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All Journal Journal of Applied Intelligent System JISA (Jurnal Informatika dan Sains) Jurnal Sistem Informasi dan Sains Teknologi
Gatot Tri Pranoto
Universitas Pelita Bangsa
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering

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Implementation of TF-IDF Algorithm and K-mean Clustering Method to Predict Words or Topics on Twitter Muhammad Darwis; Gatot Tri Pranoto; Yusuf Eka Wicaksana; Yaddarabullah Yaddarabullah
JISA(Jurnal Informatika dan Sains) Vol 3, No 2 (2020): JISA(Jurnal Informatika dan Sains)
Publisher : Universitas Trilogi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31326/jisa.v3i2.831

Abstract

The social media time line, especially Twitter, is still interesting to follow. Various tweets delivered by the public are very informative and varied. This information should be able to be used further by utilizing the topic of conversation trends at one time. In this paper, the authors cluster the tweet data with the TF-IDF algorithm and the K-Mean method using the python programming language. The results of the tweet data clustering show predictions or possible topics of conversation that are being widely discussed by netizens. Finally, the data can be used to make decisions that utilize community sentiment towards an event through social media like Twitter.  
The Design of a Monitoring Application System for The Production of Foam Products Using the UML And Waterfall Methods Henny Yulianti; Gatot Tri Pranoto
JISA(Jurnal Informatika dan Sains) Vol 4, No 2 (2021): JISA(Jurnal Informatika dan Sains)
Publisher : Universitas Trilogi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31326/jisa.v4i2.1045

Abstract

The development of information technology, which is followed by a higher level of competition in the foam product industry, encouraging companies to manage their company's resources properly and to plan effective, systematic and mature activities within the company. As a company with a variety of products, the most dominant problem is in the productivity process. Production is the most important part of a manufacturing company, where in carrying out its production activities this company produces based on orders from customers (Job Orders). And the problems that often occur are planning revisions in the midst of production and changing production schedules between groups (lines), delays in production planning in terms of prioritizing planning, and still being done manually in making daily reports. By implementing monitoring, which is the supervision and control of an activity where measurements and evaluations are completed repeatedly from time to time, monitoring is carried out for the purposes of the company and to maintain ongoing management. Monitoring will provide information about the status and trend of production activities towards the company's goals. The solution to this production problem is to build a web-based foam product production monitoring system application using the Waterfall method which is integrated with UML the method used is use case diagrams, activity diagrams, sequence diagrams, class diagrams and component diagrams and software development with PHP and MySQL technology. With Black box testing, it is proven that the design of this foam production monitoring system application can assist the company's foam product production activities in fulfilling customer orders and accurate reports so that it becomes effective and efficient. in improving the productivity and performance of the company.
Grouping of Village Status in West Java Province Using the Manhattan, Euclidean and Chebyshev Methods on the K-Mean Algorithm Gatot Tri Pranoto; Wahyu Hadikristanto; Yoga Religia
JISA(Jurnal Informatika dan Sains) Vol 5, No 1 (2022): JISA(Jurnal Informatika dan Sains)
Publisher : Universitas Trilogi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31326/jisa.v5i1.1097

