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Muhammad Nur Faiz
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Department of Informatics Engineering Politeknik Negeri Cilacap Jln. Dr.Soetomo No.01 Sidakaya, Cilacap, Indonesia
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INDONESIA
Journal of Innovation Information Technology and Application (JINITA)
Published by Politeknik Negeri Cilacap
ISSN : 27160858     EISSN : 27159248     DOI : https://doi.org/10.35970/jinita.v2i01.119
Core Subject : Science, Engineering,
Computer Science & IT Decision Sciences, Operations Research & Management Engineering
Software Engineering, Mobile Technology and Applications, Robotics, Database System, Information Engineering, Interactive Multimedia, Computer Networking, Information System, Computer Architecture, Embedded System, Computer Security, Digital Forensic Human-Computer Interaction, Virtual/Augmented Reality, Intelligent System, IT Governance, Computer Vision, Distributed Computing System, Mobile Processing, Next Network Generation, Natural Language Processing, Business Process, Cognitive Systems, Networking Technology, and Pattern Recognition
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 160 Documents
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Comparison of YOLOv5 for Classifying Mangrove Leaf Species using CNN-Based Anindita Septiarini; Rita Diana; Rahmat Kamara; Novianti Puspitasari; Anton Prafanto
Journal of Innovation Information Technology and Application (JINITA) Vol 7 No 1 (2025): JINITA, June 2025
Publisher : Politeknik Negeri Cilacap

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35970/jinita.v7i1.2676

Abstract

Indonesia has many species of mangrove plants scattered throughout the coast to the river's edge. Species of mangrove plants can be distinguished based on root type, stem size, leaf shape, flower color, and fruit. Although each type of mangrove plant has different characteristics, several types look similar, especially on the leaves. Therefore, a model was needed to classify mangrove plant species by applying current technology to make it easier to recognize the type of mangrove plant. This research aims to implement the Convolutional Neural Network (CNN) method in classifying mangrove plant species. The algorithm used is the 5th version of You Only Look Once (YOLO) with 3 different variants (YOLOv5s, YOLOv5m, and YOLOv5l). The three variants have various processing times and numbers of layers. This study uses mangrove leaf images with a total image dataset of 400 images consisting of 4 types of mangrove plants: Avicennia alba, Bruguiera gymnorhiza, Rhizopora apiculata, and Sonneratia alba. The model performance achieved 82.50%, 88.75%, and 93.75% accuracy using YOLOv5s, YOLOv5m, and YOLOv5l, respectively.
Addressing Insider Threats: The Human Factor in Cybersecurity for Financial Institutions Hewa Majeed Zangana; Harman Salih Mohammed; Mamo Muhamad Husain
Journal of Innovation Information Technology and Application (JINITA) Vol 7 No 1 (2025): JINITA, June 2025
Publisher : Politeknik Negeri Cilacap

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35970/jinita.v7i1.2686

Abstract

Financial institutions face persistent cybersecurity threats, with insider threats emerging as a particularly complex challenge due to their human-centric nature. This study aims to examine the human factor in cybersecurity within financial institutions, with a focus on insider threats and strategies to mitigate them. A hybrid research approach was used, combining a systematic literature review (SLR) and qualitative case study analysis to investigate cybersecurity risks, AI-driven solutions, and regulatory compliance. The findings reveal that AI-powered tools—such as behavioral biometrics, machine learning, and blockchain technologies—substantially enhance fraud detection and risk management. Real-world implementations in financial institutions demonstrated improved threat response, reduced regulatory penalties, and increased operational efficiency. The study concludes that integrating technological tools with a strong cybersecurity culture can significantly mitigate insider threats.
Evaluating ERD Models and RAID-Based Storage for Query Performance Optimization in Relational Databases Juanda Hakim Lubis; Sri Handayani; Herman Mawengkang; Yuliska
Journal of Innovation Information Technology and Application (JINITA) Vol 7 No 1 (2025): JINITA, June 2025
Publisher : Politeknik Negeri Cilacap

