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All Journal Jurnal Neutrino : jurnal fisika dan aplikasinya TELKOMNIKA (Telecommunication Computing Electronics and Control) Jurnal Teknologi Informasi dan Ilmu Komputer Journal of Information Technology and Computer Science Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Tri Dharma Mandiri: Diseminasi dan Hilirisasi Riset Kepada Masyarakat (Jurnal Pengabdian Kepada Masyarakat)
Barlian Henryranu Prasetio
Fakultas Ilmu Komputer, Universitas Brawijaya
Biochemistry, Genetics & Molecular Biology Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Engineering Health Professions Physics Public Health Social Sciences

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Implementasi Algoritma Naive Bayes pada Sistem Monitoring dan Klasifikasi Kualitas Air Akuarium Ikan Mas Koki Agra Firmansyah; Dahnial Syauqy; Barlian Henryranu Prasetio
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 6 No 9 (2022): September 2022
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

In this era of technological development, new innovations are needed that can help humans carry out their activities more efficiently. One of these areas of activity is measuring the water quality of the goldfish aquarium. At this time the measurement of air quality, especially in aquariums, is done manually on each attribute to be measured, while the measuring instrument for measuring the level of the attribute to be measured varies with prices that are relatively affordable to very expensive. In addition, using opinions based on direct observations of aquariums, etc. Therefore, the purpose of this research is to create a system that can help overcome this problem. In this study, the system was designed using a turbidity sensor as a measure of water turbidity levels, mq-135 as a measure of ammonia gas (NH3) and pH as a measure of acid and base levels. As for the display system using a 16x2 LCD to display the output obtained. This system component goes through the Arduino uno as a microcontroller and a laptop to enter code on the Arduino ide so that the system can run and as the main power supply. The classification used in this study is nave Bayes, nave Bayes is an algorithm method to determine the value of each class based on training data and test data, to read numerical attributes using distribution calculations, the attributes used in this Nave Bayes classification are obtained from reading turbidity, ammonia and ph of the sensor. This nave Bayes method has a fairly good accuracy, the higher the accuracy of the method, the more training data with various values.
Analisis Perbandingan Filter Finite Impulse Response, Infinite Impulse Response, dan Discrete Wavelet Transform pada Kondisi Kelelahan Mental berbasis Sinyal Electroenchephalography Dwinanda Romolo; Edita Rosana Widasari; Barlian Henryranu Prasetio
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 6 No 9 (2022): September 2022
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

The brain is one of the vital organs owned by living things. The brain will work optimally if it gets enough oxygen from the blood. If not, the work of the brain will decrease and will affect human performance in daily activities. One of the reasons for the decreased supply of oxygen to the brain is fatigue. According to the International Labor Organization in 2013, as many as two million workers experienced work accidents due to fatigue caused by mental factors. One way to find out whether humans experience mental fatigue or not is by recording brain wave signal activity or Electroenchephalography (EEG) and analyzing it. To analyze the EEG signal must use digital filters which are very numerous. This study will analyze the EEG signal using three filters, namely Finite Impulse Response (FIR), Infinite Impulse Response (IIR), and Discrate Wavelet Transform (DWT). Then the filtering results from these three filters will be compared to find which filter has the highest efficiency level by looking at the Signal to Noise Ratio (SNR) value and the resulting computational time. The result is that the FIR filter is the most efficient than the other filters, resulting in an average SNR value of 27.89975 dB. While the average value of the resulting computation time is 0.131 s
Sistem Deteksi Tingkat Stress Menggunakan Suara dengan Metode Jaringan Saraf Tiruan dan Ekstraksi Fitur MFCC berbasis Raspberry Pi Muhammad Nabil Aljufri; Barlian Henryranu Prasetio
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 6 No 11 (2022): November 2022
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Stress is a feeling of emotional tension that can be caused by many things, such as work, studies, family, etcetera. If stress is not addressed quickly, it can worsen and have an impact on your health. Several studies have produced good results with a device that can tell someone's emotion by their voice. The purpose of this research is to know whether someone is experiencing stress and to know the level of stress using their voice. By using MFCC feature extraction and Artificial Neural Network machine learning is expected to know the level of stress using speech. This system works by using Raspberry Pi, which connects to a microphone. The Raspberry Pi will wait for a command that start and stop the recording from a buttnon, then when a voice is recorded it will extract its feature and predict the result. That result will be shown on LCD. In this research, the dataset that is being used is Speech Under Simulated and Actual Stress (SUSAS) dataset, which contains 1860 utterances. The result of this research using 30 samples from the dataset is 90% accuracy, but when building the artificial neural network model, the testing accuracy is only 76%. where the average computing time while testing the system is 2.65 seconds.