Abstract

The Ministry of Villages, Development of Disadvantaged Areas and Transmigration (Ministry of Village PDTT) is a ministry within the Indonesian Government in charge of rural and rural development, empowerment of rural communities, accelerated development of disadvantaged areas, and transmigration. Village Potential Data for 2014 (Podes 2014) in West Java Province is data issued by the Central Statistics Agency in collaboration with the Ministry of Village PDTT which is in unsupervised data format, consists of 5319 village data. The Podes 2014 data in West Java Province were made based on the level of village development (village specific) in Indonesia, by making the village as the unit of analysis. Base on the Regulation of the Minister of Villages, Disadvantaged Areas and Transmigration of the Republic of Indonesia number 2 of 2016 concerning the village development index, the Village is classified into 5 village status, namely Very Disadvantaged Village, Disadvantaged Village, Developing Village, Advanced Village and Independent Village based on the ability to manage and increase the potential of social, economic and ecological resources. Village status is in fact inseparable from village development that is under government funding support. However, village development funds have not been distributed effectively and accurately according to the conditions and potential of the village due to the lack of clear information about the status of the village. Therefore, the information regarding the villages priority in term of which villages needs more funding and attention from the government is still lacking. Data mining is a method that can be used to group objects in a data into classes that have the same criteria (clustering). One of the algorithms that can be used for the clustering process is the k-means algorithm. Data grouping using k-means is done by calculating the closest distance from data to a centroid point. In this study, different types of distance calculation in the K-means algorithm are compared. Those types are Manhattan, Euclidean and Chebyshev. Validation tests have been carried out using the execution time and Davies Bouldin index. From this test, the data Village Potential 2014 in West Java province have grouped all the 5 status of the village with the obtained number of villages for each cluster is a cluster village Extremely Backward many as 694 villages, cluster Villages 567 villages, cluster village Evolving as much as 1440 villages, the cluster with Desa Maju1557 villages and the cluster Independent Village for 1061 villages. For distance calculation, Chebyshev has the most efficient accumulation time of 1 second compared to Euclidean 1.6 seconds and Manhattan 2.4 seconds. Meanwhile, the Euclidean method has the value, Davies Index most optimal which is 0.886 compared to the Manhattan method 0.926 and Chebyshev 0.990.
South German Credit Data Classification Using Random Forest Algorithm to Predict Bank Credit Receipts Yoga Religia; Gatot Tri Pranoto; Egar Dika Santosa
JISA(Jurnal Informatika dan Sains) Vol 3, No 2 (2020): JISA(Jurnal Informatika dan Sains)
Publisher : Universitas Trilogi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31326/jisa.v3i2.837

Abstract

Normally, most of the bank's wealth is obtained from providing credit loans so that a marketing bank must be able to reduce the risk of non-performing credit loans. The risk of providing loans can be minimized by studying patterns from existing lending data. One technique that can be used to solve this problem is to use data mining techniques. Data mining makes it possible to find hidden information from large data sets by way of classification. The Random Forest (RF) algorithm is a classification algorithm that can be used to deal with data imbalancing problems. The purpose of this study is to discuss the use of the RF algorithm for classification of South German Credit data. This research is needed because currently there is no previous research that applies the RF algorithm to classify South German Credit data specifically. Based on the tests that have been done, the optimal performance of the classification algorithm RF on South German Credit data is the comparison of training data of 85% and testing data of 15% with an accuracy of 78.33%.
Analysis of the Use of Particle Swarm Optimization on Naïve Bayes for Classification of Credit Bank Applications Yoga Religia Religia; Gatot Tri Pranoto; I Made Suwancita
JISA(Jurnal Informatika dan Sains) Vol 4, No 2 (2021): JISA(Jurnal Informatika dan Sains)
Publisher : Universitas Trilogi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31326/jisa.v4i2.946

Abstract

The selection of prospective customers who apply for credit in the banking world is a very important thing to be considered by the marketing department in order to avoid non-performing loans. The website www.kaggle.com currently provides South German Credit data in the form of supervised learning data. The use of data mining techniques makes it possible to find hidden patterns contained in large data sets, one of which is using classification modeling. This study aims to compare the classification of South German Credit data using the Naïve Bayes algorithm and compare the classification of South German Credit data using the Naïve Bayes algorithm with particle swarm optimization (PSO). The test was carried out using a confusion matrix to determine the accuracy, precision and recall values of the research model. Based on the test, it is known that PSO is able to increase the accuracy and recall of Nave Bayes, but PSO has not been able to increase the precision value of Nave Bayes. The test results show that PSO optimization gives Naïve Bayes an increase in the value of accuracy by 0.46%, and gives Naïve Bayes an increase in recall value by 3.02%. 
FORECASTING WITH WEIGHTED MOVING AVERAGE METHOD FOR PRODUCT PROCUREMENT STOCK Amali Amali; Gatot Tri Pranoto; Muhammad Darwis
Jurnal Sistem Informasi dan Sains Teknologi Vol 4, No 2 (2022): Jurnal Sistem Informasi dan Sains Teknologi
Publisher : Universitas Trilogi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31326/sistek.v4i2.1268

