cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
-
Contact Email
-
Phone
-
Journal Mail Official
-
Editorial Address
-
Location
Kab. minahasa utara,
Sulawesi utara
INDONESIA
CogITo Smart Journal
Published by Universitas Klabat
ISSN : 25412221     EISSN : 24778079     DOI : -
Core Subject : Science, Education, Engineering,
Computer Science & IT Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering
CogITo Smart Journal adalah jurnal ilmiah di bidang Ilmu Komputer yang diterbitkan oleh Fakultas Ilmu Komputer Universitas Klabat anggota CORIS (Cooperation Research Inter University) dan IndoCEISS (Indonesian Computer Electronics and Instrumentation Support Society). CogITo Smart Journal dua kali setahun, yaitu setiap bulan Juni dan Desember. CogITo Smart Journal menerima berbagai naskah yang sifatnya baru dan asli dari hasil penelitian, telaah pustaka, dan resensi buku dari bidang Ilmu Komputer dan Informatika yang boleh ditulis dalam Bahasa Indonesia atau Bahasa Inggris. Kata CogITo berasal dari Bahasa Latin yang berarti I Think. Sehihngga CogITo Smart berarti I Think Smart dalam Bahasa Inggris.
Arjuna Subject : -
Articles 336 Documents
 30   31   32   33   34 
Optimizing Network Traffic Classification Models with a Hybrid Approach for Large-Scale Data Handoko, Andrew C; Hendry, Hendry; Wellem, Theophilus
CogITo Smart Journal Vol. 11 No. 2 (2025): Cogito Smart Journal
Publisher : Fakultas Ilmu Komputer, Universitas Klabat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31154/cogito.v11i2.966.281-294

Abstract

The escalating threat of cyberattacks necessitates the development of intrusion detection models that are both accurate and computationally efficient for large-scale network traffic. To address this issue, this study proposes a hybrid approach combining Autoencoder, Convolutional Neural Network (CNN), and XGBoost as an adaptive and lightweight solution for network traffic classification. The key contribution of this research lies in the design of a multi-stage pipeline that performs dimensionality reduction, feature extraction, and final classification. The model was evaluated using the Moore Dataset, which contains complex and high-dimensional network traffic data. The experimental results indicate that the proposed hybrid model achieved a classification accuracy of 99.20% with a testing time of only 0.09 seconds. Furthermore, the pipeline significantly reduced computational load compared to single CNN or XGBoost models. These findings demonstrate that the hybrid approach not only offers high classification performance but also enhances scalability and efficiency, making it suitable for real-world implementation in modern network security systems. Overall, the proposed model presents a promising and practical solution for advancing future intrusion detection systems.
Transparency and Trust in Minahasa Tourism Advertising using Blockchain Rianto, Indra; Djamen, Arje Cerullo; Tampi, Tesalonika Inryanti; Langitan, Jentelino Silvester; Modeong, Merriam
CogITo Smart Journal Vol. 11 No. 2 (2025): Cogito Smart Journal
Publisher : Fakultas Ilmu Komputer, Universitas Klabat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31154/cogito.v11i2.969.323-336

Abstract

Abstract Tourism plays a vital role in driving economic growth, and Minahasa holds strong potential to optimize this sector. However, challenges remain in digital advertising, particularly regarding transparency and consumer trust. This study investigates the impact of blockchain technology on transparency, trust, and the effectiveness of digital advertising in Minahasa’s tourism industry. A quantitative explanatory design was employed using Partial Least Squares Structural Equation Modeling (SEM-PLS), with data collected from 150–250 respondents through purposive and snowball sampling techniques.The findings reveal that blockchain significantly influences all key variables. It enhances advertising transparency (T-statistic = 36.738, p = 0.000), strengthens consumer trust (T-statistic = 33.164, p = 0.000), and improves advertising effectiveness (T-statistic = 28.400, p = 0.000). These results highlight blockchain’s capacity to provide immutable records, ensure data authenticity, and optimize ad performance through verifiable real-time information. This study confirms that blockchain can serve as a strategic tool to promote transparent, trustworthy, and effective digital advertising in tourism. The findings provide practical insights for tourism stakeholders and contribute to academic discussions on technology-driven marketing innovation.
Text Similarity Analysis for Evaluating Alignment Between Lesson Plans and Teaching Reports Rachmat Chrismanto, Antonius; Sudiarto Raharjo, Willy; Gilang Purnajati, Oscar
CogITo Smart Journal Vol. 11 No. 2 (2025): Cogito Smart Journal
Publisher : Fakultas Ilmu Komputer, Universitas Klabat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31154/cogito.v11i2.976.414-429

