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Klasifikasi Bibit Sapi Peranakan Ongole Menggunakan Metode Pengolahan Citra Leylin Fatqiyah; Agus Harjoko
IJEIS (Indonesian Journal of Electronics and Instrumentation Systems) Vol 6, No 2 (2016): October
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Ongole Crossbreed cattle is the largest cattle in Indonesia. Indonesian consume it’s beef in a large amount. Classification effects beef’s quantity and quality. However, the classification process is measuring manually one by one all this time. Moreover the current standard is too high and inappropriate due to the real exist conditions. Seeing the importance of classification, it is necessary to make a system that is able to classify Ongole Crossbreed cattle stocker.This system will measure quantitative requirement parameters from the image. This system will classify using image processing. Implementation of the system is using Matlab software. This system will classify into four classes, namely class I, class II, class III, and external class III. According to the results, it is obtained that the system is able to measure the body lenght, the chest circumference, and the height with accuracy rates are 90,77%, 93,30% and 93,13%. This system is able to classify the class of Ongole Crossbreed cattle stocker with accuracy rate is 86,67%

Hybrid Power Method For Power Supply Of Public Service Computer Tri Wahyu Supardi; Agus Harjoko
IJEIS (Indonesian Journal of Electronics and Instrumentation Systems) Vol 8, No 2 (2018): October
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Public services require computer-based electricity supply. Not continuous electricity supply in some areas led to a power supply from an alternative power source becomes indispensable. One alternative power source is a solar cell. Solar cell is a sustainable source of electricity but power output is not constant depending on the sunlight. The power source is needed to anticipate the lack of power when the power generated by the solar cell is not enough.In this paper proposed a hybrid design that combines the power supply of electricity from the solar cell, the network provider of electricity, and batteries. This paper discusses methods of hybrid electric power from three sources. Hybrid power is usually done by PCC (Power Controlled Converter), which consists of a controlled power converter for each channel input, but in this study the method proposed hybrid power input specification in the form of a synchronization circuit PCC then replaced by a diode circuit.The design of hybrid power supply in this study resulted in the specification input from the solar cell with Vmp channels (maximum power voltage) 35VDC Voc (open circuit voltage) 43.78VDC, channels of electricity provider with the already converted with SMPS (Switched Mode Power Supply) to 35VDC, and channels of a battery with a minimum voltage of 21VDC maximum 27.6VDC. The test results showed that the implementation of the proposed hybrid method can perform a single capture or hybrid power, and can transfer power between the source retrieval without pause. Implementation of the proposed hybrid method has a 21VDC output voltage range - 43.78VDC and efficiency of 98.6% - 99.5%.

Penggunaan Deteksi Gerak untuk Pengurangan Ukuran Data Rekaman Video Kamera CCTV Jockie P Sagala; Ika Candradewi; Agus Harjoko
IJEIS (Indonesian Journal of Electronics and Instrumentation Systems) Vol 10, No 1 (2020): April
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Some cases the recording data of Closed Circuit Television (CCTV) is stored for future use. In the long term usage, the files size will grow larger and requiring large storage devices. In some cases, the recorded data not only image with the desired object but also the background images that may be recorded for long periods of time. This cases make data storage device usage to be less effective. So this research will design a system of CCTV devices that capable to select images to reduce the size of stored images data by image processing.The images selection of this system is based on based on adaptive median algorithm. When any object get detected, the images data to be saved is current input frame. Otherwise, the data to be saved is background model image. Background model on this system is adapted with the change visual data of background image.The results obtained from this research in the form of a CCTV system that are able to select recording data to be stored with image processing. The background model will be kept adapting with background visual data changes.

Otomasi Kamera Perangkap Menggunakan Deteksi Gerak dan Komputer Papan Tunggal Habib Dwi Cahya; Agus Harjoko
IJEIS (Indonesian Journal of Electronics and Instrumentation Systems) Vol 9, No 1 (2019): April
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

USB camera is currently used in daily life for various purposes. On its development, the use of USB camera can be used to create camara traps and can be used to observe the development of animal with integrated systems. In this research, motion detection was used to observe animals online using Single Board Computer (SBC) Camera trap in this research using Single Board camera in form of raspberry pi 3 B. Python proggramming language is used with OpenCV library. The method used to detect motion is the Mixture of Gaussian (MOG). The result image gained by motion detection will be uploaded to the dropbox API.The test performed on 11 videos, the system can process images with 320x240 resolution. The test results show the best blut value of k = 13, the best threshold value is 100 pixel with an accuracy of 80,3%, and the maximum distance system can detect animal objects as far as 6m. The response time gained for the sytem to process frame per second have average of 0,098 seconds, while for uploading image to dropbox han an average of 1,618 seconds. The test result show the system still has room for development and improvement.