Sistem Pengenalan Intensitas Emosi Sedih melalui Ucapan menggunakan Ekstraksi Bark-Frequency Cepstral Coefficient dan K-Nearest Neighbor berbasis Raspberry Pi 4 Dhimas Arfian Lazzuardhy; Barlian Henryranu Prasetio
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 6 No 11 (2022): November 2022
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

The pandemic has had a major impact on human mental health, which has had an impact on the instability of emotions. Unstable emotions can cause stress, characterized by feelings of sadness, gloom, and unhappiness. Prolonged feelings of sadness can be a sign of depression. Therefore, tools are needed that able to provide information about the intensity of sadness felt in order to reduce prolonged sadness. Some studies have successfully created tools to detect emotions with voice signal. Therefore, the purpose of this research is to make a system that is able to detect the intensity of sad emotions through human's voice. The research was conducted using BFCC feature extraction, which has a better accuracy than the MFCC method if the sample data has a lot of noise. The system will work with the help of a microphone as a device to record. In the first process, the system will record using a microphone, the recorded results will be processed to extract the features and classify them. After the prediction results are obtained, the results will be displayed on the LCD display. This research uses the Crowd Sourced Emotional Actors Dataset (CREMA-D) consisting of several emotions with high, low, and mid levels, but in this research only focuses on the use of sad emotions. The results of the study obtained an accuracy of 60% with an average signal-to-ratio (SNR) of 23.9 dB, and has an average difference of 11.76 dB better than the MFCC method.
Sistem Pengenalan Intensitas Emosi Marah melalui Ucapan menggunakan Ekstraksi Wavelet-Based Frequency Cepstral Coefficients dan Algoritma K-Nearest Neighbor berbasis Raspberry Pi 4 M. Ihsan An-Nashir; Barlian Henryranu Prasetio
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 6 No 11 (2022): November 2022
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Emotion is an intense feeling directed towards someone or something. One way that humans can express angry emotions that can be recognized is through speech. Speech emotion recognition is a technology that can be used to identify emotions from the sound of someone speaking. In this research, a speech emotion recognition process was carried out using the WFCC (Wavelet-based Frequency Cepstral Coefficients) method, which uses wavelet transformation in its extraction and has the ability to separate various frequency variations at various times. In addition, this study also tested the ability of the K-Nearest Neighbor algorithm in classifying the intensity of angry emotions from sound signals. This study was conducted using a Raspberry Pi 4. This study concluded that the WFCC extraction method is quite effective in detecting high intensity angry emotions with an accuracy of 66.67%.
Sistem Kontrol Perangkat Inframerah menggunakan Speech Recognition dengan Spectrogram dan Convolutional Neural Network berbasis Mikrokontroler Irfan Muzakky Nurrizqy; Barlian Henryranu Prasetio; Rekyan Regasari Mardi Putri
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 7 No 13 (2023): Publikasi Khusus Tahun 2023
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Dipublikasikan di JTIIK (Jurnal Teknologi Informasi dan Ilmu Komputer)
Sistem Klasifikasi Kualitas Air untuk Budidaya Ikan Nila Hitam (Oreochromis Niloticus) menggunakan Metode Support Vector Machine Dwiki Ilham Bagaskara; Dahnial Syauqy; Barlian Henryranu Prasetio
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 6 No 12 (2022): Desember 2022
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Black Tilapia or which has the scientific name Oreochromis Niloticus is a type of freshwater fish that is widely consumed throughout the world, especially in Indonesia. Tilapia production continues to increase along with the increasing demands to meet food needs both domestically and abroad. One of the efforts made to achieve this target is cultivating tilapia seeds to improve the quality and production of tilapia. In cultivating tilapia in order to obtain quality fish yields, one of the most important factors influencing this is the quality of the water in the pond where the tilapia is cultivated. In Indonesia there are still many tilapia cultivators who measure and control pond water quality manually or even don't do this at all. For this reason, a water quality classification system will be designed for black tilapia cultivation using the support vector machine method which can carry out classifications to determine water quality in ponds automatically. The system is implemented using 3 sensors, namely the temperature sensor DS18B20, the pH sensor PH-4502c, and the turbidity sensor SEN0189. In the test, 3 features were used in the system to make training and testing data, namely temperature features, pH features, and turbidity features to determine the class of pond water being tested. 60 total training data consisting of 30 pieces of data for the "good" class and 30 pieces of data for the "bad" class were used to conduct training data for the classification system. The support vector machine classification method that is programmed in the system will carry out the classification process by reading the values ​​from the sensors and storing them as features to be processed and compared with the results of the training data. The system will look for the y value as the reference value for class classification results where if the y value ≤ 1 then the water will be declared "good" and if y> 1 then the water is said to be "bad". The test results are obtained by reading the output results on the system LCD for 15 seconds. From the test results, 15 reading were obtained where 12 of the test results were correct readings and 3 were incorrect readings. From the test results it can be concluded that this system can work according to its function and purpose.
