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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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PREDIKSI CITRA MAKANAN MENGGUNAKAN CONVOLUTIONAL NEURAL NETWORK UNTUK MENENTUKAN BESARAN KALORI MAKANAN I Putu Agus Eka Darma Udayana; Putu Gede Surya Cipta Nugraha
Jurnal Teknologi Informasi dan Komputer Vol 6, No 1 (2020): Jurnal Teknologi Informasi dan Komputer
Publisher : LPPM Universitas Dhyana Pura

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (979.274 KB)

Abstract

ABSTRACTDeep learning is a subfield of machine learning which its development has been significantly increased recently. One example of the application deep learning method is the implementation of computer vision to recognize an image. In this research, the authors focus on the application of deep learning to recognize food images. Food recognition is also useful in many popular lifestyle applications such as calorie counting applications or diet-related applications. In this research, the CNN method is proposed to recognize the image of commonly consumed food by Indonesian people. This technique consists of 3 main phases, first preprocessing or normalizing of food image input data by wrapping and cropping, second the formation of models and system training, and the last is pra-training for system testing. The experiment focused on the implementation of the CNN method to recognize food images for developed calorie counter applications. This research uses 50 food image data for testing each food category with an average accuracy of 86% and the system can determine the number of food calories based on a calorie database in the system.Keywords: Convolution Neural Network (CNN), Deep Learning, Food Prediction, Food Calorie.ABSTRAKDeep Learning adalah bidang keilmuan baru pada machine learning yang akhir-akhir ini berkembang sangat pesat. Salah satu contoh penerapan metode deep learning adalah implementasi komputer vision untuk mengenali sebuah gambar. Pada penelitian ini, penulis fokus pada penerapan deep learning untuk mengenali citra makanan. Pengenalan makanan juga berguna dalam banyak aplikasi gaya hidup populer seperti aplikasi penghitung kalori atau aplikasi yang berhubungan dengan diet. Pada penelitian ini diusulkan metode CNN untuk mengenali citra makanan yang umum dikonsumsi oleh masyarakat Indonesia. Teknik ini terdiri dari 3 tahap utama, pertama preprocessing atau menormalkan data input citra makanan dengan melakukan wrapping dan cropping, kedua pembentukan model dan pelatihan sistem, dan yang terakhir adalah melakukan prapelatihan untuk pengujian sistem. Percobaan difokuskan pada bagaimana metode CNN dapat digunakan sebagai metode untuk mengenali citra makanan sehingga dapat digunakan untuk mengembangkan aplikasi penghitung kalori. Pada penelitian ini digunakan 50 data citra makanan untuk pengujian setiap kategori makanan dengan rata-rata akurasi sebesar 86% dan sistem dapat menentukan besaran kalori makanan sesuai dengan database kalori pada sistem.Kata Kunci : Convolution Neural Network (CNN), Deep Learning, Prediksi Citra Makanan, KaloriMakanan.
Implementasi Kombinasi Metode Mean Denoising dan Convolutional Neural Network pada Facial Landmark Detection I Putu Agus Eka Darma Udayana; I Kadek Dwi Gandika Supartha
Jurnal Nasional Pendidikan Teknik Informatika : JANAPATI Vol. 10 No. 1 (2021)
Publisher : Prodi Pendidikan Teknik Informatika Universitas Pendidikan Ganesha

