Articles
<![CDATA[Identifikasi Titik Api Lilin Berbasis Nilai HSV , Threshold dan Momen Citra untuk Aplikasi Robot Pemadam Api ]]>
<![CDATA[Wiyagi, Rama Okta]]>;
<![CDATA[Soesanti, Indah]]>;
<![CDATA[Susanto, Adhi]]>
<![CDATA[ Jurnal Semesta Teknika Vol 17, No 1 (2014): MEI 2014 ]]>
Publisher : <![CDATA[Jurnal Semesta Teknika ]]>
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<![CDATA[Fire fighting robot is robot that has function to find and extinguish a candle flame in the space arena. To be able carry out their duties then the robot is equipped with sensors, controllers and drivers. Phototransistor, thermopile arrays, or UVTron is sensors that usually used in fire fighting robot. These sensors have some drawbacks. Phototransistor has a relatively close distance readings. While TPA81 thermopile array has a narrow field of reading only 41 ° x 6 ° from sensor. UVtron only limited to determine whether there is any point of the fire and was unable to determine absolute position or angle of the hotspots and vulnerable to damage if the jar is touched by the hand. Additionally TPA81 sensors and sensor UVtron is relatively expensive. This research aims to build a candle light detection alternative better in terms of specification, performance, price, reliability and ease of development. As the input of the system identification using webcams camera types. The webcam running on Raspberry Pi single-board computer. Image information is converted to HSV color space (Hue, Saturation, Value) and applied threshold processing. Thresholding HSV performed on the range of values contained in the object candle flame. To get the absolute position of a candle flame using moments analysis. Identification system can identify candle flame spot with the farthest distance is 225cm. Angle readings in the horizontal plane by 60 Ë and the vertical plane by 40 Ë. The achievement of the highest FPS obtained in image resolution size of 320 x 240 pixels which is 8.129 FPS.]]>
<![CDATA[Kompresi Citra Medis Menggunakan Alihragam Kosinus Diskret Dan Sistem Logika Fuzzy Adaptif ]]>
<![CDATA[Soesanti, Indah]]>
<![CDATA[ Jurnal Semesta Teknika Vol 11, No 1 (2008): MEI 2008 ]]>
Publisher : <![CDATA[Jurnal Semesta Teknika ]]>
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<![CDATA[The required of bandwidth for communication of digital image data is increased. Limited channel capacity favors image compression techniques. These techniques attempt to minimize the number of bits needed to represent an image and to reconstruct it with little visible distortion. The image data compression techniques reduce memory of storage data and time needed to transmit data. One of the image data compression methods is using Discrete Cosine Transform and Adaptive Fuzzy Logic. The objective of this research is compressing medical image using Discrete Cosine Transform and Adaptive Fuzzy Logic System. Discrete Cosine Transform is applied to find the data will which be encoded and Adaptive Fuzzy Logic System is applied to classify sub image into certain class. The class classification of a sub image is according to their AC energy levels. The systems assign more bits to a sub image if the sub image contains much detail (large AC energy) and less bits if contains less detail (small AC energy). The result of the research shows that the accurate calculation of AC energy determines class classification of sub image and bitmaps used for image data compression must be matching with characteristic of image. Bitmaps used for image data compression determine compression ratio and reconstructed image quality. The medical image compression with ratio of 1:4.8028 result in a reconstruction image with SNR of 63.8197 dB, and visually shows that the image is similar to the original image without significant error.]]>
<![CDATA[Perancangan Perangkat Lunak untuk Ekstraksi Ciri dan Klasifikasi Pola Batik ]]>
<![CDATA[Soesanti, Indah]]>
<![CDATA[ Semesta Teknika Vol 17, No 2 (2014): NOVEMBER 2014 ]]>
Publisher : <![CDATA[Semesta Teknika ]]>
