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All Journal Bulletin of Electrical Engineering and Informatics Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) Jurnal Riset Perkebunan (JRP)
Khairur Rizki
Fakultas Pertanian, Universitas Andalas, Indonesia
Agriculture, Biological Sciences & Forestry Computer Science & IT Electrical & Electronics Engineering

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 1 
Identification Rainfall Humidity With Sugeno Methods Rizki, Khairur
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol 4, No 2 (2018)
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v4i2.11266

Abstract

Moisture identification of rainfall is very useful and petrified in the activity of utilizing the sun's heat, such as in drying clothes, drying food ingredients in household businesses, and so on. Rainfall is influenced by changes in temperature and humidity that is always changing. To predict the accuracy of rainfall in this study used the Fuzzy Sugeno method. In the Fuzzy Sugeno method, input data in the form of rainfall data, light intensity, and temperature, while output data in the form of humidity indications of rainfall with an indication of damp, very humid, and not humid. In order for all input data to be classified, the Arduino module is used to translate the results of reading input data from the sensor. The accuracy of the prediction of identification of rainfall from the results of this study is very good. So this research is very helpful in identifying the level of humidity in the rainfall.
The Efficacy Of Isopropyl Amine Glyphosate 165 Sl Herbicide Effect On Weed Control Of Coconut Cultivation Doni Hariandi; Ryan Budi Setiawan; Khairur Rizki
Jurnal Riset Perkebunan Vol. 4 No. 2 (2023): Jurnal Riset Perkebunan (JRP)
Publisher : Jurusan Budidaya Perkebunan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25077/jrp.4.2.95-104.2023

Abstract

Coconut plants are an important commodity for Indonesian people. In the cultivation process, coconut plants need a suitable environment for growth and production. One of the problems in cultivation is weeds. Weeds have a negative impact on cultivated plants, therefore appropriate control measures are needed. So far, the most effective weed control is chemical methods using herbicides. One of the herbicides that can be used is the herbicide with isopropyl amine glyphosate 165 SL. The aim of the experiment was to determine the efficacy of the herbicide isopropyl amine glyphosate 165 SL for controlling weeds in coconut cultivation was conducted from February to June 2022 at Pariaman City, West Sumatra Province. The experimental units were laid out according to a Randomized Block Design with 7 treatments and 3 groups as replications. The treatment was herbicide isopropyl amine glyphosate 165 SL at doses of 3.50 l ha-1, 5.25 l ha-1, 7.00 l ha-1, 8.75 l ha-1, 10.00 l ha-1, manual weeding and control (without weeding). The results of the research show that (1) The herbicide isopropyl amine glyphosate 165 SL can generally control weeds in coconut cultivation up to 12 weeks after application because the weed biomass in the treatment plot is relatively the same as manual weeding and is lighter than the control; (2) Herbicide isopropyl amine glyphosate 165 SL with a dose range of 3.50 l/ha – 10.50 l ha-1 up to 6 weeks after application does not show symptoms of phytotoxicity in coconut plants.
Hybrid XAI and deep learning architecture for trustworthy dental diagnostics Fadhillah, Yusra; Hasan Siregar, Muhammad Noor; Abdul Kodir, Ade Ismail; Rizki, Khairur
Bulletin of Electrical Engineering and Informatics Vol 15, No 3: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i3.11193

Abstract

Dental periodontal disease is a persistent an inflammatory disorder affecting tooth supporting tissues and stays a main motive of tooth loss. Although dental radiographs are essential for early diagnosis, their interpretation is often subjective and inconsistent due to reliance on clinician expertise. This study proposes an automated and interpretable diagnostic framework using a convolutional neural network (CNN) integrated with gradient-weighted class activation mapping (Grad-CAM). The CNN performs binary classification of periapical radiographs into periodontal and normal categories, while Grad-CAM provides visual explanations of the model’s decision-making process. Experimental results show that the proposed model achieves a classification accuracy of 94.17%, indicating reliable diagnostic performance. The generated heatmaps consistently highlight clinically relevant regions, particularly alveolar bone loss in periodontal cases, whereas normal images exhibit no pathological activation. These findings demonstrate that the proposed CNN–Grad-CAM framework enhances both diagnostic accuracy and interpretability. The study contributes a transparent and trustworthy artificial intelligence solution to support objective periodontal disease diagnosis in dental radiology.
Co-Authors Ade Ismail Abdul Kodir, Ade Ismail Doni Hariandi Fadhillah, Yusra Muhammad Noor Hasan Siregar Ryan Budi Setiawan