Publisher: Universitas Gadjah Mada   (Total: 48 journals)   [Sort by number of followers]

Showing 1 - 46 of 46 Journals sorted alphabetically
Agrinova (Agrotechnology Innovation)     Open Access   (Followers: 1)
Agritech     Open Access   (Followers: 1)
ASEAN J. of Chemical Engineering     Open Access  
Bakti Budaya     Open Access  
Berkala Ilmu Perpustakaan dan Informasi     Open Access  
Buletin Peternakan : Bulletin of Animal Science     Open Access   (Followers: 1)
Buletin Psikologi     Open Access  
Gadjah Mada Intl. J. of Business     Open Access   (SJR: 0.127, CiteScore: 0)
Gadjah Mada J. of Professional Psychology     Open Access  
IJEIS (Indonesian J. of Electronics and Instrumentation Systems)     Open Access   (Followers: 3)
IKAT : The Indonesian J. of Southeast Asian Studies     Open Access  
Indonesian Food and Nutrition Progress     Open Access  
Indonesian J. of Biotechnology     Open Access   (Followers: 1)
Indonesian J. of Chemistry     Open Access   (Followers: 1, SJR: 0.209, CiteScore: 1)
Indonesian J. of Computing and Cybernetics Systems     Open Access  
Indonesian J. of Geography     Open Access   (Followers: 1, SJR: 0.217, CiteScore: 1)
Indonesian J. of Pharmacy     Open Access  
J. of Applied Geology     Open Access   (Followers: 3)
J. of Indonesian Economy and Business     Open Access   (Followers: 1)
J. of Leadership in Organizations     Open Access   (Followers: 4)
J. of Mechanical Design and Testing     Open Access   (Followers: 4)
J. of the Civil Engineering Forum     Open Access   (Followers: 1)
JKAP (Jurnal Kebijakan dan Administrasi Publik)     Open Access  
Jurnal Filsafat     Open Access  
Jurnal Gizi Klinik Indonesia     Open Access  
Jurnal Humaniora     Open Access   (Followers: 1)
Jurnal Ilmu Kehutanan     Open Access  
Jurnal Kajian Seni     Open Access  
Jurnal Kawistara     Open Access  
Jurnal Ketahanan Nasional     Open Access  
Jurnal Manusia dan Lingkungan     Open Access  
Jurnal Media dan Komunikasi Indonesia     Open Access   (Followers: 4)
Jurnal Mimbar Hukum Fakultas Hukum Universitas Gadjah Mada     Open Access  
Jurnal Nasional Teknik Elektro dan Teknologi Informasi     Open Access  
Jurnal Pariwisata Terapan     Open Access  
Jurnal Perikanan Universitas Gadjah Mada     Open Access  
Jurnal Psikologi     Open Access  
Jurnal Sain Veteriner     Open Access  
Jurnal Teknosains     Open Access  
Majalah Geografi Indonesia     Open Access  
Majalah Kedokteran Gigi Indonesia     Open Access   (Followers: 1)
Poetika : Jurnal Ilmu Sastra     Open Access  
Populasi     Open Access  
Review of Primary Care Practice and Education (Kajian Praktik dan Pendidikan Layanan Primer)     Open Access  
Sasdaya : Gadjah Mada J. of Humanities     Open Access  
Traditional Medicine J.     Open Access  
Similar Journals
Journal Cover
Indonesian Journal of Computing and Cybernetics Systems
Number of Followers: 0  

  This is an Open Access Journal Open Access journal
ISSN (Print) 1978-1520 - ISSN (Online) 2460-7258
Published by Universitas Gadjah Mada Homepage  [48 journals]
  • Aspect-Based Sentiment Analysis of KAI Access Reviews Using NBC and SVM

    • Authors: Huda Mustakim, Sigit Priyanta
      Pages: 113 - 124
      Abstract: The existence of KAI Access from PT. KAI prove their sincerity in serving consumers in this modern era. However, many negative reviews found in Google Play Store. There has been research on the review, but the analysis stage still at document level so the aspect related to the application is not known clearly and structured. So it is necessary to do an aspect-based sentiment analysis to extract the aspects and the sentiment. This study aims to do an aspect-based sentiment analysis on user reviews of KAI Access using Naive Bayes Classifier (NBC) and Support Vector Machine (SVM), with 3 scenarios. Scenario 1 uses NBC with Multinomial Naive Bayes, scenario 2 uses SVM with default Sklearn library parameter, and scenario 3, uses SVM with hyperparameter tunning, while the data scrapped from Google Play Store. The results show the majority of user sentiment is negative for each aspect, with most discussed errors aspect shows the high system errors. The test results gives the best model from scenario 3 with an average accuracy 91.63%, f1-score 75.55%, precision 77.60%, and recall 74.47%.
      PubDate: 2022-04-30
      DOI: 10.22146/ijccs.68903
      Issue No: Vol. 16, No. 2 (2022)
       
