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All Journal Jurnal Ilmu Komputer Jurnal Pendidikan Teknologi dan Kejuruan KARMAPATI (Kumpulan Artikel Mahasiswa Pendidikan Teknik Informatika) ISSN: 2252-9063 Jurnal Sains dan Teknologi JUTI: Jurnal Ilmiah Teknologi Informasi Journal of ICT Research and Applications Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Network Engineering Research Operation [NERO] Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control JUTIK : Jurnal Teknologi Informasi dan Komputer SINTECH (Science and Information Technology) Journal RESEARCH : Computer, Information System & Technology Management Jurnal ULTIMATICS WIDYA LAKSANA Journal of Education Technology International Journal of Natural Science and Engineering Jurnal Nasional Pendidikan Teknik Informatika (JANAPATI) INSERT: Information System and Emerging Technology Journal
Gede Aditra Pradnyana
Universitas Pendidikan Ganesha
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Languange, Linguistic, Communication & Media Library & Information Science Social Sciences Other

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Journal : SINTECH (Science and Information Technology) Journal

 1 
Pengembangan Digital Story Book “Satua Bali” Berbasis Mobile Prasada, I Made Yoga; Putrama, I Made; Pradnyana, Gede Aditra
SINTECH (Science and Information Technology) Journal Vol 1 No 1 (2018): SINTECH Journal Edisi April 2018
Publisher : LPPM STMIK STIKOM Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1186.891 KB) | DOI: 10.31598/sintechjournal.v1i1.209

Abstract

This research aimed to produce a media in the form of Digital Story Book "Satua Bali" Based Mobile that could be a container to provide a collection of Satua Bali, so it could be utilized to help maintaining the existence of Satua Bali among the community, especially children. Digital Story Book "Satua Bali" Based Mobile has some additional features, such as user can add new satua, share to the social medias, give comment, like, add as favorite, etc. This research was a type of research and development with research model of ADDIE (Analysis, Design, Development, Implementation, Evaluation). The testing process was conducted in six stages, namely test: whitebox and blackbox which obtain good result, content expert which obtains result of 96,2% (very appropriate), media expert that obtains result of 92,5% (very appropriate), users responses with UEQ method obtain good result, and compatibility is successfully implemented to 10 different devices. The final result of this research is the application of Digital Story Book "Satua Bali" which can be run on smartphone with android operating system.
PENGEMBANGAN SISTEM PENDUKUNG KEPUTUSAN PENENTUAN PRIORITAS PEMBERIAN BANTUAN BIMTEK KEPADA INDUSTRI KECIL DAN MENENGAH (IKM) DENGAN METODE ANALITYCAL HIERARCHY PROCESS (AHP) DAN SIMPLE ADDITIVE WEIGHTING (SAW) Komang Sudana Yasa Pande; Made Windu Antara Kesiman; Gede Aditra Pradnyana
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 | Full PDF (3.032 KB) | DOI: 10.31598/sintechjournal.v3i1.391

Abstract

The Buleleng Regency's Office of Trade and Industry has one of the tasks to develop and empower Small and Medium Industries (IKM) in Buleleng. One of the ways undertaken by the agency is to provide assistance to Bimtek entrepreneurship training or Technical Guidance. Bimtek is technical guidance assistance from government programs to improving and developing small and medium industries. However, sometimes this assistance is inconsistent because decisions are often changed. For this reason a decision support system was developed using AHP and SAW methods with 8 criterias including: number of monthly production, average product prices, number of equipment owned, number of employees, length of business establishment, annual sales value, total annual raw material and distance to Buleleng Government Commerce and Industry Office. The system developed can help the Buleleng Government Commerce and Industry to determine bimtek beneficiaries according to predetermined criterias. The development of this decision support system was built using the SDLC Method with a waterfall model. There are 4 tests performed including: blackbox testing, whitebox testing, accuracy testing, and user response testing. This study successfully developed a decision support system after passing the blackbox test and the whitebox test. Accuracy test showed very good results with an accuracy rate of 86.67%. The user response test conducted on 4 users including: admin, staff, IKM support and the general public has a mean percentage of 92.3% which is in a very good range.
PENGEMBANGAN SISTEM CERDAS UNTUK PREDIKSI DAFTAR KEMBALI MAHASISWA BARU DENGAN METODE NAIVE BAYES (STUDI KASUS: UNIVERSITAS PENDIDIKAN GANESHA) Komang Aditya Pratama; Gede Aditra Pradnyana; I Ketut Resika Arthana
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 | Full PDF (485.109 KB) | DOI: 10.31598/sintechjournal.v3i1.523