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35970/jinita.v7i1.2707

Abstract

The amount of data stored in magnetic disks (e.g., floppy disks) increases by 100% each year for each department in a company, necessitating efforts to maintain an optimal database system. Designing a database is the initial step in creating a system with optimal performance. However, database design alone is not sufficient to enhance performance. One approach to improving data transaction speed is by optimizing query processing. This research evaluates different relational database models using varying amounts of data. Query costs are analyzed using the Cost-Based Optimizer method and access time measurements. The results of this study provide insights for database administrators in designing relational database models effectively and selecting appropriate query structures to optimize database performance. The findings indicate that: (1) database design can be optimized by separating entities based on specialized usage, and (2) factors such as record count, attribute size, query type, use of unique or primary keys, order-by clauses, index sequences, and SQL function usage significantly impact query cost and overall performance.
Sentiment Analysis Using Stacking Ensemble After the 2024 Indonesian Election Results Andy Victor Pakpahan; Fahmi Reza Ferdiansyah; Robby Gustian; Muhammad Nur Faiz; Sukma Aji
Journal of Innovation Information Technology and Application (JINITA) Vol 7 No 1 (2025): JINITA, June 2025
Publisher : Politeknik Negeri Cilacap

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35970/jinita.v7i1.2724

Abstract

Sentiment analysis is a text processing technique aimed at identifying opinions and emotions within a sentence. Machine learning is commonly applied in this area, with algorithms such as Naïve Bayes, Support Vector Machine (SVM), and Random Forest being frequently used. However, achieving optimal accuracy remains a challenge, particularly when dealing with unstructured text data, such as content from social media platforms. This research seeks to improve sentiment analysis performance by implementing a stacking ensemble learning approach, which combines the predictive strengths of several base models. The base models selected for this study are Naïve Bayes, SVM, and Random Forest, while Random Forest also serves as the meta-model to generate final predictions. The study focuses on sentiment analysis in a specific context—public opinion following the announcement of the Indonesian presidential election results in 2024. The dataset comprises 6,737 tweets collected from the X platform using web scraping techniques in 2024. Results show that individual models achieved varying levels of accuracy: Naïve Bayes at 66.84%, SVM at 77.74%, and Random Forest at 74.78%. In contrast, the stacking ensemble model achieved a significantly higher accuracy of 81.53%. This improvement highlights the effectiveness of ensemble learning in integrating different algorithmic perspectives to enhance predictive performance. By leveraging the complementary strengths of each base model, stacking not only boosts accuracy but also increases model robustness, making it highly suitable for real-world sentiment analysis applications that involve noisy and informal textual data from social media.
A Novel Principles+ Framework for Improving User Experience of Augmented Reality Tenia Wahyuningrum; Aditya Tama Isdiarto; Robert Hendra Yudianto; Rajermani Thinakaran
Journal of Innovation Information Technology and Application (JINITA) Vol 7 No 1 (2025): JINITA, June 2025
Publisher : Politeknik Negeri Cilacap

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35970/jinita.v7i1.2728

Abstract

This study introduces a framework that aligns analysis and synthesis approaches, PRInCiPleS+, to enhance the user experience (UX) for Mooi Indie paintings through Augmented Reality (AR). By integrating cultural depth, emotional engagement, and technical usability, this study aims to revive this traditional art form and engage younger audiences. The customized PRInCiPleS+ design method combines empathy mapping and sustainability considerations to develop an AR application for Instagram filters. This study highlights the role of AR in preserving traditional arts while aligning it with the Sustainable Development Goals. The results of the Mooi Indie AR test using the independent sample T Test method showed no significant difference between users who had never used AR and those who had used AR in terms of user experience. In other words, users who are using AR for the first time and those who have used AR before feel almost the same user experience. The user experience score measured using the UMUX-Lite questionnaire showed that the group of users who had never used AR had a user experience score of 78.15, while the group who had ever used AR was 71.10, which means good. Analytically, Mooi Indie AR still needs some improvement, especially in terms of filter loading time, time required to explore filters, the number of users who use filters, and then save and share them with other users to increase engagement.
Optimisation of Criminal Data Clustering Model using Information Gain Prih Diantono Abda’u; Ratih Hafsarah Maharrani; Muhammad Nur Faiz; Oman Somantri
Journal of Innovation Information Technology and Application (JINITA) Vol 7 No 1 (2025): JINITA, June 2025
Publisher : Politeknik Negeri Cilacap

