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All Journal Dinamik TELKOMNIKA (Telecommunication Computing Electronics and Control) JAIS (Journal of Applied Intelligent System) Proceeding SENDI_U Journal of Information System Sinkron : Jurnal dan Penelitian Teknik Informatika Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer Abdimasku : Jurnal Pengabdian Masyarakat SKANIKA: Sistem Komputer dan Teknik Informatika Jurnal Informatika Polinema (JIP) Jurnal Informatika: Jurnal Pengembangan IT Seminar Nasional Riset dan Teknologi (SEMNAS RISTEK)
Hadi, Heru Pramono
Universitas Dian Nuswantoro
Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Health Professions Industrial & Manufacturing Engineering Library & Information Science Social Sciences Other

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Journal : JAIS (Journal of Applied Intelligent System)

 1 
Visitor Prediction Decision Support System at Dieng Tourism Objects Using the K-Nearest Neighbor Method Eko Hari Rachmawanto; Christy Atika Sari; Heru Pramono; Wellia Shinta Sari
Journal of Applied Intelligent System Vol 7, No 2 (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.v7i2.6821

Abstract

A tourist target is anything that attracts a visitor or tourist to come to visit a place or area. Tourism goods play an important role in a country or region, becoming a source of national foreign exchange, increasing human resources, and improving the economy of surrounding communities. The problem posed in this study is how to implement a decision support system in predicting visitor numbers for Dieng tourists using the k-nearest neighbor method. The purpose of this study is to help the local government and surrounding communities to improve facilities such as restaurants, places of worship, parking lots, clean toilets so that tourists can feel safe and comfortable when visiting Dieng. Helps manage tourism targets. is what you give. These attractions using a decision support system as a process to predict visitors. The number of visitors who visited in December 2017 was 421,394, which serves as a reference for predicting the number of visitors who will visit Dieng in the following year. The predicted result is 29569.25 visitors with a parameter value of k = 8 and a minimum RMSE value of k = 1/0.
Learning Vector Quantization for Robusta and Arabica Coffee Classification Jatmoko, Cahaya; Sinaga, Daurat; Lestiawan, Heru; Hadi, Heru Pramono
Journal of Applied Intelligent System Vol. 8 No. 2 (2023): 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.v8i2.7343

Abstract

ANN or artificial neural network is a way to solve various kinds of problems to make decisions based on training. One of the methods of JSt which contains competitive and supervised learning. Where this layer will automatically learn the classification of the closest input distances and will be distributed to the same class. there are 2 types of coffee beans that are famous in the world, namely arabica and robusta, for some people or the layman it will be very difficult to distinguish these 2 types of coffee beans apart from the fact that the shape is almost the same the color looks almost the same but there are a number of differences in the two coffee beans which we can see from the shape of the seed. Robusta has a shape that tends to be round and smaller in size, and has a rougher texture. Arabica, on the other hand, is slightly flatter and longer in shape. The size is slightly bigger than Robusta but the texture of Arabica is smoother than Robusta. This is the basis of this study where the images of the two coffee beans will be extracted using the first-order texture feature extraction method based on MU parameters, standard deviation, skewness, energy, entropy, and smoothness. The method for collecting data was in the form of a quantitative method using images from each coffee bean, both Arabica and Robusta, with a total of 130 images. The comparison between training_data and test_data is 80:20. Through research conducted in the form of performance parameters with the best accuracy, including: Learning rate 0.01, max epoch or maximum iteration of 10 and 30%, the amount of training data used is 39 training images and 26 test images resulting in an accuracy presentation of 71% for the training process and error with a percentage of 96% for the test process.
Customer Segmentation Using K-Means Clustering with RFM Method (Case Study : PT. Dewangga Travindo Semarang) Winaryanti, Hida Sekar; Hadi, Heru Pramono; Rachmawanto, Eko Hari
(JAIS) Journal of Applied Intelligent System Vol. 9 No. 1 (2024): Journal of Applied Intelligent System
Publisher : LPPM Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/jais.v9i1.10440

Abstract

PT. Dewangga Travindo is a company that operates in the field of travel services which includes tours, travel, and Hajj and Umrah pilgrimages which is based in the city of Semarang and has received permission from the Ministry of Religion No. D/606 of 2013. Every year there is always an increase in sales of services. Hajj and Umrah. The higher transaction activity every day results in a large buildup of data in the database which will only become data waste. The ability to process data is increasingly sophisticated using data mining, which is an activity of looking for relationships between items to obtain patterns as information to assist in decision making. However, considering the large number of competitors offering the same services, it is necessary to increase competitiveness to overcome market segmentation at PT Dewangga Travindo. For this reason, this research was carried out which aims to overcome customer segmentation using the Clustering method with the K-Means algorithm which produces a visual cluster model with RStudio tools using RFM attributes applied to carry out segmentation. The data used in this research is data on Hajj and Umrah pilgrims in the 2018-2020 period.
Co-Authors Ali, Rabei Raad Alzami, Farrikh Ashari, Ayu Aufa Nisrina Aulia Azzami, Salman Yuris Adila Cahaya Jatmoko Cahyani, Tiara Indah Candra Irawan Christy Atika Sari Edi Faisal Edi Faisal Edi Sugiarto Edi Sugiarto Eko Hari Rachmawanto Fahmi Amiq Faisal, Edi Heru Lestiawan Hindarto, Aris Nur Indra Gamayanto Jannah, Safira Baiti Maurensa, Giacinta Moh. Yusuf, Moh. MY. Teguh Sulistyono Nurhindarto, Aris Pristyanti Nawang Putri R. Septiansa Anggoro Rabei Raad Ali Rohman, M. Hilma Minanur Sasono Wibowo Sinaga, Daurat Suhariyanto - Suhariyanto Suhariyanto Sukamto, Titien Sukamto, Titien Suhartini Titien S. Sukamto Titien Sukamto Wellia Shinta Sari Winaryanti, Hida Sekar Zaenal Arifin