Reviewerly/exHarmony huggingface dataset:
Comparative Analysis Performance of Naïve Bayes and K-NN Using Confusion Matrix and AUC To Predict Insurance Fraud

Based on claim submission data from year 2019 to 2021 can be seen that the percentage of claims in one province is much higher than other provinces. During that period, the percentage of claims in that province reached 22% while the highest percentage in other provinces was only 6%. It is suspected that there has been a claim fraud in the province. The fraud allegedly started when customer submits a policy issuance for the elderly insured with a low sum insured so that the premium is also low. The insured's health condition at that time may not be good but it is not explained in the insurance application letter. To increase the sum insured, the policy is usually added with additional coverage. Fraud claim creates big loss for insurance company since the company has to pay the claim that they should not pay. Insurance company need to have a mechanism to avoid the fraud claim. From this research, it is expected to find the best methodology to be able to predict the potential of insurance claim fraud early when customers apply for policy issuance so that additional checks can be carried out for suspected submissions. The initial data available for this research is 14,778 claim records with attributes are : the date of claim submission, policy effective date, sum assured, type of claim, cause of claim, province and fraud. In order to get the best methodology on the accuracy and performance aspect to fulfill the expectation, two methodologies (Naïve Bayes and K-NN) are compared. Both Naive Bayes and K-NN methods are used with a comparison of training data and testing data is 80:20. Several combinations were performed for each of these methods. By using Confusion Matrix and AUC to measure the accuracy and performance of the two methods, it can be concluded that the best one is Naive Bayes with accuracy is 90% and AUC is 0.761. The attributes used are province, sum assured, additional coverage and the insured is the policy holder.
Klasterisasi Konsentrasi Keahlian Siswa SMK Berdasarkan Kurikulum Merdeka

The process of determining the concentration of expertise carried out at the YPC Tasikmalaya Vocational School has shortcomings such as making decisions based on the wishes of students without paying attention to academic grades at the previous level of education. So that there are some students who feel it is not right in choosing the concentration of expertise, resulting in a lack of competence possessed by students with the concentration of expertise selected. The choice of concentration of expertise is the right of every student, but if it is wrong it can cause a decrease in learning motivation and low learning achievement. This problem can be solved by using clustering method with K-Means algorithm. This study aims to classify students' interests in choosing a concentration of expertise at YPC Tasikmalaya Vocational School based on the Merdeka Curriculum. The results showed that the grouping of students' interests in choosing the concentration of expertise was formed into 4 clusters. The cluster with the most members is cluster 0, namely students who have an average score of 79 Mathematics, then Indonesian and English 83. Furthermore, the cluster with the least number of members is cluster 2, namely students who have an average score of 78 Mathematics and English, then Indonesian 79.
Sistem Pakar Diagnosis Tingkat Stres Berbasis Android dengan Metode Certainty Factor

The number of cases of Corona Virus Diesis (COVID-19) according to the World Health Organization (WHO) is 93,217,287 people. In the case of Covid-19, one of which is making changes in behavior is education. The teaching and learning process has changed into bold learning in the education sector. Students who continuously carry out the learning process at home can increase stress because previous research data stated that the level of severe anxiety experienced by students was one of them caused by daring learning, which reached 95.59%, and students reached 97.69%. This research aims to develop an expert system for dealing with stress in high school students. The method used for making the expert system is the Certainty Factor. Based on functionality testing using black box testing, it shows that all components produce the expected and appropriate results, then for accuracy testing using a confusion matrix through a comparison between manual calculations and system calculations, so that the accuracy test results are 100%. Therefore, the expert system for diagnosing stress diseases in high school students can be said to be feasible.
Pengelompokan Cuaca Kota Palembang Menggunakan Algoritma K-Means Clustering Untuk Mengetahui Pola Karakteristik Cuaca

Weather related information is one of the things that is very important and has a big influence on all kinds of life activities such as in public safety, socio-economics, agriculture, aviation, and so on.The weather in each place or region is different, this happens because of the different weather elements in each place/region. By using data mining clustering techniques, weather clustering will be carried out in the city of Palembang. K-means is the algorithm chosen for clustering the weather in the city of Palembang. The test was carried out using daily weather data for 2020-2021 from BMKG by utilizing rapidminer application as learning techniques for data. So that we will get a group of weather characteristics of Palembang city based on similarities and dissimilarities. From the test results, the best k was obtained at k=3 with the parameters Measure Types ( NumericalMeasure ) and Divergences ( DynamicTimeWarpingDistance ) as well as a local random seed of 2500 seen from the results of the Davies-Bouldin Index (DBI). This weather grouping can later provide information on how the weather character is and reduce the impact of sudden changes in weather conditions.
Sistem Pakar Diagnosa Penyakit Pencernaan Menggunakan Metode Case Based Reasoning (CBR) Berbasis Web

