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PENENTUAN TINGKAT PEMAHAMAN MAHASISWA TERHADAP SOCIAL DISTANCING MENGGUNAKAN ALGORITMA C4.5 Sudipa, I Gede Iwan; I Nyoman Alit Arsana; Made Leo Radhitya
SINTECH (Science and Information Technology) Journal Vol 3 No 1 (2020): SINTECH Journal Edition April 2020
Publisher : LPPM STMIK STIKOM Indonesia

Pandemic Corona Disease 19 or called Covid19 raises a situation that requires every level of society to maintain health and perform activities work from home. One suggestion from the government is to implement social distancing for the academic community, one of which is students. However, not all students can do it due to the lack of understanding of social distancing and the demands of working status, this study seeks to measure the level of understanding of students in knowing social distancing and applying it in current conditions. Based on the questionnaire data collection distributed with the number of respondents 287 students with vulnerable ages 18-25 years later conducted a classification of datamining using the C4.5 algorithm with tree modeling, the results obtained that the accuracy of 93.73%, with class precision that is predictions of students understanding social distancing ( 96.97%), students understand but have to work (100%) and students hesitate (75.71%).

Face Images Classification using VGG-CNN I Nyoman Gede Arya Astawa; Made Leo Radhitya; I Wayan Raka Ardana; Felix Andika Dwiyanto
Knowledge Engineering and Data Science Vol 4, No 1 (2021)
Publisher : Universitas Negeri Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.17977/um018v4i12021p49-54

Image classification is a fundamental problem in computer vision. In facial recognition, image classification can speed up the training process and also significantly improve accuracy. The use of deep learning methods in facial recognition has been commonly used. One of them is the Convolutional Neural Network (CNN) method which has high accuracy. Furthermore, this study aims to combine CNN for facial recognition and VGG for the classification process. The process begins by input the face image. Then, the preprocessor feature extractor method is used for transfer learning. This study uses a VGG-face model as an optimization model of transfer learning with a pre-trained model architecture. Specifically, the features extracted from an image can be numeric vectors. The model will use this vector to describe specific features in an image.Â The face image is divided into two, 17% of data test and 83% of data train. The result shows that the value of accuracy validation (val_accuracy), loss, and loss validation (val_loss) are excellent. However, the best training results are images produced from digital cameras with modified classifications. Val_accuracy's result of val_accuracy is very high (99.84%), not too far from the accuracy value (94.69%). Those slight differences indicate an excellent model, since if the difference is too much will causes underfit. Other than that, if the accuracy value is higher than the accuracy validation value, then it will cause an overfit. Likewise, in the loss and val_loss, the two values are val_loss (0.69%) and loss value (10.41%).

Sistem Informasi Geografis Risiko Kemunculan Rip Current Menggunakan Decision Tree C4.5 Made Leo Radhitya; Agus Harjoko
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 10, No 2 (2016): July
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.15949

One of the dangers that occur at the beach is rip current. Rip current poses significant danger for beachgoers. This paper proposes a method to predict the rip current's occurence risk by using decision tree generated using C4.5 algorithm. The output from the decision tree is rip current's occurrence risk. The case study for this research is the beach located at Rote Island, Rote Ndao, Nusa Tenggara Timur. Evaluation result shows that the accuracy is 0.84, and the precision is 0.61. The average recall value is 0.68 and the average F-measure is 0.59 in the range 0 to 1.

PENENTUAN TINGKAT PEMAHAMAN MAHASISWA TERHADAP SOCIAL DISTANCING MENGGUNAKAN ALGORITMA C4.5 I Gede Iwan Sudipa; I Nyoman Alit Arsana; Made Leo Radhitya
SINTECH (Science and Information Technology) Journal Vol. 3 No. 1 (2020): SINTECH Journal Edition April 2020
Publisher : LPPM STMIK STIKOM Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31598/sintechjournal.v3i1.562

Pandemic Corona Disease 19 or called Covid19 raises a situation that requires every level of society to maintain health and perform activities work from home. One suggestion from the government is to implement social distancing for the academic community, one of which is students. However, not all students can do it due to the lack of understanding of social distancing and the demands of working status, this study seeks to measure the level of understanding of students in knowing social distancing and applying it in current conditions. Based on the questionnaire data collection distributed with the number of respondents 287 students with vulnerable ages 18-25 years later conducted a classification of datamining using the C4.5 algorithm with tree modeling, the results obtained that the accuracy of 93.73%, with class precision that is predictions of students understanding social distancing ( 96.97%), students understand but have to work (100%) and students hesitate (75.71%).

