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All Journal International Journal of Electrical and Computer Engineering IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Jurnal Teknologi Informasi dan Ilmu Komputer 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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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%.
Sistem Deteksi Hama Babi menggunakan CNN (Convolutional Neural Network) berbasis Raspberry Pi Aditia Reza Nugraha; Fitri Utaminingrum; Hurriyatul Fitriyah
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 5 No 9 (2021): September 2021
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Indonesia is an agricultural country, part of which contains the agricultural sector, such as maize, rice, oil palm and other food crops. The area of ​​agricultural land increases every year by cutting down forests as an additional resource. Forest encroachment for the agricultural sector has an impact on the environment in which agriculture is located, where agriculture is in direct contact with forest areas. Agricultural areas adjacent to plantations cause wild animals such as wild boar to become pests that can damage plantation products and are also dangerous if they come into direct contact with humans who are present while doing activities in the area. As a pest repellent prevention, it is usually carried out by conventional means such as pig traps, poison, or nets in the affected area. As an innovation using smart technology, a solution to this problem requires a system that functions as a security supervisor for plantations. The system that will be created serves to monitor the situation when the plantation is invaded by pests such as wild boars. The surveillance system uses a webcam that is attached to the Raspberry pi model 4 which functions to see the state of the garden when there are objects of pig pests. The monitoring system on the camera will classify wild boar and farmer objects on plantations using the Convolutional Neural Network method by YOLOv3 architecture with an accuracy rate of the data model for a value of 97%. The system will also be built using a buzzer notification as a notification when a pest is detected on hardware with a success of 100% buzzer output.
Sistem Deteksi Kematangan Buah Mangga berdasarkan kandungan Gas NH3, C2H5OH dan VOCs menggunakan metode K-Nearest Neighbor (K-NN) Luqmanul Halim Zain; Eko Setiawan; Hurriyatul Fitriyah
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 5 No 9 (2021): September 2021
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Mango is a fruit from a tropical climate besides having an interesting taste characteristic, mangoes have various nutritional content and a distinctive aroma. Mango (Mangifera indica L.) has more than 270 aroma of volatile compounds in different mango varieties (Shibamoto, T. et al, 1990). One of the problems in mango production is the classification process for whether mangoes are ripe. In the case of ripe mangoes, sometimes there are mangoes that have a fairly ripe color but still taste sour, and vice versa. Because of that we need a system that can determine the level of maturity of mangoes based on aroma. In this study, a system was designed to detect ripeness in mangoes based on aroma using the K-Nearest Neighbor method. In the process of classifying the sample data, Arduino nano is used as data processing. The training data is taken from mangoes with different ripeness levels, after that the tested mangoes will be detected their gas content with TGS2602, MQ135 and MQ5 sensors, after data has been obtained it will be processed by the K-Nearest Neigbour method. The classification results of the mango ripeness level will be displayed on the LCD screen along with the sensor readings. In system testing, the results classification accuracy with 15 test data, the highest accuracy reached 86.6% at K = 3 compared to the values of K = 5.7 and 9.
Deteksi Penyakit pada Daun Cabai berdasarkan Fitur HSV dan GLCM menggunakan Algoritma C4.5 berbasis Raspberry Pi Shafa Sabilla Zuain; Hurriyatul Fitriyah; Rizal Maulana
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 5 No 9 (2021): September 2021
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Chili plants are plants with great economic potential in Indonesia. Nevertheless, every year chili production has decreased, one of which is due to disease. Observation of conditions in chili plants can be seen in the changes that occur in chili leaves. Disease detection in chili leaves is needed to minimize the risk of crop failure in chili plants and as a strategic control effort. The number of types of diseases in chili plants is quite a lot and knowledge about the symptoms of the disease is not enough to make it quite difficult for farmers to determine the type of disease that attacks. Therefore, a system that is able to detect diseases in chili leaves is needed. Disease Detection System on Chili Leaves Based on HSV and GLCM Features Using the C4.5 Algorithm Based on Raspberry Pi is used to detect types of diseases on chili leaves. This research uses Hue, Saturation and Value (HSV) color features and Gray Level Co-occurence Matrices (GLCM) texture features. The HSV color feature was used to analyze diseased leaf discoloration. Texture features are used to analyze changes in the texture of chili leaves with the help of five features from GLCM, namely correlation, dissimilarity, homogeneity, contrast, and energy with four variations of angles, namely angles 0, 45, 90 and 135. The classification method used is using a decision tree from C4.5 algorithm with classification results in the form of sercospore spot disease, curly mosaic and normal conditions. Detection of disease in chili leaves using this method using 21 test data to get an accuracy of 86%. The average execution time required by the system to detect is 1.045 seconds.
