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All Journal Jurnal Mahasiswa Matematika JAIS (Journal of Applied Intelligent System) Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Abdimasku : Jurnal Pengabdian Masyarakat Jurnal Inovasi Penelitian Mathematics and Applications (MAp) Journal Jurnal Citrakara Jurnal Ekonomi dan Manajemen (JEKMA) Sejahtera: Jurnal Inspirasi Mengabdi Untuk Negeri Journal of Artificial Intelligence and Digital Business Jurnal Pengabdian Kepada Masyarakat Synergy In Economic & Business Management (SEBM)
Dibyo Adi Wibowo
Fakultas Ilmu Komputer Universitas Dian Nuswantoro
Agriculture, Biological Sciences & Forestry Arts Humanities Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Mathematics Social Sciences Other

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Journal : JAIS (Journal of Applied Intelligent System)

 1 
Implementation of the K-Nearst Neighbor (k-NN) Algorithm in Classification of Angora and Country Cats Andriana, Wiwin; Wisnumurti, Reza; Lestari, Yuni; Indriyono, Bonifacius Vicky; Wibowo, Dibyo Adi; Udayanti, Erika Devi
Journal of Applied Intelligent System Vol. 8 No. 1 (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.v8i1.7129

Abstract

There are so many types of mixed cats from various cat breeds, so many people find it difficult to identify and classify them. Therefore, we need a method that can classify the type of cat breeds. In this study the authors used the K-Nearest Neighbor (k-NN) algorithm to make it easier to recognize and classify cat breeds based on certain characteristics. The author took samples of 2 types of cat races, namely the Anggora race and the Kampung race. The implementation stage is to determine the euclidean distance and sort it and then determine the value of K to find the nearest neighbor. In testing, the authors used 50 training data and 50 test data with 6 attributes used, namely body shape, nose width, nose height, food type, hair type and hair length. The results of the classification of cat breeds using the k-NN method obtained an accuracy rate of 94% and an error rate of 6%.
Poverty Modeling in East Java Province Using the Spatial Seemingly Unrelated Regression (Sur) Method Wibowo, Dibyo Adi; Hidajat, Moch Sjamsul; Widyatmoko, Widyatmoko
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.v8i2.8178

Abstract

Poverty is a complex problem because it relates to various aspects of human life. In Indonesia, there is one province that has a very high percentage of poverty, namely East Java Province. Although from year to year the poverty rate has decreased, when viewed from the national level it is still very far from the government's expectations of reducing the poverty rate. Cases of poverty can be modeled by Econometrics. Econometric models are often applied to problems involving one or more related equations. One method that can be used to solve several interrelated equations because there is a correlation error regression between one another, namely Seemingly Unrelated Regression which is usually abbreviated as SUR, in this case Spatial Seemingly Unrelated Regression (SUR-Spatial) is development that takes into account the spatial influence between locations. From the results of tests conducted in the SUR-Spatial Lagrange Multiplier model, the poverty data generated by the East Java Province is the SUR-Spatial Autoregressive Model (SUR-SAR). So with the SUR-SAR model it can be seen that the variable that has a significant effect on the percentage of poor people is the growth rate of Gross Regional Domestic Product based on the constant price of the minimum wage for each district, as well as the average length of school years. Meanwhile, the Poverty Depth Index has an effect because of the growth rate of Gross Regional Domestic Product on the basis of constant prices and the average length of schooling. The Poverty Severity Index is influenced by the growth rate of Gross Regional Domestic Product at constant prices and average years of schooling.
Covid-19 Classification using Convolutional Neural Networks Based on Adam, RMSP, and SGD Optimalization Hidajat, Moch Sjamsul; Wibowo, Dibyo Adi
(JAIS) Journal of Applied Intelligent System Vol. 8 No. 3 (2023): Journal of Applied Intelligent System
Publisher : LPPM Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33633/jais.v8i3.9492