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23887/janapati.v10i1.29779

Abstract

Facial landmark detectionmerupakan bagian dari facial recognition,bertujuan untuk mengidentifikasi titik fokus pada wajah berdasarkan ciri penampakan bagian wajah yang cenderung menonjol, seperti area mata, hidung, bibir, serta tulang pipi. Facial landmark detection sering diimplementasikan pada bidang pengenalan wajah, prediksi pose wajah, rekonstruksi wajah 3 dimensi, serta pengembangan sistem deteksi kelelahan karyawan berdasarkan ekspresi wajah. Seiring bertambahnya ketersediaan citra wajah dan kebutuhan proses komputasi yang cepat, metode Convolutional Neural Network (CNN) diimplementasikan pada facial landmark detection. Namun beragamnya kualitas citra menyebabkan CNN kurang optimal dalam melakukan deteksi. Oleh karena itu guna mengatasi permasalahan terkait kualitas citra ini, diimplementasikan metode mean denoising sebagai upaya peningkatan nilai akurasi CNN dalam melakukan pendeteksian landmark wajah. Dataset citra wajah diperoleh dari platform Kaggle, LFW-People, AFLW200 dan Female Facial Image Dataset, dengan total sebanyak 2.050 citra wajah, dan terbagi menjadi 2.000 data latih dan 50 data uji. Berdasarkan hasil pengujian, kombinasi metode CNN dengan mean denoising menghasilkan peningkatan akurasi yang lebih baik dalam pengenalan objek pada wajah pada kualitas citra yang heterogen dengan rata-rata akurasi pengujian sebesar 81,33%.Akurasi yang cukup baik ini didapatkan karena citra wajah masukan dilakukan penghilangan noise terlebih dahulu sehingga fitur dari citra yang seringkali menyebabkan sistem CNN salah dalam mengidentifikasi objek pada wajah dapat diminimalisir.
Comparison of Final Results Using Combination AHP-VIKOR And AHP-SAW Methods In Performance Assessment (Case Imanuel Lurang Congregation) Devi Valentino Waas; I Gede Iwan Sudipa; I Putu Agus Eka Darma Udayana
IJISTECH (International Journal of Information System and Technology) Vol 5, No 5 (2022): February
Publisher : Sekolah Tinggi Ilmu Komputer (STIKOM) Tunas Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (871.609 KB) | DOI: 10.30645/ijistech.v5i5.185

Abstract

Determination of the final result in determining the decision is to determine the best alternative from several existing alternatives based on several predetermined criteria. The criteria are measures, rules, or standards for making decisions. It can be done by combining several Multi-Criteria Decision Making (MCDM) methods such as AHP, VIKOR, SAW, TOPSIS, and others to get the best decision results. The Analytical Hierarchy Process (AHP) method is one of the MCDM methods with advantages at the criteria weighting stage. It uses a consistency test to see whether the weights obtained are consistent. In comparison, the VIKOR and SAW methods are also of MCDM methods but do not apply the weighting consistency test. With the advantages and disadvantages of each MCDM method, it is possible to combine several existing methods to provide better solutions or alternatives. This study compares the ranking results between the combination of the AHP-VIKOR method and the combination of the AHP-SAW method in a performance appraisal case study. The AHP method is used to weight the criteria and sub-criteria, while the VIKOR and SAW methods are used in the alternative ranking process. The test results show differences in the alternative ranking results between the two combinations of MCDM methods used.
Detection of Student Drowsiness Using Ensemble Regression Trees in Online Learning During a COVID-19 Pandemic I Putu Agus Eka Darma Udayana; Ni Putu Eka Kherismawati; I Gede Iwan Sudipa
Telematika Vol 19, No 2 (2022): Edisi Juni 2022
Publisher : Jurusan Teknik Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31315/telematika.v19i2.7044

Abstract

Online lectures are mandatory to deal with the implementation of education during the COVID-19 pandemic. This significant change certainly creates a different experience for students. Regarding online learning, several public health experts and ophthalmologists say that residual radiation from electronic screens is causing an epidemic of eye fatigue. Research on smart classrooms actually appeared several years ago, but in reality it has not been implemented according to the planned concept. The current smart classroom research environment only uses outdated methods, which make the computer system incongruent (such as decision trees in video feeds) or only to the level of empirical studies or blueprints, which are not much help for other academic footing or reference materials. to students. This study aims to build an intelligent system that can evaluate students' attention during online classes, use teaching videos as learning feeds and input for predictions and also use advanced algorithms in several computational domains, namely face segmentation, landmarking, PERCLOS observations, Yawning and decision analysis using Ensemble Regression Trees to detect students' sleepiness, which is expected to patch up the shortcomings of the PERCLOS algorithm and the problems found in the single regression tree-based implementation. Based on the results of the tests that have been carried out, the system developed has been able to observe sleepy objects in learning videos with an accuracy of 80% so that later it can be a lesson for teachers why there are students who are sleepy during online classes either because of uninteresting material or other reasons.
Decision Support System for Sentiment Analysis of Youtube Comments on Government Policies I Putu Agus Eka Darma Udayana; I Gusti Agung Indrawan; I Putu Dwi Guna Ambara Putra
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%.
Effect on signal magnitude thresholding on detecting student engagement through EEG in various screen size environment I Putu Agus Eka Darma Udayana; Made Sudarma; I Ketut Gede Darma Putra; I Made Sukarsa
Bulletin of Electrical Engineering and Informatics Vol 12, No 4: August 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v12i4.4850