PENDEKATAN Z-SCORE DAN FUZZY DALAM PENGUJIAN AKURASI PERAMALAN CURAH HUJAN Made Leo Radhitya; Gede Iwan Sudipa
SINTECH (Science and Information Technology) Journal Vol. 3 No. 2 (2020): SINTECH Journal Edition Oktober 2020
Publisher : LPPM STMIK STIKOM Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31598/sintechjournal.v3i2.567

Determination of rainfall is important to determine the intensity of rain that occurs in an area. Rain intensity that is too high will certainly have a bad impact. Forecasting or prediction techniques are used to determine the likelihood of intensity occurring in the following year. However, rainfall data are continuous numerical data. Measurement of accuracy becomes more difficult if the data type is like that. So, this study tests the accuracy of rainfall forecasting in the city of Denpasar from a different perspective. This test combines the Z-score method and the Fuzzy set theory to normalize and classify rainfall data. This combination determines the degree of rainfall membership divided into Upper, Middle, and Lower levels. Based on the results of rainfall accuracy testing starting in 2012-2016 obtained an average value of accuracy of 85% with training data that is data in 2007-2015. The normalization process greatly affects the value of the training data.

SISTEM REKOMENDASI MUSIK BERDASARKAN DATA KONTEKS PADA LISTENING HISTORY MUSIK DAN KETERKAITAN ARTIS INDONESIA Gst Ayu Vida Mastrika Giri; Made Leo Radhitya; Made Agung Raharja; I Wayan Supriana
Jurnal RESISTOR (Rekayasa Sistem Komputer) Vol. 5 No. 1 (2022): Jurnal RESISTOR Edisi April 2022
Publisher : Prahasta Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31598/jurnalresistor.v5i1.1044

A large number of digital music circulates online today. It makes music listeners confused to choose which music is suitable to listen to in certain circumstances or contexts, for example certain time, weather, activity, and desired mood. Playlist creation can make it easy for music listeners to collect their favorite music for a particular context, but creating playlists is time consuming and of course a lot of playlists will have to be created to accommodate all combinations of contexts. In this study, an automated music recommendation system was built using context data consisting of time, weather, activities, and desired mood which were also adjusted for the listener's age, gender, and favorite artist. The method used is Case-Based Reasoning (CBR), using listeners' listening history data as a knowledge base and the artist relatedness of Indonesian artists to improve solutions at the revision stage. Output of this system is in the form of music playlist presented in a website. The overall precision average for music recommendations is 0.78.

RANCANG BANGUN SISTEM INFORMASI PENGOLAHAN NILAI SISWA PADA SMK KESEHATAN PANCA ATMA JAYA I Putu Eka Andika Cakra; Made Leo Radhitya; Ketut Laksmi Maswari
Jurnal Teknologi Informasi dan Komputer Vol 6, No 2 (2020): Jurnal Teknologi Informasi dan Komputer
Publisher : LPPM Universitas Dhyana Pura

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (851.781 KB)

