ANALISIS SENTIMEN PENGGUNA MEDIA SOSIAL TERHADAP APLIKASI M-HEALTH PEDULI LINDUNGI DENGAN METODE LEXICON BASED DAN NAÏVE BAYES

Pedulilindungi atau satusehat merupakan aplikasi yang dirilis secara resmi guna menangani penyebaran virus Covid-19 dan vaksinasi. Namun, dikarenakan suatu insiden besarnya kebocoran data pribadi, terutama identitas pribadi, kepercayaan masyarakat terhadap aplikasi tersebut sangat rendah. Untuk mengetahui pendapat masyarakat saat ini maka dilakukanlah penelitian dengan mengkombinasikan metode Lexicon Based dan Naïve Bayes. Hasil klasifikasi sentiment memperoleh nilai yaitu 62% negative, 32% netral, 6% positif pada Tiktok. 56% negative, 37% netral, 7% positif pada Youtube. 100% positif pada Twitter, dengan jumlah keseluruhan 118 skor negative, 69 skor netral, 113 skor positif, maka dapat disumpulkan masyarakat memiliki opini negative pada aplikasi peduli lindungi. Hasil evaluasi kinerja model memperoleh akurasi 91%, presisi 94%, recall 82%, f1_scores 86% pada Tiktok, pada Youtube akurasi sebesar 90%, presisi 93%, recall 81%, f1_scores 84%. Pada Twitter akurasi 70%, presisi 23%, recall 33%, f1-scores 28%. Pengkombinasian metode Lexicon Based dan Naïve Bayes ini memiliki akurasi yang sangat tinggi pada media sosial Tiktok dan Youtube, sehingga untuk penelitian selanjutnya pada media sosial Twitter perbanyak data yang diambil. Juga penelitian ini diharapkan dapat membantu membangun kembali aplikasi supaya lebih optimal

IMPLEMENTASI METODE K-NEAREST NEIGHBOUR (K_NN) UNTUK MENDUGA SALINITAS AIR LAUT

In this research, trying to predict the salinity of sea water using the K-Nearest Neighbor method, this method serves to clarify the input data using the distance measurement method with training data, the variable used in this study is the value of the location of coordinates (latitude and longitude) and the output is in the form of salinity, the case study in this study is the southern waters of Sumenep, the system has been able to make an estimate but with an error rate of 1.00 so that there is a need for re-analysis because the data used is only small, the need for additional data so that the results will be more optimal, it is also necessary to experiment with changing methods or simplifying rules or by adding input variables in the system that have been created so that it produces better accuracy values, because the existing system still requires a long time in estimating

SISTEM CERDAS PENDUGAAN SALINITAS AIR LAUT BERDASARKAN CITRA LANDSAT MENGGUNAKAN METODE Adaptive Neuro Fuzzy Inference System ( ANFIS )

Abstract. The purpose of this research is to predict the sea surfce salinity, so that it can be used as refractory material for salt production. Salinity is the soluble salt content in water and the suitable the salinity standard in salt industry will give an impact on the quality of the salt produced. The method of this research is Adaptive Neuro Fuzzy Inference System (ANFIS). The system in this research works by extracting landsat 8 image to produce some value variable which is used as dataset in ANFIS system such as red , green, blue, Longitude and Latitude value. Its dataset will be divided to training and testing data. Training data is used to train the ANFIS system while testing data is used to measure the prediction accuracy resulted by ANFIS. in order to know the level of accuracy by using Root Means Square Error ( RMSE ) method is used to measure the accuracy level. The system has been able to make predictions with error rate of 2,0267 in average.Keywords: Salinity, Landsat Image, Smart System, ANFIS.Abstrak. Penelitian ini bertujuan untuk memprediksi salinitas air laut yang bisa dijadikan sebagai bahan refrensi untuk produksi garam. Salinitas adalah kadar garam terlarut dalam air, dengan salinitas yang sesuai standart dalam industri garam akan berdampak pada kualitas garam yang dihasilkan. Metode yang digunakan dalam penelitian ini adalah Adaptive Neuro Fuzzy Inference System ( ANFIS ). Sistem kerja dalam penelitian ini dengan mengekstraksi citra landsat 8 sehingga menghasilkan beberapa variabel yang dijadikan sebagai dataset dalam sistem ANFIS diantaranya adalah variabel red, green, blue, Longitude dan Latitude. Dataset tersebut akan dibagi menjadi data Training dan data Testing. Data Training digunakan untuk melatih sistem ANFIS sedangkan data Testing digunakan untuk mengukur akurasi prediksi yang dihasilkan oleh ANFIS. Pengukuran tingkat akurasi menggunakan metode Root Means Square Error ( RMSE ). Sistem yang dibuat telah mampu melakukan prediksi dengan tingkat error rata – rata 2,0267.Kata Kunci: Salinitas, Citra Landsat, Sistem Cerdas, ANFIS

