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All Journal Jurnal Teknik ITS Proceeding of the Electrical Engineering Computer Science and Informatics International Journal of Advances in Intelligent Informatics Proceeding of the Electrical Engineering Computer Science and Informatics Knowledge Engineering and Data Science JAVA Journal of Electrical and Electronics Engineering Jurnal Nasional Teknik Elektro dan Teknologi Informasi
Yoyon Kusnendar Suprapto
Departemen Teknik Komputer Institut Teknologi Sepuluh Nopember Surabaya
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Energy Engineering

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Penerapan Algoritma K-MeansDengan Optimasi Jumlah Cluster Untuk Pengelompokan Angkatan Kerja Propinsi Jatim Kuswantoro, Endik; Suprapto, Yoyon Kusnendar
JAVA Journal of Electrical and Electronics Engineering Vol 13, No 1 (2015)
Publisher : JAVA Journal of Electrical and Electronics Engineering

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1085.854 KB)

Abstract

This Jumlah angkatan kerja di Indonesia terus meningkat seiring dengan pertambahan jumlah penduduk. Semakin besar jumlah penduduk maka angkatan kerja jadi semakin besar. Hal itu dapat menjadi beban tersendiri bagi perekonomian. Karena jika meningkatnya angkatan kerja yang tidak diimbangi dengan bertambahnya lapangan kerja akan menyebabkan masalah pengangguran. Kondisi tersebut dapat menyebabkan kesejahteraannya menurun. Oleh karena itu permasalahan penganggguran juga tidak terlepas dari bagian jumlah angkatan kerja, Pada Propinsi Jawa Timurjuga mengalami permasalahan tersebut. Dalam penelitian ini akan bertujuan untuk mendapatkan PengelompokanAngkatan kerja pada wilayah propinsi Jawa timur dengan menggunakan algoritma K-Means, dengan pemodelan tersebut akan menghasilkan tingkat penganggurannya dari hasil masing – masing cluster yang dihasillkan, dan persebaran kelompok – kelompok tenaga kerja di pedesaan dan perkotaan ,sehingga bisa memberikan informasi kebutuhan tenaga kerja apa saja yang ada di propinsi Jawa Timur.setelah dilakukan pengklasteran maka hasil yang didapat akan di visualisasi ke dalam grafik chart. Dari proses pengelompokan dengan algoritma K-Means dari jumlah sample sebanyak 17.576 sample rumah tanggadidapat 2 (Dua) kluster yang optimal yang mampu mewakili analisa data yaitu kluster 1 memiliki jumlah anggota kluster paling banyak dan mempunyai karakteristik pengangguran sebanyak 7.936 rumah tangga sample terdiri dari 2.982 sample data tergolong pengangguran setengah dan pekerja paruh waktu sisanya sebesar 4.954 tergolong pekerja aktif diprosentasekan cluster 1 sebanyak 92,59 %. Kluster 2 (dua) kluaster yang mempunyai karakteristik pengangguran Terbuka sebanyak 730 rumah tangga atau sebanyak 8,42 % dari jumlah rumah tangga sample, faktor paling banyak penyebab pengangguran terbuka ialah merasa putus asa dalam memperoleh pekerjaan bisa dikarenakan cacat fisik atau lainnya.
Alerting System for Sport Activity Based on ECG Signals using Proportional Integral Derivative Octaviani, Vika; Kurniawan, Arief; Kusnendar Suprapto, Yoyon; Zaini, Ahmad
Proceeding of the Electrical Engineering Computer Science and Informatics Vol 4: EECSI 2017
Publisher : IAES Indonesia Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (699.555 KB) | DOI: 10.11591/eecsi.v4.1005

Abstract

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. 
Guitar Simulator Based on Realtime Recording Ragil Bintang Brilyan; Eko Mulyanto Yuniarno; Yoyon Kusnendar Suprapto
Jurnal Teknik ITS Vol 10, No 2 (2021)
Publisher : Direktorat Riset dan Pengabdian Masyarakat (DRPM), ITS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j23373539.v10i2.67225

Abstract

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 Vika Octaviani; Arief Kurniawan; Yoyon Kusnendar Suprapto; Ahmad Zaini
Proceeding of the Electrical Engineering Computer Science and Informatics Vol 4: EECSI 2017
Publisher : IAES Indonesia Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (699.555 KB) | DOI: 10.11591/eecsi.v4.1005

Abstract

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. 
Analisis Tingkat Kepuasan Pelayanan E-Procurement Menggunakan Service Quality Model (Studi di Kabupaten Sidoarjo) Tri Hasti Wulandari; Yoyon Kusnendar Suprapto; Achmad Affandi
Jurnal Nasional Teknik Elektro dan Teknologi Informasi Vol 8 No 3: Agustus 2019
Publisher : Departemen Teknik Elektro dan Teknologi Informasi, Fakultas Teknik, Universitas Gadjah Mada

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1143.967 KB)

Abstract

The development of a region is related to the process of procurement of goods/services. Good and quality service will improve the image of the procurement of goods/services in serving the procurement of goods/services in Sidoarjo Regency. For this reason, measurement of service satisfaction level is needed to determine the quality of services that have been provided so far. The purpose of this study is to determine the distribution of user satisfaction based on five dimensions of service quality,i.e., tangibles, reliability, responsiveness, assurance, empathy with two variables of reality and expectations, and knowing things that hinder the tender process. Retrieval of data is done with two kinds of internal and external questionnaires using simple random sampling method. Overall, 100% of respondents express satisfaction but there are some things that need to be improved. The results of the study from the external side show a very large gap in the completeness aspect of the electronic pre-tender process of 5.60%, while the biggest internal gap is the aspect of tender planning, which is 6.97%. Dimensions that need to be prioritized for improving the quality of service to reduce the gap are assurance dimension from the external and responsiveness from the internal.
Automatic note generator for Javanese gamelan music accompaniment using deep learning Arik Kurniawati; Eko Mulyanto Yuniarno; Yoyon Kusnendar Suprapto; Aditya Nur Ikhsan Soewidiatmaka
International Journal of Advances in Intelligent Informatics Vol 9, No 2 (2023): July 2023
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v9i2.1031

Abstract

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.
Deep Learning for Multi-Structured Javanese Gamelan Note Generator Arik Kurniawati; Eko Mulyanto Yuniarno; Yoyon Kusnendar Suprapto
Knowledge Engineering and Data Science Vol 6, No 1 (2023)
Publisher : Universitas Negeri Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.17977/um018v6i12023p41-56

Abstract

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.
Co-Authors Achmad Affandi Aditya Nur Ikhsan Soewidiatmaka Arik Kurniawati Arik Kurniawati Eko Mulyanto Yuniarno Eko Mulyanto Yuniarno Kurniawan, Arief Kuswantoro, Endik Octaviani, Vika Ragil Bintang Brilyan Tri Hasti Wulandari Vika Octaviani Wulandari, Tri Hasti Zaini, Ahmad