ABSTRACTSMK Kesehatan Panca Atma Jaya is one of the vocational school in the Klungkung area, where every semester students will recieve report cards as a result of teaching and learning process. The process of recording report at SMK Kesehatan Panca Atma Jaya is hampered by late collection of student grades made by the honorary teacher causing the preparation of report cards a little delayed. This research was created to assist in the processing of report cards grades and assist teachers in collecting grades because the process inputing grades can be done online. In addition this system is equipped with e-mail notifications. Functional and non-fuctional testing, it can be concluded that the design of students report cards processing information system has een running in accordance with fuctional and non-fucntional testing.Keywords: Information System, Report Cards, Email Notification..ABSTRAKSMK Kesehatan Panca Atma Jaya merupakan salah satu sekolah kejuruan yang ada didaerah klungkung, dimana setiap semester siswa akan menerima rapor sebagai hasil proses belajar mengajar. Proses perekapan nilai rapor pada SMK Kesehatan Panca Atma Jaya terkendala pada pengumpulan nilai yang sering terlambat yang dilakukan oleh guru honorer menyebabkan penyusunan rapor sedikit terhambat. Penelitian ini dibuat untuk membantu dalam pengolahan data nilai rapor dan membantu guru dalam pengumpulan nilai karena proses penginputan nilai bisa dilakukan secara online. Selain itu sistem ini dilengkapi dengan notifikasi email sebagai pemberitauan dalam pengumpulan nilai. Pengujian fungsional dan non fungsional, dapat diperoleh kesimpulan bahwa rancang bangun sistem informasi pengolahan nilai siswa sudah berjalan sesuai dengan pengujian fungsional dan non fuungsional.Kata Kunci: Sistem Informasi, Rapor, Notifikasi Email

APLIKASI E-SEWA BARANG BERBASIS MOBILE I Dewa Gede Agung Pandawana; Made Leo Radhitya; I Made Subrata Sandhiyasa; Bagus Tresna Bramstya
Jurnal Krisnadana Vol 1 No 3 (2022): Jurnal Krisnadana - Mei 2022
Publisher : Yayasan Sinergi Widya Nusantara (Sidyanusa)

Aktivitas sewa-menyewa merupakan suatu aktivitas memakai suatu barang yang bukan barang milik sendiri dengan membayarkan sejumlah uang kepada pihak penyewa dengan persetujuan kedua belah pihak dalam pemenuhan proses bisnis si penyewa, sewa-menyewa tidak lepas dari kehidupan sehari-hari dikarenakan efektivitas dan efisiensinya dalam membantu menyelesaikan proses bisnis setiap pelaku usaha karena pelaku usaha tidak perlu membeli barang tersebut yang membuat tidak banyak biaya yang dikeluarkan dalam pemenuhan perawatan barang tersebut. Penerapan sistem informasi sangat diperlukan agar dapat mengoptimalkan fungsi dan pemanfaatan aktivitas sewa-menyewa, sehingga lebih mudah dan efisien. Penelitian ini menghasilkan sebuah sistem informasi sewa barang yang dapat digunakan oleh pihak pelaku usaha dalam mencari barang yang akan disewa dalam membantu menyelesaikan proses bisnis mereka. Terdapat fungsi utama yaitu Pencarian Barang yang membantu user dalam menemukan barang yang dicari serta terdapat detail barang yang memudahkan user dalam memilih spesifikasi barang yang diinginkan, juga terdapat fungsi Riwayat yang mencatat setiap aktivitas yang dilakukan user pada sistem serta memudahkan mencari barang yang sama ketika ingin melakukan penyewaan terhadap barang yang pernah disewa. Proses perancangan sistem menggunakan metode Waterfall dengan perancangan terstruktur, menggunakan React Native sebagai framework dan menggunakan Unified Modelling Language sebagai bahasa dalam memvisualisasikan rancangan model sistem. Metode pengujian menggunakan model Black Box. Hasil pengujian menunjukkan performa sistem yang secara fungsional sangat baik.

KLASIFIKASI GENRE MUSIK MENGGUNAKAN TEKNIK PEMBELAJARAN MESIN Gst. Ayu Vida Mastrika Giri; Made Leo Radhitya
Jurnal Teknologi Informasi dan Komputer Vol 9, No 1 (2023): Jurnal Teknologi Informasi dan Komputer
Publisher : LPPM Universitas Dhyana Pura

