Jurnal Politeknik Negeri Batam (PoliBatam)
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Implementation of the K-Nearest Neighbors (KNN) Regressor Method to Predict Toyota Used Car Prices
Get PDFThe development of the automotive industry in Indonesia has experienced significant growth in recent decades, especially in the used car market segment. One of the used car brands that has high demand is Toyota, because it has a reliable reputation and quality. However, there are challenges that are often faced by sellers and buyers of used cars, namely in determining prices correctly and accurately. Incorrect pricing can be detrimental to one party, either the price is too high or too low. Prices that are too high can slow down the turnover of goods in the market. While low prices can cause sellers to experience losses. The purpose of this study is to help find good performance in determining the price of used Toyota cars. This study will use one of the Machine Learning methods, namely K-Nearest Neighbors Regressor. The KNN method is one method that can be used for classification and regression. In addition, this algorithm is a simple algorithm and can provide accurate prediction results based on its proximity to existing data. This study uses selected relevant features, namely model, year, kilometer, tax, mpg, and cc. The results of this study obtained MAE = 3.31686, MSE = 26.43640, RMSE = 5.14163, and R2-Score = 0.99501 using 90:10 data division and k = 1. This proves that KNN Regressor is an effective method in predicting the price of used Toyota cars. Therefore, the K-Nearest Neighbors (KNN) Regressor method is able to provide a fairly accurate price estimate with a minimal error rate.The development of the automotive industry in Indonesia has experienced significant growth in recent decades, especially in the used car market segment. One of the used car brands that has high demand is Toyota, because it has a reliable reputation and quality. However, there are challenges that are often faced by sellers and buyers of used cars, namely in determining prices correctly and accurately. Incorrect pricing can be detrimental to one party, either the price is too high or too low. Prices that are too high can slow down the turnover of goods in the market. While low prices can cause sellers to experience losses. The purpose of this study is to help find good performance in determining the price of used Toyota cars. This study will use one of the Machine Learning methods, namely K-Nearest Neighbors Regressor. The KNN method is one method that can be used for classification and regression. In addition, this algorithm is a simple algorithm and can provide accurate prediction results based on its proximity to existing data. This study uses selected relevant features, namely model, year, kilometer, tax, mpg, and cc. The results of this study obtained MAE = 3.31686, MSE = 26.43640, RMSE = 5.14163, and R2-Score = 0.99501 using 90:10 data division and k = 1. This proves that KNN Regressor is an effective method in predicting the price of used Toyota cars. Therefore, the K-Nearest Neighbors (KNN) Regressor method is able to provide a fairly accurate price estimate with a minimal error rate
Colors and Texture Feature Extraction Using Learning Vector Quantization 3 Algorithm in Optimization of Beef Identification
Get PDFThe Assessment Institute for Foods, Drugs, and Cosmetics of the Indonesian Ulama Council (LPPOM MUI) is responsible for conducting research, evaluations, and determining the halal status of products in accordance with Islamic teachings. In Indonesia, where religious diversity is prevalent, the halal certification process is crucial, particularly due to differences in the halal status of certain foods, such as beef and pork, across religions. One of the challenges in this process lies in ensuring a rapid and accurate determination of various types of meat, including beef, pork, goat, and buffalo, which currently tends to be time-consuming within the LPPOM MUI Halal Center. To address this issue, there is a need for a technological solution that can quickly and accurately identify different types of meat, thereby reducing consumer uncertainty when selecting halal products. This study aims to develop an Android-based application utilizing the Learning Vector Quantization 3 (LVQ3) method to facilitate the classification of meat types by analyzing patterns specific to beef, pork, goat, and buffalo. This system is expected to expedite the halal verification process, thereby supporting more efficient and accurate decision-making in the halal certification sector
Comparing Machine Learning Algorithms to Enhance Volumetric Water Content Prediction in Low-Cost Soil Moisture Sensor
