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INDONESIA
INFOKUM
Published by SEAN INSTITUTE
ISSN : 23029706     EISSN : 27224635     DOI : -
Core Subject : Science,
Computer Science & IT
The INFOKUM a scientific journal of Decision support sistem , expert system and artificial inteligens which includes scholarly writings on pure research and applied research in the field of information systems and information technology as well as a review-general review of the development of the theory, methods, and related applied sciences. Software Engineering. Image Processing Datamining Artificial Neural Networks
Arjuna Subject : Ilmu Komputer (semua kategori) - Kecerdasan Buatan
Articles 84 Documents
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Search results for , issue "Vol. 10 No. 02 (2022): Juni, Data Mining, Image Processing, and artificial intelligence" : 84 Documents clear
APPLICATION OF DATA MINING TO IDENTIFY DIABETES MELLITUS USING THE SUPPORT VECTOR MACHINE (SVM) ALGORITHM AND KNN Windania Purba; Yessy Yessy; Riski Nofarianus Gulo
INFOKUM Vol. 10 No. 02 (2022): Juni, Data Mining, Image Processing, and artificial intelligence
Publisher : Sean Institute

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Abstract

Damage to the performance of human organs is very detrimental Received Revised Accepted And is the source of the most problems at this time. One of the diseases that is the number one killer in the world is diabetes mellitus. Diabetes mellitus is a metabolic disease characterized by hyperglycemia caused by and obstacle in insulin secretion from insulin action or both. Diabetes mellitus is divided into several types, type 1 diabetes mellitus generally gives rise to indications before the patient is 30 years old. Although in fact the indications of the disease can arise at any time. This study aims to apply the Support Vector Machine (SVM) and K-Nearest Neighbor (KNN) method to identify diabetes mellitus and calculate the comparison value of the accuracy of the two algorithms. From the results of this study. It can be concluded that the Support Vector Machine (SVM) algorithm produces an accuracy value of 76% while the accuracy value of the K-Nearest Neighbor (KNN) algorithm is 75%
APPLICATION OF DATA MINING TO PREDICATE STOCK PRICE USING LONG SHORT TERM MEMORY METHOD Sonia Novel Lase; Yenny Yenny; Owen Owen; Mardi Turnip; Evta Indra
INFOKUM Vol. 10 No. 02 (2022): Juni, Data Mining, Image Processing, and artificial intelligence
Publisher : Sean Institute

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Abstract

Investing some of our wealth to invest in stocks is highly recommended considering the fluctuating nature of stock prices, meaning that stock prices can go up and down at any time depending on the conditions and phenomena that occur on the stock market. Stock investment includes having a high risk of loss but also by taking that risk it is also possible to get high profits (High Risk High Return). Shares are proof of ownership of company value or proof of equity interest. Shareholders are also entitled to receive dividends (profit sharing) according to the number of shares they own. This study aims to make it easier for everyone who wants to invest in Google and Tesla stocks and implement the long short term memory method for stock price prediction. This data mining research resulted in a Root Mean Square Error (RMSE) value of 1.80%, which means the prediction results are very accurate with real data and the average difference between real stock price data and predicted data is $3 -$15.
USING THE NAIVE BAYES CLASSIFIER METHOD ON SOCIAL MEDIA SENTIMENT ANALYSIS Windania Purba; Ade Syahpitri; Grace Fitri Anggi Munthe
INFOKUM Vol. 10 No. 02 (2022): Juni, Data Mining, Image Processing, and artificial intelligence
Publisher : Sean Institute

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Abstract

Social media is one of the many technological developments that greatly affect human communication and socialization systems. Most people voice their opinions through social media, with the aim that they can be heard and seen by the general public. However, the use of social media often backfires for the owners themselves due to their excessive use. In particular, this study discusses the grouping of sentiment data from Prima Indonesia University students where to seek negative and positive opinions from students as a benchmark for online learning methods carried out in the campus environment. The data grouping process uses the nave Bayes algorithm, because this algorithm has been widely used in data processing. The tests carried out in this study resulted in an accuracy of 68% of the dataset selected as the data training process. This results in a classification of new data to find out a sentiment on students belonging to the negative or positive class.
IMPLEMENTATION OF DATA MINING WITH NAVE BAYES CLASSIFIER TO SUPPORT THE MARKETING STRATEGY OF THE MARKETING SECTION AT PT. MEGA PANCA JAYA BUMI Wahyudi, Ricki; Ramadhani, Putri Saumi; Utomo, Ardy Budi; Anita, Anita
INFOKUM Vol. 10 No. 02 (2022): Juni, Data Mining, Image Processing, and artificial intelligence
Publisher : Sean Institute

