Paradigma - Jurnal Komputer dan Informatika
Paradigma is a journal in the field of Computer and Informatics published by LPPM Bina Sarana Informatika and has an ISSN, both in print (1410-5063) and online version (2579-3500). This journal contains scientific research results on the themes of Computer Science, Informatics Engineering, Computer Engineering, Expert Systems, Information Systems, Web Programming, Mobile Programming, Games Programming, Data Mining, Text Mining, Image processing, and Decision Support Systems. Publish Frequency: 2 times a year (March and September) Paradigma has been accredited with Sinta 3 (S3) rank by Arjuna Ristekbrin with Accreditation Decree Number: 72/E/KPT/2024, starting Vol. 25, No. 1, year 2023. and indexed by DOAJ, EBSCO, Google Scholar, Academic Resource Index, Crossref, One Search, SINTA, Base, Scilit. Please click here to view the history of the Paradigma journal.
LPPM Universitas Bina Sarana Informatika
Paradigma is an open-access article distributed under the terms of the Creative Commons Attribution-ShareAlike 4.0 International License (https://creativecommons.org/licenses/by-sa/4.0/) , This license permits: Share  copy and redistribute the material in any medium or format for any purpose, even commercially, Adapt  remix, transform, and build upon the material for any purpose, even commercially.
Comparative Analysis of Email Spam Detection Using SVM with TF-IDF and Word2Vec on Multilingual Datasets
The rapid growth of email communication has increased the prevalence of spam emails, which can disrupt productivity and compromise information security. This study presents a comparative analysis of two text representation methods—TF-IDF and Word2Vec—for spam email classification using a Support Vector Machine (SVM) with a Radial Basis Function kernel. The experiments utilized Indonesian and English email datasets totaling 5,421 emails, split into 75% training and 25% testing sets. Two scenarios were evaluated: baseline with default parameters and after hyperparameter optimization using Grid Search combined with K-Fold Cross Validation. The results indicate that TF-IDF consistently outperformed Word2Vec across both languages, achieving the highest accuracy of 0.9562 on the English dataset after tuning. Word2Vec showed substantial improvement following parameter adjustment, reducing the performance gap with TF-IDF. The findings highlight the importance of hyperparameter optimization for enhancing the quality of feature representations and improving classification performance. This study also demonstrates that TF-IDF provides more stable results across different linguistic contexts, while Word2Vec benefits significantly from careful tuning. The results provide practical insights for implementing efficient spam email detection systems in multilingual environments. Future research could explore additional classifiers, deep learning approaches, and contextual embeddings to further improve classification accuracy and robustness.
Copyright (c) 2026 Kaifa Ahlal Katamsyi, Ahmad Taufiq Akbar, Andi Nurkholis, Hari Prapcoyo, Bagus Muhammad Akbar, Shoffan Saifullah (Author) https://creativecommons.org/licenses/by-sa/4.0
Integration of LODECI Weighting Method and SPOTIS in Employee Performance Evaluation Based on Multi-Criteria Decision Making
Employee performance evaluation in many organizations often faces challenges due to numerous assessment criteria and potential subjectivity in the decision-making process, making the evaluation results less consistent and objective. Multi-Criteria Decision Making (MCDM) methods have been widely used to address this problem; however, previous approaches generally still rely on subjective weight determination and do not fully consider the stability of results against data variation. Therefore, this study aims to develop a more objective and stable decision-making model by integrating the LODECI method to determine criteria weights based on data and the SPOTIS method to rank alternatives based on their distance from the ideal solution. Five evaluation criteria are used, namely productivity, work quality, discipline, teamwork, and responsibility, with data collected from eight employees as alternatives. The analysis process was carried out through the stages of constructing a decision matrix, calculating criterion weights using LODECI, and ranking using SPOTIS which produced a total distance value as a quantitative evaluation metric. The research results show that GS Employee achieved the smallest distance value of 0.058, thus ranking first, followed by CR Employee with a value of 0.086 and AN Employee with a value of 0.321. These findings indicate that the proposed model is capable of providing more measurable and consistent evaluation results. The main contribution of this study lies in the integration of objective weighting and ideal-solution-based ranking methods supported by sensitivity analysis, thereby producing a performance evaluation system that is more reliable, transparent, and robust compared to previous approaches.