Get PDFMeasuring soil moisture is possible either with directly using gravimetric test or indirectly using soil moisture sensor. Direct measurements offer accuracy but are not efficient in field measurements. On the other hand, indirect measurement offers remote measurement that will facilitate the user but lacks in accuracy. This research aims to compare and identify the best machine learning model that can improve indirect measurement (soil moisture sensor prediction) using direct measurement (gravimetric test) as a response variable. This research uses linear regression, K-Nearest Neighbours (KNN) and Decision Tree models. The three models were then compared based on Root Mean Square Error (RMSE). The results suggested that KNN (0.02939128) had the smallest RMSE value followed by decision tree (0.05144186) and linear regression model (0.05172371)
Comparison of Support Vector Machine and Decision Tree Algorithm Performance with Undersampling Approach in Predicting Heart Disease Based on Lifestyle
Get PDFHeart disease is one of the leading causes of death in the world with risk factors such as atherosclerosis, high blood pressure, and smoking. Early diagnosis is essential to reduce mortality and improve patients\u27 quality of life. This study evaluates the performance of two machine learning algorithms, namely Support Vector Machine (SVM) and Decision Tree (DT), in predicting heart disease risk by applying undersampling techniques to handle data imbalance. The K-fold cross-validation method with K=10 and hyperparameter tuning were applied to obtain the optimal performance of both models. The results showed that SVM without undersampling achieved 92% accuracy, while with undersampling the accuracy decreased to 76%. DT without undersampling has 91% accuracy, while with undersampling the accuracy reaches 75%. The undersampling technique successfully improved the balance in recognizing minority classes, although it reduced the overall accuracy. This finding confirms that SVM is more reliable in predicting heart disease in datasets with unbalanced class distribution.Heart disease is one of the leading causes of death in the world with risk factors such as atherosclerosis, high blood pressure, and smoking. Early diagnosis is essential to reduce mortality and improve patients\u27 quality of life. This study evaluates the performance of two machine learning algorithms, namely Support Vector Machine (SVM) and Decision Tree (DT), in predicting heart disease risk by applying undersampling techniques to handle data imbalance. The K-fold cross-validation method with K=10 and hyperparameter tuning were applied to obtain the optimal performance of both models. The results showed that SVM without undersampling achieved 92% accuracy, while with undersampling the accuracy decreased to 76%. DT without undersampling has 91% accuracy, while with undersampling the accuracy reaches 75%. The undersampling technique successfully improved the balance in recognizing minority classes, although it reduced the overall accuracy. This finding confirms that SVM is more reliable in predicting heart disease in datasets with unbalanced class distribution
Aspect-Based Sentiment Analysis with LDA and IndoBERT Algorithm on Mental Health App: Riliv
Get PDFIndonesia\u27s mental health crisis in 2024 is increasing along with the high growth of internet users. Thus, high internet usage provides an opportunity for mobile applications including Riliv, a popular mental health application in Indonesia to become the most complete solution for overthinking, anxiety, and depression. This research aims to analyze the sentiment correlation of aspects based on App Store and Play Store reviews through scraping to effectively expose Riliv’s user satisfaction knowledge to developers using topic labeling with Latent Dirichlet Allocation (LDA) and sentiment labeling using Bidirectional Encoder Representations from Transformers (BERT) indobenchmark/indobert-base-p1 model on Aspect-Based Sentiment Analysis (ABSA). This study used 3068 reviews from September 2015 to December 2024. The main results obtained were 1) Identified the sentiment that positive is highest in 2020, neutral is highest in 2020, and negative is highest in 2018. 2) Identified 4 main aspects of the Riliv application: Access Support, Counseling Services, Meditation Features, and User Interface with LDA. 3) The majority distribution was negative on User Interface, neutral on Counseling Services, and positive on Meditation Features. 4) The effectiveness of IndoBERT compared to the non-transformer baseline algorithm. 5) The most optimal results were obtained with 70% training, 10% validation, and 20% testing, resulting in 95% accuracy
Twitter Sentiment Classification towards Telecommunication Provider Users in Indonesia