Sistem Klasifikasi Air Mineral Layak Minum berdasarkan Nilai PH dan Kekeruhan Menggunakan Metode Naive Bayes berbasis Arduino Uno Faza Gustaf Marrera; Barlian Henryranu Prasetio; Hurriyatul Fitriyah
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 7 No 1 (2023): Januari 2023
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Water is a very important natural resource for the survival of humans, one of which is as a source of consumption. The safety standards for drinking water established by the government are very important to ensure the safety of the drinking water we consume. To be safe for health, drinking water must meet physical, microbiological, chemical, and radioactive requirements. However, there are still some areas in Indonesia that face difficulties in ensuring the safety of the drinking water they consume. From this problem, researchers want to make a drinking water quality detection system that can provide information related to pH and water clarity to users. The drinking water quality detection system is made using the pH-4502C and turbidity SEN0189 sensors and the Arduino UNO microcontroller connected to the 16x2 LCD as an information display. The results of the two sensors will then be classified using the Naive Bayes method. In this study, sensor testing and method testing have been conducted. The results obtained in the pH-4502 accuracy test are 96.54%. Then, based on the results of the SEN0189 turbidity sensor test, it can be seen that the SEN0189 turbidity sensor can work well in reading the condition of water being cloudy or not. From the readings taken on drinking water (clear) and coffee water (cloudy), it can be seen that the voltage values produced by the sensor are different. A higher voltage value indicates that the water is clearer, while a lower voltage value indicates that the water is cloudier. In the Naive Bayes accuracy test, the result obtained is 100% of 15 total test data and 30 training data. The data is in the form of drinking water in a public environment.
Sistem Klasifikasi Kualitas Daging Ayam Broiler sebagai Bahan Pembuatan Bakso berdasar Nilai Resistansi dan Kadar Amonia dengan menggunakan Metode Fuzzy Iqbal Maulana Susanto; Barlian Henryranu Prasetio; Achmad Ridok
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 7 No 1 (2023): Januari 2023
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

The availability of chicken meat is inversely proportional to the demand, resulting in meatball producers being unable to choose proper chicken meat. In making meatballs, quality meat is needed that is fresh, firm, and fit for consumption. Then a classification system was created that could detect quality of chicken meat based on the aroma and texture. System input values come from the MQ-135 gas sensor and resistance circuit (voltage divider). Input data is processed using the fuzzy - Mamdani method on Arduino Pro Mini. Processed data is displayed on the Oled ST7789 LCD and the buzzer sound indicator. The system is small in size with a voltage source from the battery, so the classification system is portable. The results of functional testing of the MQ-135 sensor in measuring ammonia gas levels affect the output voltage value by 96.78%. While the voltage range has a measurement of 96.29% with a measurement range of 1K-100KΩ. Broiler filet breast meat has an average resistance value of 26.32K - 33.92KΩ and an ammonia content of 5.41 - 7.41 ppm with wet-processed meat, while the average resistance value and ammonia content of dry processed meat is 27.02K - 34.52KΩ and 5.45 - 7.33 ppm. System accuracy in classifying using the fuzzy - Mamdani method has a proportion of 83.33% in 24 tests.
Pengembangan Sistem Monitoring pada Identifikasi Kesehatan Pencernaan menggunakan Bluetooth Low Energy (BLE) berbasis Aplikasi Smartphone Ryan Anggito Priono; Barlian Henryranu Prasetio; Eko Setiawan
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 7 No 2 (2023): Februari 2023
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

A research with the title “Identification System for Digestive Health by Bowel Sound using Convolutional Neural Network” is a research done by a college student named Ryzaldi Ananda Fabiana with the purpose of making a system that could detect ileus obstruction from the person's gastrointestinal sound. But, the implementation of the system uses VNC viewer as an application which needs internet connection to run and showing the result of identification on the raspberry pi's terminal. A solution to the problem is to create a specific application from scratch specifically for the system using a protocol that doesn't require any internet such as BLE or Bluetooth Low Energy. This research will modify the previous research's raspbberry pi untuk a BLE peripheral and create and application that will run as a BLE central that will initiate the connection with the raspberry pi. The system works by initiating a connection between the application and raspberry pi where from the application side it will show every service and characteristic that is initialized on the raspberry pi side and includes several buttons to call the identification functions, merge and downsample the signal data and send the data. This study found that the average time it took for the application and raspberry pi to finish creating a connection or the delay is about 2.060369 seconds, sending a message has an average delay of 0.190982 seconds and sending data has an average delay of 0.0564674 seconds.
