Articles
<![CDATA[Distributed rule execution mechanism in smart home system ]]>
<![CDATA[Agung Setia Budi]]>;
<![CDATA[Hurriyatul Fitriyah]]>;
<![CDATA[Eko Setiawan]]>;
<![CDATA[Rakhmadhany Primananda]]>;
<![CDATA[Rizal Maulana]]>
<![CDATA[ International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 4: August 2022 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijece.v12i4.pp4439-4448
<![CDATA[Smart home systems become an interesting topics in the last few years. Many researchers have been studied some features. Most of smart home system use a centralized architecture know as centralized smart home system (CSHS). The centralizedmechanism is easy to manage and to configure. However, in fault-tolerant systemparadigm it produces a problem. The entire system will fail, if the master station fails.Another problem of CSHS is centralized mechanism gives more data-flow. This condition makes the system has a greater delay time. To solve the problem, we proposea distributed rule execution mechanism (DREM). Compared to the centralized mechanism, the DREM allows a device to provide its service without any commands fromthe master station. In this mechanism, since the information does not need to go tothe master station, the data-flow and the delay-time can be decreased. The experimentresults show that the DREM is able to mask the failure in the master station by directlytransmit the data from trigger device to service device. This mechanism makes the services provision without master station possible. The mathematical analysis also shows that the delay time of the service provision of the DREM is less than the delay time ofCSHS.]]>
<![CDATA[Deteksi Kesegaran Ikan Tongkol (Euthynnus Affinis) secara Otomatis Berdasarkan Citra Mata Menggunakan Binary Similarity ]]>
<![CDATA[Hurriyatul Fitriyah]]>;
<![CDATA[Dahnial Syauqy]]>;
<![CDATA[Faizal Andy Susilo]]>
<![CDATA[ Jurnal Teknologi Informasi dan Ilmu Komputer Vol 7 No 5: Oktober 2020 ]]>
Publisher : <![CDATA[Fakultas Ilmu Komputer, Universitas Brawijaya ]]>
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DOI: 10.25126/jtiik.2020753839
<![CDATA[Ikan tongkol (Euthynnus Affinis) adalah salah satu ikan yang paling banyak diminati di Indonesia karena kandungan proteinnya hampir setara ikan tuna, namun dengan harga relatif lebih murah. Ikan termasuk komoditi pangan yang mudah rusak tanpa adanya penanganan khusus ketika ikan ditangkap. Padahal, mutu dan nilai jual ikan sangat tergantung dari parameter kesegaran ikan itu sendiri. Penelitian ini mengembangkan metode deteksi kesegaran ikan tongkol menggunakan fitur berupa citra mata ikan. Mata ikan dapat digunakan untuk mengetahui tingkat kesegarannya. Ikan segar memiliki pupil bulat berwarna hitam yang utuh dan jernih di tengahnya. Hal tersebut kemudian dijadikan knowledge-based dari proses deteksi kesegaran ikan. Sebelum dilakukan proses deteksi, dilakukan proses pre-processing untuk mendapatkan gambar kepala ikan secara otomatis. Selanjutnya dilakukan perhitungan similarity antara citra biner kepala ikan dengan 2 buah template, yakni Template-Mata untuk mendeteksi mata dan Template-Tengah untuk mendeteksi bulat hitam di tengah mata. Sebanyak 30 citra mata ikan dengan kriteria segar dan tidak segar digunakan sebagai data pengujian. Dari pengujian, kedua template tersebut mampu membedakan ciri morfologis dari mata ikan yang segar dengan tepat.AbstractTongkol fish (Euthynnus Affinis) is one of the most popular fish in Indonesia because it has more protein than tuna, but with a relatively cheaper price. Fish is a perishable food commodities if it is caught without any special handling. In fact, the quality and value of fish selling depends on the parameters of the freshness of the fish itself. This study developed a method for detecting freshness of tongkol fish using features that is extracted from the image of a fish's eye. Fish eye can be used to determine the level of freshness. Fresh fish have whole round and clear black pupils in the middle. This is then made into knowledge-base on the process of detecting the freshness. First, this fully automatic detection performed a pre-processing process to obtain automatic fish head images. It was then compared with two templates, which are eye-template and middle-template. If the fish head image has similarity below certain threshold then it is classified as fresh fish, or else it is non-fresh fish. A total of 30 images of fish with fresh and non-fresh criteria were used as test data. From the test, the two templates can classify the morphological characteristics of fresh fish eyes precisely.]]>
