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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Jurnal SPEKTRUM SINTECH (Science and Information Technology) Journal Jurnal Nasional Pendidikan Teknik Informatika (JANAPATI) Journal of Computer Science and Informatics Engineering (J-Cosine) Journal of Electrical, Electronics and Informatics SPEKTA (Jurnal Pengabdian Kepada Masyarakat : Teknologi dan Aplikasi) Jurnal Ilmiah Wahana Pendidikan Journal of Law and Social Politics Jurnal SPEKTRUM
Duman Care Khrisne
Program Studi Teknik Elektro, Fakultas Teknik, Universitas Udayana
Religion Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Social Sciences Other

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Journal : Journal of Electrical, Electronics and Informatics

 1 
ASALTAG : Automatic Image Annotation Through Salient Object Detection and Improved k-Nearest Neighbor Feature Matching Theresia Hendrawati; Duman Care Khrisne
Journal of Electrical, Electronics and Informatics Vol 2 No 1 (2018): JEEI (February 2018)
Publisher : Institute for Research and Community Services Udayana University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24843/JEEI.2018.v02.i01.p02

Abstract

Image databases are becoming very large nowadays, and there is an increasing need for automatic image annotation, for assiting on finding the desired specific image. In this paper, we present a new approach of automatic image annotation using salient object detection and improved k-Nearest Neigbor classifier named ASALTAG. ASALTAG is consist of three major part, the segmentation using Minimum Barirer Salienct Region Segmentation, feature extraction using Block Truncation Algorithm, Gray Level Co-occurrence Matrix and Hu’ Moments, the last part is classification using improved k-Nearest Neigbor. As the result we get maximum accuracy of 79.56% with k=5, better than earlier research. It is because the saliency object detection we do before the feature extraction proccess give us more focused object in image to annotate. Normalization of the feature vector and the distance measure that we use in ASALTAG also improve the kNN classifier accuracy for labeling image.
Indonesian Alphabet Speech Recognition for Early Literacy using Convolutional Neural Network Approach Duman Care Khrisne; Theresia Hendrawati
Journal of Electrical, Electronics and Informatics Vol 4 No 1 (2020): JEEI (February 2020)
Publisher : Institute for Research and Community Services Udayana University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24843/JEEI.2020.v04.i01.p06

Abstract

Games are considered capable of being used as a learning medium that can help teachers to teach children how to pronounce the Indonesian alphabet in early literacy, we try to build one aspect of the game in this study. The approach we use is a speech recognition approach that uses the convolutional neural network method. The results of this study indicate that CNN can recognize speech, with input data is in the form of sound. We use the MFCC feature vector sound feature to make a 3-dimensional matrix of input sound into CNN input. We also use the Sequential CNN architecture made from a simple 10 layer neural network, which produces a model with a small size, approximately only about 6 MB, with high accuracy (84%) and an F-Measure of 0.91.
Geographic Information System of Potential Tsunami Impact Areas and Safe Gathering Places for Coastal Tourism Area in Badung Regency, Bali Province I Made Arsa Suyadnya; Duman Care Khrisne
Journal of Electrical, Electronics and Informatics Vol 1 No 2 (2017): JEEI (September 2017)
Publisher : Institute for Research and Community Services Udayana University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24843/JEEI.2017.v01.i02.p07

Abstract

The Southern part of Bali especially Badung regency, in addition to having natural beauty and popular tourist attraction, it has a high potential for disaster. The fact is the coastline of Bali in the south is prone to tsunami because Bali is located close to the colliding zone between the Indo-Australian plate and the Eurasia plate, which presents the main source of the local tsunami that could hit the island of Bali. This research undertakes the design and development of a Geographic Information System (GIS) that can provide information and socialization of potential tsunami impact areas and safe gathering places for coastal tourism area in Badung regency. This application is built web-based by using Google Maps API v3. In this Geographic Information System, users can identify potential tsunami impact areas, obtain information on evacuation methods in the event of a tsunami disaster and can find the nearest safe gathering places to do an evacuation. By utilizing geolocation and direction services from Google Maps API v3, simulation of the nearest evacuation route has been successfully built. Evacuation is done by considering two possible evacuation sites. The first possibility is to evacuate to the nearest vertical high building, and the second evacuation site is away from the danger zone (red zone) and towards the safe zone (yellow zone or outside the yellow zone).
Android Based Application for Rhizome Medicinal Plant Recognition Using SqueezeNet Krisna Hany Indrani; Duman Care Khrisne; I Made Arsa Suyadnya
Journal of Electrical, Electronics and Informatics Vol 4 No 1 (2020): JEEI (February 2020)
Publisher : Institute for Research and Community Services Udayana University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24843/JEEI.2020.v04.i01.p02

