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All Journal JISA (Jurnal Informatika dan Sains) Journal of Computer Science, Information Technology and Telecommunication Engineering (JCoSITTE) Jurnal Teknisi Jurnal Citrakara The Indonesian Journal of Computer Science
Gibran, M Khalil
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Arts Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering

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Journal : The Indonesian Journal of Computer Science

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Enhancing Water Potability Assessment Using Hybrid Fuzzy-Naïve Bayes Azmi, Fadhillah; Gibran, M Khalil; Ridwan, Achmad
The Indonesian Journal of Computer Science Vol. 12 No. 3 (2023): The Indonesian Journal of Computer Science
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v12i3.3232

Abstract

In an effort to ensure a safe and high-quality water supply, the assessment of water potability is of paramount importance. An accurate and efficient assessment of water potability can be a challenge due to various influencing factors. Therefore, an innovative and integrated approach is needed to improve the assessment of water potability. In this study, we introduce a new approach to improving the assessment of water potability. This approach aims to overcome the shortcomings of traditional methods by using a hybrid fuzzy-Naïve Bayes approach to obtain a more accurate level of water potability. Fuzzy techniques are used to overcome uncertainty and ambiguity in the initial data. This method describes the probability weights in a fuzzy manner for various parameters. Then, the Naïve Bayes method is used to classify water samples based on the probability generated by the fuzzy system. This hybrid approach makes it possible to consider the relationship between parameters and generate more realistic probability values. This study uses datasets collected from various sources that include water potability parameters. A hybrid fuzzy-Naïve Bayes approach was then applied to this data set to make a more effective and accurate assessment of water potability. The experimental results show that the proposed method obtains an accuracy of 90%, which significantly improves the water potability assessment compared to the conventional method, which results in an accuracy of 84%. By combining fuzzy and Naïve Bayes techniques, we can overcome uncertainty in data and produce more accurate judgments.
Machine Learning and Fuzzy C-Means Clustering for the Identification of Tomato Diseases Saleh, Amir; Ridwan, Achmad; Gibran, M Khalil
The Indonesian Journal of Computer Science Vol. 12 No. 5 (2023): The Indonesian Journal of Computer Science (IJCS)
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v12i5.3379

Abstract

Diseases in tomato plants can cause economic losses in the agricultural industry. Identification of tomato plant diseases is important to choosing the right action to control their spread. In this research, we propose an approach to identify tomato plant diseases using a machine learning algorithm and lab colour space-based image segmentation using the fuzzy c-means (FCM) clustering algorithm. The segmentation method aims to separate the infected area, leaf image, and background in the tomato plant image. In the first step, the tomato image is represented in the Lab colour space, which allows for combining information on brightness (L), red-green colour components (a), and yellow-blue colour components (b). Then, the FCM algorithm is applied to segment the image. The segmentation results are then evaluated through an identification process using machine learning techniques such as k-Nearest Neighbors (kNN), Random Forest (RF), Support Vector Machine (SVM), and Naïve Bayes (NB) to measure the level of accuracy. The dataset used in this research is tomato images, which include various plant diseases obtained from the Kaggle dataset. The performance results of the proposed method show that the segmentation approach based on Lab colour space with the FCM clustering algorithm is able to identify infected areas well. The accuracy value of each machine learning method used is kNN of 85.40%, RF of 88.87%, SVM of 80.73%, and NB of 74.60%. The proposed method shows success in accurately identifying types of tomato plant diseases and obtains improvements compared to without using segmentation.
Co-Authors Achmad Ridwan Alfaruqy, OK. Mhd Fahri Amir Saleh Badria, Lailatul Berutu, Asro Hayati Fadhillah Azmi, Fadhillah FIKRI HAIKAL Hasrul Hsb, Mhd Fikry Hendrawan, Aurellia Aknesia Lubis, Fitra Hidayat Muharram, Muhammad Raihan Nurzanah, Laila Ramadhani, Fredy Kusuma Ritonga, Saprina Putri Utama Rizka Rizka, Rizka