Operasi Morfologi Dan Kode Rantai Untuk Menghitung Luas Area Basah Kertas Saring Nafiatun Sholihah; Agus Harjoko
IJEIS (Indonesian Journal of Electronics and Instrumentation Systems) Vol 11, No 1 (2021): April
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijeis.46130

Calculation of wet areas carried out with the help of millimeter block paper has the disadvantage of copying the edges that are less precise and the calculation time is quite long. Another problem is the consistency and accuracy that is generated depends on the subjective factors of the person and one's fatigue. In order for the process to be faster and more consistent, the calculation process using image processing is very necessary. Image preprocessing includes cropping, grayscalling, lowpass filter averaging, convertion to binary image based on otsu thresholding, and complementing images to pixel objects of value 1. Segmentation with morphological operations, including opening operations to remove small objects around objects, Holes Filling operations to fill holes in objects, opening operations again to remove objects other than wet areas. The process of calculating wet areas uses chain code. Based on the results of testing of 81 images, the use of morphological operations is able to produce segmentation of wet areas that approach the original wet area. The scale value affects the accuracy and the best scale is obtained from the use of the ruler. The use of chain code is able to calculate the wet area on filter paper with an average accuracy of 95.73%, the value is higher than extensive use by summing the pixel value even though it is not significant. The average calculation of wet areas uses a system of about 0.8 seconds or 379 times faster than using millimeter block.

Klasifikasi Sel Darah Putih dan Sel Limfoblas Menggunakan Metode Multilayer Perceptron Backpropagation Apri Nur Liyantoko; Ika Candradewi; Agus Harjoko
IJEIS (Indonesian Journal of Electronics and Instrumentation Systems) Vol 9, No 2 (2019): October
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Leukemia is a type of cancer that is on white blood cell. This disease are characterized by abundance of abnormal white blood cell called lymphoblast in the bone marrow. Classification of blood cell types, calculation of the ratio of cell types and comparison with normal blood cells can be the subject of diagnosing this disease. The diagnostic process is carried out manually by hematologists through microscopic image. This method is likely to provide a subjective result and time-consuming.The application of digital image processing techniques and machine learning in the process of classifying white blood cells can provide more objective results. This research used thresholding method as segmentation and multilayer method of back propagation perceptron with variations in the extraction of textural features, geometry, and colors. The results of segmentation testing in this study amounted to 68.70%. Whereas the classification test shows that the combination of feature extraction of GLCM features, geometry features, and color features gives the best results. This test produces an accuration value 91.43%, precision value of 50.63%, sensitivity 56.67%, F1Score 51.95%, and specitifity 94.16%.

Pengenalan Ucapan Suku Kata Bahasa Lisan Menggunakan Ciri LPC, MFCC, dan JST Abriyono Abriyono; Agus Harjoko
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 6, No 2 (2012): July
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.2149

AbstrakSuara adalah salah satu alat komunikasi antar manusia yang efektif dan digemari. Selain sebagai alat komunikasi antar manusia, suara manusia telah digunakan sebagai alat komunikasi antara manusia dan komputer (mesin). Penelitian menggunakan suara sebagai alat komunikasi manusia dan mesin telah banyak dilakukan dengan menggunakan berbagai bahasa. Bahkan ada beberapa penelitian yang telah menghasilkan kemampuan pengenalan yang baik dan dikomersilkan (menggunakan bahasa Inggris). Bagaimana dengan penelitian pengenalan suara menggunakan Bahasa Indonesia? Peneliti mengamati penelitian pengenalan ucapan kata dalam Bahasa Indonesia masih minim dan cakupan jumlah katanya pun masih kecil. Oleh karena itu, pada penelitian ini, peneliti melakukan pengenalan ucapan kata Bahasa Indonesia. Pengenalan ucapan kata Bahasa Indonesia dilakukan dengan memecah kata Bahasa Indonesia ke dalam bentuk suku kata bahasa lisan. Pemecahan ke dalam bentuk lafal kata diharapkan mampu mengurangi jumlah kata yang sangat besar, namun tetap mengakomodasi seluruh kata yang dalam Bahasa Indonesia. Total jumlah lafal kata yang ditemukan oleh peneliti adalah 1741 suku kata bahasa lisan. Peneliti membagi sistem dalam 4 bagian besar, yakni proses perekaman, pre-processing, ekstraksi ciri, dan pengenalan. Pada proses perekaman digunakan frekuensi 11025 Hz, Mono, 8 bit. Pada pre-processing digunakan proses bantuan seperti pre-emphasis, segmentasi, framing, dan windowing. Sedangkan untuk ekstraksi ciri dan pengenalan digunakan ciri LPC/MFCC dan identifier jaringan syaraf tiruan backpropagation. Hasil pengenalan dengan pendekatan yang dibangun menunjukkan hasil yang belum memuaskan, yakni dengan kemampuan pengenalan terbaik sebesar 0.65% dengan ciri MFCC. Kata kunci—pengenalan kata Bahasa Indonesia, LPC, MFCC, JST, backpropagation. Abstract Voice is one of effective and convinienced communication’s medium among human. However, the used of voice is not only for communication among human but also has another role nowadays. Voice becomes communication medium for human and computer (machine). One of its application is speech to text application. Some of speech to text research already claimed good accuracy for some languages. How about Indonesian language? The research for Indonesian word recognition was still at low amount. The word used for research was at small amount too. Because of some of the reasons, researcher focus on Indonesian word recognition in this research. This research will divide the word into the speech syllable. The aim for the dividing system is to reduce the large amount of the word, but still cover all of the word. We found and used 1741 speech syllables. For managing the recognition, we used several approaches. The approaches are 11025 Hz, Mono, 8 bit for recording, pre-emphasized, segmentation, framing, and windowing for pre-processing, LPC and MFCC for the features, and back-propagation neural network for the identifier. The result using this approach was not reached good performance. The best result performed 0.65% by using MFCC feature. Keywords—Indonesian’s syllable recognition, LPC, MFCC, neural network, backpropagation

