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Jurnal Ilmiah Merpati (Menara Penelitian Akademika Teknologi Informasi)
Published by Universitas Udayana
ISSN : 22523006     EISSN : 26852411     DOI : https://doi.org/10.24843/JIM
Core Subject : Science,
Computer Science & IT
The journal publishes work from all disciplinary, theoretical and methodological perspectives. It is designed to be read by researchers, scholars, teachers and advanced students in the fields of Information Systems and Information Science, as well as IT developers, consultants, software vendors, and senior IT executives seeking an update on current experience and future prospects in relation to contemporary information and communications technology.
Arjuna Subject : Ilmu Komputer (semua kategori) - Sistem Informasi
Articles 280 Documents
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Comparison of Support Vector Machine and K-Nearest Neighbor for Baby Foot Identification based on Image Geometric Characteristics Angga Pratama Nugraha; I Nyoman Piarsa; I Made Suwija Putra
Jurnal Ilmiah Merpati (Menara Penelitian Akademika Teknologi Informasi) Vol. 9, No. 1, April 2021
Publisher : Lembaga Penelitian dan Pengabdian kepada Masyarakat Universitas Udayana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24843/JIM.2021.v09.i01.p08

Abstract

Biometric recognition of infant identification systems is critical in security access for identification and verification systems. However, until now, hospitals or health centres in Indonesia still use conventional biometric identification, such as stamping or inking on the soles of babies' feet affixed to paper and are very vulnerable to the risk of damage or loss of data. To resolve this problem, computer vision technology can accurately identify the baby's feet' soles with the final result in the form of digital data. This study compares the classification method of baby feet using the SVM (Support Vector Machine) algorithm with the K-Nearest Neighbor algorithm. The baby's feet understudy image was taken using a cellphone camera with sample data of 3 months old babies. Comparing the SVM and KNN classification methods obtained high accuracy, precision and recall values, namely 98.80% accuracy, 89.51% precision and 88.00% recall. (for the SVM Gaussian kernel classification), with an accuracy of 99.08%, 92.65% precision and 90.75% recall (for the KNN Ecluidean Distance classification), it can be concluded that the KNN classification method using Euclidean distance is the best for applied in the baby palm identification system using the geometric image feature.
Rancang Bangun Chatbot Sebagai Penghubung Komunikasi Antara Aplikasi Line Messenger Dengan Telegram Messenger Defri Gentia; I Made Sukarsa; Kadek Suar Wibawa
Jurnal Ilmiah Merpati (Menara Penelitian Akademika Teknologi Informasi) Vol. 8, No. 3, December 2020
Publisher : Lembaga Penelitian dan Pengabdian kepada Masyarakat Universitas Udayana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24843/JIM.2020.v08.i03.p01

Abstract

Teknologi informasi seiring waktu telah berkembang sangat pesat dengan adanya berbagai aplikasi instant messaging yang bermunculan bertujuan sebagai media komunikasi secara digital. Aplikasi instant messaging telah digunakan diberbagai bidang seperti di dunia pendidikan salah satunya untuk perkuliahan. Penggunaan instant messaging di perkuliahan bertujuan sebagai media untuk berkomunikasi antara mahasiswa dan dosen terkait aktivitas kuliah. Contoh aplikasi instant messaging yang telah digunakan mahasiswa dan dosen yaitu Line Messenger dan Telegram Messenger. Perbedaan instant messaging yang digunakan oleh dosen dan mahasiswa menimbulkan permasalahan dimana mahasiswa biasa menggunakan Line dan dosen menggunakan Telegram, sehingga saat mahasiswa ingin berkomunikasi online dengan dosen maka harus menggunakan aplikasi yang sama dengan yang digunakan oleh dosen yaitu Telegram. Solusi untuk mempermudah komunikasi online antara mahasiswa dan dosen, yaitu dengan pembuatan chatbot, karena bisa dirancang sebagai sistem penghubung komunikasi antara mahasiswa dengan dosen. Chatbot yang dirancang berfungsi sebagai jembatan antara aplikasi Line dengan Telegram. Komunikasi berupa pesan teks yang diberikan baik oleh mahasiswa maupun dosen melalui aplikasi Line ataupun Telegram disimpan oleh chatbot dan dikirim ke instant messaging lawan bicara.
E-Readiness of Integrated Information Systems Using STOPE Framework in Udayana University Theca Difa Yulian Syahputri; Dwi Putra Githa; I Putu Agus Eka Pratama
Jurnal Ilmiah Merpati (Menara Penelitian Akademika Teknologi Informasi) Vol. 9, No. 1, April 2021
Publisher : Lembaga Penelitian dan Pengabdian kepada Masyarakat Universitas Udayana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24843/JIM.2021.v09.i01.p02