Abstract

Rhizome is modification of stem that grows below the surface of the soil and produce new bud and roots from its segments. Besides being used as spices, rhizome also used by people as ingredients of traditional medicine to treat various diseases. This proves that rhizome has many benefits. However, the ability to recognize types of rhizome can only be done by certain people because rhizome has variety of types, aromas, and different colors. This study was designed to build an Android based application to recognize the types of rhizome, so that people can recognize types of rhizome without having special knowledge. The application was built using Convolutional Neural Network (CNN) methods with SqueezeNet architecture model. This study used 9 class of rhizome with Zingiberaceae Family, namely Bangle, Jahe, Kunyit Kuning, Kencur, Lengkuas, Temu Kunci, Temu Ireng, Temu Mangga, and Temulawak. Testing is carried out to know the performance of application such as accuracy level of application in recognize types of rhizome. Based on the results of testing with 54 rhizomes sample images, the application is capable of recognizing rhizomes types by obtaining a top-1 accuracy value of 41% and top-5 accuracy value of 81%.
Detecting the Ripeness of Harvest-Ready Dragon Fruit using Smaller VGGNet-Like Network I Made Wismadi; Duman Care Khrisne; I Made Arsa Suyadnya
Journal of Electrical, Electronics and Informatics Vol 3 No 2 (2019): JEEI (August 2019)
Publisher : Institute for Research and Community Services Udayana University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24843/JEEI.2019.v03.i02.p01

Abstract

This study has a purpose to develop an application to detect the ripeness of the dragon fruit with the deep learning approach using the Smaller VGGNet-like Network method. In this study, the dragon fruit are classified into two classes: ripe or ready for harvest and still raw, by using the Convolutional Neural Network (CNN). The training process utilize the hard packages in python with the backend tensorflow. The model in this research is tested using the confusion matrix and ROC method with the condition that 100 new data are tested. Based on the test conducted, the level of accuracy in classifying the ripeness of the dragon fruit is 91%, and the test using 20 epoch, 50 epoch, 100 epoch, and 500 epoch produced an AUROC value of 0,95.
Automatic Cigarette Object Concealment in Video using R-CNN Kadek Utari Widiarsini; Duman Care Khrisne; I Made Arsa Suyadnya
Journal of Electrical, Electronics and Informatics Vol 5 No 1 (2021): JEEI (February 2021)
Publisher : Institute for Research and Community Services Udayana University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24843/JEEI.2021.v05.i01.p04

Abstract

Cigarettes are packaged processed tobacco products, produced from the Nicotiana Tabacum, Nicotiana Rustica plants and other species or synthetics that contain nicotine with or without additives. Smoking is known to the public as one of the causes of death in the world that is quite large such as asthma, lung infections, oral cancer, throat cancer, lung cancer, heart attacks, strokes, dementia, erectile dysfunction (impotence), and so on. This research aims to build an application that can recognize cigarettes automatically and conceal pictures so that people especially minors are not affected by cigarettes. The application is built using the Region-based Convolutional Neural Network (R-CNN) method. The study uses images that have cigarette objects in them. The test is carried out to find out the application performance such as the level of application accuracy in recognizing cigarette objects. Based on the test results with a sample of 126 cigarette images, the application built is able to recognize cigarette objects by obtaining an accuracy value of 63%.
Carrier Frequency Offset Effects on OFDM System over Rayleigh Fading Channel N. M. A. E. Dewi Wirastuti; I.G.A.K. Diafari Djuni Hartawan; I Made Arsa Suyadnya; Duman Care Khrisne
Journal of Electrical, Electronics and Informatics Vol 3 No 2 (2019): JEEI (August 2019)
Publisher : Institute for Research and Community Services Udayana University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24843/JEEI.2019.v03.i02.p03

