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INTENSIF: Jurnal Ilmiah Penelitian dan Penerapan Teknologi Sistem Informasi
Published by Universitas Nusantara PGRI Kediri
ISSN : 2580409X     EISSN : 25496824     DOI : https://doi.org/10.29407/intensif
Core Subject : Science,
Computer Science & IT Decision Sciences, Operations Research & Management
INTENSIF Journal is a publication container for research in various fields related to information systems. These fields includeInformation System, Software Engineering, Data Mining, Data Warehouse, Computer Networking, Artificial Intelligence, e-Bussiness, e-Government, Big Data, Application Development, Geograpic Information System, Information Retrieval, Information Technology Infrastructure, Knowledge Management System, Enterprise Architecture.Published periodically in February and August.
Arjuna Subject : -
Articles 168 Documents
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Enhancing Multi-Class Classification of Non-Functional Requirements Using a BERT-DBN Hybrid Model Suris, Badzliana Aqmar; Thobirin, Aris; Surono , Sugiyarto; Abdulnazar, Mohamed Naeem Antharathara
INTENSIF: Jurnal Ilmiah Penelitian dan Penerapan Teknologi Sistem Informasi Vol 9 No 2 (2025): August 2025
Publisher : Universitas Nusantara PGRI Kediri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29407/intensif.v9i2.24637

Abstract

Background: Software requirements classification is essential to group Non-Functional Requirements (NFR) into several aspects, such as security, usability, performance, and operability. The main challenges in NFR classification are data limitations, text complexity, and high generalization needs. Objective: This research seeks to create a classification model using a hybrid of BERT and DBN, optimize hyperparameters, and improve data representation. Methods: A BERT and DBN-based approach is used, where DBN enhances BERT's ability to extract hierarchical features. Bayesian Optimization determines the optimal hyperparameters and data augmentation is applied to enrich the dataset variation. The model is tested on the PROMISE dataset consisting of 625 data. Results: The BERT-DBN model achieves 95% accuracy on the baseline configuration and 94% on the extensive configuration, better than the previous model, BERT-CNN. The model shows stability without any indication of overfitting. Conclusion: The combination of data augmentation, hyperparameter optimization, and DBN's ability to capture hierarchical patterns improves the accuracy of NFR classification, making it more effective than existing methods, and is expected to enhance text-based classification for software requirements.
Enhancing Accessibility, Engagement, and Motivation in Counseling Services for Secondary Schools through Gamified Blended Mobile and Virtual Reality Therapy Prasetyaningrum, Putri Taqwa; Ibrahim, Norshahila; Aryani, Eka; Ningsih, Rully; Subagyo, Ibnu Rivansyah
INTENSIF: Jurnal Ilmiah Penelitian dan Penerapan Teknologi Sistem Informasi Vol 9 No 2 (2025): August 2025
Publisher : Universitas Nusantara PGRI Kediri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29407/intensif.v9i2.24814

Abstract

Background: Secondary school counseling services often face challenges such as limited counselor availability and low student participation. Traditional counseling methods frequently fail to engage students, thus reducing both accessibility and impact. Integrating Virtual Reality (VR) and mobile-based interventions presents a promising solution to address these issues. Objective: This study aims to evaluate the effectiveness of a gamified blended mobile and VR therapy in enhancing accessibility, cognitive-emotional-behavioral engagement, and motivation within secondary school counseling services. Methods: A mixed-methods research design was employed, combining quantitative methods (pre- and post-intervention surveys, along with behavioral tracking) and qualitative methods (semi-structured interviews and thematic analysis of focus group discussions). These methods were chosen to capture both measurable impacts and participants’ perceptions of the intervention. A total of 384 students and 10 counselors participated in an 8-week intervention. Results: The intervention led to a significant improvement in the Accessibility Index (from 3.2 to 4.6). Additionally, engagement across cognitive, emotional, and behavioral dimensions showed marked improvements. Thematic analysis revealed that students appreciated the safety and realism provided by the digital counseling environment. Conclusion: The gamified blended therapy approach effectively enhanced counseling accessibility and multidimensional engagement, offering a scalable, student-centered solution for secondary school counseling services.
Enhancing Vision Transformer Performance with Rotation Based Augmentation for Classifying Images of Colon Cancer Pathology Prasetya, Rudy Eko; Soeleman, M. Arief; Al Zami, Farrikh; Affandy, Affandy; Marjuni, Aris; Assaqty, Mohammad Iqbal Saryuddin
INTENSIF: Jurnal Ilmiah Penelitian dan Penerapan Teknologi Sistem Informasi Vol 9 No 2 (2025): August 2025
Publisher : Universitas Nusantara PGRI Kediri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29407/intensif.v9i2.24918

