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Sutrisno Sutrisno
Fakultas Ilmu Komputer , Universitas Brawijaya
Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Engineering

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Pengembangan Aplikasi Pendeteksi Kantuk Pada Pengendara Kendaraan Bermotor Dengan Menggunakan Sensor Detak Jantung Pada Smartwatch Imam Farouqi Faisal; Agi Putra Kharisma; Sutrisno Sutrisno
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 3 No 10 (2019): Oktober 2019
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

The majority of traffic accidents that occur are caused by human error, one of which is drowsy. When drowsy, the driver's concentration tends to decrease, so it can cause loss of control over the vehicle, this can lead to traffic accidents. Therefore, there's a need for a solution that can prevent traffic accidents caused by drowsiness. Based on these problems, the author offers a system that can detect driver drowsiness, so if the driver is drowsy, he can be given a warning immediately. This system is an Android based application that uses heart rate data to detect drowsiness. To obtain heart rate, this application is using the heart rate monitor sensor on the smartwatch which the driver must wear. In its development, this application is designed using Object Oriented Design (OOD) approach and implemented using Object Oriented Programming (OOP) method. Black box, accuracy, and usability are tested. Black box testing result shows that this application has 100% validity rate, based on accuracy testing conducted on five respondents, this application has 86,3% accuracy rate, showing that this application can detect the respondents' drowsiness accurately, whereas based on usability testing, this application has 83,5 score which means it got B (Excellent) rate.
Peramalan Curah Hujan Menggunakan Metode Extreme Learning Machine Rich Juniadi Domitri Simamora; Tibyani Tibyani; Sutrisno Sutrisno
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 3 No 10 (2019): Oktober 2019
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Rainfall is the height of rain water that is found and collected in a flat, not absorbed, does not evaporate and does not flow. Information about rainfall is very important especially in agriculture and civil. In agriculture, rainfall information is used to determine the type of plants to be planted in accordance with the intensity of rainfall, predicting the start of the growing season in the planting calendar to minimize the risk of planting. In the civil field, it is used as a determinant of engineering design standards in planning flood disaster control buildings. Above normal rainfall will cause natural disasters such as floods and landslides. Rainfall is part of the weather element and one of the meteorological processes that is quite difficult to predict. Rainfall forecasting is needed so that the community and the government can take preventative measures against the existing problems. The forecasting process is divided into several processes which include data normalization, forecasting with the Extreme Learning Machine algorithm, data denormalization and the results of errors with MAPE. Based on the test results using rainfall data in the Poncokusumo area with a span of years 2002 to 2015 obtained the smallest MAPE value of 3.6852%, with as many features as 4, many neurons in the hidden layer as much as 2, the percentage of training data 90%.
Klasifikasi Gangguan Jiwa Skizofrenia Menggunakan Algoritme Decision Tree C5.0 Febriyani Riyanda; Imam Cholissodin; Sutrisno Sutrisno
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 3 No 10 (2019): Oktober 2019
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Schizophrenia is one of psychiatric disorders which gets much concern all over the world. Schizophrenia requires a quick and precise treatment due to its chronic tendency. However, the insufficient service on mental disorder can cause patients are not addressed immediately and the height subjectivity amongst psychiatrists in determining the type of schizophrenia has similar symtoms can lead to schizophrenia classification errors. In this classification of mental disorders use the C5.0 decision tree algorithm, which has additional functions such as boosting. The data used were 106 data taken from the Dr. Radjiman Wediodiningrat Lawang Psychiatric Hospital, this data consists of 89 trained data and 17 test data. The test method being used is accuracy. Based on the results of testing the C5.0 parameter, the highest average accuracy value was 85.884% with the number of data = 71 samples, the number of trials or the number of decision tree = 100.
Pengembangan Aplikasi Perangkat Bergerak Layanan berbasis Lokasi Penghubung Sukarelawan dengan Kegiatan Sukarela Rahmatsyah Rahmatsyah; Agi Putra Kharisma; Sutrisno Sutrisno
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 3 No 10 (2019): Oktober 2019
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Volunteers are individuals or groups who carry out an voluntary activity. Volunteering are usually intended to provide a service without making a profit. From a survei of 144 Indonesian over the age of 17, found that 82.2% difficult to register as a volunteer because of a lack of information regarding volunteer activities, 54.2% were hesitent to register as volunteers because of a lack information about volunteerism activities, and 96,5% felt that they needed a platform to connect volunteers with social activities. Therefore an application is made to connect volunteers with voluntary activities. Location-based application that displays a number of voluntary activities to volunteers based on the closest location. From the results of exploring needs, there are two types of actors namely volunteers as a community who want to become volunteers and social organizations as implementing activities. Therefore two applications were developed that represented the behavior of the two actors. The application was developed with the Model-View-ViewModel (MVVM) architecture and data storage using Firebase services with the results of all functional testing successfully implemented and an average of usability test results above 70% which means it can be accepted by the user, and can be operated on the Android operating system with a minimum version of 16 through 29.
