificial intelligence algorithms enable intelligent decision-making for energy distribution, minimizing grid pressure while ensuring optimal charging speeds. By blending ontological philosophy with technology analysis, this paper offers insights into how artificial intelligence-driven systems are redefining the relationship between electric vehicles, high-voltage grids, and sustainable energy ecosystems. The findings highlight the potential of artificial intelligence to improve electric vehicle charging efficiency, grid integration, and long-term sustainability in the energy transition.
Square Transposition Method with Adaptive Key Flexibility and Strong Diffusion Performance Magdalena Ariance Ineke Pakereng; Alz Danny Wowor; Yos Richard Beeh; Felix David; Erwien Christianto; Vincent Exelcio Susanto; Claudio Canavaro
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol. 24 No. 3 (2025)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v24i3.5004

Abstract

The Square Transposition method has notable potential in enhancing diffusion within block encryption systems; however, its application is typically limited to perfect square key lengths. The objective of this study is to reconstruct the method to accommodate non-square key lengths by utilizing two square matrices. To assess the effectiveness of the proposed approach, the method of this study uses a comparative analysis conducted against the transposition structures found in DES and AES algorithms, both of which are cryptographic standards established by NIST. The comparison is strictly limited to the transposition component, excluding other components of the full encryption framework. The evaluation involves Monobit, Block Bit, and Run Tests, along with Pearson correlation analysis between plaintext and ciphertext. Tests are conducted on 16 input variations across three key sizes: 128-bit, 256-bit, and 512-bit. The results of this study show that the proposed method achieves lower correlation values (r = 0.02) compared to DES (r = 0.07) and AES (r = 0.05). The conclusion of this study is that these findings indicate the approach offers improved key flexibility and diffusion capability, making it a promising transposition component for block cipher encryption systems. This reconstruction contributes a novel transposition structure that is compatible with non-square key sizes, thereby enhancing both diffusion strength and adaptability in modern cryptographic applications.
Cranioplasty Training Innovation Using Design Thinking: AugmentedReality and Interchangeability-Based Mannequin Prototype Djoko Kuswanto; Athirah Hersyadea Alifah Putri; Ellya Zulaikha; Tedy Apriawan; Yuri Pamungkas; Evi Triandini; Nadya Paramitha Jafari; Thassaporn Chusak
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol. 24 No. 3 (2025)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v24i3.5055

Abstract

Cranioplasty, a surgical procedure to reconstruct the anatomical structure of the human skull, is commonlyperformed in Indonesia due to the malignancy of diseases, traffic accidents, and workplaceinjuries. If left untreated, this condition can lead to serious complications. Although cranioplasty isgenerally considered a relatively easy surgery, it has a fairly high postoperative complication rate ofaround 10.3%. The decreasing availability of cadavers for anatomical studies has significantly limitedtraining opportunities. Therefore, efficient and effective training tools are essential, especially whentraditional resources are insufficient to meet educational needs. Additionally, the training capabilitiesof commercially available mannequins or replicas used in medical institutions remain limited. Themain objective of this project was to develop a smart, modular cranioplasty training mannequin designedfor repeated use, incorporating Augmented Reality (AR) technology to visualize anatomicalstructures that cannot be physically replicated. Using a design thinking approach, data was collectedthrough interviews with neurosurgeons, neurosurgery residents, and cranioplasty specialists, as well asthrough a review of relevant literature. Usability testing of the developed prototype yielded promisingresults, with high ratings for ease of use (4.8), training effectiveness (4.5), anatomical realism (4.3),and material durability (4.5) on a 5-point Likert scale. These findings demonstrated strong user approvaland confirmed the model’s potential to support surgical skill development in a practical andreproducible manner. The resulting AR-integrated training mannequin offers an innovative, engaging,and durable solution to address current challenges in neurosurgical education, especially in resourceconstrainedsettings.
Comparative Evaluation of Data Clustering Accuracy through Integration of Dimensionality Reduction and Distance Metric Paska Marto Hasugian; Devy Mathelinea; Siska Simamora; Pandi Barita Nauli Simangunsong
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol. 24 No. 3 (2025)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v24i3.5057

Abstract

The primary issue in clustering analysis of multivariate data is the low accuracy resulting from a mismatch between the Distance Metric used and the characteristics of the data. This study aims to comprehensively evaluate the effect of eight Distance Metric in the KMeans algorithm integrated with the Principal Component Analysis (PCA)dimension reduction technique. The analysis process was conducted by transforming the data into two principal components using PCA, then applying K-Means to each Distance Metric. Performance evaluation was conducted based on five internal metrics: Silhouette Score, Davies-Bouldin Index, Sum of Squared Errors, Calinski-Harabasz Index, and Dunn Index. The results show that the Bray-Curtis formula provides the best performance, with a Silhouette Score of 0.4291 and SSE of 30.3673. This is followed by Euclidean and Minkowski, which yield the highest Calinski-Harabasz Index value of 2239.85 and Dunn Index of 0.0108, respectively. In contrast, Hamming’s formula yielded the lowest performance across all metrics, with a Silhouette Score of 0.0000 and an SSE of 1996.00. The ANOVA test revealed significant differences between the Distance Metric, with a p-value of ¡0.000 for all metrics, which was further supported by the Tukey HSD follow-up test results. The implications of these findings confirm the importance of selecting an appropriate Distance Metric in the clustering process to ensure the validity, efficiency, and interpretability of multivariate data analysis results.

Page 2 of 2 | Total Record : 15

 1   2 

Filter by Year

2025 2025


Reset
Filter By Issues
All Issue Vol. 25 No. 1 (2025) Vol. 24 No. 3 (2025) Vol. 24 No. 2 (2025) Vol. 24 No. 1 (2024) Vol. 23 No. 3 (2024) Vol. 23 No. 2 (2024) Vol. 23 No. 1 (2023) Vol. 22 No. 3 (2023) Vol. 22 No. 2 (2023) Vol. 22 No. 1 (2022) Vol. 21 No. 3 (2022) Vol. 21 No. 2 (2022) Vol. 21 No. 1 (2021) Vol. 20 No. 2 (2021) Vol. 20 No. 1 (2020) Vol. 19 No. 2 (2020) Vol. 19 No. 1 (2019) Vol. 18 No. 2 (2019) Vol. 18 No. 1 (2018) Vol. 17 No. 2 (2018) Vol. 17 No. 1 (2017) Vol. 16 No. 2 (2017) Vol. 16 No. 1 (2016) Vol. 15 No. 2 (2016) Vol. 15 No. 1 (2015) More Issue