Abstract

Background: In medical imaging, classifying images of colon cancer pathology is still an essential challenge, especially for facilitating early diagnosis and successful intervention. Recently, Vision Transformer (ViT) models have demonstrated great promise for a variety of computer vision tasks, including the classification of medical images. However, the lack of annotated medical datasets and the intrinsic unpredictability of histopathology pictures sometimes restrict their performance. Objective: This study aims to enhance the performance of ViT models in colon cancer pathology classification by introducing a targeted data augmentation strategy, with a particular focus on rotation-based augmentation. Methods: We proposed a data augmentation pipeline that uses controlled changes to improve the number and diversity of training data. Like Rotation, Flip and Geometry are emphasized to replicate the real-world tissue orientation variations that are frequently seen in colon pathology slides. 10,000 JPEG pictures of colon cancer pathology, each with a resolution of 768 x 768 pixels, are used to train the models. We use models trained with and without the suggested augmentation pipeline to compare ViT performance across accuracy, sensitivity, and specificity in order to assess the impact of augmentation. Results: According to study results, rotation-based augmentation enhances ViT performance, achieving up to 99.30% accuracy and 99.50% sensitivity while preserving training times. In real-world pathology settings, where slide orientation varies greatly and can affect categorization consistency, these enhancements are especially pertinent. Conclusion: The proposed rotation-centric data augmentation technique enhances the performance of the ViT model in the classification of images showing colon cancer pathology.
Understanding Student Acceptance of AI in Mojokerto Regency High Schools and a Framework for Effective Integration Iswanti, Usmanur Dian; Ridwandono, Doddy; Faroqi, Asif; Chuttur, Mohammad Yasser; Suryanto, Tri Lathif Mardi
INTENSIF: Jurnal Ilmiah Penelitian dan Penerapan Teknologi Sistem Informasi Vol 9 No 2 (2025): August 2025
Publisher : Universitas Nusantara PGRI Kediri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29407/intensif.v9i2.24993

Abstract

Background: The use of AI in education is growing rapidly, especially in adaptive learning and automated feedback. Recent studies show widespread adoption of AI in higher education, but research at the secondary school level is limited. Factors such as ease of use, motivation, and institutional support play an important role accepting these technologies. Objective: The objective of this study is to investigate the acceptance and usage of the Question.AI application among high school students in Mojokerto Regency, to identify the factors that influence its adoption and effectiveness in enhancing learning outcomes. Methods: The methodology adopted for this research comprises a quantitative study design using a probability sampling method, specifically the Stratified Random Sampling technique. A total of 400 high school students from Mojokerto Regency participated. Data collection was conducted through structured questionnaires designed to evaluate factors influencing the adoption of the Question.AI application. Result: The result revealed that Facilitating Conditions (FC), Habit (H), and Hedonic Motivation (HM) significantly influence students' behavioral intention to use the Question.AI application. Among these, Habit and Hedonic Motivation showed the strongest effect, indicating that students are more likely to adopt AI tools when their use becomes routine and satisfied. Conclusion: These results support the UTAUT2 framework and highlight the need for enjoyable user experiences and adequate support systems to drive sustained adoption. The findings contribute to understanding AI acceptance at the secondary education level and offer practical insights for integrating AI applications more effectively into school environments.
Smart Governance Decision-Support System for Fisheries Development in Southeast Maluku: A Conceptual Framework Hasyim, Cawalinya Livsanthi; Somnaikubun, Glenty B.A; Teniwut, Wellem Anselmus
INTENSIF: Jurnal Ilmiah Penelitian dan Penerapan Teknologi Sistem Informasi Vol 9 No 2 (2025): August 2025
Publisher : Universitas Nusantara PGRI Kediri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29407/intensif.v9i2.25166

Abstract

Background: Southeast Maluku Regency has vast marine and fishery resources; hence, the fisheries sector has not been a major economic contributor. The fisheries sector is still below its maximum capacity; this problem is caused by unsustainable fishing sector development planning. Objective: This research aimed to build framework tools to help plan and manage a sustainable and integrated fisheries sector based on empirical conditions. Methods: In this research, a suitable application framework was designed to support the development and planning of the fisheries sector in this region, the design of the input process, the input used, the interface, and the output produced to achieve smart government and a smart city. Results: This study built a conceptual framework tailored to the empirical conditions of the region in terms of geographical location and limited internet coverage for the Southeast Maluku Regency fisheries supporting master plan. Conclusion: The study provides guidance for researchers and practitioners in similar small island regions worldwide to construct a web-based intelligent DSS (decision support system) consistent with geographical conditions for planning the fisheries and marine sectors in their respective regions. The conceptual framework is adaptive which based on empirical condition both data and assessment of ranking for suitability location.
Machine Learning-Based Naïve Bayes Classification of Pineapple Productivity: A Case Study in North Sumatra Suendri, Suendri; Aprilia, Rima; Br. Rambe, Ramadiani; Zakaria, Nur Haryani
INTENSIF: Jurnal Ilmiah Penelitian dan Penerapan Teknologi Sistem Informasi Vol 9 No 2 (2025): August 2025
Publisher : Universitas Nusantara PGRI Kediri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29407/intensif.v9i2.24034

