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All Journal Jurnal Informatika Jurnal Ilmiah Kursor Jurnal Teknologi Informasi dan Ilmu Komputer Fountain of Informatics Journal Jurnal Matematika Sains dan Teknologi
Edi Satriyanto
Politeknik Elektronika Negeri Surabaya
Agriculture, Biological Sciences & Forestry Computer Science & IT Decision Sciences, Operations Research & Management Engineering Mathematics Other

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PENGENDALI POINTER DENGAN GAZE TRACKING MENGGUNAKAN METODE HAAR CLASSIFIER SEBAGAI ALAT BANTU PRESENTASI (EYE POINTER) Satriyanto, Edi; Ardilla, Fernando; Agustriany Lubis, Risa Indah
Jurnal Matematika Sains dan Teknologi Vol 11 No 2 (2010)
Publisher : LPPM Universitas Terbuka

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

The application that builded in this research is a pointer controller using eye movement (eye pointer). This application is one of image processing applications, where the users just have to move their eye to control the computer pointer. This eye pointer is expected able to assist the usage of manual pointer during the presentation. Since the title of this research is using gaze tracking that follow the eye movement, so that is important to detect the center of the pupil. To track the gaze, it is necessary to detect the center of the pupil if the eye image is from the input camera. The gaze tracking is detected using the three-step hierarchy system. First, motion detection, object (eye) detection, and then pupil detection. For motion detection, the used method is identify the movement by dynamic compare the pixel ago by current pixel at t time. The eye region is detected using the Haar-Like Feature Classifier, where the sistem must be trained first to get the cascade classifier that allow the sistem to detect the object in each frame that captured by camera. The center of pupil is detect using integral projection.The final step is mapping the position of center of pupil to the screen of monitor using comparison scale between eye resolution with screen resolution. When detecting the eye gaze on the screen, the information (the distance and angle between eyes and a screen) is necessary to compute pointing coordinates on the screen. In this research, the accuracy of this application is equal to 80% at eye movement with speed 1-2 second. And the optimum mean value is between 5 and 10. The optimum distance of user and the webcam is 40 cm from webcam.
VIRTUAL POINTER UNTUK IDENTIFIKASI ISYARAT TANGAN SEBAGAI PENGENDALI GERAKAN ROBOT SECARA REAL-TIME Irawan, M. Isa; Satriyanto, Edi
Jurnal Informatika Vol 9, No 1 (2008): MAY 2008
Publisher : Institute of Research and Community Outreach - Petra Christian University

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (467.837 KB) | DOI: 10.9744/informatika.9.1.78-85

Abstract

Many previous researches have been done in relation to human-robot interface, which is an interaction between human and robot using hand gesture. Hand gesture that is used in this research is a moving hand gesture with pointing position. The most important factor to identify hand gesture is the ability to differentiate hands with other objects based on the skin colour. A method to detect hand skin colour is using Fuzzy C-Means (FCM) which can refine a cluster centre and the membership value of each data iteratively by minimizing objective function. Hence, the cluster centre moves to the correct location. Recognition result with moving detection method was able to detect the movement of a moving object 91.07944% in 1 second. Skin detection using FCM was able to segment skin colour and not the skin in real time with the successful rate 90.2834%. The successful rate of the hand gesture pattern identification using rule base is 86.67%. The successful rate of virtual hand writing using LVQ artificial neural network as a command for controlling a robot is 79.2%. Abstract in Bahasa Indonesia : Banyak penelitian sebelumnya berrhubungan dengan human robot interface, interaksi manusia dengan robot menggunakan isyarat tangan sebagai bahasa tubuh manusia. Isyarat tangan yang digunakan dalam penelitian ini adalah isyarat tangan bergerak yang berposisi menunjuk untuk identifikasi isyarat tangan, faktor yang paling penting adalah kemampuan membedakan tangan dengan obyek lain berdasarkan warna kulitnya. Metode untuk mendeteksi warna kulit tangan adalah Fuzzy C-Means (FCM) yang memiliki kemampuan memperbaiki pusat cluster dan nilai keanggotaan tiap data secara berulang dengan meminimumkan fungsi obyektif, sehingga pusat cluster akan bergerak menuju lokasi yang tepat. Hasil pengenalan dengan metode moving detection, mampu mendeteksi pergerakan obyek bergerak. secara baik sebesar 91.07944% dalam 1 detik. obyek Skin detection dengan Fuzzy C-Means (FCM) mampu melakukan segmentasi warna kulit dan bukan kulit secara real-time dengan tingkat keberhasilan. 90.2834% Identifikasi pola isyarat tangan dengan rule base tingkat keberhasilannya 86.67%. Identifikasi hasil virtual hand writing menggunakan jaringan syaraf tiruan metode LVQ sebagai perintah untuk mengendalikan robot tingkat keberhasilannya mencapai 79.2%. Kata Kunci : virtual pointer , Fuzzy C-Mean (FCM), jaringan syaraf tiruan LVQ
PENGENDALI POINTER DENGAN GAZE TRACKING MENGGUNAKAN METODE HAAR CLASSIFIER SEBAGAI ALAT BANTU PRESENTASI (EYE POINTER) Edi Satriyanto; Fernando Ardilla; Risa Indah Agustriany Lubis
Jurnal Matematika Sains dan Teknologi Vol. 11 No. 2 (2010)
Publisher : LPPM Universitas Terbuka

