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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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Modified Grey Wolf Optimizer with Lévy Flight for Waste Collection Routing: A Case Study in Bandung Rudi Hartono; Nanang Maulana Yoeseph; Abdul Aziz; Agus Purnomo
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.v7i2.2883

Abstract

Efficient urban waste management is a critical challenge driven by rapid urbanization, with collection routes strongly influencing operational costs and environmental sustainability. This study addresses the optimization of waste collection routes by modeling the problem as a Travelling Salesman Problem (TSP), serving as a foundational step toward more complex routing frameworks. We propose a Lévy-flight-enhanced Grey Wolf Optimizer (LGWO), which extends the standard Grey Wolf Optimizer (GWO) by integrating a lévy flight mechanism designed to strengthen global exploration and mitigate premature convergence to local optima. The performance of LGWO is evaluated against six other metaheuristic algorithms (GWO, ACOR, WOA, PSO, ALO, and ABC) using a real-world dataset of 36 waste collection points in Bandung, Indonesia. Experimental results based on 30 independent trials per algorithm show that LGWO achieves the best overall performance, obtaining the shortest tour (60.85 km) and the lowest mean distance (77.72 km), whereas the Ant Lion Optimizer (ALO) yields the poorest performance with the highest average distance of 89.90 km. These findings indicate that incorporating a lévy flight mechanism into GWO improves solution quality and convergence behavior for TSP-based waste collection routing. This research offers a practical optimization tool for developing more efficient and cost-effective urban waste management strategies. Future work will extend this approach by incorporating dynamic factors such as service times and vehicle capacities, enabling a more realistic treatment of Vehicle Routing Problem (VRP) variants.  
Improving Computational Efficiency and Accuracy of Damerau-Levenshtein Distance for Indonesian Spelling Correction using Cosine Similarity husni husni; Yoga Dwitya Pramudita; Mohammad Syarief; Army Justitia; Ika Oktavia Suzanti
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.v7i2.2893

Abstract

Spelling correction is an automatic correction feature useful in detecting spelling errors and providing word suggestions if necessary. Spelling correction is one of the crucial preprocessing phases in text mining. The Damerau-Levenshtein Distance method is one of the spelling correction methods that has high accuracy. This method has four types of operations: insertion, deletion, substitution, and transposition. The basic approach in detecting spelling errors in the Indonesian language is to use a dictionary search. Despite its accuracy, the Damerau-Levenshtein Distance method has a slow computation time. Furthermore, when the dictionary contains several suggested words that have the same distance from the target word, it will be difficult to prioritize the most appropriate suggestions. To overcome this problem, we introduce a caching mechanism to store previously calculated corrections, thereby speeding up the computation process. In addition, we use the cosine similarity method to rank words in Damerau-Levenshtein Distance results. The results of our approach have a significant improvement in accuracy, increasing from 72.13% to 83.60% by integrating caching and cosine similarity for ranking, which shows a significant improvement in both efficiency and effectiveness
Penetration Testing Through NIST SP 800-115 and OWASP TOP 10 With Risk Analysis Using CVSS on the XY Diskominfo Website Ilham Akbar; Khairunnisak Nur Isnaini; Banu Dwi Putranto
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.v7i2.2907