Get PDFInternet services have become essential for communication and information sharing. Nowadays, daily activities are conducted through the internet. This study aims to gain a better understanding of the components that influence user perception and satisfaction using textual, sentiment, and statistical analysis techniques. By applying machine learning algorithms such as Naïve Bayes and Support Vector Machine (SVM), this research analyzes customer perceptions of telecommunication service providers in Indonesia. The dataset consists of 300 tweets obtained from the Kaggle platform. The objective is to identify elements that affect customer satisfaction, particularly those related to network stability and service quality. Data preprocessing is carried out using methods such as case folding, normalization, stemming, and stopword removal to enhance sentiment analysis model performance. The results show that SVM outperforms Naïve Bayes in precision and recall, achieving an accuracy of 90% compared to Naïve Bayes\u27 87%. This demonstrates SVM\u27s ability to classify positive and negative sentiments more accurately. Common topics found in the analysis include customer satisfaction with network stability and affordable pricing, while dissatisfaction arises from poor connectivity and slow customer service response. These findings provide valuable insights for service providers to improve service quality and enhance customer satisfaction. Real-time sentiment analysis using machine learning has great potential, and this study highlights how telecommunication companies can leverage strategic recommendations to improve service quality and retain customers.Internet services have become essential for communication and information sharing. Nowadays, daily activities are conducted through the internet. This study aims to gain a better understanding of the components that influence user perception and satisfaction using textual, sentiment, and statistical analysis techniques. By applying machine learning algorithms such as Naïve Bayes and Support Vector Machine (SVM), this research analyzes customer perceptions of telecommunication service providers in Indonesia. The dataset consists of 300 tweets obtained from the Kaggle platform. The objective is to identify elements that affect customer satisfaction, particularly those related to network stability and service quality. Data preprocessing is carried out using methods such as case folding, normalization, stemming, and stopword removal to enhance sentiment analysis model performance. The results show that SVM outperforms Naïve Bayes in precision and recall, achieving an accuracy of 90% compared to Naïve Bayes\u27 87%. This demonstrates SVM\u27s ability to classify positive and negative sentiments more accurately. Common topics found in the analysis include customer satisfaction with network stability and affordable pricing, while dissatisfaction arises from poor connectivity and slow customer service response. These findings provide valuable insights for service providers to improve service quality and enhance customer satisfaction. Real-time sentiment analysis using machine learning has great potential, and this study highlights how telecommunication companies can leverage strategic recommendations to improve service quality and retain customers
Baby Cry Classification Using Ensemble Learning and Whisper Method Comparison
Get PDFBaby cry classification is an important topic in Machine Learning, especially in the healthcare field, as crying is the primary form of communication for infants to convey their needs or conditions. Many inexperienced parents tend to interpret baby cries in a limited way, even though each cry has unique characteristics that represent specific needs such as hunger, discomfort, sleepiness, flatulence, and abdominal pain. With the advancement of technology, identification of baby cries can now be done automatically through AI-based applications, but the implementation is still limited. This study compares the performance of ensemble learning methods, namely Random Forest and XGBoost, with the Whisper model in classifying baby cries. The results show that the Whisper-small model has the best performance with precision 0.9115 and recall 0.9007, followed by XGBoost with slightly degraded performance after hyperparameter optimization. Random Forest showed the lowest performance among the three models. Transformer-based models such as Whisper-small proved to be superior in capturing the complex patterns of infant cries, compared to tree-based models. These findings indicate the great potential of accurate and reliable models to help parents understand the needs of infants more effectively, thereby improving the quality of infant care
Analisis Perancangan Video Promosi Koran Digital Batam Pos: Case Study Menggunakan Metode Design Thinking