Abstract

Orthogonal Frequency Division Multiplexing (OFDM) system showed the use of Discrete Fourier Transform (DFT) and Inverse Discrete Fourier Transform (IDFT) to perform the baseband modulation and demodulation. So that, it can increase and improve the efficiency of the modulation and demodulation. Currently, the OFDM is being utilized in the field of broadband wireless communication, which transmit signals orthogonally, that increases speed of information transmission. It also has high proficiency with high bandwidth and provide large data rates and robust against the multipath delay spread. On the other hand, there are some issues faced OFDM system which are high Peak Average Power Ratio (PAPR), and sensitive to Phase Noise (PN) and Carrier Frequency Offset (CFO). This paper presents Orthogonal Frequency Division Multiplexing (OFDM) performance evaluation in the presence of CFO with two different environment scenarios were used: an AWGN channel and a Rayleigh fading channel. The simulation was performed to evaluate the effects of CFO based on Bit Error Rate (BER) vs. Energy Bit per Noise Ratio (Eb/No). The results showed that for BER degradation caused by CFO effects have presented in our simulation for both AWGN and Rayleigh fading channel.
Expert System for Early Diagnosis of Heart Disease Using the Random Forest Method Yogi Prawira Putra; Duman Care Khrisne; I Made Arsa Suyadnya
Journal of Electrical, Electronics and Informatics Vol 3 No 1 (2019): JEEI (February 2019)
Publisher : Institute for Research and Community Services Udayana University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24843/JEEI.2019.v03.i01.p03

Abstract

In Indonesia, coronary heart disease continues to grow. However, the efforts to prevention it can still be done by diagnosing the initial symptoms caused by using an expert system. This study was designed to build an expert system application to diagnose early coronary disease by random forest methods. The application interface was built using the PHP programming language using framework bootstrap, and uses the python programming language to build a random forest. To make an early diagnosis of coronary heart disease, a decision tree was built by training data from the UCI Dataset Machine Learning Repository using the random forest method. Followed by patient classification data that has been collected through 13 questions to get the diagnosis. The diagnosis results were normal, stadium 1, stadium 2, stadium 3 and stadium 4. Based on the tests that had been carried out, the application was able to provide results in accordance with the sample data collected using a confusion matrix resulting in an accuracy of 92.25% +/- 0.62 with 70% precision, remember 46%, which obtained a score of f0,5 72%.
Residual Neural Network Model for Detecting Waste Disposing Action in Images Suyadnya, I Made Arsa; Khrisne, Duman Care
Journal of Electrical, Electronics and Informatics Vol 5 No 2 (2021): JEEI (September 2021)
Publisher : Institute for Research and Community Services Udayana University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24843/JEEI.2021.v05.i02.p03

Abstract

Waste in general has become a major problem for people around the world. Evidence internationally shows that everyone, or nearly everyone, admits to polluting at some point, with the majority of people littering at least occasionally. This research wants to overcome these problems, by utilizing computer vision and deep learning approaches. This research was conducted to detect the actions carried out by humans in the activities/actions of disposing of waste in an image. This is useful to provide better information for research on better waste disposal behavior than before. We use a Convolutional Neural Network model with a Residual Neural Network architecture to detect the types of activities that objects perform in an image. The result is an artificial neural network model that can label the activities that occur in the input image (scene recognition). This model has been able to carry out the recognition process with an accuracy of 88% with an F1-Score of 0.87.
Helpdesk Ticketing Information System Based on Android at Communication and Information Department of Badung Regency Damayanti, Dewi Ayu Sulistyo; Suyadnya, I Made Arsa; Khrisne, Duman Care
Journal of Electrical, Electronics and Informatics Vol 5 No 2 (2021): JEEI (September 2021)
Publisher : Institute for Research and Community Services Udayana University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24843/JEEI.2021.v05.i02.p01