Abstract

Currently, websites are widely used in various fields, including government. However, government websites are often the target of hacking. The XY Regency Communication and Information Agency experienced a website breach in 2023, which affected one of its subdomains, e-office.xy-regency.go.id, which stored employee data. This hack resulted in the leakage of sensitive data, user account takeovers, service disruptions, and a decline in public trust. The purpose of this study was to identify and test the vulnerabilities of the e-office.xy-regency.go.id website using the NIST SP 800-115 method, which includes the planning, discovery, attack, and reporting phases. Vulnerabilities were classified using the Common Vulnerability Scoring System (CVSS) and the OWASP Top 10 standard. This study identified two high-level vulnerabilities, namely Cross-Site Scripting (XSS) with a CVSS score of 8.2, classified as High severity, and Cross-Site Request Forgery (CSRF) with a CVSS score of 9.3, classified as Critical severity. These vulnerabilities could allow hackers to execute malicious scripts and manipulate users without their knowledge. Recommendations include implementing input validation on text boxes by limiting characters to letters or combinations of letters and numbers, and ensuring that all forms and endpoints that handle sensitive data are protected with unique and unpredictable CSRF tokens. Future research should focus on analyzing domain vulnerabilities, identifying the origin and potential of attacks, and developing effective protection and recovery strategies.
A Culinary Recommendation System in Lombok Using Latent Dirichlet Allocation and Content-Based Filtering Jihadul Akbar; Sofiansyah Fadli
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.v7i2.2932

Abstract

This research develops a culinary recommendation system in Lombok by integrating the Latent Dirichlet Allocation (LDA) and Content-Based Filtering (CBF) methods. This integration aims to overcome the limitations of pure CBF, which relies solely on basic restaurant attributes and is less capable of capturing the semantic context of tourist reviews. Data was obtained through web scraping of Google Maps using the Apify.com platform, covering 825 restaurants and 20,114 reviews. The research stages included data collection, text preprocessing, topic modeling using LDA, feature engineering, similarity calculation using cosine similarity, and system evaluation. Evaluation was performed using Precision@K, Recall@K, F1-Score, and Mean Average Precision (MAP). The results show that the hybrid CBF+LDA model provides a significant improvement compared to pure CBF, with Precision@3 of 0.9333, Recall@3 of 0.1312, F1-Score of 0.2300, and MAP of 0.9628. These findings indicate that the integration of LDA topics enriches the semantic representation of reviews, thereby improving the relevance of recommendations. This research contributes to the development of artificial intelligence-based tourism recommendation systems and provides practical implications for promoting local cuisine, enhancing tourist experiences, and utilizing digital reviews as a basis for decision-making in the regional tourism sector. 
IoT Based Hydroponic Water Nutrient and pH Monitoring with Real-Time Notifications Tri Agusti Farma; Dora Palupi; Weri Sirait
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.v7i2.2940

Abstract

Indonesia is an agricultural country whose population lives from the agricultural sector, the Government has encouraged agriculture to realize the use of modern technology for the agricultural sector which is useful for increasing farmer productivity and making farmers' work easier with quality harvests, as well as more efficient food security in Indonesia. One way to save time and energy for farmers to find out nutrients and pH is by using a nutrient and pH monitoring system for hydroponic plant water. The purpose of this research is to create an IoT-based automatic system, used for systematic real-time monitoring of water nutrients in hydroponic plants using a TDS sensor to measure the mass weight of nutrients and a pH meter to measure the acidity of water, a DHT22 sensor to measure temperature and humidity, and a DSB18B20 sensor to measure water temperature, the data obtained from these sensors will be displayed on the LCD. The final result of the tool created is a nutrient and pH monitoring system for water in pakcoy plants, the results of sensor readings will be sent in real-time to the dashboard and WhatsApp. This research method uses the R and D (Research and Development) method with the ADDIE (analyze, design, develop, implement, evaluate) model. The IoT expert validation value was 0.91 (very appropriate) and the farmer effectiveness validation value was 0.85 (very effective). The outputs generated from this research can be used to make it easier for hydroponic farmers to monitor plant nutrient balance automatically and systematically.
Comparative Analysis of C4.5 and Random Forest for Analyzing Factors Affecting Undergraduate Students’ Final Project Completion in Higher Education Nelci Dessy Rumlaklak; Derwin Rony Sina; Tifanny Sooai
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.v7i2.2941