Abstract

Many industries and institutions have switched to digital technology, an integrated information technology system, since we entered the era of the industrial revolution 4.0. In implementing new strategies, problems often occur because they do not assess technology readiness (e-readiness) to be built. Therefore, it is necessary to conduct a technology readiness assessment (e-readiness) either before or after the new system is implemented to assess how ready it is. The review can use the STOPE framework to be flexible so that this framework can be adjusted to the case study handled. IMISSU is an integrated management information system technology belonging to Udayana University. The results of the IMISSU e-readiness assessment found that the readiness of Udayana University in implementing IMISSU is at a very ready level (4 of 4). Assessment using the STOPE framework includes three levels, namely the domain, sub-domain, and sub-sub-domain levels. The study was conducted by distributing 100 questionnaires divided among various academicians in the university, adjusted to the STOPE framework domain.
Reengineering Business Process Manufacturing Company Sales Module Using Odoo V12.0 Application Rika Hari Wahyuni; I Made Sukarsa; Dewa Made Sri Arsa
Jurnal Ilmiah Merpati (Menara Penelitian Akademika Teknologi Informasi) Vol 9 No 3 (2021): Vol. 9, No. 3, December 2021
Publisher : Lembaga Penelitian dan Pengabdian kepada Masyarakat Universitas Udayana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24843/JIM.2021.v09.i03.p01

Abstract

The manufacturing industry applies the use of ERP to run corporate business processes, build good communication and improve service quality. This study aims to provide a Business Process Reengineering proposal to the sales department using the Odoo V12.0 so that the system can be integrated. The methodology used in this research is Business Process Reengineering, where the research only focuses on the sales field. Existing business processes, namely purchasing products, sales orders and invoicing manuall. Reengineering that has been carried out is the product sales process, the sales order process, and the payment process. Reengineering has been carried out quite well by using the User Acceptance Test method and obtaining agreed responses as the greatest value in every aspect. The results obtained are based on 5 categories, namely content items by 63%, module items by 53%, multimedia element items by 88%, navigation aspects by 70% and usability items by 82%. Keywords: Business Process Reengineering, Enterprise Resource Planning, Odoo V12.0, Sales, User Acceptance Test
Improvement of MSME Sales and Capital Management through a Website-Based Marketplace System I Putu Sura Sanjaya; Oka Sudana; I Putu Arya Dharmaadi
Jurnal Ilmiah Merpati (Menara Penelitian Akademika Teknologi Informasi) Vol. 9, No. 2, August 2021
Publisher : Lembaga Penelitian dan Pengabdian kepada Masyarakat Universitas Udayana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24843/JIM.2021.v09.i02.p01