Abstract

In this study, a new method was developed to detect student involvement in the online learning process. This method is based on convolutional neural network (CNN) as a classifier with an emphasis on the preprocessing process combined with a new feature in the form of signal magnitude area (SMA) thresholding. In this study, the data used as training data is a public dataset that emphasizes the decomposition of electroencephalography (EEG) signals into individual signal processing. Twenty subjects were taken to be used as test data, with each subject watching online learning lectures in the field of computer science on three different devices, either with a flat screen, a curved screen or a smartphone screen that is smaller than two standard computer monitors. Based on the study's results, it is known that the change in screen size is inversely proportional to the level of student attention, the smaller the screen, the lower the student's attention. For classification results, the model equipped with SMA thresholding outperformed the standard classifier by 8.33% with a test set of 20 people.
RAINFALL FORECASTING USING THE HOLT-WINTERS EXPONENTIAL SMOOTHING METHOD I Komang Arya Ganda Wiguna; Ni Luh Putu Ayu Cintia Utami; Wayan Gede Suka Parwita; I Putu Agus Eka Darma Udayana; I Gede Iwan Sudipa
Jurnal Info Sains : Informatika dan Sains Vol. 13 No. 01 (2023): Jurnal Info Sains : Informatika dan Sains , Maret 2023
Publisher : SEAN Institute

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Abstract

Abiansemal District is dominated by agricultural land, with Subak Blahkiuh being one of the agricultural lands in the district. Agriculture is extremely weather-dependent, particularly during the monsoon season. Therefore, precipitation forecasting is necessary for determining a favorable sowing season. Researchers use the Holt-Winters Exponential Smoothing method with data from 2012 to 2022 for forecasting. Holt-Winters Exponential Smoothing employs two forecasting models. The results of the calculations indicate that the additive model has alpha equal to 0.653467, beta equal to 0.0036348, and gamma equal to 0.1182400. While the multiplicative model has alpha values of 0.8889286, 0.0001 and 0.0246825 for beta and gamma, respectively. For accurate forecasting, it is necessary to examine the error data, specifically the MAE and MAPE. The multiplicative model yielded an MAE of 158.87 and a MAPE of 55.18%, whereas the additive model yielded an MAE of 186.59 and a MAPE of 70.18%. The MAPE values of the additive model and multiplicative model are greater than 50 percent, indicating that the additive and multiplicative models provide inaccurate forecasts. Between the additive model and the multiplicative model, the Holt-Winters Exponential Smoothing method favors the multiplicative model for future precipitation forecasting.
Pengabdian Berbasis Teknologi dalam Menunjang Pengalaman dan Interaksi Pengunjung Terhadap Destinasi Wisata Melalui Aplikasi AR Gamification I Komang Arya Ganda Wiguna; Rikcy Sanusi; I Gede Iwan Sudipa; Ketut Ngurah Semadi; Ida Bagus Ary Indra Iswara; I Putu Agus Eka Darma Udayana; Made Dona Wahyu Aristana
Faedah : Jurnal Hasil Kegiatan Pengabdian Masyarakat Indonesia Vol. 1 No. 3 (2023): Agustus : Jurnal Hasil Kegiatan Pengabdian Masyarakat Indonesia
Publisher : FKIP, Universitas Palangka Raya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59024/faedah.v1i3.292

Abstract

Technology's role in tourism can have a positive effect on attracting tourists. In providing solutions to non-optimal media in supporting alternative entertainment and services for tourists, the service activity's goal of integrating augmented reality (AR) in tourism has given rise to the concept of AR gamification, in which game elements are integrated into the tourist experience to create a more enjoyable and interactive experience. Potential users are assisted in comprehending and optimizing their use of AR gamification applications. The implementation of augmented reality games at The Sila's Agrotourism benefited from the active participation of collaborators in all PKM activities. The results demonstrated that the use of augmented reality (AR)-based interactive games in tourist destinations enhances the destination's appeal and encourages visitor participation in associated activities. The implementation of augmented reality technology makes the tourist experience at The Sila's Agrotourism more engaging and enjoyable for guests, which has positive repercussions for boosting tourist attraction and satisfaction.
Smart Mobile Application for Detecting Balinese Masks to Introduce Balinese Culture to World Tourism Putu Gede Surya Cipta Nugraha; Putu Satria Udyana Putra; I Putu Agus Eka Darma Udayana; I Putu Dwi Guna Ambara Putra
Jurnal Info Sains : Informatika dan Sains Vol. 13 No. 02 (2023): Jurnal Info Sains : Informatika dan Sains , Edition September  2023
Publisher : SEAN Institute

