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Information Visualization of Lesson Study Activity Mardhia, Murein; Azhari, Ahmad; Ardiansyah, Ardiansyah
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol 3, No 2 (2017)
Publisher : Universitas Ahmad Dahlan

This research explores how to design an interface dashboard to visualize information about evaluation activities occurred during lesson study. Data were collected in two stages using interview and questionnaire techniques whom the respondents were experts and team teaching who had been experienced the whole activities of Lesson Study. We conducted a case study in Mathematics Education Program of Universitas Ahmad Dahlan, where the teaching staffs have participated and conducted the Lesson Study activities for more than two semesters. The interface dashboard prototype was tested by conducting User Experience method and Software Usability Testing assessment. Results obtained show a fairly good acceptance level qualitatively and so does from SUS scored 75 from the average value of all participants

Analisis Fitur Warna dan Tekstur untuk Metode Deteksi Jalan Prahara, Adhi; Azhari, Ahmad
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol 2, No 2 (2016)
Publisher : Universitas Ahmad Dahlan

Deteksi jalan digunakan untuk mengidentifikasi area jalan pada citra atau frame video. Tantangan dalam mendeteksi jalan diantaranya warna dan tekstur jalan yang beragam serta masalah pencahayaan. Oleh karena itu diperlukan fitur yang sesuai untuk menghadapi permasalahan tersebut. Pada penelitian ini dilakukan analisis fitur warna dan tekstur untuk mendeteksi jalan. Kumpulan 50 sampel jalan diambil untuk diekstrak fitur warna di tiga ruang warna yang berbeda yaitu RGB (Red-Green-Blue), HSV (Hue-Saturation-Value), dan CIE L*a*b* serta diekstrak fitur teksturnya dengan GLCM (Gray Level Co-occurrence Matrix). Fitur-fitur tersebut kemudian dianalisis untuk didapatkan fitur dengan variasi yang rendah dari semua sampel jalan yang digunakan untuk menentukan threshold warna maupun tekstur. Hasil pengujian metode deteksi jalan dari 150 citra uji jalan menggunakan batasan fitur hasil analisis menunjukkan akurasi 90,54%.

Deep Learning on EEG Study Concentration in Pendemic Garnis Ajeng Pamiela; Ahmad Azhari
Informatika Mulawarman : Jurnal Ilmiah Ilmu Komputer Vol 16, No 2 (2021): Informatika Mulawarman : Jurnal Ilmiah Ilmu Komputer
Publisher : Mulawarman University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30872/jim.v16i2.6474

Brainwaves are one of the biometric properties that can be used to identify individuals based on their physical and behavioural characteristics. An electroencephalogram (EEG) can be used to measure and capture brain wave activity. The activities required are in the form of giving complex tasks to get thinking and concentration processes called Cognitive Tests, in the form of a Culture Fair Intelligence Test (CFIT) stimulus and Competency Test (UK). This study aims to obtain a pattern of the relationship between concentration and learning outcomes for late adolescent students during the pandemic. The object of research involved in this research is the 10th grade students of TKJ SMK. Data acquisition was carried out twice on the Beta signal by doing cognitive test questions which were done twice at school and at home. Then the data obtained from the test results will be extracted using Fast Fourier Transform (FFT). Furthermore, after the data extraction results are obtained, the classification process will be carried out using the CNN algorithm. The results of the FFT obtained the average value of the signal peak. The results of the CNN classification show that the pandemic does not affect student concentration. The average signal concentration in schools when testing using CFIT is 0.2445 and at the time of testing using UK Mathematics is 0.1330 with an average CFIT score of 77.05 and for UK average is 53.33 with an accuracy value of 83.33 %. While the average signal concentration at home when testing using CFIT is 0.2252 and at the time of testing using UK Mathematics is 0.1301 with an average CFIT score of 77.13 and for UK average is 57.50 with an accuracy value of 83, 33%.