Co-Authors Achmad Ridok Adharul Muttaqin Adi Setiawan Adven Edo Prasetya Adven Edo Prasetya, Adven Edo Agra Firmansyah Ahmad Afif Supianto Aldi Jayadi Ali, Zidane Allaam, Fakhrul Arief Kurniawan Aryo Pinandito Ash-Shadiq, Aqsath Muhammad Aswin Suharsono, Aswin Atmojo Pamungkas, Handoko Bagus Ayu Astina Sari, Ni Made Baariu, Rahagi Abdu Bagus Priyo Pangestu Brylliano Maza Putra Budi Darma Setiawan Budy Prakoso, Khrisna Shane Chatarina Umbul Wahyuni Dahnial Syauqy Dayat, Fauzi Syarifulloh Defri Alif Raihan Denny Sagita Rusdianto Dhimas Arfian Lazzuardhy Dini Eka Ristanti Dini Ismawati Dwiki Ilham Bagaskara Dwinanda Romolo Edita Rosana Widasari Edita Rosana Widasari, Edita Rosana Eko Setiawan Eko Setiawan Eko Setiawan Fabiana, Ryzaldi Ananda Fachry Ananta Fadhilah, Khairian Fadhillah, Muhammad Galih Faisal Natanael Lubis Faviansyah Arianda Pallas Faza Gustaf Marrera Fitra Abdurrachman Bachtiar Fitriyah, Hurriyatul Gembong Edhi Setiawan Gembong Edhi Setyawan Ghifari, Ahmad Hafidz Abdillah Masruri Hanifa Maulani Ramadhan Haqyah, Saprina Hani Heru Nurwarsito Hilal Imtiyaz I Wayan Boby Astagina Naghi Imam Cholissodin Iqbal Maulana Susanto Irfan Muzakky Nurrizqy Irwanda Adhi Firmantara Isnandar, Muhammad Fawwaz Dynoeputra Iwasawa, Takeru Jevandika Joan Chandra Kustijono Julisya Thana Khriswanti Kamal, Attar Syifa Kusuma, Lindhu Parang La Ode Adriyan Hazmar Lavanna Indanus Ramadhan M. Hannats Hanafi Ichsan M. Ihsan An-Nashir Mahardika, Aryanta Seta Mochammad Hannats Hanafi Mochammad Hannats Hanafi Ichsan Muchlas Mughniy Muflih, Aufada Muhammad Fatikh Hidayat Muhammad Ghifari, Muhammad Muhammad Habib Jufah Alhamdani Muhammad Nabil Aljufri Muhammad Rizki Chairurrafi Nadi Rahmat Endrawan Nashrullah, Ega Rasendriya Naviaddin, Arsal Wildan Ngulandoro, Mochammad Giri Wiwaha Nobel Edgar Novaria Elsari Ryzkiansyah Novea, Leisha Nur, Farhan Marwandi Nurrizqy, Irfan Muzakky Nurul Hidayat Ovriawan Aldo Pribadi Putra Paleva, Haidar Rheza Panggabean, Riki Boy Parja, Mujianto Anda Perkasa, Septiyo Budi Permana, Galih Pierl Kritzenger Sinaga Prawironegoro, Abdul Harris Putera, Thariq Andhita Putra Pamungkas, Dimas Resha Putra, Brylliano Maza Putra, Ravelino Adhianto Surya Raden Galih Paramananda Rahmawan, Muhammad Fuad Rajasa, Mohammad Fariq Rakhmadhany Primananda, Rakhmadhany Ramadhan, Dimas Ramadhan, Muhammad Fitrah Randy Cahya Wihandika Rekyan Regasari Mardi Putri, Rekyan Regasari Mardi Reza Hastuti Riyad Febrian Rizal Maulana Rizal Maulana, Rizal Rosyana Lencie Mampioper Ryan Anggito Priono Sabriansyah Rizkiqa Akbar Sabriansyah Rizqika Akbar Sabriansyah Rizqika Akbar Septiyo Budi Perkasa Sifaunnufus Ms, Fi Imanur Sigit Priyo Jatmiko Subianto, Aflah Fadhlurrahman Syahrul Chilmi, Syahrul Tampubolon, Jeremya Tiara Mahardika Tibyani Tibyani Utaminingrum, Fitri Valensiyah Rozika Widasari, Edita Rosana Wijaya Kurniawan Wijaya Kurniawan Yosia Nindra Kristiantya Yudhistira, Gevan Putra Yunan Alamsyah Nasution Yusril Dewantara Yusuf, Delfi Olivia