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All Journal IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Bulletin of Electrical Engineering and Informatics Telematika : Jurnal Informatika dan Teknologi Informasi Jurnal Teknologi Informasi dan Ilmu Komputer JUTIK : Jurnal Teknologi Informasi dan Komputer Jurnal RESISTOR (Rekayasa Sistem Komputer) WIDYABHAKTI Jurnal Ilmiah Populer Jurnal Nasional Pendidikan Teknik Informatika (JANAPATI) IJISTECH (International Journal Of Information System & Technology) International Journal of Engineering and Emerging Technology Indonesian Journal of Data and Science Journal of Computer Networks, Architecture and High Performance Computing IJISTECH International Journal of Engineering, Science and Information Technology Jurnal Info Sains : Informatika dan Sains JSIKTI (Jurnal Sistem Informasi dan Komputer Terapan Indonesia) Jurnal Pengabdian Masyarakat Bangsa Jurnal Pengabdian Kepada Masyarakat SEWAGATI Faedah: Jurnal Hasil Kegiatan Pengabdian Masyarakat Indonesia KOMET KREATIF: Jurnal Pengabdian Masyarakat Nusantara Lontar Komputer: Jurnal Ilmiah Teknologi Informasi
I Putu Agus Eka Darma Udayana, I Putu Agus Eka
Institut Bisnis Dan Teknologi Indonesia (INSTIKI)
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Sentimen Analisis Inisiatif Vaksin Nasional Menggunakan Naïve Bayes dan Laplacian Smoothing Pada Komentar Video Youtube Udayana, I Putu Agus Eka Darma; Sudipa, I Gede Iwan; Risaldi, Risaldi
Jurnal RESISTOR (Rekayasa Sistem Komputer) Vol. 5 No. 2 (2022): Jurnal RESISTOR Edisi Oktober 2022
Publisher : Prahasta Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31598/jurnalresistor.v5i2.1108

Abstract

COVID-19 pandemic that has been declared by who in march 2020 Has been Indonesia biggest health crisis end in the decade. WHO said one of the quickest way to end the pandemic is through immunity through vaccine thu's theory is a national vaccine program initiated by the government in the middle of 2021. YouTube is of de facto public space in Indonesia cyberspace for its netizen for various conversation. from gossiping to discuss in public policy YouTube has been a gold mine for social media data mining enthusiast since 2010. but has been not utilized much by Indonesia Academic. do lack of popularity compared to Twitter which has been a media darling what Indonesian Acdemic ever since This research is focused on sentiment analysis pantydeal YouTube about the national vaccine initiation on a news channel in YouTube. This research is primarily consist of naive bayes classifier a a popular algorithm Indonesian data mining enthusiast which has some limitation such as the problem known as zero probability problem and also the use of non-public data which can be fixed by the use of Laplacian smoothing algorithm which when tested Using 100 of random comments as a data testing has resulted in 71% percent of succes rate and when we do a statistical analysis the precision , recall rate and the F-meassurement score of the classifier all resulted in above 75% score which is satisfactory.
Comparison of Deep Learning Methods for Detecting Tuberculosis Through Chest X-Rays Udayana, I Putu Agus Eka Darma; Indrawan, I Gusti Agung; Prawira, I Made Karang Satria
Journal of Computer Networks, Architecture and High Performance Computing Vol. 6 No. 3 (2024): Articles Research Volume 6 Issue 3, July 2024
Publisher : Information Technology and Science (ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/cnahpc.v6i3.4345

