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All Journal International Journal of Electrical and Computer Engineering IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Journal of ICT Research and Applications Proceeding of the Electrical Engineering Computer Science and Informatics Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Jurnal Sistem Komputer & Kecerdasan Buatan Jurnal Abdi Masyarakat Indonesia
Fitriyah, Hurriyatul
Universitas Brawijaya
Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Public Health

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Pengembangan Sistem Pengenalan Bahasa Isyarat dengan Sensor Akselerometer menggunakan Metode Naive Bayes Gunawan Wahyu Andreanto; Dahnial Syauqy; Hurriyatul Fitriyah
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 5 No 3 (2021): Maret 2021
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Communication is the process of delivering information in social life. However, people with hearing impairments have difficulty exchanging information because of their limitations. The Indonesian Government's solution to this problem is the standardization of the Indonesian Sign Language System (SIBI). Previous research has been carried out related to SIBI alphabet sign recognition with flex sensor and MPU6050 using the Naive Bayes method. However, from 130 tests, the research only produced an accuracy about 43.85%. The problem is due to the limitations of the flex sensor orientation reading accuracy. From these, development research was carried out to improve the accuracy of SIBI sign recognition. From these, development research was carried out to improve the accuracy of SIBI sign recognition. In this study, there were 6 units of MPU6050 that functioned as finger and opisthenar orientation readers. Sign recognition using the Naive Bayes method based on the sensor orientation reading. The research produces data suitability between the controller and the Android application is 100% that the application can be used as a data media representation, Mean Absolute Percentage Error (MAPE) of the sensor readings at 1,471% from 24 tests, classification accuracy rate at 92.31% from 78 tests, and the average processing time at 20 ms from 20 tests.
Sistem Pengendalian Gerak Sendok Menggunakan Sensor Mpu6050 Dan Metode Pid Berbasis Arduino Ahmad Fahmi AdamSyah; Hurriyatul Fitriyah; Rizal Maulana
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 5 No 3 (2021): Maret 2021
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Difficulty eating is a condition in which a person is unable to eat normally or comfortably. Researchers want to propose a spoon system that can be controlled its stability. The system of this research is the movement of the spoon to be read using the MPU6050 sensor. The value obtained from the sensor will be forwarded to the Arduino Nano processing unit for calculations using the PID controller method where the Kp, Ki, and Kd variables are searched using the trial and error tuning method. The results of these calculations will be used as a spoon mover. Based on the tests that have been carried out, there are conclusions on the test results, first on testing the accuracy of the sensor angle, it can be seen that from 7 experiments from an angle of 0 ° to 180 ° with a multiple of 30 ° on both axes, namely pitch and roll, the percentage error is 0. 59% or less than 1%. The second test of the accuracy of the servo angle based on the setpoint is done, it can be seen that from 7 experiments from an angle of 0 ° to 180 ° with a multiple of 30 ° on both axes, namely roll and pitch, the percentage error is 0.8. Finally, the system speed test has reached stable or commonly called the settling time that has been carried out, it can be seen that from 10 experiments, the initial speed of the system reaches the settling time, which is the average time of both axes, namely 0.99 seconds or under 1 second.
Alat Pendeteksi Uang untuk Tunanetra menggunakan Metode Histogram of Oriented Gradients dan K-Nearest Neighbor Nico Dian Nugraha; Fitri Utaminingrum; Hurriyatul Fitriyah
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 5 No 4 (2021): April 2021
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Banknotes that distributed by scammers would causes restlessness in society, including blind people. With impairment vision, blind people would hard to distinguish between genuine and fake money. From that problem there would be a research about nominal and authenticity detection system for blind people. The detection system consists of camera as a sensor device to detect picture from the banknote, followed by ultraviolet lamp to tell about the genuine banknote, and speaker as the output from this system. Output would generate voice as the nominations and tell if it is genuine or fake banknote. Code program on this system were written in Pyhton language with Raspberry Pi hardware, Webcam sensor camera, and ultraviolet lamp. Detecting banknotes would use Histogram of Oriented Gradients method and using K-Nearest Neighbour method to classify banknote. Around 3370 data training were used to detect about authenticity of the banknotes and the detections were tried for 56 times. implementation of K-Nearest Neighbor method using k=3 obtained an accuracy result of 98.21% with an average compute time of 3608 ms.
