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<![CDATA[Application of Data Mining to Predict Product Sales Using the K-Means Method ]]>
<![CDATA[Titania Delfiano]]>;
<![CDATA[Desiyanna Lasut]]>
<![CDATA[ bit-Tech Vol. 6 No. 2 (2023): bit-Tech ]]>
Publisher : <![CDATA[Komunitas Dosen Indonesia ]]>
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<![CDATA[Sales activities that run every day generate large amounts of transaction data which can become data stacks. This also happened in a bag-selling shop called Toko Blessing. Toko Blessing is a bag sales business that focuses on children's school supplies with various categories such as school bags, drink bottle bags, and lunch box bags which have several variants of models and motifs in each category. With so many product variations and manual reporting, Toko Blessing faces difficulties in determining which products are best-selling and need to be added in large quantities to meet buyer demands and avoid the accumulation of less desirable products. With the availability of large sales data, if processed properly this data can be used to design the right business strategy. The K-means method is used because it makes it easier for the store to analyze and classify data to find out the level of the product through large amounts of sales transaction data that can be done quickly. The K-means method aims to determine sales patterns by looking at Blessing Shop sales transactions to help find out which products are often sold/best-selling and to predict future sales. From the data mining application using the K-means method, sales reports were generated based on sales transaction data from January 2021 to December 2022 totaling 1,188 data, which can later be used to assist Blessing Stores in making decisions on which products are superior to predict sales in the coming year.]]>
<![CDATA[Designing Home Security With Esp32-Cam and IoT-Based Alarm Notification Using Telegram ]]>
<![CDATA[Hery Kurniawan]]>;
<![CDATA[Susanto Hariyanto]]>
<![CDATA[ bit-Tech Vol. 6 No. 2 (2023): bit-Tech ]]>
Publisher : <![CDATA[Komunitas Dosen Indonesia ]]>
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DOI: 10.32877/bt.v6i2.932
<![CDATA[Nowadays, technological advances have entered human life. One of the security system solutions is the use of Closed Circuit Television (CCTV) as a monitoring medium, but it is still lacking. This is because more and more crimes are committed in homes where the owner is away. As a result, each home needs a security system that can protect the wealth and assets of the owner. Similarly, currently existing security systems are rarely able to provide data directly to homeowners if an outsider wants to commit a crime. So, research was conducted with the title "Home Security Design with ESP32-CAM and Internet of Things (IoT) Based Alarm Notification Using Telegram Application". With this device plan, it is believed that individuals who leave the house will feel safer. This device can detect an infrared wave produced by humans within its range and is equipped with an alarm/buzzer that produces output in the form of notifications on Telegram and alarm sounds on the device. The PIR sensor and ESP32-CAM integrated with the Telegram mobile device form the basis of this security system. This research has successfully implemented a device aimed at reducing home burglary issues by detecting human motion, triggering alarms, and enabling remote monitoring through Telegram. Additionally, the adapter utilized in the system produces an average voltage value of 5.196 V with an average error rate of approximately 3.9%.]]>
<![CDATA[Sistem Informasi Pendugaan Stock dengan Metode Holt-Winters ]]>
<![CDATA[Johnathan Agusman]]>
<![CDATA[ bit-Tech Vol. 6 No. 2 (2023): bit-Tech ]]>
Publisher : <![CDATA[Komunitas Dosen Indonesia ]]>
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DOI: 10.32877/bt.v6i2.934
<![CDATA[In sales, prediction is an important element carried out by traders to help their sales efforts. Predictions involve assessing future values based on historical data. Problems that can arise from the combination of stock and transactions. The aim of this research is to create a sales prediction system, monitor sales and input transactions. This research uses the Holt-Winters method to predict sales. The main goal is to implement the findings of this research into a web administration site. In this research, transaction data is used as a basis for predictions using the Holt-Winters method. The Holt-Winters method uses a series of actual values for predictions. Parameters like Alpha, Gamma, and Beta are defined for weighting, and initial values are calculated for Trend, Level, and Seasonality attributes to achieve predicted results. Evaluation of the Holt-Winters method in this research involves searching for the Mean Absolute Percentage Error (MAPE) to determine the percentage of prediction error. The application of this research involves creating a website administration to assist sellers in combining transactions and available products. The average results of the evaluation of the administration of the website created resulted in a satisfaction percentage of 82.61%. This shows that the system can help in unifying transactions and products.]]>
<![CDATA[Analysis and Design of Disease Diagnosis Systems and Patient Medicine Recommendations with Forward Chaining Method ]]>
<![CDATA[Samuel Ryon Elkana]]>;
<![CDATA[Verri Kuswanto]]>
<![CDATA[ bit-Tech Vol. 6 No. 2 (2023): bit-Tech ]]>
Publisher : <![CDATA[Komunitas Dosen Indonesia ]]>
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DOI: 10.32877/bt.v6i2.937
