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All Journal KARMAPATI (Kumpulan Artikel Mahasiswa Pendidikan Teknik Informatika) ISSN: 2252-9063 Jurnal Pseudocode Journal of ICT Research and Applications SINTECH (Science and Information Technology) Journal WIDYA LAKSANA Jurnal Nasional Pendidikan Teknik Informatika (JANAPATI) Indonesian Journal of Electrical Engineering and Computer Science INSERT: Information System and Emerging Technology Journal Jurnal Nasional Teknik Elektro dan Teknologi Informasi TEKNOSIA
I Md. Dendi Maysanjaya
Universitas Pendidikan Ganesha
Automotive Engineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Energy Engineering Transportation Other

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Journal : SINTECH (Science and Information Technology) Journal

 1 
IDENTIFIKASI CITRA UKIRAN ORNAMEN TRADISIONAL BALI DENGAN METODE MULTILAYER PERCEPTRON I Gede Rusdy Mahayana Putra; Made Windu Antara Kesiman; Gede Aditra Pradnyana; I Made Dendi Maysanjaya
SINTECH (Science and Information Technology) Journal Vol. 4 No. 1 (2021): SINTECH Journal Edition April 2021
Publisher : LPPM STMIK STIKOM Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31598/sintechjournal.v4i1.552

Abstract

Balinese ornament carving are a cultural heritage that is owned by especially the Balinese people. However, especially Balinese people only know the shape of the carving without knowing the name and characteristics of the Balinese traditional carving ornaments. Based on these problems, the researchers have a solution to research about Balinese Ornament Carving Identification by utilizing digital image processing technology. In this study uses Gabor Filter as a feature extraction from the carved image that used and Multilayer Perceptron as a classifier. There are 18 (eighteen) classes of Balinese carving ornaments use in this study with a total of dataset is 268 (two hundred and sixty eight). The purpose of this study was to determine the level of identification  accuracy  of Balinese ornament carving with Multilayer Perceptron method. In the implementation using digital image processing technic with Multilayer Perceptron method was based on backpropagation learning algorithm with 10560 neuron input layers, 50 neuron hidden layers, and 18 neuron output layers as classifier obtained the accuracy for testing is 43%. Classification testing based on k-fold cross validation with K=5 results in average accuracy of 41.14% with optimum accuracy of 56% and accuracy testing with Confusion Matrix obtained the accuracy 43.3%, sensitivity 42.68% and specificity 96.87%. 
Analisis Sentimen Program Mbkm Pada Media Sosial Twitter Menggunakan KNN Dan SMOTE Komang Pramayasa; I Md Dendi Maysanjaya; I Gusti Ayu Agung Diatri Indradewi
SINTECH (Science and Information Technology) Journal Vol. 6 No. 2 (2023): SINTECH Journal Edition Agustus 2023
Publisher : Prahasta Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31598/sintechjournal.v6i2.1372

Abstract

The Merdeka Belajar-Kampus Merdeka (MBKM) program is a relatively new program implemented in Indonesia since February 2020. Like a new program, the implementation of the MBKM program is also followed by various pro and con attitudes. Therefore, a sentiment analysis technique is needed to determine the public opinion towards the MBKM program. The purpose of this study is to determine the performance of the KNN method in performing sentiment classification optimized by the SMOTE method in overcoming the problem of unbalanced data and to determine the tendency of public sentiment towards the implementation of the MBKM program. Based on the research results, the KNN method optimized with the SMOTE method is proven to improve classification performance. From initially producing an accuracy value of 76.13%, precision of 76.03%, recall of 76.13% and f1-score of 76.01% there was an increase in accuracy value to 76.13%, precision to 76.03%, recall to 76.13%, and f1-score to 76.01%. In this study, it was found that community responses tended to be neutral towards the MBKM program. The community feels that the MBKM program is a program that can increase student experience. However, there are still program systems that are considered complicated and need to be evaluated.
Co-Authors Ali Djamhuri Anak Agung Istri, Callysta Athalia Cahyadi, Kadek Wawan Dessy Seri Wahyuni Dharma Putra, I Gede Wira Driya, Putu Dhanu E. Elsa Herdiana Murhandarwati Gede Aditra Pradnyana Gede Saindra Santyadiputra, Gede Saindra Gede Surya Mahendra Hanung Adi Nugroho I Gede Agus Krisna Perdana I Gede Mahendra Darmawiguna I Gede Riyan Ardi Darmawan I Gede Rusdy Mahayana Putra I Gusti Ayu Agung Diatri Indradewi I Made Angga Darma Putra I Made Ardwi Pradnyana I Made Edy Listartha I Made Gede Sunarya Irfanda, Achmad Kadek Suwis Satria Atmaja Komang Jepri Kusuma Jaya Komang Pramayasa Kusumadewi, Ni Made Ayu Mita Made Satria Wibawa Made Windu Antara Kesiman Muhammad Ridwan Ni Putu, Ana Rainita Noor Akhmad Setiawan Putu Hendra Suputra Putu Setiari, Gusti Ayu Putu Yudia Pratiwi Saraya, Fashan Sudiasta Putri, Nyoman Dinda Indira Walhidayah, Irfan Widhia K.Z Oktoeberza Yasa, I Gede Agus Sukariana