Abstract:Abstract: Question classification is a computer science system, which aims to analyze questions and can label each question based on existing categories. Questions can be collected from several materials or topics that are…
re many and different. Therefore, the researcher intends to create a classification system for quiz questions Data Warehouse and Business Intelligence which can be grouped into topics Data Warehouse, Business Intelligence, Data Analytics, and Performance Measurement. One way to solve this problem is by approach machine learning. In this study, researchers used a comparison of machine learning algorithms, namely the algorithm NaïveBayes and SupportVectorMachine using SMOTE and methods Cross-Validation The results of this study show the best accuracy results and are very helpful. The results obtained in the method cross-validation before SMOTE resulted in an accuracy rate of 82.02% for the results after going through the SMOTE stage of 94.79% on the algorithm Naïve Bayes, while the algorithm SupportVectorMachine get accuracy of 81.39% in the process before SMOTE for the results after going through SMOTE of 96.52%.
Keywords: Cross-Validation; Machine Learning; Naive Bayes; Support Vector Machine; Question Classification
Abstrak: Klasifikasi pertanyaan merupakan sebuah sistem ilmu komputer, yang bertujuan untuk menganalisis pertanyaan serta dapat memberi label pada setiap pertanyaan berdasarkan kategori yang ada. Pertanyaan soal dapat dikumpulkan dari beberapa materi atau topik yang banyak dan berbeda. Oleh karena itu, bermaksud untuk membuat sistem klasifikasi pertanyaan soal kuis Data Warehouse dan Business Intelligence yang dapat dikelompokkan menjadi topik Data Warehouse, Business Intelligence, Data Analitik, dan Pengukuran Kinerja. Cara yang dapat dilakukan untuk permasalahan ini dengan menggunakan pendekatan MachineLearning. Pada penelitian kali ini menggunakan perbandingan algoritma MachineLearning yaitu algoritma NaïveBayes dan SupportVectorMachine menggunakan metode SMOTE dan Cross-Validation. Hasil penelitian ini menunjukkan hasil akurasi yang terbaik dan sangat membantu. Hasil yang diperoleh pada metode cross-validation sebelum SMOTE menghasilkan tingkat akurasi sebesar 82.02% untuk hasil sesudah melalui tahap SMOTE sebesar 94.79 % pada algoritma Naïve Bayes, sedangkan pada algoritma Support Vector Machine menghasilkan akurasi sebesar pada proses sebelum SMOTE 81.39% untuk hasil sesudah melalui SMOTE sebesar 96.52%.
Kata kunci: Klasifikasi Pertanyaan; Pembelajaran Mesin; Naive Bayes; Support Vector Machine; Cross-Validation
Abstract:Abstract: The human eye can distinguish objects from digital images, however, computers do not have the ability as human eyes that can directly distinguish objects from digital images. Therefore the bag of visual words method…
ethod was created. Bag of visual words is a method for presenting digital images based on local features. Bag of visual words illustrates how an image can be taken its characteristics, so that computers can distinguish objects on digital images. The test results show that the bag of visual words are still not maximal in classifying digital image categories, especially the chair category, which is only able to produce the most accurate accuracy of 75%. To improve the performance quality of bag of visual words in classifying digital image categories, especially the chair category, you can add an approach to determine the good number of K in clustering the visual words pattern.
Keywords: Bag Of Visual Words, Classification, Digital Image, Speed-Up Robust Feature, Support Vector Machine
Abstrak: Secara kasat mata manusia bisa membedakan objek pada citra digital, namun, komputer tidak memiliki kemampuan sebagai mata manusia yang dapat secara langsung membedakan objek pada citra digital. Maka dari itu diciptakanlah metode bag of visual words. Bag of visual words adalah metode untuk menyajikan citra digital berdasarkan fitur lokal. Bag of visual words menggambarkan bagaimana suatu gambar dapat diambil karakteristiknya, sehingga komputer dapat membedakan objek pada citra digital. Hasil pengujian menunjukkan bag of visual words masih belum maksimal dalam mengklasifikasi kategori citra digital khususnya kategori chair, yang hanya mampu menghasilkan akurasi paling akurat sebesar 75 %. Untuk meningkatkan kualitas kinerja bag of visual words dalam mengklasifikasi kategori citra digital khususnya kategori chair, dapat menambahkan pendekatan untuk menentukan jumlah K yang baik dalam mengkluster pola visual words.
Kata kunci: Bag Of Visual Words, Klasifikasi, Citra Digital, Speed-Up Robust Feature, Support Vector Machine
Abstract:Meat is one of the essential food ingredients in meeting the nutritional needs. The current problem lies in the consumers' lack of knowledge on how to differentiate between pork, beef, goat, and lamb meat. This is because…
e when the meat is already cut, their appearances may seem similar at first glance. Many consumers are unaware of the practice of mixing different types of meat for consumption. One way to classify animal meat is by using image processing. In this research, an image processing system is created to classify meat, specifically pork, beef, goat, and lamb. Support Vector Machine (SVM) is a development of Machine Learning that can be used in classifying images into specific classes. SVM method as a classifier is performed using a confusion matrix. The test results show the highest accuracy value obtained in the class of Goat Meat 91.4%, the highest precision in the class of goat meat 80%, the highest recall in the class of beef 81.3%, and the highest F1-score in the class of beef 0.76.