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<![CDATA[The popularity of batik patterns in Indonesia has varied. Industrial modern devices in imaging have supported batik pattern recognition and classification. The important of product pattern information could not naturally visible. The information about batik pattern can be achieved by using the appropriate software design of image processing for extracting the features. One of the potential procedures is the unsupervised classification method based on specific feature. In this research, the specific feature extraction based on the eigenimage of batik pattern was done. In the final step, the nearest distance eigenimage between reference batik image and test batik image was used to identify the batik from the classical pattern field point of view. The results of batik image identification conformed 96.67% with the reference batik images.]]>
<![CDATA[Klasifikasi Wajah Kambing Peranakan Ettawa (PE) Jantan Berbasis Perseptron ]]>
<![CDATA[Chamim, Anna Nur Nazilah]]>;
<![CDATA[Soesanto, Adhi]]>;
<![CDATA[Soesanti, Indah]]>
<![CDATA[ Semesta Teknika Vol 17, No 2 (2014): NOVEMBER 2014 ]]>
Publisher : <![CDATA[Semesta Teknika ]]>
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<![CDATA[Goat Peranakan Ettawa ( PE ) is a kind of superior goat derived from goat crosses, between Ettawa (Jamnapari ) from India and Kambing Kacang (Bean Goat) from Java. A factor to determine quality of goat PE is itâs face. More than 30 cm ears length and the head color is black represents good quality. More better the quality of goat face, means higher selling price. In this study, male goat face is classified into class good quality, less good, and not good at data such as photo / image In the market, classification done by visual observation, so many farmers have difficulty in classifying the face of a goat. For that purpose, a system is needed that capable for classifying a goat face to facilitate farmers in classifying.This classification system uses Perceptron Method, is a method of guided learning using characteristic as input those are ears length, black value and brown face value. Images are used as training images as much as 9 images, and test images are 20 images. This system could classificating PE goat face with success rate of 95% and 1 error from 20 testing images. Error occured because the background was detected as black and image taking that not precise. ]]>
<![CDATA[Prediksi Beban Listrik Menggunakan Algoritma Jaringan Syaraf Tiruan Tipe Propagasi-Balik ]]>
<![CDATA[Syahputra, Ramadoni]]>;
<![CDATA[Syahfitra, Febrian Dhimas]]>;
<![CDATA[Putra, Karisma Trinanda]]>;
<![CDATA[Soesanti, Indah]]>
<![CDATA[ Semesta Teknika Vol 23, No 2 (2020): NOVEMBER 2020 ]]>
Publisher : <![CDATA[Semesta Teknika ]]>
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<![CDATA[Artikel ini mengusulkan prediksi beban puncak menggunakan metode jaringan syaraf tiruan tipe propagasi-balik. Prediksi beban puncak transformator tenaga merupakan tugas penting dalam mengantisipasi pertumbuhan beban listrik di masa mendatang. Prediksi yang tepat dan akurat akan memfasilitasi perencanaan kapasitas pembangkit listrik yang memadai pada waktu yang tepat. Metode jaringan syaraf tiruan tipe propagasi-balik memiliki akurasi yang baik dalam tugas-tugas prediksi. Pada penelitian ini dilakukan prediksi beban puncak pada dua buah transformator tenaga dengan studi kasus di Gardu Induk Bumiayu, Brebes, Jawa Tengah, Indonesia. Parameter pelatihan adalah data pertumbuhan penduduk, produk domestik regional bruto (PDRB), dan data beban puncak selama sepuluh tahun terakhir. Hasil penelitian menunjukkan bahwa kedua unit transformator tenaga tersebut masih dapat melayani beban listrik di wilayah pelayanan Gardu Induk Bumiayu selama sepuluh tahun ke depan.   This article proposes a peak load prediction using the backpropagation neural network method. Predicting the peak load of power transformers is an important task in anticipating load growth in the future. Precise and accurate predictions will facilitate the planning of sufficient power generation capacity at the right time. The backpropagation type neural network method has good accuracy in the prediction task. In this study, a case study was carried out by predicting the peak load of power transformers at Bumiayu Substation, Brebes, Central Java, Indonesia. Training parameters consists of population growth data, gross regional domestic product (GRDP), and peak load data for the last ten years. The results showed that the two power transformer units could still serve the electricity load in the Bumiayu substation service area for the next ten years.   ]]>
