Publication Search

Cardiovascular disease is a leading cause of death globally, necessitating effective predictive systems. This research aims to analyze the effectiveness of various machine learning (ML) models—Logistic Regression (LR), Random Forest (RF), Naive Bayes (NB), Support Vector Classifier (SVC), and K-Nearest Neighbors (KNN)—in predicting heart disease using publicly available health data. The study involved pre-processing data, training models, and evaluating them using accuracy, precision, recall, F1-score, and G-Mean metrics. The results show that KNN is the most reliable model, with the highest accuracy of 92%. Significant health features were identified, such as chest pain type and maximum heart rate. The study contributes to improving clinical decision support systems by identifying optimal ML models for heart disease prediction.

Data mining atau penambangan data merupakan proses pengumpulan dan pengolahan data untuk mengekstrak informasi penting. Metode data mining K-Nearest Neighbor dapat menganalisis pada aplikasi Redbus. RedBus merupakan salah satu aplikasi resmi pembelian tiket bus kota di Indonesia. Permasalahan yang muncul setelah pembaruan aplikasi RedBus adalah bertambahnya ulasan bintang satu yang menyatakan bahwa versi terbaru tidak sesuai dengan versi sebelumnya. Data Mining yang dugunakan untuk menganalisis sentimen Access by Bus Kota di seluruh Indonesia menggunakan metode K-Nearest Neighbors. Data yang digunakan adalah data yang diperoleh dari ulasan pengguna aplikasi redBus selama satu bulan terhitung dari tanggal 20 September 2024 sampai dengan 20 Oktober 2024 dengan total 1291 ulasan. Analisis sentimen pada penelitian ini menggunakan metode K-Nearest Neighbors melalui bahasa pemrograman Python. Hasil penelitian menunjukkan bahwa kinerja terbaik pada percobaan dengan pembagian data latih dan data uji, serta nilai k yang bervariasi diperoleh pada percobaan dengan pembagian 90% data latih, 10% data uji dan menggunakan nilai k = 5 dengan nilai akurasi, presisi, dan recall masing-masing sebesar 90,23%; dan nilai recall sebesar 72,38%. Klasifikasi sentimen dengan model terbaik menggunakan parameter k = 3 menghasilkan 79,26% sentimen positif, 17,25% sentimen netral, dan 3,49% sentimen negatif. 

Data mining atau penambangan data merupakan proses pengumpulan dan pengolahan data untuk mengekstrak informasi penting. Metode data mining K-Nearest Neighbor dapat menganalisis pada aplikasi Redbus. RedBus merupakan salah satu aplikasi resmi pembelian tiket bus kota di Indonesia. Permasalahan yang muncul setelah pembaruan aplikasi RedBus adalah bertambahnya ulasan bintang satu yang menyatakan bahwa versi terbaru tidak sesuai dengan versi sebelumnya. Data Mining yang dugunakan untuk menganalisis sentimen Access by Bus Kota di seluruh Indonesia menggunakan metode K-Nearest Neighbors. Data yang digunakan adalah data yang diperoleh dari ulasan pengguna aplikasi redBus selama satu bulan terhitung dari tanggal 20 September 2024 sampai dengan 20 Oktober 2024 dengan total 1291 ulasan. Analisis sentimen pada penelitian ini menggunakan metode K-Nearest Neighbors melalui bahasa pemrograman Python. Hasil penelitian menunjukkan bahwa kinerja terbaik pada percobaan dengan pembagian data latih dan data uji, serta nilai k yang bervariasi diperoleh pada percobaan dengan pembagian 90% data latih, 10% data uji dan menggunakan nilai k = 5 dengan nilai akurasi, presisi, dan recall masing-masing sebesar 90,23%; dan nilai recall sebesar 72,38%. Klasifikasi sentimen dengan model terbaik menggunakan parameter k = 3 menghasilkan 79,26% sentimen positif, 17,25% sentimen netral, dan 3,49% sentimen negatif. 

