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All Journal Techno.Com: Jurnal Teknologi Informasi JOIV : International Journal on Informatics Visualization Jurnal Rekayasa elektrika The Indonesian Journal of Computer Science Emitor: Jurnal Teknik Elektro
Rokhana, Rika
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Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Energy Engineering

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Level Kualitas Air Nutrisi pada Hidroponik Berdasarkan Sistem Klasifikasi Fuzzy Sanaba, Utari; Rokhana, Rika; Setiawardhana, Setiawardhana
Techno.Com Vol. 23 No. 2 (2024): Mei 2024
Publisher : LPPM Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/tc.v23i2.10538

Abstract

Tingginya jumlah penduduk telah menyebabkan perubahan lahan pertanian menjadi lahan non-pertanian. Solusi inovatif untuk mengatasi keterbatasan lahan yaitu urban agriculture, khususnya hidroponik. Namun, kondisi nutrisi pada air hidroponik sering kali dalam kondisi buruk sehingga perlu dimonitoring dan dideteksi tingkat kualitasnya untuk menjaga kondisi air nutrisi dalam bak hidroponik dalam keadaan baik. Kondisi air nutrisi yang baik akan mengoptimalkan proses penyerapan akar dan pertumbuhan tanaman. Parameter kualitas air nutrisi dapat dideteksi melalui suhu air nutrisi, kadar TDS (Total Dissolved Solids) di dalam nutrisi, dan tingkat keasamaan atau pH dari air nutrisi di dalam bak hidroponik. Metode fuzzy logic classification memungkinkan dalam mengolah kondisi aktual nutrisi dari ketiga parameter tersebut menjadi sebuah keputusan level kualitas air nutrisi tanaman dalam kondisi baik, sedang, buruk, ataupun sangat buruk. Penelitian ini menggunakan sensor suhu air, TDS, dan pH dalam pengukuran masing-masing parameter yang kemudian ditampilkan pada website. Hasil pengukuran parameter nutrisi mencapai error rendah yaitu ±5%. Hasil klasifikasi kualitas dari kondisi air nutrisi tanaman yang diputuskan dengan fuzzy logic sudah sesuai dengan yang diinginkan oleh petani dan berhasil 100% ditampilkan pada website pengguna. Sistem ini memudahkan pengguna dalam memantau, mengevaluasi, dan meningkatkan kondisi dan kualitas nutrisi tanaman dari jarak jauh.
Penentuan Tingkat Stres berdasarkan Bio-Parameter Menggunakan Variasi Kernel Support Vector Machine Daffa Syah Alam; Rokhana, Rika; Arief, Zainal
The Indonesian Journal of Computer Science Vol. 13 No. 6 (2024): 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.v13i6.4495

Abstract

System for detecting a person's stress level based on bio-parameters is blood pressure, heart rate, and respiratory rate. Measurements of blood pressure, heart rate, and respiratory rate in order to detect the condition of a person's stress level are carried out non-invasively or don’t damage the nervous tissue in the body and routinely. Heart rate measurement using MAX30102 sensor on the finger. Measurement of blood pressure using the MPX2050GP pressure sensor by placing cuff on the person's arm. While measuring the breathing rate using the MAX9814 micondensor sensor. In determining or classifying stress level conditions from non-invasive measurement parameters of blood pressure, heart rate and respiratory rate using Support Vector Machine (SVM) method with specified kernel variations. The classification of stress level conditions consists of four classes including normal, mild stress, moderate stress and severe stress. So that a dataset of 71 data is obtained with the data augmentation process and the accuracy of each SVM kernel variation used is obtained.
Nutrition Temperature and TDS Control System with Fuzzy Logic on Pak Choy Hydroponics (Brassica rapa subsp. chinensis) Sanaba, Utari; Rokhana, Rika; Setiawardhana, Setiawardhana; Wijayanto, Ardik
Jurnal Rekayasa Elektrika Vol 20, No 3 (2024)
Publisher : Universitas Syiah Kuala

