Article Per Year (5 Year)
p-Index From 2021 - 2026
1.323
P-Index
This Author published in this journals
All Journal Jurnal Infra
Leo Willyanto Santoso
Program Studi Informatika
Computer Science & IT

Published : 25 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 25 Documents
Search

 1   2   3 
Perancangan dan Pembuatan Data Warehouse dan Business Intelligence pada Market Research Motorcycle Honda MPM Motor Erriv Septianfan Budi; Leo Willyanto Santoso; Lily Puspa Dewi
Jurnal Infra Vol 7, No 2 (2019)
Publisher : Jurnal Infra

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (770.603 KB)

Abstract

Honda MPM (Mitra Pinasthika Mulia) Distributor have a market research system that only covers internal matters such as sales results, and Honda MPM Distributors also use government data in making decisions. However, in collecting and analyzing government data, Honda MPM Distributor company is done by typing government data on the Microsoft Excel program manually and table reporting that is not connected with sales data. This led to the slow pace of the market research process and wasted considerable time coupled with the large number of areas divided between villages and cities.Therefore, the data warehouse as business intelligence is needed by Honda MPM Motor companies in accelerating company data management process and as a tool to help create reports that could simplify their analysis on market research.Data collected in the data warehouse is correct and complete. The resulting report helps facilitate the provision of needed division head information for market research in a city / regency concerning economic GRDP (Gross Regional Domestic Product) crops, livestock, services, population development and motor population, and motorized sales details (buyer’s gender, motor type purchased, buyer's job) through the Power BI application. But, the application can continue to grow with increasing data obtained from the government. Based on the results of the questionnaire conducted 80% both 20% is very good for the convenience provided by this reporting application as an effort to help market analysis and 60% good 40% is very good for the overall reporting program.
Klasifikasi Pakaian Berdasarkan Gambar Menggunakan Metode YOLOv3 dan CNN Michael Christianto Wujaya; Leo Willyanto Santoso
Jurnal Infra Vol 9, No 1 (2021)
Publisher : Jurnal Infra

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

Abstract

Clothing is one of the primary human needs and have many functions. It’s function not solely to cover and protect the wearer, but also to look stylish. Mass media, the internet, and social media are the main place for people to find inspiration to look fashionable. But sometimes it is difficult to determine the type of clothing so it will be easy to find. Therefore, a program that is able to differentiate and classify clothes will be a great help. The method we used are You Only Look Once to detect the clothing object from an image. The output of detection will be cropped and the result will be processed and classified by Convolutional Neural Network using ResNet50 architecture. In the training process of ResNet50, various things will be tuned which is learning rate, dropout, epoch, number of dense layer and its value, freezing layer, and data augmentation. Then program will search similar image using k-nearest neighbor.The result of this study will classify clothes in an image that is worn by the model in the image. The average accuracy obtained using the fine-tuned ResNet50 is 86.44%.
Klasifikasi Topik dan Analisa Sentimen Terhadap Kuesioner Umpan Balik Universitas Menggunakan Metode Long Short-Term Memory Doenny Auddy Lionovan; Leo Willyanto Santoso; Rolly Intan
Jurnal Infra Vol 8, No 2 (2020)
Publisher : Jurnal Infra

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

Abstract

In evaluating and improving the quality of services and facilities, Petra Christian University (PCU) took feedback questionnaires given to students via online. At this time, reading the comments in the suggestion column is still read manually. So that it becomes less effective and efficient in analyzing sentiments and classifying topics for many comments. This study will apply word2vec and Long Short-Term Memory method to create a program that can help classify topics and sentiment analysis.Word2vec is used as a method to convert a word into a vector along with mapping the meaning of existing words. The parameter tested on word2vec are the number of iterations and windows size. Whereas the Long Short-Term Memory method is used to classify sentiment and topics. The parameter tested are the number of layers, the number of units, the batch size, and the percentage of dropout.The result of this study indicates that the word2vec method along with Long Short-Term Memory can be used to analyze sentiment and topic classification. The best configuration results obtained average accuracy in the implementation of the sentiment classification is 89,16 % and for implementation of the topic classification is 92,98 %.
Sistem Rekomendasi Film menggunakan User-based Collaborative Filtering dan K-modes Clustering Ichwanto Hadi; Leo Willyanto Santoso; Alvin Nathaniel Tjondrowiguno
Jurnal Infra Vol 8, No 1 (2020)
Publisher : Jurnal Infra

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

Abstract

Film is one of the popular entertainment media in the community. The number of titles that have been released makes it difficult for people to find which movies they want to watch. To overcome this problem, it is necessary to have information about the film that will make it easier for the public to find films that fit the user's preferences, therefore the user needs a system that can provide movie recommendations.The movie recommendation system using User-based Collaborative Filtering is one method that is able to provide recommendations. K-mode Clustering can also be used as an additional accuracy of recommendations by grouping user preferences history.According to the results of the testing of the k-modes clustering method, the best number of clusters for K-Modes Clustering for film recommendations obtained using the Elbow Method and Silhouette Coefficient is k = 3. From the results of testing the accuracy of the recommendations with Mean Reciprocal Rank (MRR) generated average MRR of 0.17092270381865 for film recommendations with a data train and test ratio of 80%: 20% and an average MRR of 0.15072658511145 for film recommendations with a data train and test ratio of 60%: 40%. From the results of the two tests above, it can be concluded that the level of accuracy of the film recommendations according to the MRR is sufficient because the MRR is close to 0.
Pengenalan Jenis Masakan Melalui Gambar Mengunakan YOLO Alexander William Sutjiadi; Kartika Gunadi; Leo Willyanto Santoso
Jurnal Infra Vol 9, No 2 (2021)
Publisher : Jurnal Infra

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

Abstract

Most input systems in food calorie information applications still use manual input, where the user needs to write the name of the food. This can cause difficulties for the user who does not know the name of the food. But with the development of technology in object detection, this can be an easier and faster process by using images to recognize the type of food.This research uses You Only Look Once method to recognize the type of food from the input images. The type of YOLO model used is the YOLOv3 model which has a modified convolution layer with Xception model that has high accuracy and detection speed.The result obtained is that the modified YOLO model has higher accuracy and faster detection speed than the standard YOLOv3 model. The Highest mAP result achieved was 85.13% with 0.088742 seconds average detection time.
Co-Authors Adam Putra Sulaiman Adrian Evan Alexander Setiawan Alexander William Sutjiadi Alvin Nathaniel Tjondrowiguno Anthony Setiawan Billy Faith Susanto Daniel Adi Daniel Valentino Hidayat Djoni Haryadi Setiabudi Doenny Auddy Lionovan Erriv Septianfan Budi Handry Khoswanto Hendry Gozali Hendyk Cendana Santoso Hennyta Sutanto Henry Novianus Palit Ichwanto Hadi Isak Imanuel Leong Justinus Andjarwirawan Kartika Gunadi Kevin Alexander Harianto Kevin Oktovio Lauw Lettisia Apriolita Harjono Liliana Liliana Lily Puspa Dewi Lucy Rosalind Michael Christianto Wujaya Michael Christiawan Resmana Lim Reyner Reyner Richard Putra Sugijanto Ricky Handoyo Rolly Intan Rudy Adipranata Silvia Rostianingsih Valerie Caroline Alverina Yulia Yulia Zachary Osborn