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All Journal Sistemasi: Jurnal Sistem Informasi JURNAL MEDIA INFORMATIKA BUDIDARMA Jurnal TAM (Technology Acceptance Model) Journal of Electrical, Electronics and Informatics JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) Tematik : Jurnal Teknologi Informasi Komunikasi PROFICIO: Jurnal Pengabdian Masyarakat JURNAL ELEKTRO DAN INFORMATIKA SWADHARMA (JEIS) Jurnal Pengabdian Masyarakat Tapis Berseri Darma Abdi Karya: Jurnal Pengabdian Kepada Masyarakat Jurnal TAM (Technology Acceptance Model)
Hendrawati, Theresia
Institut Bisnis Dan Teknologi Indonesia
Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Social Sciences Other

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EVALUASI PENGARUH TEKNOLOGI, PENGGUNA DAN ORGANISASI TERHADAP MANFAAT DARI PENERAPAN E-PROCUREMENT I Gusti Ayu Agung Mas Aristamy; Theresia Hendrawati
Sistemasi: Jurnal Sistem Informasi Vol 9, No 2 (2020): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (708.244 KB) | DOI: 10.32520/stmsi.v9i2.667

Abstract

Pemerintah tengah berupaya mewujudkan pemerintahan yang terbuka dan demokratis dengan cara meningkatkan dan mengoptimalkan layanan publik melalui dibentuknya E-procurement atau pengadaan barang/jasa berbasis elektronik. Tujuan dari dibentuknya E-procurement adalah untuk mewujudkan pengadaan barang/jasa pemerintah yang efektif, efisien, transparan, adil, tidak diskriminatif dan akuntabel. Namun, dalam penerapannya masih banyak kendala yang ditemukan pada penerapan E-procurement. Melihat dari banyaknya kendala yang ditemui, penelitian ini bertujuan untuk melakukan evaluasi sistem E-procurement yang mengambil studi kasus pada E-procurement milik Pemerintah Provinsi Bali. Evaluasi dilakukan untuk mengetahui faktor yang menjadi pendukung serta penghambat dari penerapan sistem E-Procurement di Pemerintah Provinsi Bali. Metode evaluasi sistem yang dipilih untuk penelitian ini adalah metode HOT-Fit. Metode ini dipilih karena mencakup aspek Human (Pengguna), Organization (Instansi/Organisasi) dan Technology (Teknologi/Sistem). Hasil dari penelitian ini menyatakan bahwa faktor Teknologi, Pengguna dan Organisasi memiliki pengaruh yang positif dan signifikan terhadap penerapan sistem E-Procurement, sehingga pengguna sistem merasakan manfaat yang diberikan oleh adanya sistem E-Procurement. Faktor yang menjadi penghambat sejauh ini adalah masih adanya beberapa tahapan dalam proses pengadaan barang dan jasa yang masih manual atau belum sepenuhnya dilaksanakan by sistem.
Analysis of Twitter Users Sentiment against the Covid-19 Outbreak Using the Backpropagation Method with Adam Optimization Theresia Hendrawati; Christina Purnama Yanti
Journal of Electrical, Electronics and Informatics Vol 5 No 1 (2021): JEEI (February 2021)
Publisher : Institute for Research and Community Services Udayana University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24843/JEEI.2021.v05.i01.p01

Abstract

This research tries to take advantage of Twitter by analyzing Indonesian-language tweets that discuss the Covid-19 virus outbreak to find out what Twitter users think about the Covid-19 virus outbreak. This study tries to analyze sentiment to see opinions on Covid-19 tweets that contains Posittive, Negative or Neutral sentiments using Multi-layer Perceptron (MLP) using Backprogragation with Adam optimization. We collected 200 documents (tweets) in Indonesian about Covid-19 that were tweeted since November 2019 and then trained them to get our MLP Backpropagation model. Our model managed to get an accuracy of up to 70% with f1-scores for positive, negative, and neutral classes respectively 0.77, 0.75, and 0.5 from a maximum value of 1. This shows that our model is quite successful in carrying out the sentiment classification process for Indonesian tweets with the Covid-19 theme.
ASALTAG : Automatic Image Annotation Through Salient Object Detection and Improved k-Nearest Neighbor Feature Matching Theresia Hendrawati; Duman Care Khrisne
Journal of Electrical, Electronics and Informatics Vol 2 No 1 (2018): JEEI (February 2018)
Publisher : Institute for Research and Community Services Udayana University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24843/JEEI.2018.v02.i01.p02