Copyright (c) 2026 Fadila Shely Amalia, Dedi Darwis, Abhishek R. Mehta (Author) https://creativecommons.org/licenses/by-sa/4.0
Facial Skin Disease Classification Using Swin Transformer V2 and ResNet-50 in a Flask-Based System
Facial skin diseases are common health conditions that can significantly affect both physical and psychological well-being. Early identification is essential to minimize the risk of disease progression. However, in many areas, there is still a lack of access to dermatological care. Although deep learning algorithms have been widely used in medical image categorization, few studies offer a direct comparison between convolutional neural networks (CNN) and transformer-based architectures within a cohesive experimental framework, especially concerning the classification of facial skin diseases. This study compares the effectiveness of ResNet-50 with Swin Transformer V2 and develops a deep learning system to classify six different types of skin problems on the face. The models were evaluated using accuracy, precision, recall, and F1-score after the dataset was divided into subsets for testing, validation, and training. According to the trial results, Swin Transformer V2 achieves an astounding accuracy of 97.54%, outperforming ResNet-50, which achieves 94.44%. The training curves indicate stable learning behavior with minimal overfitting. Grad-CAM visualization is applied to improve interpretability by highlighting relevant regions in the images. The best-performing model is implemented in a Flask-based web application as a prototype system for early detection. These results demonstrate how transformer-based architectures can improve classification performance and highlight their potential applications in practical diagnostic support systems
Copyright (c) 2026 Shinta Arum Imaniyah, Febrian Murti Dewanto, Nur Latifah Dwi Mutiara Sari (Author) https://creativecommons.org/licenses/by-sa/4.0
Comparative Analysis of Multi-Classifier Models with Resampling Techniques for Imbalanced Student Graduation Prediction
<table class="MsoNormalTable" style="border-collapse: collapse; mso-table-layout-alt: fixed; border: none; mso-border-top-alt: #7F7F7F; mso-border-left-alt: black; mso-border-bottom-alt: #7F7F7F; mso-border-right-alt: black; mso-border-style-alt: solid; mso-border-width-alt: .5pt; mso-yfti-tbllook: 1024; mso-padding-alt: 0cm 5.4pt 0cm 5.4pt; mso-border-insideh: .5pt solid black; mso-border-insidev: .5pt solid black;" border="1" width="586" cellspacing="0" cellpadding="0"> Student graduation prediction supports early academic intervention but commonly suffers from class imbalance, where on-time graduates dominate the dataset. This study evaluates five classifiers—Random Forest (RF), XGBoost, Logistic Regression (LR), k-Nearest Neighbors (k-NN), and Gaussian Naïve Bayes (GNB)—under five class-imbalance handling scenarios: Baseline (no resampling), Random Undersampling (RUS), SMOTE, ADASYN, and Borderline-SMOTE. Experiments were conducted on 796 student records (10 attributes) with an imbalanced distribution (634 on-time vs. 162 not on-time; ratio 1:3.9) using Stratified 5-Fold Cross-Validation. Performance was assessed using confusion-matrix metrics and AUC-ROC to reflect minority-class detection. Under baseline, RF achieved the highest accuracy (0.873) but limited minority recall (0.573), confirming majority-class bias. Resampling consistently improved minority recall across models; for example, LR recall increased to 0.802 with RUS, while GNB reached 0.833 with ADASYN, although accuracy decreased due to the sensitivity–specificity trade-off. Overall, RF and XGBoost showed the most stable discrimination across resampling scenarios based on AUC (RF: 0.870–0.883; XGBoost: 0.847–0.866). The main contribution is a systematic, reproducible comparative evaluation of classifier–resampling combinations for imbalanced graduation prediction, providing practical guidance for selecting robust models to identify students at risk of delayed graduation.
Copyright (c) 2026 Irmawati Carolina, Resti Lia Andharsaputri, Suharjanti, Titin Prihatin, Hafis Nurdin (Author) https://creativecommons.org/licenses/by-sa/4.0
Modification of Additive Ratio Assessment Method through Distance-Based Weighting Approach for Optimizing Assessment Accuracy
The Additive Ratio Assessment (ARAS) method is one of the approaches in multi-criteria decision making (MCDM) used to determine the best alternative based on a number of predetermined criteria. The drawback of this method is its heavy reliance on the accuracy of the criterion weighting determination; non-objective weights can lead to biased results. This study aims to improve the accuracy of ranking in multicriteria decision-making through the modification of the ARAS method with a distance-based weighting approach called ARAS-D. The ARAS method, known for its simplicity in calculation, was modified to be more responsive to the distribution of alternative data on each criterion. This distance-based weighting approach objectively determines the weight of the criteria based on variations in data performance, thereby reducing subjectivity in the weighting process. A case study was conducted on the selection of a new store location with six main criteria: rental cost, building area, accessibility, consumer traffic, parking availability, and infrastructure. The results of the evaluation show that the ARAS-D method is able to produce more precise ratings than the standard approach. Store locations with the highest utility value are recommended as the best choice, proving the effectiveness of the method in supporting strategic decisions. The results of the New Store Location 5 alternative rating obtained the highest score with a value of 0.9083, indicating that this location is the most optimal choice overall. This is followed by New Store Location 3 with a value of 0.8617 and New Store Location 1 with a value of 0.8415, which also shows excellent performance against the criteria that have been set. This research contributes to the development of more adaptive and data-based decision-making methods.