<![CDATA[Deteksi Gulma Berdasarkan Warna HSV dan Fitur Bentuk Menggunakan Jaringan Syaraf Tiruan ]]>
<![CDATA[Hurriyatul Fitriyah]]>;
<![CDATA[Rizal Maulana]]>
<![CDATA[ Jurnal Teknologi Informasi dan Ilmu Komputer Vol 8 No 5: Oktober 2021 ]]>
Publisher : <![CDATA[Fakultas Ilmu Komputer, Universitas Brawijaya ]]>
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DOI: 10.25126/jtiik.2021854719
<![CDATA[Gulma merupakan tanaman pengganggu dalam lahan pertanian. Herbisida merupakan obat yang efektif membunuh gulma tersebut. Penyemprotan herbisida harus tepat sasaran kepada gulma saja dan tidak mengenai tanaman. Penelitian ini membuat sistem yang dapat mendeteksi gulma secara otomatis di antara tanaman pada lahan pertanian riil. Sistem ini menggunakan gambar lahan pertanian riil dimana tanaman tampak utuh (daun dapat lebih dari satu) yang diambil menggunakan kamera dengan posisi vertikal menghadap ke bawah. Algoritma yang dibuat menggunakan segmentasi berdasarkan warna hijau dalam ruang warna HSV untuk mendeteksi daun, baik gulma maupun tanaman pada beragam pencahayaan. Sebanyak tiga fitur bentuk domain spasial digunakan untuk membedakan gulma dengan tanaman yang memiliki karakteristik bentuk daun yang berbeda. Fitur bentuk yang digunakan adalah Rectangularity, Edge-to-Center distances function, dan Distance Transform function. Klasifikasi gulma dan tanaman menggunakan metode Jaringan syaraf tiruan (JST) yang dapat dilatih secara offline. Dari 149 tanaman yang terdeteksi dimana 70% sebagai data training, 15% data validasi dan 15% data uji, didapati akurasi pengujian sebesar 95.46%.AbstractWeed is a major challenge in a crop plantation. A herbicide is the most effective substance to kill this unwanted vegetation. Spraying the herbicide must be done carefully to target the weeds only. Here in this research, we develop an algorithm that detects weeds among the plants based on the shape of their leaves. The detection is based on images that were acquired using a camera. The leaves of weeds and plants were detected based on their green color using segmentation in HSV color-space as it is more effective to detect objects in various illumination. Three shape features were extracted, which are Rectangularity that is based on Rectangularity, Edge-to-Center distance function, and Distance Transform function. Those features were fed into a learning algorithm, Artificial Neural Network (ANN), to classify whether it is the plant or the weed. The testing on the weed classification in a real outdoor environment showed 95.46% accuracy using a total of 149 detected plants (70% as training data, 15% as validation data, and 15% as testing data).]]>
<![CDATA[Face Detection of Thermal Images in Various Standing Body-Pose using Facial Geometry ]]>
<![CDATA[Hurriyatul Fitriyah]]>;
<![CDATA[Edita Rosana Widasari]]>
<![CDATA[ IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 14, No 4 (2020): October ]]>
Publisher : <![CDATA[IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia. ]]>
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DOI: 10.22146/ijccs.59672
<![CDATA[ Automatic face detection in frontal view for thermal images is a primary task in a health system e.g. febrile identification or security system e.g. intruder recognition. In a daily state, the scanned person does not always stay in frontal face view. This paper develops an algorithm to identify a frontal face in various standing body-pose. The algorithm used an image processing method where first it segmented face based on human skin’s temperature. Some exposed non-face body parts could also get included in the segmentation result, hence discriminant features of a face were applied. The shape features were based on the characteristic of a frontal face, which are: (1) Size of a face, (2) facial Golden Ratio, and (3) Shape of a face is oval. The algorithm was tested on various standing body-pose that rotate 360° towards 2 meters and 4 meters camera-to-object distance. The accuracy of the algorithm on face detection in a manageable environment is 95.8%. It detected face whether the person was wearing glasses or not.]]>
<![CDATA[Controlling the Nutrition Water Level in the Non-Circulating Hydroponics based on the Top Projected Canopy Area ]]>
