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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 418 Documents
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Performance Evaluation of Artificial Intelligence Models for Classification in Concept Map Quality Assessment Wahyu Styo Pratama; Didik Dwi Prasetya; Triyanna Widyaningtyas; Muhammad Zaki Wiryawan; Lalu Ganda Rady Putra; Tsukasa Hirashima
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.v24i3.4729

Abstract

Open-ended concept maps generated by students give better flexibility and present a complex analysis process for teachers. We investigate the application of classification algorithms in assessing openended concept maps, with the purpose of providing assistance for teachers in evaluating student comprehension. The method used in this study is experimental methods, which consists of data collection, preprocessing, representation generation, and modelling with Feedforward Neural Network, Random Forest, Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Decision Tree, and Logistic Regression. Our dataset, derived from concept maps, consists of 3,759 words forming 690 propositions, scored carefully by experts to ensure high accuracy in the evaluation process. Results of this study indicate that K-NN outperformed all other models, achieving the highest accuracy and Receiver Operating Characteristic-Area Under the Curve scores, demonstrating its robustness in distinguishing between classes. Support Vector Machine excelled in precision, effectively minimizing false positives, while Random Forest showcased a balanced performance through its ensemble learning approach. Decision Tree and Linear Regression showed limitations in handling complex data patterns. FeedforwardNeural Network can model intricate relationships, but needs further optimization. This research concluded that Artificial Intelligence classification enables a better assessment for teachers, enables the path for personalized learning strategies in learning.
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.
Hostage Liberation Operations using Wheeled Robots Based on LIDAR (Light Detection and Ranging) Sensors Kasiyanto Kasiyanto; Aripriharta Aripriharta; Dekki Widiatmoko; Dodo Irmanto; Muhammad Cahyo Bagaskoro
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol. 23 No. 2 (2024)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v23i2.3493

Abstract

Hostage release operations require a high level of precision, alertness, and skill, which are carried out manually by soldiers of the Indonesian National Army. This medium presents a significant risk to soldiers. This research aims to improve the effectiveness of hostage release operations by integrating wheeled robot technology based on Light Detection and Ranging (LIDAR) sensors. The research method used is experiment-based in developing and testing a prototype of a mobile robot equipped with LIDAR technology and a web camera capable of mapping the location of hostages in three dimensions. The research showed that this robot has high accuracy, reaching 97.87%, and can createthree-dimensional route maps and display real-time video on a computer. The use of this technology has the potential to reduce risks to soldiers and improve the accuracy of mapping hostage locations, which can ultimately improve the safety and effectiveness of hostage release operations in the context of special operations tasks by soldiers of the Army.
Development of a Smart System for Optimizing Treatment Using Forward Chaining Method Muhamad Azwar; Eka Nurul Qomaliyah; Nurul Indriani
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol. 23 No. 2 (2024)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v23i2.3371

Abstract

The utilization of traditional herbal medicine among the inhabitants of Lombok is notably prevalent yet frequently hindered by a lack of comprehension regarding the efficacy of herbal remedies for specific ailments. Addressing this challenge, this study proposes the development of an Android application called "sopherbal", aimed at delivering personalized herbal plant recommendations via easily accessible mobile devices. Employing forward chaining methodology, the application identifies optimal herbal remedies based on ailment type, processing techniques, usage instructions, and recommended dosage and treatment duration. Notably, while effective in this context, the forward chaining approach entails certain trade-offs and hurdles. Previous research indicates that forward chaining facilitates accurate recommendation generation, and it may be constrained by its reliance on predefined rules and limited adaptability to complex, evolving scenarios. Despite these challenges, the ”sopherbal” application, featuring 50 Sasak medicinal plants curated for 15 common ailments, achieved an 86% validation rate, affirming its efficacy in bridging the gap between traditional herbal knowledge and modern healthcare needs.
Forecasting the Poverty Rates using Holt’s Exponential Smoothing Riza Prapascatama Agusdin; Sylvert Prian Tahalea; Vynska Amalia Permadi
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol. 23 No. 2 (2024)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v23i2.2672

Abstract

As a developing country with many provinces, Indonesia has a poverty problem that needs to be overcome. This research aimed to predict the poverty level in the Special Region of Yogyakarta using poverty data provided by the Central Statistics Agency for the Special Region of Yogyakarta. The method used in this research was Holt exponential smoothing to predict poverty levels in Yogyakarta City and four districts (Sleman, Bantul, Kulon Progo, and Gunungkidul) in this province. Three performances were measured to evaluate forecast results: sum squared error, mean squared error, and root mean squared error. The research results showed that the best configuration for the cities of Yogyakarta and Bantul is , = 0.9, 0.4; Kulon Progo and Gunungkidul are , = 0.9, 0.9; and Sleman are , = 0.9, 0.6. The forecasting results for 2022 to 2024, using a 95% confidence interval, showed that the poverty rate will increase in every city and district in the Special Region of Yogyakarta.
Learning Accuracy with Particle Swarm Optimization for Music Genre Classification Using Recurrent Neural Networks Muhammad Rizki; Arief Hermawan; Donny Avianto
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol. 23 No. 2 (2024)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v23i2.3037

Abstract

Deep learning has revolutionized many fields, but its success often depends on optimal selection hyperparameters, this research aims to compare two sets of learning rates, namely the learning set rates from previous research and rates optimized for Particle Swarm Optimization. Particle Swarm Optimization is learned by mimicking the collective foraging behavior of a swarm of particles, and repeatedly adjusting to improve performance. The results show that the level of Particle Swarm Optimization is better previous level, achieving the highest accuracy of 0.955 compared to the previous best accuracy level of 0.933. In particular, specific levels generated by Particle Swarm Optimization, for example, 0.00163064, achieving competitive accuracy of 0.942-0.945 with shorter computing time compared to the previous rate. These findings underscore the importance of choosing the right learning rate for optimizing the accuracy of Recurrent Neural Networks and demonstrating the potential of Particle Swarm Optimization to exceed existing research benchmarks. Future work will explore comparative analysis different optimization algorithms to obtain the learning rate and assess their computational efficiency. These further investigations promise to improve the performance optimization of Recurrent Neural Networks goes beyond the limitations of previous research.

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