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Dinamik
Published by Universitas Stikubank
ISSN : 08549524     EISSN : 26231786     DOI : 10.35315/dinamik.v28i1
Core Subject : Science,
Computer Science & IT
The Jurnal DINAMIK aims to: Promote a comprehensive approach to informatics engineering and management incorporating viewpoints of different applications (computer graphics, computer networks and security, computer vision, computational intelligence, databases, big data, IT project management, and other fields relevant to information technology. Encourage scientists, practicing engineers, and others to conduct research and similar activities.
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 455 Documents
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Comparasi Model DeepSeek dan OpenAI dalam Meningkatkan Efisiensi Pencarian Informasi pada Sistem Pencarian Algoritma Mahenra, Ridwan; Setiawan, Dandi
Dinamik Vol 31 No 1 (2026)
Publisher : Universitas Stikubank

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35315/dinamik.v31i1.10287

Abstract

This study evaluates the efficiency of two artificial intelligence models, DeepSeek and OpenAI, in generating code for algorithmic systems. Efficiency is assessed through execution speed, code accuracy, and the number of code characters produced. Data were collected from 100 tests covering search, sorting, graph, dynamic programming, optimization, data processing, text, and machine learning algorithms. The objective is to compare the performance of both models to support the development of efficient information retrieval systems. The method involves algorithm testing with statistical analysis of execution time, accuracy, and code length. Results indicate that DeepSeek has an average execution time of 28.74 seconds, slightly slower than OpenAI’s 28.49 seconds. However, DeepSeek’s accuracy (85.88%) surpasses OpenAI’s (85.03%). The average number of code characters is identical at 96.35 characters. The study concludes that DeepSeek excels in accuracy, while OpenAI is faster in certain cases, providing valuable insights for developers in selecting AI models for information retrieval applications.
Analisis Efektivitas Pembelajaran Bahasa Jepang Melalui Anime dan Buku Teks dengan Algoritma AI K-Means dan Decision Tree Al Farhan, M Haidar Amir; Mahenra, Ridwan
Dinamik Vol 31 No 1 (2026)
Publisher : Universitas Stikubank

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35315/dinamik.v31i1.10288

Abstract

The growing interest in learning the Japanese language in Indonesia, driven by popular culture such as anime, creates a need to understand the effectiveness of different learning media. The non-uniform effectiveness of media for each individual poses a major challenge. Therefore, this study aims to analyze the effectiveness of both anime and textbooks by segmenting learner profiles and identifying key determinants of success using an artificial intelligence approach. This research employed a quantitative method through a questionnaire survey of 120 respondents. The data were analyzed in two stages: the K-Means Clustering algorithm was used to group respondents into learner profiles, and the Decision Tree algorithm was used to identify the most significant factors that differentiate these profiles. The analysis successfully identified three distinct learner profiles: "Intensive & Adaptive Learner," "Flexible Learner," and "Passive Learner." The decision tree revealed that the perception of textbook effectiveness and the frequency of anime use are the strongest predictors in determining a learner's profile, more so than theoretical learning style preferences. It is concluded that media effectiveness is highly dependent on the learner's behavioral and perceptual profile, which underscores the importance of a personalized approach in language education technology.
Dampak Model Mental Pengguna terhadap Implementasi Multi-Factor Authentication untuk Mitigasi Risiko Password Guessing di Konteks Organisasi Triantoro, Ery; Widyarto, Setyawan
Dinamik Vol 31 No 1 (2026)
Publisher : Universitas Stikubank