Get PDFIn terms of news delivery, especially after the Covid-19 pandemic, the transition from print media to digital media has accelerated, just as Batam Pos must be prepared to face various challenges in the digital realm. One solution implemented by Batam Pos is to shift the medium of news delivery from a print newspaper to a digital newspaper. Although it has been running since the end of 2023, many people are still unaware that the Batam Pos print newspaper can be accessed digitally. As of January 2025, the total number of digital newspaper subscribers for Batam Pos is only 60, with a target of 2500 subscribers to be achieved by the end of 2025. Therefore, this research was conducted to promote the Batam Pos digital newspaper to the public using the design thinking method. This research uses a quantitative approach with validation testing by the content production manager of Batam Pos, followed by testing using the Single Ease Question (SEQ) method on 100 respondents. The evaluation results show an SEQ score of 4.06, which falls into the good/successful category. This research concludes that the designed promotional video is able to meet the information needs of potential customers regarding the subscription process and benefits and has the potential to increase the number of Batam Pos digital newspaper subscribers.Dalam aspek penyampaian berita, terlebih setelah pandemi Covid-19, semakin mempercepat peralihan dari media cetak ke media digital, sama halnya dengan Batam Pos yang harus siap dalam menghadapi berbagai tantangan dalam hal digital. Salah satu solusi yang diterapkan oleh Batam Pos ialah berpindah medium dalam penyampaian berita, yaitu dari berupa koran cetak berpindah menjadi koran digital. Walaupun sudah berjalan sejak akhir 2023, masih banyak masyarakat yang belum mengetahui bahwa koran cetak Batam Pos bisa diakses secara digital. Terhitung sejak Januari 2025 total pelanggan koran digital Batam Pos baru mencapai 60 orang, dengan target yang ingin dicapai pada tahun 2025 ini berjumlah 2500 orang. Sehingga penelitian ini dilakukan bertujuan untuk mempromosikan koran digital Batam Pos kepada masyarakat menggunakan metode design thinking. Penelitian ini menggunakan pendekatan kuantitatif dengan uji validasi dari penanggung jawab produksi konten Batam Pos, diikuti oleh pengujian menggunakan metode Single Ease Question (SEQ) pada 100 responden. Hasil evaluasi menunjukkan nilai SEQ sebesar 4,06 yang termasuk dalam kategori baik/berhasil. Penelitian ini menyimpulkan bahwa video promosi yang dirancang mampu memenuhi kebutuhan informasi kepada calon pelanggan terkait alur dan manfaat berlangganan, serta berpotensi meningkatkan jumlah pelanggan koran digital Batam pos
Leveraging Convolutional Neural Networks for Multiclass Waste Classification
Get PDFThe impact of population growth on waste production in Indonesia emphasizes the urgent need for effective waste management to mitigate environmental and health risks. Segregating waste into organic and inorganic categories is essential for sustainable management, enabling processes like composting and recycling. Employing convolutional neural networks (CNN) through machine learning presents a promising solution for waste classification. This study utilizes a CNN algorithm to achieve significant accuracy and precision in multi-class waste classification, with particular attention to areas for improvement, such as cardboard classification. Based on the MobileNetV2 architecture and Adam optimizer, the model demonstrates high accuracy and precision, with training and validation accuracy of 95.28% and 89.48%, respectively. High precision and recall values confirm its accurate waste classification. The evaluation of unseen data maintains an accuracy of 86.36%, indicating its generalization ability. However, variations in accuracy among waste classes suggest opportunities for refinement, particularly in cardboard classification
Implementation of Blockchain Smart Contract for Online Concert Ticket Transactions Based on NFTs
Get PDFThe development of the entertainment industry, especially music concerts, has driven the transformation of ticket sales systems from conventional to digital methods. Although online concert ticket sales offer greater convenience and reach, they still face the risks of fraud, counterfeit tickets, and unfair distribution. This study, Blockchain Smart Contract Implementation for NFT-Based Online Music Concert Ticket Transactions, aims to develop a ticket sales system using blockchain technology by integrating smart contracts and Non-Fungible Tokens (NFTs). The main objectives are to design and implement smart contracts on the Ethereum network, implement ERC-721-based digital tickets, ensure transparency in transaction history, and verify ticket authenticity through unique identifiers. This study adopts the Agile method, with implementation on the Ethereum Sepolia Testnet and testing using the meta mask digital wallet. The results show that the developed system can automatically hold funds through an escrow mechanism until the ticket is downloaded, generate unique and tamper-proof NFT tickets, display transaction details transparently, and facilitate ticket verification effectively. In conclusion, the use of smart contracts and NFTs significantly improves the security, transparency, and trustworthiness of online music concert ticket transactions