Abstract

Micro, Small and Medium Enterprises (MSMEs) have a good enough potential to overcome poverty, because they have an absorption contribution of 99.45 percent. MSMEs can be developed by utilizing e-marketplaces, but the existing marketplaces have not been able to solve the problems faced by MSMEs. One of the problems faced by MSME actors is difficulty in accessing capital. In general, MSME capital is obtained from the bank, but MSME actors are burdened by the need for collateral to apply for loans. Based on these problems, a marketplace information system for MSME products was built with peer-to-peer lending features that can help MSMEs do product marketing as well as apply for loans on easy terms and without any collateral. Testing the system with the black box method shows that all features are functioning properly and in accordance with the expected functionality.Testing with the acceptance testing method for MSME actors obtained an average score of 89.25%, so it can be concluded that the system is acceptable and in accordance with the needs of MSME players in Gianyar Regency.
Peramalan Jumlah Kunjungan Wisatawan Menggunakan Triple Exponential Smoothing I Wayan Agus Surya Darma; I Putu Eka Giri Gunawan; Ni Putu Sutramiani
Jurnal Ilmiah Merpati (Menara Penelitian Akademika Teknologi Informasi) Vol. 8, No. 3, December 2020
Publisher : Lembaga Penelitian dan Pengabdian kepada Masyarakat Universitas Udayana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24843/JIM.2020.v08.i03.p06

Abstract

Bali merupakan salah satu destinasi pariwisata terbaik di dunia. Berdasarkan berita resmi statistik yang dipublikasikan oleh Badan Pusat Statistik Bali, jumlah kunjungan wisatawan mancanegara ke Bali pada bulan Juni 2019 mencapai 549.751 kunjungan. Peramalan kunjungan wisatawan merupakan faktor yang sangat penting untuk menentukan kebijakan tempat tujuan wisata, meminimalkan ketidakpastian dan resiko investasi. Hal ini merupakan hal yang sangat penting karena sektor pariwisata merupakan tulang punggung ekonomi di Bali. Penelitian ini mengangkat topik bagaimana mengimplementasikan metode Triple Exponential Smoothing pada proses peralaman jumlah wisatawan. Kami menggunakan data historis kunjungan wisatawan ke Bali yang diperoleh dari Badan Pusat Statistik Provinsi Bali. Hasil peramalan dievaluasi menggunakan mean absolute error untuk menunjukan rata-rata kesalahan dalam perhitungan peramalan. Rata-rata Mean Absolute Error yang dihasilkan pada peramalan ini adalah 18 dengan hasil evaluasi terbaik dengan menggunakan Alpha 0.9, Beta 0 dan Gamma 0.8.
Forecasting Number of COVID-19 in Bali Province Using Neural Network Algorithm Ida Ayu Utari Dewi; I Kadek Noppi Adi Jaya; Kadek Oky Sanjaya
Jurnal Ilmiah Merpati (Menara Penelitian Akademika Teknologi Informasi) Vol. 9, No. 1, April 2021
Publisher : Lembaga Penelitian dan Pengabdian kepada Masyarakat Universitas Udayana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24843/JIM.2021.v09.i01.p07

Abstract

COVID-19 (coronavirus disease 2019) is a large family of viruses that cause mild to severe illness, such as the common cold or colds and serious illnesses such as MERS and SARS. COVID-19 has become a pandemic, meaning that there has been an increase in cases of the disease which is quite fast and there has been spread between countries and caused enormous losses in various countries. The increasing number of COVID-19 cases every day in Indonesia, including in Bali Province and the resulting losses underlie the forecasting of the number of COVID-19 in Bali Province. Forecasting is carried out using the Neural Network algorithm for time series data on the number of COVID-19 in Bali Province. The data used is data on the number of COVID-19 in the Bali Province in the form of time series data sourced from the Bali Provincial Health Office. The entire forecasting process uses the Rapidminer Studio tools starting from preprocessing, modeling, testing and validation. The results of the RMSE (Root Mean Square Error) evaluation value based on testing for the positive patients were 18.956, the patients recovered were 15.413, the patients under treatment were 5.066 and the patients who died was 0.233.
Decision Support System for Tour Package Recommendation in Bali Using BWM-MARCOS Method Ni Ketut Pradani Gayatri S; I Made Candiasa; Kadek Yota Ernanda Aryanto
Jurnal Ilmiah Merpati (Menara Penelitian Akademika Teknologi Informasi) Vol. 9, No. 2, August 2021
Publisher : Lembaga Penelitian dan Pengabdian kepada Masyarakat Universitas Udayana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24843/JIM.2021.v09.i02.p05