<![CDATA[Hurriyatul Fitriyah]]>;
<![CDATA[Agung Setia Budi]]>;
<![CDATA[Rizal Maulana]]>;
<![CDATA[Eko Setiawan]]>
<![CDATA[ IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 16, No 2 (2022): April ]]>
Publisher : <![CDATA[IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia. ]]>
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DOI: 10.22146/ijccs.70556
<![CDATA[Deep Water Culture Hydroponics is suitable for a large-scale plantation as it does not require turn-on the electric pump constantly. Nevertheless, this method needs an electric aerator to give Oxygen to the roots. Kratky’s and Dry Hydroponics are the two methods that suggest an air gap between the raft and the nutrient water level. The gap gives Oxygen to the roots without an aeration pump. Controlling the nutrient water level is required to give a good distance of air gap for Precision Agriculture. The root length estimation used to be done manually by opening the raft, but this research promotes automatic and non-contact estimation using the camera. The images are used to predict the root length based on the Top Projected Canopy Area (TPCA) using various Regression Methods. The test shows that the TPCA gives a high correlation toward the Root Length (>0.9). To control the nutrient water level, this research compares If-Else and the Linear Regression. The error between the actual level that is measured using an Ultrasonic sensor and the setpoint is fed to an Arduino Uno to control the duration of an inlet pump and the outlet pump. The If-Else and the Linear Regression method show good results.]]>
<![CDATA[Automatic Estimation of Human Weight From Body Silhouette Using Multiple Linear Regression ]]>
<![CDATA[Hurriyatul Fitriyah]]>;
<![CDATA[Gembong Edhi Setyaw]]>
<![CDATA[ Proceeding of the Electrical Engineering Computer Science and Informatics Vol 5: EECSI 2018 ]]>
Publisher : <![CDATA[IAES Indonesia Section ]]>
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DOI: 10.11591/eecsi.v5.1688
<![CDATA[Estimating weight based on 2D image is advantageous especially for contactless and rapid measurement. Several researches used additional thermal camera or Kinect camera, required subjects to do front and side pose and manually extract body measures. This research propose an algorithm to estimate body weight automatically using 2D visual image where subject only do front pose. This research studied 4 features of body measures which are: (F1) height, and width of (F2) shoulder, (F3) abdomen/waist plus arm, (F4) feet. Each feature was simply subtracted based on body proportion where normal body has 8 equal segments. Shoulder is in 2nd segment, abdomen/waist is in 4th segment and feet is in the last segment. Multiple Linear Regression is used to determine weight estimation formula of all combination of 4 features, 15 in total. The highest significance R2 (0.80) and RMSE 2.68 Kg is given when using all 4 features in the estimation formula.]]>
<![CDATA[Sistem Deteksi Jumlah, Jenis dan Kecepatan Kendaraan Menggunakan Analisa Blob Berbasis Raspberry Pi ]]>
<![CDATA[Gembong Edhi Setyawan]]>;
<![CDATA[Benny Adiwijaya]]>;
<![CDATA[Hurriyatul Fitriyah]]>
<![CDATA[ Jurnal Teknologi Informasi dan Ilmu Komputer Vol 6 No 2: April 2019 ]]>
Publisher : <![CDATA[Fakultas Ilmu Komputer, Universitas Brawijaya ]]>
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DOI: 10.25126/jtiik.2019621405
<![CDATA[Penghitungan kondisi lalu lintas guna analisa kualitas jalan raya umumnya dilakukan secara manual. Hal ini tentunya membutuhkan biaya dan SDM yang tinggi serta tidak dapat dianalisa secara langsung. Dalam penelitian ini telah dikembangkan metode pengenalan jenis, jumlah dan kecepatan kendaraan secara otomatis menggunakan pengolahan citra digital. Metode yang berdasarkan analisa terhadap BLOB (Binary Large OBject) tersebut ditanamkan pada sistem berbasis Raspberry Pi. Setiap blob merupakan connected-component yang diperoleh dari proses thresholding terhadap perubahan nilai pixel dari sebuah frame dan frame rujukan dalam metode background subtraction. Jenis kendaraan ditentukan oleh jumlah piksel dalam bounding-box setiap blob. Jumlah kendaraan yang melaju dihitung dengan memberikan garis virtual dimana jumlahnya akan bertambah jika centroid dari setiap bounding-box kendaraan melewatinya. Kecepatan kendaraan dihitung dengan membagi jarak sebenarnya dari koordinat awal hingga garis virtual sepanjang 12 