Abstract

Chronic diseases are the leading cause of death worldwide, accounting for 73% of deaths in 2020. Tuberculosis (TB), caused by the bacterium Mycobacterium tuberculosis, is one of these diseases and has a significant impact on countries with a high TB burden due to a lack of radiologists and medical equipment. Early diagnosis of TB is crucial but challenging because of its similarity to lung cancer and the shortage of radiologists. A semi-automatic TB detection system is needed to support medical diagnosis and improve public health services. Deep learning technology, such as Convolutional Neural Networks (CNN), offers an effective solution for disease diagnosis with high accuracy. This study compares deep learning methods using an 8-layer CNN and VGG-19, both enhanced with Histogram Equalization (HE) for improved image quality. The study utilizes chest X-ray images of normal lungs and TB-affected lungs from Kaggle. Model performance is evaluated using accuracy, precision, recall, and F1-score metrics. Results indicate that the VGG-19 model outperforms the 8-layer CNN across all evaluation metrics, achieving an accuracy of 72.00% compared to 65.00% for the 8-layer CNN. VGG-19 also demonstrates better precision, recall, and F1-score, making it a more suitable choice for TB detection with enhanced image quality.
Pelatihan Sistem Informasi Pengelolaan Keuangan pada Kantor Kelurahan Sanur Berbasis Web Udayana, I Putu Agus Eka Darma; Prawira, Putu Yoka Angga; Hibur, Filipus Yobili; Widantari Suandana, Ni Putu; Kherismawati, Ni Putu Eka
Jurnal KOMET Vol 1 No 3 (2025): Jurnal Komet: Kolaborasi Masyarakat Berbasis Teknologi : INPRESS Volume 1 Nomor 3
Publisher : Yayasan Sinergi Widya Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70103/komet.v1i3.50

Abstract

Kelurahan Sanur, sebagai salah satu instansi pemerintahan di Denpasar Selatan, menghadapi permasalahan dalam pengelolaan keuangan yang masih dilakukan secara manual menggunakan buku besar. Pada pelaksanaan kegiatan operasional sehari-hari, pihak kantor kelurahan Sanur masih melakukan pengelolaan keuangan dalam bentuk pencatatan buku transaksi yang tersedia. Berdasarkan permasalahan mitra ini, pengabdi memberikan fasilitas sistem informasi keuangan untuk mitra serta melakukan pelatihan penggunaan aplikasi tersebut. Sistem ini dirancang untuk meningkatkan efisiensi dan transparansi pengelolaan keuangan mitra. Metode pelatihan yang dilakukan tim pengabdi meliputi pengenalan konsep dasar sistem informasi, pelatihan teknis penggunaan sistem, dan simulasi pengelolaan data keuangan. Kegiatan ini dilaksanakan melalui pendekatan partisipatif yang melibatkan staf kelurahan dalam setiap tahap implementasi. Hasil dari pelatihan menunjukkan peningkatan pemahaman dan keterampilan peserta dalam menggunakan sistem secara mandiri. Pelatihan ini menjadi langkah awal menuju digitalisasi pengelolaan keuangan yang lebih modern dan akuntabel.
Comparison of Artificial Intelligence Methods for Tuberculosis Detection Using X-Ray Images Udayana, I Putu Agus Eka Darma; Prawira, I Made Karang Satria; Tika, I Gede Bagus Arya Merta
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 19, No 1 (2025): January
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.102601