Copyright (c) 2025 Rakhmat Dedi Gunawan, Muhammad Waqas Arshad, Agung Deni Wahyudi, Ryan Randy Suryono, Tri Widodo, Faruk Ulum (Author) https://creativecommons.org/licenses/by-sa/4.0
Interactive Live Streaming Website with Cloud Computing and AI Assistant Integration
Live streaming platforms continue to face persistent challenges related to high latency, limited scalability, and insufficient real-time interactivity, which degrade user experience and hinder audience engagement. This study aims to address these limitations by developing StreamCats, a third-party interactive web-based live streaming system that integrates cloud computing infrastructure, WebSocket-based real-time communication, and an AI assistant. The system was developed using Object-Oriented Methodology (OOM) and the Rational Unified Process (RUP), implemented with Next.js, Socket.IO, Supabase, and Puter.js, while TikTok live event connectivity was achieved through WebSocket reverse engineering. System performance was evaluated through stress testing using Apache JMeter under 10, 50, and 100 concurrent user scenarios, functional validation of six core modules, and usability testing involving ten participants. The results demonstrate that under 100 concurrent users, the system maintained an average latency of 2,180.3 ms (well below the 5,000 ms threshold), a response time of 139 ms, CPU usage of 1.13%, and a throughput of 89.8 requests per second, outperforming comparable platforms Indofinity (57 req/s) and Tikfinity (19.5 req/s). All six modules passed functional tests, and 90% of participants successfully completed usability tasks with an average comprehension time of under five minutes. These findings confirm that the integration of cloud computing and AI-driven assistance significantly enhances scalability, responsiveness, and user experience in interactive live streaming environments, establishing StreamCats as a viable and competitive foundation for multi-platform real-time streaming solutions
Copyright (c) 2026 Satria Tantra Adlillah, Ai Rosita (Author) https://creativecommons.org/licenses/by-sa/4.0
Support Vector Machine with FastText Word Embedding for Hate Speech Aspect Categorization
Freedom of expression on Twitter often leads to issues such as hate speech, which may include provocation, incitement, or insults based on race, religion, gender, and other aspects. To address this issue, machine learning techniques can be applied to automatically classify hate speech. Therefore, this study aims to implement a machine learning–based approach for automatic hate speech aspect classification and to evaluate the accuracy of the obtained results. Support Vector Machine is used as the classifier method, with FastText as the word embedding method in the categorization process of hate speech aspects. The categorized aspects include abusive, individual, group, religion, race, physical, gender and other. The dataset used in this research is a collection of Indonesian tweets from Kaggle, which have been classified into each aspect. This study also tested combinations of preprocessing methods, namely filtering with stemming and the FastText pre-trained model. From the test results of the application of the Support Vector Machine method with FastText word embedding, with parameters C value = 1.0, gamma = 1.0 and RBF kernel and the ratio between training data and testing data is 90:10, the best results were obtained accuracy 98%, precision 98%, recall 98% and F1-Score 97% on Physical and Gender aspects. In addition, this study also tested if it did not use fasttext word embedding and the accuracy results showed 84%, precision 74%, recall 86% and F1 Score 79% in the abbusive aspect.
Copyright (c) 2025 Aida Milati Mardiana, Imam Fahrur Rozi, Rudy Arianto (Author) https://creativecommons.org/licenses/by-sa/4.0
Fine-Tuned Autoencoder Neural Network for Anomaly Detection in Accounting Transactions
Anomaly detection in accounting transactions plays a crucial role in identifying irregularities that may signal fraud, errors, or unusual financial behavior. Traditional rule-based and statistical methods often struggle to detect complex and hidden patterns in large-scale financial datasets. This paper presents a fine-tuned Autoencoder Neural Network for detecting anomalies in structured accounting records. The model processes feature such as date, account type, debit, credit, transaction category, and payment method. Preprocessing includes handling missing values, encoding categorical data, and extracting temporal features. The Autoencoder architecture was optimized using multiple hidden layers and dropout regularization to prevent overfitting. Reconstruction errors were used to determine anomaly scores, with a dynamic threshold set at the 98th percentile. Experimental results show that the model accurately distinguishes normal and anomalous transactions, identifying 2,000 outliers from a total of 100,000 records. Additional analysis indicates that anomalies often occur during weekends or holidays and involve unusual payment methods. These findings demonstrate the potential of the fine-tuned Autoencoder as a scalable and intelligent anomaly detection framework to support auditors and financial analysts in proactive fraud prevention.
Copyright (c) 2025 Nur Alamsyah, Hani Fitria Rahmani, Wala Erpurini, Budiman https://creativecommons.org/licenses/by-sa/4.0
An Objective Sales Team Performance Assessment Model: Integrating Entropy Weighting and Multi-Attribute Utility Theory
Evaluation of sales team performance is essential for measuring the effectiveness of marketing strategies and achieving corporate goals. The main problem in evaluating sales team performance lies in the high subjectivity of assessments, unclear performance indicators, and difficulties in determining objective and consistent criteria weights. This situation results in evaluation outcomes that are unstable and potentially lead to suboptimal managerial decisions. To address this, the study applies a hybrid approach combining Entropy weighting and Multi-Attribute Utility Theory (MAUT). Entropy objectively derives criterion weights from data variability, while MAUT systematically transforms performance scores into utility values, enabling more consistent comparisons than traditional assessment methods. The research results show that the proposed model produces stable and quantitatively consistent rankings, with a utility score range between 0 and 1.0048 reflecting measurable performance differentiation among teams. Team G achieved the highest score of 1.0048, while Team D scored 0, indicating a significant performance gap. Compared to conventional methods, which tend to yield more homogeneous values, this hybrid approach is more effective in minimizing bias, enhancing discriminative power, and strengthening the reliability of managerial decision-making. This research makes a significant contribution to the development of scientific knowledge by presenting an innovation in the form of integrating the Entropy and MAUT methods in the context of sales team performance evaluation that is more objective, systematic, and data-based.