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35315/dinamik.v31i1.10290

Abstract

This study conducts a Systematic Literature Review (SLR) to explore the impact of users’ mental models on the implementation of Multi-Factor Authentication (MFA) as a strategy for mitigating password guessing risks in organizational environments. Amid the growing landscape of cyber threats, single-factor authentication has proven to be vulnerable, making MFA an essential layered security solution. However, the adoption of MFA remains slow. Existing studies indicate that expert users perceive MFA as a useful additional layer of verification, whereas non-expert users often view it as a burdensome task (a chore) and may even struggle to distinguish between different types of MFA. Dependence on mobile devices emerges as a common source of frustration for both groups. These findings emphasize that understanding users’ mental models is crucial for improving the implementation and usability of MFA. Innovations such as adaptive MFA or Single Input Multi-Factor Authentication (SIMFA) show potential as solutions to balance security requirements and user experience.
Implementation of Machine Learning for Credit Card Fraud Detection using Logistic Regression and Gradient Boosting Zebua, Ernest Duta Haga; Tanjung, Juliansyah Putra; Simatupang, Jonfiter; Sianturi, Magdalena
Dinamik Vol 31 No 1 (2026)
Publisher : Universitas Stikubank

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35315/dinamik.v31i1.10291

Abstract

Credit card fraud is a critical issue in digital financial transactions. This study aims to develop and evaluate fraud detection models using Logistic Regression and Gradient Boosting on an imbalanced dataset, where fraudulent transactions constitute only a small portion of the data. To address this imbalance, the Synthetic Minority Over-sampling Technique (SMOTE) was applied during preprocessing. Logistic Regression, used as a baseline model, achieved 95% accuracy, 78.6% precision, 55.9% recall, and a 65.3% F1-score. After applying class weighting and SMOTE, recall improved to 88.7%, but precision dropped to 52%, indicating that the model became overly sensitive and prone to false positives. Gradient Boosting initially produced better results, with 98% accuracy, 95.5% precision, 84.3% recall, and an 89.5% F1-score. After hyperparameter tuning and resampling, its performance improved further to 96.7% precision, 86.1% recall, and a 91.1% F1-score. These results indicate that Gradient Boosting is more effective in handling imbalanced data and offers greater reliability in detecting fraudulent transactions. The findings support the growing evidence in favor of ensemble learning techniques in fraud detection applications. This research contributes practical insights into improving the accuracy and security of machine learning-based fraud detection systems in financial services.
Pesediaan Barang Skincare dengan Metode Scm pada PT. Nindy Glow Beauty Aesthetic di Sei Piring Margolang, Ririn Yulia Sari; Anggraeni, Dewi; Sumantri, Sumantri
Dinamik Vol 31 No 1 (2026)
Publisher : Universitas Stikubank

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35315/dinamik.v31i1.10307

Abstract

Persaingan industri distribusi yang semakin ketat menuntut perusahaan untuk memiliki sistem manajemen persediaan yang efisien dan terintegrasi. PT. Nindy Glow Beauty Aesthetic, sebuah klinik kecantikan yang bergerak di bidang penjualan produk skincare di Sei Piring, saat ini masih menggunakan nota pembelian manual sebagai acuan informasi persediaan barang. Hal ini mengakibatkan data stok tidak akurat dan menghambat pengambilan keputusan. Penelitian ini bertujuan untuk mengembangkan sistem informasi persediaan barang berbasis metode Supply Chain Management (SCM) yang dapat membantu perusahaan dalam merencanakan kebutuhan stok berdasarkan data penjualan, permintaan, dan ketersediaan barang. Hasil dari pengembangan sistem ini diharapkan dapat meningkatkan efisiensi pengelolaan persediaan, mengurangi kerugian akibat kelebihan atau kekurangan stok, serta mendukung proses distribusi produk skincare secara optimal. Studi ini juga mengacu pada penelitian sebelumnya yang menunjukkan keberhasilan penerapan metode SCM di berbagai sektor industri
Analisis Deteksi Komunitas Louvain, Infomap, dan Walktrap pada Konstruksi Social Network Analysis Jaringan Undang-Undang Republik Indonesia 2014-2024 Al Amin, Imam Husni; Wibisono, Setyawan; Hadikurniawati, Wiwien; Lestariningsih, Endang; Eniyati, Sri
Dinamik Vol 31 No 1 (2026)
Publisher : Universitas Stikubank