<![CDATA[Technological developments have had a significant impact on many aspects of life, one of which is patient care in hospitals. Apart from using technology in patient care, technology also provides access to effective and efficient information storage and management to record patient data for treatment purposes. In outpatient services at hospitals, there are often complaints from employees regarding the health recording system which is less than optimal. Therefore, a system that manages disease diagnoses and patient treatment recommendations is something that needs to be developed, with the hope of speeding up the performance of medical personnel, so that they can help more patients who need help. The application system design aims to help manage information related to disease diagnosis and patient drug recommendations, where this system uses Forward Chaining to assist users in identifying diseases and prescribing drugs according to the diagnosis the patient is complaining about. By using the Forward Chaining methodology, medical personnel are able to obtain patient diagnosis results more quickly. The result is an application that can help medical personnel in serving outpatients from registration, examination, to exchanging medicines. This application has been tested using black-box testing involving several respondents, where respondents can feel that this application works well and helps hospital staff in examining patients.]]>
<![CDATA[Design of Diabetes Prediction Application Using K-Nearest Neighbor Algorithm ]]>
<![CDATA[Alvin Gunawan]]>;
<![CDATA[Indah Fenriana]]>
<![CDATA[ bit-Tech Vol. 6 No. 2 (2023): bit-Tech ]]>
Publisher : <![CDATA[Komunitas Dosen Indonesia ]]>
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DOI: 10.32877/bt.v6i2.939
<![CDATA[The development of diabetes continues to increase accompanied by an increase in unhealthy lifestyles with a high number of cases, making diabetes need to be continuously researched and developed to obtain useful information in terms of research related to diabetes. This study aims to predict diabetes using the K-Nearest Neighbor Algorithm and make a simulation of checking the disease and test the quality of the K-Nearest Neighbor Algorithm for diabetes and make comparisons with the Naïve Bayes algorithm. The K-Nearest Neighbor algorithm is the method used in this study because it has the advantage of being able to train data that is fast, simple, and easy to learn. The way this algorithm works is by calculating the distance between each row of training data and test data based on a predetermined K value. In the process of using the K-Nearest Neighbor, there is a Z-Score normalization stage which is used to adjust the values for each attribute of diabetes symptoms so that they have a range of values that are not too far away. Based on the results of the research and testing of the K-Nearest Neighbor that has been carried out, an accuracy of 97.12% is obtained and the Area Under Curve value is 0.872 which is included in the good classification category and these results have a greater accuracy value compared to previous studies on the same disease, namely Diabetes with the Naïve Bayes algorithm which produces the most optimal accuracy of 87.69%.]]>
<![CDATA[Penerapan Markerless Augmented Reality pada E-Katalog Variasi Mobil Menggunakan Metode Natural Feature Tracking ]]>
<![CDATA[Stevens Khouw]]>;
<![CDATA[Edy]]>
<![CDATA[ bit-Tech Vol. 6 No. 2 (2023): bit-Tech ]]>
Publisher : <![CDATA[Komunitas Dosen Indonesia ]]>
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DOI: 10.32877/bt.v6i2.941
<![CDATA[Information technology is widely used by business people in promoting the products and services they sell, this is very important especially for businesses that sell products that are not common, for example car variations. Car variation business is already widely found in various regions in Indonesia, but the car variation business has a relatively small and specialized market. The main cause is the lack of promotion of product knowledge to the public and the lack of interesting and practical forms of promotional media that make car variation products rarely seen by vehicle owners. Therefore, in this study, attempted on the application of augmented reality technology into the e-catalog application of car variations using the natural feature tracking method to help the visual augmentation process of car variation products that want to be seen as a form of promotional media for car variation products in general, the e-catalog itself were made using unity engine. From this research, the results obtained are: the application of augmented reality was successfully carried out on the Android e-catalog application, the attractiveness of the application display with a good indicator of 100%, the attractiveness of augmented reality with a percentage of 88.9% on good indicators and the appropriateness of application development with a percentage of 100% on good indicators, these result are obtained from questionnaires that are distributed to both coworker and random users as sample.]]>
<![CDATA[Pendeteksi Serangan Jaringan Komputer Menggunakan Metode Intrusion Detection System dengan Memanfaatkan Snort Berbasis Telegram ]]>
<![CDATA[Juan Adi Dharma]]>;
<![CDATA[Rino]]>
<![CDATA[ bit-Tech Vol. 6 No. 2 (2023): bit-Tech ]]>
Publisher : <![CDATA[Komunitas Dosen Indonesia ]]>
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DOI: 10.32877/bt.v6i2.943
<![CDATA[The constantly evolving of information technology landscape has made information security something of paramount importance, yet the development of information technology is not met with a corresponding advancement in its security systems. As a result, in the current era, there is a multitude of cybercrimes in the realm of the internet. Therefore, this research aims to create a computer network attack detector using the Linux operating system by leveraging the Telegram-based Snort application and employing the Intrusion Detection System (IDS) method through an IDS-based application, namely Snort. Additionally, this study incorporates features for blocking IP addresses and changing the Linux server password through the Telegram application for initial response when an attack is detected, accomplished by sending specific commands within the Telegram application. Furthermore, this paper also introduce a feature for categorizing the risk of computer network attacks into three categories: Low, Medium, and High within the Telegram application. The results of this research demonstrate that Snort can detect predefined rules and send alerts to the Telegram application for every attack occurring within the Wireless Local Area Network (WLAN). Successful IP address blocking is achieved through Telegram integration with the Iptables application, and changing the Linux server password is also accomplished through Telegram by integrating the bash shell programming language found in the Terminal of the Linux operating system. Finally, the risk of attacks can be viewed within the Telegram application.]]>