Abstract

Penyakit tuberkulosis (TB), yang disebabkan oleh bakteri Mycobacterium tuberculosis, merupakan penyakit menular yang sangat berbahaya. Di Indonesia, TB adalah penyakit menular paling mematikan setelah COVID-19 dan menempati urutan ke-13 sebagai penyebab kematian global. Deteksi dini TB sangat penting untuk meningkatkan peluang kesembuhan, namun keterbatasan jumlah ahli radiologi menjadi tantangan utama. Teknologi deep learning, khususnya Convolutional Neural Network (CNN), mejadi solusi efektif untuk masalah ini. Oleh karena itu, pada penelitian ini akan membandingkan dua arsitektur CNN, yaitu AlexNet dan VGG-19, dalam mendeteksi TB pada citra rontgen paru-paru, dengan penerapan metode perbaikan kualitas citra, seperti Histogram Equalization (HE), Adaptive Histogram Equalization (AHE), Contrast Limited Adaptive Histogram Equalization (CLAHE), dan Gamma Correction. Dataset yang digunakan diperoleh dari Kaggle dan mencakup citra rontgen paru-paru normal serta TB. Evaluasi performa dilakukan berdasarkan akurasi, presisi, recall, dan F1-score. Hasil penelitian menunjukkan bahwa VGG-19 dengan CLAHE memberikan performa terbaik dengan akurasi 93.5%, presisi 98.88%, recall 88%, dan F1-score 93.12%. VGG-19 dengan Gamma Correction juga menunjukkan hasil yang sangat baik dengan akurasi 91%, presisi 97.67%, recall 84%, dan F1-score 90.32%. Temuan ini menggarisbawahi efektivitas kombinasi CNN dan metode pemrosesan citra dalam meningkatkan deteksi TB.
EEG-Based Focus Analysis to Evaluate the Effectiveness of Active Learning Approaches Udayana, I Putu Agus Eka Darma; Sudarma, Made; Putra, I Ketut Gede Darma; Sukarsa, I Made; Jo, Minho
International Journal of Engineering, Science and Information Technology Vol 5, No 4 (2025)
Publisher : Malikussaleh University, Aceh, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52088/ijesty.v5i4.1068

Abstract

Electroencephalography (EEG) has emerged as a non-invasive and objective technique for monitoring brain activity in real time, widely applied to measure cognitive states such as concentration and alertness. Its ability to capture brain responses during learning processes makes EEG a promising tool to evaluate student engagement more accurately than conventional methods. This study investigates the effectiveness of two active learning methods, Project-Based Learning (PjBL) and Problem-Based Learning (PBL), in the context of English tutoring for elementary students using EEG signals as a cognitive indicator. A total of 20 students aged 8–12 years from ThinkerBee Learning Centre Bali participated in the study. EEG data were recorded using the Muse 2 Headband while students completed test-based tasks designed for each learning method. The EEG signals were preprocessed using bandpass filtering, Continuous Wavelet Transform (CWT), and frequency band decomposition. Concentration scores were then calculated using two approaches: a heuristic method based on the Beta/(Theta + Alpha) ratio and a Long Short-Term Memory (LSTM) model. The heuristic method produced average scores of 0.3991 (PjBL) and 0.3822 (PBL), with a 4.42% difference, while the LSTM model showed a more substantial difference, with scores of 0.5454 (PjBL) and 0.4265 (PBL). A Spearman correlation test between EEG-derived scores and students’ academic results yielded a perfect correlation value of 1.0000, indicating a strong relationship between cognitive engagement and learning outcomes. These results demonstrate the potential of EEG as a reliable tool for objectively assessing learning effectiveness in primary education contexts.
Pelatihan dan Penerapan Sistem Single Sign-On untuk Meningkatkan Efisiensi dan Keamanan Layanan Digital Baliyoni Saguna Udayana, I Putu Agus Eka Darma; Meinarni, Ni Putu Suci; Febyanti, Putu Ayu; Arta, I Putu Utama; Kerlania, I Gusti Ayu Agung Randhika
Jurnal KOMET Vol 2 No 2 (2025): Jurnal Komet: Kolaborasi Masyarakat Berbasis Teknologi : Volume 2 Nomor 2, Oktobe
Publisher : Yayasan Sinergi Widya Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70103/komet.v2i2.73