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35315/dinamik.v31i1.10308

Abstract

Penelitian ini mengevaluasi performa tiga algoritma deteksi komunitas Louvain, Infomap, dan Walktrap dalam konteks social network analysis pada jaringan undang-undang Republik Indonesia periode 2014–2024. Jaringan dibangun dari hubungan kutipan antar undang-undang Republik Indonesia pada rentang waktu antara tahun 2014 sampai dengan tahun 2024. Kutipan antar undang-undang diperoleh pada bagian “Mengingat” pada setiap undang-undang, menghasilkan sebuah konstruksi struktur graf berarah dan tak berbobot. Setiap algoritma diuji berdasarkan empat metrik evaluasi: modularity, coverage, conductance, dan inter-cluster density. Evaluasi terhadap tiga algoritma deteksi komunitas Infomap, Louvain, dan Walktrap pada jaringan undang-undang menunjukkan perbedaan karakteristik dalam membentuk struktur komunitas. Louvain unggul dalam hal modularity (0.522387) dan conductance (0.287157), yang mencerminkan kemampuan optimal dalam memisahkan komunitas besar yang kohesif dan minim koneksi keluar. Infomap menempati posisi menengah dengan modularity dan inter-cluster density yang cukup baik, menawarkan keseimbangan antara segmentasi dan kepadatan komunitas. Walktrap memiliki keunggulan pada coverage (0.809586) dan inter-cluster density (0.50640), menandakan kemampuannya membentuk komunitas kecil yang sangat padat secara internal, meskipun cenderung kurang terstruktur secara global karena modularity-nya paling rendah (0.464787). Dengan demikian, Louvain direkomendasikan sebagai algoritma paling sesuai untuk analisis jaringan undang-undang, terutama jika tujuan utama adalah memperoleh segmentasi komunitas yang terstruktur kuat dan representatif secara makro terhadap arsitektur hukum nasional.
Pemodelan Tren Kasus Hiv dan Klasterisasi Wilayah menggunakan Algoritma K-Means dan Decision Tree - Studi Kasus di Kabupaten Bogor Bintang, Bagus; Triantoro, Ery; Wibowo, Arief
Dinamik Vol 31 No 1 (2026)
Publisher : Universitas Stikubank

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35315/dinamik.v31i1.10310

Abstract

Infectious diseases remain a dynamic and evolving public health threat, requiring data-driven approaches for early detection and targeted policy planning. This study aims to model spatio-temporal trends and clustering patterns of HIV transmission in Bogor Regency during the period 2020–2023 by utilizing a combination of unsupervised and supervised machine learning techniques. The dataset was obtained from the Bogor Regency Health Office and includes annual data on the number of HIV cases across 40 sub-districts. The research methodology consists of data preprocessing stages, clustering using the K-Means algorithm, and classification using a Decision Tree model. The preprocessing steps include data integration, attribute selection, temporal aggregation, handling of missing data, and normalization using Z-score. K-Means clustering is applied to identify hidden patterns in the development of HIV cases, resulting in three distinct clusters based on multi-year trends. The resulting cluster labels are then used as target classes in the supervised classification process. The Decision Tree classification model demonstrates high accuracy in predicting cluster membership, indicating a strong relationship between the temporal patterns of HIV cases and cluster identity. The integration of clustering and classification techniques provides a robust analytical framework for understanding the dynamics of HIV transmission, while also supporting the formulation of more precise, evidence-based, and region-specific public health interventions.
Faktor-Faktor yang Mempengaruhi Siswa SMA Al-Kautsar dalam Penggunaan Media Pembelajaran Berbasis Metaverse Putra, Satya Setiawan; Suryono, Ryan Randy; Rahmanto, Yuri
Dinamik Vol 31 No 1 (2026)
Publisher : Universitas Stikubank