Abstract

ABSTRACTDhanty Store is a family start-up located in East Jakarta. It was initiated in 2018, engaged in retail with the main product in the form of women's clothing and accessories. One of the important processes in Dhanty Store operations is the product procurement process. Currently, Dhanty Store request products according to their wishes without looking at their sales data. This causes their product stock is not well controlled. When there is a lot of demand, sometimes Dhanty Shops run out of stock so their customers will move to other stores. In addition, the process of requesting and procuring products to suppliers also takes a long time so that it can further disrupt the operations of Dhanty Store. This study develops a forecasting application prototype with the Weighted Moving Average method to assist Dhanty Store in the process of requesting and procuring their products. Forecasting results in the period (t) of the 1st week of January were 275 products. In addition, this study predicts product stock with a 4-week moving average and the MAD tracking signal value is ranged from -1.51 to 3.86 and the MAPE value is 35.4%. As for the reliability and level of user acceptance of the prototype model in this study, tested using the System Usability Scale (SUS) method and it is known that the average value given by respondents was 82 with details 0% considered inappropriate, 40% considered neutral and 60% rated it according to need.                                                                                                                                                        Keywords: data mining, forecasting, weighted moving average, MAD, MAPE, SUS
Manhattan, Euclidean And Chebyshev Methods In K-Means Algorithm For Village Status Grouping In Aceh Province Amali Amali; Gatot Tri Pranoto
Journal of Applied Intelligent System Vol 7, No 3 (2022): Journal of Applied Intelligent System
Publisher : Universitas Dian Nuswantoro and IndoCEISS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33633/jais.v7i3.7037

Abstract

The Ministry of Villages, Development of Disadvantaged Regions and Transmigration (Ministry of Villages PDTT) is a ministry within the Government of Indonesia in charge of developing villages and rural areas, empowering rural communities, accelerating the development of disadvantaged areas, and transmigration. The 2014 Village Potential Data (Podes 2014) is data released by the Central Statistics Agency in collaboration with the Ministry of Villages PDTT in unsupervised form and consists of 6474 villages in the province of Aceh. Podes 2014 data is based on the level of village development (village specific) in Indonesia by using the village as the unit of analysis. Data mining is a method that can be used to group objects in a data into classes that have the same criteria (clustering). One of the algorithms that can be used for the clustering process is the k-means algorithm. Grouping data using k-means is done by calculating the shortest distance from a data point to a centroid point. In this study, a comparison of the distance calculation method on k-means between Manhattan, Euclidean and Chebyshev will be carried out. Tests will be performed using the execution time and the davies boulder index. From the tests that have been carried out, it is found that the number of villages in each cluster is 2,639 developing villages, 1,188 independent villages, 1,182 very underdeveloped villages, 1,266 developed villages and 199 disadvantaged clusters. The Chebyshev distance calculation method has the most efficient accumulation of time compared to Manhattan and Euclidean, while the Euclidean method has the most optimal Davies Index.
Decision Support System Recommendation Housing Using AHP And Saw Method Palangka Raya City Gatot Tri Pranoto; Ismasari Nawangsih; Edy Widodo
Journal of Applied Intelligent System Vol 7, No 3 (2022): Journal of Applied Intelligent System
Publisher : Universitas Dian Nuswantoro and IndoCEISS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33633/jais.v7i3.7038