Sistem Klasifikasi Kesegaran Daging Sapi berdasarkan Citra menggunakan Metode Naive Bayes berbasis Raspberry Pi Habib Muhammad Al-Jabbar; Hurriyatul Fitriyah; Rizal Maulana
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 5 No 4 (2021): April 2021
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Beef is one of the commodities that has contributed to the improvement of public nutrition, particularly the need for animal protein. Fresh beef is meat that is fresh red in color, starting from being cut up to 10 hours. So far, evaluation of freshness and identification of meat composition has been done manually by means of human visual observations. Due to human limitations, there are often different perceptions of each observer. On this basis, as an effort to obtain beef freshness accurately, this research has made a tool that can detect the freshness of beef with the help of digital image computing. By using the Raspberry Pi as a mini computer, a camera as a sensor and image processing which is then classified by Naive Bayes, this system can work properly, it can be proven by the output of the accurate classification of beef freshness. The choice of the naive Bayes method is based on the fact that this method is a very good classification method in which the class of freshness types is known from the start. This method can also work even though it only uses a little training data. When there is a slight change in training data, the naive Bayes method also adapts quite well. The results of the beef color conversion process are then classified at the color level based on SNI standards. From 40 training data and 20 tested data, an accuracy of 95% and an average computation rate of 0.009094 seconds.
Sistem Klasifikasi Langit Cerah dan Berawan menggunakan Gray Level Co-Occurrence Matrix dan K-Nearest Neighbor berbasis Raspberry Pi Marrisaeka Mawarni; Hurriyatul Fitriyah; Rizal Maulana
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 5 No 6 (2021): Juni 2021
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Weather is a condition in the atmosphere that occurs in an area in a short time. Weather can be predicted by observing weather elements, such as clouds. This research will perform a weather classification based on sky and clouds conditions in the form of sunny and cloudy weather. Sunny weather occurs when the sun shines brights, and there are no clouds in the sky. Cloudy weather occurs when sunlight is covered by clouds that contain water vapor in the atmosphere. The system used fisheye images so that we have a wider field of view images than panoramic images. The classification process for clear and cloudy skies uses the Gray Level Co-occurrence Matrices (GLCM) method to extract the texture features. The features for the classification of sky conditions in the GLCM method are energy, contrast, correlation, homogeneity, and dissimilarity with four angular variations, namely 0,45,90, and 135. The sky classification process is using the K-Nearest Neighbor method with k = 3,5,7, and 9. The Raspberry Pi 4 is used for the whole data processing, and then the classification results are displayed on the LCD. the Accuracy testing was carried out using k-fold with 5 fold, obtaining an average accuracy result of 100% and an average computation time of 2.3516 seconds.
Sistem Rute Terpendek Pencarian Buku Di Perpustakaan Menggunakan Algoritme Dijkstra Muh. Syifau Mubarok; Dahnial Syauqy; Hurriyatul Fitriyah
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 5 No 6 (2021): Juni 2021
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

The library is considered as a convenient place to read, because the library provides a collection of books and other publications services that are provided to the general public or certain groups. Lots of information can be obtained from the library includes scientific, recreational, religious and entertainment that are human needs. However, according to a survey conducted by UNESCO in 2015 stated that the reading interest of Indonesian society is very low. The lack of interest in reading the community can be affected by several factors such as, the minimal number of libraries and the lack of decent facilities in the library. For example, the more extensive a library, the more variations of book categories and certainly directly proportional to the large number of shelves arranged. Doing a random search manually is the way that most often done, so it will wasting time, especially if the library visitors are visiting the library for the first time and don't know the location of the shelves and the arrangement of the book placement. With the research located on the white label room of the central library of Brawijaya University, the writer made an Arduino-based system which was planted in a book basket, which in the system contained a list of book categories in the library as well as the shelf location where the books were stored. The visitors only need to select the category of books that they want to borrow then the system will process it. The resulting output is the shortest bookshelf route through the LCD on the system by first mapping the existing bookshelves in the library by calculating the shortest distance of the book sought on the nearest shelves from the starting point. With the research located on the white label room of the central library of Universitas Brawijaya, the results from 10 random route search samples the system succeeded in determining the right route with an accuracy value of 100%. It is proven that the system works well besides of that the kind system is an embedded system that is installed in a book basket, so it will make it easier for the visitors if they want to borrow a lot of books in large quantities. On another test was done by testing the execution time required by the system to find the shortest route, the results of 10 tests using random samples only require a very short time with an average of 4.2 milliseconds.