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35970/jinita.v7i1.2741

Abstract

Crime is a phenomenon that significantly impacts society, necessitating mapping efforts that can be utilized for further analysis. Clustering, as a data analysis technique, groups objects based on similarities or differences in their characteristics. This approach enhances the understanding of data by identifying patterns and relationships between criminal events, such as crime type, time, and location. By clustering crime data based on similar characteristics, authorities can make more effective and efficient decisions in crime prevention and control. However, selecting too many attributes can negatively affect clustering performance. To address this issue, this study applies Information Gain reduction to reduce data dimensionality by eliminating attributes with low informational contribution. Additionally, three clustering methods K-Medoid, K-Means, and X-Means are compared to evaluate their performance. The concept of Information Gain is also integrated to optimize cluster formation, measuring how much an attribute contributes to distinguishing objects within a cluster. By leveraging Information Gain, this study aims to identify the most relevant and influential attributes in forming clusters that accurately represent crime data characteristics. Furthermore, the number of clusters generated is evaluated using the Davies-Bouldin Index (DBI). The results indicate that the K-Means algorithm outperforms the other two methods, achieving the best clustering quality with an optimal number of clusters (k = 6) and the lowest DBI value.
Interpretable Deep Learning Model for Grape Leaf Disease Classification Based on EfficientNet with Grad-CAM Visualization Castaka Agus Sugianto; Dini Rohmayani; Jhoanne Fredricka; Mohamed Doheir
Journal of Innovation Information Technology and Application (JINITA) Vol 7 No 1 (2025): JINITA, June 2025
Publisher : Politeknik Negeri Cilacap

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35970/jinita.v7i1.2745

Abstract

Grape leaf diseases pose a significant threat to agricultural productivity, especially in regions with fluctuating climatic conditions that create favorable environments for pathogen growth. Early and accurate disease detection is essential for preventing severe crop losses. Traditional manual inspection methods are inefficient and prone to human error, highlighting the need for an automated approach. This study proposes a computer vision-based solution using Convolutional Neural Networks (CNN) improved by EfficientNetB0 to classify grape leaf diseases. The model was trained on a publicly available dataset from Kaggle, which consists of 9,027 images in four classes: ESCA, Leaf Blight, Black Rot, and Healthy. Each image has a resolution of 300 × 300 pixels with a 24-bit color depth, ensuring sufficient detail for analysis. To enhance model performance, data augmentation and hyperparameter tuning were applied. The EfficientNetB0 model was employed due to its strong feature extraction capabilities and computational efficiency. The proposed model achieved 99.36% accuracy, with evaluation metrics including precision (99%), recall (99%), and F1-score (99%), demonstrating its reliability in distinguishing disease categories. Further analysis using a confusion matrix and Grad-CAM visualization provided insights into the model’s decision-making process. The results indicate that this deep learning-based approach is highly effective for grape leaf disease classification. Future research can explore real-time field data collection, attention mechanisms, and self-supervised learning to further improve classification accuracy and model generalization for large-scale agricultural applications.
TARKAM: The Advanced Robotic Kicker and Automation Machine for Goalkeeper Training Hendi Purnata; Supriyono; Sugeng Dwi Riyanto; Dwi Aji Nugroho; Wahidun Sholih
Journal of Innovation Information Technology and Application (JINITA) Vol 7 No 1 (2025): JINITA, June 2025
Publisher : Politeknik Negeri Cilacap