Abstract

Mitra Baliyoni Saguna merupakan perusahaan yang mengelola berbagai layanan digital internal untuk mendukung aktivitas operasionalnya. Seiring bertambahnya jumlah aplikasi, perusahaan mengalami kesulitan dalam pengelolaan akses pengguna yang menyebabkan pengguna harus mengingat banyak akun dan kata sandi berbeda untuk tiap sistem. Kondisi ini tidak hanya menurunkan efisiensi kerja, tetapi juga meningkatkan risiko keamanan informasi karena kredensial sering disimpan secara tidak aman atau digunakan secara berulang. Berdasarkan permasalahan tersebut, tim pengabdi dari Institut Bisnis dan Teknologi Indonesia (INSTIKI) melaksanakan kegiatan pelatihan dan pendampingan kepada mitra dalam penerapan sistem autentikasi terpusat berbasis Single Sign-On (SSO) menggunakan protokol OAuth 2.0. Kegiatan ini dilaksanakan secara langsung di kantor Baliyoni Saguna dan melibatkan tim IT serta staf operasional sebagai peserta pelatihan. Materi yang diberikan mencakup pengenalan konsep dasar SSO, pemahaman alur kerja OAuth 2.0, serta simulasi penggunaan sistem secara langsung melalui antarmuka pengguna dan dashboard admin. Setelah pelatihan, sistem SSO langsung diimplementasikan dan diintegrasikan dengan berbagai aplikasi internal yang digunakan mitra. Hasil kegiatan menunjukkan bahwa sistem SSO berhasil diterapkan secara fungsional dan memberikan dampak positif terhadap efisiensi akses pengguna, keamanan autentikasi, serta pengelolaan hak akses secara terpusat. Peserta pelatihan juga menunjukkan peningkatan signifikan dalam pemahaman teknis dan kemampuan operasional. Kegiatan ini menjadi solusi nyata terhadap permasalahan autentikasi dan menjadi langkah awal menuju transformasi digital yang berkelanjutan dan terstandarisasi di lingkungan kerja Baliyoni Saguna.
Optimasi Convolutional Neural Network Untuk Deteksi Covid-19 pada X-ray Thorax Berbasis Dropout Suryawan, I Gede Totok; Darma Udayana, I Putu Agus Eka
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 9 No 3: Juni 2022
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jtiik.2022935143

Abstract

Pandemi COVID-19 yang melanda Indonesia sejak pertengahan tahun 2020 telah memberikan dampak luar biasa pada infrastruktur medis di Indonesia. Angka rata-rata penyebaran virus COVID-19 yang cukup tinggi membuat monitoring bed occupancy rate menjadi sebuah tantangan tersendiri. Dengan adanya penetrasi Artificial Intelligence yang tepat pada sistem medis di Indonesia, diharapkan dapat membantu terjadinya transfer knowledge antar paramedis menjadi lebih efektif. Salah satunya dengan menggunakan Deep learning yaitu Convolutional Neural Network (CNN) yang sudah terbukti merupakan salah satu metode yang dapat digunakan untuk melakukan skrining pasien dan mendeteksi COVID-19. Namun untuk melatih sebuah classifier CNN yang ampuh dan siap digunakan di dunia nyata membutuhkan computing power yang besar dan umumnya training rate yang lama.  Penelitian ini bertujuan untuk membuat arsitektur jaringan syaraf tiruan berbasis deep learning yang lebih cepat dan efisien dengan pembuatan network yang  lebih ramping sehingga lebih mudah dibuat oleh orang lain tanpa harus memiliki computing power yang besar. Metode yang digunakan adalah dengan menyisipkan dropout layer pada sistem jaringan syaraf tiruan. Metode ini akan memaksa sistem untuk belajar memakai rute yang tersingkat dengan cara menghilangkan beberapa node secara acak. Arsitektur ini kemudian diuji pada data ronsen thorax penyintas COVID-19 dan kemudian dibandingkan dengan arsitektur lainnya yang sama-sama memakai pendekatan deep learning. Setelah ditraning menggunakan 500 data COVID-19 thorax X-Ray public database dan diuji dengan jumlah data yang sama, classifier yang menggunakan arsitektur ini mampu menghasilkan akurasi sebesar 95,20%, precision 94,80%, recall 95,58%, specificity 94,88%, NVP sebesar 95,60%, F-Score sebesar 95,18 dan dapat menghemat waktu training sampai 62% dibandingkan dengan arsitektur deep learning lainnya. AbstractThe COVID-19 pandemic that hit Indonesia in mid-2020 had a tremendous impact on medical infrastructure in Indonesia. The virus made monitoring the bed occupancy rate became a challenge in itself. New approach can be taken to fight the crisis. The Convolutional Neural Network (CNN), which has proved to be one of the methods that can use to screen patients and detect COVID-19.also have its own problem because it requires enormous computing power and generally a long training rate. Therefore, this study aimed to tackle that problem by creating a leaner network. Thus, it is easier for others to build without having enormous computing power. The method used was to insert a dropout layer on the artificial network system. This method will force the system to learn using the shortest route by eliminating some nodes at random. Then, this architecture was tested on chest X-ray data of COVID-19 survivors and compared with other architectures that both used a deep learning approach. It proved that when this system was tested with COVID-19 thorax x-ray public database data, the classifier that used this architecture could achieve an accuracy rate of 95.20% followed by precision and recall value reaching 94.80% and 94.80%. respectively and last but not least F-score of 95.18% and Negative Predictive value of 95.60%  It could also save training time up to 62% compared to other deep learning architectures. Using dropout layers proved could produce more efficient layers and more powerful classifiers while keeping training time to a minimum.
Decision Support System for Sentiment Analysis of Youtube Comments on Government Policies Udayana, I Putu Agus Eka Darma; I Gusti Agung Indrawan; Putra, I Putu Dwi Guna Ambara
Journal of Computer Networks, Architecture and High Performance Computing Vol. 5 No. 1 (2023): Article Research Volume 5 Issue 1, January 2023
Publisher : Information Technology and Science (ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/cnahpc.v5i1.1999

