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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.
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Articles 168 Documents
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Development of Drip Irrigation Monitoring and Control System Model Based on the Internet of Things Using Android Applications Walid, Miftahul; Horiyah, Horiyah; Rofiuddin, Rofiuddin; Susilo, Purnomo Hadi; Wahyudi, Muhammad Hasan
INTENSIF: Jurnal Ilmiah Penelitian dan Penerapan Teknologi Sistem Informasi Vol 9 No 1 (2025): February 2025
Publisher : Universitas Nusantara PGRI Kediri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29407/intensif.v9i1.23113

Abstract

Background: Efficient water management is crucial for sustainable agriculture, particularly in regions with limited water resources. Drip irrigation systems, when integrated with the Internet of Things (IoT), offer a promising solution to optimize water usage and enhance agricultural productivity. Objective: This study aims to develop an IoT-based drip irrigation system to improve water efficiency and support small-scale farmers by providing a cost-effective and adaptable solution. Methods: The system employs multiple sensors to monitor key environmental parameters, including soil moisture, air temperature, and water levels in the tank. The collected data is transmitted to the ThingSpeak cloud platform via an Android application for real-time monitoring and control. Performance metrics such as sensor reaction time, solenoid valve response time, and pump response time were analyzed to evaluate system effectiveness. Results: Experimental findings show that the system effectively monitors and regulates irrigation, with an average sensor reaction time of 2.95 seconds, a solenoid valve response time of 2.75 seconds, and a pump response time of 2.3 seconds. The system successfully automates irrigation, ensuring optimal water usage. Conclusion: The IoT-based drip irrigation system enhances water resource management, increases crop yield, and reduces operational costs. While the system demonstrates high efficiency, further research could focus on scalability, integration with predictive analytics, and adaptation to different crop types and environmental conditions.
Identifying Key Features in Yelp Data for Success in Different Types of Restaurants Priyadi, Andrianshah; Lande, Nelly Malik; Faradilla, Anita; Hasan, Ma’arif; Widianti, Evi
INTENSIF: Jurnal Ilmiah Penelitian dan Penerapan Teknologi Sistem Informasi Vol 9 No 1 (2025): February 2025
Publisher : Universitas Nusantara PGRI Kediri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29407/intensif.v9i1.23476

Abstract

Background: The purpose of this research is to measure of customer satisfaction for newly established independent restaurants and, consequently, good predictors of independent restaurant success. Urban communities face several challenges, including how to best use scarce resources like real estate and support small enterprises. Smart businesses are essential to the development of smart cities because they use data analytics to inform their strategic planning and design choices, and the target of this topic is restaurant. Objective: Restaurants control a sizable portion of the city market's small business sector. As part of the Yelp Data Challenge, Yelp just made available an open dataset that includes important details, ratings, and Yelp scores for every restaurant in different cities. Methods: Our methodology utilizes a vector of crucial factors to accurately forecast a business’s prospective success and exclusively evaluate eateries located inside the city limits of Las Vegas. The dependent variables will consist of the mean Yelp ratings for each restaurant and constructed our model by following the subsequent stages. Conclusion: The findings of this research is corroborated by the discovery that the statistically significant properties of restaurants, shown by a low p-value, varied across various restaurant categories, the unique modeling technique to forecast future restaurants' Yelp rankings based on their design choices. This will assist owners of restaurants in making better design choices, which will result in more prosperous small enterprises in urban settings.
Environmental Acoustic Features Robustness Analysis: A Multi-Aspecs Study Semma, Andi Bahtiar; Kusrini, Kusrini; Setyanto, Arif; da Silva, Bruno; Braeken, An
INTENSIF: Jurnal Ilmiah Penelitian dan Penerapan Teknologi Sistem Informasi Vol 9 No 1 (2025): February 2025
Publisher : Universitas Nusantara PGRI Kediri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29407/intensif.v9i1.23723

