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Muhammad Nur Faiz
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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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Design and Implementation of Intelligent Traffic Lights for One-Way Open and Close Roads Based on Reinforcement Learning Yoanda Alim Syahbana; Sugeng Purwantoro E.S.G.S; Muhammad Wahyudi; Muhammad Imam Akbar; Ibnu Zachri Baihaqi
Journal of Innovation Information Technology and Application (JINITA) Vol 7 No 2 (2025): JINITA, December 2025
Publisher : Politeknik Negeri Cilacap

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35970/jinita.v7i2.2691

Abstract

This study aims to design and implement an intelligent traffic light system for one-way open and close road conditions, commonly encountered during road repair projects. These situations often cause congestion due to alternating vehicle flow in a single lane. To address this issue, the system utilizes a Reinforcement Learning (RL) algorithm to dynamically adjust the traffic light timing based on real-time traffic conditions. The research was conducted in three main stages: (1) designing the network topology and IoT devices using Raspberry Pi, ESP modules, and Access Points (APs), (2) implementing the intelligent traffic light system, and (3) conducting a functional evaluation. A key performance metric evaluated was the response time of the system. Experimental results showed that the traffic light system achieved an average response time of 0.51 seconds, indicating that it is responsive and suitable for real-time operation. The successful integration of RL and MQTT-based communication also demonstrates the feasibility of deploying this system in dynamic traffic environments. Further research is recommended for field testing with additional sensor integration and advanced RL models to enhance system accuracy and efficiency
Enhanching User Experience in E-Commerce Website Design Through the User Centered Design Approach: A Case Study Hamidatun Nisa; Dr. Tenia Wahyuningrum; Khoem Sambath
Journal of Innovation Information Technology and Application (JINITA) Vol 7 No 2 (2025): JINITA, December 2025
Publisher : Politeknik Negeri Cilacap

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35970/jinita.v7i2.2786

Abstract

Many small-scale businesses in Indonesia rely on manual customer interaction workflows, leading to delays, inefficiencies, and missed opportunities in digital transactions. This study proposes designing and implementing a user-centered e-commerce website to streamline order processing, improve user autonomy, and reduce the operational burden on the business owner. Addressing management issues in small businesses requires creating a website that meets user needs. The User-Centered Design (UCD) methodology includes iterative stages of user research, requirements specification, interface design, and usability evaluation. A high-fidelity prototype was tested by 65 participants, resulting in 56 valid responses after data cleaning. Usability was evaluated through ISO/IEC 25022-based metrics, achieving a 67.86% completion rate, an average task time of 91.66 seconds, and an overall relative efficiency of 66.96%. Using the User Experience Questionnaire (UEQ), we measured user satisfaction, with five out of six dimensions rated as “Excellent”. The final system was implemented using Laravel and Filament, integrated with Midtrans for payment automation, Mailtrap for email testing, and a management dashboard for order tracking and status updates. This study demonstrates the practical application of UCD in the digital transformation of SMEs by delivering a fully functional, user-validated interface that enhances transactional clarity and customer experience. Compared to prior methods, the approach enables self-service ordering with reduced reliance on real-time manual responses. The findings offer a replicable reference for similar businesses seeking to implement user-focused digital solutions efficiently.
Improved Malnutrition Classification in Toddlers Using Mutual Information-Guided Feature Selection and Hybrid KNN–MLP Ensemble Learning Syahrani Lonang; Anton Yudhana; Shoffan Saifullah
Journal of Innovation Information Technology and Application (JINITA) Vol 7 No 2 (2025): JINITA, December 2025
Publisher : Politeknik Negeri Cilacap