Classification of Concentration Levels in Adult-Early Phase using Brainwave Signals by Applying K-Nearest Neighbor Ahmad Azhari; Fathia Irbati Ammatulloh
Signal and Image Processing Letters Vol. 1 No. 1: March 2019
Publisher : Association for Scientific Computing Electrical and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/simple.v1i1.170

The brain controls the center of human life. Through the brain, all activities of living can be done. One of them is cognitive activity. Brain performance is influenced by mental conditions, lifestyle, and age. Cognitive activity is an observation of mental action, so it includes psychological symptoms that involve memory in the brain's memory, information processing, and future planning. In this study, the concentration level was measured at the age of the adult-early phase (18-30 years) because in this phase, the brain thinks more abstractly and mental conditions influence it. The purpose of this study was to see the level of concentration in the adult-early phase with a stimulus in the form of cognitive activity using IQ tests with the type of Standard Progressive Matrices (SPM) tests. To find out the IQ test results require a long time, so in this study, a recording was done to get brain waves so that the results of the concentration level can be obtained quickly.EEG data was taken using an Electroencephalogram (EEG) by applying the SPM test as a stimulus. The acquisition takes three times for each respondent, with a total of 10 respondents. The method implemented in this study is a classification with the k-Nearest Neighbor (kNN) algorithm. Before using this method, preprocessing is done first by reducing the signal and filtering the beta signal (13-30 Hz).The results of the data taken will be extracted first to get the right features, feature extraction in this study using first-order statistical characteristics that aim to find out the typical information from the signals obtained. The results of this study are the classification of concentration levels in the categories of high, medium, and low. Finally, the results of this study show an accuracy rate of 70%.

Vehicle pose estimation for vehicle detection and tracking based on road direction Adhi Prahara; Ahmad Azhari; Murinto Murinto
International Journal of Advances in Intelligent Informatics Vol 3, No 1 (2017): March 2017
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v3i1.88

Vehicle has several types and each of them has different color, size, and shape. The appearance of vehicle also changes if viewed from different viewpoint of traffic surveillance camera. This situation can create many possibilities of vehicle poses. However, the one in common, vehicle pose usually follows road direction. Therefore, this research proposes a method to estimate the pose of vehicle for vehicle detection and tracking based on road direction. Vehicle training data are generated from 3D vehicle models in four-pair orientation categories. Histogram of Oriented Gradients (HOG) and Linear-Support Vector Machine (Linear-SVM) are used to build vehicle detectors from the data. Road area is extracted from traffic surveillance image to localize the detection area. The pose of vehicle which estimated based on road direction will be used to select a suitable vehicle detector for vehicle detection process. To obtain the final vehicle object, vehicle line checking method is applied to the vehicle detection result. Finally, vehicle tracking is performed to give label on each vehicle. The test conducted on various viewpoints of traffic surveillance camera shows that the method effectively detects and tracks vehicle by estimating the pose of vehicle. Performance evaluation of the proposed method shows 0.9170 of accuracy and 0.9161 of balance accuracy (BAC).

Brainwaves feature classification by applying K-Means clustering using single-sensor EEG Ahmad Azhari; Leonel Hernandez
International Journal of Advances in Intelligent Informatics Vol 2, No 3 (2016): November 2016
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v2i3.86

The use of brainwave signal is a step in the introduction of the individual identity using biometric technology based on characteristics of the body. Brainwave signal has unique characteristics and different on each individual because the brainwave cannot be read or copied by people so it is not possible to have a similarity of one person with another person. To be able to process the identification of individual characteristics, which obtained from the signal brainwave, required a pattern of brain activity that is prominent and constant. Cognitive activity testing using a single-sensor EEG (Electroencephalogram) divided into two categories, called the activity of cognitive involving the ability of the right brain (creativity, imagination, holistic thinking, intuition, arts, rhythms, nonverbal, feelings, visualization, tune of songs, daydreaming) and the left brain (logic, analysis, sequences, linear, mathematics, language, facts, think in words, word of songs, computation) give a different cluster based on two times the test on mathematical activities (no cluster slices of experiment 1 and experiment 2). The result showed that cognitive activity based on math activity can provide a signal characteristic that can be used as the basis for a brain-computer interface applications development by utilizing EEG single-sensor.