Abstract

Background: Pineapple is a major agricultural commodity in Indonesia, especially in North Sumatra, where increasing demand calls for improved productivity. Although machine learning has been widely applied in agriculture, most prior studies on pineapple focus on fruit quality assessment or employ complex, less interpretable models, leaving a gap in lightweight and practical approaches for productivity classification. Objective: This study aims to evaluate the novelty and effectiveness of the Naïve Bayes algorithm in classifying pineapple productivity based on agronomic characteristics, addressing the underexplored use of this method for productivity prediction in pineapple cultivation. Methods: A descriptive quantitative approach was applied using secondary data from the Labuhan Batu Agricultural Extension Center, consisting of 52 records with seven agronomic parameters. The dataset was divided into 31 training and 21 testing samples, and the Naïve Bayes model was implemented using RapidMiner 7.1, with performance measured by accuracy. The small dataset size is recognized as a limitation that may affect generalizability. Results: The Naïve Bayes model achieved an accuracy of 86.67%, effectively distinguishing between productive and unproductive pineapples and demonstrating its suitability for agricultural classification tasks even with limited data. Conclusion: This study highlights the novelty and practicality of applying Naïve Bayes for pineapple productivity classification, offering an interpretable and computationally efficient alternative to more complex models. Future work should address dataset limitations by incorporating larger and more diverse samples and exploring hybrid or ensemble approaches to further enhance performance and support precision agriculture.
A Comparative Analysis of UTAUT and UTAUT 2 in M-Commerce and M-Banking Ginting, Tri Wulandari; Sinaga, Joy Nasten
INTENSIF: Jurnal Ilmiah Penelitian dan Penerapan Teknologi Sistem Informasi Vol 9 No 2 (2025): August 2025
Publisher : Universitas Nusantara PGRI Kediri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29407/intensif.v9i2.24179

Abstract

Background: The debate over the superiority between UTAUT and UTAUT 2 has driven the development of UTAUT 3. However, this latest model is still not the best solution, as several variables remain less influential. Objective: This study aims to determine which UTAUT model is superior in technology acceptance, particularly for m-commerce and m-banking. Methods: Multiple linear regression was used to determine the direction and magnitude of the influence of independent variables on the dependent variable, analyzing key factors affecting the adoption of mobile banking and m-commerce by comparing UTAUT and UTAUT 2. Results: In m-commerce, UTAUT highlights behavioral intention and facilitating conditions, while UTAUT 2 adds habit and price value as key influencing factors. Conclusion: For m-banking, both models are equally effective, but UTAUT 2 is superior due to the strong influence of habit. In m-commerce, UTAUT 2 is also preferable, as price value significantly affects behavioral intention.
Palm Oil Quality Based on Free Fatty Acid Using SVM Prayogi, Andi; Aly, Moustafa H.; Ikhwan, Ali; Pane, Muhammad Akbar Syahbana; Siregar, Ratu Mutiara
INTENSIF: Jurnal Ilmiah Penelitian dan Penerapan Teknologi Sistem Informasi Vol 9 No 2 (2025): August 2025
Publisher : Universitas Nusantara PGRI Kediri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29407/intensif.v9i2.24797

Abstract

Background: Palm oil is one of the key commodities in both the food and non-food industries, with its quality largely influenced by the level of Free Fatty Acid (FFA). Obejctive: High FFA content can reduce the stability and market value of the oil. Classify palm oil quality based on FFA levels using the Support Vector Machine (SVM) algorithm. Methods: FFA levels were measured across multiple samples with varying usage frequencies (0, 5, 7, and 9 cycles) using the alkalimetric titration method. The measured data was categorized as "Suitable" if FFA ≤ 0.3% and "Unsuitable" if it exceeded this threshold. The developed SVM model was trained using 70% of the data and tested with the remaining 30%. Results: Evaluation results indicate that the model achieved an accuracy of 95%, a precision of 92%, and a recall of 94%, demonstrating SVM's effectiveness in classifying data. Additionally, hyperplane visualization using PCA provided a clearer distinction between oil categories based on FFA levels. Conclusion: This study highlights that SVM can serve as an effective alternative for FFA-based palm oil quality classification. The implementation of this model is expected to enhance efficiency in the palm oil industry, particularly.

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