The main factors causing digestive disorders are food that is not good in terms of hygiene and health, nutritional balance, improper diet, infection, and abnormalities in the digestive organs so that they will contract several digestive diseases such as appendicitis, ulcers, stomach ulcers, hepatitis. , diarrhea and constipation. Early treatment is needed before consulting specifically with a digestive disease specialist before the disease is severe. In this case, it is necessary to have a system that can replace specialist doctors in diagnosing early symptoms of digestive diseases considering the high cost of consulting a doctor. This diagnostic expert system is very much needed in diagnosing early symptoms of the causes of digestive diseases. The application of the system is equipped with a Case Based Reasoning that solves new cases based on reasoning from old cases. The implementation has resulted in gastric cancer of 67.01% according to the symptoms selected by the patient, then the system also provides treatment advice, and provides information on the handling or treatment of gastric cancer, so that with this application it can help doctors in diagnosing the disease quickly.
Perancangan Sistem Otomatis Transaksi Pembayaran Pada Marketplace UMKM Menggunakan Metode Crawling Horspool

UMKM MAPAN is one of the UMKM communities consisting of 300 UMKM entrepreneurs who are currently developing and located in the Depok area. These UMKM provide a wide variety of products to be marketed. In marketing these products, these UMKM group still uses the conventional method, is still by face-to-face with the buyer, so they have to rent a place first and are still limited in marketing online. This method has many weaknesses, such as the high cost of renting a place and the difficulty of finding a strategic location to market the product. Therefore, in this study, an e-commerce marketplace web system was created that could accommodate 300 UMKM MAPAN entrepreneurs in marketing their products. In addition, an automatic system for payment transactions on the UMKM marketplace was also created using the Horspool crawling method so that it could make it easier for UMKM entrepreneurs to print payment transaction reports. Based on the research results, the success rate of report printing is 100%. In this study, the complexity of the Horspool algorithm is O(n) with n is length of pattern while the time complexity of the Horspool algorithm is O(m+σ) with m is length of search string.
Rancang Bangun Kamus Digital Berbasis Chatbot Menggunakan Pendekatan Pattern Matching

The rapid development of technology today has had an impact on various fields of life, one of which is in the world of education. The application of information technology in the learning process at school or on campus has become commonplace. Smart devices such as smartphones connected to the internet can help the learning process and help encourage the communication process between educational stakeholders. In online learning, the process of communication and interaction between students and teachers switches through instant messaging providers. Taken from the 2019 digital report data issued by We Are Social Hotsuit, it is stated that the most popular instant messaging application in Indonesia is WhatsApp, where 83% of internet users in Indonesia are WhatsApp users, with this data making Indonesia one of the countries with the largest number of users in the world. Based on this, to support the performance of teachers in delivering learning materials, as well as provide faster access in searching for the required terms, this study built a digital chatbot-based dictionary that was embedded in the instant messaging application as the most popular message delivery medium in Indonesia. The process of building a chatbot-based digital dictionary in this study was carried out using the pattern matching method, which is a method included in the study of artificial intelligence that recognizes input from users to be matched with data stored in the database, and the appropriate response will be returned to the user. The subject content that is sampled in making this digital dictionary is physics. The result of this study is the existence of a chatbot-based digital dictionary that can assist students in finding definitions of various terms and formulas related to physics material, given by the teacher which students can access at any time as long as they are connected to the internet.
Smartphone Purchase Recommendation System Using the K-Nearest Neighbor (KNN) Algorithm

Indonesia is in the fourth position of the countries with the most smartphone users worldwide. Smartphones are needed in today's modern times. Smartphones are also used not only for long-distance communication but also for carrying out daily work. Smartphones are currently used for study and work and also become entertainment to play. Therefore, smartphones are very much sought after for the suitability of users who carry out their daily activities. So this research is very helpful for users to find smartphones that support their daily activities such as studying, working, and playing. This research is based on a website that can make it easier for users to see their smartphone recommendations directly. The analysis uses the K-Nearest Neighbor (KKN) method to see the ratings reviewed by other users who have tried using their smartphones with different phone brands. The calculation method in the current study uses 3 KNN calculations and uses the concept of combining calculations to find the maximum recommendation results. The result of the recommendation system using the K-Nearest Neighbor method is in the form of a review stating whether the user agrees or disagrees. In the current study, there have been 100 reviews from users, and it has a percentage of 78% for success and 22% for failure.
Sistem Pencacah Sampah Berbasis Computer Vision Menggunakan Metode Eigenface