Abstract. This study presents a web-based system for identifying traditional herbal leaves using K-Nearest Neighbors (KNN) and image processing techniques focused on analyzing leaf shape and color. The dataset used consists of images of various types of herbal leaves, providing a basis for classification and medicinal benefit information retrieval. The system was tested with multiple leaf samples to assess accuracy, speed, and effectiveness in identifying leaf types based on visual characteristics. Results show that the system can recognize different types of herbal leaves and display information on their medicinal properties in a user-friendly interface..

The growth of the internet has influenced the tourism industry because the internet makes it easier for individuals to obtain reviews about places to visit and because the internet is a tool used by tourist site managers to assess the quality of their offerings. The increase in the number of tourists of almost two million in just three years in West Sumba is proof of this influence. Social media is a tool that people use to interact with each other online; some people have multiple accounts on platforms such as Instagram, WhatsApp, Facebook, Telegram, Twitter, and so on. Tourists can receive recommendations for tourist attractions based on price and type of trip desired through a tourist attraction recommendation system that uses the KNN algorithm. Three factors were used in this research: activity, type of tourism, and type of price. An accuracy of 63.16% is found in the test results using the KNN algorithm and the Rapid Miner application with a K value of 5. The analysis results show that the K-Nearest Neighbor (K-NN) approach can be used as a guideline for recommending tourist destinations to visitors in West Sumba.

The Merdeka Belajar Kampus Merdeka (MBKM) program provides students the opportunity to study for one semester outside of their major, aiming to develop the soft and hard skills required in the workforce. One key component of this program is internships or practical work, which gives students hands-on experience in the professional world and the chance to build professional networks. This research uses the K-Nearest Neighbor (K-NN) method to predict the impact of MBKM activities on undergraduate students at STMIK Kaputama. Using the RapidMiner application, student data was tested to obtain the accuracy of predicting students' engagement in the MBKM program in the future. The test results show that the K-NN model has an accuracy of 75.34%, indicating that the model is fairly good at predicting the impact of the MBKM program on students.    

Handling of dental disease problems requires that it be handled quickly and correctly, but not all teams of dental experts can carry out treatment quickly due to the lack of a team of dental experts who are in the workplace or hospital 24 hours a day.  Apart from that, the public also has very little knowledge of information about dental disease, so that to treat dental disease, people have to consult a dentist. To classify images of dental disease, feature extraction is needed. Feature extraction is taking characteristics of an object that can describe the image. One example of image feature extraction used is Red, Green, Blue (RGB). This feature extraction is often used to identify or classify an image. Dental image data that will be used in the classification process are tooth abrasion, anterior crosbite, cavities and gingivitis. K-Nears Neigbor is the simplest data mining algorithm.  The aim of this algorithm is to find the results of the closest distance classification for each object.  In determining the distance, the data is initially divided into two parts, namely training data and testing data. After receiving the training data and testing data, the distance from each testing data (Equilidence Distance) to the training data is calculated. The K-Nearest Neighbors method can be applied to classify dental disease based on images of types of dental disease using Matlab software. As a result of the image data training process, 40 image data were input, training results obtained were 100%.

Penelitian ini bertujuan untuk mengimplementasikan metode K-Nearest Neighbors (K-NN) dalam klasifikasi minat calon siswa-siswi di Able Ballet berdasarkan profil mereka. Metode K-NN digunakan karena kemampuannya dalam mengklasifikasikan data dengan tingkat akurasi yang tinggi. Penelitian dilakukan dengan mengumpulkan data profil siswa baru dan menggunakan metode K-NN untuk memprediksi minat mereka terhadap berbagai program yang ditawarkan. Hasil penelitian menunjukkan bahwa metode K-NN dapat mencapai tingkat akurasi sebesar 90% dalam klasifikasi minat siswa. Hal ini menunjukkan bahwa metode ini efektif dalam mendukung pengambilan keputusan untuk penempatan program yang sesuai bagi siswa baru. Penelitian ini juga menyarankan penggunaan dataset yang lebih besar dan eksplorasi metode lain seperti Support Vector Machine (SVM) untuk meningkatkan akurasi model di masa depan.