Abstract

In this comprehensive study, a meticulous analysis of the application of Convolutional Neural Network (CNN) methodologies in the classification of Covid-19 and non-Covid-19 cases was conducted. Leveraging diverse optimization techniques such as RMS, SGD, and Adam, the research systematically evaluated the performance of the CNN model in accurately discerning intricate patterns and distinct features associated with Covid-19 pathology. the implementation of the RMS and Adam optimization methods resulted in the highest accuracy levels, with both models achieving an impressive 98% accuracy in the classification of Covid-19 and non-Covid-19 cases. Leveraging the robust capabilities of these optimization techniques, the study successfully demonstrated the effectiveness of the RMS and Adam models in enhancing the precision and reliability of the Convolutional Neural Network (CNN) for the accurate identification and differentiation of Covid-19 patterns within the medical imaging datasets. The notable achievement of 98% accuracy further emphasizes the potential of these optimization methods in advancing the capabilities of CNN-based diagnostic tools, thus contributing significantly to the ongoing efforts in Covid-19 diagnosis and management.  
Predicting Gold Price Movement Using Long Short-Term Memory Model Nagata, Azaria Beryl; Hidajat, Moch Sjamsul; Wibowo, Dibyo Adi; Widyatmoko, Widyatmoko; Yaacob, Noorayisahbe Bt Mohd
(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.v9i1.10305

Abstract

Gold, as a valuable commodity, has been a primary focus in the global financial market. It is often utilized as an investment instrument due to the belief in its potential price appreciation. However, the unpredictable and complex movement of gold prices poses a significant challenge in investment decision-making. Therefore, this research aims to address this issue by proposing the use of the Long Short-Term Memory (LSTM) model in time series analysis. LSTM is a robust approach to understanding patterns and trends in gold price data over time. In the context of time series analysis, historical gold price data includes daily, weekly, and monthly datasets. Each model with its respective dataset is useful for identifying patterns in gold prices. The daily model achieves an MSE of 452.2284140627481 and an RMSE of 21.26566279387379. The weekly model achieves an MSE of 1346.1816584357384 and an RMSE of 36.69034830082345. The monthly model achieves an MSE of 11649.597907584808 and an RMSE of 107.93330305139747. With these RMSE results, the LSTM model can predict gold prices effectively. Based on the trained models, it can also be concluded that gold prices exhibit long-term temporal dependence.
Data Mining Application Analyzing Customer Purchase Patterns Using The Apriori Algorithm Prayugo, Moh. Lambang; Wibowo, Dibyo Adi; Hidajat, Moch. Sjamsul; Mintorini, Ery; Ali, Rabei Raad
(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.v9i1.10308

Abstract

The study aims to implement Data Mining with Apriori Algorithm and Association Methods (shop cart analysis) to analyze the sales pattern of Kaffa Beauty Shop stores as a case study. Sales information obtained from stores is used to find out the repeated buying habits of cosmetic products. This analysis provides store owners with valuable information to make more useful decisions about product inventory management, marketing strategies, and other aspects of their business. The Apriori Algorithm implementation follows steps including data preprocessing, subsetting, frequent dataset search, and strong association rules (strong Association Rules). The results of the analysis show that there are important purchasing patterns among some cosmetic products that can be the basis of a more effective sales strategy. The study helps understand how data mining and Apriori Algorithms can be applied in business contexts such as Kaffa Beauty Shop stores. Therefore, the results of this analysis are expected to contribute greatly to improving business efficiency and optimizing marketing strategies for store owners and stakeholders. The research is also expected to show the enormous potential of data analysis to support optimal business decision making.
Co-Authors Ali, Rabei Raad Alvina Dwi Andriani Andriana, Wiwin Bonifacius Vicky Indriyono Bonifacius Vicky Indriyono, Bonifacius Vicky Daffa Hafizh Dzamara Erika Devi Udayanti Ery Mintorini Imam Nurhadi Purwanto Iqlima Zahari Khamadi Khamadi Khamadi Moch. Sjamsul Hidajat Muhammad Burhanudin, Muhammad Nagata, Azaria Beryl Natanael, David Febrian Pratama, Zudha Prayugo, Moh. Lambang Puspa, Silfi Andriana Rizky Nur Annisa Sayyidah Farhani Tri Esti Rahayuningtyas Widyatmoko Widyatmoko Wildan Mahmud Wisnumurti, Reza Yaacob, Noorayisahbe Bt Mohd Yanuar Rahman Yulianti, Shinta Rachma Yuni Lestari