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Hapnes Toba
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JuTISI (Jurnal Teknik Informatika dan Sistem Informasi)
Published by Universitas Kristen Maranatha
ISSN : 24432210     EISSN : 24432229     DOI : https://doi.org/10.28932/jutisi
Core Subject : Science,
Computer Science & IT
Paper topics that can be included in JuTISI are as follows, but are not limited to: • Artificial Intelligence • Business Intelligence • Cloud & Grid Computing • Computer Networking & Security • Data Analytics • Datawarehouse & Datamining • Decision Support System • E-Systems (E-Gov, E-Health, E-Commerce, etc.) • Enterprise System (SCM, ERP, CRM) • Human-Computer Interaction • Image Processing • Information Retrieval • Information System • Information System Audit • Enterprise Architecture • Knowledge Management • Machine Learning • Mobile Computing & Application • Multimedia System • Open Source System & Technology • Semantic Web & Web 2.0
Arjuna Subject : Ilmu Komputer (semua kategori) - Aplikasi Ilmu Komputer
Articles 479 Documents
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Perancangan Sistem Registrasi berdasarkan Estimasi Waktu Penanganan Pasien untuk Mencegah Kerumunan Antrian Astrid Lestari Tungadi; Erick Alfons Lisangan
Jurnal Teknik Informatika dan Sistem Informasi Vol 8 No 2 (2022): JuTISI
Publisher : Maranatha University Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28932/jutisi.v8i2.4411

Abstract

Along with the increase in population and the development of information technology, it is necessary to have a system that is able to support the process of improving health care. One of the weaknesses of the current health care system is the patient waiting time which is still below the established standard, which is less than 60 minutes. This study designed a patient registration system that can estimate the patient's arrival time based on the input symptoms. The use of QR Code technology and meeting applications helps to support patient registration and consultation. The research methodology uses the waterfall method by involving doctors and patients as data sources. Test results using black boxes indicate that the system is able to categorize patient care as expected. The system has also been able to function well functionally. It takes a process of updating symptoms periodically by doctors so that the system is able to recognize new symptoms that have not been recorded previously. With the estimated arrival time, the queue or crowd of patients in the waiting room can be minimized properly.
Peramalan Jumlah Kasus COVID-19 Menggunakan Joint Learning Muhammad Rizqi Nur; Faris Mushlihul Amin; Ahmad Yusuf
Jurnal Teknik Informatika dan Sistem Informasi Vol 8 No 1 (2022): JuTISI
Publisher : Maranatha University Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28932/jutisi.v8i1.4469

Abstract

COVID-19 is a dangerous illness because it spreads quickly and easily. Vaccines are already available but the pandemic isn’t likely to end soon. Forecasting is hoped to help handle the pandemic. Deep learning, specially LSTM, has been used to forecast COVID-19 case count in some regions. However, deep learning models generally need a lot of training data while COVID-19 daily data are scarce. However, COVID-19 pandemic happens in many regions. This research aims to use joint learning with data from other regions to improve model performance with fewer data and to use the model to forecast until 9 months since the date of last data taken. Joint learning was done by making models share some parts and training the models together. To overcome the different data scale and pandemic age in the regions, the data was first transformed into discrete SIRD variables and was evaluated using RMSSE. Joint learning failed to improve the model performance in this research. The proposed model performance was signficantly better than ARIMA-SIRD and SIRD model but wasn’t better than normal encoder-decoder LSTM. The models only reached RMSSE below one occasionally. Additionally, it was found that doing joint learning with all regions without selecting them by clustering can make the model performance worse instead. It was also found that RMSSE is too sensitive to a mostly stagnant time-series due to its division by the error of one-step naïve forecast.
BESKlus : BERT Extractive Summarization with K-Means Clustering in Scientific Paper Feliks Victor Parningotan Samosir; Hapnes Toba; Mewati Ayub
Jurnal Teknik Informatika dan Sistem Informasi Vol 8 No 1 (2022): JuTISI
Publisher : Maranatha University Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28932/jutisi.v8i1.4474

Abstract

This study aims to propose methods and models for extractive text summarization with contextual embedding. To build this model, a combination of traditional machine learning algorithms such as K-Means Clustering and the latest BERT-based architectures such as Sentence-BERT (SBERT) is carried out. The contextual embedding process will be carried out at the sentence level by SBERT. Embedded sentences will be clustered and the distance calculated from the centroid. The top sentences from each cluster will be used as summary candidates. The dataset used in this study is a collection of scientific journals from NeurIPS. Performance evaluation carried out with ROUGE-L gave a result of 15.52% and a BERTScore of 85.55%. This result surpasses several previous models such as PyTextRank and BERT Extractive Summarizer. The results of these measurements prove that the use of contextual embedding is very good if applied to extractive text summarization which is generally done at the sentence level.
Prediksi Kinerja Pegawai sebagai Rekomendasi Kenaikan Golongan dengan Metode Decision Tree dan Regresi Logistik Erik Dwi Anggara; Andreas Widjaja; Bernard Renaldy Suteja
Jurnal Teknik Informatika dan Sistem Informasi Vol 8 No 1 (2022): JuTISI
Publisher : Maranatha University Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28932/jutisi.v8i1.4479