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Abstract

In the business world, competition is something that is already familiar to business actors. The bigger the business can compete, the bigger the challenges faced by business actors. One of them is a company in residential property owned by PT. Mega Panca Jaya Bumi. The newly developing company should have thought of strategies in marketing its business. In order to avoid a decrease in interest and profits, it is only natural that these new strategies can be implemented in data processing techniques or Data Mining which is expected to increase marketing and the number of markets is wider. The data processing in this study uses the Naïve Bayes Classifier Algorithm in its calculations. Data taken from the company as many as 300 records from 2019 to 2021. In this study, the process of the data experiment was carried out by applying the Python programming language version 3.8 technology. The results of testing the data got an accuracy rate of 56%
SMART HOME SYSTEM BASED ON THE INTERNET OF THINGS USING NODEMCU AND ANDROID APPLICATIONS Muhammad Syahputra Novelan; Aminuddin Indra Permana
INFOKUM Vol. 10 No. 02 (2022): Juni, Data Mining, Image Processing, and artificial intelligence
Publisher : Sean Institute

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Abstract

Smart home systems aim to maximize surveillance, monitoring, security and so on. The way this system works is that it integrates telecommunications and system controllers from a chip or NodeMCU, so it is called the Internet of Things. In this study, create or design a smart home that aims to make it easier to control existing equipment at home remotely and when traveling out of town with the Internet Of Things system. Besides, it's not just controlling remotely but by monitoring it by using an Android smartphone. The devices used in the Smart Home system design are divided into several parts, namely input, process and output. Part of the input consists of a room temperature sensor, part of the process consists of a Microcontroller or uses a NodeMCU, and part of the output is an LED as an indicator and sound or using a Buzzer module. The results of the tests carried out by the system designed that the smart home runs well overall starting from the android application and receiving data from the microcontroller and sending notifications to the sensor readings
Asistensi Implementation of Database-Based CodeIgniter PHP Framework on School Alumni Data (Case Study of Alumni Data for SMK Taman Siswa Medan) Aripin Rambe
INFOKUM Vol. 10 No. 02 (2022): Juni, Data Mining, Image Processing, and artificial intelligence
Publisher : Sean Institute

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Abstract

By using the CodeIgniter PHP framework, an information system web application software can be created to help manage data. In this study, software was developed to help manage school alumni data. By using the security layer in the CodeIgniter PHP framework, data filtering can be done to prevent the exploitation of security vulnerabilities, which include Cross-site Scripting (XSS) and SQL Injection. This can be seen from the test results that only produce low level warnings. So the quality of the software developed in terms of security is quite good.
SENTIMENT ANALYSIS OF PRODUCT REVIEWS DATA ON TOKOPEDIA BY COMPARING THE PERFORMANCE OF CLASSIFICATION ALGORITHMS Dwi Widiastuti; Isram Rasal; Dessy Wulandari Asfary Putri
INFOKUM Vol. 10 No. 02 (2022): Juni, Data Mining, Image Processing, and artificial intelligence
Publisher : Sean Institute

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Abstract

Social media is a medium where people can express their opinion on something. Opinion mining or sentiment analysis, which is studying people's sentiments towards certain entities. This can be used by companies to find out people's responses to a sales product. Sentiment analysis has received a lot of attention in recent years. Sentiment analysis is one of the main tasks of NLP (Natural Language Processing). In this paper, sentiment polarity categorization becomes the basis for sentiment analysis problems in product reviews. A general process for sentiment polarity categorization is proposed with a detailed description of the process. The data used in this study is an online product review collected from the Tokopedia application. Classification is carried out on sentence level categorization and star rating level categorization. There are three models used to compare the classification process, namely SVM, Random Forest, and Naïve Bayes models. The results of this research paper are in the form of a comparison of the performance of the three models against the polarity categorization of product review sentiment on Tokopedia
DESIGN AND CONSTRUCTION OF CAR RENTAL MONITORING SYSTEM BASED ON MICROCONTROLLER INTEGRATED THROUGH SMARTPHONE USING FUZZY MAMDANI METHOD Rakhmat Kurniawan R; Abdul Halim Hasugian; Alwy Azyari Harahap
INFOKUM Vol. 10 No. 02 (2022): Juni, Data Mining, Image Processing, and artificial intelligence
Publisher : Sean Institute