<![CDATA[Prediksi Beban Listrik Menggunakan Algoritma Jaringan Syaraf Tiruan Tipe Propagasi-Balik ]]>
<![CDATA[Syahputra, Ramadoni]]>;
<![CDATA[Syahfitra, Febrian Dhimas]]>;
<![CDATA[Putra, Karisma Trinanda]]>;
<![CDATA[Soesanti, Indah]]>
<![CDATA[ Semesta Teknika Vol 23, No 2 (2020): NOVEMBER 2020 ]]>
Publisher : <![CDATA[Universitas Muhammadiyah Yogyakarta ]]>
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DOI: 10.18196/st.v23i2.9940
<![CDATA[Artikel ini mengusulkan prediksi beban puncak menggunakan metode jaringan syaraf tiruan tipe propagasi-balik. Prediksi beban puncak transformator tenaga merupakan tugas penting dalam mengantisipasi pertumbuhan beban listrik di masa mendatang. Prediksi yang tepat dan akurat akan memfasilitasi perencanaan kapasitas pembangkit listrik yang memadai pada waktu yang tepat. Metode jaringan syaraf tiruan tipe propagasi-balik memiliki akurasi yang baik dalam tugas-tugas prediksi. Pada penelitian ini dilakukan prediksi beban puncak pada dua buah transformator tenaga dengan studi kasus di Gardu Induk Bumiayu, Brebes, Jawa Tengah, Indonesia. Parameter pelatihan adalah data pertumbuhan penduduk, produk domestik regional bruto (PDRB), dan data beban puncak selama sepuluh tahun terakhir. Hasil penelitian menunjukkan bahwa kedua unit transformator tenaga tersebut masih dapat melayani beban listrik di wilayah pelayanan Gardu Induk Bumiayu selama sepuluh tahun ke depan.   This article proposes a peak load prediction using the backpropagation neural network method. Predicting the peak load of power transformers is an important task in anticipating load growth in the future. Precise and accurate predictions will facilitate the planning of sufficient power generation capacity at the right time. The backpropagation type neural network method has good accuracy in the prediction task. In this study, a case study was carried out by predicting the peak load of power transformers at Bumiayu Substation, Brebes, Central Java, Indonesia. Training parameters consists of population growth data, gross regional domestic product (GRDP), and peak load data for the last ten years. The results showed that the two power transformer units could still serve the electricity load in the Bumiayu substation service area for the next ten years.   ]]>
<![CDATA[Klasifikasi Wajah Kambing Peranakan Ettawa (PE) Jantan Berbasis Perseptron ]]>
<![CDATA[Chamim, Anna Nur Nazilah]]>;
<![CDATA[Soesanto, Adhi]]>;
<![CDATA[Soesanti, Indah]]>
<![CDATA[ Semesta Teknika Vol 17, No 2 (2014): NOVEMBER 2014 ]]>
Publisher : <![CDATA[Universitas Muhammadiyah Yogyakarta ]]>
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DOI: 10.18196/st.v17i2.420
<![CDATA[Goat Peranakan Ettawa ( PE ) is a kind of superior goat derived from goat crosses, between Ettawa (Jamnapari ) from India and Kambing Kacang (Bean Goat) from Java. A factor to determine quality of goat PE is it’s face. More than 30 cm ears length and the head color is black represents good quality. More better the quality of goat face, means higher selling price. In this study, male goat face is classified into class good quality, less good, and not good at data such as photo / image In the market, classification done by visual observation, so many farmers have difficulty in classifying the face of a goat. For that purpose, a system is needed that capable for classifying a goat face to facilitate farmers in classifying.This classification system uses Perceptron Method, is a method of guided learning using characteristic as input those are ears length, black value and brown face value. Images are used as training images as much as 9 images, and test images are 20 images. This system could classificating PE goat face with success rate of 95% and 1 error from 20 testing images. Error occured because the background was detected as black and image taking that not precise.]]>
<![CDATA[Metode Ekstraksi Ciri untuk Membedakan Citra Wajah Asli dan Foto Berbasis Perceptron ]]>
<![CDATA[Afri Yudamson , Indah Soesanti, Warsun Najib]]>
<![CDATA[ Semesta Teknika Vol 16, No 1 (2013): MEI 2013 ]]>
Publisher : <![CDATA[Universitas Muhammadiyah Yogyakarta ]]>
