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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 191 Documents
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Shortest Path Calculation for Bandung Tourist Destinations Using Ant Colony Optimization Juanda Hakim Lubis; Sri Handayani; Akhyar Lubis; Fajrul Malik Aminullah Napitupulu
Journal of Innovation Information Technology and Application (JINITA) Vol 8 No 1 (2026): JINITA, June 2026
Publisher : Politeknik Negeri Cilacap

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35970/jinita.v8i1.2839

Abstract

Efficient route planning is essential in modern tourism, particularly for multi-destination travel in complex urban environments. This study proposes a tourism route optimization model based on Ant Colony Optimization (ACO) integrated with real-world geographic distance data. The problem is modeled as a weighted graph, where tourist destinations are represented as nodes and edge weights are derived from distances obtained via the Google Maps Directions API. The objective is to determine an optimal route that minimizes total travel distance across multiple destinations. The ACO algorithm employs a probabilistic search mechanism based on pheromone trails and heuristic visibility to iteratively construct candidate routes. Experimental results show that the model consistently converges to the optimal route sequence 1 → 4 → 2 → 5 with a total distance of 8323 meters under appropriate parameter settings. The findings also indicate that heuristic influence plays a critical role in ensuring convergence and solution quality. Compared to a deterministic baseline based on Dijkstra’s algorithm, ACO demonstrates greater flexibility in exploring multiple route combinations, making it more suitable for multi-destination routing. The integration of real-world data enhances the practical applicability of the model, although it introduces a higher computational cost.
3D Visualization of Post-Disaster Priorities Using CILOS–MARCOS for Decision Support and Mitigation Siti Asmiatun; Agusta Praba Ristadi Pinem; Nurtriana Hidayati; Mohammad Burhan Hanif
Journal of Innovation Information Technology and Application (JINITA) Vol 8 No 1 (2026): JINITA, June 2026
Publisher : Politeknik Negeri Cilacap

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35970/jinita.v8i1.2908

Abstract

One of the main challenges in disaster mitigation is delivering information that various stakeholders, including the public, policymakers, and emergency response teams, can easily understand. Visualization of disaster damage serves as an intuitive and informative tool to support better decision-making during emergencies. It also enhances public and stakeholder understanding of risk levels, thereby improving preparedness for future disasters. This study develops a disaster damage visualization model integrated with a decision-support system, using Multiple Criteria Decision-Making (MCDM) techniques. The model applies the CILOS method to determine objective weights of disaster criteria by evaluating the influence and loss of importance when a criterion is optimized. These weights are then used in the MARCOS method to perform ranking analysis, combining normalization and ideal-solution concepts to ensure a robust evaluation. The results demonstrate high reliability, with a Spearman rank correlation coefficient of 0.96, indicating strong agreement between the CILOS-MARCOS ranking results and the official JITUPASNA priority document. The final rankings are visualized in an interactive 3D format using the Unity Engine, allowing users to explore affected areas spatially, view detailed sectoral damage values, and compare subdistrict-level data. Integrating MCDM-based analysis with 3D visualization provides a novel, data-driven approach to enhance the effectiveness and transparency of disaster mitigation and rehabilitation planning. This model contributes to more adaptive and efficient strategies by linking disaster data, multi-criteria evaluation, and interactive visualization for comprehensive decision support.
Deep Learning Approach for Human Tracking by Utilizing Convolutional Neural Network and Long Short-Term Memory Kenny Susanto; Rila Mandala
Journal of Innovation Information Technology and Application (JINITA) Vol 8 No 1 (2026): JINITA, June 2026
Publisher : Politeknik Negeri Cilacap