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Abstract

Indonesia, known for its remarkable cultural diversity, encompasses many ethnic groups, each preserving its distinct cultural heritage. Among Indonesia's cultural treasures is the ancient art of traditional mask-making, referred to as "topeng." Bali, in particular, stands out as a hub for topeng artistry, with roots tracing back to prehistoric eras, serving as a profound representation of Bali's rich cultural values. Bali showcases a broad spectrum of traditional dances incorporating masks as props. The use of shows in these performances often piques the curiosity of residents and tourists, prompting questions about the symbolism and cultural context surrounding their use. People recognize the physical appearance of masks but need to gain knowledge of their names and deeper cultural meanings. The inherent similarities among mask forms further confound both locals and foreign visitors in distinguishing between various types of Balinese masks. This research endeavors to tackle this issue by developing an Artificial Intelligence (AI) system that is integrated into a mobile application using the CNN (VGG-16) method. The primary objective is to introduce and promote the captivating Balinese mask culture and artistry, bolstering cultural tourism through cutting-edge mobile technology. The anticipated outcomes include a nationally accredited journal publication, a user-friendly mobile application, and the acquisition of intellectual property rights. This research constitutes a transition from Technology Readiness Level (TRL) 2 to TRL 3, wherein the AI framework will be rigorously validated, incorporating accuracy, precision, recall, and F1-score assessments, all seamlessly integrated within the mobile system.
Peningkatan Efektivitas Bisnis Pada Kelompok Peternakan Ayam Petelur Melalui Penerapan Sistem Informasi I Putu Agus Eka Darma Udayana; I Gusti Agung Indrawan; Bagus Kusuma Wijaya
Jurnal Pengabdian Masyarakat Bangsa Vol. 1 No. 8 (2023): Oktober
Publisher : Amirul Bangun Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59837/jpmba.v1i8.378

Abstract

Kelompok peternakan ayam petelur Sarinadi yang terletak di Desa Sawan, Kabupaten Buleleng Provinsi Bali menjalankan bisnis dari pembibitan ayam petelur sampai distribusi dan penjualan telur ayam. Sarinadi memiliki 14.000 ekor ayam petelur. Terdapat masalah yang dialami yaitu perputaran keuangan yang disebabkan oleh pencatatan kas yang tidak sistematis dan masalah lain yang dialami adalah pada bidang inventory. Selain itu masalah transparansi keuangan juga menjadi permasalah yang menjadi polemik antar anggota kelompok peternakan. Berdasarkan masalah tersebut, tim pengabdian kepada masyarakatv mengimplementasikan sistem keuangan serta inventori pada mitra. Selain melakukan implementasi sistem, pengabdian ini juga akan meberikan pelatihan kepada mitra dan mengevaluasi sistem yang telah dikembangkan menggunakan pendekatan usability testing. Berdasarkan evaluasi pelatihan, hasil pre-test berkisar 30-55% (cukup rendah) dan mengalami peningkatan pada post-test yaitu berkisar 75-95% dan hasil pelatihan lainnya yaitu dari pre-test berkisar 20-55% (cukup rendah) dan mengalami peningkatan pada post-test yaitu berkisar 75-95%. Pada evaluasi usability testing sistem Sarinadi memiliki nilai diatas 3, dimana nilai tersebut dapat dikategorikan diatas rata-rata dari nilai maksimum adalah 5. Berdarkan beberapa evaluasi yang telah dilakukan, ini menunjukan denga nadanya pelatihan mitra telah mampu mengoperasikan sistem dengan baik dan sistem dapat diterima oleh mitra untuk meningkatkan manajemen keuangan dan inventori pada usaha mitra.
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