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35315/dinamik.v31i1.10311

Abstract

This study aims to investigate the factors influencing the continuance intention of Al-Kautsar Senior High School students in using metaverse-based learning media. The background of this research lies in the rapid adoption of immersive technologies in education, while students’ levels of acceptance have not yet been fully understood. The objective is to identify the antecedents of satisfaction, which subsequently influence continuous intention. The research model examines the effects of perceived interactivity, perceived sociability, perceived enjoyment, perceived ease of use, perceived security, and social influence on satisfaction. A quantitative approach was employed by distributing questionnaires to students, and the data were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). The results indicate that satisfaction is a very strong and statistically significant predictor of continuous intention to use metaverse applications (β = 0.716, p < 0.001). The six hypothesized antecedent variables were not found to have a significant individual effect on satisfaction. In conclusion, for digital native students at Al-Kautsar Senior High School, factors such as ease of use, interactivity, and enjoyment have shifted from being drivers of satisfaction to becoming basic expectations (hygiene factors). Satisfaction itself emerges as the primary determinant, likely influenced by more substantive elements such as content quality or pedagogical design rather than merely the technical features of the platform.
Penerapan E-SCM dalam Usaha Bisnis Rumahan di NSH Group Julita, Rizka; Helmiah, Fauriatun; Sudarmin, Sudarmin
Dinamik Vol 31 No 1 (2026)
Publisher : Universitas Stikubank

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35315/dinamik.v31i1.10313

Abstract

Business is an economic activity carried out by individuals or organizations to produce and sell goods or services with the aim of making a profit. The NSH Group Store is a business that sells carpets, pillows, bolsters, and dolls located in the Sei Dadap I/II Plantation, Sei Dadap District, Asahan Regency, North Sumatra 21225. The NSH Group Store was established in 2016 and is owned by Mrs. Siti Komariah Siregar. Among the challenges faced by the NSH Group Store owner are irregular stock procurement. Sales transaction processes still use conventional methods, reducing efficiency and time effectiveness, and potentially leading to data errors. Supply Chain Management is a series of approaches used to efficiently integrate suppliers so that goods can be distributed in the right quantities, locations, and at the right time, with the aim of minimizing overall system costs. A bolster pillow is a pillow that can function as both a pillow and a bolster. Bolster pillows are oval and long, so they can be hugged while sleeping. The benefits of a bolster pillow include maintaining a proper sleeping position, reducing pressure on joints, helping reduce aches, improving sleep quality, and improving overall health. Therefore, by implementing Supply Chain Management (SCM), data processing will be faster and more accurate.
Penerapan SCM dalam Pengendalian Sembako sebagai Inventory di UD. Putri 2 Rahma Diffa, Rafi Alif; Dalimunthe, Ruri Ashari; Sudarmin, Sudarmin
Dinamik Vol 31 No 1 (2026)
Publisher : Universitas Stikubank

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35315/dinamik.v31i1.10314

Abstract

Business ventures are activities carried out by individuals or organizations involving the production, sale, purchase, or exchange of goods and services, with the aim of generating profit. A basic necessities store (commonly known as a “sembako” store in Indonesia) sells daily staple needs, especially the nine essential commodities (sembako), which include items such as rice, sugar, cooking oil, eggs, salt, and other key food ingredients. UD. Putri 2, located in Dusun 1A, Sumber Harapan Village (21261), Tinggi Raja Subdistrict, Asahan Regency, was established in 2018 and has since become an essential part of the local community. This has required UD. Putri 2 to constantly monitor their stock inventory. However, the company still faces inefficiencies in managing sales data processing, which often leads to inventory shortages. When the supply of goods is insufficient to meet customer demand, customers may turn to other stores. If this occurs repeatedly, the store risks losing profit due to the unavailability of goods. Supply Chain Management (SCM) refers to the integrated processes and production activities starting from the acquisition of raw materials from suppliers, the value-adding processes that turn raw materials into finished products, the inventory storage process, and the distribution of finished goods to retailers and consumers. The implementation of SCM can optimize inventory management of staple goods, minimize inventory costs, and improve supply chain efficiency at UD. Putri 2.

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