Abstract

Image databases are becoming very large nowadays, and there is an increasing need for automatic image annotation, for assiting on finding the desired specific image. In this paper, we present a new approach of automatic image annotation using salient object detection and improved k-Nearest Neigbor classifier named ASALTAG. ASALTAG is consist of three major part, the segmentation using Minimum Barirer Salienct Region Segmentation, feature extraction using Block Truncation Algorithm, Gray Level Co-occurrence Matrix and Hu’ Moments, the last part is classification using improved k-Nearest Neigbor. As the result we get maximum accuracy of 79.56% with k=5, better than earlier research. It is because the saliency object detection we do before the feature extraction proccess give us more focused object in image to annotate. Normalization of the feature vector and the distance measure that we use in ASALTAG also improve the kNN classifier accuracy for labeling image.
Indonesian Alphabet Speech Recognition for Early Literacy using Convolutional Neural Network Approach Duman Care Khrisne; Theresia Hendrawati
Journal of Electrical, Electronics and Informatics Vol 4 No 1 (2020): JEEI (February 2020)
Publisher : Institute for Research and Community Services Udayana University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24843/JEEI.2020.v04.i01.p06

Abstract

Games are considered capable of being used as a learning medium that can help teachers to teach children how to pronounce the Indonesian alphabet in early literacy, we try to build one aspect of the game in this study. The approach we use is a speech recognition approach that uses the convolutional neural network method. The results of this study indicate that CNN can recognize speech, with input data is in the form of sound. We use the MFCC feature vector sound feature to make a 3-dimensional matrix of input sound into CNN input. We also use the Sequential CNN architecture made from a simple 10 layer neural network, which produces a model with a small size, approximately only about 6 MB, with high accuracy (84%) and an F-Measure of 0.91.
ANALYSIS OF E-LEARNING ACCEPTANCE IN GENERATION Z STIKI INDONESIA DURING THE COVID-19 PANDEMIC I Gusti Ayu Agung Mas Aristamy; Theresia Hendrawati
Jurnal TAM (Technology Acceptance Model) Vol 13, No 2 (2022): Jurnal TAM (Technology Acceptance Model)
Publisher : LPPM STMIK Pringsewu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56327/jurnaltam.v13i2.1302

Abstract

E-Learning has been applied for a long time in Indonesia and other developed and developing countries. However, research by Salloum et.al., said that E-Learning systems in developing countries were partially or completely not adopted; utilization has not been completed and is considered less than a satisfactory level. This refers to the lack of understanding of the factors that influence adoption. One of the latest studies on distance learning during the COVID-19 pandemic said that there were several obstacles experienced by students, and teachers. This study aims to analyze the adoption of E-Learning technology in Generation Z at one of university in Bali during the COVID-19 pandemic. Results of this study indicate that there is a positive impact of computer self-efficacy and accessibility on the perception of the ease with which students use the E-Learning system. The factors of information quality and content quality positively affect the students' perceived ease of use and usefulness of the E-Learning system. It is this perception of the usefulness and ease of use of the E-Learning system that has an increasing impact on students' intentions and attitudes to use the E-Learning system in the future.
Analysis of Sales Forecasting on Galah Kopi Using the Fuzzy Time Series Method Christina Purnama Yanti; Kadek Listy Mas Setya Devi; Santi Ika Murpratiwi; Theresia Hendrawati
Journal of Electrical, Electronics and Informatics Vol 7 No 1 (2023): JEEI (July 2023)
Publisher : Institute for Research and Community Services Udayana University