Deteksi Orang Bermasker Medis Menggunakan Metode Convolutional Neural Network Berbasis Raspberry Pi Bimo Dimas Nugraraga; Hurriyatul Fitriyah; Dahnial Syauqy
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 5 No 6 (2021): Juni 2021
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

In the COVID - 19 pandemic, mask is an important commodities. The usage of masks is very important to prevent transmission of COVID - 19, especially in important institutions such as Hospital. Medical mask is very good to prevent transmission of COVID - 19 because medical mask has 3 protective layers. The Detection Of Medical Mask Using Raspberry Pi Based On Convolutional Neural Network aims to prevent the spread of COVID - 19 in hospitals by preventing people who do not use medical mask from entering hospitals. This system consist of a webcam, Raspberry Pi 4, and solenoid lock. Image processing is done by converting the color from RGB to YCbCr to detect medical masks and remove the background. This system use Convolutional Neural Network for classification method. The solenoid lock will open if the result of the classification is a medical mask and will be locked if the result of the classification is a non-medical. In this study, testing was carried out at 5 different distances, namely distances of 0.5 meters, 1.0 meters, 1.5 meters, 2.0 meters, 2.5 meters. Overall accuracy of this system is 97%. The average execution time of this system is 0.563271 seconds.
Sistem Klasifikasi Diabetes Melitus Berdasarkan Kondisi Urin, Gas Buang Pernapasan, Dan Tekanan Darah Menggunakan Metode Naive Bayes Berbasis Arduino Dwi Fitriani; Rizal Maulana; Hurriyatul Fitriyah
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 5 No 6 (2021): Juni 2021
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Diabetes mellitus is a disease that attacks humans regardless of age. A person will have diabetes mellitus when blood sugar levels in the body increase, resulting in reduced insulin in the body. Diabetes examination can be done using a blood sugar test tool using a blood sample taken from a person's fingers. Examination in this way causes pain and discomfort. With this problem, this study aims to create a system that can be used to detect a person without using blood samples (non-invasive), saving time and costs in the examination. In the study of diabetes mellitus detection system using parameters of urine condition, respiratory exhaust gas and blood pressure. The conditions used are ammonia gas levels in human urine, respiratory exhaust gases used are acetone gas levels from human breath and human blood pressure. Data processing is carried out using arduino uno microcontroller. The data was obtained from the output sensors MQ-135, TGS-822 and MPX5700AP sensors. From the test results obtained the correlation value of sensors MQ-135 and TGS-822 with the output voltage of 99.29% and 98.56%, while for the sensor MPX5700AP known percentage of errors sistole and diastole by 8.90% and 4.64%. The system classifies diabetes mellitus using the Naive Bayes method. It uses 12 test data and 24 training data to determine the accuracy of Naive Bayes classification. Of the 12 test data there is 1 data whose class is not appropriate so that the accuracy value becomes 91.67%. Meanwhile, the average compute time of the system obtained in 10 tests is 1.02 seconds.
