MOTION FEATURE SEBAGAI FITUR PADA SISTEM DETEKSI ASAP KEBAKARAN MENGGUNAKAN SUPPORT VECTOR MACHINE

Kebakaran merupakan bahaya yang dihadapi bangunan/gedung selain dari pencurian atau perampokan. Jika terjadi kebakaran, asap merupakan tanda yang mudah terlihat di awal terjadinya kebakaran. Keberadaan asap akan terlihat pada kamera (Closed Circuit Television) CCTV yang terpasang. Dengan demikian video citra keluaran kamera CCTV dapat digunakan untuk membantu petugas pengawasmendeteksi sejak dini kebakaran. Deteksi asap pada video memiliki kesulitan karena asap merupakan objek non kaku dimana bentuk dan ukurannya yang berubah-ubah serta objek latar belakang yang bervariasi. Selain itu kepadatan asap juga bervariasi mulai dari transparan hingga hitam pekat. Dalam penelitian ini akan diterapkan penggunaan fitur gerakan asap sebagai salah satu fitur deteksi asap dengan menggunakan video. Dalam penelitian ini, objek asap dipisahkan dari gambar latar belakangnya dengan menggunakan metodeo Adaptive Gaussian Mixture-based Background/Foreground Segmentation. Selanjutnya dari kontur asap diektraksi fitur gerakan, warna dan bentuk kontur asap. Fitur-fitur ini menjadi fitur masukan mesin klasfikasi support vector machine (SVM) untuk mengenali keberadaan asap dalam video tersebut. Kata kunci: Video Detection, Smoke Detection, Adaptive Gaussian Background/Foreground Segmentation, Motion Feature, Support Vector Machine

Deep Learning for Multi-Structured Javanese Gamelan Note Generator

Javanese gamelan, a traditional Indonesian musical style, has several song structures called gendhing. Gendhing (songs) are written in conventional notation and require gamelan musicians to recognize patterns in the structure of each song. Usually, previous research on gendhing focuses on artistic and ethnomusicological perspectives, but this study is to explore the correlation between gendhing as traditional music in Indonesia and deep learning technology that replaces the task of gamelan composers. This research proposes CNN-LSTM to generate notation of ricikan struktural instruments as an accompaniment to Javanese gamelan music compositions based on balungan notation, rhythm, song structure, and gatra information. This proposed method (CNN-LSTM) is compared with LSTM and CNN. The musical data in this study is represented using numerical notation for the main melody in balungan notation. The experimental results showed that the CNN-LSTM model showed better performance compared to the LSTM and CNN models, with accuracy values of 91.9%, 91.5%, and 91.2% for CNN-LSTM, LSTM, and CNN, respectively. And the value of note distance for the Sampak song structure is 4 for the CNN-LSTM model, 8 for the LSTM model, and 12 for the CNN model. The smaller the note distance, the closer it is to the original notation provided by the gamelan composer. This study provides relevance for novice gamelan musicians who are interested in learning karawitan, especially in understanding ricikan struktural music notation and gamelan art in composing musical compositions of a song

Guitar Simulator Based on Realtime Recording

Music is an art that combines several compositions of musical instruments. Among them are vocals, piano, guitar, bass, drums, and so on. To play a musical instrument also requires a technique to a formula, so that the music game becomes more harmonious. Techniques and formulas in playing musical instruments include tempo, rhythm, how to play a musical instrument, to chords. But for people who are just learning to play a musical instrument, it is certainly difficult to know the formula for the chords to be played. Not to mention when the sound of the chord being played is different from the sound of the intended chord. This can change a song being played sound fake or deviate from the actual song. Often in learning to play musical instruments, some media do not explain or explain how the chords are played and whether the chords are played correctly. One way to determine the accuracy of the chord sound in a self-taught music game, can be done using the help of Machine Learning. This method records the sound of guitar chords being played and classifies guitar chords according to their original sound. However, chords that can be classified are still limited to basic chords, because they are intended for the most basic learning. And for the display of the chord formula that is played it will be more interactive when using game design

Alerting System for Sport Activity Based on ECG Signals using Proportional Integral Derivative

Exercise makes the body fit, but most people do not know the intensity of the exercise they are doing right or otherwise can be dangerous, because not everyone knows the maximum heart rate (MHR), Heart Rate Resting (HRRest), heart rate reserve (HRR) and Target Heart Rate (THR) for each individual, it is proposed an ECG signal-based warning system to find out how much a person's maximum limit in exercise based on age, gender, body mass index, MHR, RHR, THRmin and THRmax. The data is taken by using ECG sensors from the subjects who are doing sport activities using a treadmill by noting the resulted feature when the subject reaches the maximum limit of the heart rate (THRmax) target. Range is calculated from 50% of the THR value, which increases periodically during treadmill activities up to 85% of THR. When already exceed THRmax, then the system will automatically warn and decrease the level of exercise to medium to low levels in the cooling down level. For the % hardware errors in a row from 1 minute, 3 minutes, 5 minutes, and 10 minutes obtained % error with 0.77±0.14. The RMSE of the hardware and software test showed high accuracy because of the small value of error. The system succeeds to alert any intensity level of sport based on the Proportional integral derivative according to the bpm value generated by the subject during the treadmill exercise.