Copyright (c) 2026 Rio Efendi, Adhie Thyo Priandika (Author) https://creativecommons.org/licenses/by-sa/4.0
Combination of Logarithmic Least Square Weighting and MAUT Method for Best Employee Selection in Retail Companies
Selecting the best employees plays a crucial role in enhancing the performance of retail companies. Given that each employee has unique roles, responsibilities, and working conditions, creating a truly fair and consistent assessment standard can be challenging. Additionally, subjective factors such as personal bias or preferences of the assessor can influence the evaluation outcome. The integration of LLSW and the MAUT method in employee selection offers a systematic approach that combines precise weighting with multi-criteria utility analysis. This combination aims to improve the accuracy, objectivity, and transparency of the decision-making process. By utilizing both methods, retail companies can establish a more effective, transparent, and data-driven selection system, ensuring that the best employees are chosen based on rational and fair evaluations. The results of the employee selection process using LLSW and MAUT showed that Employee RS ranked first with the highest score of 0.7485, indicating the strongest qualifications compared to the other candidates. Employee LK and Employee ML ranked second and third with scores of 0.6035 and 0.572, respectively, demonstrating solid performance. These selection outcomes can assist companies in recruiting the most suitable workforce for their operational needs and vision, ultimately leading to improved productivity and service quality in the long run. The main contribution of this research is capable of improving accuracy and fairness in employee performance evaluation. This approach reduces the subjectivity that often occurs in conventional assessment processes in the retail sector, as well as providing a basis for transparent and measurable decision-making.
Copyright (c) 2025 Aditya Saputra, Adhie Thyo Priandika (Author) https://creativecommons.org/licenses/by-sa/4.0
K-Means++ and TF-IDF for Grouping Library Books by Topic
The grouping of library materials in the Department of Informatics and Computer Engineering (JTIK) at Universitas Negeri Makassar (UNM) is still conducted using a conventional system that relies on predefined categories and librarian intuition. This approach often leads to inconsistencies in book categorization, making it difficult for users to find relevant references efficiently. To address this issue, this research applies the K-Means++ clustering method, which optimizes centroid initialization for more accurate cluster formation. Books are grouped based on the TF-IDF weighting matrix, resulting in six distinct clusters characterized by unique centroid values. Analysis of the top 10 words per cluster highlights dominant topics within each group. The clustering quality was evaluated using the Silhouette Coefficient, with the highest value of 0.04299, indicating a well-separated cluster structure. These findings demonstrate that K-Means++ effectively organizes books based on word similarity, enhancing library material management and improving information retrieval in the JTIK library.
Copyright (c) 2025 Jessicha Putrianingsih Pamput, Aindri Rizky Muthmainnah, Andi Akram Nur Risal, Dewi Fatmarani Surianto https://creativecommons.org/licenses/by-sa/4.0
Implementation of the Standard Deviation Multi-Objective Optimization by Ratio Analysis Method in Warehouse Staff Recruitment Selection
The warehouse staff selection process has a crucial role in ensuring optimal operational efficiency and logistics management. A selection approach that considers aspects of technical skills, work experience, and compatibility with the organization's culture is essential in ensuring the efficiency and effectiveness of logistics management. The labor selection process, including in the context of warehouse staff recruitment, often faces challenges due to subjectivity in decision-making. The implementation of the SD-MOORA method is the main goal in this study in the process of accepting warehouse staff to improve the objectivity and accuracy of candidate selection, the results of this study are expected to contribute to improving the efficiency of the labor selection process and support data-based decision-making in human resource management. The data used in this study consists of 8 candidates and 6 criteria in the selection of warehouse staff admission. The final outcome of optimizing the SD-MOORA method for ranking warehouse staff admissions shows that GT secured the top rank with a value of 0.3827, indicating it is the most suitable candidate according to the selection criteria. AN followed in second place with a score of 0.3752, and BD placed third with a score of 0.3579. This study significantly contributes to advancing the development of decision support systems for warehouse staff selection by applying the SD-MOORA method. By objectively considering the weighting of criteria using standard deviations, this approach enhances both the accuracy and transparency of candidate rankings.
Copyright (c) 2025 Farhan Nopransyah Putra, Adhie Thyo Priandika (Author) https://creativecommons.org/licenses/by-sa/4.0
Optimizing Employee Admission Selection Using G2M Weighting and MOORA Method
An objective and effective employee admission selection process is a crucial step for the success of the organization in achieving its goals. Problems in employee recruitment selection often arise due to a lack of good planning and system implementation, namely decisions are often influenced by personal preferences, stereotypes, or non-relevant factors, thus reducing objectivity in choosing the best candidates. Objective selection ensures that candidate assessments are conducted based on measurable, relevant, and bias-free criteria, so that only individuals who truly meet the company's needs and standards are accepted. The purpose of developing an optimal approach in employee admission selection using G2M weighting and MOORA is to create a more objective, efficient, and accurate selection process. This approach aims to integrate the calculation of criterion weights mathematically, such as those offered by G2M, in order to eliminate subjective bias in determining criterion prioritization. The MOORA method of evaluating alternative candidates is carried out through ratio analysis that takes into account various criteria simultaneously, resulting in a transparent and data-driven ranking. The results of the employee admission selection ranking based on the criteria that have been evaluated, Candidate 3 obtained the highest score of 0.4177, indicating that this candidate best meets the expected criteria. The second position was occupied by Candidate 6 with a score of 0.3886, followed by Candidate 9 with a score of 0.3528. This research contributes to the recruitment process, by providing a more reliable, transparent, and less subjective way of selecting the right candidates for the positions that companies need.