meter yang dibagi dengan waktu centroid tersebut untuk menempuhnya. Algoritma tersebut diimplementasikan pada sistem berbasis Raspberry Pi dengan input kamera yang terhubung dengan serial monitor untuk menampilkan output penghitungan. Pengujian akurasi deteksi jenis kendaraan yakni sepeda motor, kendaraan ringan dan berat menghasilkan akurasi 93,39%. Pengujian jumlah kendaraan menghasilkan rata-rata akurasi 93,48% untuk semua jenis kendaraan. Pengujian laju kendaraan yang dideteksi dengan dibandingkan kecepatan pada spedometer kendaraan menunjukkan akurasi 93,9%. AbstractAn analysis on traffic condition usually carried out manually by visual observation. This method demands high human resource and cannot be analysed immediately. This paper present an algorithm to analyse type, number and speed of vehicles that passing by a road automatically using BLOB (Binary Large Object) analysis. Each blob is a connected-component as a result of thresholding after background subtration process. Type of vehicles was determined by measuring pixel number of blob’s bounding box. Number of vehicles was determined by drawing virtual line where the number was increased once a centroid of bounding box passed it. Speed of vehicles was determined using basic speed formula where 12 meters of actual distance between the beginning coordinate and virtual line was divided by time to travel it. The algorithm was embedded in Raspberry Pi where videos were acquired using attached web camera. The analysis result was shown in connected serial monitor. Testing on vehicles’ type detection (motorcycle, light vehicle, heavy vehicle) result accuracy of 93.9%, number of vehicles result accuracy of 93.48%, whilst speed of vehicles result accuracy of 93.9%.]]>
<![CDATA[Pengukuran Panjang-Berat Ikan dan Sayuran pada Budikdamber (Budi Daya Ikan dalam Ember) Menggunakan Visi Komputer dan Regresi Linier ]]>
<![CDATA[Hurriyatul Fitriyah]]>
<![CDATA[ Jurnal SISKOM-KB (Sistem Komputer dan Kecerdasan Buatan) Vol. 4 No. 1 (2020): Volume IV - Nomor 1 - September 2020 ]]>
Publisher : <![CDATA[Teknik Informatika ]]>
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DOI: 10.47970/siskom-kb.v4i1.166
<![CDATA[Budi daya ikan dalam ember atau Budikdamber kini banyak diminati masyarakat Indonesia karena dapat dilakukan pada lahan yang terbatas. Lele dan kangkung adalah ikan dan sayuran yang sering dikembangbiakkan pada budikdamber ini. Meski keduanya tergolong mudah untuk tumbuh, namun pemantauan kondisi masih perlu dilakukan. Diantaranya adalah ukuran ikan dan sayuran yang dapat menunjukkan apakah kondisi keduanya baik atau memerlukan penanganan. Penelitian ini membuat sistem pengukuran berat dan panjang ikan dan sayuran pada budikdamber menggunakan visi komputer untuk mengambil fitur dan regresi linier untuk memprediksi ukuran. Kamera dipilih sebagai sensor agar pengukuran dapat dilakukan secara non-kontak. Fitur yang digunakan adalah luas area dari lele dan kangkung yang diperoleh berdasarkan proses segmentasi pada ruang warna Hue dan Saturation. Prediksi berat dan panjang menggunakan Regresi Linier dengan input luas area dan output berat-panjang melalui proses pelatihan data. Hasil pengujian menggunakan Kfold cross Validation pada gambar uji menunjukkan nilai koefisien determinasi R2 yang tinggi pada panjang & berat lele (0,92 & 0,88), namun rendah pada panjang & berat kangkung (0,43 & 0,07). Meski demikian, Mean Absolute Percentage Error (MAPE) dari prediksi ukuran kangkung masih baik yakni 13,37% untuk berat dan 5,78% untuk panjang. MAPE dari pengukuran panjang lele adalah 1,49% dan berat lele adalah 4,49%. Nilai-nilai tersebut menunjukkan bahwa algoritma yang dibangun sudah memiliki akurasi yang baik, namun masih perlu perbaikan dalam hal prediksi ukuran kangkung.]]>
<![CDATA[Sistem Object Tracking pada Quadcopter Menggunakan Segmentasi Citra dengan Deteksi Warna HSV dan Metode Regresi Linier Berbasis Raspberry Pi ]]>
<![CDATA[Fahmi Erza]]>;
<![CDATA[Hurriyatul Fitriyah]]>;
<![CDATA[Eko Setiawan]]>
<![CDATA[ Jurnal Teknologi Informasi dan Ilmu Komputer Vol 9 No 7: Spesial Issue Seminar Nasional Teknologi dan Rekayasa Informasi (SENTRIN) 2022 ]]>
Publisher : <![CDATA[Fakultas Ilmu Komputer, Universitas Brawijaya ]]>
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DOI: 10.25126/jtiik.2022976808