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DOI: 10.18196/st.v16i1.431
<![CDATA[Face is one of media for human identification. Previous studies aimed at identifying human face were for a two-dimensional images. Thus, fraud may occur when providing input in two-dimensional face images (photos). This study aims to distinguish the original three-dimensional face image with two-dimensional face image. Feature extraction based on facial geometry principles (Incomplete sentence, subject only, do not know what the authors mean). Face images (both the original and the photos) were captured at deviated angle, to the left and to the right. Each image is then sliced for each face components (eyes and nose) and sought the position of the center point of each component. Comparison between the value of the right eye-nose projection vector to the left-right eye vector and the value of the left-right eye vector become the characteristics of each image. The perceptron method was used for the classifiers. The result, the software can distinguish the original three-dimensional and two-dimensional face image with an error of 8.33% of the 24 tested images. Error occurred for some samples that show big round nose.]]>
<![CDATA[Kompresi Citra Medis Menggunakan Alihragam Kosinus Diskret Dan Sistem Logika Fuzzy Adaptif ]]>
<![CDATA[Indah Soesanti]]>
<![CDATA[ Semesta Teknika Vol 11, No 1 (2008): MEI 2008 ]]>
Publisher : <![CDATA[Universitas Muhammadiyah Yogyakarta ]]>
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DOI: 10.18196/st.v11i1.772
<![CDATA[The required of bandwidth for communication of digital image data is increased. Limited channel capacity favors image compression techniques. These techniques attempt to minimize the number of bits needed to represent an image and to reconstruct it with little visible distortion. The image data compression techniques reduce memory of storage data and time needed to transmit data. One of the image data compression methods is using Discrete Cosine Transform and Adaptive Fuzzy Logic. The objective of this research is compressing medical image using Discrete Cosine Transform and Adaptive Fuzzy Logic System. Discrete Cosine Transform is applied to find the data will which be encoded and Adaptive Fuzzy Logic System is applied to classify sub image into certain class. The class classification of a sub image is according to their AC energy levels. The systems assign more bits to a sub image if the sub image contains much detail (large AC energy) and less bits if contains less detail (small AC energy). The result of the research shows that the accurate calculation of AC energy determines class classification of sub image and bitmaps used for image data compression must be matching with characteristic of image. Bitmaps used for image data compression determine compression ratio and reconstructed image quality. The medical image compression with ratio of 1:4.8028 result in a reconstruction image with SNR of 63.8197 dB, and visually shows that the image is similar to the original image without significant error.]]>
<![CDATA[Perancangan Perangkat Lunak untuk Ekstraksi Ciri dan Klasifikasi Pola Batik ]]>
<![CDATA[Indah Soesanti]]>
<![CDATA[ Semesta Teknika Vol 17, No 2 (2014): NOVEMBER 2014 ]]>
Publisher : <![CDATA[Universitas Muhammadiyah Yogyakarta ]]>
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DOI: 10.18196/st.v17i2.424
<![CDATA[The popularity of batik patterns in Indonesia has varied. Industrial modern devices in imaging have supported batik pattern recognition and classification. The important of product pattern information could not naturally visible. The information about batik pattern can be achieved by using the appropriate software design of image processing for extracting the features. One of the potential procedures is the unsupervised classification method based on specific feature. In this research, the specific feature extraction based on the eigenimage of batik pattern was done. In the final step, the nearest distance eigenimage between reference batik image and test batik image was used to identify the batik from the classical pattern field point of view. The results of batik image identification conformed 96.67% with the reference batik images.]]>