Copyright (c) 2025 Yuri Rahmanto, Junhai Wang, Setiawansyah Setiawansyah, Aditia Yudhistira, Dedi Darwis, Ryan Randy Suryono https://creativecommons.org/licenses/by-sa/4.0
Efficient Image Transmission for Autonomous Systems Using Residual Dense Feature Networks Over LoRa Networks
Autonomous systems face challenges in transmitting high-quality images over bandwidth-constrained networks like LoRa, which operates at data rates of 0.3–50 kbps. This study proposes the Residual Dense Feature Network (RDF Net), a super-resolution model designed to optimize image transmission within the constraints of LoRa networks. By leveraging Contrast-Aware Channel Attention (CCA), Enhanced Spatial Attention (ESA), Blueprint Separable Convolution (BSConv), and a progressive approach, RDF Net achieves 20x upscaling, enabling low-resolution images (40x40 pixels) to be reconstructed into high-resolution outputs (800x800 pixels) on a central server. Experimental evaluations demonstrate that Model-4, combining CCA and ESA, delivers state-of-the-art perceptual quality and structural fidelity, while Model-3, using ESA, offers a computationally efficient alternative for resource-constrained scenarios. Simulations of LoRa’s bandwidth limitations reveal that transmitting a single 40x40 image requires approximately 0.208–0.56 seconds at a data rate of 50 kbps. While this demonstrates the feasibility of near real-time communication, the trade-off between latency and visual fidelity remains a critical consideration, particularly for latency-sensitive applications. These findings underscore RDF Net’s potential to address the challenges of high-quality visual communication in bandwidth-constrained environments, paving the way for enhanced autonomous system applications. Further optimization, including adaptive compression strategies, and testing on actual LoRa hardware are recommended to validate its performance in real-world scenarios and explore its applicability to diverse autonomous systems.
Copyright (c) 2025 Muhamad Fadly Rizqy Praptawilaga, Galura Muhammad Suranegara, Arief Suryadi Satyawan https://creativecommons.org/licenses/by-sa/4.0
Prediction Customer Loyalty Using Random Forest Algorithm on Shopee Reviews
This research develops a Shopee customer loyalty prediction model using Random Forest algorithm, utilizing customer reviews from Google Play Store. One of the key issues in e-commerce is maintaining customer loyalty amidst intense competition, so it is important to identify loyal customers and understand the factors that influence their commitment. This study involves data collection through web scraping, data cleaning, loyalty labeling, and Random Forest-based prediction model building and evaluation. The evaluation process was conducted using a confusion matrix to measure accuracy, precision, recall, and F1-score. The model classified customers into loyal, neutral, and disloyal categories, with an overall accuracy of 97%. The model showed precision, recall, and F1-score of 0.98 for loyal customers, and 0.99, 1.00, and 0.99 for disloyal customers. However, identification of neutral customers is still a challenge, with precision, recall, and F1-score of 0.92, 0.85, and 0.88, respectively. The results of this study provide strategic insights for Shopee in improving customer retention strategies and demonstrate the effectiveness of the Random Forest algorithm in analyzing review data.
Copyright (c) 2025 Ferdi Saputra, Fersellia https://creativecommons.org/licenses/by-sa/4.0
Classification of Vegetable Types Using the Convolutional Neural Network (CNN) Algorithm
This study aims to classify vegetable types using the Convolutional Neural Network (CNN) algorithm with a dataset encompassing 15 vegetable classes and a total of 31,000 images. By utilizing the TensorFlow and Keras libraries, the CNN model was designed with convolutional, pooling, and dense layers to recognize visual features such as color, texture, and shape. The results indicate a highest validation accuracy of 95.83% and a testing accuracy of 93%. This research contributes to the application of the CNN algorithm for image classification and demonstrates its potential in handling multi-class datasets effectively. However, since the vegetables used have very distinct shapes and textures, this study is more relevant in the context of the technical application of the CNN algorithm rather than practical benefits. The research would be more impactful if applied to vegetables with similar shapes and characteristics, thereby supporting farmers or individuals studying vegetable traits in greater depth. Additionally, such an approach could address challenges in differentiating visually similar vegetable types, making the technology more valuable in real-world agricultural or educational settings.
Copyright (c) 2025 Wilis Arum Karunia, Safriya Murni Puspita, Dwi Rolliawati, Ahmad Yusuf (Author) https://creativecommons.org/licenses/by-sa/4.0
Classification of Dog Emotion Using Transfer Learning on Convolutional Neural Network Algorithm
Recognizing your pet's emotions are very important to improve health, welfare and to detect certain diseases in the animal. The emotions in question are categorized into four categories, namely anger, happiness, calmness, and sadness. The model is designed by utilizing transfer learning techniques using the VGG16 architecture to perform image feature extraction for dog emotion classification based on the image of the animal's facial expression. The research produced an accuracy value of 96.72% on the training set and 88.05% on the validation set, as well as an average F1-Score value of 84.30% on the test set. This research shows the great potential of utilizing transfer learning in dog emotions classification and contributes to more advanced emotion recognition techniques to improve pet’s welfare.
