COMPARISON OF ALGORITHM BETWEEN CLASSIFICATION & REGRESSION TREES AND SUPPORT VECTOR MACHINE IN DETERMINING STUDENT ACCEPTANCE IN STATE UNIVERSITIES

Higher education entrance selection activities are intended to obtain superior student candidates. The opportunity to take part in the selection is given to all high school graduate students and equivalent. The student entrance test at PTN consists of three types of selection routes, namely the SNMPTN or invitation route, the SBMPTN, and the independent examination held by state universities. Starting from the dataset, data selection was carried out from 143 students' data and 7 attribute selections were carried out using preprocessing using data transformation first. The aim of using data transformation is to simplify the data training process for MAN 1 students in Cirebon. Preprocessing for prediction of classification results, accuracy of testing data for 143 students is implemented in the program and the resulting calculation process will be more efficient. After going through the preprocessing stage, the data is divided into training data and testing data using 10-fold cross validation. Next, for the classification process, a comparison of two methods will be used, namely for the first method using CART, the second method using SVM by adding Gain ratio weighting. The results of the research show that in the first experiment the researcher carried out a comparative trial of cross validation and classification performance and used the CART and SVM algorithms. The results comparison using the CART algorithm gets an accuracy of 86.10% and the SVM algorithm method for classifying students entering PTN was 86.71%

REAL TIME ONLINE EXAM PROCTORING SYSTEM IN HIGHER EDUCATION USING WEBRTC TECHNOLOGY

The low level of trust in online exam results from students is a major problem because it is difficult to monitor whether test takers are taking the exam honestly according to their own abilities. Even though it has been assisted by the presence of video conferencing applications such as Zoom, Google Meet, Cisco Webex and similar applications, online exam proctoring is still unable to run effectively. Cheating in online exams, such as using dual monitors, is very possible for exam participants. Therefore, as a future preventive measure in the online exam process, a system is needed that can accommodate this concern. This research will create an online exam supervision system with WebRTC technology which has features to accommodate real-time supervision. The System Development Life Cycle method will be used in software development with 5 main stages, namely Requirement Analysis, Design, Development, Testing, and Maintenance. Implementation of the system was carried out during the online examination process for a class at one of the universities in Surabaya. Finally, the test results show that features such as: Live Proctoring get a score of 4.5; Attention Alert gets a score of 5; Exam Lock scored 4.5; Live Alert scored 4.5; and Tab & Window Detection got a score of 4; shows that this system has succeeded in providing a solution in online exam proctoring needs

TEXT CLASSIFICATION USING INDOBERT FINE-TUNING MODELING WITH CONVOLUTIONAL NEURAL NETWORK AND BI-LSTM

The technological advancements in goods delivery facilities have been increasing year by year in tandem with the growing online trade, which necessitates delivery services to fulfill the transactional process between sellers and buyers. Since 2000, top brand awards have often conducted official survey analyses to provide comparisons of goods or services, one of which includes delivery services. However, the survey rankings based on public opinion are less accurate due to users of delivery services and service companies being unaware of the specific success factors and weaknesses in their services. The aim of this research is to analyze the comparison of text mining using the Indonesian language transformation method, IndoBert. The algorithm utilized to demonstrate analysis performance employs Convolutional Neural Network (CNN) and Bidirectional Long Short-Term Memory (Bi-LSTM). This method is utilized to determine the impact of opinion data from Twitter on the J&T Express expedition delivery service, incorporating both text preprocessing and data without text preprocessing. The IndoBert parameters vary in the learning rate section based on four factors: price, time, returns, and others. The research data consisted of 2525 comments from Twitter users regarding the delivery service spanning from January 1, 2021, to March 31, 2023. The testing showed that Bi-LSTM with text preprocessing performed 2% higher, achieving 79% at a learning rate of 1x10-6, compared to without text preprocessing at the same learning rate, which reached 77%. Additionally, CNN outperformed by 3% with a rate of 83%, compared to 80% without text preprocessing at a learning rate of 1x10-5. The highest accuracy, reaching 83%, was obtained by CNN with parameters set at 1x10-5, and the preprocessing technique was considered superior to Bi-LSTM

ANALYSIS OF THE INFLUENCE OF BUSINESS INTELLIGENCE ON BEVERAGE SALES KONNICHIWA COFFEE USING THE METHOD EQUIVALENCE CLASS TRANSFORMATION