<![CDATA[Saat ini, banyak aplikasi perangkat cerdas yang dikembangkan untuk melakukan tugas secara mandiri tanpa menerima perintah dari manusia. Oleh karena itu, mengembangkan sistem yang memungkinkan perangkat untuk melakukan tugas pengawasan seperti mendeteksi dan melacak objek bergerak akan memungkinkan tugas yang lebih canggih untuk diterapkan pada robot di masa depan. Teknologi Quadcopter sesungguhnya dapat memudahkan pekerjaan manusia dalam melakukan pengawasan dan pelacakan seperti pada kasus pelacakan lansia atau ABK (Anak Berkebutuhan Khusus) secara otomatis agar kerabat dapat melakukan pengawasan dengan menggunakan drone. Sehingga penelitian ini dilakukan untuk membuat sebuah sistem pada drone atau quadcopter agar dapat mendeteksi objek dan mengikutinya. Pada implementasinya, orang yang berkebutuhan khusus dan membutuhkan pengawasan akan mengenakan atribut berupa topi dengan warna solid. Warna topi tersebut akan dijadikan acuan untuk threshold segmentasi warna untuk mendeteksi objek topi tersebut dengan pemrosesan citra digital. Pergerakan drone ditentukan oleh prediksi jarak, sudut, dan ketinggian objek berdasarkan regresi linier yang dihasilkan dari 123 data latih. Hasil deteksi sistem juga cukup sesuai dengan pergerakan drone ketika diuji dengan 27 data. Akurasi dari prediksi gerak pitch adalah 84%, prediksi gerak yaw adalah 94%, dan prediksi gerak up/down adalah 91,5%. Adapun waktu komputasinya adalah 0.175829662 detik per frame. Abstract Nowadays, many intelligent device applications are developed to perform tasks independently without receiving commands from humans. Therefore, developing systems that allow devices to perform surveillance tasks such as detecting and tracking moving objects will allow more sophisticated tasks to be applied to robots in the future. Quadcopter technology can actually facilitate human work in monitoring and tracking, such as in the case of tracking the elderly or children with special needs automatically so that relatives can carry out surveillance using drones. So this research was planned to create a system on a drone so it can detect objects and follow them. In its implementation, people with special needs and need supervision will wear an attribute in the form of a hat with a solid color. The color of the hat will be used as references for the color segmentation threshold to detect the hat object with digital image processing. The movement of the drone is determined by the prediction of the distance, angle, and height of the object based on linear regression generated from 123 training data. The system detection results are also quite in accordance with the movement of the drone when tested with 27 data. The accuracy of pitch motion prediction is 84%, yaw motion prediction is 94%, and up/down motion prediction is 91.5%. The computation time is 0.175829662 seconds per frame.]]>
<![CDATA[Accuracy of Various Methods to Estimate Volume and Weight of Symmetrical and Non-Symmetrical Fruits using Computer Vision ]]>
<![CDATA[Hurriyatul Fitriyah]]>
<![CDATA[ Journal of ICT Research and Applications Vol. 16 No. 3 (2022) ]]>
Publisher : <![CDATA[DRPM - ITB ]]>
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DOI: 10.5614/itbj.ict.res.appl.2022.16.3.2
<![CDATA[Many researchers have used images to measure the volume and weight of fruits so that the measurement can be done remotely and non-contact. There are various methods for fruit volume estimation based on images, i.e., Basic Shape, Solid of Revolution, Conical Frustum, and Regression. The weight estimation generally uses Regression. This study analyzed the accuracy of these methods. Tests were done by taking images of symmetrical fruits (represented by tangerines) and non-symmetrical fruits (represented by strawberries). The images were processed using segmentation in saturation color space to get binary images. The Regression method used Diameter, Projection Area, and Perimeter as features that were extracted from the binary images. For symmetrical fruits, the best accuracy was obtained with the Linear Regression based on Diameter (LDD), which gave the highest R2 (0.96 for volume and 0.93 for weight) and the lowest RMSE (5.7 mm3 for volume and 5.3 gram for volume). For non-symmetrical fruits, the highest accuracy for non-symmetric fruits was given by the Linear Regression based on Diameter (LRD) and Linear Regression based on Area (LRA) with an R2 of 0.8 for volume and weight. The RMSE for LRD and LRA for strawberries was 3.3 mm3 for volume and 1.4 grams for weight.]]>