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35970/jinita.v7i1.2770

Abstract

This research aims to design and implement a ball-throwing robot that is used to train goalkeeper skills, such as reflexes and the ability to read the direction of the ball. The main focus of the research is to develop a ball throwing system that can provide a variety of shots with high precision, as well as overcome the limitations of manual training that relies on the individual skills of the kicker. This ball-throwing robot is controlled through a smartphone using a Bluetooth HC-05 module as a means of communication, and is equipped with two DC spindle motors, two DC PG36 motors, and two DC 12V motors to regulate the direction and speed of the ball. The ball direction and rotation control uses PID (Proportional-Integral-Derivative) control to ensure stable and accurate ball launching. The system dynamically adjusts the ball speed and direction based on position and angle sensor feedback. Tests were conducted at three different distances, namely 5 meters (100% accuracy), 7 meters (100% accuracy), and 11 meters (80% accuracy). The test results show that this ball-throwing robot is able to deliver shots with high precision, move flexibly and quickly, and adjust the direction and speed of the ball effectively. The results of the ANOVA test (p < 0.005) showed a statistically significant difference in average firing accuracy at all three distances. This research makes a practical contribution to improving the effectiveness of goalkeeper training, as well as offering a more efficient solution in football training. This research makes a practical contribution in improving the effectiveness of goalkeeper training, as well as offering a more efficient solution in soccer training.
Visual Content Captioning and Audio Conversion using CNN-RNN with Attention Model Aldy Agil Hermanto; Giat Karyono; Imam Tahyudin; Boby Sandityas Prahasto
Journal of Innovation Information Technology and Application (JINITA) Vol 7 No 1 (2025): JINITA, June 2025
Publisher : Politeknik Negeri Cilacap

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35970/jinita.v7i1.2788

Abstract

The primary objective of this research is to develop an image captioning and audio conversion system based on Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN) with the integration of an Attention Mechanism, aimed at improving accessibility for visually impaired individuals. The research design follows a systematic approach involving data collection, preprocessing, model development, training, evaluation, and implementation. The methodology utilizes CNN for visual feature extraction, RNN for language modeling, and an Attention Mechanism to enhance contextual relevance in caption generation. Google Text-to-Speech (gTTS) is also integrated to convert generated captions into audio format. The main outcomes demonstrate that the model is capable of generating coherent and contextually relevant captions, as validated through qualitative assessment and quantitative measurement using the BLEU score. Experimental results show decreasing training and validation loss over 8 epochs without signs of overfitting, indicating stable model performance. The attention visualization confirms the model’s ability to focus on relevant image regions during caption generation. In conclusion, the proposed CNN-RNN architecture with Attention effectively generates descriptive captions and converts them into speech, showing strong potential for real-world accessibility applications.
Analysis of Vegetation Index in Ambon City Using Sentinel-2 Satellite Image Data with Normalized Difference Vegetation Index (NDVI) Method based on Google Earth Engine Rakuasa, Heinrich; Sihasale, Daniel Anthoni
Journal of Innovation Information Technology and Application (JINITA) Vol 5 No 1 (2023): JINITA, June 2023
Publisher : Politeknik Negeri Cilacap

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35970/jinita.v5i1.1869

Abstract

Rapid urban development and increasing human activities in the city can affect the decline in the Vegetation Index in Ambon City. The research aims to analyze the vegetation index using sentinel-2 satellite image data with the Normalized Difference Vegetation Index (NDVI) method based on Google Earth Engine (GEE) in Ambon City in 2023. This research uses Sentinel-2 Satellite Image data which is analyzed using Google Earth Engine with the Normalized Difference Vegetation Index (NDVI) method. The results showed that the vegetation index value in Ambon City in 2023 was the lowest value of -0.672381 and the highest value of 0.949297. The vegetation index value is then divided into four classes, namely No Vegetation which has an area of 4,448.99 ha or 13.67%, Low Vegetation areas have an area of 1,611.06 ha or 4.95%, Moderate Vegetation areas have an area of 2,895.12 ha or 8.89% and High Vegetation areas have an area of 23,597.35 ha or 72.49%. Analysis of the vegetation index in Ambon City is very important to maintain environmental balance and a healthy and sustainable environment.

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