Abstract

This study analyzes factors influencing students’ final project completion status in a higher education context using six classification models: C4.5, Random Forest (RF), C4.5 with SMOTE, RF with SMOTE, Cost-Sensitive Random Forest (RF-CS), and Cost-Sensitive C4.5 (C4.5-CS). The dataset consists of 1,017 student records categorized into Ideal and Tidak Ideal, with a severe class imbalance where the minority class represents only 16.49% of the data.The results indicate that baseline models achieved high overall accuracy but showed limited effectiveness in identifying the minority Tidak Ideal class. SMOTE-based models improved minority-class recall but introduced a higher number of false positives, highlighting a trade-off between recall and precision. In contrast, cost-sensitive learning produced the most substantial improvement in minority-class detection. Among all evaluated models, Cost-Sensitive Random Forest demonstrated the most balanced performance by significantly reducing false-negative errors while maintaining reasonable overall accuracy.These findings confirm that algorithm-level cost-sensitive approaches are more effective than oversampling techniques for handling severe class imbalance in educational datasets. The proposed model provides a reliable basis for early identification of students at risk of delayed final project completion and supports data-driven academic decision-making
5G NR Coverage Optimization Using Legacy 4G Infrastructure: A Machine Learning-Enhanced Empirical Study in Indonesian Urban Environment AFRIZAL YUHANEF; Muhammad Putra Pamungkas; Nasrul Nasrul; Dikky Chandra; Herry Setiawan; Sonya Purna Faradisya
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.v7i2.2942

Abstract

The deployment of 5G New Radio (NR) networks requires substantial infrastructure investment, posing challenges for emerging markets. This empirical study explores coverage optimization by retrofitting existing 4G LTE Base Transceiver Stations (BTS) using machine learning-enhanced Self-Organizing Network (SON) algorithms. Over six months, 6,895 drive test measurements were collected across 28 Telkomsel BTS sites in Padang, Indonesia, enabling before-and-after optimization analysis. Paired t-test results showed significant improvements in coverage quality: the service area with excellent SINR increased from 54.5% to 58.1% (p < 0.001, Cohen’s d = 0.27), while maintaining 99.3% RSRP compliance (≥ -92 dBm, 95% CI: 97.8%–100%). Automatic Cell Planning (ACP) effectively identified parameter configurations that enhanced performance without additional infrastructure cost, reducing deployment expenses. However, 5G NSA deployment remained limited to only 2.17% of measurements, which is valid for early-stage insights but restricts generalization to full 5G deployment scenarios. Therefore, the findings primarily apply to NSA overlay on existing 4G infrastructure rather than full 5G standalone deployment. This underscores ongoing economic and technical challenges in emerging markets’ 5G rollout. Despite this, the study provides strong empirical evidence supporting infrastructure reuse via SON-based optimization as a cost-effective way to improve coverage and quality. These results offer valuable guidance for operators and policymakers aiming to accelerate 5G adoption while managing costs in similar regions. Future work should expand validation as 5G NSA and standalone (SA) deployments grow and investigate integration with advanced AI-driven network management techniques
A Comparative Analysis of KIP-K Acceptance Prediction Based on School Type Using XGBoost, Random Forest, and SVM-RBF: Evaluation Through Accuracy and Data Visualization Riyadi Purwanto; Fajar Mahardika; Muhammad Nur Faiz
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: 10.35970/10.35970/jinita.v7i2.2948