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35970/jinita.v7i2.2831

Abstract

Malnutrition remains a significant public health challenge in Indonesia, with early detection being crucial for effective intervention. Previous studies utilizing the K-Nearest Neighbor (KNN) algorithm demonstrated promising results in classifying malnourished toddlers based on anthropometric data. However, single-model approaches often suffer from sensitivity to noise and limited generalization. This study proposes a hybrid ensemble model combining KNN and Multi-Layer Perceptron (MLP), integrated with mutual information-based feature selection, to improve classification performance. Using a dataset from Puskesmas Ubung, Bali, comprising 1,319 records with nine anthropometric features and a binary malnutrition label, the model was evaluated under stratified five-fold cross-validation. The proposed KNN–MLP ensemble with top-ranked features achieved 94.3% accuracy, surpassing both standalone KNN and MLP models. Additional metrics, including precision (91.7%), recall (89.4%), F1-score (90.5%), and MAE (0.05), confirmed the model's robustness and reliability. These findings demonstrate that ensemble learning combined with feature selection significantly improves early-stage malnutrition classification, offering a scalable approach for decision-support systems in public health interventions.
Automated Esophagitis Detection from Endoscopy Using Deep Learning Imam Kharits N; Imam tahyudin; Dhanar Intan Surya Saputra
Journal of Innovation Information Technology and Application (JINITA) Vol 7 No 2 (2025): JINITA, December 2025
Publisher : Politeknik Negeri Cilacap

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35970/jinita.v7i2.2835

Abstract

Gastro-esophageal reflux disease (GERD) is a widespread condition that often leads to severe complications, including esophagitis, which significantly affects patient health and quality of life. While endoscopy is the gold standard for diagnosing esophagitis, its reliance on specialized equipment and trained professionals can limit accessibility and efficiency. This study introduces an innovative approach to diagnosing esophagitis by leveraging Convolutional Neural Networks (CNN) for automated classification of endoscopic images. By utilizing the Kvasir dataset, which includes a comprehensive collection of gastrointestinal endoscopy images, the model is trained to distinguish between esophagitis and normal-Z-line conditions with remarkable accuracy. The CNN model achieved outstanding results, with an accuracy of 96.04%, precision of 98.94%, recall of 93.00%, and an F1-score of 95.88%, demonstrating its potential to outperform traditional diagnostic methods. These findings underscore the ability of CNN to not only enhance diagnostic precision but also to reduce human error, making the process faster, more reliable, and more accessible. This research contributes to the growing body of work in medical image analysis, suggesting that CNN-based models can transform clinical practices by supporting timely, accurate diagnoses while alleviating the burden on medical professionals. The integration of deep learning in this domain holds the promise of advancing healthcare accessibility and efficiency globally
Comparison Method of Convolutional Neural Network and Support Vector Machine for Facial Expression Recognition Imam Riadi; Restu Prima Yudha; Abdul Fadlil
Journal of Innovation Information Technology and Application (JINITA) Vol 7 No 2 (2025): JINITA, December 2025
Publisher : Politeknik Negeri Cilacap

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35970/jinita.v7i2.2836

Abstract

Facial expressions are an important component of nonverbal communication that enable humans to understand each other's emotional states intuitively. Although humans can easily recognize expressions such as smiles or frowns, replicating this ability in computational systems remains a complex challenge. Therefore, an automated system capable of accurately and efficiently identifying facial expressions is needed. This research aims to compare the accuracy of CNN and SVM methods in facial expression recognition using the JAFFE dataset, which is limited to one demographic (Japanese women) with 284 images (80% for training, 10% for validation, and 10% for testing). CNN extracts features through convolution and pooling processes, while SVM is used as a classification algorithm based on statistical learning. The recognition process is divided into three main stages: data preprocessing, feature extraction, and facial expression classification. The system recognizes seven emotional categories: anger, disgust, fear, happiness, neutral, sadness, and surprise. Results show that CNN outperforms SVM with an accuracy of 86%, while SVM achieves 81%. The limitations of the dataset may affect generalizability, and further research can use larger, more diverse datasets
The Effect of Light Intensity, Camera Pixel Quality, Camera Distance, and Object Altitude on Detection Accuracy in a Real-Time Drone Surveillance System Using YOLOv5 Astika Ayuningtyas; Imam Riadi; Anton Yudhana
Journal of Innovation Information Technology and Application (JINITA) Vol 7 No 2 (2025): JINITA, December 2025
Publisher : Politeknik Negeri Cilacap