Abstract

Ganesha University of Education or Undiksha is one of the state universities in Bali, precisely in the city of Singaraja. In the admission of new students, Undiksha applies 3 admissions paths, as follows the State University National Admission Selection (SNMPTN), State University Joint Entrance Test (SBMPTN), and Independent Entrance Test (SMBJM) consisting of 2 parts namely Computer Based Test (CBT) and Interests and Talents. Each year the committees are busy with the re-registration of prospective students. In determining the number of students quota for re-registration, they are still using the manual method in form of an excel file, so they want to use a system to do the process. These problems can be overcome by using ?Intelligent System for Re-Registration of New Students Prediction using the Naive Bayes Method (Case Study: Ganesha University of Education)?. The Naive Bayes method is used to determine the re-register probability of the new students so that the number of students who re-register can be determining the new students quota. In developing the system, the researcher use the CRISP-DM methodology as a standard of data mining process as well as a research method. The results of this prediction system research show that the system can predict well with the average predictive system accuracy value of 75.56%.
Pengembangan Digital Story Book “Satua Bali” Berbasis Mobile I Made Yoga Prasada; I Made Putrama; Gede Aditra Pradnyana
SINTECH (Science and Information Technology) Journal Vol. 1 No. 1 (2018): SINTECH Journal Edition April 2018
Publisher : LPPM STMIK STIKOM Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31598/sintechjournal.v1i1.209

Abstract

This research aimed to produce a media in the form of Digital Story Book "Satua Bali" Based Mobile that could be a container to provide a collection of Satua Bali, so it could be utilized to help maintaining the existence of Satua Bali among the community, especially children. Digital Story Book "Satua Bali" Based Mobile has some additional features, such as user can add new satua, share to the social medias, give comment, like, add as favorite, etc. This research was a type of research and development with research model of ADDIE (Analysis, Design, Development, Implementation, Evaluation). The testing process was conducted in six stages, namely test: whitebox and blackbox which obtain good result, content expert which obtains result of 96,2% (very appropriate), media expert that obtains result of 92,5% (very appropriate), users responses with UEQ method obtain good result, and compatibility is successfully implemented to 10 different devices. The final result of this research is the application of Digital Story Book "Satua Bali" which can be run on smartphone with android operating system.
PENGEMBANGAN SISTEM PENDUKUNG KEPUTUSAN PENENTUAN PRIORITAS PEMBERIAN BANTUAN BIMTEK KEPADA INDUSTRI KECIL DAN MENENGAH (IKM) DENGAN METODE ANALITYCAL HIERARCHY PROCESS (AHP) DAN SIMPLE ADDITIVE WEIGHTING (SAW) Komang Sudana Yasa Pande; Made Windu Antara Kesiman; Gede Aditra Pradnyana
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.v3i1.391

Abstract

The Buleleng Regency's Office of Trade and Industry has one of the tasks to develop and empower Small and Medium Industries (IKM) in Buleleng. One of the ways undertaken by the agency is to provide assistance to Bimtek entrepreneurship training or Technical Guidance. Bimtek is technical guidance assistance from government programs to improving and developing small and medium industries. However, sometimes this assistance is inconsistent because decisions are often changed. For this reason a decision support system was developed using AHP and SAW methods with 8 criterias including: number of monthly production, average product prices, number of equipment owned, number of employees, length of business establishment, annual sales value, total annual raw material and distance to Buleleng Government Commerce and Industry Office. The system developed can help the Buleleng Government Commerce and Industry to determine bimtek beneficiaries according to predetermined criterias. The development of this decision support system was built using the SDLC Method with a waterfall model. There are 4 tests performed including: blackbox testing, whitebox testing, accuracy testing, and user response testing. This study successfully developed a decision support system after passing the blackbox test and the whitebox test. Accuracy test showed very good results with an accuracy rate of 86.67%. The user response test conducted on 4 users including: admin, staff, IKM support and the general public has a mean percentage of 92.3% which is in a very good range.
PENGEMBANGAN SISTEM CERDAS UNTUK PREDIKSI DAFTAR KEMBALI MAHASISWA BARU DENGAN METODE NAIVE BAYES (STUDI KASUS: UNIVERSITAS PENDIDIKAN GANESHA) Komang Aditya Pratama; Gede Aditra Pradnyana; I Ketut Resika Arthana
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.v3i1.523

Abstract

Ganesha University of Education or Undiksha is one of the state universities in Bali, precisely in the city of Singaraja. In the admission of new students, Undiksha applies 3 admissions paths, as follows the State University National Admission Selection (SNMPTN), State University Joint Entrance Test (SBMPTN), and Independent Entrance Test (SMBJM) consisting of 2 parts namely Computer Based Test (CBT) and Interests and Talents. Each year the committees are busy with the re-registration of prospective students. In determining the number of students quota for re-registration, they are still using the manual method in form of an excel file, so they want to use a system to do the process. These problems can be overcome by using “Intelligent System for Re-Registration of New Students Prediction using the Naive Bayes Method (Case Study: Ganesha University of Education)”. The Naive Bayes method is used to determine the re-register probability of the new students so that the number of students who re-register can be determining the new students quota. In developing the system, the researcher use the CRISP-DM methodology as a standard of data mining process as well as a research method. The results of this prediction system research show that the system can predict well with the average predictive system accuracy value of 75.56%.
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%. 
Optimasi Parameter Support Vector Machine Dengan Algoritma Genetika Untuk Analisis Sentimen Pada Media Sosial Instagram I Putu Dedy Wira Darmawan; Gede Aditra Pradnyana; Ida Bagus Nyoman Pascima
SINTECH (Science and Information Technology) Journal Vol. 6 No. 1 (2023): SINTECH Journal Edition April 2023
Publisher : Prahasta Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31598/sintechjournal.v6i1.1245