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35970/jinita.v8i1.2914

Abstract

The world of digitalization is evolving at a fast pace, and automation growth in large sectors of industry, as well as in all aspects of life, creates a necessity for a robust and dependable product in tracking objects. Any pre-recorded or live-stream video can benefit from the ability to track an object in it to increase its value. This research explores a new approach to building a human-tracking model using deep learning architectures such as CNNs and LSTMs. It aims to assess the deep learning capability in handling tasks compared to the baseline algorithm GOTURN. The proposed model is constructed using a combination of CNN+LSTM and a secondary model built on LSTM. The dataset is gathered from a publicly available resource, LaSOT (Large Single Object Tracking), which specializes in collecting objects for object tracking operations. The proposed model achieves higher accuracy than the baseline model, with up to a 100% difference. The result of this research may bring new insight to the rather unpopular field of computer vision in tracking objects. The development of this research will be discussed further in this paper.
Multiclass Waste Image Classification Based on Convolutional Neural Network and Transfer Learning Arif Rahman; Devaldi Caliesta Octadiani; Imam Riadi; Suprihatin
Journal of Innovation Information Technology and Application (JINITA) Vol 8 No 1 (2026): JINITA, June 2026
Publisher : Politeknik Negeri Cilacap

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35970/jinita.v8i1.2934

Abstract

The escalating waste generation presents significant challenges for waste management systems, driving the need for innovative and efficient waste separation technologies. Efficient waste management requires the accurate separation of organic and inorganic waste, which automation can significantly enhance. This paper uses Convolutional Neural Network (CNN) architectures, optimized through transfer learning, to classify waste images as part of an automatic waste separation system. The study evaluates three architectures (InceptionV3, ResNet152, and MobileNetV3) by fine-tuning and testing them on the Waste Images Dataset and Waste Classification Data datasets. The InceptionV3 model demonstrated superior performance on the Waste Images Dataset, achieving the highest accuracy across nine classes. In comparison, the MobileNetV3 model excelled on the Waste Classification Data with the best accuracy across the two classes. These results highlight the effectiveness of CNN in automating waste classification and underscore the potential of InceptionV3 for multiclass tasks and MobileNetV3 for binary classifications in diverse environmental contexts.
Enhancing The Accuracy of Academic Document Plagiarism Detection Through Big Data and Similarity Measure Approaches Debora Chrisinta; Miko Purnomo; Iis Aprianti; Justin Eduardo Simarmata
Journal of Innovation Information Technology and Application (JINITA) Vol 8 No 1 (2026): JINITA, June 2026
Publisher : Politeknik Negeri Cilacap

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35970/jinita.v8i1.2946

Abstract

This study develops a Big Data-based plagiarism detection system using Similarity Measures to enhance objectivity and efficiency in evaluating student assignments. Employing a Research and Development (R&D) approach, the system processes .pdf and .docx documents through text preprocessing and computes similarities using Jaccard and Cosine Similarity. Testing on assignments from the Information Technology Program at the University of Timor showed that Cosine Similarity is more sensitive to sentence structure, while Jaccard better detects identical phrases. Around 40% of documents exceeded a 40% similarity threshold, indicating potential plagiarism. The system offers fast, flexible detection via a web interface, though it remains limited in identifying semantic or paraphrased content. Future improvements will incorporate NLP techniques to enhance accuracy and academic integrity
Construction of an Indonesian Language Corpus for the Evaluation of the JMO Application Using UMUX-Lite Based on Google Play Store User Reviews Vern Rahmah Solehah; Tenia Wahyuningrum; Adnan Purwanto; Singgih Briandoko; Singgih Setia Andiko; Teotino Gomes Soares
Journal of Innovation Information Technology and Application (JINITA) Vol 8 No 1 (2026): JINITA, June 2026
Publisher : Politeknik Negeri Cilacap