Penelitian ini bertujuan untuk mengimplementasikan metode K-Nearest Neighbors (K-NN) dalam klasifikasi minat calon siswa-siswi di Able Ballet berdasarkan profil mereka. Metode K-NN digunakan karena kemampuannya dalam mengklasifikasikan data dengan tingkat akurasi yang tinggi. Penelitian dilakukan dengan mengumpulkan data profil siswa baru dan menggunakan metode K-NN untuk memprediksi minat mereka terhadap berbagai program yang ditawarkan. Hasil penelitian menunjukkan bahwa metode K-NN dapat mencapai tingkat akurasi sebesar 90% dalam klasifikasi minat siswa. Hal ini menunjukkan bahwa metode ini efektif dalam mendukung pengambilan keputusan untuk penempatan program yang sesuai bagi siswa baru. Penelitian ini juga menyarankan penggunaan dataset yang lebih besar dan eksplorasi metode lain seperti Support Vector Machine (SVM) untuk meningkatkan akurasi model di masa depan.

Business legality is the identity of a business that legalizes a business so that it is recognized by the community. Business legality must be valid according to applicable laws and regulations so that the business can be protected by various documents that are valid in the eyes of the law. One of the supporting factors for the sustainability of a business is influenced by the existence of legal elements of the business being run. Business permits that must be owned by the community are a business establishment deed, business entity NPWP, trade business license (SIUP), company domicile certificate (SKDP) and business registration number (NIB). The increase in community businesses in Sei Bingai District, Langkat Regency has triggered many business permits that are not directly supervised by the local government. Community business permits are important documents in supervising the running of these community businesses. The types of businesses in Sei Bingai District also vary, such as tourism, C mining, trade, factories and so on.

This research focuses on using the K-Nearest Neighbor (KNN) algorithm to model student satisfaction with campus services. The study finds that the quality of the dataset strongly influences the accuracy of the KNN classification results. Factors such as data cleanliness, balanced class distribution, and sufficient training data volume are highlighted as crucial for a successful model. The research also emphasizes the significance of proper feature selection in enhancing classification performance, suggesting that irrelevant features can introduce noise and decrease model accuracy. The model was evaluated using a dataset of 1032 data points and K=5, achieving an accuracy of 93.72%. While the model performed well for certain classes such as "Very Good" and "None", challenges were encountered in classifying the "Fair" and "Deficient" classes. The study concludes that KNN is effective in identifying student satisfaction patterns but highlights the need for improvements in accurately classifying these challenging classes. Ultimately, the research underscores the importance of data quality and feature selection in enhancing the performance of classification models for student satisfaction analysis.

Diabetes Mellitus (DM) is a metabolic disorder characterized by high blood sugar levels due to insulin deficiency. Factors causing Diabetes Mellitus (DM) are lifestyle which includes diet, lack of exercise, monitoring blood sugar, and medication. Most people do not realize that they have DM and only find out when they experience severe symptoms. To avoid this, the k-Nearest Neighbor (KNN) method can be used to predict the possibility of developing diabetes. The aim of this research is to classify diabetes mellitus using the K-Nearest Neighbor (KNN) method and make people more aware of the risk of disease through healthy lifestyle changes. Data received from the Dharma Husada Clinic is categorized based on researchers' needs, including age, BMI, insulin, skin thickness, glucose, diabetes, genetics, and insulin. This research was carried out in three main steps: dataset input, preprocessing, and evaluation. The first stage is data analysis which begins by entering a dataset to train and test the model, where each data element has certain characteristics (attributes) and classes. Preprocessing steps include training data generation and data cleaning, which includes sanitization, lowercase, normalization, stopwords, stemming, and tokenizing. The final step is evaluating. Evaluation includes building an evaluation model and measuring the level of accuracy, building a predictive model, and saving the model. This research shows that the K-Nearest Neighbor (KNN) method can be used to classify diabetes mellitus (DM), but especially in a small dataset consisting of 245 dates and 8 attributes it is not accurate for patients aged 30 years. . A k value that is too small can cause overfitting, and a k value that is too large can cause underfitting. However, if the amount of data is small, the choice of k can have a large impact.    