<![CDATA[Designing Prototype of Volume Detector for Medical Oxygen Cylinder Using NodeMCU ESP8266 ]]>
<![CDATA[Mamad Muhamad Mansur]]>;
<![CDATA[Rahmat Ismatullah Ibrahim]]>;
<![CDATA[Ade Surya Budiman]]>
<![CDATA[ bit-Tech Vol. 6 No. 2 (2023): bit-Tech ]]>
Publisher : <![CDATA[Komunitas Dosen Indonesia ]]>
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DOI: 10.32877/bt.v6i2.980
<![CDATA[The COVID-19 pandemic posed significant challenges to the healthcare sector, particularly due to the imbalance between patient numbers and available medical staff, resulting in difficulties during emergencies. One such challenge was the prompt replacement of oxygen cylinders. Leveraging Internet of Things (IoT) technology, we developed a prototype oxygen cylinder volume detector aimed at providing early notifications to healthcare professionals. This prototype incorporates the NodeMCU ESP8266, equipped with an Infrared (IR) Obstacle Sensor. The sensor communicates with a relay to activate an MP3 module, delivering audible alerts. Furthermore, the NodeMCU ESP8266 system integrates CTBot to send text notifications through the Telegram application to the nearest healthcare personnel. Visual alerts are also provided through red LED lights attached to the cylinders, indicating which oxygen cylinder is approaching depletion. Comprehensive testing validated the proper functionality of all system components. This innovative oxygen cylinder detector prototype is designed to streamline healthcare worker and nurse workflows by offering quicker access to accurate information regarding the status of medical oxygen cylinders. Not only does this enhance patient care efficiency, but it also ensuring the availability of oxygen is crucial for critical patients.]]>
<![CDATA[Application Of Deep Learning For Image Deepfake Detector Using Convolutional Neural Network Algorithm ]]>
<![CDATA[Ananda Adhicitta]]>
<![CDATA[ bit-Tech Vol. 6 No. 2 (2023): bit-Tech ]]>
Publisher : <![CDATA[Komunitas Dosen Indonesia ]]>
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DOI: 10.32877/bt.v6i2.1000
<![CDATA[Social media has long been used by the public in general as a means of exchanging information. Behind this commonly exchange of information, hide the malicious intent of those who are not responsible for spreading false information or hoaxes. This false information, which can come in various forms such as images, sounds, or videos, can actually be useful when used as stock photos or simply used as caricatures and satire. Unfortunately, false information often used on famous people instead to make them look like they said or did something that never happened. This certainly needs to be controlled, one of which is by using deepfake detector that aims to recognize false information pattern. Deepfake detector utilizes the computer's ability to self-learn to recognize that invisible patterns in images using one of deep learning algorithms, namely Convolutional Neural Network, which converts images into a collection of arrays containing numbers and then performs mathematical operations repeatedly on each layer. The result of the mathematical operation can then be used as a reference to determine whether an image is real or hoax. Author’s deepfake detector application using Convolutional Neural Network, specifically using the Resnet-50 model on hoax images created using AI with the ProGAN model, appears to be able to detect hoax images with the same model, with an accuracy of 85%, precision of 100%, and recall of 65%, but appears to experience decrease in accuracy when used in deepfakes with other models such as StyleGAN and BigGAN.]]>
<![CDATA[Penggunaan Sistem Informasi dalam Perhitungan Z-Score dan Penentuan Kategori Stunting pada Balita ]]>
<![CDATA[Krisna Widatama]]>;
<![CDATA[Putry Wahyu Setyaningsih]]>
<![CDATA[ bit-Tech Vol. 6 No. 2 (2023): bit-Tech ]]>
Publisher : <![CDATA[Komunitas Dosen Indonesia ]]>
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DOI: 10.32877/bt.v6i2.1013
<![CDATA[Information technology is an inseparable part of human work today. It is growing very rapidly along with the human need for the speed at which information is processed until it is displayed to the user. Information systems are now widely used in all sectors, ranging from: economy, agriculture, health and so on. In the health sector, many health facilities have used information systems to improve services, one of which is Posyandu. Posyandu is the front line in providing family health services at the village level. Currently, Posyandu focus is preventing malnutrition or stunting. Standardization of stunt calculation data often changes. In addition, the calculation and documentation of toddler examination data is also still done manually. This has an impact on the efficiency of the performance of staff in determining toddlers who are affected by stunting. Data integration with the Ministry of Health is also a problem when recorded manually. Therefore, it is proposed that an information system that functions to perform Z-Score calculations and classify them into stunting or non-stunting groups. The existence of this information system can also support the government to encourage data integration between local governments and the center regarding stunting data. It is hoped that this information system can help the work efficiency of health cadres to record toddlers who fall into the Stunting category. In addition, it is also possible for the government to access data stored in the system to support data integration.]]>