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35970/jinita.v8i1.2955

Abstract

The JMO application is a key digital service platform developed by the Social Security Administration for Employment. This public legal entity provides social security protection for all workers in Indonesia, offering them online access. It offers a comprehensive suite of services, including participant registration, Old Age Security benefit simulations, balance checks, same-day claims with Electronic Know Your Customer (e-KYC) verification, and workplace accident reporting. This study aims to evaluate the application's usability by constructing an Indonesian-language corpus from user reviews on the Google Play Store, utilizing the Usability Metric for User Experience-Lite (UMUX-Lite) instrument. The objective is to assess whether the application meets user requirements and delivers a user-friendly experience. A dataset of 2,500 reviews was manually labeled based on two core UMUX-Lite indicators: perceived ease of use (P1) and perceived usefulness (P3). The labeling results were as follows: 35.1% irrelevant, 29.5% relevant to P1, 28.1% relevant to P3, and 7.4% relevant to both P1 and P3. The text underwent preprocessing, including stop word removal, tokenization, and stemming, before being analyzed via word cloud visualization, Term Frequency-Inverse Document Frequency (TF-IDF) weighting, and multiple classification algorithms (Naive Bayes, Random Forest, Support Vector Machine (SVM), Logistic Regression, K-Nearest Neighbors (K-NN), and Decision Tree). To mitigate class imbalance, the Synthetic Minority Oversampling Technique (SMOTE) was applied. Post-oversampling, all classifiers achieved accuracy rates above 80%. The results objectively indicate that users generally perceive the JMO application as easy to use and effective in meeting their needs, validating the proposed evaluation framework. The constructed corpus also serves as a valuable resource for future research on evaluating Indonesian-language mobile applications.
User-Centered Feature Prioritization in Stroke Self-Care Applications: A Kano Model Study Winny Setyonugroho; Attar Husna Fathiya; Moch Zihad Islami; Fandi Setiawan; Sentagi Sesotya Utami; Musa Abdul Rahman; Iman Permana
Journal of Innovation Information Technology and Application (JINITA) Vol 8 No 1 (2026): JINITA, June 2026
Publisher : Politeknik Negeri Cilacap

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35970/jinita.v8i1.2966

Abstract

Stroke survivors often require ongoing support for long-term recovery, yet many face limited access to healthcare, particularly in developing countries like Indonesia. Mobile health technology enables supporting self-care. However, their usefulness and effectiveness depend heavily on their suitability to user needs. This study aims to identify and prioritize user-centered features in self-care apps for stroke survivors using the Kano Model. A questionnaire was distributed to 66 stroke survivors across Indonesia; after data filtering, 37 valid responses were analyzed using basic categorization, mean average scoring, and the Better-Worse method. Features were classified into Performance and Attractive categories and ranked by R-value, which measures sensitivity to user satisfaction. The highest-ranked Performance features were prevention of stroke recurrence (R = 1.03), emergency alert/warning system (R = 1.01), first aid handling (R = 0.97), and daily health data monitoring (R = 0.97). In the Attractive category, speech therapy (R = 0.93), notification reminders (R = 0.91), and online doctor consultation (R = 0.86) were top priorities. Features like font size adjustment (R = 0.70), voice commands (R = 0.70), and dark/light mode (R = 0.66) were lower in priority. These findings provide clear guidance for developing user-centered mobile applications that meet stroke survivors’ most valued needs.
Performance Evaluation of a Standalone OFDM System for Digital Image Transmission over AWGN Channels Ade silvia handayani; Sarjana; Aryanti; Nurhajar Anugraha; Muhammad Hanif Fatin; Devi Wahyuni; Muhammad Rafly Wijaya; Dely Andini
Journal of Innovation Information Technology and Application (JINITA) Vol 8 No 1 (2026): JINITA, June 2026
Publisher : Politeknik Negeri Cilacap