  • Mangrove-based Ecotourism Sustainability Analysis using NDVI and AHP
           Approach

    • Authors: Yerik Afrianto Singgalen, Danny Manongga
      Pages: 125 - 136
      Abstract:  This article aims to analyze the sustainability of mangrove ecotourism using the Normalized Difference Vegetation Index (NDVI) and Analytical Hierarchy Process (AHP) approaches. Based on Landsat 8 OLI satellite imagery calculation using the NDVI technique, there has been a decrease in vegetation value on Dodola Island in 2017. This condition needs to be analyzed scientifically, considering the Dodola Island mangrove area to be preserved. In addition to the interests of tourism infrastructure development. The research method used is a mixed research method through a case study approach in Dodola Island, Morotai Island Regency, North Maluku Province, Indonesia. This study adopts remote sensing techniques and decision support systems to describe the results of sustainable mangrove ecotourism analysis. This study indicates that the calculation results of Landsat 8 OLI spatial data from 2013-to 2021 show a significant decrease in vegetation value in 2017, where the maximum NDVI value is 0.30, and the minimum NDVI value is 0.11. Specifically, the mangrove area also experienced a decrease in vegetation value with a maximum NDVI value is 0.23 and a minimum NDVI value is 0.02. To anticipate environmental damage in mangrove areas, this study recommends mangrove conservation programs, namely rehabilitation, restoration, reclamation, and conservation of mangrove areas. In addition, the results of the priority analysis using the AHP approach show that the rehabilitation program is a program that needs to be prioritized because it follows the existing conditions and capabilities of the Dodola Island managers.
      PubDate: 2022-04-30
      DOI: 10.22146/ijccs.68986
      Issue No: Vol. 16, No. 2 (2022)
       
  • On the Design of a Blockchain-based Fraud-prevention Performance Appraisal
           System

    • Authors: Bryan Andi Gerrardo, Agus Harjoko, Nai Wei Lo
      Pages: 137 - 148
      Abstract:  The job recruitment process takes a lot of process and number of documents. It is very well known for applicants to exaggerated and falsify their work history data. It may put a company at legal risk and significant commercial losses. Generally, company use third-party to verify applicant’s work history data which is time-consuming and costly. It also makes companies relies on third-party which may not trustworthy and cause several other risks. Generally, experience letters is used as a proof of work history documents of employee. However, the process of publishing an experience letter may contain conflict of interest between company and employee. Yet, publishing an experience letter is not mandatory in several places. In this research, we propose a system to verify applicant’s work history data by using performance appraisal as proof of work history and utilizing Blockchain to provide secure system, tampered-proof and real-time verification. The proposed approach also minimizes trust issues and privacy of data sharing by adding encryption and digital signature schema using Elliptic Curve Cryptography (ECC) algorithm. Furthermore, we have implemented a prototype to demonstrate how the proposed system work using a Quorum-based consortium blockchain.
      PubDate: 2022-04-30
      DOI: 10.22146/ijccs.67669
      Issue No: Vol. 16, No. 2 (2022)
       
  • Mobile-based Primate Image Recognition using CNN

    • Authors: Nuruddin Wiranda, Agfianto Eko Putra
      Pages: 149 - 158
      Abstract: Six out of 25 species of primates most endangered are in Indonesia. Six of these primates are namely Orangutan, Lutung, Bekantan, Tarsius tumpara, Kukang, and Simakobu. Three of the six primates live mostly on the island of Borneo. One form of preservation of primate treasures found in Kalimantan is by conducting studies on primate identification. In this study, an android app was developed using the CNN method to identify primate species in Kalimantan wetlands. CNN is used to extract spatial features from primate images to be very efficient for image identification problems. The data set used in this study is ImageNets, while the model used is MobileNets. The application was tested using two scenarios, namely using photos and video recordings. Photos were taken directly, then reduced to a resolution of 256 x 256. Then, videos were taken in approximately 10 to 30 seconds with two megapixel camera resolution. The results obtained was an average accuracy of 93.6% when using photos and 79% when using video recordings. After calculating the accuracy, the usability test using SUS was performed. Based on the SUS results, it is known that the application developed is feasible to use.
      PubDate: 2022-04-30
      DOI: 10.22146/ijccs.65640
      Issue No: Vol. 16, No. 2 (2022)
       