Abstract

Abstract—Background: Acoustic signals are complex, with temporal, spectral, and amplitude variations. Their non-stationarity complicates analysis, as traditional methods often fail to capture their richness. Environmental factors like reflections, refractions, and noise further distort signals. While advanced techniques such as adaptive filtering and deep learning exist, comprehensive acoustic feature analysis remains limited.  Objective: This study investigates which acoustic features maintain the highest robustness across diverse environments while preserving discriminative power.  Methods: Audio samples were recorded in controlled environments (jungles, cafés, factories, streets) with varying noise levels. Standardized equipment captured 22050 Hz, 16-bit audio at multiple positions and distances. After amplitude standardization, various acoustic features were extracted and analyzed.  Results: MFCCs demonstrated exceptional reliability, with correlation coefficients of 0.98819 and 0.98889 for closely positioned devices and a robustness score of 0.99. Across different acoustic scenes and sample lengths (1, 3, 5s), MFCCs maintained high correlation (≈0.978) and robustness (0.98), confirming their versatility.  Conclusion: MFCCs proved highly effective for acoustic fingerprinting across settings. Despite limitations in tested environments (≤5m distance, ≤5s samples), their consistent performance validates the methodology. Future research should explore combining MFCCs with spectral features and expanding studies to broader environments and device types.
Sentiment Analysis of Suicide on X Using Support Vector Machine and Naive Bayes Classifier Algorithms Mardianto, M. Fariz Fadillah; Pratama, Bagas Shata; Audilla, Marfa; Pusporani, Elly
INTENSIF: Jurnal Ilmiah Penelitian dan Penerapan Teknologi Sistem Informasi Vol 9 No 1 (2025): February 2025
Publisher : Universitas Nusantara PGRI Kediri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29407/intensif.v9i1.23742

Abstract

Background: The World Health Organization (WHO) defines health as a state of physical, mental, and social well-being, not just the absence of disease. Mental health, essential for overall well-being, is often neglected, leading to disorders like depression, a major cause of suicide. In Indonesia, suicide cases have surged, with 971 reported from January to October 2023. Objective: This study aims to analyze public sentiment regarding the rise in suicide cases in Indonesia using sentiment analysis methods, specifically Support Vector Machine (SVM) and Naive Bayes Classifier (NBC). The findings are expected to raise public awareness and provide policy recommendations to support mental health initiatives. Methods: One method used to understand public perception regarding the issue of suicide is text mining. This research employs text mining techniques with the Support Vector Machine (SVM) and Naive Bayes Classifier algorithms to analyze public sentiment related to suicide cases in Indonesia. Data was collected from tweets on social media platform X using crawling methods with snscrape and Python, totaling 1,175 tweets. Results: The results indicate that the Linear SVM model achieved higher accuracy than Naive Bayes in classifying tweet sentiments, with an accuracy rate of 80%. Conclusion: The SVM algorithm with a linear kernel achieved 80% accuracy and an identical ROC-AUC score. Word cloud visualization highlighted terms like "kill," "self," "depression," and "stress" as key negative sentiments. This study aims to raise public awareness and support better mental health policies in Indonesia.
Extending the Expectation Confirmation Model to Examine Continuous Use Mobile Banking: Security, Trust, and Convenience Habib, Ahmad; Pramana, Edwin; Junaedi, Hartarto; Ronando, Elsen
INTENSIF: Jurnal Ilmiah Penelitian dan Penerapan Teknologi Sistem Informasi Vol 9 No 1 (2025): February 2025
Publisher : Universitas Nusantara PGRI Kediri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29407/intensif.v9i1.23751

Abstract

Background: Mobile banking adoption continues to grow, but user retention remains a challenge. Understanding the factors influencing continuance intention is crucial for improving long-term engagement. Prior research highlights the importance of confirmation, perceived usefulness, security, satisfaction, trust, and convenience, yet their interrelationships require further exploration. Objective: This study examines key determinants of users' intention to continue using mobile banking services, focusing on how confirmation, perceived usefulness, security, satisfaction, trust, and convenience influence this decision. Methods: A quantitative study was conducted using structural equation modeling (SEM) to analyze relationships among these factors. Data were collected from mobile banking users and assessed for statistical significance. Results: Confirmation significantly impacts perceived usefulness (0.576) and satisfaction (0.527). Perceived usefulness influences satisfaction (0.289) and continuance intention (0.396), while satisfaction also affects continuance intention (0.240). Trust plays a role (0.211), and perceived security strongly influences trust (0.651). Perceived convenience also impacts continuance intention (0.304), emphasizing its importance in user experience. Conclusion: Confirmation and security are critical for satisfaction and trust, which drive continued mobile banking use. Strengthening security, improving perceived usefulness, and fostering trust can enhance user retention. Future studies should explore additional variables, test the model across demographics, and assess the impact of emerging technologies like AI and blockchain. Longitudinal and experimental research may offer deeper insights into these evolving relationships.
Uncovering Key Topics in Indonesian Political Discourse Through Twitter Analysis After the 2024 Presidential Inauguration Using Clustering methods Hidayatullah, Syarif; Nuraini, Ulfa Siti
INTENSIF: Jurnal Ilmiah Penelitian dan Penerapan Teknologi Sistem Informasi Vol 9 No 1 (2025): February 2025
Publisher : Universitas Nusantara PGRI Kediri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29407/intensif.v9i1.23771