Konnichiwa Coffee shop is one of the business beverage that sells various types of drinks. Determination of price discounts or product promotions sometimes doesn’t match what customers wants and needs. Another obstacle found was that there were no promotions for consumers who bought directly at Konnichiwa Coffee outlets. This causes less than optimal sales strategies and store promotion strategies. Determining menus that are often purchased simultaneously by consumers can be a reference for owners in determining promotional strategies. Therefore, this research was conducted to look for association patterns between menus that can implement business intelligence (BI) in the association rules method. One of the association rules algorithms is the ECLAT algorithm. The ECLAT algorithm is used because it is more efficient and faster in terms of time. The data used in this research were 214 products from 100 transactions with 26 types of drink menus. The resulting pattern refers to a minimum support value of 3% and a minimum confidence of 30%. This means that transaction data that has association patterns or that were purchased together is only 3% of the total transaction data with a confidence level of 30%. From the results obtained, the Java Latte, Kopi Latte and Sapporo Latte menus are the menus that are most often purchased together so they can be used as a marketing strategy for Konnichiwa Coffee

PROTOTYPE OF THE INTERNET OF THINGS-BASED SWALLOW BUILDING MONITORING AND SECURITY SYSTEM

The Swallow nest production in a cultivation building have high commercial value. There are several factors that affect nest productivity such as light intensity, temperature and humidity conditions in the cultivation building. Better factors can be found in cultivation building that located far from residential area. However, this led to a risk of theft. Therefore, this study proposes a system that able to monitor the productivity factors and safety status of the building. System development uses a comparison method with black box testing. The system controller uses the Raspberry Pi B+ and the python programming language. Sensor Modules (GY-302, DHT-22, BME-280) are used to monitor light intensity, temperature and humidity. The security system uses sensor modules (LDR LM393, PIR HC-SR501 and SW-420) as flashlight, motion and vibration detectors. The black box testing result shows a good performance of the proposed system. The monitoring and security system can monitor the condition of swallow cultivation parameters and security status that can turn on alert alarms, send WhatsApp messages, store log data and send data to the website periodically according to the level of conditions that occur

EXPERT SYSTEM FOR INITIAL IDENTIFICATION OF DISEASES CAUSED BY HELICOBACTER PYLORI BACTERIA USING CASE BASED REASONING APPROACH

Helicobacter pylori, which is a bacterium that can live in the stomach. Infection can occur when bacteria invade and damage the stomach wall. Lack of information and ignorance of the public about the seriousness of these bacteria causes various very serious diseases such as inflammation of the digestive tract (gastritis), gastric bleeding, gastric perforation (leak stomach), infection of the peritoneal wall (peritonitis) and gastric cancer. This expert system aims to provide information and also early identification of diseases caused by the bacterium Helicobacter pylori. After the expert system has identified the type of disease, it will then suggest the actions that need to be taken. The method used is CBR, this method works with the stages of Retrieve, Reuse, Revise, and Retain. The data that will be processed in this study are 30 data, with the results of 29 data in accordance with the doctor's diagnosis. From these results, it can be said that the accuracy of this expert system is 97% so that it can be used as an alternative in identifying diseases caused by the bacterium Helicobacter pylori

IMPLEMENTATION OF CERTAINTY FACTOR METHOD IN PEST AND DISEASE DIAGNOSIS IN HYDROPONIC PLANTS

Pest and disease attacks often occur in hydroponic plants so that many plants are damaged and result in losses for farmers or crop failure. The problem of pests and plant diseases is the main obstacle in increasing agricultural productivity, these conditions affect farmers' income and the provision of hydroponic plant food. The farmers do not know what types of pests and symptoms of diseases attack plants, making it difficult to handle diseases on hydroponic plants. Likewise for hydroponic plants located at the Agricultural Service of Asahan Regency which are attacked by pests, hydroponic experts say that the appearance of small leaf spots appears on the upper side of the leaves that damage hydroponic plants and because the cause of pests that cause disease in hydroponic plants is unknown, so farmers are hesitant to take countermeasures. An expert system is a computer program designed to make decisions such as decisions taken by an expert or expert. Therefore, an expert system with the Certainty Factor method is made in the diagnosis of pests and diseases in hydroponic plants. The purpose of this study was to apply the certanity factor method to diagnose pests and diseases in hydroponic plants. The results of the pest data test found spider mites with a confidence level of 79% and wet rot disease with a confidence level of 62%. The conclusion obtained is that the certainty factor method can detect early types of pests and diseases on hydroponic plants quickly and accurately