The advancement of e-commerce has changed the way people shop. However, there is a mismatch between the actual quality of a product and the seller’s description. Product reviews are an important source of information for making purchasing decisions. However, processing large numbers of reviews manually is difficult. This research aims to detect emotions in Indonesian language product review texts using contextual embeddings. The public dataset used was PRDECT-ID, which comprises five emotion labels. The methods used include data preprocessing, feature extraction using contextual embeddings such as Bidirectional Encoder Representations from Transformers (BERT), and classification using Decision Tree, Naïve Bayes, and k-Nearest Neighbors (KNN). Among the compared models, the KNN model demonstrated the highest improvement, achieving a 15.09% enhancement over the decision tree results. This research provides insights into the effectiveness of contextual embeddings in detecting emotions in Indonesian language product review texts.

The insurance business within an insurance company offers insurance products owned by the insurance company. In every insurance product there is a premium payment and the premium is the income of an insurance company at the rate of the amount insured. The problem that PT BNI Life Insurance has is that there are many stops in premium payments such as policy redemptions due to errors in the benefits received or incorrect selection of the insurance product, this can reduce the achievement of targets for an insurance company. The aim of this research is to find out the best classification algorithm compared between K-Nearest Neighbor and Naive Bayes to predict the type of insurance product that customers will choose. In this research, data mining methods are applied to compare two different methods, namely the K-Nearest Neighbor method and the Naïve Bayes method. The level of accuracy results for the K-Nearest Neighbor method is 80% and the Naïve Bayes method is 70.53%, which means that the K-Nearest Neighbor method is the best method to apply to an insurance product classification system based on the demographics of prospective customers.

The process of identifying the ripeness level of cayenne peppers is an important step in cultivation and post-harvest handling. Dependence on the quality factors of farmers, such as visual diversity and differences in ripeness perception, results in subjective harvest outcomes. This manual process is also prone to inconsistent results, as humans have time limitations, fatigue, and sometimes lack concentration when sorting for long periods. To minimize these issues, technological intervention is needed to mechanically classify the ripeness level of cayenne peppers. This research aims to develop a classification model for the maturity level of cayenne pepper plants. This research proposes the use of the CNN method for feature extraction and KNN for data classification based on the features extracted by CNN. From the test scenarios carried out, the classification carried out by KNN based on CNN feature extraction got the best accuracy of 99.33%, while the CNN classification model got the best accuracy of 87.33%.

This study aims to apply the K-Nearest Neighbors (KNN) algorithm to predict stunting status in young children based on height and weight data. Stunting is a growth failure condition caused by chronic malnutrition that negatively impacts children's physical and mental development. The dataset includes height, weight, and stunting status of children. The results show that the KNN model with k=3 achieved 100% accuracy on the test data. Evaluation using the confusion matrix and classification report indicates perfect precision, recall, and F1-score for each class. Data normalization with StandardScaler improved the model's performance by ensuring all features are on the same scale. The KNN algorithm proves to be a simple yet effective method for predicting stunting, demonstrating significant potential for early detection and health intervention in children. This study recommends using a larger and more diverse dataset, as well as incorporating additional relevant features to enhance model accuracy. Implementing the model in a web or mobile application is also suggested to assist healthcare professionals in the field.