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Galah Kopi is one of the coffee shops in Tabanan. Addressed at Jl. Raya Babadan Senganan No. 13, Penebel District, Tabanan Regency. Galah Kopi's sales only use previous sales data as a benchmark without the aid of calculations using a more accurate scientific method. The coffee shop also experienced erratic sales problems. The solution that can be used is to do forecasting. This study uses the Fuzzy Time Series method for sales forecasting. The results of this study show that the model method has an accuracy value where the results of the coffee category with an MSE value of 901,917, MAE 27,715 and MAPE 6,115, the Chen model with an MSE value of 4939,505, MAE 57,952 and MAPE 12,574. Fuzzy time series model Singh in the non-coffee category with MSE values ??of 3249.019, MAE 50.177 and MAPE 6.96, with the Chen model with MSE values ??of 23536.2, MAE 125.904 and MAPE 19.00. Fuzzy time series model Singh for the Food category with MSE values ??of 1286.453, MAE 32.187 and MAPE 8.211, with the Chen model with MSE values ??of 14175.61, MAE 98.273 and MAPE 103.45. Fuzzy Time Series Singh model in snack category with MSE value of 1285.114, MAE 30.845 and MAPE 41.967, with the Chen model with MSE value of 14175.61, MAE 98.273 and MAPE 103.45. So that the model method that has the smallest accuracy is the fuzzy time series Singh
Penerapan Deep Learning Dalam Pengenalan Endek Bali Menggunakan Convolutional Neural Network Theresia Hendrawati; Dewa Ayu Putri Wulandari; I Gde Swiyasa Surya Dharma; Ni Luh Wiwik Sri Rahayu Ginantra, M.Kom; Christina Purnama Yanti
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 7, No 4 (2023): Oktober 2023
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v7i4.6721

Abstract

Endek Bali has been recognized as one of the Intellectual Property of Traditional Cultural Expressions, with registration number EBT 12.2020.0000085 on December 22, 2020. In the present era, many people find it difficult to distinguish between endek fabric and batik fabric because their patterns are quite similar. This research aims to help identify Bali's Endek fabric based on digital images. One of the approaches used is the Convolutional Neural Network method with ResNet50, which is a deep learning method used to recognize and classify objects in digital images. Evaluation result from testing the best model with new testing model using confession matrix get result of 90,69% accuracy, 90,69% recall, 90,60% precision and 90,68% f1-score. Thus, the model developed in this research demonstrates optimal performance in classifying images of Bali's Endek.
Implementation and Evaluation of Accounting Information Systems in Manufacturing Company Using System Usability Scale Sandhiyasa, I Made Subrata; Yanti, Christina Purnama; Hendrawati, Theresia
Journal of Electrical, Electronics and Informatics Vol 5 No 2 (2021): JEEI (September 2021)
Publisher : Institute for Research and Community Services Udayana University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24843/JEEI.2021.v05.i02.p05

Abstract

Information systems can facilitate business actors to control and evaluate business reporting effectively and efficiently. Anugrah Sri Jaya is a business engaged in manufacturing. This company still uses a manual recording system which causes several problems, namely frequent errors in calculations and mismatch of inventory stock records with actual conditions. Therefore, the researcher proposes to implement an information system in the accounting process at Anugrah Sri Jaya. From the results of the study, researchers have succeeded in building a web-based accounting information system at Anugrah Srijaya. Information system testing uses the System Usability Scale (SUS) method where the test uses a questionnaire as an assessor and the number of responses is 5 respondents where the respondents from this study are business owners and several employees who will later use this system. From the results of the tests carried out, it can be seen that the average score obtained from the calculation of the System Usability Scale (SUS) is 74.5. Based on the results of the average score, this system can be said to be in the category of acceptance (Acceptable) and on the adjective rating scale, the information system built is rated as excellent.
PKM IMPLEMENTASI APLIKASI PEMBELAJARAN KERANGKA TULANG BERBASIS AUGMENTED REALITY PADA SD NO. 4 ABIANSEMAL: Indonesia Yanti, Christina Purnama; Ginantra, Ni Luh Wiwik Sri Rahayu; Sastaparamitha, Ni Nyoman Ayu J; Hendrawati, Theresia; Dwipayana, Ida Bagus Rama
PROFICIO Vol. 5 No. 1 (2024): PROFICIO: Jurnal Abdimas FKIP UTP
Publisher : FKIP UNIVERSITAS TUNAS PEMBANGUNAN SURAKARTA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36728/jpf.v5i1.2959