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Abstract

The increasing human needs to follow human needs as well as the lack of employment opportunities make some people do the theft mode. Some of them are by borrowing a car rental, but in certain cases the borrower does not return the car or it can be called as running away or stealing with borrowed mode. Therefore, the author wants to make a tool that can monitor the car remotely using a microcontroller, where the MPU-6050 sensor will be used to determine the status of the car when it is on, off, and running. The Ublox Neo6M GPS module will also be used to track the car's location. The output to be received will be sent through the intermediary of the 800L SIM module. In this study using the Mamdani fuzzy method because this method is suitable for use in most real-time problems such as making decisions to look for engine vibrations in cars that change and are less certain. In this study, the average percentage difference between the MPU-6050 sensors is 6.722%. With this fuzzy logic method, a mathematical framework is obtained that is used to represent uncertainty, ambiguity, imprecision, lack of information and partial truth. Keywords: Monitoring System, Car Rental, Microcontroller, Fuzzy Mamdani.
ANALISIS KEBUTUHAN PENGGUNA APLIKASI RUANGGURU DAN ZENIUS SEBAGAI MEDIA PEMBELAJARAN DALAM MENGATASI KESULITAN BELAJAR SISWA PADA PEMBELAJARAN DARING DI MASA COVID -19 MENGGUNAKAN METODE FUZZY KANO Palma Juanta; Dimas Prayoga
INFOKUM Vol. 10 No. 02 (2022): Juni, Data Mining, Image Processing, and artificial intelligence
Publisher : Sean Institute

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Abstract

Ruangguru dan zenius merupakan aplikasi bimbingan belajar online yang sangat banyak digunakan selain itu ruang guru dan zenius menawarkan produk dengan solusi terlengkap kepada Siswa/i, Dampak COVID -19 terhadap dunia pendidikan di Indonesia sangat dirasalkan oleh semua siswa terutama pada kegiatan belajar –mengajar dengan hadirnya ruang guru dan zenius dapat membantu siswa kesulitan belajar. Berdasarkan hasil wawancara dan observasi, penggunaan Ruangguru dan zenius memiliki pengaruh positif dalam proses pembelajaran. Tetapi ada permasalahan yang dirasakan oleh pengguna Ruangguru dan zenius. Tujuan pmenelitian ini adalah menganalisis Aplikasi dengan cara mengidentifikasi, mengategorikan, dan memprioritaskan kebutuhan berdasarkan kepuasan pengguna Aplikasi Ruanggurudan zenius sehingga dapat mengurangi permasalahan pengguna sehingga kualitasnya dapat meningkat. Metode yang digunakan Fuzzy Kano, merupakan teknik pengembangan produk secara kuantitatif dan obyektif berdasarkan penilaian dari kepuasan pelanggan untuk meningkatkan kualitas produk. Dalam penelitian ini menggunakan purposive sampling sehingga target minimal respoden sebesar 77 responden. Hasil dari penelitian ini, terdapat 20 requirement yang didefinisikan sebagai fitur aplikasi, yang dikagorikan kedalam 3 katagori yang terdiri dari 7 fitur termasuk ke dalam kategori one-dimensional quality attribute, 4 fitur termasuk kategori attractive quality attribute, dan 9 fitur termasuk ke kategori indifferent quality attribute. Prioritas paling tinggi yaitu, fitur “Mendapatkan penjelasan jawaban” pada kategori one- dimensional quality attribute, “melanjutkan vidio ” pada kategori attractive quality attribute, “mengganti kurikulum” pada kategori indifferent quality attribute.
IMPLEMENTATION OF THE C.50 ALGORITHM IN ASSESSING EMPLOYEE PERFORMANCE ON PT SMARTFREN TELECOM TBK Purba, Windania; Caprio, Calvin Di; Sabrian, Muhammad Ryan
INFOKUM Vol. 10 No. 02 (2022): Juni, Data Mining, Image Processing, and artificial intelligence
Publisher : Sean Institute

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Abstract

This study aims to create a model in measuring the performance of PT Smartfren Telecom employees which is measured based on several attributes such as performance targets, service orientation, integrity, discipline, cooperation and leadership. By using data mining data analysis methods, decision trees and the C.50 Algorithm, so that it can make a pattern of decisions that can be proposed as a basis for giving rewards and punishments for the performance of PT Smartfren employees. The population of the data is 30 respondents with 7 categories of leaders and 23 as employees. The results of this study are the C5.0 algorithm can process employee performance data into a decision tree and useful rules as input. The results obtained can be developed into a decision-making system so that it can be used to assist in determining employee performance decisions. In general, based on the evaluation results for staff data with training data as much as 23 data obtained an accuracy rate of 97%, where one of the factors that affect accuracy is that performance data does not meet the requirements which are still small, this can be increased by increasing the amount of training data related to the data

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