Abstract

RPS (Rencana Pembelajaran Semester, or called Lesson Plans) is a class activity planning document in the higher education learning process that includes learning outcomes, methods, learning strategy, and evaluation criteria. It is created by the lecturers in charge of the course and coordinated with the relevant department. This document needs to be monitored throughout the semester for its conformity with the implementation document (Borang Pelaksanaan Perkuliahan (BPP)). It was done manually through our eRPS system, but it requires a lot of effort and precision and is not time-efficient. This research focused on evaluating the effectiveness of several content-based text similarity methods to detect RPS conformity compared with the BPP, or called Teaching Reports document. The Boyer-Moore (B), Rabin-Karp (R), Jaccard (JC), Jaro-Winkler (JW), Smith-Waterman (SW), Knuth-Morris-Pratt (K), Levenehtein cosine similarity (C), Dice (D), Jaro (J), and Soundex (S) algorithms were evaluated in this paper. In the vector-based similarity method, TF-IDF was used. The evaluation of 11 string-matching algorithms across four scenarios demonstrated clear performance trends. Fuzzy algorithms (SW with accuracy 0,845–0,870, and JW with accuracy 0,840-0,850) achieved the highest accuracy in a single row of lecturer scenario, while exact/pattern-based algorithms (B, K, and S with accuracy 0,8625–0,8725) on a combination of all rows of lectures with minimal variance (≈0,005–0,015).  Pre-processing benefits fuzzy algorithms (+2.5%) but is neutral for exact/pattern-based algorithms. The combined scenario improves the exact/phonetic algorithms (+6–7%) but reduces the fuzzy performance algorithm (−10–14%). The optimal thresholds were generally 40–50%, except for JW and J, which were 65%.
Neural Dynamic Network for Brain Tumor Classification: An Attention-Based Feature Selection Approach Naseer, Muchammad; Agustina, Nova; Gusdevi, Harya; Riyanti, Niken
CogITo Smart Journal Vol. 11 No. 2 (2025): Cogito Smart Journal
Publisher : Fakultas Ilmu Komputer, Universitas Klabat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31154/cogito.v11i2.989.430-446

Abstract

Magnetic Resonance Imaging (MRI) plays a vital role in the early detection of brain tumors. However, standard Convolutional Neural Network (CNN) models often struggle to extract truly relevant features from complex MRI structures. This limitation creates a gap in achieving robust and clinically interpretable classifications, as feature redundancy and weak attention toward tumor-specific regions may reduce diagnostic reliability. To address this gap, this study introduces a Neural Dynamic Network (NDN) that integrates EfficientNetV2S with a dynamic attention-based mechanism to adaptively highlight informative features while suppressing noise. The proposed model was evaluated using a 5-fold cross-validation scheme and tested on unseen data. Compared with the baseline CNN, the NDN consistently demonstrated higher accuracy, precision, recall, and F1-score across folds and final testing, reflecting improved robustness and balanced sensitivity. NDN yielded significant improvements, with the 5-fold validation averaging an accuracy of 88.44%, a precision of 87.84%, a recall of 87.88%, and an F1-score of 87.82%.  Beyond numerical performance, interpretability analysis utilizing Grad-CAM demonstrated that NDN generates more concentrated and clinically consistent heatmaps. In contrast, the baseline CNN produced dispersed activations that exhibited less alignment with tumor regions. Overall, the findings confirm that incorporating a dynamic attention-based mechanism substantially enhances both feature selection and visual interpretability. This makes the NDN architecture more reliable for MRI-based brain tumor classification and highly suitable as a decision-support tool in clinical workflows.
Funnel-Based Predictive Modeling for Forecasting Student Admissions in Higher Education Lumbanraja, Obaja Marum
CogITo Smart Journal Vol. 11 No. 2 (2025): Cogito Smart Journal
Publisher : Fakultas Ilmu Komputer, Universitas Klabat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31154/cogito.v11i2.1002.337-348