Indonesia is one of the largest countries in Asia, with a very dense population. According to data from The World Bank, the human population indicator in Indonesia in 2019 increased by 270,625.57 people. It shows that population density in Indonesia is related to world problems related to household waste. The household sector contributes as the top waste producer in Indonesia. Adipura data in 2019 stated that at least household waste contributed 36%. This figure is higher than the heap of waste generated from the traditional market, which reaches 24%. The accumulation of waste that occurs without any sorting of waste makes the waste more difficult to decompose and difficult to recycle. Apart from that, the current landfill causes air pollution. Therefore, it is necessary to increase public awareness regarding waste sorting and processing to overcome these problems. We propose to make a device that can help sort organic and inorganic waste with Artificial Intelligence technology based on Computer Vision using the Eigenface method and the Internet of Things. Eigenface is a method that has the working principle of using an XML file to perform face recognition. 1. Implementation of a Computer Vision-Based Garbage Counting System Using the Eigenface Method can run well, where when the system detects an organic object, the door of the counting machine can be opened, and vice versa if it detects inorganic, the machine door is closed. Accuracy results for organic are 70% and for inorganic 75%. It is due to the lack of variation in the dataset and changes in the object's physical condition.
News Recommender System Based on User Log History Using Rapid Automatic Keyword Extraction

There are many ways to find information; one of them is reading online news. However, searching for news online becomes more difficult because we should visit multiple platforms to find information. Sometimes, the recommended news doesn't match the user's interests. In many prior works, news recommendations are based on trending. Thus, the recommended news may not necessarily match the user's interests. To overcome this, we built a web-based news recommender system to make it easier for users to find news. We use the Rapid Automatic Keyword Extraction (RAKE) method in the recommendation process because this method can recommend news based on user preferences by utilizing user history logs. RAKE converts the title and content of the news into vector representation using Count vectorizer and applies the Cosine Similarity function to compare similarities between news. The test results show that the average performance of our proposed system is 90.8%, this accuracy outperforms earlier systems in terms of performance by the purpose of the recommender system, i.e., diversity, novelty, and relevance.
Penerapan Algoritma Fisher Yates Shuffle pada Game Edukasi Pembelajaran Untuk Pendidikan Anak Usia Dini (PAUD)

Games can be used as educational tools, helping students learn about a particular subject or concept. Just like playing learning educational games for Early Childhood Education (PAUD), it can be a way to learn. In learning activities such as letters, numbers, colors, names of animals, plants, and so on, they still use conventional learning media, so they are less effective in implementing learning, where currently children interact more with smartphones than books, this can This causes parents whose children are still in elementary school, such as reading, counting, writing using books that are less attractive, which can cause children to be lazy to study, and less attractive to children in learning. From that problem, the author is interested in making educational game applications by utilizing smartphone media. And the application of this learning educational game was made by utilizing the Fisher Yates Shuffle Algorithm, by randomizing the questions in this application. Based on the test results, the implementation of the Fisher Yates Shuffle Algorithm for question randomization in the early childhood education learning educational game application (PAUD) went well, was in demand by PAUD children and there were no repeated questions that made PAUD children happier in study.
Penerapan Algoritma Asosiasi dengan Metode Apriori Untuk Strategi Meningkatkan Hasil Penjualan Spare Part Motor

Sales transactions in everyday life are very common, but in the sales business you must have a strategy to increase sales results optimally. This research was conducted on PD. Tri Jaya Motor, where this trading company is engaged in trading motorcycle spare parts for all types of motorcycles, both Honda, Yamaha, Kawasaki and so on. Currently PD. Tri Jaya Motor wants to make a strategy to increase sales results, but the problem is that PD. Tri Jaya Motor still doesn't get the right and effective way, one of which is in the preparation of goods that still don't have the right placement pattern, so the items that should be close together, but this stored far apart, to solve these problems a method is needed, therefore the method used in this study is the association algorithm with the a priori method to analyze motorcycle spare part sales transaction data, which in this study takes motorcycle spare part sales transaction data in one year. The purpose of this study is to find out which items are purchased by consumers simultaneously so that from this information it can be used to make sales strategies, one of which is by arranging items that are close to the items purchased by consumers simultaneously. The results of this study show that when buying a kilometer cable, 86% will buy a brake cable with a support value of 30% and 70% confidence. By knowing the products that are purchased a lot and the interrelationships between goods with one another, so the company can develop a strategy by increasing the inventory of these goods for PD. Tri Jaya Motor.
Implementasi Algoritma Multiplicative Congruential Random Number Generator Pada Aplikasi Seleksi Mojang Jajaka

