Jurnal Teknologi Informasi dan Komunikasi (TIKomSiN - STMIK Sinar Nusantara)
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PREDIKSI KEPRIBADIAN BERDASARKAN STATUS SOSIAL MEDIA FACEBOOK MENGGUNAKAN METODE NAIVE BAYES DAN KNN
Full text linkMedia sosial sekarang hampir menjadi bagian hidup semua orang dari hampir semua kalangan umur dengan beragam kepribadian. Penelitian ini dilakukan untuk memprediksi kepribadian seseorang berdasarkan status media sosial media facebook menggunakan algoritma Naïve Bayes dan K-NN kepribadian yang berdasarkan lima besar. Data yang di ambil dari MyPersonality yang dibagi menjadi 2 yaitu 40% data latih, dan 60% data uji menghasilkan tingkat akurasi sebesar 100%, precision 100%, Recall 100% dapat disimpulkan bahwa variabel prediksi yang (Signifikan) dengan metode Naive Bayes . Sedangkan prediksi menggunakan metode KKNmenunjukkan rata-rata nilai prediksi kepribadian dengan akurasi 58,96% (Tidak signifikan), presisi 99,12% (Signifikan), dan recall 2,34% (Tidak signifikan)
ALGORITMA C4.5 UNTUK PENENTUAN TIM PEMAIN UTAMA OLAHRAGA VOLI
Full text linkThe current system in the DX JUNIOR Volleyball Team still uses a manual process, because there is no application or method used in determining players on the first team. However, with so many prospective players who want to play to become the main players, it becomes an obstacle in determining who deserves to enter the first team, and requires a long process in determining prospective players who are eligible and not eligible to enter the first team. So a determination classification system is needed by applying the C4.5 Algorithm method which can simplify the process of determining the main players. The purpose of this research is to create an application for Classification of Volleyball First Team Determination Using the C4.5 Algorithm in the DX JUNIOR volleyball team. The methods used include the type of data for data collection techniques using observation and interview methods and literature studies to determine the theoretical basis for research related to the matters under study. As for the system analysis method, for system design using UML, Matlab. The application of the C4.5 Algorithm method for determining first team players was made to make it easier to determine prospective players in the first team based on criteria, namely Physical Strength, Attitude, Cooperation, and Test Score. The results of the research were tested using the blackbox test the system runs accordingly, the validity test was tested on 72 data, resulting in an analysis value which can be concluded that the results of the comparison of Entropy and Gain calculated manually with Entropy and Gain calculated by the program process the results are the same, so the application is in accordance with the results of the C4.5 algorithm analysis
PENGUKURAN KINERJA OPTIMASI ALGORITMA BAT PADA ALGORITMA NAIVE BAYES, KNN DAN DECISION TREE UNTUK SENTIMEN ANALISIS DI LINI MASA TWITTER
Full text linkSocial media is a very effective communication media in today's digital era. Of the social media, Twitter is the most widely used social media. Many tweets entered on Twitter have encouraged research in the field of text mining. One of the branches of text mining is sentiment analysis. Sentiment analysis in this study was formed from 3 classification algorithms, namely Naïve Bayes and Decission Tree. In practice, the results of the 3 classification algorithms often produce very low levels of accuracy. Bat algorithm is an algorithm that can optimize the results from the accuracy of the Naïve Bayes, K-NN algorithm and Decission Tree. In this study, two research scenarios were made: first, calculating the accuracy of the Naïve Bayes, K-NN algorithm, and Decission Tree. Second, optimizing the classification results of the 3 algorithms with the Bat algorithm method, which then re-tested the accuracy value. In the first scenario the percentage is generated from the accuracy value of Naïve Bayes of 33,58, K-NN of 33,61 and Decission Tree of 32,82. In the second scenario, using one of the objective functions, namely f(x) = x2, the Naïve Bayes value is obtained 39,01, K-NN 66,15 and Decission Tree 76,63. From the results of 3 the optimization test of classification Algorithm, it was found that the overall objective functions of the Bat algorithm were all able to increase the data accuracy value from before optimization. From all the tests, it was found that the Decision Tree algorithm has the highest average value of optimization increment, namely 43,81
PENERAPAN MOVING AVERAGE PADA PREDIKSI PENJUALAN ACCU
Full text linkThe problem faced by TIO ACCU is the difficulty of providing stock according to consumer needed, it caused every month has difference sale of product. The purpose of this research is to create a battery sales prediction system using the Moving Average method. The Moving Average algorithm used for past sales data doesn’t has seasonal trends or elements. This method is applied to predict the number of battery sales in future periods. The results from 5 periods and validity test with 31 sales data for the MAD method is 3.90, for the MSE method is 20.09, and for the MAPE method is 7.72%. Meanwhile, the results of calculation 7 periods with validity test of 29 sales data for the MAD method is 3.70, the MSE method is 18.90, and for the MAPE method is 7.28%. The test results of the battery sales prediction system using the Moving Average method have run well and optimally with accuracy rate of 92.28% for 5-period predictions and 92.72% for 7-period predictions can be classified as very good criteria, because it has an error rate of less than 10%.