Abstract:This study aims to examine the short-term and long-term relationships between inflation, exchange rate, BI rate, and GDP on third-party funds (DPK) in Islamic commercial banks. This research uses a quantitative approach…
and secondary data, which consist of 60 monthly observations from January 2020 to December 2024. The analysis tool employed is the Vector Error Correction Model (VECM), with several tests conducted, including the stationarity test, optimal lag test, VAR stability test, cointegration test, Granger causality test, VECM estimation, impulse response function (IRF) test, and forecast error variance decomposition (FEVD) test. The results indicate that inflation has a significant negative effect on DPK in both the short and long term. The exchange rate has no significant effect on DPK in either the short or long term. The BI rate does not affect DPK in the short term, while in the long term, it tends to show a negative effect, though not significant. Gross Domestic Product (GDP) has a significant positive effect on DPK in both the short and long term.
Abstract:This study examines the effect of export value on Indonesia’s economic growth during the 2020–2024 period. The purpose of the research is to determine whether export performance contributes to the increase of national economic…
l economic growth in both the short and long term. The research applies a quantitative design using secondary time series data obtained from official institutions, including export data from the Ministry of Trade of the Republic of Indonesia and economic growth data from the Central Statistics Agency. The analysis technique employed is the Vector Error Correction Model, which identifies short-run and long-run relationships between variables. The results show that in the short run, export value has a positive and significant influence on economic growth, while in the long run, it shows a positive but insignificant relationship.
Abstract:Gangguan tidur seperti insomnia dan sleep apnea merupakan masalah kesehatan global yang dapat menurunkan kualitas hidup. Deteksi dini terhadap gangguan ini penting dilakukan, khususnya dengan bantuan teknologi seperti algoritma…
goritma data mining untuk meningkatkan ketepatan diagnosis. Data mining adalah bagian esensial dari analitik data dalam disiplin ilmu data science, yang memberikan berbagai manfaat luas dan aplikasi yang relevan. Penelitian ini menggunakan dataset Sleep Health and Lifestyle dari Kaggle untuk mengevaluasi kinerja tiga algoritma data mining, yaitu Naïve Bayes, Support Vector Machine (SVM), dan Neural Network, dalam mengklasifikasi gangguan tidur. Proses pengembangan model mengikuti tahapan CRISP-DM dengan pengujian akurasi menggunakan Cross Validation dan evaluasi menggunakan Confusion Matrix dan kurva ROC. Berdasarkan hasil pengujian, algoritma Neural Network menunjukkan kinerja terbaik dengan akurasi 93,08% dan nilai AUC yang termasuk dalam klasifikasi "Excellent." Temuan ini menunjukkan bahwa Neural Network efektif dalam mengklasifikasi gangguan tidur, sehingga dapat mendukung proses diagnosa dan penanganan gangguan tidur secara lebih akurat.
Abstract:This study aims to determine the effect of short-term and long-term macroeconomic factors on the financial performance of Islamic banking in Indonesia. The dependent variables in this study are Return on Assets (ROA), Financial…
nancial to Deposit Ratio (FDR) and Operating Costs of Operating Income (BOPO) as proxies of financial performance. While the independent variables are industrial production index (IPI), inflation, BI rate, composite stock price index (CSPI) and exchange rates. The analytical method used is Vector Error Correction Models (VECM). The data used in this study are monthly time series data from January 2010 - December 2015. The results of the study state that in the long term the influence of the Industrial Production Index (IPI), inflation and exchange rates have a negative and significant effect on Return on Assets ( ROA) and Financial to Deposit Ratio (FDR). Meanwhile, the BI rate and the Composite Stock Price Index (JCI) have a positive and significant impact on Return on Assets (ROA) and Financial to Deposit Ratio (FDR). Industrial Production Index (IPI), inflation and exchange rates have a positive and significant influence on the Operating Cost of Operating Income (BOPO). Meanwhile, the BI rate and the Composite Stock Price Index (JCI) have a negative and significant effect on the Operating Cost of Operating Income (BOPO). The short-term effect of macroeconomic variables on financial performance (ROA, FDR and BOPO) does not show a significant relationship.
Abstract:Background: Pulmonary tuberculosis (pulmonary TB) is a chronic infectious disease caused by the bacterium Mycobacterium tuberculosis. Mycobacterium tuberculosis is a bacterium that causes tuberculosis infection which is…
transmitted by droplet splashes and becomes a vector of transmission when a person interacts physically. Objective: This study aims to determine the relationship between the level of knowledge and motivation with adherence to taking anti-tuberculosis medication at An-Nisa Hospital Tangerang. Methods: This study used descriptive analytic quantitative research with a cross sectional approach. The sampling technique used was purposive sampling method, where this technique selected subjects in a population that matched the criteria as a sample, namely pulmonary TB patients who were undergoing initial and advanced treatment at An-Nisa Hospital, Tangerang, totaling 102 respondents. Data analysis was performed using the chi-square test. Results: The results of the respondent's data processing showed that the level of knowledge with adherence to taking anti-tuberculosis medication showed a significant relationship with value (p-value 0.021), and motivation with adherence to taking anti-tuberculosis medication showed a significant relationship with value (p-value 0.027 ). Conclusion: It can be concluded that there is a relationship between the level of knowledge and motivation with adherence to taking anti-tuberculosis medication in patients with pulmonary TB. Therefore, to reduce the occurrence of drug-resistant TB, efforts should be made to increase adherence to treatment in TB patients, either through the method of providing direct motivation or through counseling about pulmonary TB disease.