Abstract

Background: Social media, especially Twitter, plays a key role in political discourse, shaping public opinion. In Indonesia, the 2024 presidential Inauguration , with candidates Prabowo Subianto and Gibran Rakabuming Raka, has generated significant online conversations. Understanding public sentiment and identifying key topics is urgent for analyzing and grouping these discussions, offering insights into political views. Objective: The purpose of this research is to analyze Twitter conversations surrounding the 2024 Indonesian presidential election. The goal is to identify the main topics in these conversations and assess the effectiveness of different clustering algorithms in grouping similar tweets. Methods: This study applies a quantitative approach, using a dataset of 29,905 tweets collected from October 20 to October 25, 2024. The method includes text preprocessing, such as tokenization, stemming, and word weighting. PCA is used for dimensionality reduction. The clustering algorithms K-means, DBSCAN, PAM, and Agglomerative Hierarchical are employed, with performance evaluated based on the Silhouette Score. Results: The results reveal that the Agglomerative Hierarchical Clustering algorithm with Ward linkage and two PCA components produced the highest Silhouette Score of 0.8018. The clustering identified three distinct topics: political leadership, work and collaboration, and unity. Conclusion: This research successfully identified key discussion topics in Twitter conversations about the 2024 Indonesian presidential election. The Agglomerative Hierarchical method with Ward linkage was the most effective clustering algorithm. These findings offer valuable insights into public opinion, and future studies could expand to other social media platforms or investigate the relationship between sentiment and political outcomes.
Optimizing Rice Plant Disease Classification Using Data Augmentation with GANs on Convolutional Neural Networks Agustin, Tinuk; Saputro, Indrawan Ady; Rahmadi, Mochammad Luthfi
INTENSIF: Jurnal Ilmiah Penelitian dan Penerapan Teknologi Sistem Informasi Vol 9 No 1 (2025): February 2025
Publisher : Universitas Nusantara PGRI Kediri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29407/intensif.v9i1.23834

Abstract

Background: Rice disease classification using CNN models faces challenges due to limited data, particularly in minority classes, and inconsistent image quality, which affect model performance. Data augmentation techniques can potentially enhance classification accuracy by improving data diversity and quality. Objective: This study evaluates the effectiveness of data augmentation techniques, specifically classical augmentation and Deep Convolutional Generative Adversarial Networks (DCGAN), in improving CNN performance for rice disease classification. Methods: A quantitative study was conducted using four CNN training scenarios: no augmentation, classical augmentation, DCGAN augmentation, and a combination of both. Model accuracy was analyzed to determine the impact of each augmentation technique. Results: The baseline CNN model achieved an accuracy of 91.88%. Classical augmentation improved accuracy by 2.56%, while DCGAN augmentation led to a 5.44% increase. The combination of classical augmentation and DCGAN yielded the highest accuracy of 98.13%. Conclusion: Data augmentation significantly enhances CNN performance in rice disease classification, with the combined approach of classical augmentation and DCGAN proving to be the most effective. These findings highlight the importance of augmentation techniques in addressing data limitations and improving classification accuracy. Future research should explore additional augmentation strategies and test the model across different datasets to further validate its effectiveness.
Artificial Bee Colony-Based Optimization for Public Electric Vehicle Charging Station Placement Samsurizal, Samsurizal; Cahyo, Agung Dwi; Afandi, Arif Nur; Ardina, Andi Ahyina; Sari, Resi Kumala
INTENSIF: Jurnal Ilmiah Penelitian dan Penerapan Teknologi Sistem Informasi Vol 9 No 1 (2025): February 2025
Publisher : Universitas Nusantara PGRI Kediri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29407/intensif.v9i1.23918