RANCANG BANGUN SISTEM PEMANTAUAN KUALITAS UDARA BERBASIS ARDUINO UNTUK MENDETEKSI POLUSI UDARA DI PERKOTAAN
Full text linkAir pollution is a pressing environmental issue that poses significant risks to human health and the environment. To address this issue, the development of a reliable air quality monitoring system is essential. This research focuses on the design and implementation of a monitoring system based on Arduino Uno microcontroller and sensors such as MQ135, MQ136, and DHT11. The system aims to detect and measure air pollutants, temperature, and humidity levels. The Arduino Uno processes the data collected from the sensors and displays the information on the LCD screen. The system provides real-time monitoring of air quality, allowing quick action to be taken to improve the environment. The use of an Arduino Uno microcontroller enables efficient data processing and control of the overall system operation. The MQ135 sensor is used to detect and measure levels of air pollutants, particularly carbon monoxide (CO). The MQ136 sensor, on the other hand, is used to detect and measure levels of certain air pollutants, such as sulfur dioxide (SO2). In addition, the DHT11 sensor is used to measure the temperature and humidity levels in the air. The results of testing carried out 16 times under the condition of good conditions resulted in an average of CO 8.6 and SO2 15,25 where it is included in the high accuracy category
RANCANG BANGUN GAME EDUKASI ‘DEN SARWO’ BERBASIS MOBILE MATA PELAJARAN BAHASA JAWA MATERI AKASARA JAWA DI KELAS IVB SD NEGERI MOJOSONGO 3
Full text linkThe purpose of this research is to design a mobile-based educational game "Den Sarwo" on Javanese language subjects on Javanese script material to help students to understand Javanese script. Researchers used the Research and development (R&D) method with the ADDIE development model. Data collection using interviews, questionnaires, and documentation. Testing of the educational game "Den Sarwo" was carried out using the Blackbox Testing method and validation by experts who were converted using a Likert Scale. The results showed that the educational game "Den Sarwo" obtained very feasible criteria according to media experts having a feasibility level of 92%, according to material expert validation having a feasibility level of 92% with very feasible criteria, and feasibility assessment by educators getting a percentage of 97.3% with very feasible criteria, so that the educational game "Den Sarwo" is feasible to use as a learning media and entertainment facility when studying independently anytime and anywhere
Prediksi Persediaan Obat Untuk Proses Penjualan Menggunakan Metode Decision Tree Pada Apotek
Full text linkInventory of drugs in a pharmacy is information that is needed to see the availability of drugs for the sales process. Oftentimes, the stock of drugs that are needed by the community is empty, while drugs that are not needed are actually abundant in stock in the warehouse. The unavailability of the drugs needed, of course, disappointed the people who were in dire need of these drugs. Meanwhile, the abundance of drugs that are not needed will cause losses because the drugs have expired due to being stored in the warehouse for too long. Another problem is that pharmacies feel overwhelmed in predicting which drugs are needed a lot and which drugs are not needed by the community. Considering that the prediction process is still manual, it is only by estimating it without any mathematical calculations.Based on these problems, the authors decided to design a drug inventory prediction system for the drug sales process using the decision tree method. The purpose of this study is how to build an application to predict drug sales that can be used to optimize drug stock control and increase sales at pharmacies.The Decision Tree algorithm is used because it is a suitable algorithm for classification problems and data mining, mapping attribute values into classes that can be applied to new classifications. The concept of the Decision Tree Algorithm is to convert data into a decision tree and decision rules. The Decision Tree Algorithm was introduced by (Quinlan, C.45: Programs for Machine Learning, 1993) which is a development of the ID3 Algorithm, the algorithm is used to form a decision tree. Decision tree is considered as one of the most popular approaches.The results of functional testing indicate that the application can run properly according to its design. The results of the validity test stated that the Prediction of Drug Sales at Pharmacies with the C.45 Method with 30 samples of sales transaction data had an accuracy of 89%, thus indicating that the system that has been created has a fairly good performance and can be used by pharmacies to predict drug sales in the future. which will come
