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All Journal JIKO (Jurnal Informatika dan Komputer) International Journal of Social Science Journal of Intelligent Software Systems Jurnal Teknologi Informasi
Danny Kriestanto
Universitas Teknologi Digital Indonesia
Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Education Environmental Science Health Professions Languange, Linguistic, Communication & Media Medicine & Pharmacology Public Health Social Sciences Other

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Journal : journal of intelligent software systems

 1 
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
PERFORMANCE ANALYSIS OF LOGISTIC REGRESSION ALGORITHM IN OPINION SEGMENTATION OF INDOSAT NETWORK SERVICE REVIEWS Sandy Ananda Dwi; Danny Kriestanto; Ajie Al Qadri Anwar; Syahrur Ro'uf; Lintang Suci Rochmana; Muhammad Agung Nugroho
Journal of Intelligent Software Systems Vol 4, No 1 (2025): Juli 2025
Publisher : LPPM UTDI (d.h STMIK AKAKOM) Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26798/jiss.v4i1.2004

Abstract

In the era of the industrial revolution 4.0, where the use of network services has become a basic need and cannot be separated from daily activities, the massive number of network service users can be proven by the increasing number of people using digital platforms to search for information, express opinions or even just to communicate with each other, currently network services are available in the form of digital platforms that can be used to purchase network data packages or just to monitor the quality of network services, therefore this study aims to analyze user sentiment towards network services that have been launched by the Indosat provider based on the results of user reviews sourced from the digital platform using a machine learning approach and a logistic regression algorithm model to determine the segmentation of opinions that are widely expressed on the digital platform. The results of this study indicate that the logistic regression algorithm is able to analyze patterns of consumer characteristics with good accuracy in the algorithm model, and the results of the accuracy of the algorithm model in finding segmentation patterns in sentiment opinions reach an accuracy value of 85%, precision 81%, recall 77% and f1-score 79% to predict an opinion that has negative and positive sentiment during testing, then network speed, connection disruption and network data package prices are one of the factors that can influence an opinion regarding negative and positive sentiment.
CLASSIFICATION OF OIL PALM FRUIT CROSS-SECTIONS USING HSV FEATURE EXTRACTION AND GAUSSIAN NAÏVE BAYES Teguh Junian Kuswanto; WIDYASTUTI ANDRIYANI; Rikie Kartadie; Bambang Purnomosidi D.P; Danny Kriestanto
Journal of Intelligent Software Systems Vol 4, No 2 (2025): Desember 2025
Publisher : LPPM UTDI (d.h STMIK AKAKOM) Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26798/jiss.v4i2.2310

Abstract

Accurate identification of oil palm fruit varieties is essential for supporting breeding programs and optimizing seed quality in plantation operations. Manual approaches often lead to inconsistencies due to the high visual similarity among fruit types, particularly between dura and tenera. This study proposes an automatic classification model for oil palm fruit cross-sections using HSV-based color feature extraction combined with a Gaussian Naïve Bayes classifier. A dataset of 186 cross-sectional fruit images was used, consisting of 90 training samples and 96 testing samples representing the dura, pisifera, and tenera varieties. The methodology includes preprocessing, segmentation, HSV feature extraction, model training, and performance evaluation through a confusion matrix. Experimental results show that the proposed model achieves an accuracy of 85%, with misclassifications primarily occurring in the tenera class due to its close resemblance to the dura variety. Compared to Linear Discriminant Analysis (LDA), the proposed approach demonstrates faster computation time and competitive accuracy. These findings indicate that Gaussian Naïve Bayes, supported by HSV feature descriptors, provides an efficient solution for lightweight and cost-effective digital classification of oil palm fruit varieties
Co-Authors Agung Prasetyo Ajie Al Qadri Anwar Andriyani, Widyastuti Arrio Saputra Bambang Purnomosidi Dwi Putranto Femi Dwi Astuti Heru Agus Triyanto Lintang Suci Rochmana Moh. Abdul Kholik Muhammad Agung Nugroho Rikie Kartadie Sandy Ananda Dwi Sur Yanti Syahrur Ro'uf Teguh Junian Kuswanto