TRANSFER LEARNING IMPLEMENTATION ON IMAGE RECOGNITION OF INDONESIAN TRADITIONAL HOUSES

Indonesia is the largest archipelago in the world that has cultural diversity, one of Indonesia's cultural wealth is the architectural uniqueness of the types of traditional houses that come from different tribes and regions. in this era of digitalization, the younger generation of this nation must continue to preserve cultural wealth, one of which is by building a system that can document and provide learning about image recognition of the archipelago's traditional houses. Thanks to Artificial Intelligence Technology, it is possible to create a smart model that functions as an image recognition with system learning by working with a neural network called deep learning, which is supported by a transfer learning algorithm that can utilize previous models that have been trained, one of which is the MobileNetV2, Resnet50, VGG16 and Xception models as an effort to get a model with high accuracy with limited dataset conditions. So, the purpose as well as the update of this research is to build an image recognition model of Indonesian traditional houses with the transfer learning method. The methods and stages used are CRISP-DM (Cross Industry Standard Process for Data Mining), a standard used to build applications that aim to gain insight from a dataset, the image dataset used in this study was created with the image scraper technique from the internet.  The conclusion of this research is that an image recognition model of Indonesian traditional houses is produced by training experiments from 5 transfer learning models that have been determined and the greatest accuracy is obtained, namely 0.96% of the MobileNetV2 transfer training method, the potential for further development for future research is to add more classes and amount of data and design a more detailed and detailed deployment model

A NEW MODEL FOR HYDROPONIC LETTUCE NUTRITION ADAPTIVE CONTROL SYSTEM BASED ON FUZZY LOGIC SUGENO METHOD USING ESP32

In the last few years, the terms Smart Agriculture, Smart Farming, Urban Farming, or Precision Farming have been increasingly recognized and growing rapidly. Hydroponics is one part that is currently a trend, both in industrial or household scale businesses and hobbies. One of the most important things to consider in maintaining the quality of hydroponic plant growth is the concentration of nutrients in the water. A series of studies have been conducted to improve the quality of hydroponic plants. However, the developments that have been carried out have not focused on optimal nutritional control. The previous hydroponic plant nutrition control system still used conventional methods, namely the use of a rule base with firm values ​​, and did not consider the quantity and quality of water. Therefore, this study proposes a new model for an adaptive control system for hydroponic lettuce nutrition based on the Fuzzy Logic Sugeno method using ESP32. The fuzzy logic Sugeno method is used to create a new model of the inference system for determining the amount of nutrient dosage based on supporting data obtained from sensors installed on hydroponic growing media. Compared with the conventional method, the resulting test results show that the proposed method can adapt the amount of added nutrients, provide optimal nutrient addition output, and prevent excess nutrient additions that can potentially accumulate toxic ions in water that degrade water quality

4G LTE NETWORK WALK TEST ANALYSIS USING ANDROID APPLICATION G-NET TRACK ON SWCU FTI BUILDING

The poor performance of a cellular network can hinder student academic activities such as the difficulty of accessing online materials. Therefore, this research aims to obtain data on the quality of the 4G LTE cellular network in the SWCU FTI building area so that the data can be used as a reference for improving network quality. The parameters used for analysis are RSRP (Reference Signal Received Power), RSRQ (Reference Signal Received Quality) and SINR (Signal Interference to Noise Ratio) parameters. The results of measurements in 21 data collection points show that the RSRP parameter shows that the overall average of Telkomsel operators is -111.05 dBm and Indosat operators is -114.48 dBm and based on KPI standards, it is categorized as very poor signal quality. For the RSRQ parameter, the average overall measurement results for Telkomsel operators is -16.25 dB and Indosat operators are -19.57 dB and based on KPI standards, it is categorized as poor signal quality. For SINR parameters, the overall average of Telkomsel operators is -7.5 dB and Indosat operators is -7.8 dB and based on KPI standards, it is categorized as poor signal quality