Abstract

Bali is one of the provinces with profitable tourism opportunities. It has led to many businesses related to tourism, which is a travel agent. Travel agents in Bali usually offer variety of tour packages with different prices and specifications. The problem experienced by tourists in determining tour packages is that the price of tour packages is quite high and does not match the tourist budget. In addition, the schedule of visits from tour packages is also inflexible. This problem can be overcome by making a decision support system for forming tour packages. This study uses the BWM method to determine each criterion’s optimal weight and the MARCOS to rank alternative tourism objects that will form a tour package. Testing results using confusion matrix get an accuracy value of 74.19%, precision of 81.25%, recall / sensitivity of 72.22% and specificity of 76.92%.
Website-Based Application for Classification of Diabetes Using Logistic Regression Method Muhamad Soleh; Naufal Ammar; Indrati Sukmadi
Jurnal Ilmiah Merpati (Menara Penelitian Akademika Teknologi Informasi) Vol. 9, No. 1, April 2021
Publisher : Lembaga Penelitian dan Pengabdian kepada Masyarakat Universitas Udayana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24843/JIM.2021.v09.i01.p03

Abstract

Machine learning is a one of computer science field, machine-learning studies how computers are able to learn from data to improve their intelligence. Machine learning consists of many classification methods, including Neural Networks, Support Vector Machines, Logistics Regression, and others. In this study, a classification process carried out using the Logistics Regression method for cases of Diabetes. Diabetes is an increase in glucose in the bloodstream due to a lack of insulin, which is responsible for the transfer of glucose from the blood to tissues or cells. This study created with the aim of improving previous paper. The data used in this study are the same data as previous studies published by the Pima Indian Diabetes Dataset. In this study, several stages used, those are pre-processing, processing, evaluation, and website-based application development. The data in this study divided into two, 75% for training data, and 25% for testing data. This study produces an evaluation with an accuracy 80%, which means it is better than the previous paper, which is 75, 97%.
Identification of Baby's Feet Using Principal Component Analysis (PCA) Method Character Extraction with K-Nearest Neighbor (KNN) Classification in Matlab Application Geyge Andika Lesmana; I Nyoman Piarsa; I Made Suwija Putra
Jurnal Ilmiah Merpati (Menara Penelitian Akademika Teknologi Informasi) Vol 9 No 3 (2021): Vol. 9, No. 3, December 2021
Publisher : Lembaga Penelitian dan Pengabdian kepada Masyarakat Universitas Udayana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24843/JIM.2021.v09.i03.p02

Abstract

Biometric recognition systems or human identification are very important in security access for identification and verification systems. The biometric recognition system can be used as an identification system based on the characteristics possessed by the body part of each individual. The soles of the feet can be used for identification because the soles of the feet have certain and unique characteristics which include major lines, protrusions, small dots, single points, and textures. The introduction of biometrics in babies is still conventional, which is a standard operating procedure such as attaching bracelets on baby's feet and imprinting or inking on the soles of baby's feet which are affixed to paper and are very vulnerable to the risk of damage or loss of data, there is a need for a system that can store data automatically digital and able to do the baby identification process. The Principal Component Analysis method is used for the extraction process of the characteristics of the baby's feet. The classification uses the K-Nearest Neighbor (K-NN) method with the euclidean distance approach. Tests were carried using 120 images of baby feet, there are 20 classes, each class contains 3 images of the right foot and 3 images of the foot of the left foot, and a dataset of 280 training images. The highest accuracy result obtained in system testing is 91% with a computation time of 5.63 seconds using the Principal Component Analysis method with the K-Nearest Neighbor (K-NN) classification.Keywords: Footprint, Feature Extraction, Principal Component Analysis, K-Nearest Neighbor.

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