Abstract

The Indonesia Smart College Card (Kartu Indonesia Pintar-Kuliah / KIP-K) is a national initiative aimed at expanding access to higher education for students from socioeconomically disadvantaged backgrounds. This study, conducted at Politeknik Negeri Cilacap, investigates the prediction of KIP-K acceptance based on the type of high school attended by applicants. A comparative analysis was carried out using three supervised machine learning algorithms: Extreme Gradient Boosting (XGBoost), Random Forest, and Support Vector Machine with Radial Basis Function (SVM-RBF). The dataset, sourced from institutional admission records between 2022 and 2024, comprises information on school types (public, private, vocational, madrasah, and others), demographic attributes, and the KIP-K acceptance status. The data were split into training and testing sets using a 50:50 stratified sampling technique to preserve class distribution. Model performance was evaluated using standard classification metrics, including accuracy, precision, recall, and F1-score. Additionally, confusion matrices, ROC curves, and feature importance visualizations were used to enhance model interpretability. The experimental results demonstrate that the XGBoost algorithm consistently outperformed the other models across all performance metrics. Specifically, XGBoost exhibited the highest discriminatory power with an AUC of 0.93, followed by Random Forest (0.90) and SVM-RBF (0.85). These findings affirm the suitability of tree-based ensemble methods for classification tasks in educational domains and emphasize the predictive relevance of school type in determining KIP-K eligibility. The study presents a data-driven decision support framework that can contribute to more objective, transparent, and equitable scholarship allocation practices, particularly within the context of vocational higher education institutions in Indonesia
Polarized Amplitude Time Spiral Encoding for Infant Cry Audio Augmentation and CNN Classification Nuk Ghurroh Setyoningrum; Ema Utami; Kusrini; Ferry Wahyu Wibowo
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: 10.35970/10.35970/jinita.v7i2.2960

Abstract

Recognizing infant cries is essential for healthcare, yet conventional representations such as spectrograms and MFCC often fail to capture temporal dynamics, limiting classification performance. This study introduces Polarized Amplitude Time Spiral Encoding (PATSE), a novel transformation that encodes amplitude and time into spiral-based polar representations, enabling richer visual features for deep learning. To address data scarcity and imbalance, audio augmentation techniques time stretching, time shifting, pitch scaling, and polarity inversion were applied, expanding the dataset from 457 to 6855 samples. A Convolutional Neural Network (CNN) trained on PATSE images achieved notable improvements, with overall accuracy increasing from 80% before augmentation to 93% after augmentation. The model attained high performance on the dominant Hungry class (F1-score = 0.96) while also enhancing recognition of minority classes such as belly pain, burping, discomfort, and tired. These results confirm the effectiveness of PATSE in improving generalization and reducing bias, offering a distinctive advantage over linear representations. The proposed framework provides a foundation for intelligent infant cry monitoring and early detection systems in healthcare.
Development of a Hybrid CNN–SVM-Based Acute Lymphoblastic Leukemia Detection System on Hematology Image Data Linda Perdana Wanti; Annisa Romadloni; Kukuh Muhammad; Abdul Rohman Supriyono; Muhammad Nur Faiz
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.v7i2.3002

Abstract

Acute Lymphoblastic Leukemia (ALL) is among the most common pediatric blood cancers and progresses rapidly, necessitating early and accurate detection. Manual diagnosis via microscopic analysis of blood samples is time-consuming and highly dependent on specialist expertise. This study proposes a hybrid model that combines a Convolutional Neural Network (CNN) with a Support Vector Machine (SVM) to automatically detect ALL from blood-cell images. The CNN performs deep feature extraction from images, while the SVM serves as the classifier to determine ALL status. The dataset comprises microscopic images labeled as ALL or normal and is processed through preprocessing steps such as augmentation and normalization. The adopted CNN produces optimized feature representations. Experimental results show that the hybrid CNN–SVM model with an RBF kernel achieves the best performance, with an accuracy of 96.4%, precision of 95.8%, recall of 96.1%, and an F1-score of 96.0%, surpassing pure CNN-based baselines. Training converged at the 41st epoch, with a training accuracy of 97.2%, validation accuracy of 95.9%, training loss of 0.09, and validation loss of 0.11, indicating stable learning without overfitting. The model’s ROC curve lies well above the chance diagonal, with an Area Under the Curve (AUC) of 0.914, means there is a 91.4% chance the model assigns a higher score to a truly positive (leukemia) image than to a negative (normal) image.These findings suggest that the CNN–SVM hybrid approach enhances leukemia detection performance compared with conventional CNN-only methods and holds promise as a fast, accurate, and efficient image-based decision-support tool for early leukemia diagnosis in digital hematology.

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