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35970/jinita.v7i2.2843

Abstract

This research evaluates the performance of the drone detection system based on YOLOv5 in a variety of environmental conditions. The four main variables under test were drone height, camera type, light intensity, and camera-to-object distance. Thirty-six different scenarios were used with three different camera types (1080p, 2K, and Canon 600D). The height of the drones varied from 1 to 14 meters, and the variations in illumination ranged from 0 to 46 lux. Results showed consistent YOLOv5 performance with an average accuracy of 60%, precision of 62%, recall of 58%, F1-score of 60%, and IoU of 75%. ANOVA revealed that light intensity, camera distance, and drone height all had a significant impact on detection accuracy (p < 0.05), but camera type was not statistically significant. The best results were obtained under the following conditions: high light levels (>40 lux), camera distances <10 m, and drone altitudes between 6 and 9 m. These findings demonstrate the importance of environmental setup in improving the performance of object detection systems based on deep learning. This research helps design a more reliable and adaptable drone detection system for real-world applications. This work provides practical guidelines for implementing deep learning-based aerial surveillance and highlights optimal operational parameters for YOLOv5 systems.
Facial Image-Based Autism Detection Using ConvNeXt Tiny: A Lightweight Deep Learning Approach for Early Screening Putut Pamilih Widagdo; Muhammad Bambang Firdaus; Gubtha Mahendra Putra; Akhmad Irsyad
Journal of Innovation Information Technology and Application (JINITA) Vol 7 No 2 (2025): JINITA, December 2025
Publisher : Politeknik Negeri Cilacap

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35970/jinita.v7i2.2848

Abstract

This study proposes a deep learning model using the ConvNeXt Tiny architecture to detect autism spectrum disorder (ASD) from facial images, addressing the need for an early, efficient, and accessible diagnostic tool. The model integrates facial image preprocessing techniques like Contrast Limited Adaptive Histogram Equalization (CLAHE) and data augmentation, with facial segmentation performed by MTCNN. The ConvNeXt Tiny model is trained using transfer learning and evaluated through metrics such as accuracy, precision, recall, and F1-score, and compared with traditional CNN models like ResNet50 and EfficientNet-B0. The results demonstrate that the proposed model outperforms ResNet50 and EfficientNet in all evaluation metrics, achieving a classification accuracy of 84%. It also demonstrates a balanced performance across both classes (autistic and non-autistic), with high precision and recall for both, leading to a high F1-score. Furthermore, the model's computational efficiency makes it suitable for web and mobile applications, enabling scalable and real-time screening for ASD in children. The study's contributions include the development of a novel, lightweight ASD classification system, a comparative analysis of ConvNeXt with other CNN models, and the creation of a prototype for early ASD detection. This approach not only provides a promising alternative to conventional diagnostic methods but also sets the groundwork for further research and practical implementation in clinical settings.
Sentiment Analysis Model for VTuber Live Stream Chat using Decision Tree and Support Vector Machine Herman Yuliansyah; Habib Aulia Raihan; Murinto
Journal of Innovation Information Technology and Application (JINITA) Vol 7 No 2 (2025): JINITA, December 2025
Publisher : Politeknik Negeri Cilacap