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.17529/jre.v20i3.34322

Abstract

Hydroponics is a method of cultivation that does not use soil as a medium, allowing it to be applied in limited spaces such as urban households. One of the vegetable plants that can be grown using hydroponics is pak choy (Brassica rapa subsp. chinensis). To produce healthy pak choy plants that can efficiently absorb nutrients in a hydroponic system, several factors need to be considered, such as the level of Total Dissolved Solids (TDS) in the nutrient solution, nutrient solution temperature, and air humidity in the hydroponic environment. The ideal nutrient solution temperature for hydroponic plants falls within the range of 25-27C. In this system, a monitoring and control system will be designed to optimize the growth of pak choy plants in a Deep Flow Technique (DFT) hydroponic system. In this system, the nutrient solution temperature will be controlled with a set point of 25C using an on/off control for a peltier device. To maintain the TDS level at a set point of 1200 ppm in the nutrient solution, fuzzy logic control will be employed, generating timer-based control signals for the nutrient pump A, nutrient pump B, and water pump. The monitoring system will be displayed on an Internet of Things (IoT) dashboard platform, such as ThingSpeak.
Design and Development of an EMG-Based Interactive Musical Instrument Using the Decision Tree Method Pratiwi, Reniantika Dwi; Rokhana, Rika; Waya Rahmaning Gusti, Agrippina
Emitor: Jurnal Teknik Elektro Vol 25, No 3: November 2025
Publisher : Universitas Muhammadiyah Surakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23917/emitor.v25i3.12866

Abstract

Hand motor limitations often hinder individuals from expressing their musical creativity, particularly those affected by neurological disorders, musculoskeletal injuries, or playing-related musculoskeletal disorders. Such impairments restrict access to traditional instruments and highlight the need for alternative modes of musical interaction. This study addresses the problem by designing an interactive musical instrument based on surface electromyography (EMG), enabling the conversion of forearm muscle activity into digital notes via a MIDI controller in real time. The system integrates a Muscle Sensor v3, Arduino Uno, and Python-based software equipped with a graphical user interface. The processing pipeline consists of EMG signal acquisition, feature extraction using three widely adopted time-domain features—Mean Absolute Value (MAV), Root Mean Square (RMS), and Waveform Length (WL)—and gesture classification with a Decision Tree algorithm implemented in scikit-learn. Once classified, the gestures are mapped to corresponding MIDI note values and transmitted to a Digital Audio Workstation (DAW) for sound production. Experimental evaluation was performed on eight distinct hand gesture classes. For each class, 20 repetitions were collected for training, and 10 additional repetitions were used for testing, resulting in 80 independent test trials. The system achieved an overall accuracy of 82.5%, with 66 correct predictions out of 80. Simple gestures such as Hand Open and Index Bend reached 100% accuracy, whereas gestures with overlapping muscle activation patterns, notably Form Number 1 and Form Number 2, achieved only 60% accuracy due to their highly similar EMG features. These results demonstrate that the Decision Tree algorithm, while computationally efficient and interpretable, has limitations when handling non-linearly separable data. Nonetheless, the study establishes the feasibility of using Decision Trees as a lightweight baseline for real-time EMG-based musical interfaces. The findings suggest potential for further development through multi-subject, multi-channel EMG datasets and advanced classifiers such as Support Vector Machines (SVM) or Artificial Neural Networks (ANN). Ultimately, this work contributes to the advancement of inclusive and adaptive digital musical technologies for individuals with motor impairments.
Face Recognition Using Convolution Neural Network Method with Discrete Cosine Transform Image for Login System Setiawan, Ari; Sigit, Riyanto; Rokhana, Rika
JOIV : International Journal on Informatics Visualization Vol 7, No 2 (2023)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.7.2.1546

Abstract

These days, the application of image processing in computer vision is becoming more crucial. Some situations necessitate a solution based on computer vision and growing deep learning. One method continuously developed in deep learning is the Convolutional Neural Network, with MobileNet, EfficientNet, VGG16, and others being widely used architectures. Using the CNN architecture, the dataset consists primarily of images; the more datasets there are, the more image storage space will be required. Compression via the discrete cosine transform technique is a method to address this issue. We implement the DCT compression method in the present research to get around the system's limited storage space. Using DCT, we also compare compressed and uncompressed images. All users who had been trained with each test 5 times for a total of 150 tests were given the test. Based on testing findings, the size reduction rate for compressed and uncompressed images is measured at 25%. The case study presented is face recognition, and the training results indicate that the accuracy of compressed images using the DCT approach ranges from 91.33% to 100%. Still, the accuracy of uncompressed facial images ranges from 98.17% to 100%. In addition, the accuracy of the proposed CNN architecture has increased to 87.43%, while the accuracy of MobileNet has increased by 16.75%. The accuracy of EfficientNetB1 with noisy-student weights is measured at 74.91%, and the accuracy of EfficientNetB1 with imageNet weights can reach 100%. Facial biometric authentication using a deep learning algorithm and DCT-compressed images was successfully accomplished with an accuracy value of 95.33% and an error value of 4.67%.
Co-Authors Ardik Wijayanto Ari Setiawan Daffa Syah Alam Gusti, Agrippina Waya Rahmaning Kemalasari Ningsih, Vita Kusuma Pratiwi, Reniantika Dwi Riyanto Sigit, Riyanto Sanaba, Utari Setiawardhana, Setiawardhana Waya Rahmaning Gusti, Agrippina Zainal Arief