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All Journal JURNAL ISTEK JOIN (Jurnal Online Informatika) Al Irsyad: Jurnal Studi Islam Tabsyir: Jurnal Dakwah Dan Sosial Humaniora Moral : Jurnal Kajian Pendidikan Islam ISTEK
Deden Suparman, Deden
UIN Sunan Gunung Djati Bandung
Religion Agriculture, Biological Sciences & Forestry Humanities Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Education Electrical & Electronics Engineering Library & Information Science Social Sciences

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Automatic Detection of Hijaiyah Letters Pronunciation using Convolutional Neural Network Algorithm Gerhana, Yana Aditia; Azis, Aaz Muhammad Hafidz; Ramdania, Diena Rauda; Dzulfikar, Wildan Budiawan; Atmadja, Aldy Rialdy; Suparman, Deden; Rahayu, Ayu Puji
JOIN (Jurnal Online Informatika) Vol 7 No 1 (2022)
Publisher : Department of Informatics, UIN Sunan Gunung Djati Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15575/join.v7i1.882

Abstract

Abstract— Speech recognition technology is used in learning to read letters in the Qur'an. This study aims to implement the CNN algorithm in recognizing the results of introducing the pronunciation of the hijaiyah letters. The pronunciation sound is extracted using the Mel-frequency cepstral coefficients (MFCC) model and then classified using a deep learning model with the CNN algorithm. This system was developed using the CRISP-DM model. Based on the results of testing 616 voice data of 28 hijaiyah letters, the best value was obtained for accuracy of 62.45%, precision of 75%, recall of 50% and f1-score of 58%.
Implementation of Rule-Base and Internet Methods of Things Optimizing Water Mangement For Improving Seed Quality Gerhana, Yana Aditia; Suparman, Deden
ISTEK Vol. 13 No. 1 (2024): Juni 2024
Publisher : Fakultas Sains dan Teknologi UIN Sunan Gunung Djati Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15575/istek.v13i1.930

Abstract

System hydroponics Nutrients Film Technique (NFT) is one of the increasingly popular plant cultivation techniques used because it can increase the efficiency of water and nutrient use as well as crop yields. The NFT Hydroponic System has problems that are often faced in the form of control that must be optimal for important parameters like pH, temperature water, And concentration nutrition, so that can influence plant health and growth and need a good environment controlled To avoid decline quality plant or withering plant. Study This design uses Arduino Uno as a center control system monitoring hydroponics NFTs Which in add sensors pH For read value from pH water, sensors TDS used For read density nutrition, sensors temperature DS18B20 used For read temperature water Because own waterproff and water sensor features flow to read the amount of water flow. Data is read by the sensor and Then sent to Firebase through module NodeMCU which has been connected to the Arduino Uno then from Firebase it is created output form information to the user through the application mobile. Results testing done with the use 3 media Which were different as much 60 time experienced 58 successes and 2 failures resulted in a score accuracy of 96.6% of the total testing.
Implementation of Convolutional Neural Network CNN Algorithm to Detect Coffe Fruit Maturity Gerhana, Yana Aditia; Heryanto, Rafi Rai; Syaripudin, Undang; Suparman, Deden
ISTEK Vol. 13 No. 2 (2024): Desember 2024
Publisher : Fakultas Sains dan Teknologi UIN Sunan Gunung Djati Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15575/istek.v13i2.1247

Abstract

Fruit ripeness detection is important in the agriculture and food processing industries to ensure optimal product quality. Proper fruit ripeness can affect flavour, texture and nutrition, making it a key focus in production process monitoring and control. The fruit ripeness detection process still needs to be done manually, which can be inefficient and inaccurate. This research aims to address these challenges by implementing the CNN algorithm with VGG-19 architecture to detect coffee fruit ripeness automatically. The process involves collecting datasets of fruit images with various ripeness levels, image pre-processing including cropping and resizing, training the CNN VGG-19 model with feature learning and hyperparameter optimisation and evaluating model performance using a confusion matrix. This experiment aims to evaluate the model's performance in detecting fruit ripeness and measure the speed and efficiency of the CNN-based detection system with VGG-19 architecture. The results of this research are expected to help develop a better system for identifying fruit ripeness.
Co-Authors Aaz Muhammad Hafidz Azis ALAWIYAH, RIZKA Aldy Rialdy Atmadja Aulia, Clarissa Syahnaz Ayu Puji Rahayu Azis, Aaz Muhammad Hafidz Bashori, Abdul Hamid Diena Rauda Ramdania Dzulfikar, Wildan Budiawan Heryanto, Rafi Rai Luthfi Hana Fadiah Mimin Mintarsih Mintarsih, Mimin Moch Naufal Ramdhani Muchtar, Subhanallah Nurin Fatnata Rizka Alawiyah Sunandar, Dadan Undang Syaripudin Undang Syaripudin, Undang Wildan Budiawan Zulfikar Yana Aditia Gerhana Yana Aditia Gerhana Yana Aditia Gerhana, Yana Aditia