SISTEM PENDUKUNG KEPUTUSAN PENENTUAN POTENSI ANGIN UNTUK PEMBANGKIT LISTRIK TENAGA BAYU (PLTB) MENGGUNAKAN METODE FUZZY MAMDANI

In order to assist the government in realizing the use of 23% of EBT in 2025, to support the direction of government policies and strategies to improve accessibility by providing electricity to remote islands and villages. so in this study, the researcher makes a decision support system for determining the potential of renewable energy, especially energy produced by wind for wind power plants, The method used in this research is the Mamdani Fuzzy Logic, a system consisting of 3 Input criteria, including wind speed, temperature and air pressure, and output is potential for wind energy, output is presented in the form of a percentage unit with a range of 0-100%. The research was conducted in Sumenep Regency, after processing with the Fuzzy Mamdani method, the value of wind potential was generated with the average of the total output data = 43.51%, the value of min = 31.27%, the max value = 49.87%

SENTIMENT ANALYSIS ON LGBT ISSUES IN INDONESIA WITH LEXICON-BASED AND SUPPORT VECTOR MACHINE ALGORITHMS

Non-heterosexual sexual orientation (LGBT) behavior today is one of the most pervasive issues in Indonesian culture. Because of its domino effect on social stability and physical and mental health, the phenomenon known as lesbian, gay, bisexual, and transgender (LGBT) has always been under scrutiny. The development of LGBT people in Indonesia reflects cultural changes that concern many people. Freedom of speech for LGBT people on social media has many public implications. Observation of this phenomenon gives rise to views of anomalies and discrepancies that have drawn criticism. Various attempts have been made to prevent the movement of LGBT people. However, until now, many still debate the pros and cons of this LGBT movement. The lexicon-based method uses a support vector machine to classify public opinion in TikTok video comments about LGBT issues. The lexicon-based method is used as a weighting method, and the support vector machine method is used as a classification method. The results show that the highest gain in sentiment is neutral, with percentage values of 61%, 56%, 68%, 69%, and 63%. The second is positive sentiment, with percentage values of 27%, 27%, 20%, 20%, and 29%. The rest have negative sentiments. With a relatively high accuracy of the five data sets sequentially at 93%, 89%, 95%, 97%, and 91%. This shows that the majority of Indonesians prefer to ignore the issue

Penyerapan Tenaga Kerja Di Provinsi Jawa Timur

One of the factors that can create a successful economic development in each country is the absorption of labor. The high absorption of labor will reduce poverty and increase economic growth. Thus, the government of each country is no exception, Indonesia is also carrying out reforms related to labor issues. The purpose of this study is to determine the factors that influence employment in East Java Province in 2010-2017. The variables used in this study are employment, wages, population and Gross Regional Domestic Product (GRDP). The data used in this study is panel data. The analysis tool used is panel data regression analysis. The results in this study using the model fixed effect are the variables of labor, wages, population and Gross Regional Domestic Product (GRDP) affecting employment in East Java. The policy recommendations carried out are programs to improve the quality and productivity of the workforce, programs for expanding and placing workers, developing industrial relations and sharia work programs and labor inspection and labor protection programs

Smart Drip Irrigation System Based on IoT Using Fuzzy Logic

The absence of a water drip rate control system in drip irrigation systems has impacted water use efficiency and normalization of soil moisture. Therefore, this research aims to develop an intelligent system using the fuzzy logic method to control the rate of water droplets in a drip irrigation system and maintain soil moisture in normal conditions. The DHT22 sensor is used to obtain temperature and humidity values, which are then used as input data and processed by the ESP32 microcontroller, which includes a fuzzy system. The Internet of Things (IoT) is also used to send data from the microcontroller to the Thingspek web server. The Blynk application is used to make it easier to monitor temperature, humidity, and water droplet rate values. The results of this research show that the temperature accuracy values produced using the MSE evaluation were 6.66667 and RMSE were 2.58199, while for temperature, the values for MSE were 0.128333 and RMSE were 0.358236. The average value of soil moisture produced in the planting medium is 44.46%; this value is within normal conditions for chili plants, where normal soil moisture conditions range between 40% - 60

K-Means Clustering and Multilayer Perceptron for Categorizing Student Business Groups