Copyright (c) 2024 Steven Tribethran, Nicolas Jacky Pratama Hasan, Abdul Rahman https://creativecommons.org/licenses/by-sa/4.0
Selection of Marine Tourism Destinations in West Kalimantan Using the Analytical Hierarchy Process Method
Tourism in West Kalimantan offers various destinations rich in meaning and history. However, there are issues in managing tourism in this region, such as the lack of easily accessible information for tourists and the uneven distribution of visits to tourist destinations. Tourists often struggle to choose destinations that match their preferences due to the limitations of comprehensive and objective information systems. This results in some destinations becoming overcrowded while others are less visited. Technological advancements enable the development of Decision Support Systems (DSS) to address these issues and enhance decision-making in selecting tourist destinations. This research aims to develop a decision support system using the Analytical Hierarchy Process (AHP) method to simplify the decision-making process in choosing maritime tourist destinations in West Kalimantan. The primary goal of this research is to help tourists select maritime tourist destinations that best suit their preferences, improve their tourism experience, and support sustainable tourism development in West Kalimantan. This study employs the AHP method in the decision-making process. The research stages include problem identification, literature review, data collection through questionnaires, data analysis, and final weight calculation. Data were collected from 400 respondents in West Kalimantan and analyzed to determine the priority of tourist destinations based on criteria such as scenery, distance, accessibility, facilities, cleanliness, and cost. Based on AHP analysis, it was found that Temajuk Sambas is the most preferred tourist destination, followed by Temajuk Mempawah, Jawai Bahari, Samudera Indah, Pulau Lemukutan, and Tanjung Bajau. The developed decision support system provides clear guidance for tourists in selecting maritime tourist destinations in West Kalimantan according to their preferences.
Copyright (c) 2025 Susy Rosyida, Hendi Minarto https://creativecommons.org/licenses/by-sa/4.0
Dempster Shafer Analysis in Mental and Emotional Health Monitoring
Monitoring and diagnosing mental and emotional health is a significant challenge in the healthcare field due to its complex and subjective nature. This research aims to develop an expert system using the Dempster-Shafer method in monitoring and diagnosing mental and emotional health conditions. The Dempster-Shafer method was chosen because of its ability to handle uncertainty and combine various evidence from different information sources. This analysis is designed to identify seven types of mental and emotional illness by considering twenty-four related symptoms. The results of the analysis show that this expert system can provide a more accurate and comprehensive assessment compared to conventional methods. It is hoped that the implementation of this expert system can be an effective tool for medical personnel in making diagnoses and determining appropriate treatment steps for patients with mental and emotional health conditions. This study also highlights the potential of the Dempster-Shafer method in other applications that require evidence-based analysis under uncertainty.
Copyright (c) 2024 Asyahri Hadi Nasyuha, Dini Fakta Sari, Azanuddin, Muhammad Hafidz Ady Khoiri https://creativecommons.org/licenses/by-sa/4.0
Implementation MFEP Method in Developing Recommendation System for Program Keluarga Harapan (PKH) Recipients
Poverty occurs because of the imbalance between unlimited human needs and limited resources. This results in a lack of income to meet basic living needs. The Indoonesian government's efforts to alleviate poverty include providing assistance to the poor or underprivileged with assistance called Social Assistance, one of which is the Program Keluarga Harapan (PKH). Problems often occur in determining who is entitled to receive PKH assistance. The conventional selection process is considered inefficient because it requires a long process and the influence of the committee's subjectivity in the assessment, the criteria used in the survey are not in accordance with government regulations and the limited quota of total PKH recipients, so there are still people who do not receive PKH even though they meet the criteria. This research uses the Multi Factor Evaluation Process (MFEP) method. System testing uses the black box method and Boundary Value Analysis techniques which focus on finding system errors. To test the system's accuracy by comparing the MFEP process from the system results and facts based on PKH recipients in 2022 and producing an accuracy value of 91%.
Copyright (c) 2024 Wawan Nugroho, Galih Setiawan Nurohim, Heribertus Ary Setyadi, Doddy Satrya Perbawa https://creativecommons.org/licenses/by-sa/4.0
Sipkumhamai Application Success Analysis Using the Delone And Mclean Model
Evidence-based policy aims to increase the efficiency and effectiveness of policy settings and increase alternative opportunities. The Legal and Human Rights Policy Strategy Agency created the SIPKUMHAMAI application to support evidence-based legal and human rights policies, support legal and human rights research with better data, and provide information to the public about legal and human rights issues. It is very important to make efforts to provide comprehensive and systematic data and information on legal and human rights issues originating from real situations on the ground. In addition to overall legal and human rights issues, this data and information can be used to find out more about the causes of legal and human rights problems, identify deficiencies in law enforcement and human rights protection, and carry out analyzes and provide various recommendations to strengthen systems and mechanisms for enforcing law and human rights in Indonesia. To achieve this goal, a system evaluation must be carried out to determine which components need to be improved. This is necessary to determine whether the system used provides significant benefits for users and the organization. Using the Delone and McLean model, from the six relationships of Information System Success Model, it was obtained that only Hypothesis 7, Hypothesis 8, and Hypothesis 9 were significantly supported and accepted by the data. These findings provide several implications for eGovernment research and practice, especially regarding how to maximize applications. This paper concludes by discussing the limitations that the proposed hypotheses are not fully supported by the research results.