Klasifikasi Penyimpangan Tumbuh Kembang Anak Menggunakan Algoritme C5.0 Dyah Ayu Wahyuning Dewi; Imam Cholissodin; Sutrisno Sutrisno
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 3 No 10 (2019): Oktober 2019
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Developmental deviation of the child's development is a disruption of the process of growth and development resulting in the child experiencing a phase that is inhibited compared to other normal children. If it is not immediately treated, it is feared that the developmental deviation of the child's growth will be increasingly difficult to handle. For that we need the awareness of parents to immediately check the condition of the child at the doctor, in order to alleviate these irregularities. However, the number of patients is not proportional to the number of doctors available. Lack of doctors can result in slow handling of patients. To deal with this, a system of diversification of child growth and development was made using the C5.0 algorithm. In this study will be classified into three types of developmental deviations of children, namely autism, down syndrome, and ADHD (Attention Deficit Hyperactivity Disorder). C5.0 algorithm is one of the decision tree algorithms and is a development of C4.5. The difference in C4.5 and C5.0 is that in the C5.0 algorithm there is a boosting process, so that it can provide better accuracy than the C4.5 algorithm. From the research that has been done, the average value of accuracy in testing the amount of training data is 95.9%, the average accuracy in testing the number of trials is 97.3%, and the comparison testing of C4.5 and C5.0 results in accuracy at C5.0 is 93.33% while the accuracy at C4.5 is 87.61%. The things that affect the accuracy value are the large amount of data, and the number of trials used.
Identifikasi Jenis Attention Deficit Hyperactivity Disorder pada Anak menggunakan Learning Vector Quantization dengan Seleksi Fitur menggunakan Algoritme Genetika Chalid Ahmad Aulia; Dewi Candra; Sutrisno Sutrisno
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 3 No 11 (2019): November 2019
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Attention Deficit Hyperactivity Disorder is one of the common disorders that may occur on children which is indicated by certain kinds of behaviors such as the inability to calm down, not being able to pay much attention, and sudden desire to do excessive things. There are three types of ADHD in general: inattention, impulsiveness, and hyperactivity. Unfortunately, a lot of people are unaware of the dangers of this disorder if not treated from an early stage. Therefore, a system to identify the type of ADHD in a child is needed. This study implements Learning Vector Quantization as the algorithm to classify the types of ADHD and genetic algorithm as the selector of relevant features. In this study, there are 45 features which are the symptoms of ADHD that will be selected in advance by the genetic algorithm to determine which features are going to be used in the LVQ process to determine its accuracy value. The testing includes finding the numbers of variables that may have impacts to the results and can result the highest accuracy numbers. The best parameters with the highest accuracy results are the population size of 15, crossover rate of 0.9, mutation rate of 0.1, number of generations of 7, and the learning rate of 0.5 where the average accuracy is 96%.
Seleksi Fitur dengan Information Gain pada Identifikasi Jenis Attention Deficit Hyperactivity Disorder Menggunakan Metode Modified K-Nearest Neighbor Muhammad Hafidzullah; Sutrisno Sutrisno; Marji Marji
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 3 No 11 (2019): November 2019
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Attention Deficit Hyperactivity Disorder (ADHD) is a disorder that usually occurs in early childhood. In general there are three types of behavior associated with this disorder, namely: inattentiveness, impulsiveness, and hyperactivity. ADHD mental illness can only be recognized through changes in patient behavior. In this study, the data used using 45 features, where the number of features can affect the performance and accuracy of the classification method. Feature selection aims to reduce features that are of equal importance to make classification algorithms easier to operate more quickly and effectively so as to produce better accuracy. The method to be used for the selection of features is the Information Gain method. The use of the Information Gain method in this case is to select the best features that have relevance to the related data. These features are selected based on the magnitude of the gain value obtained, where the greater the gain value, the more relevant the feature is with the related data. The best average accuracy results obtained from this system are obtained in the second test scenario with the highest average accuracy value of 88% obtained at k = 37 and k = 42 and the number of features 36 and 41, and at k = 1 in testing without using Information Gain. These results indicate the use of the Information Gain feature selection method in this case has a fairly good accuracy value.
Ekstraksi Topik Dokumen Berita Menggunakan Term-Cluster Weighting dan Clustering Large Application (CLARA) Rizal Maulana; Sigit Adinugroho; Sutrisno Sutrisno
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 3 No 11 (2019): November 2019
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

The growth of technology makes it easy to get informations and a kind of informations is often used is news media. As technology growth, news can be spread through news portals in form of web-bases such as Kompas, Detik, Tempo, and many others. Users of information technology sometimes don't have time to read news all the time and sometime can't get the news that they need. One of many solution to solve the problem is to do clustering news documents and after that topic extraction is used to get get important topics from the news cluster. In this research using Clustering Large Application (CLARA) for the clustering algorithm because CLARA is an optimization of k-medoid which is better than k-means from various aspects and on topic extraction uses term-cluster weighting to calculate term weights in the cluster. The proses of this research is used text preprocessing documents so it become structured data, after that Singular Value Decomposition (SVD) used to decomose features. Then CLARA is used to clustering documents and for topic extraction is using term frequency-inverse cluster frequency (TF-ICF). Data in this research is secondary data that obtained from Kaggle website which is an English language news documents. The result of silhoette sore from using 226 documents and 2 clusters is 0,005. As for accuracy topic extraction is 1 with taken number topic from 1 to 10.