Abstract

In the operational activities of Diskominfo at Badung Regency, there are often reports of service problems every day. Currently, the reporting of these problems is still done in person via telephone or short message, which affects the speed of handling the report. The complaint system is a solution to problems related to reporting and handling of these service problems. In this study, a mobile application-based complaint system will be developed for the client-side to make it easier for clients to report or respond to reports and for the server-side, it will be developed on a web-based basis to still facilitate the management of complaint data. This study uses Data Flow Diagrams to model the process and Entity Relationship Diagrams to model its database. The evaluation process is carried out using the black box testing method and usability evaluation using SEQ and SUS. The system that has been successfully developed is able to carry out the main processes related to adding reports, viewing report progress, verifying reports, following up on reports, and changing report status. Testing using the black box testing method on 7 test classes with 74 test scenarios to get 74 valid test scenarios. Testing the installation and use of applications on 3 types of smartphones has also been successfully carried out without any problems. Usability testing on users using SEQ gets a middle value of 6 (for Administrator and IT Staff) and 7 (for Verification), which shows that the application developed is easy to use by the user, with the results of the SUS the questionnaire, the user shows that the application is in the Excellent class with an average value obtained from 15 respondents of 80.5.
Co-Authors A.A Ngurah Amrita ADITYA PRATAMA Agma Tinoe Mauludy Agus Wisnu Kusuma Nata Ahmad Sulton Anak Agung Dewi Sintyarianti Ardy Wijaya Arya Mertasana , Putu Arya Ramadhan, Fauzul Boy Aribana Depari Budi Dharma Prabhawa, I Dewa Gede Cokorda Gde Wahyu Pramana Damayanti, Dewi Ayu Sulistyo Darma Putra Dewa Made Wiharta Dwika Prihambodo, Prakoso Fajar Purnama Faridzky, Fadel Ferry Angga Irawan Gede Edy Purna Sastriya Gede Sukadarmika Gusman Saleh, Arya Hartawan, I Gusti Agung Komang Dlafari Djuni Hendrawati, Theresia I Dewa Gede Shunu Kendrawan I G. A. K. Diafari Djuni Hartawan I Gede Agus Satya Dharma I Gusti Made Andi Dipayana I Kadek Agung Bagus Satria Bumi Kelana I Kadek Arya Wiratama I Kadek Yuda Setiadi I Ketut Putra Swastika I Ketut Wijaya I Made Arsa Suyadnya I Made Cakra Pustaka1 I Made Rian Yuliawan I MADE SUDARMA I Made Sukarsa I Made Wismadi I Made Yudi Adnyana Putra I Nyoman Sumitra Tanaya I Putu Gede Mahendra Sanjaya I Putu Prasna Mahardika I Wayan Adi Setyadi I Wayan Shandyasa Ida Ayu Dwi Giriantari Ida Bagus A. Swamardika Jaelani, Maulana Jauzaa Maylia Suhendro Kadek Utari Widiarsini Karda, Putu Adistyanda Timoti Raja Kendrawan, I Dewa Gede Shunu Komang Oka Saputra Krisna Hany Indrani Lie Jasa Made Ngurah Satya Wibawa Putra Made Sudarma Made Sudarma Made Surdarma Made Surdarma Mkwawa, Is-haka Ni Made Ary Esta Dewi Wirastuti Nunut Asihanna, Ester Nur Adl, Waliyin Nyoman Putra Sastra Purna Sastriya, Gede Edy Putra Dharma, Wisnu Wardhana Putra Sentana, Kadek Wibawa Putra, I Made Yudi Adnyana Putri Sintya Dewi Putu Adistyanda Timoti Raja Karda Putu Agus Indra Purnama Putu Arya Mertasana Putu Aryasuta Wicaksana Putu Pande Deva Ryana Putra Risqa Purma Pratama Salsabila, Unik Hanifah Sebayang, Deo Armanta Suartama, Putu Dandy Surya Puja Anggara Tjok Gede Indra Partha Widyadi Setiawan Wijaya Kusuma Yasa, Kadek Yogi Prawira Putra