Klasifikasi Sumber Nektar Madu berdasarkan Kecerahan dan Warna dengan Metode Naive Bayes berbasis Embedded System Syarief Taufik Hidayatullah; Dahnial Syauqy; Hurriyatul Fitriyah
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 5 No 8 (2021): Agustus 2021
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Honey is a liquid has many benefits for humans and also as a food reserve for bees, honey stored by bees undergoing chemical processes and fermentation through evaporation, air exchange and changes in increasing heat temperatures. The bees job is divided into three kind, female bees lay eggs, male bees to mate with female bees and the worker bees looking for nectar which is stored in the bee hive as food reserves. Honey from different nectars produces different colors and tastes, so it have different benefits. Honey can be used as a sugar substitute and has many contain beneficial. This design system with naive bayes classification uses acacia honey, eucalyptus honey, coffee honey longan honey, Sengon multiflora honey and mango honey with Arduino Uno microcontroller, TCS3200 sensor and LDR sensor. The classification process is carried out by pouring honey into a 50ml beaker glass then placing it above the LDR sensor and under the TCS3200 sensor according to the prototype design of the calcification tool. The LDR sensor gets light from the color sensor and then will give output red, green, blue, and honey clarity then the data is processed by the naive bayes method so the result will be a classification of honey. Classifications are displayed on the LCD so that users can see the classification results. Naive Bayes classification was chosen because it is effective and requires a bit of training data, in this study using 10 samples of training data per honey. From the test results with 67 honey samples, the results obtained 94% accuracy with an average computation time of 1007.28ms, an average LCD accuracy 100%, an average LDR sensor accuracy 98.9% and an average error on the color sensor 5.69%. This means that the results can be said it's good
Sistem Klasifikasi Mutu Air PDAM berdasarkan Zat Terlarut, PH dan Turbidity menggunakan Metode Fuzzy Sugeno berbasis Arduino Fatchullah Wahid Afifi; Hurriyatul Fitriyah; Eko Setiawan
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 5 No 8 (2021): Agustus 2021
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Some characteristics of clean drinking water are fresh water, colorless and not smoky. One of the water sources that are considered clean and worth drinking is through PDAM (Perusahaan Daerah Air Minum). Water from PDAM was chosen because it has gone through various physical and chemical processes, so that PDAM-assisted water is considered clean enough to be used for daily needs in the future. Factors that affect the level of water feasibility are usually determined from the mixed content of the water. One of the ingredients used by PDAM is chlorine as a disinfectant to maintain water cleanliness and protect from bacteria. Referring to the Regulation of the Minister of Health of the Republic of Indonesia 492 / MENKS / PER / IV / 2017, the upper limit of chlorine content in drinking water is 5 mg / L. Water quality classification system designed by utilizing pH sensors to know the acidity level in water, turbidity sensors to know the level of turbidity in water, and TDS sensors to know the amount of dissolved substance content in water. Then the system will store and process input data from the sensor using the fuzzy method of takagi-sugeno model to classify water quality based on its content into three groups, namely safe, processed and dangerous. During the testing phase of the system, from 4 experiments with 3 PDAM water samples, the accuracy rate obtained from the system and compared to existing tools was 100%.