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1479.979 KB)

Abstract

The application that builded in this research is a pointer controller using eye movement (eye pointer). This application is one of image processing applications, where the users just have to move their eye to control the computer pointer. This eye pointer is expected able to assist the usage of manual pointer during the presentation. Since the title of this research is using gaze tracking that follow the eye movement, so that is important to detect the center of the pupil. To track the gaze, it is necessary to detect the center of the pupil if the eye image is from the input camera. The gaze tracking is detected using the three-step hierarchy system. First, motion detection, object (eye) detection, and then pupil detection. For motion detection, the used method is identify the movement by dynamic compare the pixel ago by current pixel at t time. The eye region is detected using the Haar-Like Feature Classifier, where the sistem must be trained first to get the cascade classifier that allow the sistem to detect the object in each frame that captured by camera. The center of pupil is detect using integral projection.The final step is mapping the position of center of pupil to the screen of monitor using comparison scale between eye resolution with screen resolution. When detecting the eye gaze on the screen, the information (the distance and angle between eyes and a screen) is necessary to compute pointing coordinates on the screen. In this research, the accuracy of this application is equal to 80% at eye movement with speed 1-2 second. And the optimum mean value is between 5 and 10. The optimum distance of user and the webcam is 40 cm from webcam.
Algoritma Deep Learning-LSTM untuk Memprediksi Umur Transformator Ayu Ahadi Ningrum; Iwan Syarif; Agus Indra Gunawan; Edi Satriyanto; Rosmaliati Muchtar
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 8 No 3: Juni 2021
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jtiik.2021834587