Alas Kaki Penimbang Berat Badan Dengan Berjalan berbasis Sensor Load Cell dan Metode Regresi Linier Afflatuslloh Adi Salung; Hurriyatul Fitriyah; Dahnial Syauqy
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 5 No 9 (2021): September 2021
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

The ideal body is everyone's dream. From teenagers to old people, both male and female, definitely want the ideal body. The ideal body can be obtained from sufficient exercise, a healthy diet, and adequate sleep. We can find out whether our bodies are ideal or not by measuring their body height and body weight, which are adjusted from the person's age. Monitoring weight is one of the needs to maintain a healthy body. We should always check our weight, so we can control the body's healthy patterns. Sometimes measuring weight takes time, for busy people it may not be important. In fact it is very necessary to always know the weight. It will be very helpful if there is a weight meter that can be used at any time and is not required to stand still on a regular weight scale. Therefore we need a tool that can make it easier for us to monitor our weight at any time. We can check our weight when sleeping, running, eating, and all daily activities. In this study, we will create a wearable device that adds the function of sandals to a weight monitoring tool. By using several Load Cell sensors on the sandal, these sensors function to get the value of the Load Cell sensor which will be processed with the Wemos D1 microcontroller. The results of the process will come out on android devices that are commonly used every day. We can use this tool like an ordinary scale, when standing upright we can get a weight value, and this tool can also be used when walking conditions. In this study, we will focus on examining the device while walking. With this tool, we can make it easier for users to monitor their weight during daily activities without using ordinary scales.
Rancang Bangun Sistem Klasifikasi Tingkat Kematangan Pisang berdasarkan Warna Kulit dan Berat menggunakan Metode K-Nearest Neighbor berbasis Arduino Pramandha Saputra; Dahnial Syauqy; Hurriyatul Fitriyah
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 5 No 10 (2021): Oktober 2021
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Currently, bananas are one of the favorite fruits that have good nutrition and taste that is liked by most people, because bananas have good nutritional content for the body. Banana is a fruit that is very beneficial for human life, which can be consumed at any time and at all ages. Constraints on the community in distinguishing ripe bananas that are suitable for management are sometimes experienced by ordinary people who do not know about the characteristics of ripe bananas and are good to manage. To make processed products, bananas with the right maturity are needed, for that a study was made on the ripeness of bananas based on the weight and color of banana peels that classify them using the K-Nearest Neighbor method. In this system there are several components, namely: Arduino Mega 2560 microcontroller to process k-nearest neighbor calculation data, TCS3200 sensor whichs is use to detect skin color on bananas, loadcell sensor as a weight gauge on bananas. The system in distinguishing the ripeness of bananas using the k-nearest neighbor method got an accuracy of 86.6%. Perform testing on the value of K = 3 and then the results of the changed K value are compared to see a more accurate K value in the k-nearest neighbour method applied to the system.
Deteksi Orang Bermasker untuk Akses Pintu berdasarkan Rasio Bounding Box Wajah dan Roundness menggunakan Naive Bayes berbasis Raspberry Pi Aditya Rafly Syahdana; Hurriyatul Fitriyah; Rizal Maulana
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 5 No 11 (2021): November 2021
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

COVID-19 is a disease caused by the corona virus and it has caused a global pandemic since December 2019. The use of masks is highly recommended to stop the spread of the corona virus, especially in closed rooms with many people inside, such as the classrooms and the office spaces. Detection of people with mask to access door based on the ratio of face Bounding Box and Roundness using Naive Bayes on Raspberry Pi is used to ensure people wearing masks who want to enter classrooms and office spaces. Webcam is used to capture images of people who want to enter. The image is processed and classified on the Raspberry Pi 4. Image processing begins with converting RGB to YCbCr and performing the morphological dilation, the morphological opening, and the morphological closing of morphology. Image processing aims to segment the human faces and remove their backgrounds. Human facial features were extracted using the ratio of Bounding Box and Roundness analysis which aims to determine the detected human face. The method for classifying faces is the Naive Bayes method. The solenoid lock opens when the classification result uses a mask, and it will be locked when the classification result does not use a mask. In the process of testing the Naive Bayes model using 60 data, the highest accuracy is 90%. To prove the accuracy of the Naive Bayes model, a test was carried out by inputting images directly into the system at 5 different distances, namely at 0.5 meters, 1 meter, 1.5 meters, 2 meters, and 2.5 meters. The test at each distance got an average accuracy of 86.6%. The average execution time required for system to detect masker is 8.82412 milliseconds.
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 Benny Adiwijaya 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 Eko Setiawan Erdano Sedya Dwiprasajawara Esa Prakasa Fadhilatur Rahmah Fahmi Erza 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