Abstract

Sentiment analysis is the process of classifying a text dataset as positive, negative or neutral. Youtube is one of the popular media used to provide responses to a problem. In the Jokowi era, infrastructure development was carried out massively and evenly, one of which was in Bali Province, namely the construction of the Mengwi-Gilimanuk Toll Road. The construction of the Mengwi-Gilimanuk Toll Road consumed a lot of people's agricultural land, which resulted in various pro and con responses from the community. From these problems, sentiment analysis is carried out to get community reviews related to the object being analyzed by utilizing algorithms to be able to classify opinions, in the construction of this system the naïve bayes algorithm is used with testing methods namely accuracy, precision, and recall. From the sentiment analysis conducted by utilizing 18 video links on YouTube with 701 comments, it produces positive sentiment as much as 50.64%, negative sentiment as much as 7.70% and neutral sentiment as much as 39.23%.
Comparison of Deep Learning Methods for Detecting Tuberculosis Through Chest X-Rays Udayana, I Putu Agus Eka Darma; Indrawan, I Gusti Agung; Prawira, I Made Karang Satria
Journal of Computer Networks, Architecture and High Performance Computing Vol. 6 No. 3 (2024): Articles Research Volume 6 Issue 3, July 2024
Publisher : Information Technology and Science (ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/cnahpc.v6i3.4345

Abstract

Chronic diseases are the leading cause of death worldwide, accounting for 73% of deaths in 2020. Tuberculosis (TB), caused by the bacterium Mycobacterium tuberculosis, is one of these diseases and has a significant impact on countries with a high TB burden due to a lack of radiologists and medical equipment. Early diagnosis of TB is crucial but challenging because of its similarity to lung cancer and the shortage of radiologists. A semi-automatic TB detection system is needed to support medical diagnosis and improve public health services. Deep learning technology, such as Convolutional Neural Networks (CNN), offers an effective solution for disease diagnosis with high accuracy. This study compares deep learning methods using an 8-layer CNN and VGG-19, both enhanced with Histogram Equalization (HE) for improved image quality. The study utilizes chest X-ray images of normal lungs and TB-affected lungs from Kaggle. Model performance is evaluated using accuracy, precision, recall, and F1-score metrics. Results indicate that the VGG-19 model outperforms the 8-layer CNN across all evaluation metrics, achieving an accuracy of 72.00% compared to 65.00% for the 8-layer CNN. VGG-19 also demonstrates better precision, recall, and F1-score, making it a more suitable choice for TB detection with enhanced image quality.
PELATIHAN DAN PENDAMPINGAN PENGGUNAAN TEKNOLOGI INFORMASI UNTUK PENINGKATAN KAPASITAS PRAJURU DESA ADAT DI BALI Ni Wayan Wardani; I Putu Agus Eka Darma Udayana; Komang Redy Winatha; I Putu Yoga Indrawan; Putu Gede Surya Cipta Nugraha; Eddy Hartono; I Nyoman Agus Suarya Putra; Ni Made Mila Rosa Desmayani
Sewagati Vol. 1 No. 2 (2023): Sewagati
Publisher : Fakultas Teknik dan Informatika Universitas PGRI Mahadewa Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (370.165 KB) | DOI: 10.59819/sewagati.v1i2.2734

