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All Journal IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Lontar Komputer: Jurnal Ilmiah Teknologi Informasi Majalah Ilmiah Teknologi Elektro 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 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 Majalah Ilmiah Teknologi Elektro KOMET

I Putu Agus Eka Darma Udayana, I Putu Agus Eka

Institut Bisnis Dan Teknologi Indonesia (INSTIKI)

Author-ID : 2511492

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Pelatihan Pengelolaan Website, Media Sosial, dan Google My Business di Black Garlic Bali I Gede Totok Suryawan; I Putu Agus Eka Darma Udayana
WIDYABHAKTI Jurnal Ilmiah Populer Vol. 3 No. 2 (2021): Maret
Publisher : STIKOM Bali

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30864/widyabhakti.v3i2.237

Black Garlic Bali merupakan salah satu usaha kecil yang dimiliki oleh kelompok masyarakat di Banjar Sangging Desa Kelanting Kecamatan Kerambitan Tabanan Bali. Produk Black Garlic Bali adalah hasil olahan dari bawang putih mentah menjadi fermentasi black garlic. Kelompok usaha ini beranggotakan sebagian dari anggota PKK Banjar Sangging yang dibentuk sebagai mata pencarian tambahan bagi masyarakat Banjar Sangging Desa Kelanting. Salah satu tingkat urgensi PKM ini harus dilakukan adalah adanya fakta kalau petani di desa tersebut mulai meninggalkan produksi bawang putih lokal ini akibat maraknya bawang impor yang sekarang banyak ditemukan di pasaran. Dengan semakin banyaknya permintaan bawang putih oleh Black Garlic Bali petani di sekitar akan melihat kembali potensi produksi bawang putih, sehingga secara tidak langsung akan membantu menjaga produksi bawang putih di Desa Kelanting Kecamatan Kerambitan Tabanan Bali. Kegiatan PKM ini dilakukan untuk membantu meningkatkan kapasitas kelompok usaha Black Garlic Bali dalam bidang pemasaran dan penerapan teknologi informasi untuk mendukung kemajuan usaha dengan memberikan sebuah website untuk usaha mereka. Selain memberikan website, juga dilakukan program pelatihan pengelolaan website menggunakan Wordpress, pelatihan manajemen sosial media menggunakan Canva, serta pelatihan membuat listing bisnis di google menggunakan google my business. Dengan adanya kegiatan PKM, saat ini mitra sudah memiliki website yang bisa diakses di : www.blackgarlicbali.com, akun page Facebook dan Instagram. Selain itu mitra juga memiliki pengetahuan tentang manajemen website menggunakan wordpress, manajemen sosial media dengan canva serta listing bisnis di google.

IMPLEMENTASI SISTEM INFORMASI MONITORING DAN EVALUASI TENANT INBIS STIKI INDONESIA I Putu Agus Eka Darma Udayana; Ni Putu Eka Kherismawati
Jurnal Teknologi Informasi dan Komputer Vol 5, No 2 (2019): Jurnal Teknologi Informasi dan Komputer
Publisher : LPPM Universitas Dhyana Pura

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (743.331 KB)