Abstract

Background: The urgency of developing Electric Vehicle Charging Stations (EVCS) infrastructure is increasing alongside the need for low-emission mobility and energy efficiency. Objective: This study aims to optimize the determination of EVCS locations using the Artificial Bee Colony (ABC) method. Methods: This method was selected for its capability to find optimal solutions through an iterative population-based approach. Simulations were conducted by limiting the maximum iterations to 1000 to evaluate the impact of iteration numbers on optimization quality. Results: The results show that the ABC method successfully identified the shortest distance from three initial locations to the optimal EVCS locations. In the second simulation, the shortest distance obtained was 0.6420 km, indicating that an increase in the number of iterations correlates directly with the quality of optimization results. Specifically, the optimal distance from the first initial location to the EVCS at Danareksa Tower was 1.7018 km using the ultra-fast charging type. From the second initial location to the EVCS at the Ministry of State-Owned Enterprises Building, the optimal distance was 0.6420 km using the fast-charging type. Meanwhile, from the third initial location to the EVCS at PLN UID Greater Jakarta, the optimal distance was 1.1787 km using the ultra-fast charging type. Conclusion: This study demonstrates that the ABC method can deliver accurate results in determining optimal EVCS locations with efficient distances. These findings are expected to support the development of more effective and integrated electric vehicle infrastructure.
Implementation of SMOTE to Improve the Performance of Random Forest Classification in Credit Risk Assessment in Banking Nanda, Nafa Nur Adifia; Farida, Yuniar; Utami, Wika Dianita
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.v9i2.23930

Abstract

Background: Credit is essential in banking operations, facilitating investment, corporate expansion, and financial satisfaction. Credit risk may emerge if the borrower defaults on payment commitments. Objective: This study aims to evaluate an individual's creditworthiness by classifying and assessing their eligibility for credit. Methods: This study uses the Random Forest technique to categorize credit risk evaluation. Random Forest is a decision tree technique recognized for its high accuracy in data classification, utilizing an ensemble method of many decision trees. Before executing the classification process, issues frequently arise when data cannot be directly processed due to class imbalance. This study employs the SMOTE (Synthetic Minority Over-sampling Technique) algorithm to address class imbalance. The SMOTE algorithm is a method that emphasizes oversampling and is designed to augment the data in the minority class by generating synthetic data that aligns with the minority class data. The findings indicated that the ideal ratio for partitioning training and testing data was 80:20, and implementing the SMOTE technique within Random Forest enhanced performance assessment. Results: This research contributes to improving the accuracy of credit risk classification using the Random Forest algorithm, which effectively handles complex data and is supported by the implementation of SMOTE to overcome the class imbalance in the data. The classification accuracy value rose from 91.54% to 94.41%. The precision value rose from 90.83% to 97.03%, while the recall value increased from 60.26% to 91.55%. Conclusion: This method helps banks identify high-risk debtors more objectively and efficiently and supports appropriate credit decision-making.
Evaluating YOLOv8-Based Distance Estimation: A Comparison of OpenCV and Coordinate Attention Weighting in Blind Navigation Systems Syahrudin, Erwin; Utami, Ema; Hartanto, Anggit Dwi; Raharjo, Suwanto
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.v9i2.24395

Abstract

Background: Recent developments in assistive technologies for the visually impaired have increasingly utilized computer vision techniques for real-time distance estimation. However, challenges remain in balancing accuracy, latency, and robustness under dynamic environmental conditions. Objective: This study aimed to evaluate and compare the performance of OpenCV and Coordinate Attention Weighting (CAW) models for distance estimation in blind navigation systems, particularly focusing on their effectiveness in real-time scenarios. Methods: A quantitative experimental study was conducted using an image dataset labeled with actual distances. The baseline performances of OpenCV and CAW were measured and compared. Subsequently, targeted optimizations were applied to the OpenCV model, including adaptive image filtering, hyperparameter tuning, and integration of a Kalman filter. Results: Initial evaluation showed that CAW achieved a higher baseline accuracy of 88% compared to OpenCV. However, after optimizations, OpenCV’s accuracy improved by 15%, reaching approximately 85%. Additionally, the optimized OpenCV model demonstrated reduced latency, outperforming CAW in real-time detection speed. Under varying lighting and motion conditions, OpenCV also exhibited superior robustness compared to CAW. Conclusion: The findings suggest that with proper optimization, OpenCV can match or exceed CAW in key performance aspects, making it a viable and efficient alternative for real-time distance estimation in blind navigation systems. Future research should explore further model integration and hardware acceleration for deployment in wearable devices.

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