Show Abstract | Download Original | Original Source | Check in Google Scholar

ABSTRACTMusic classification plays an important role in music information retrieval because the results of music classification based on genre can be used by music listeners to find new music according to their preferences. Currently, online music streaming providers create playlists and also provide music recommendations based on genres. Therefore, developing a good and accurate machine learning model to classify music automatically based on genre will be very beneficial for online music streaming providers. In this research, automatic classification of music genres was carried out using content-based features that have been extracted from audio signals. The dataset used contains musical features that have been processed from the GTZAN music dataset which consists of 57 content-based features. The machine learning techniques used for music classification were K-Nearest Neighbor (K-NN), Support Vector Machine (SVM), and Multi Layer Perceptron (MLP). The MLP machine learning technique produced the greatest accuracy value when compared to K-NN and SVM in this study with an accuracy value of 71.6%, followed by SVM with an accuracy value of 71%, and K-NN with an accuracy value of 69.6%.Keywords: genre, k-nearest neighbor, multi layer perceptron, music classification, support vector machineABSTRAKKlasifikasi musik memainkan peran penting dalam temu kembali informasi musik karena hasil klasifikasi musik berdasarkan genre dapat digunakan oleh pendengar musik untuk menemukan musik baru yang sesuai dengan preferensi. Saat ini, penyedia streaming musik online membuat playlist dan juga memberikan rekomendasi musik berdasarkan genre. Oleh karena itu, pengembangan model pembelajaran mesin yang baik dan akurat untuk mengklasifikasikan musik secara otomatis berdasarkan genre akan sangat bermanfaat bagi penyedia streaming musik online. Pada penelitian ini, dilakukan klasifikasi genre musik otomatis menggunakan fitur berbasis konten yang telah diekstrak dari sinyal audio. Dataset yang digunakan berupa fitur yang telah diproses dari dataset musik GTZAN yang terdiri dari 57 fitur berbasis konten. Teknik pembelajaran mesin yang digunakan untuk klasifikasi musik adalah K-Nearest Neighbor (K-NN), Support Vector Machine (SVM), dan Multi Layer Perceptron (MLP). Teknik pembelajaran mesin MLP menghasilkan nilai akurasi terbesar jika dibandingkan dengan K-NN dan SVM pada penelitian ini dengan nilai akurasi 71,6%, disusul dengan SVM dengan nilai akurasi 71%, dan K-NN dengan nilai akurasi sebesar 69,6%.Kata Kunci: genre, k-nearest neighbor, klasifikasi musik, multi layer perceptron, support vector machine

APLIKASI E-SEWA BARANG BERBASIS MOBILE I Dewa Gede Agung Pandawana; Made Leo Radhitya; I Made Subrata Sandhiyasa; Bagus Tresna Bramstya
Jurnal Krisnadana Vol 1 No 3 (2022): Jurnal Krisnadana - Mei 2022
Publisher : Yayasan Sinergi Widya Nusantara (Sidyanusa)

Aktivitas sewa-menyewa merupakan suatu aktivitas memakai suatu barang yang bukan barang milik sendiri dengan membayarkan sejumlah uang kepada pihak penyewa dengan persetujuan kedua belah pihak dalam pemenuhan proses bisnis si penyewa, sewa-menyewa tidak lepas dari kehidupan sehari-hari dikarenakan efektivitas dan efisiensinya dalam membantu menyelesaikan proses bisnis setiap pelaku usaha karena pelaku usaha tidak perlu membeli barang tersebut yang membuat tidak banyak biaya yang dikeluarkan dalam pemenuhan perawatan barang tersebut. Penerapan sistem informasi sangat diperlukan agar dapat mengoptimalkan fungsi dan pemanfaatan aktivitas sewa-menyewa, sehingga lebih mudah dan efisien. Penelitian ini menghasilkan sebuah sistem informasi sewa barang yang dapat digunakan oleh pihak pelaku usaha dalam mencari barang yang akan disewa dalam membantu menyelesaikan proses bisnis mereka. Terdapat fungsi utama yaitu Pencarian Barang yang membantu user dalam menemukan barang yang dicari serta terdapat detail barang yang memudahkan user dalam memilih spesifikasi barang yang diinginkan, juga terdapat fungsi Riwayat yang mencatat setiap aktivitas yang dilakukan user pada sistem serta memudahkan mencari barang yang sama ketika ingin melakukan penyewaan terhadap barang yang pernah disewa. Proses perancangan sistem menggunakan metode Waterfall dengan perancangan terstruktur, menggunakan React Native sebagai framework dan menggunakan Unified Modelling Language sebagai bahasa dalam memvisualisasikan rancangan model sistem. Metode pengujian menggunakan model Black Box. Hasil pengujian menunjukkan performa sistem yang secara fungsional sangat baik.

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