Sistem Informasi Geografis Risiko Kemunculan Rip Current Menggunakan Decision Tree C4.5 Made Leo Radhitya; Agus Harjoko
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 10, No 2 (2016): July
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.15949

One of the dangers that occur at the beach is rip current. Rip current poses significant danger for beachgoers. This paper proposes a method to predict the rip current's occurence risk by using decision tree generated using C4.5 algorithm. The output from the decision tree is rip current's occurrence risk. The case study for this research is the beach located at Rote Island, Rote Ndao, Nusa Tenggara Timur. Evaluation result shows that the accuracy is 0.84, and the precision is 0.61. The average recall value is 0.68 and the average F-measure is 0.59 in the range 0 to 1.

Klasifikasi Varietas Cabai Berdasarkan Morfologi Daun Menggunakan Backpropagation Neural Network Kharis Syaban; Agus Harjoko
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 10, No 2 (2016): July
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.16628

Compared with other methods of classifiers such as cellular and molecular biological methods, using the image of the leaves become the first choice in the classification of plants. The leaves can be characterized by shape, color, and texture; The leaves can have a color that varies depending on the season and geographical location. In addition, the same plant species also can have different leaf shapes. In this study, the morphological features of leaves used to identify varieties of pepper plants. The method used to perform feature extraction is a moment invariant and basic geometric features. For the process of recognition based on the features that have been extracted, used neural network methods with backpropagation learning algorithm. From the neural-network training, the best accuracy in classifying varieties of chili with minimum error 0.001 by providing learning rate 0.1, momentum of 0.7, and 15 neurons in the hidden layer foreach of various feature. To conduct cross-validation testing with k-fold tehcnique, obtained classification accuracy to be range of 80.75%±0.09% with k=4.

Case-Based Reasoning for Stroke Disease Diagnosis Nelson Rumui; Agus Harjoko; Aina Musdholifah
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 12, No 1 (2018): January
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.26331

Stroke is a type of cerebrovascular disease that occurs because blood flow to the brain is disrupted. Examination of stroke accurately using CT scan, but the tool is not always available, so it can be done by the Siriraj Score. Each type of stroke has similar symptoms so doctors should re-examine similar cases prior to diagnosis. The hypothesis of the Case-based reasoning (CBR) method is a similar problems having similar solution.This research implements CBR concept using Siriraj score, dense index and Jaccard Coeficient method to perform similarity calculation between cases.The test is using k-fold cross validation with 4 fold and set values of threshold (0.65), (0.7), (0.75), (0.8), (0.85), (0.9), and (0.95). Using 45 cases of data test and 135 cases of case base. The test showed that threshold of 0.7 is suitable to be applied in sensitivity (89.88%) and accuracy (84.44% for CBR using indexing and 87.78% for CBR without indexing). Threshold of 0.65 resulted high sensitivity and accuracy but showed many cases of irrelevant retrieval results. Threshold (0.75), (0.8), (0.85), (0.9) and (0.95) resulted in sensitivity (65.48%, 59.52%, 5.95%, 3,57% and 0%) and accuracy of CBR using indexing (61.67%, 55.56%, 5.56%, 3.33%, and 0%) and accuracy of CBR without indexing (62.78% 56.67%, 55.56%, 5.56%, 3.33%, and 0%).

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