Abstract

Social media is an online media that users use to interact with each other by expressing themselves by giving comments, and one example is Instagram. All the collected comments will form a public opinion. This opinion can be used with sentiment analysis to become information. The method commonly used to carry out sentiment analysis is classification using machine learning. One of the machine learning that is often used is the Support Vector Machine (SVM). However, on non-linear problems such as sentiment analysis, SVM requires the kernel to map vectors into high-dimensional spaces to solve non-linear problems. The problem faced in using the kernel is to choose the optimal parameters for the classification model to produce a good classification model. This study proposes a new approach to obtain optimal parameters for SVM using Genetic Algorithm (GA). This study designed an SVM-GA classification model from the data collection, processing, classification, and evaluation stages. The results showed that the best accuracy produced with parameters optimized with the genetic algorithm was 81.6%, or an increase of 2.4% from the SVM sentiment analysis method without GA optimization.
Co-Authors ., I Dewa Gede Angga Sitangga Putra ., I Gede Herri Yudiana Sucitra ., I Kadek Supriandana ., I Made Agus Oka Wijaya ., I Putu Aditya Narayana ., Kadek Adi Sidiantara ., Pande Komang Saputra A. A. Gede Yudhi Paramartha Achmad Yogie Setiawan Adityastika, Putu Angga Adnyana, Gede Ari Adnyani, Ni Luh Putu Sri Agus Aan Jiwa Permana Agus Ari Premana Agus Kamiana Agus Seputra I Ketut Arif Djunaidy Arya, Ketut Brahma, A.A. Gede Raka Wahyu Candra Sulistyawati Darma, Komang Agus Satia Devi Dwi Hariyanti Dewa Gede Hendra Divayana, Dewa Gede Hendra Dewantara, Ari Indrawan Dewi, Ni Putu Sri Indra Padma Dharma Putra, I Gede Wira Didit Kurniawan Driya, Putu Dhanu Fahrul Rizal, Fahrul Gede Saindra Santyadiputra Gede Saindra Santyadiputra Gede Saindra Santyadiputra, Gede Saindra Gede Saindra Santyadiputra, S.T., M.Cs . Gede Yogi Wiryawan Hartini, Nyoman Sugi Hartini, Nyoman Sugi Hartini, Ria I Dewa Gede Angga Sitangga Putra . I Gede Dedy Prasetia I Gede Herri Yudiana Sucitra . I Gede Mahendra Darmawiguna I Gede Riyan Ardi Darmawan I Gede Rusdy Mahayana Putra I Kadek Ary Prahayuda I Kadek Arya Budi Artana I Kadek Supriandana . I Ketut Resika Arthana I Komang Agus Suryantara I Komang Ari Mogi I Made Agus Oka Wijaya . I Made Ardwi Pradnyana I Made Dedi Suardika I Made Edy Listartha I Made Putrama I Made Windu Antara Kesiman I Made Yoga Prasada I Md. Dendi Maysanjaya I Putu Aditya Narayana . I Putu Andika Subagya Putra I Putu Dedy Wira Darmawan I Putu Gede Hendra Suputra Ida Bagus Nyoman Pascima Ida Bagus Putu Suarma Putra Ign Edo Paska Kadek Adi Sidiantara . Kadek Krisna Kadek Yota Ernanda Aryanto Kamiana, Agus Ketut Agustini Ketut Arya Komang Aditya Pratama Komang Aditya Pratama Komang Agus Satia Darma Komang Sudana Yasa Pande Komang Sudana Yasa Pande Komang Wisnu Baskara Putra Kusumadiputra, Made Novta Luh Putu Eka Damayanthi, Luh Putu Eka M.Cs ., Gede Saindra Santyadiputra, S.T., M.Cs M.Cs S.Kom I Made Agus Wirawan . Made Aristia Prayudi Made Novta Kusumadiputra Made Windu Antara Kesiman Made Windu Antara Kesiman Mogi, I Komang Ari Ni Komang Arie Suwastini Ni Made Rai Wisudariani Ni Made Sthiti Nur Hita Ni Nyoman Sugihartini Ni Putu Sri Indra Padma Dewi Nur Hita, Ni Made Sthiti P. WAYAN ARTA SUYASA Pande Komang Saputra . Paska, Ign Edo Prahayuda, I Kadek Ary Prasada, I Made Yoga Prasetia, I Gede Dedy Premana, Agus Ari Purnandita, Ida Bagus Putra, I Gusti Kade Ari Satria Putra, Komang Wisnu Baskara Putu Angga Adityastika Putu Moni Lestari Putu Setiari, Gusti Ayu Ria Hartini Samgraha, Kadek Dwi Loka Sasmita, Ade Suardika, I Made Dedi Suarma Putra, Ida Bagus Putu Sulistyawati, Candra Sunarya, I Made Arisetiawan Sunarya, I Made Arisetiawan Suryantara, I Komang Agus Yogi Aditya Yunita Purnama Sari Yunita Purnama Sari