Abstract

Kualitas dan ketersediaan pasokan listrik menjadi hal yang sangat penting. Kegagalan pada transformator menyebabkan pemadaman listrik yang dapat menurunkan kualitas layanan kepada pelanggan. Oleh karena itu, pengetahuan tentang umur transformator sangat penting untuk menghindari terjadinya kerusakan transformator secara mendadak yang dapat mengurangi kualitas layanan pada pelanggan. Penelitian ini bertujuan untuk mengembangkan aplikasi yang dapat memprediksi umur transformator secara akurat menggunakan metode Deep Learning-LSTM. LSTM adalah metode yang dapat digunakan untuk mempelajari suatu pola pada data deret waktu. Data yang digunakan dalam penelitian ini bersumber dari 25 unit transformator yang meliputi data dari sensor arus, tegangan, dan suhu. Analisis performa yang digunakan untuk mengukur kinerja LSTM adalah Root Mean Squared Error (RMSE) dan Squared Correlation (SC). Selain LSTM, penelitian ini juga menerapkan algoritma Multilayer Perceptron, Linear Regression, dan Gradient Boosting Regressor sebagai algoritma pembanding.  Hasil eksperimen menunjukkan bahwa LSTM mempunyai kinerja yang sangat bagus setelah dilakukan pencarian komposisi data, seleksi fitur menggunakan algoritma KBest dan melakukan percobaan beberapa variasi parameter. Hasil penelitian menunjukkan bahwa metode Deep Learning-LSTM mempunyai kinerja yang lebih baik daripada 3 algoritma lain yaitu nilai RMSE= 0,0004 dan nilai Squared Correlation= 0,9690. AbstractThe quality and availability of the electricity supply is very important. Failures in the transformer cause power outages which can reduce the quality of service to customers. Therefore, knowledge of transformer life is very important to avoid sudden transformer damage which can reduce the quality of service to customers. This study aims to develop applications that can predict transformer life accurately using the Deep Learning-LSTM method. LSTM is a method that can be used to study a pattern in time series data. The data used in this research comes from 25 transformer units which include data from current, voltage, and temperature sensors. The performance analysis used to measure LSTM performance is Root Mean Squared Error (RMSE) and Squared Correlation (SC). Apart from LSTM, this research also applies the Multilayer Perceptron algorithm, Linear Regression, and Gradient Boosting Regressor as a comparison algorithm. The experimental results show that LSTM has a very good performance after searching for the composition of the data, selecting features using the KBest algorithm and experimenting with several parameter variations. The results showed that the Deep Learning-LSTM method had better performance than the other 3 algorithms, namely the value of RMSE = 0.0004 and the value of Squared Correlation = 0.9690.
Sistem Akuaponik untuk Peternakan Lele dan Tanaman Kangkung Hidroponik Berbasis IoT dan Sistem Inferensi Fuzzy Fachrul Rozie; Iwan Syarif; Muhammad Udin Harun Al Rasyid; Edi Satriyanto
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 8 No 1: Februari 2021
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jtiik.0814025

Abstract

Akuaponik adalah penggabungan sistem budidaya akuakultur dan hidroponik yang dapat menjadi solusi untuk mengatasi keterbatasan lahan, keterbatasan sumber air serta meningkatkan ketahanan pangan. Pada sistem akuaponik, kualitas air pada budidaya ikan merupakan salah satu syarat utama dalam keberhasilan proses budidaya. Penelitian ini mengkombinasikan peternakan lele dengan penanaman kangkung hidroponik. Kotoran ikan lele dan sisa makanan terakumulasi di air dan dapat menjadi racun bagi ikan lele karena mengandung kadar anomia yang tinggi sehingga sangat berbahaya jika tidak dibuang. Air ini kemudian dialirkan ke tanaman kangkung hidroponik melalui biofilter yang bermanfaat sebagai pengurai air kotor dari kolam menjadi nitrat dan nitrit yang berguna sebagai nutrisi tanaman. Selanjutnya setelah air menjadi bersih dan mempunyai kadar oksigen yang tinggi, air tersebut dialirkan kembali ke kolam ikan lele. Penelitian ini bertujuan untuk mengembangkan sistem cerdas pada budidaya akuaponik dengan memanfaatkan teknologi Internet of Things yang dilengkapi dengan beberapa jenis sensor untuk memantau dan mengendalikan kualitas air dengan menerapkan algoritma Sistem Inferensi Fuzzy / Fuzzy Inference System (FIS) untuk mengatur kecepatan sirkulasi air kolam agar menghemat daya listrik pada pompa. Peralatan ini juga dilengkapi dengan layanan pemberian pakan ikan secara otomatis yang dapat diprogram sesuai kebutuhan. Sistem akuaponik ini dapat dipantau melalui web maupun ponsel pintar berbasis android. Pengujian yang dilakukan terhadap perbandingan keputusan oleh pakar dan sistem FIS pada kecepatan sirkulasi air sistem akuaponik menunjukkan nilai akurasi 83,33%, dan hasil dari pengujian ketepatan alat pemberi pakan yang dibuat secara otomatis terhadap ketepatan pemberian pakan secara manual memiliki nilai akurasi 90,97%. AbstractAquaponics is a combination of aquaculture and hydroponic cultivation systems that can be a solution to overcoming limited land, limited water sources and increasing food security. In the aquaponics system, water quality in fish farming is one of the main requirements in the success of the cultivation process. This research combines catfish farming with hydroponic kale cultivation. Catfish feces and food scraps accumulate in water and can be toxic to catfish because they contain high levels of anomia so it is very dangerous if not disposed of. This water is then flowed to hydroponic kale plants through a biofilter which is useful as decomposing dirty water from the pond into nitrates and nitrites which are useful as plant nutrients. Furthermore, after the water becomes clean and has high oxygen levels, the water is flowed back into the catfish pond. This study aims to develop a smart system in aquaponic cultivation by utilizing Internet of Things technology which is equipped with several types of sensors to monitor and control water quality by applying the Fuzzy Inference System (FIS) algorithm to regulate the speed of pool water circulation in order to save electric power on the pump. This equipment is also equipped with an automatic fish feeding service which can be programmed as needed. This aquaponics system can be monitored via the web or an Android-based smart phone. Tests carried out on the comparison of decisions by experts and the FIS system on the water circulation speed of the aquaponics system show an accuracy value of 83.33%, and the results of testing the accuracy of the feeder that is made automatically against the accuracy of manual feeding have an accuracy value of 90.97% .
Aplikasi Analisis Manajemen Resiko Untuk Membantu Pengambilan Keputusan Menggunakan Metode Decision Table Berbasis Web (Studi Kasus SMKN 1 Jenangan Ponorogo) Dihin Muriyatmoko; Edi Satriyanto; Eru Puspita
Fountain of Informatics Journal Vol 1, No 2 (2016): November
Publisher : Universitas Darussalam Gontor