Klasifikasi Risiko Hipertensi menggunakan Fuzzy K-Nearest Neighbor Deby Chintya; Budi Darma Setiawan; Sutrisno Sutrisno
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 3 No 11 (2019): November 2019
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Hypertension, also known as high blood pressure, is a condition where there's increase of blood pressure above the normal level of 120/80 mmHg. Hypertension can cause cardiovascular disease and increased death risk by coronary heart disease and stroke. According to Riset Kesehatan Dasar in Indonesia, hypertension is the most prevalent health problems with 25,8% percentage in 2013. Development of classification system for hypertension risk can be used to detect early hypertension disease. Classification of hypertension risk in this research uses Fuzzy K-Nearest Neighbor method, with Information Gain feature selection. Accuracy value resulted from the test is 84.0002% with value k=5 using 6 features of blood pressure, fitness, age, fatty foods consumption and caffeine consumption.
Klasifikasi Isu Suku, Antar Golongan, Ras, Agama (SARA) pada Twitter Berbahasa Indonesia menggunakan Metode Improved K-Nearest Neighbor (K-NN) Firhad Rinaldi Saputra; Indriati Indriati; Sutrisno Sutrisno
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 4 No 1 (2020): Januari 2020
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Twitter is a social network that has one of the most active users today. With the openness of information users move to send texts or tweets about other users, the number of Twitter users makes a lot of tweets related to ethnic issues, between groups, races, religions (SARA). Twitter cannot access the content of tweets that contain Sara's Issues, research is needed to classify tweets to understand including categories of Sara's Issues or Not Sara's Problems. Classification The Sara issue starts in several ways, namely preprocessing which consists of several stages, namely cleaning, folding cases, tokenisation, filtering and stemming. Followed by the term weghting process, to the classification process using the Improved K-Nearest Neighbor method. Based on the implementation and testing carried out in the research on Sara's Issue Classification on Twitter Using K-NN Increase, get the best results based on Precision averages of 0.976422, Remember at 1, F-Measure of 0.987944444 and Accuracy of 96%. Where the number of documents used as training data are 320 documents and test data as many as 80 documents. Where the number of documents, comparison or balance of training data and the value of k-value used determine the good or not classification process of the document.
Co-Authors Abas Saritua Gultom Achmad Dwi Noviyanto Adinugroho, Sigit Aditya Negara Aditya Sudarmadi Agi Putra Kharisma Agus Prayogi Ahmad Galang Satria Anandita Azharunisa Sasmito Andi Amaliyah Maryama Arthur Julio Risa Ashshiddiqi Axel Iskandar Budi Darma Setiawan Candra Dewi Chalid Ahmad Aulia Chindy Putri Beauty Cindy Inka Sari Danastri Ramya Mehaninda Deby Chintya Dewi Syafira Dhavin Putra Alamsyah Dhimas Tungga Satya Dina Dahniawati Dita Sundarningsih Dyah Ayu Wahyuning Dewi Edy Santoso Endah Utik Wahyuningtyas Enny Trisnawati Fajar Pradana Faraz Dhia Alkadri Febriyani Riyanda Filan Maula Andini Firhad Rinaldi Saputra Fran's Dwi Saputra Atmanagara Galih Aulia Rahmadanu Heru Budiyanto Ian Lord Perdana Imam Cholissodin Imam Farouqi Faisal Inas Nabila Indri Monika Parapat Indriati Indriati Jeowandha Ria Wiyani Jodi Irjaya Kartika Karuniawan Susanto Kukuh Wicaksono Wahyuditomo M. Ali Fauzi Mahardhika Hendra Bagaskara Marji Marji Miracle Fachrunnisa Almas Mochamad Ali Fahmi Mochamad Rafli Andriansyah Mohamad Yusuf Arrahman Muhammad Abdan Mulia Muhammad Alfian Nuris Shobah Muhammad Hafidzullah Muhammad Tanzil Furqon Nanda Firizki Ananta Nurul Hidayat Putra Pandu Adikara Putri Indhira Utami Paudi Rachmad Faqih Santoso Rachmad Ridlo Baihaqi Rahmatsyah Rahmatsyah Rakhmadina Noviyanti Randy Cahya Wihandika Ratih Kartika Dewi Rayindita Siwie Mazayantri Rekyan Regasari Rekyan Regasari Mardi Putri, Rekyan Regasari Mardi Retno Indah Rokhmawati Rezza Hary Dwi Satriya Rich Juniadi Domitri Simamora Riski Adam Elimade Rizal Maulana Sabrina Nurfadilla Safira Dyah Karina Siti Utami Fhylayli Supraptoa Supraptoa Thariq Muhammad Firdausy Tibyani Tibyani Tri Halomoan Simanjuntak Tunggul Prastyo Sriatmoko Wayan Firdaus Mahmudy Widya Amala Sholikhah Yose Parman Putra Sinamo Yuita Arum Sari