Co-Authors Abdurrahman Diewa Prakarsa Abimanyu Sri Setyo Achmad Baichuni Zain Aditia Reza Nugraha Aditya Rafly Syahdana Afflatuslloh Adi Salung Agi Putra Kharisma Agif Sasmito Agung Setia Budi Ahmad Fahmi AdamSyah Ahmad Fatchi Machzar Ahmad Haris Wahyudi Ahmad Wildan Farras Mumtaz Alfatehan Arsya Baharin Ali Ilham Ainur Rahman Allif Maulana Ananda Ribelta Andhika Rizky Fariz Andi Dwi Angga Prastya Andy Hartono Aprilo Paskalis Polii Aries Suprayogi Bagus Sawung Timur Barlian Henryranu Prasetio Belsazar Elgiborado Giovani Djoedir Bilawal Haesri Bimo Dimas Nugraraga Boris Wiyan Pradana Chandra Gusti Nanda Putra Cut Fahrani Dhania Dahnial Syauqy David Isura Dede Satriawan Denis Andi Setiawan Dewi Pusparini Dian Bagus Setyo Budi Diego Yanda Setiawan Dimas Bagus Jatmiko Dimas Dwi Saputra Dimas Firmanda Al Riza Dimas Guntoro Dipatya Sakasana Dody Kristian Manalu Dwi Fitriani Edhi Setyaw, Gembong Eko Ardiansyah Eko Setiawan Erdano Sedya Dwiprasajawara Esa Prakasa Fadhilatur Rahmah Faizal Andy Susilo Fajra Rizky Falachudin Akbar Fatchullah Wahid Afifi Faza Gustaf Marrera Fikriza Ilham Prasetyo Gembong Edhi Setiawan Gembong Edhi Setyaw Gembong Edhi Setyawan Gunawan Wahyu Andreanto Habib Muhammad Al-Jabbar Hafizh Hamzah Wicaksono Hamdan Zuhdi Dewanul Arifin Hamzah Attamimi Handi Handi Handy Yusuf Herwin Yurianda Ichwanul Muchlis Imam Pratama Setiady Indera Ulung Mahendra Iqbal Koza Irham Manthiqo Noor Issa Arwani Ivana Agustina Julisya Thana Khriswanti Khairul Anwar Komang Candra Brata Lashot Ria Ingrid Melanika Lintang Cahyaning Ratri Luqmanul Halim Zain M Ilham Fadilah Akbar M Nuzulul Marofi M. Fiqhi Hidayatulah Marrisaeka Mawarni Mimi Hamidah Moch Zamroni Mochammad Hannats Hanafi Mochammad Hannats Hanafi Ichsan Mohamad Abyan Naufal Fachly Mohamad Misfaul May Dana Mohammad Isya Alfian Mohammad Lutfi Zulfikri Muh. Syifau Mubarok Muhamad Delta Rudi Priyanto Muhamad Ichwan Sudibyo Muhammad Ammar Hassan Muhammad Daffa Bintang Nugroho Muhammad Fatham Mubina Akbar Muhammad Irfan Reza Muhammad Junifadhil Caesariano Muhammad Raihan Al Hakim Muhammad Rifqi Radifan Masruri Muhammad Riyyan Royhan Muhammad Rizki Chairurrafi Muhammad Rizky Rais Muhammad Rizqi Zamzami Muhlis Agung Saputro Musada Teguh Andi Afandi Nafisa Nafisa Nashir Umam Hasbi Nico Dian Nugraha Nur Aini Afifah Isbindra Nur Syifa Syafaat Okky Nizka Pratama Oktaviany Setyowati Olivia Rumiris Sitanggang Pandy Aldrige Simanungkalit Pramandha Saputra Putra Wijaya Putri Harviana Raden Galih Paramananda Rakhmadhany Primananda Rando Rando Refsi Ilham Cahya Rejeki Puspa Dinasty Rekyan Regasari Mardi Putri, Rekyan Regasari Mardi Retno Damayanti Rian Ari Hardianyah Ricky Zefani Aria Zurendra Rifqi Alvaro Rifqi Imam Ramadhan Rizal Maulana Rizka Ayudya Pratiwi Rizqy Maulana Rosa Mulyanis Chan Sabriansyah Rizqika Akbar Salsabiil Hasanah Samuel Lamhot Ladd Palmer Simarmata Satyaki Kusumayudha Septian Mukti Pratama Shafa Sabilla Zuain Sulthan Ghiffari Awdihansyah Syarief Taufik Hidayatullah Tatit Kisyaprakasa Thomas Oddy Chrisdwianto Tibyani Tibyani Tri Oktavia Mayasari Tunggal Manda Ary Triyono Utaminingrum, Fitri Wahyu Hari Suwito Widasari, Edita Rosana Wijaya Kurniawan Wildo Satrio Wisnumurti Wisnumurti Xavierro Lawrenza Yusuf Hendrawan Zultoni Febriansyah