Principal component analysis implementation for brainwave signal reduction based on cognitive activity Ahmad Azhari; Murein Miksa Mardhia
International Journal of Advances in Intelligent Informatics Vol 3, No 3 (2017): November 2017
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v3i3.118

Human has the ability to think that comes from the brain. Electrical signals generated by brain and represented in wave form. To record and measure the activity of brainwaves in the form of electrical potential required electroencephalogram (EEG). In this study a cognitive task is applied to trigger a specific human brain response arising from the cognitive aspect. Stimulation is given by using nine types of cognitive tasks including breath, color, face, finger, math, object, password thinking, singing, and sports. Principal component analysis (PCA) is implemented as a first step to reduce data and to get the main component of feature extraction results obtained from EEG acquisition. The results show that PCA succeeded reducing 108 existing datasets to 2 prominent factors with a cumulative rate of 65.7%. Factor 1 (F1) includes mean, standard deviation, and entropy, while factor 2 (F2) includes skewness and kurtosis.

Information Visualization of Lesson Study Activity Murein Mardhia; Ahmad Azhari; Ardiansyah Ardiansyah
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol 3, No 2 (2017)
Publisher : Universitas Ahmad Dahlan

This research explores how to design an interface dashboard to visualize information about evaluation activities occurred during lesson study. Data were collected in two stages using interview and questionnaire techniques whom the respondents were experts and team teaching who had been experienced the whole activities of Lesson Study. We conducted a case study in Mathematics Education Program of Universitas Ahmad Dahlan, where the teaching staffs have participated and conducted the Lesson Study activities for more than two semesters. The interface dashboard prototype was tested by conducting User Experience method and Software Usability Testing assessment. Results obtained show a fairly good acceptance level qualitatively and so does from SUS scored 75 from the average value of all participants

Analisis Fitur Warna dan Tekstur untuk Metode Deteksi Jalan Adhi Prahara; Ahmad Azhari
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol 2, No 2 (2016)
Publisher : Universitas Ahmad Dahlan

Deteksi jalan digunakan untuk mengidentifikasi area jalan pada citra atau frame video. Tantangan dalam mendeteksi jalan diantaranya warna dan tekstur jalan yang beragam serta masalah pencahayaan. Oleh karena itu diperlukan fitur yang sesuai untuk menghadapi permasalahan tersebut. Pada penelitian ini dilakukan analisis fitur warna dan tekstur untuk mendeteksi jalan. Kumpulan 50 sampel jalan diambil untuk diekstrak fitur warna di tiga ruang warna yang berbeda yaitu RGB (Red-Green-Blue), HSV (Hue-Saturation-Value), dan CIE L*a*b* serta diekstrak fitur teksturnya dengan GLCM (Gray Level Co-occurrence Matrix). Fitur-fitur tersebut kemudian dianalisis untuk didapatkan fitur dengan variasi yang rendah dari semua sampel jalan yang digunakan untuk menentukan threshold warna maupun tekstur. Hasil pengujian metode deteksi jalan dari 150 citra uji jalan menggunakan batasan fitur hasil analisis menunjukkan akurasi 90,54%.

PENERAPAN TEKNOLOGI TOKO ONLINE UNTUK PEMASARAN PRODUK BAGI IBU-IBU AISYIYAH GUNUNG KIDUL Gita Indah Budiarti; Murein Miksa Mardhia; Ahmad Azhari
JCES (Journal of Character Education Society) Vol 3, No 1 (2020): Januari
Publisher : Universitas Muhammadiyah Mataram

Abstrak: Pemasaran dan promosi merupakan hal terpenting bagi penjualan produk. Ibu-ibu Aisyiyah Gunung Kidul (mitra) memiliki produk, salah satunya makanan ringan dari mokaf. Perlu adanya inovasi pada pemasaran untuk meningkatkan penjualan mitra. Kegiatan ini bertujuan untuk membuat toko online bagi mitra kemudian melatih mitra untuk menggunakannya. Metode yang digunakan adalah pelatihan dan evaluasi. Hasil dari kegiatan ini mitra mampu menggunakan website dan aplikasi dengan baik. Mitra yang dapat menunggah produknya sebesar 63%. Mitra sangat antusias pada pelatihan ini, dan berharap ada tindak lanjut dari kegiatan ini.Abstract: Marketing and promotion are the most important things for product sales. Aisyiyah Gunung Kidul mothers (partners) have products, one of which is snacks from the mokaf. There needs to be innovation in marketing to increase partner sales. This activity aims to create an online shop for partners and then train partners to use it. The method used is training and evaluation. The results of this activity partners are able to use the website and application well. Partners who can upload their products by 63%. Partners are very enthusiastic about this training, and hope that there will be a follow-up to this activity.

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