Copyright (c) 2024 Fadillah Said, Chintia Octenta, Adi Octaviantara https://creativecommons.org/licenses/by-sa/4.0
Mobile-based Application Development on Admission of New Students with Design Science Research Methodology
Increased use of mobile devices in recent years has led to a change in human behavior as users. Mobile devices today are being used for a wide range of sectors ranging from entertainment, and business to education. In the field of education, it can be used to interact between teachers and students, and lecturers with students, and can also be done for registration of New Student Admission. The presence of PMB registration through mobile devices can help prospective students apply wherever they are without having to come directly to the campus. It's not implemented by the Indonesian Siber University. (Cyber University). The Cyber University campus is currently implementing New Student Admission registration directly through the campus, so this process is still likely to take a long time. To solve the problem, this study will solve the problem of new student enrolment that is still being done manually to be digitized by building mobile-based applications. The method to be used is the Design Science Research Methodology (DSRM) known as the fast method because it includes the Agile software development model. The programming language used is the Dart-based Flutter framework. As a result of the research carried out, the mobile-based PMB application on the Cyber University was successfully constructed and in line with expectations. Candidate students can download the app on the Google PlayStore with the keyword Cyber PMB
Copyright (c) 2024 Yuris Alkhalifi, Rino Ramadan, Rahdian Kusuma Atmaja, Ispandi https://creativecommons.org/licenses/by-sa/4.0
Building a Predictive Model for Chronic Kidney Disease: Integrating KNN and PSO
This study examines the improvement of prediction accuracy for Chronic Kidney Disease (CKD) through the integration of the K-Nearest Neighbors (KNN) method with Particle Swarm Optimization (PSO). Amidst the rising prevalence of CKD, closely related to diabetes and hypertension, early detection of CKD becomes a significant challenge, especially in Indonesia where access to healthcare facilities and public awareness remain limited. This study utilizes the Chronic Kidney Disease dataset from the UCI Machine Learning repository, encompassing 400 patient records with 24 clinical, laboratory, and demographic variables. With the KNN method, this approach classifies data based on feature proximity, while PSO is used for feature selection and parameter optimization, enhancing the model's accuracy and efficiency in identifying CKD at early stages. The findings indicate a significant improvement in prediction accuracy, from 80.00% using KNN to 97.75% after integration with PSO. These results affirm that the combined approach of KNN and PSO holds great potential in improving early detection and management of CKD, paving the way for further research into practical applications in the healthcare field.
Copyright (c) 2024 Slamet Widodo, Herlambang Brawijaya, Samudi Samudi https://creativecommons.org/licenses/by-sa/4.0
Analysis of IT Service Desk Applications Using the Servqual Method at The Republic of Indonesia National Library
The National Library has implemented an Electronic-Based Government System (SPBE) so that library service operations and operations for the national library staff have used a computerized system which results in the PERPUSNAS requiring centralized management of IT problems, namely the IT Service Desk. As an IT problem service, IT Service Desk needs to be analyzed to measure the level of user satisfaction to determine the quality of its services. In this study, the measurement of the level of user satisfaction of IT service desk services was carried out using the Servqual method which has 5 dimensions of service quality, namely Tangible, Reliability, Responsiveness, Assurance, and Emphaty by looking for the value of the gap between the value of perception and the value of expectations from users. The results of the IT Service Desk measurement at PERPUSNAS RI obtained a gap of -0.78 with the category "Good Enough".
National Library of the Republic of Indonesia
Copyright (c) 2024 Siti Nur Khasanah, Dewi Masyithoh https://creativecommons.org/licenses/by-sa/4.0
Predicting Graduation Outcomes: Decision Tree Model Enhanced with Genetic Algorithm
This research aims to improve the accuracy of predicting student permit results in the digital era by utilizing machine learning techniques. The main focus is the use of a Decision Tree (DT) model optimized with a Genetic Algorithm (GA) to overcome the limitations of accuracy and testing of conventional methods. This research began with collecting student academic data, followed by preprocessing to eliminate incompleteness and organize the data format. The DT model is then built and optimized with GA, which is inspired by biological evolutionary processes to improve feature selection and parameter tuning. The results show a significant increase in prediction accuracy, from 86.19% to 87.68%, and an increase in the Area Under Curve (AUC) value from 0.755% to 0.788%. This research not only proves the effectiveness of GA integration in improving DT models, but also paves the way for the application of evolutionary techniques in educational data analysis and other fields. The main contributions of this research include the development of more accurate prediction models and practical applications in educational contexts, with the hope of assisting educational institutions in making more informed decisions for their students.
Copyright (c) 2024 Sinta Rukiastiandari, Luthfia Rohimah, Aprillia, Fara Mutia https://creativecommons.org/licenses/by-sa/4.0
Phyton-Based Machine Learning Algorithm To Predict Obesity Risk Factors In Adult Populations
Obesity is a serious health problem because it can lead to a variety of diseases. Adults are prone to obesity due to several factors such as age, physical activity, weight, diet, gender, lifestyle and so on. Machine Learning as one of the methods for predicting and classifying factors of obesity especially in the adult population. In machine learning, there are various types of algorithms that can be used to classify data. In this study, using the K-Nearest Neighbor, Decision Tree and Naïve Bayes algorithms, 2111 datasets were used and processed using the Phyton programming language. The results were obtained from the comparison of the three algoritms with the highest accuracy of 93.6%, the Decision Trees with 79.6% and the Naïv Bayes with 60%.