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35970/jinita.v7i2.2872

Abstract

Virtual YouTuber (VTuber) has a feature that allows fan and viewer interaction through live streaming chat that contains textual data based on emotions and opinions. The previous study examined sentiment analysis in various domains. However, live-streaming chat has short, informal, and unstructured text characteristics, making it challenging to analyze its sentiment. Decision Trees (DT) have advantages in interpretability and training speed, while Support Vector Machines (SVM) can handle high-dimensional data and avoid overfitting. Still, few studies examine DT and SVM in live streaming chat. This study aims to propose a sentiment analysis model in VTuber live streaming chat by comparing the performance of DT and SVM. VTuber Lives streaming chat was collected and preprocessed through cleaning, expansion-contraction, case folding, tokenization, stopword removal, and lemmatization. VADER and AFINN Lexicon labeled positive, neutral, or negative sentiments. Later, TF-IDF is used for feature extraction, and K-Fold cross-validation is used to evaluate the sentiment analysis model based on DT and SVM. A confusion matrix measures the model’s performance by knowing the accuracy, precision, recall, and F1 score values. The results of 10-fold cross-validation show that the proposed model with a combination of DT+AFINN with hyperparameter optimization achieves an accuracy of 96.26%. The combination of DT+AFINN shows its superiority in sentiment analysis of VTuber live chat data compared to DT+VADER, SVM+AFINN, and SVM+VADER.
Gold Price Forecasting using Time Series Modeling on a Web Platform Dwi Ratna Puspita Sari; Sirli Fahriah; Kurnianingsih; Santi Purwaningrum
Journal of Innovation Information Technology and Application (JINITA) Vol 7 No 2 (2025): JINITA, December 2025
Publisher : Politeknik Negeri Cilacap

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35970/4p33wz16

Abstract

Gold is one of the most favored investment instruments due to its stability and its ability to preserve value against inflation. However, its price movements are volatile and influenced by various global economic factors, currency exchange rates, and geopolitical conditions, making gold price forecasting a significant challenge. This study aims to develop a gold price forecasting system using the Long Short-Term Memory (LSTM) algorithm, a variant of the Recurrent Neural Network (RNN) that excels in processing time-series data. The dataset consists of historical daily gold buying and selling prices from 2015 to 2025, collected from Yahoo Finance, Logam Mulia, and the official website of Bank Indonesia. The modeling process follows the CRISP-DM methodology, which includes business understanding, data preparation and exploration, modeling, and evaluation stages. Time Series Cross Validation (TSCV) is used to validate the model. LSTM performance is compared with other models such as GRU, CNN-1D, and Simple RNN to identify the best-performing architecture. Evaluation results indicate that LSTM achieved the highest performance with an R² score of 0.99 for selling prices and 0.98 for buying prices on the final test dataset. The system is deployed online, making it accessible in real-time. This research is expected to assist investors, financial analysts, and the general public in making smarter investment decisions based on valid historical data and advanced forecasting technology.
Comparative Study of PID Control and Self-Tuning Neural Network Adaptive Control on an Octarotor for Motion Optimization Hendi Purnata; Moh Khairudin; Sarwo Pranoto; Galih Mustiko Aji; Nanda Pranandita
Journal of Innovation Information Technology and Application (JINITA) Vol 7 No 2 (2025): JINITA, December 2025
Publisher : Politeknik Negeri Cilacap

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35970/jinita.v7i2.2882

Abstract

This study aims to compare the performance between a PID cascade controller and a self-tuning Neural Network adaptive control (PID NN) in controlling an ROV on an octarotor platform with 6 degrees of freedom (DOF), namely Surge, Sway, Heave, Roll, Pitch, and Yaw. The conventional PID control system is used as a proven baseline, while the Neural Network-based adaptive control is applied to adjust parameters in real-time, expected to address the nonlinearity and external disturbances that are difficult to handle by static PID. This study involves an analysis of overshoot, rise time, settling time, and final position for each channel, as well as a comparison of the performance of the two control methods. The results show that PID NN provides faster rise times and lower overshoot in most channels, such as Surge overshoot of 8.3%, rise time of 6.2 seconds, and Sway overshoot of 2.1%, settling time of 90 seconds, compared to PID, which has higher overshoot and longer stabilization times. However, for the Yaw and Heave channels, although PID NN showed larger overshoot and longer settling time, PID was faster in achieving stability. Overall, although PID NN demonstrated superiority in terms of rapid stabilization for Roll and Pitch, further adjustments are needed to optimize Yaw and Heave to achieve faster stabilization without compromising system stability and overall control performance. This study opens opportunities for further development in the field of adaptive control for high-complexity multirotor systems.

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