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21111/fij.v1i2.671

Abstract

Decision making by considering is very important in an institution. Generally, in choosing a solution of the problem is only take from a meeting, discussion or vote among the leader of each division. But it usually needs more time if the problems are the repetitive and less measurable,  then it takes an application so that decision-making to be effective, efficient, systematic and measurable. This application use decision table method to classifies each risk by considering the scale of likelihood and consequence, then of both these parameters will be taken risk management matrix that will determine the best solution.  This application not absolutely and only help to give the recommendation with the minimum value, so on each solution and risk have a value and the test results of the respondents indicated that the application is able to assist institutions in decision-making as much as 50 percent.
APPLICATION OF MULTISTAGE CLUSTERING FOR MAPPING ECONOMIC POTENTIAL IN EAST JAVA PROVINCE Ronny Susetyoko; Edi Satriyanto; Alfi Fadliana; Muhammad Syahfitra
Jurnal Ilmiah Kursor Vol 12 No 1 (2023)
Publisher : Universitas Trunojoyo Madura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21107/kursor.v12i01.325

Abstract

This study aims to map the economic potential in East Java Province based on GRDP according to business field category. Multistage clustering is a method developed for outlier data and datasets with large variance. Multistage clustering is a combination of Ordering Points to Identify the Clustering Structure (OPTICS) and K-Means. The first stage was grouped using OPTICS. The outlier data resulting from the clustering stage is used as a dataset in the second stage using K-Means. The performance of this method is compared with several other methods, namely: K-Means, DBSCAN – K-Means, Agglomerative, Fuzzy C-Means (FCM), Possibilistic C-Means (PCM), and Fuzzy Possibilistic C-Means (FPCM) based on the characteristics of the Silhouette score and Davies-Bouldin score. Multistage clustering was chosen as the best method with a Silhouette score of 0.442 and Davies-Bouldin score of 0.388. With the Elbow method and the two metrics, the optimum number of clusters is 8 clusters. The results of this mapping method, the City of Surabaya forms a separate cluster which has the highest economic potential in 15 categories of business fields. Next Gresik, Pasuruan, Sidoarjo, and Probolinggo have the second highest economic potential with 10 categories of business fields ranking in the top 3.
Co-Authors Agus Indra Gunawan Agustriany Lubis, Risa Indah Alfi Fadliana Ardilla, Fernando Ayu Ahadi Ningrum Dihin Muriyatmoko, Dihin Eru Puspita Fachrul Rozie Fernando Ardilla iwan Syarif M Udin Harun Al Rasyid, M Udin Harun Mohammad Isa Irawan Muhammad Syahfitra Risa Indah Agustriany Lubis Ronny Susetyoko Rosmaliati, Rosmaliati