The research conducted in this study was driven by the East Java provincial government's requirement to assess the transaction levels of the Student Business Group (KUS) in the SMA Double Track program. These transaction levels are a basis for allocating supplementary financial aid to each business group. The system's primary objective is to assist the provincial government of East Java in making well-informed choices pertaining to the distribution of supplementary capital to the KUS. The classification technique employed in this study is the multilayer perceptron. However, the K-Means Clustering method is utilised to generate target data due to the limited availability during the classification process, which involves dividing the transaction level attributes into three distinct groups: (0) low transactions, (1) medium transactions, and (2) high transactions. The clustering process encompasses three distinct features: (1) income, (2) spending, and (3) profit. These three traits will be utilized as input data throughout the categorization procedure. The classification procedure employing the Multilayer Perceptron technique involved processing a dataset including 1383 data points. The training data constituted 80% of the dataset, while the remaining 20% was allocated for testing. In order to evaluate the efficacy of the constructed model, the training error was assessed using K-Fold cross-validation, yielding an average accuracy score of 0.92. In the present study, the categorization technique yielded an accuracy of 0.96. This model aims to classify scenarios when the dataset lacks prior target data

Analysis and Development of Seawater Density Measurement Algorithm Using Arduino Uno and YL-69 Sensor

In this research, an analysis was carried out to develop a measuring instrument for seawater density in salt production using a microcontroller (Arduino Uno) and YL-69 sensor, this sensor was commonly used to measure soil moisture. The experimental method was used in this research to produce initial data in the form of resistance and seawater density values, then calculations are carried out using statistical methods to find equations and produce a constant variable that connects the resistance and seawater density values. The equation was used to compile the algorithm into Arduino Uno. As for the results of this research,  From six experiments conducted, two experiments produced the same sea water density value between the actual and the predicted, namely the 2nd and 5th experiments, while for other experiments there was a difference between the actual and predicted values, however, it was not too significant, the difference occurs between the value range 0 ~ 1, to determine the level of error, use the Mean Square Error (MSE) with an error level of = 0.5 and Mean Absolute Error (MAE) with an error level of = 0.6. The contribution of this research is an algorithm that can predict the density value (baume) based on the resistance value obtained from the YL 69 sensor.In this research, an analysis was carried out to develop a measuring instrument for seawater density in salt production using a microcontroller (Arduino Uno) and YL-69 sensor, this sensor was commonly used to measure soil moisture. The experimental method was used in this research to produce initial data in the form of resistance and seawater density values, then calculations are carried out using statistical methods to find equations and produce a constant variable that connects the resistance and seawater density values. The equation was used to compile the algorithm into Arduino Uno. As for the results of this research,  From six experiments conducted, two experiments produced the same sea water density value between the actual and the predicted, namely the 2nd and 5th experiments, while for other experiments there was a difference between the actual and predicted values, however, it was not too significant, the difference occurs between the value range 0 ~ 1, to determine the level of error, use the Mean Square Error (MSE) with an error level of = 0.5 and Mean Absolute Error (MAE) with an error level of = 0.6. The contribution of this research is an algorithm that can predict the density value (baume) based on the resistance value obtained from the YL 69 sensor

Seleksi Karyawan Baru Menggunakan Metode Composite Perfomence Index (CPI ) dan Rank Order Centroid (ROC)

Abstrak: Dalam meminimalisir kesalahan serta subyektifitas keputusan untuk seleksi karyawan baru diperlukan sebuah system pendukung keputusan (Decision Support System / DSS ) yang dapat membantu bagian SDM untuk memutuskan karyawan yang akan diterima atau tidak, dalam penelitian ini digunakan kombinasi metode Composite Perfomence Index (CPI) dan Rank Order Centroid (ROC) pada system pembobotannya, luaran dari penelitian  ini adalah  berupa nilai perengkingan, dimana dari empat alternatif yang dihitung, alternatif pertama yaitu A1 memiliki rengking tertinggi dengan nilai 145,25 dikuti oleh A2 dengan nilai 140,25, selanjutnya A4 dengan nilai 128,3 dan rengking terahir adalah A3 dengan nilai 126. Dapat disimpulkan bahwa metode Composite Perfomence Index (CPI) yang dikombinasikan dengan metode Rank Order Centroid (ROC) dalam system pembobotan setiap kriteria dapat melakukan perengkingan yang baik dan mampu meminimalisir subjektifitas dari sistem pembobotan secara manual.Kata Kunci : DSS, CPI, ROC, Seleksi Karyawan baruAbstract: In minimizing errors and decision subjectivity for the selection of new employees, a decision support system (Decision Support System / DSS) is needed that can help the HR department to decide which employees will be accepted or not, in this study used a combination of Composite Performance Index (CPI) and Rank Order Centroid (ROC) in the weighting system, the output of this study is in the form of a ranking value, where of the four alternatives calculated, the first alternative, namely A1 has the highest ranking with a value of 145.25, followed by A2 with a value of 140.25, then A4 with a value of 128.3 and the last rank is A3 with a value of 126. It can be concluded that the Composite Performance Index (CPI) method combined with the Rank Order Centroid (ROC) method in the weighting system of each criterion can perform a good ranking and is able to  minimize the subjectivity of the weighting system as a whole. manually .Keywords: DSS, CPI, ROC, New Employee Selectio