Abstract

A traditional village in Bali is an institution that is responsible for managing and maintaining the sustainability of a traditional village in Bali. The development of information technology has developed so rapidly that even village officials must keep up with the development of information technology. However, in reality some village officials are not fluent in using information technology either in the form of Microsoft Word, Excel, Power Point, especially now that online meetings are also developing with many positive impacts. Based on these problems, community service was carried out by conducting Microsoft Word, Excel, Power Point training, and online meetings using the Zoom application. This training was conducted with the aim of increasing the skills of indigenous village communities in using information and communication technology. In this training, participants will be taught how to use Microsoft Word, Excel, and Power Point to create documents, process data, and make presentations. In addition, participants will also be taught how to use the Zoom application to conduct online meetings. This training is expected to provide benefits for indigenous village communities in improving their skills and productivity. Based on the evaluation results that have been carried out using the pre-test and post-test methods, in general there is an increase in the ability of village officials to operate the information technology material that has been provided in this community service.
Co-Authors Aristana, Made Dona Wahyu Arta, I Putu Utama Bagus Kusuma Wijaya Devi Valentino Waas Dewa Putu Yudhi Ardiana Eddy Hartono Febyanti, Putu Ayu Haris, Lucky Hardiyanti Kartika Hibur, Filipus Yobili I Gede Iwan Sudipa I Gede Totok Suryawan I Gede Totok Suryawan I Gusti Agung Indrawan I Gusti Agung Indrawan I Gusti Made Ngurah Desnanjaya I Kadek Dwi Gandika Supartha I Ketut Gede Darma Putra I ketut Gede Darma Putra I Komang Arya Ganda Wiguna I Komang Arya Ganda Wiguna I Made Avendias Mahawan I Made Sukarsa I Made Yoga Apriliana I Nyoman Agus Suarya Putra I Putu Dwi Guna Ambara Putra I Putu Yoga Indrawan Ida Bagus Ary Indra Iswara Jo, Minho Kerlania, I Gusti Ayu Agung Randhika Ketut Ngurah Semadi Made Dona Wahyu Aristana Made Sudarma Made Sudarma MADE WAHYU ADHIPUTRA Meinarni, Ni Putu Suci Minho Jo Ni Luh Putu Ayu Cintia Utami Ni Made Mila Rosa Desmayani, Ni Made Mila Rosa Ni Putu Eka Kherismawati Ni Putu Eka Kherismawati Ni Wayan Sri Ariyani Nyoman Putra Sastra Pangestu, Ni Made Diah Nandita Prawira, I Made Karang Satria Prawira, Putu Yoka Angga Putra, I Putu Dwi Guna Ambara Putu Gede Surya Cipta Nugraha Putu Gede Surya Cipta Nugraha Putu Satria Udyana Putra Radhitya, Made Leo Rikcy Sanusi risaldi, risaldi Sandhiyasa, I Made Subrata Satya, I Wayan Wira Tika, I Gede Bagus Arya Merta Wardani, Ni Wayan Wayan Gede Suka Parwita Widantari Suandana, Ni Putu Wijaya, Bagus Kusuma Winatha, Komang Redy Yogiswara Dharma Putra