  • Face Expression Classification in Children Using CNN

    • Authors: Yusril Ihza, Danang Lelono
      Pages: 159 - 168
      Abstract: One of the turbulent emotions can be recognized from facial expressions. When compared with adults, children's facial expressions are more expressive for positive emotions and ambiguous for negative emotions so that they are much more difficult to recognize. Ambiguous in terms of negative emotions, for example, when children are angry, sometimes they show an expressionless face, making it difficult to know what emotions the child is experiencing. Therefore, it is proposed research using Convolutional Neural Network with ResNet-50 architecture. According to [1] CNN Resnet-50 is superior to other facial recognition methods, specifically in the classification of facial expressions. CNN ResNet-50 generates a model during the training process, and the model will be used during the testing process. The dataset used is Children's Spontaneous facial Expressions (LIRIS-CSE) data proposed by [2]. CNN ResNet-50 can identify children's expressions well, including expressions of anger, disgust, fear, happy, sad and surprise. The results showed a very significant increase in accuracy, namely in testing data testing reached 99.89%.
      PubDate: 2022-04-30
      DOI: 10.22146/ijccs.72493
      Issue No: Vol. 16, No. 2 (2022)
       
  • Rice Planting Calendar Application Development using Scrum

    • Authors: Gita Fadila Fitriana, Novian Adi Prasetyo
      Pages: 169 - 180
      Abstract: Indonesia is an agricultural country that produces more rice commodities than secondary crops. Many people who work as farmers choose the land to plant rice. Farmers experience several obstacles in determining the correct planting time to improve the rice harvest quality. A planting calendar is a method used by farmers to determine the scheduling of planting for one year. The rice planting calendar works based on rainfall and climate patterns. With the help of the latest technology, determining the rice planting calendar can be done quickly. The utilization of computer technology and algorithms such as Artificial Neural Network is helpful for forecasting rainfall using time series data accurately in the following month. The planting calendar is connected to data from the Meteorology, Climatology and Geophysics Agency (BMKG) from each station in each region. The rice planting calendar is made on a mobile basis with the aim of providing convenience for users in their hands. This cropping calendar application was developed using the Scrum method. The application development stages consist of sprint planning, first sprint, second sprint, third sprint and usability testing. The results of the development of the sprint went well. After completing the story, it was continued with the usability testing stage using the System Usability Scale (SUS). The SUS test was given to 20 respondents who had criteria including farmers and landowners. The results of SUS on the rice planting calendar application got a score of 72.75, which was categorized as Good.
      PubDate: 2022-04-30
      DOI: 10.22146/ijccs.70155
      Issue No: Vol. 16, No. 2 (2022)
       
  • Controlling the Nutrition Water Level in the Non-Circulating Hydroponics
           based on the Top Projected Canopy Area

    • Authors: Hurriyatul Fitriyah, Agung Setia Budi, Rizal Maulana, Eko Setiawan
      Pages: 181 - 192
      Abstract: Deep Water Culture Hydroponics is suitable for a large-scale plantation as it does not require turn-on the electric pump constantly. Nevertheless, this method needs an electric aerator to give Oxygen to the roots. Kratky’s and Dry Hydroponics are the two methods that suggest an air gap between the raft and the nutrient water level. The gap gives Oxygen to the roots without an aeration pump. Controlling the nutrient water level is required to give a good distance of air gap for Precision Agriculture. The root length estimation used to be done manually by opening the raft, but this research promotes automatic and non-contact estimation using the camera. The images are used to predict the root length based on the Top Projected Canopy Area (TPCA) using various Regression Methods. The test shows that the TPCA gives a high correlation toward the Root Length (>0.9). To control the nutrient water level, this research compares If-Else and the Linear Regression. The error between the actual level that is measured using an Ultrasonic sensor and the setpoint is fed to an Arduino Uno to control the duration of an inlet pump and the outlet pump. The If-Else and the Linear Regression method show good results.
      PubDate: 2022-04-30
      DOI: 10.22146/ijccs.70556
      Issue No: Vol. 16, No. 2 (2022)
       