Abstract

Forecasting student admissions remains a challenge due to fluctuating online engagement and complex administrative processes. Existing predictive models rarely integrate website behavioral data with institutional admission funnels, resulting in lower accuracy. This study bridges that gap by combining web analytics from Google Analytics 4 (GA4) with administrative enrollment funnel data from the admission of new students (Penerimaan Mahasiswa Baru/PMB) system to develop a unified predictive framework. The approach strengthens forecasting by aligning digital behavior with verified enrollment milestones. A quantitative explanatory design was employed, applying Pearson correlation to identify linear relationships and Seasonal ARIMA (SARIMA) to model cyclical admission trends. The dataset includes GA4 metrics sessions, engagement rate, bounce rate, and events per session and PMB funnel stages from account creation to confirmed enrollment. Results reveal strong correlations (r > 0.9, p < 0.001) between digital engagement and mid-funnel conversions, while SARIMA achieved its highest accuracy for early-stage predictions (MAPE ≈ 19%). Forecasts for final outcomes were less accurate, reflecting administrative variability. These findings confirm that web engagement metrics are reliable leading indicators of student interest and mid-stage commitment. This research establishes a reproducible pipeline unifying web analytics (GA4) with institutional funnel data (PMB), providing empirical evidence that digital engagement is a reliable leading indicator of early and mid-stage commitment, thereby forming a novel and adaptable foundation for data-driven enrollment planning.
Examining Lecturers’ Learning Management System Usage Using TAM: Eastern Indonesia Case Study Sondakh, S.Kom, M.T, Ph.D, Debby Erce; Liem, Andrew Tanny; Kasihidi, Tesalonika Angelina; Tauran, Veronica Joan Amelia
CogITo Smart Journal Vol. 11 No. 2 (2025): Cogito Smart Journal
Publisher : Fakultas Ilmu Komputer, Universitas Klabat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31154/cogito.v11i2.1011.349-367

Abstract

The implementation of Learning Management Systems (LMS) in higher education institutions continues to increase in line with the growing demand for flexible digital learning, with the assumption that LMS is an easy-to-use platform that will be naturally accepted by lecturers. This study aims to analyze the factors that influence the adoption of LMS among lecturers at higher education institutions in Eastern Indonesia. This study uses a quantitative cross-sectional survey. The research instrument, comprising 25 items classified into five constructs —Constructivist Pedagogical Beliefs, Traditional Pedagogical Beliefs, Perceived Ease of Use, Perceived Usefulness, and LMS Use —was administered to lecturers at a private university in North Sulawesi. Using the Partial Least Squares-Structural Equation Modeling approach, this study incorporates the Technology Acceptance Model with a constructivist and traditional pedagogical belief orientation. The results show that three of the eight variables significantly influence LMS usage. The findings indicate that constructivist pedagogical beliefs and perceived usefulness have a significant influence on LMS adoption, whereas traditional pedagogical beliefs do not have a significant impact. These results have practical implications for universities in designing training policies and strategies to optimize LMS usage.

Page 34 of 34 | Total Record : 336

 30   31   32   33   34 

Filter by Year

2015 2025


Reset
Filter By Issues
All Issue Vol. 11 No. 2 (2025): Cogito Smart Journal Vol. 11 No. 1 (2025): Cogito Smart Journal Vol. 10 No. 2 (2024): Cogito Smart Journal Vol. 10 No. 1 (2024): Cogito Smart Journal Vol. 9 No. 2 (2023): Cogito Smart Journal Vol. 9 No. 1 (2023): Cogito Smart Journal Vol. 8 No. 2 (2022): Cogito Smart Journal Vol. 8 No. 1 (2022): Cogito Smart Journal Vol 7, No 2 (2021): Cogito Smart Journal Vol 7, No 1 (2021): Cogito Smart Journal Vol 6, No 2 (2020): Cogito Smart Journal Vol 6, No 1 (2020): Cogito Smart Journal Vol 5, No 2 (2019): Cogito Smart Journal Vol 5, No 1 (2019): Cogito Smart Journal Vol 4, No 2 (2018): CogITo Smart Journal Vol 4, No 1 (2018): CogITo Smart Journal Vol 3, No 2 (2017): CogITo Smart Journal Vol 3, No 1 (2017): CogITo Smart Journal Vol 2, No 2 (2016): CogITo Smart Journal Vol 2, No 1 (2016): CogITo Smart Journal Vol 1, No 1 (2015): CogITo Smart Journal More Issue