SISTEM PENUNJANG KEPUTUSAN PENERIMAAN SISWA BARU BERPRESTASI DI SMP IT SURAKARTA MENGGUNAKAN METODE NEAREST NEIGHBOR DAN SIMPLE ADDAPTIVE WEIGHTING
Full text linkSMP IT Nur Hidayah Surakarta, was established in 2004/2005 academic year. SMP IT Nur Hidayah has been graduated 3 generation of graduation, on the academic year of 2006/2007, 2007/2008 and 2008/2009. In the new student admission program there is an acceptance limit for students who are eligible for scholarships. The criteria used in the selection process include: average grades of report cards, written tests, parents' income and achievements. In decision new students, the school wants quality selection results by predicting new prospective students and then decide new students. The purpose of this study was to design a decision support system for new students using the Nearest Neighbor (NN) and Simple Additive Weighting (SAW) methods. This method was chosen because it is able to carry out a prediction process and a ranking process to determine the best alternative. The technique used in this research is observation, interview and literature study. In the design of this system is made with Context Diagram, HIPO, DAD, relations between tables and database design. The application is made using the PHP programming language and the database uses My SQL. The final results are in the form of student data reports, prediction reports, selection reports and reports on the best selection results. System testing is done by testing the functionality which shows the accepted results and the validity test which shows the valid results. The results of system testing with the MAPE test showed an accuracy of 88.7%
IMPLEMENTASI METODE LOCATION BASED SERVICES PADA SISTEM PRESENSI PEGAWAI
Full text linkThe attendance system is a digital attendance record, this is needed to evaluate employee attendance data. During the corona virus pandemic, the employee attendance system having some problems related to physical distancing policies, it needs problem solving to solve the problem, it is namely by maximizing the attendance coordinates of each employee, which use Location Based Services (LBS) technology method and haversine formula that functions to calculate the distance of the attendance coordinates with the employee's position. Creating the system using Software Development Life Cycle (SDLC), Waterfall, Node.js, Express.js, Flutter and MySQL as well as a VPS server. The final result of the employee attendance system can run on two platforms, the frontend version on android mobile devices and the backend version on the website. The test results on the employee attendance system received a "reasonable" predicate with an achievement range of 70-74 %. The conclusion of this research is creating the creation of an employee attendance system use location coordinates which is answered the problems on this research.
SISTEM INFORMASI COVID-19 BERBASIS MOBILE DENGAN FRAMEWORK FLUTTER DAN APPLICATION PROGRAMMING INTERFACE (API)
Full text linkThe corona virus (COVID-19) has been happen for long time on the world, recently there is a new variant, namely omicron, which is rapidly spreading throughout the world, especially in Indonesia. The lack and difficulty to creating of spreading data of corona virus, it makes difficulty to know the information of corona virus cases for people especially for people who want doing kinds of trip. This research uses Information system of Covid-19 to plan the flutter framework and uses APIs to be able to solve these problems. The implementation of Flutter and API can easily collaborate to exchange the data in JSON format, so that data of covid-19 cases and their problems can be handled in the information system.