Automatic note generator for Javanese gamelan music accompaniment using deep learning

Javanese gamelan is a traditional form of music from Indonesia with a variety of styles and patterns. One of these patterns is the harmony music of the Bonang Barung and Bonang Penerus instruments. When playing gamelan, the resulting patterns can vary based on the music’s rhythm or dynamics, which can be challenging for novice players unfamiliar with the gamelan rules and notation system, which only provides melodic notes. Unlike in modern music, where harmony notes are often the same for all instruments, harmony music in Javanese gamelan is vital in establishing the character of a song. With technological advancements, musical composition can be generated automatically without human participation, which has become a trend in music generation research. This study proposes a method to generate musical accompaniment notes for harmony music using a bidirectional long-term memory (BiLSTM) network and compares it with recurrent neural network (RNN) and long-term memory (LSTM) models that use numerical notation to represent musical data, making it easier to learn the variations of harmony music in Javanese gamelan. This method replaces the gamelan composer in completing the notation for all the instruments in a song. To evaluate the generated harmonic music, note distance, dynamic time warping (DTW), and cross-correlation techniques were used to measure the distance between the system-generated results and the gamelan composer's creations. In addition, audio features were extracted and used to visualize the audio. The experimental results show that all models produced better accuracy results when using all features of the song, reaching a value of around 90%, compared to using only 2 features (rhythm and note of melody), which reached 65-70%. Furthermore, the BiLSTM model produced musical harmonies that were more similar to the original music (+93%) than those generated by the LSTM (+92%) and RNN (+90%). This study can be applied to performing Javanese gamelan music

IRAWNET: A Method for Transcribing Indonesian Classical Music Notes Directly from Multichannel Raw Audio

A challenging task when developing real-time Automatic Music Transcription (AMT) methods is directly leveraging inputs from multichannel raw audio without any handcrafted signal transformation and feature extraction steps. The crucial problems are that raw audio only contains an amplitude in each timestamp, and the signals of the left and right channels have different amplitude intensities and onset times. Thus, this study addressed these issues by proposing the IRawNet method with fused feature layers to merge different amplitude from multichannel raw audio. IRawNet aims to transcribe Indonesian classical music notes. It was validated with the Gamelan music dataset. The Synthetic Minority Oversampling Technique (SMOTE) overcame the class imbalance of the Gamelan music dataset. Under various experimental scenarios, the performance effects of oversampled data, hyperparameters tuning, and fused feature layers are analyzed. Furthermore, the performance of the proposed method was compared with Temporal Convolutional Network (TCN), Deep WaveNet, and the monochannel IRawNet. The results proved that proposed method almost achieves superior results in entire metric performances with 0.871 of accuracy, 0.988 of AUC, 0.927 of precision, 0.896 of recall, and 0.896 of F1 score

Gamelan Music Dataset

Gamelan music is a traditional music from Indonesia. It is a handcrafted music ensemble. It is tuned based on the feeling of expert hearing called Pangrawit, hence, instrument frequencies among Gamelan ensembles are slightly different. Therefore, this dataset presents three different Gamelan music ensembles to serve more varied data. Audio from the first ensemble was collected from the Association of Music and Art at Institut Teknologi Bandung. Audio are isolated tone recordings from Saron barung, Demung, Peking, Bonang barung, Bonang Penerus, Slenthem, and Kendhang instruments. A Gamelan expert arranged orchestra recordings from isolated tone recordings using FL studio software. The targets of the instrument sources in the orchestra are also provided. Furthermore, Audio from the second ensemble was obtained from the GamelanTron Project at Universitas Dian Nuswantoro. GamelanTron project acquired audio recordings from Kendhang rhythms to control the tempo of the other Gamelan instruments. The last ensemble was used Gamelan ensemble from Institut Teknologi Sepuluh Nopember. The audio is isolated tone recordings from Saron barung, Peking, and Demung instruments. The number of audio in the Gamelan music dataset is 279 recordings with various durations. This dataset can be used in music information retrieval research.The dataset was validated by Aditya Nur Ikhsan Soewidiatmaka who is a Gamelan exper