Mojang Jajaka (MOKA) is one of icons of tourism ambassadors who come from most districts/cities in West Java. The selection of tourism ambassadors in Kuningan was organized by the Department of Youth, Sports and Tourism (DISPORAPAR) of Kuningan, and the Mojang Jajaka Association of Kuningan. One of the series of activities carried out is participant registration and written test selection. Currently the registration process and the written test selection are still carried out conventionally by collecting hardcopy files and writing tests using a sheet of paper. This is considered less effective and efficient because using paper often causes problems, namely tucked, damaged, and easily lost. In the written test activities, cheating often occurs when filling out answers. This study aims to apply the Multiplicative Congruential Random Number Generator Algorithm in randomizing the questions on the MOKA selection test. The selection process begins with participants registering on the application. Then the participant logs in and after that the admin sees the participant data and then verifies the participant data. After that the admin will do question management, where the admin will input 30 questions. Then the participants will carry out the exam process. The Multiplicative Congruential Random Number Generator algorithm will work to randomize the questions from 30 questions and 25 questions will be issued. This application is made using the Java programming language, PHP, Javascript and MySQL database. This study uses the RUP (Rational Unified Process) methodology which consists of 4 stages, namely Inception, Elaboration, Construction, and Transition. This study results a MOKA Selection Application that can simplify the registration process and written test selection for both participants and the Mojang Jajaka selection committee, Kuningan.
Metode Algoritma Support Vector Machine (SVM) Linier Dalam Memprediksi Kelulusan Mahasiswa

The accumulation of student databases can occur if students are unable to complete their studies, namely graduating at a predetermined time. Data mining techniques are often used to process student data so that they can produce predictions of student graduation in order to graduate at a predetermined time. One of the data mining techniques that is often used is the Support Vector Machine (SVM) algorithm. This study aims to analyze the performance of the SVM algorithm to produce a predictive model of student graduation in order to graduate at a predetermined time in the Public Health Study Program, Faculty of Public Health, Deli Husada Health Institute. The method used in this study is a linear SVM algorithm starting from data retrieval by selecting the attributes that will be used for the next stage, data processing consists of cleaning data whose contents do not exist and data transformation which is the determination of the category of each data, modeling is done with the SVM algorithm. from training data and testing and evaluation data to validate and measure the accuracy of the model. The test results with the amount of training data as much as 70% and testing data as much as 30% shows that the linear SVM algorithm provides an accuracy value of 90%
Study of Classification Method to Detect Coronary Heart Disease Based On Signal Photoplethysmography (PPG)

Coronary heart disease (CHD) is one of the deadliest diseases in the world, especially in Indonesia. This disease is caused by the accumulation of fat in blood vessels and can cause heart attacks that can endanger a person's health and safety. There are several methods for detecting CAD, such as using Electrocardiogram (ECG) signals and Photophlethysmograph (PPG) signals. However, studies that have tested machine learning classification methods to detect CAD using PPG signals are rarely found compared to detection using ECG. This study uses PPG signals taken from smartphone cameras to detect CHD, so that CHD detection is easier and affordable. To be able to diagnose CHD, machine learning assistance is needed to determine whether CHD is positive or negative. This study proposes a classification algorithm study to detect CAD. There are 3 classification methods used in this study. The three methods are KNN, SVM, and decision tree. The final results obtained in this study resulted in the best classification for KNN 81%, SVM 90%, and Decision Tree 90%. Each classification used has been carried out before and after tuning
Implementasi Metode Moora Pada Sistem Pendukung Keputusan Penilaian Kinerja Karyawan

Employee performance appraisal is needed by an agency or company with the aim of evaluating performance and improving the quality of competent human resources and high loyalty for each employee at work, then an agency or company can give awards to each of its employees such as contract extensions, salary increases , get special promotions, appointments, and allowances, which can motivate every employee. This study aims to facilitate a planner in a company PT. SUPRACO INDONESIA in providing performance appraisals of each employee uses a decision support system using the Multi Objective Optimization On The Basic Of Ratio Analysis (MOORA) method. This employee performance appraisal decision support system uses a sample of 3 employees from 11 employees using the MOORA method of calculation. the final results of the calculations carried out are: for the first rank in alternative 2 with a value of 5.7805, while the second rank in alternative 1 with a value of 5.7736, and third place in alternative 3 with a value of 5.7671. In the tests carried out using Blackbox Testing, for all the features on the system running 100% with very good information and testing using the UAT (User Acceptance Test) method, it showed that the results of system user acceptance were 92%.