Abstract

Kerangka tulang manusia merupakan salah satu materi pembelajaran IPA pada kelas 5 SD. Di SD No. 4 Abiansemal proses pembelajaran kerangka tulang manusia hanya dijelaskan dengan media buku, hal ini disebabkan oleh alat peraga yang dimiliki mengalami kerusakan seperti ada beberapa bagian yang rusak dan hilang. Dari kuesioner yang disebarkan sebanyak 57 responden disimpulkan bahwa siswa merasa bosan dan kurang tertarik dalam pembelajaran tanpa menggunakan alat peraga. Augmented reality adalah sebuah teknologi yang dapat menyatukan dunia nyata dengan virtual secara beriringan, dengan adanya augmented reality diharapkan dapat menjadi pengganti alat visual dalam proses pembelajaran di dalam kelas. Oleh karena itu tim pengabdi melakukan pengabdian kepada masyarakat dimana tahapan awal yaitu pembuatan aplikasi Augmented Reality pengenalan Kerangka Tulang Manusia dan dilanjutkan dengan pelatihan dan pendampingan kepada siswa – siswi SD No. 4 Abiansemal dalam menggunakan aplikasi tersebut. Setelah dilakukan pelatihan, dilanjutkan dengan evaluasi kepada siswa dengan mengerjakan kuis yang ada pada aplikasi dan evaluasi terhadap aplikasi dengan pemberian kuesioner kepada 23 siswa. Dari hasil evaluasi didapat tingkat persetujuan terhadap aplikasi rata – rata adalah 83%.
Analisis Sentimen Larangan Impor Pakaian Bekas Menggunakan Metode Support Vectore Machine dan Lexicon Based Hendrawati, Theresia; Ginantra, Ni Luh Wiwik Sri Rahayu; Saiman, Clarita Mutiara
TEMATIK Vol. 11 No. 1 (2024): Tematik : Jurnal Teknologi Informasi Komunikasi (e-Journal) - Juni 2024
Publisher : LPPM POLITEKNIK LP3I BANDUNG

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.38204/tematik.v11i1.1890

Abstract

X as a social media platform, provides real-time tweets after the event, making it very relevant for sentiment analysis related to certain topics. This research discusses the prohibition on imports of used clothing in Indonesia using the SVM and Lexicon-based methods. The research aims to determine public sentiment regarding this government policy. The SVM method achieved 85.87% accuracy, with 93.83% recall for positive sentiment and 62.00% recall for negative sentiment. There were 76 wrong positive predictions with a precision of 88.11% and 37 wrong negative predictions with a precision of 77.02%. Meanwhile, the Lexicon Method achieved 60% accuracy, with a positive precision of 69% and a negative precision of 31%. Recall for the negative class is 25%, while for the positive class it is 75%. The results of sentiment analysis applying the Support Vector Machine method to build a classification model resulted in 753 data being successfully classified as positive sentiment, while 147 data were classified as negative sentiment. With different accuracies, it shows that sentiment analysis using the Support Vector Machine method has a higher level of accuracy than Lexicon.
Co-Authors Aristamy, I Gusti Ayu Agung Mas Christina Purnama Yanti Christina Purnama Yanti Christina Purnama Yanti Dewa Ayu Putri Wulandari Dewa Ayu Putri Wulandari Duman Care Khrisne Dwipayana, Ida Bagus Rama I Gde Swiyasa Surya Dharma Kadek Listy Mas Setya Devi Ni Komang Ita Cahyani Ni Luh Wiwik Sri Rahayu Ginantra Ni Luh Wiwik Sri Rahayu Ginantra, M.Kom Pramita, Dewa Ayu Kadek Prasetya, I Kadek Diksa Bayu Saiman, Clarita Mutiara Sandhiyasa, I Made Subrata Santi Ika Murpratiwi Sastaparamitha, Ni Nyoman Ayu J Wulandari, Dewa Ayu Putri Yuri Prima Fittryani, Yuri Prima