Copyright (c) 2024 Mari Rahmawati, Ade Fitria Lestari, Sri Hardani https://creativecommons.org/licenses/by-sa/4.0
Development of a Website-Based Rubber Tender System for Cooperatives Tanjung Telang Village
The development of technology in today's modern era is progressing very rapidly, where everyone can find various technologies in various fields around human life, one of which is information technology. The existence of information technology can simplify and speed up data processing, information technology that is often used by an agency or organization is information system technology. Tanjung Telang Village Cooperative is a village engaged in processing and selling rubber residents. Currently, the promotional process carried out by the Village Cooperative only uses via telephone, resulting in very limited competition for the sale of rubber residents. The purpose of this research is to build a website-based rubber tender system that is expected to be more efficient and transparent to residents. This research uses descriptive quantitative and uses User Centered Design (UCD) system development. And the results obtained in this research are the creation of an online-based tender system that can be used by the Tanjung Telang Village Cooperative in the auction process to prospective buyers in the South Sumatra region.
Copyright (c) 2024 Khana Wijaya, Riza Kartina https://creativecommons.org/licenses/by-sa/4.0
User Centered Design in Analysis and Design of UI UX in the Simpeg Application of Dharmais Cancer Hospital
Dharmais Cancer Hospital as a health service facility that is experienced in its field, of course, has human resources which are important, so to support employee performance in terms of employee rights such as employee leave, media is needed that really supports employee needs. Through the employee support system, namely the Employee Information System (SIMPEG) application, several problems were found in terms of appearance and the proposal process as well as the leave recapitulation process which did not exist in the previous SIMPEG application. From the problems found in the Personnel Information System (SIMPEG), a User Centered Design (UCD) method is offered which places the Personnel Information System (SIMPEG) users as the main consideration for building a new system. In the design process there are three steps that are carried out starting from the initial stage, the development stage and the final stage, where at the initial stage testing is carried out using a questionnaire, the average result is 53 which means OK but still low or not good, after the development of the system is carried out and the distribution of questionnaires was carried out again, the results were 71, which means Good. With the design of a Personnel Information System (SIMPEG) using the User Centered Design (UCD) method, it produces a new User Interface (UI) and User Experience (UX) design for the Personnel Information System (SIMPEG), which consists of a forgot password menu feature, leave recap and display easier to use for employees.
Copyright (c) 2024 Normah, Bakhtiar Rifai, Fani Nurona Cahya, Pratiwi Indah Widiastuti https://creativecommons.org/licenses/by-sa/4.0
Comparison of Supervised Learning Classification Methods on Accreditation Data of Private Higher Education Institutions
This research aims to analyze and compare supervised learning classification methods using a case study of accreditation data for private higher education institutions within the LLDikti Region III contained in BAN-PT. In addition, this research also uses Weka machine learning software in its calculations. The initial step taken is to prepare the software used for supervised learning analysis, then pre-processing the data, namely labeling data that has a categorical data type, after that determining data for testing data. The next step is to test each classification method. The methods used for comparison are logistic regression, K-nearest neighbor, naive bayes, super vector machine, and random forest. Based on the calculation results, the Kappa Statistic and Root mean squared error values obtained are 1 and 0 for the logistic regression method, 0.979 and 0.0061 for the K-nearest neighbor method, 1 and 0.2222 for the super vector machine method, 0.969 and 0.0341 for the naive bayes method, 1 and 0 for the decision tree method, and 0.5776 and 0.1949 for the random forest method, respectively. The logistic regression and decision tree methods in this study get Kappa Statistic and Root mean squared error values of 1 and 0 respectively so that they are said to be good and acceptable, thus the two classification methods are the most appropriate methods and are considered to have the highest accuracy.
Copyright (c) 2024 Noviyanto, Mochamad Wahyudi, Sumanto https://creativecommons.org/licenses/by-sa/4.0
Optimizing Heart Failure Detection: A Comparison between Naive Bayes and Particle Swarm Optimization
This research focuses on the importance of early detection of heart failure which is a serious global health problem. Given the variety of symptoms of heart failure, accurate early detection methods are needed with the aim of reducing the impact of this disease. This study uses the Naïve Bayes (NB) method which has been proven effective in classifying heart failure with significant variations in accuracy by integrating Particle Swarm Optimization (PSO) to improve the model. The evaluation model involves a confusion matrix including accuracy, precision, recall, and Area Under the Curve. The research results show that the integration of PSO in NB results in an increase in accuracy of 7.73%, an increase in precision of 6.42%, and an increase in recall of 1.93%. Although there was a small decrease in AUC. This research shows that the success of NB with PSO can help improve the performance of early detection of heart failure. This indicates the importance of this research in developing more accurate and effective detection methods for critical health conditions such as heart failure.
Copyright (c) 2024 Abdul Hamid, Ridwansyah https://creativecommons.org/licenses/by-sa/4.0