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Journal of Intelligent Software Systems
Published by Universitas Teknologi Digital Indonesia
ISSN : -     EISSN : 29627702     DOI : https://doi.org/10.26798/jiss
Core Subject : Science,
Computer Science & IT
Journal of Intelligent Software Systems (JISS) is open access, peer-reviewed international journal that will consider any original scientific article that expands the field of Intelligent Software Systems. The journal publishes articles in all Intelligent Software Systems specialities of interest to Intelligent Software Systems, physicians, and researchers.
Arjuna Subject : Ilmu Komputer (semua kategori) - Kecerdasan Buatan
Articles 5 Documents
 1 
Search results for , issue "Vol 2, No 1 (2023): July" : 5 Documents clear
ETL DATA WAREHOUSE ON PERFORMANCE MEASUREMENT Linda Sutriani; Indra Yatini Buryadi; Hera Wasiati; Sudarmanto Sudarmanto
Journal of Intelligent Software Systems Vol 2, No 1 (2023): July
Publisher : LPPM UTDI (d.h STMIK AKAKOM) Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26798/jiss.v2i1.928

Abstract

Measuring the organizational performance correctly will turn out accurate data which can be analyzed to provide important information for management in making decisions to improve company performance. This is because in the past, the performance appraisal was only based on financial measures. However, the editor-in-chief needs more than just financial indicators to improve performance. The Balanced Scorecard measures four organizational dimensions, such as customer, financial, internal business, and learning and growth. Although originally designed for the private sector, many public organizations apply it as modifications to suit their needs. The Balanced Scorecard is an effective method for measuring and achieving organizational performance. This research involves ETL in the Balanced Scorecard process to combine data from various sources into a large central repository called a data warehouse
Mushroom Image Classification Using C4.5 Algorithm Cucut Hariz Pratomo; Widyastuti Andriyani
Journal of Intelligent Software Systems Vol 2, No 1 (2023): July
Publisher : LPPM UTDI (d.h STMIK AKAKOM) Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26798/jiss.v2i1.930

Abstract

This study applied five types of Mushrooms, they are Button mushrooms, Wood Ear mushrooms, Straw mushrooms, Reishi mushrooms and Red Oyster mushrooms. The feature extraction used is Order 1 with the parameters of mean, skewness, variance, kurtosis, and entropy. The process carried out to identify mushroom images by preparing image objects. There were 15 images of each mushroom class were taken for each mushroom and stored in .jpg format. The image processing is carried out by a feature extraction process. Then five images for each mushroom class are chosen. They were used as test images which will be classified so that identification results are obtained. This study applies the Classification Algorithm C4.5 to build a decision tree, which will also identify the results of the accuracy of processed mushroom images. The obtained result of accuracy was 84% in the classification of feature extraction Order 1
ANALYSIS AND DESIGN OF DATA WAREHOUSE AND DATA MART BUDGET Hendra Maryanto; Bambang Purnomosidi Dwi Putranto; Rikie Kartadie; Muhammad Guntara; Robertus Saptoto
Journal of Intelligent Software Systems Vol 2, No 1 (2023): July
Publisher : LPPM UTDI (d.h STMIK AKAKOM) Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26798/jiss.v2i1.927

Abstract

University as higher education institutions must be able to manage budgets properly. The budget is a future financial plan which includes the expectations of university management. This research will design a data warehouse, which is a place where data can be stored on a large scale. In this research, a data warehouse will be designed as a place to store budget data. The method applied in this study is the Kimball method with a nine-step methodology. The result of this research is a data warehouse design and budget data mart
IOT BASED SOIL MOISTURE MONITORING AND SOIL MOISTURE PREDICTION USING LINEAR REGRESSION (CASE STUDY OF VINCA PLANTS) Kuindra Iriyanta; Bambang Purnomosidi Dwi Putranto; Widyastuti Andriyani
Journal of Intelligent Software Systems Vol 2, No 1 (2023): July
Publisher : LPPM UTDI (d.h STMIK AKAKOM) Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26798/jiss.v2i1.929

Abstract

Soil moisture is something that becomes important. Indonesia as an agricultural country, most of the population has a profession as a farmer. In agriculture, one of the important parts is the water composition in the soil or soil moisture. One attempt to maintain soil moisture is to provide sufficient water intake to the soil. However, in practice, it is sometimes complicated for farmers to do proper irrigation of their agricultural land. This humidity condition will ultimately determine the success of vinca plant cultivators. The accuracy of giving water both in terms of time management and volume are two things which are an important focus of vinca crop growing. This system is designed using a humidity sensor which is used to measure the moisture composition contained in the soil, and an air temperature sensor. The NodeMCU ESP2866 microcontroller acts as a link between Google spreadsheet sensors. NodeMCU ESP2866 will send humidity and temperature sensor reading data to Google spreadsheets using a RESTfull API which can connect one application to another. The sensor data is then saved to Google spreadsheet and processed using the linear regression method. The processing results will be displayed on the Google Data Studio dashboard. The output of this process is to provide information about soil moisture conditions, notification of soil moisture conditions if it is too dry or damp, thus the prevention of the death of vinca plants can be carried out. The benefit for users is that they can carry out periodic and real-time monitoring by simply using the Telegram instant messaging application, which is expected to reduce the risk of plant death due to drought or excessive watering
Polynomial Regression Method and Support Vector Machine Method for Predicting Disease Covid-19 in Indonesia Bambang Purnomosidi Dwi Putranto; Moh. Abdul Kholik; Muhammad Agung Nugroho; Danny Kriestanto
Journal of Intelligent Software Systems Vol 2, No 1 (2023): July
Publisher : LPPM UTDI (d.h STMIK AKAKOM) Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26798/jiss.v2i1.931

Abstract

The COVID-19 pandemic has become a major threat to the entire country. According to the WHO report, COVID-19 is a severe acute respiratory syndrome transmitted through respiratory droplets resulting from direct contact with patients. This study of data history is then processed using data mining prediction methods, namely the Polynomial Regression method compared to the Support Vector Machine method. Of the two methods will be sought the most accurate method by testing accuracy with MAE, MSE, and also MAPE to get the results of covid-19 predictions in Indonesia. Based on the comparison of test results through various scenarios against both methods, the Polynomial Regression method obtained the smallest test value, resulting in an accuracy value of MAE = 4146.025749867596, MSE = 19031800.02642069, MAPE = 0.006174164877416524. Polynomial regression is the best-recommended method

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