Pada tahun 2024, Indonesia akan menyelenggarakan pemilihan umum serentak yang meliputi pemilihan presiden dan pemilihan wakil rakyat di seluruh Indonesia. Masyarakat menanggapi kejadian ini dengan perasaan campur aduk, membagikan pemikirannya di situs media sosial seperti Twitter. Penelitian analisis sentimen calon presiden Indonesia tahun 2024 dilakukan terkait peristiwa ini. Sebanyak 1458 tweet digunakan dalam penelitian ini. Dengan 40,31% responden menyatakan sikap positif dan 43,46% menyatakan sentimen negatif, temuan analisis menunjukkan keseimbangan antara kedua sentimen tersebut. Menggunakan frasa "calon presiden," program Python di situs web Google Colab mengambil data twitter. Pendekatan K-Nearest Neighbor digunakan dalam proses klasifikasi. Selain itu data latih dibagi 6 : 4. 40% data uji dan 60% data latih. Nilai evaluasi yang diperoleh dari pengujian model dengan teknik K-Nearest Neighbor adalah akurasi sebesar 90,95%, presisi sebesar 62,17%, recall sebesar 62,33%, dan F-Measure sebesar 61,87%.

Customer attrition has become the focus of many businesses today – since the online market space has continued to proffer customers, various choices and alternatives to goods, services, and products for their monies. Businesses must seek to improve value, meet customers' teething demands/needs, enhance their strategies toward customer retention, and better monetize. The study compares the effects of data resampling schemes on predicting customer churn for both Random Forest (RF) and XGBoost ensembles. Data resampling schemes used include: (a) default mode, (b) random-under-sampling RUS, (c) synthetic minority oversampling technique (SMOTE), and (d) SMOTE-edited nearest neighbor (SMOTEEN). Both tree-based ensembles were constructed and trained to assess how well they performed with the chi-square feature selection mode. The result shows that RF achieved F1 0.9898, Accuracy 0.9973, Precision 0.9457, and Recall 0.9698 for the default, RUS, SMOTE, and SMOTEEN resampling, respectively. Xgboost outperformed Random Forest with F1 0.9945, Accuracy 0.9984, Precision 0.9616, and Recall 0.9890 for the default, RUS, SMOTE, and SMOTEEN, respectively. Studies support that the use of SMOTEEN resampling outperforms other schemes; while, it attributed XGBoost enhanced performance to hyper-parameter tuning of its decision trees. Retention strategies of recency-frequency-monetization were used and have been found to curb churn and improve monetization policies that will place business managers ahead of the curve of churning by customers.

The development of image processing technology today can create systems that are able to effectively recognize digital images, one of which is in the field of agriculture for plant disease identification. Citrus plants experience a decrease in productivity due to pathogen attacks on leaves such as Black Spot, Cancer, and CVDP so that disease identification is needed. The classification method that can be used to classify images is the K-Nearest Neighbor (K-NN) algorithm because it is simple and has high accuracy in image management. This study aims to implement and determine the performance of the K-NN algorithm in identifying citrus plant diseases based on leaf images. This research uses a dataset from the Kaggle website of 1,096 images. There are 12 research scenarios using the comparison between test data and training data as much as 4, namely (90% training data + 10% test data, 80% training data + 20% test data, 70% training data + 30% test data, 60% training data + 40% test data) and testing with 3 random state values (42, 32, 22). The results showed that the K-NN algorithm is very effective in identifying citrus plant diseases with the highest accuracy value in the 90% training data scenario and 10% test data with a value of K = 2 which is 98.5%.

Mitra Telur is an egg producer located in Pirakbulus, Sidumulyo, Godean District, Sleman Regency, Special Region of Yogyakarta. So far, Mitra Telur UMKM has not determined a travel route to distribute the eggs. The distribution carried out does not take into account the distance traveled to reach the shop points. This research uses two methods at once, namely saving matrix and nearest neighbor. Based on the calculation results, the initial route has a total distance of 114.9km with 4 delivery routes, while the final route has a total distance of 95.5km with 3 delivery routes. The initial route has a fixed cost of IDR 1,550,000 and a variable cost of IDR 402,150 with a total delivery cost of IDR 1,952,150, while the final route has a fixed cost of IDR 1,550,000 and a variable cost of IDR 334,250 with a total delivery cost of IDR 1,884,250. there was a reduction in distribution routes by 16.9% and a reduction in production costs by 3.5%.