  • Analysis of Covid-19 Cash Direct Aid (BLT) Acceptance Using K-Nearest
           Neighbor Algorithm

    • Authors: Ahmad Ari Aldino, Ryan Randy Suryono, Riyama Ambarwati
      Pages: 193 - 204
      Abstract: During the COVID-19 pandemic, the government imposed Large-Scale Social Restrictions (PSBB) to reduce or slow down the spread of COVID-19. This causes people to be unable to work as usual, and not even a few people have lost their jobs. This prompted the government to launch the Covid-19 direct cash assistance (BLT) program. One of the areas affected by the PSBB is Batu Ampar Village, which distributing BLT is considered less effective by residents because there are BLTs that are not well-targeted. The cause of the ineffectiveness of the distribution of aid was assessed because the data was out of sync; it was difficult to verify and validate the new data due to the size of the area and the constantly changing number of underprivileged residents. To overcome these problems, a model is needed to predict the recipients of this Covid-19 BLT. This study uses the K-Nearest Neighbor (K-NN) algorithm and RapidMiner tools to make predictions and validate using Cross-Validation. The data used are 711 lines with 474 training data and 237 testing data resulting in an accuracy of 89.68% for training data and 88.61% for testing data.
      PubDate: 2022-04-30
      DOI: 10.22146/ijccs.70801
      Issue No: Vol. 16, No. 2 (2022)
       
  • Identification of Incung Characters (Kerinci) to Latin Characters Using
           Convolutional Neural Network

    • Authors: Tesa Ananda Putri, Tri Suratno, Ulfa Khaira
      Pages: 205 - 216
      Abstract: Incung script is a legacy of the Kerinci tribe located in Kerinci Regency, Jambi Province. On October 17, 2014, the Incung script was designated by the Ministry of Education and Culture as an intangible heritage property owned by Jambi Province. But in reality, the Incung script is almost extinct in society. This study aims to identify the characters of the Incung (Kerinci) script with the output in the form of Latin characters from the Incung script. The classification method used is the Convolutional Neural Network (CNN) method. The dataset used as many as 1400 incung character images divided into 28 classes. In this study, an experiment was conducted to obtain the most optimal model. Showing the results using the CNN method during the training process that the accuracy of the training data reaches 99% and the accuracy of the testing data reaches 91% by using the optimal hyperparameters from the tests that have been done, namely batch size 32, epoch 100, and Adam's optimizer. It evaluates the CNN model using 80 images in words (a combination of several characters) with 4 test scenarios. It shows that the model can recognize image data from scanning printed books, digital writing test data, test data with images containing more than two characters, and check images with different font sizes
      PubDate: 2022-04-30
      DOI: 10.22146/ijccs.70939
      Issue No: Vol. 16, No. 2 (2022)
       
  • Sentiment Analysis of Stakeholder Satisfaction Measurement

    • Authors: Ni Luh Ratniasih, Ni Wayan Ninik Jayanti
      Pages: 217 - 226
      Abstract: Measuring the satisfaction of stakeholders is very impoirtant in order to get feedback and input for the purposes of developing and implementing the improvement strategies. ITB STIKOM Bali routinely measures student stakeholder satisfaction every semester. This study aims to analyze stakeholder comments to generate sentiment analysis on stakeholder satisfaction. The data used are comments on the results of the measurement of stakeholder satisfaction (students) for the Odd Semester of 2020/2021 which are filled out through questionnaire. The algorithm used in this research is the Naïve Bayes Classifier (NBC). The research method in this study consisted of several stages, namely problem identification and literature study, data collection on stakeholder satisfaction (students), data preprocessing, feature extraction in order to facilitate classification using the Naïve Bayes Classifier (NBC) algorithm. The training data used is 200 data while the training data is 2133 data. The results of this study can provide recommendations to ITB STIKOM Bali for the results of student comments as a whole where the percentage of sentiment generated is 58% positive sentiment and 42% negative sentiment.
      PubDate: 2022-04-30
      DOI: 10.22146/ijccs.72245
      Issue No: Vol. 16, No. 2 (2022)
       
 
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