ABSTRACTInkubator Bisnis (INBIS) is an institution to commercialize the results of research, innovation and creativity of universities. The main task of INBIS is to provide business space, assistance, monitoring and evaluation, access to capital, and network access. To achieve these objectives, monitoring and evaluation was carried out by the monitoring and evaluation team. The results of the monitoring and evaluation process are recorded in a hard copy file of monitoring and evaluation tenants. The monitoring file is not effective and efficient to see the report on monitoring and evaluating tenants when needed in an urgent time. With the importance of the report, monitoring and evaluation tenant information system was developed to record the results of tenant monitoring and evaluation, and later the report will be an evaluation material for INBIS performance, both from the stages of recruitment, assistance, and monitoring and evaluation. The system developed will manage tenant data, monitoring and evaluation team data, incubator data, and produce reports on the results of monitoring and evaluation tenants. To test all existing modules on the system, black box testing was carried out and to see how the user system responses developed were used usability testing.Keywords: Monitoring And Evaluation, Information Systems, Tenant, INBIS.ABSTRAKInkubator bisnis (INBIS) merupakan lembaga yang berfungsi sebagai wadah komersialisasi hasil penelitian, inovasi dan kreativitas perguruan tinggi. Tugas utama INBIS adalah menyediakan ruang usaha, pendampingan, monitoring dan evaluasi, akses permodalan, serta akses jejaring. Dengan adanya program tersebut diharapkan perguruan tinggi bisa menghasilkan produk inovasi yang siap bersaing di industri sebagai perusahaan pemula berbasis teknologi. Untuk mencapai tujuan tersebut dilaksanakan proses monitoring dan evaluasi yang dilaksanakan oleh tim monev. Hasil proses monitoring dan evaluasi tersebut direkam dalam file hard copy berupa catatan hasil monitoring dan evaluasi tenant. Pada implementasinya berkas tersebut kurang efektif dan efisien untuk melihat report monitoring dan evaluasi tenant ketika dibutuhkan dalam waktu yang mendesak. Mengingat pentingnya keberadaan laporan tersebut, maka dikembangkan sebuah sistem informasi monitoring dan evaluasi tenant untuk melakukan pencatatan hasil monitoring dan evaluasi tenant, serta nantinya laporan tersebut akan menjadi bahan evaluasi terhadap kinerja INBIS, baik dari tahapan rekrutmen, pendampingan, serta monitoring dan evaluasi. Sistem yang dikembangkan nantinya akan mengelola data tenant, data tim monev, data inkubator, serta menghasilkan laporan hasil monitoring dan evaluasi tenant. Untuk menguji semua modul yang ada pada sistem telah bekerja sesuai dengan rancangan dilakukanlah pengujian black box dan untuk melihat bagaimana tanggapan user sistem yang dikembangkan digunakan pengujian usability testing.Kata Kunci : Monitoring Dan Evaluasi, Sistem Informasi, Tenant, INBIS.

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)

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

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

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.

Optimasi Convolutional Neural Network Untuk Deteksi Covid-19 pada X-ray Thorax Berbasis Dropout I Gede Totok Suryawan; I Putu Agus Eka Darma Udayana
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

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.

Rancang Bangun Sistem Informasi Akuntansi Di Stiki Copy Center Lucky Hardiyanti Kartika Haris; Dewa Putu Yudhi Ardiana; I Putu Agus Eka Darma Udayana
Jurnal Sistem Informasi dan Komputer Terapan Indonesia (JSIKTI) Vol 2 No 2 (2019): December
Publisher : INFOTEKS (Information Technology, Computer and Sciences)

Abstract STIKI Copy Center is one place of business that provides print, copy, scan and sells office stationery services. Financial reporting process that occurs in STIKI Copy Center is still not in accordance with the financial statements should be and still use Ms.Excel which makes the owner feel difficulty in the process of calculating the current formula. The financial statements made at Ms.Excel can be manipulated by someone or the owner can do inputting with the same data so that it can cause redundancy and can also cause incompatibility of data and data that could be lost or deleted if infected by a virus or accidentally deleted. This research aims to design and build an accounting information system at STIKI Copy Center which begins with the stages of identifying problems, conducting data collection, analyzing system modeling using UML, building systems using the laravel framework and MySQL database. The results of this research are financial applications based website that will process the transaction income and expenses and print reports general ledger, balance sheet, income statement, statement of changes in capital and is also equipped with a revenue graph for each month. From the test results by using black box testing can be seen that the system has been going well as expected

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

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

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

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.

Co-Authors Arta, I Putu Utama Bagus Kusuma Wijaya Devi Valentino Waas Dewa Putu Yudhi Ardiana Eddy Hartono Febyanti, Putu Ayu Hibur, Filipus Yobili I Gede Iwan Sudipa I Gede Totok Suryawan I Gusti Agung Indrawan I Gusti Agung Indrawan 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 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 Komang Redy Winatha Lucky Hardiyanti Kartika Haris Made Dona Wahyu Aristana Made Sudarma Made Sudarma Meinarni, Ni Putu Suci 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 Ni Wayan Wardani Nyoman Putra Sastra Prawira, I Made Karang Satria Prawira, Putu Yoka Angga Putu Gede Surya Cipta Nugraha Putu Gede Surya Cipta Nugraha Putu Gede Surya Cipta Nugraha Putu Satria Udyana Putra Rikcy Sanusi risaldi, risaldi Tika, I Gede Bagus Arya Merta Wayan Gede Suka Parwita Widantari Suandana, Ni Putu

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