Abstract

Employee performance is one element that greatly determines the quality of an organization, both government and private. Employee performance appraisal has become a routine for most companies. Performance appraisal is required for the process of salary increases, promotions, and demotions. Until this research was carried out, the processing of employee performance appraisal and evaluation at Prasama Bhakti Foundation was still done manually, so that sometimes employee promotions were carried out late or even on an inconsistent basis for each employee. Therefore, it is necessary to group data with the help of machine learning that can help predict the eligibility of an employee to get a promotion based on his performance. Classification is one method for classifying or classifying data that are arranged systematically. Decision tree and logistic regression methods are classification or grouping methods that have been widely used for solving classification problems. In this study, it will be explained how the process of processing employee performance appraisal data starts from data preparation to determine the accuracy of the decision tree model and logistic regression that is formed. The two classification models are used to predict employee performance as a recommendation for employee promotion at the Prasama Bhakti Foundation.    
Optimasi Penentuan Menu Makanan Pendamping Air Susu Ibu Menggunakan Algoritma Genetika Wardatus Sa'adah; Umi Chotijah
Jurnal Teknik Informatika dan Sistem Informasi Vol 8 No 1 (2022): JuTISI
Publisher : Maranatha University Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28932/jutisi.v8i1.4486

Abstract

 Indonesia has a bad situation in stunting. Stunting is a problem because of the consumption of less nutrition. This is caused by giving food that is not in accordance with the nutritional needs of infants. Infants 0 until 24 months old need attention in choosing a weaning food to support the baby’s growth. The purpose of this research is to find an optimized way to improve the menu of weaning food with the genetic algorithm method. It is expected to be able to reduce stunting suffering in Indonesia. The process of the genetic algorithm method uses the fitness function of a baby’s daily nutritional needs. The selection is Roulette wheel selection method, the crossover is whole arithmetic crossover with 0.5 crossover rate, and the last is the mutation process with 0.6 mutation rate. The research used 100 generations and the result is menus for breakfast, lunch, and dinner for 6 days. The genetic algorithm can determine optimal weaning food’s menu.
Manajemen Risiko Pemasangan Wifi pada Perusahaan Telekomunikasi dengan Framework Risk Information Technology Loudry Palmarums Mustamu; Mewati Ayub; Swat Lie Liliawati
Jurnal Teknik Informatika dan Sistem Informasi Vol 8 No 1 (2022): JuTISI
Publisher : Maranatha University Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28932/jutisi.v8i1.4491

Abstract

 In this research, an analysis of Wi-Fi products was carried out using Risk Framework, Information Technology (IT) Domain Risk Response, and decision tree method to determine decision making at the Telecommunication companies which occurs from January to September 2021. The Wi-Fi installation process was carried out to assess how far Telecommunication company had responded to problems related to IT risks. This analysis is carried out to help the Telecommunication company create a framework to respond to IT risks that have occurred, such as human risk errors, system disturbances, interference fromoutside parties, inventory control, as well as responding to problems related to IT risks such as problems related to possible risks to the system used. The goal is to provide recommendations to the company in accordance with the IT Risk Framework. Thedata sources are derived from a direct interview with the manager of the Telecommunication company and customer service data. The analysis refers to the process of installing Wi-Fi for the customers. Customer service data is analyzed using the Decision Tree in Weka. The results of the analysis are expected to support the Telecommunication company to be better inresponding and reacting to IT risk and incidents that have occurred, those that may occur at telecommunication installation.
Pendeteksi Sampah Metal untuk Daur Ulang Menggunakan Metode Convolutional Neural Network Ranti Holiyanti; Sukma Wati; Ikbal Fahmi; Chaerur Rozikin
Jurnal Teknik Informatika dan Sistem Informasi Vol 8 No 1 (2022): JuTISI
Publisher : Maranatha University Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28932/jutisi.v8i1.4492