Abstract

Palangka Raya City, as one of the provincial capitals in Indonesia, which has an area of around 2,400,000 km2, is a strategic city as a service and distribution hub for the industrial, trade, government and education sectors. Regional Policy of the city government with the existence of a development plan in the City of Palangka Raya as an implementation of the city space with all the disadvantages of its designation resulting in a distribution pattern of urban land types which in fact is not evenly distributed throughout the city. This research was conducted based on the results of observations made in several Marketing Agents in the Palangkaraya Region, which included 5 districts where the survey results obtained several marketing agents for KPR housing with the aim of facilitating the purchase of KPR housing. The purpose of this study is to design a decision support system that is used to support the decision to purchase housing loans in the Palangkaraya area. Based on the research that has been done, it is expected that the results of the purchase decision support system for the KPR recommendation with the best value can be a recommendation for the purchase. This system is designed with the AHP and SAW methods to help prospective residents to determine the house based on the desired criteria.
Comparison of Holt Winters and Simple Moving Average Models to Identify the Best Model for Predicting Flood Potential Based on the Normalized Difference Water Index Raka Hikmah Ramadhan; Roni Yusman; Gatot Tri Pranoto
JISA(Jurnal Informatika dan Sains) Vol 5, No 2 (2022): JISA(Jurnal Informatika dan Sains)
Publisher : Universitas Trilogi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31326/jisa.v5i2.1316

Abstract

Flood is a condition in which water cannot be accommodated in a drainage channel such as a river or river. An area is said to be flooded if the water in the area is inundated in large quantities so that it can cover all or most of a large area. Determining forecasting or prediction on a potential in the long or short term, especially changes in water content levels in an area, requires a method, model, or approach that must be well tested. The lower the error value in a model, the better the model for testing a forecast. One of the data that can be used for analysis of potential flood models is the use of remote sensing data with technology from Landsat 8. The advantage of sensing data from Landsat 8 is that it has data good history and allows to see changes in land cover from year to year in an area. The purpose of this study was to determine the best model for forecasting the potential for flooding in an area using the Holt Winters model and the Simple Moving Average. The result of this research is that the RMSE, MAE, MAPE, MSE values in the Holt Winters model are 0.03598683, 0.02748707, 0.13944356, 0.00129505 while the RMSE, MAE, MAPE, MSE values on the Simple Moving Average are 0, 09681483, 0.06338657, 0.53775228, 0.00937311. The Holt Winters model is the best model of the Simple Moving Average because the forecast error value has a low value. 
Decision Support System to Select the Best Customers Using Analytical Hierarchy Process (AHP) Methods, Simple Additive Weighting (SAW) Methods, Weight Aggregated Sum Product Assessment Methods (WASPAS) at the Kebaya Shop Syafran Nurrahman; Gatot Tri Pranoto; Tjahjanto Tjahjanto; Samidi Samidi
JISA(Jurnal Informatika dan Sains) Vol 5, No 2 (2022): JISA(Jurnal Informatika dan Sains)
Publisher : Universitas Trilogi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31326/jisa.v5i2.1317

Abstract

Style Queen Kebaya Store (SQ Kebaya) is a store that is engaged in apparel, its product sales focus includes adult and children's kebaya. The negative impact of the Covid 19 Pandemic has proven that the Store (SQ Kebaya) has experienced a decline in sales turnover in 2020, therefore the SQ Kebaya Store's efforts to restore its sales activities are by giving gifts for customer appreciation during the COVID 19 season through selecting the best customers for the 2020 period. However, the problem faced by SQ Kebaya Stores in the process of evaluating the best customer selection is that there is no criterion weight so that the decision making is not right on target, making the best customer decisions less efficient because they have to look for customer sales records manually in the sales record book. This study produces a web-based decision support system for selecting the best customers at SQ Kebaya Stores using the AHP (criteria weight), SAW and WASPAS (best customer ranking) methods, this study produces priority weights and importance levels of each criterion, namely status (0.37 ), method of payment (0.23), total spending (0.14), quantity (0.13), intensity of visits (0.07), length of subscription (0.07) and the result of ranking the percentage of the largest alternative value is the alternative SAW method with an average of 0.6952 , while the WASPAS method is 0.6405. It can be concluded that the right method used to obtain the best alternative value is the SAW method.  
Co-Authors Amali Amali Edy Widodo Egar Dika Santosa I Made Suwancita Muhammad Darwis Muhammad Darwis Nawangsih, Ismasari Raka Hikmah Ramadhan Roni Yusman Samidi Samidi Syafran Nurrahman Tjahjanto Tjahjanto Wahyu Hadikristanto Yaddarabullah Yaddarabullah Yoga Religia Yoga Religia Religia Yulianti, Henny Yusuf Eka Wicaksana