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35970/jinita.v8i1.2991

Abstract

This study investigates the performance of a standalone digital communication system based on Orthogonal Frequency Division Multiplexing (OFDM) for digital image transmission over Additive White Gaussian Noise (AWGN) channels. The system was tested using two digital modulation techniques: Binary Phase Shift Keying (BPSK) and Quadrature Phase Shift Keying (QPSK). Unlike previous studies that focused on OFDM implementation in large-scale communication systems such as cellular networks, this research explores a simplified standalone scenario without any supporting network infrastructure. Simulations were carried out in the MATLAB environment using grayscale image inputs ranging from 1 MB to 10 MB, with signal-to-noise ratio (SNR) levels varying from 0 to 30 dB. The results show that BPSK modulation maintains a low Bit Error Rate (BER) of approximately 0.077 even under low SNR conditions (as low as 0 dB). In contrast, QPSK modulation achieves optimal performance when the SNR reaches at least 15 dB, where the BER drops to 0.00000. The image size did not significantly affect system performance, indicating robustness across varying data loads. Overall, the findings suggest that BPSK provides higher reliability in noisy channels, whereas QPSK offers better spectral efficiency under cleaner conditions. Therefore, the proposed standalone OFDM system demonstrates strong potential for lightweight data communication applications such as sensor networks and remote monitoring systems operating in limited network environments.
A Hybrid K-Means-Random Forest Approach for Optimizing CO Air Pollution Prediction Fadiana; Ria Andara; Azaila Dwi; Rona Nisa
Journal of Innovation Information Technology and Application (JINITA) Vol 8 No 1 (2026): JINITA, June 2026
Publisher : Politeknik Negeri Cilacap

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35970/jinita.v8i1.2997

Abstract

Air quality is becoming an increasingly worrying global issue due to increasing air pollution. The increase in air pollution in the environment encourages the presence of innovative solutions in terms of countermeasures and prevention. This study compares regression algorithms (Random Forest Regression, Linear Regression, SVR, Decision Tree Regression, and KNN Regression) to find the best prediction model. Feature development is carried out using a clustering algorithm (K-means) to produce new features that are able to support the optimization of the model search process and prediction results. Model quality measurement was carried out by applying Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and R2 metrics. The results showed that the best model was RFR, which excelled at the R2 approach = 0.75, MSE = 0.008823, RMSE = 0.093931, MAE = 0.060096, and ROC-AUC = 0.951. These findings suggest the effectiveness and quality of prediction models in supporting efforts to develop an early warning system based on ensemble learning dashboards. This research contributes practically to the application of machine learning in air pollution mitigation, as well as supporting the achievement of SDGs 3: Good Health and Well-Being through the provision of a healthier environment.
Hybrid Genetic Algorithm and XGBoost Model for Optimizing Sharia Compliant Stock Portfolios in the Indonesian Capital Market Gellysa Urva; Welly Desriyati
Journal of Innovation Information Technology and Application (JINITA) Vol 8 No 1 (2026): JINITA, June 2026
Publisher : Politeknik Negeri Cilacap

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35970/jinita.v8i1.2999

Abstract

This study proposes a hybrid portfolio optimization model that integrates Genetic Algorithm (GA) and Extreme Gradient Boosting (XGBoost) to optimize Sharia-compliant stock portfolios in the Indonesian capital market. The model leverages the predictive capability of XGBoost and the adaptive optimization mechanism of GA to achieve an optimal balance between return and risk under Sharia investment principles.  The dataset consists of historical daily stock price data from six Sharia-compliant stocks (ADRO, INDF, KLBF, TLKM, UNVR, and ICBP) during 2023. XGBoost was employed to predict stock returns using selected technical indicators, and its performance was evaluated using Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE). The predicted returns were then used as inputs for portfolio optimization using GA.  The results show that the hybrid model produces an optimized portfolio with KLBF (22.7%) and ICBP (21.3%) receiving the highest allocations, indicating favorable risk–return characteristics. The model achieved RMSE values ranging from 0.0081 to 0.0142 and MAPE values between 4.24% and 6.11%, demonstrating high predictive accuracy. Furthermore, the hybrid portfolio outperformed benchmark strategies, achieving an expected return of 6.42% with a Sharpe Ratio of 0.30.  These findings confirm that the integration of GA and XGBoost enhances both prediction reliability and portfolio optimization performance. This study contributes to the development of artificial intelligence-based portfolio optimization in Islamic finance and provides a practical framework for ethical and data-driven investment decision-making.

Page 17 of 20 | Total Record : 191

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