Abstract

Waste is part material that has no value within the scope of production. If you no longer need it, metal cans can take about 80 to 200 years to decompose. CNN is part of the supervised learning method that exists in deep learning, where those who have expertise in representing images or images from several categories increase recognition, namely in classifying objects, doing scene recognition, and detecting object detection. In this study, using the CNN method as a development model and applying the ResNet 50 network design, which includes the type Convolutional Neural Network (CNN) that operates by way of working, namely receive an input in the form of an image or images. The input will be carried out by training that is set using the CNN architecture so that later it will produce an output that can recognize objects as expected in knowing the types of cardboard and glass waste. The implementation of this research uses the Python programming language, Anvil, and the TensorFlow and Keras libraries. The system has succeeded in detecting the type of metal waste from general waste and assisting third parties, namely implementing it through the website using Anvil. The input shape for CNN modeling in this study is 512x384 pixels, which has a value of 100 eras, and the data set used contains images of metal waste and general waste found 547 images, resulting in an accuracy of 96%.
Analisis Pengalaman Pengguna Aplikasi Gojek dan Grab dengan Pendekatan User Experience Questionnaire Diana Khuntari
Jurnal Teknik Informatika dan Sistem Informasi Vol 8 No 1 (2022): JuTISI
Publisher : Maranatha University Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28932/jutisi.v8i1.4499

Abstract

Technological developments have an impact on the presence of Super Apps, such as: Gojek and Grab. The presence of these two applications has an effect on the lifestyle in society which is increasingly facilitated by various services that are presented in only one application. The Gojek and Grab applications have many similarities, including in terms of their use. These two applications are very popular and favored by the community because they are practical and help various community activities, for example: food ordering services, delivery of goods, transportation, and non-cash payments. This study was conducted to determine whether there are differences in user experience with the application with case studies in people living in Yogyakarta. User experience measurement is carried out using the User Experience Questionnaire (UEQ) approach on attractive, perspicuity, efficiency, dependability, stimulation, and novelty variables. This study uses a comparative descriptive method with a quantitative approach. The results showed that all Gojek and Grab application user experience variables got positive values ??and there were no significant differences in all variables. However, based on the UEQ measurement, it is known that the Gojek application is superior to the Grab application in perspicuity and novelty variables. Meanwhile, the Grab application is superior to the Gojek application in terms of efficiency, dependability, and stimulation variables. To improve the user experience for the Gojek and Grab applications, it is necessary to improve the quality of the perspicuity and dependability variables.
Optimasi Hyperparameter pada Penerapan Ensemble Regression Tree untuk Simulasi Pewarnaan Bibir Andi Hakim Arif; Achmad Solichin
Jurnal Teknik Informatika dan Sistem Informasi Vol 8 No 2 (2022): JuTISI
Publisher : Maranatha University Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28932/jutisi.v8i2.4611

Abstract

Technology helps us in many activities and keeps growing, so it makes activities more efficient, time-saving, using fewer resources and also information and entertainment are accessible. Machine Learning technology is the fastest-growing field in computer science that is used in many areas such as marketing, healthcare, manufacturing, information security, and transportation. One of the machine learning methods is the Ensemble of Regression Tree (ERT) which has succeeded in detecting facial features on the eyebrows, eyes, nose, and lips. However, utilization ERT method has not been found to detect specific areas such as lips only for gaining optimization. Then this research will be conducted to extract the facial feature annotation dataset from the iBUG 300W dataset with 68 facial features to 20 lip area points. The results of the extraction are reduced error rate, resources saving, lip features still detected and lip coloring simulation was successfully carried out using the configuration of hyperparameter values, tree = 4, regularization = 0.25, cascade = 8, feature pool = 500, oversampling = 40 and translation jitter = 0. From observations also discovered optimization that hard disk resource savings are 69.36%, RAM 30.8%, and CPU 3.8%; reduce the error rate by 0.058%; and increase inference speed by 39%.
Pengembangan Admisi Universitas Berbasis Sistem Pengelola Pengetahuan Nathanael Liman; Maresha Caroline Wijanto; Mewati Ayub; Bernard Renaldy Suteja; Try Atmaja Linggan Jaya
Jurnal Teknik Informatika dan Sistem Informasi Vol 8 No 2 (2022): JuTISI
Publisher : Maranatha University Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28932/jutisi.v8i2.4651

Abstract

 The study will develop a prototype to implement a knowledge management system using the information retrieval method. As a study case, the knowledge about university admission will be used. The users of the system consist of guests, admin, and admission staff. The guest can search for information in the dashboard and give suggestions. The admission staff can add new knowledge or modify the existing knowledge. The new knowledge should be verified and approved by the admin. The testing was performed to verify that the system works as it should be, especially for information searching. The results show that searchingusing lowercase and without stopword, or punctuation gives better similarity index. Searching using unigram also has better similarity index.

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