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INDONESIA
IJCCS (Indonesian Journal of Computing and Cybernetics Systems)
Published by Universitas Gadjah Mada
ISSN : 19781520     EISSN : 24607258     DOI : https://doi.org/10.22146/ijccs
Core Subject : Science, Engineering,
Computer Science & IT Control & Systems Engineering
Indonesian Journal of Computing and Cybernetics Systems (IJCCS), a two times annually provides a forum for the full range of scholarly study . IJCCS focuses on advanced computational intelligence, including the synergetic integration of neural networks, fuzzy logic and eveolutionary computation, so that more intelligent system can be built to industrial applications. The topics include but not limited to : fuzzy logic, neural network, genetic algorithm and evolutionary computation, hybrid systems, adaptation and learning systems, distributed intelligence systems, network systems, human interface, biologically inspired evolutionary system, artificial life and industrial applications. The paper published in this journal implies that the work described has not been, and will not be published elsewhere, except in abstract, as part of a lecture, review or academic thesis.
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 476 Documents
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Rice Planting Calendar Application Development using Scrum Gita Fadila Fitriana; Novian Adi Prasetyo
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 16, No 2 (2022): April
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.70155

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.
Controlling the Nutrition Water Level in the Non-Circulating Hydroponics based on the Top Projected Canopy Area Hurriyatul Fitriyah; Agung Setia Budi; Rizal Maulana; Eko Setiawan
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 16, No 2 (2022): April
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.70556

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.
Analysis of Covid-19 Cash Direct Aid (BLT) Acceptance Using K-Nearest Neighbor Algorithm Ahmad Ari Aldino; Ryan Randy Suryono; Riyama Ambarwati
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 16, No 2 (2022): April
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.70801

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.
Identification of Incung Characters (Kerinci) to Latin Characters Using Convolutional Neural Network Tesa Ananda Putri; Tri Suratno; Ulfa Khaira
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 16, No 2 (2022): April
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.70939

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
SENTIMENT ANALYSIS OF STAKEHOLDER SATISFACTION MEASUREMENT Ni Luh Ratniasih; Ni Wayan Ninik Jayanti
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 16, No 2 (2022): April
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.72245

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.
Face Expression Classification in Children Using CNN Yusril Ihza; Danang Lelono
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 16, No 2 (2022): April
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.72493

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%.
Implementation of Factor Analysis and BiClustering in Classifying Multidimensional Under-Five Poverty in East Nusa Tenggara Rahmadathul Wisdawati; Rani Nooraeni; Bagaskoro Cahyo Laksono; Bintang Izzatul Fatah
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 16, No 3 (2022): July
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.70433

Abstract

Under-five poverty is a condition where the needs of toodlers are not met, resulting in undernourished children and unable to reach their full potential in the social sphere. East Nusa Tenggara is a province that still faces the biggest nutritional problems in Indonesia in 2019. This study aims to explain the variables that form toodlers multidimensional poverty in East Nusa Tenggara (ENT), form the Multidimensional Under-Five Poverty Index (MUPI), and compare the results of index formed with the results of bicluster. Data source used in this study is SUSENAS KOR 2019. The analytical method used is a factor and bicluster analysis. The results shows that 11 multidimensional poverty indicators form three dimensions, namely the Adequate Food and Beverage Facility Factor, Health Protection Factor, and Housing and Nutrition Factor, which is used to form the index. Based on regional grouping, there are five areas with low MUPI scores, fourteen areas with medium MUPI scores, and three areas with high MUPI scores. However, biclustering results show that there are two areas with low poverty category, thirteen regions with moderate poverty category, and seven regions with high poverty category. The result of the comparison of MUPI grouping with the biclustering method obtained different results based on the composition of the resulting area.
Application of the Weighted Product Method in a Decision Support System to Determine Children's Multiple Intelligence Yuliana Astiti Fonga Wea Tae; Rambu Yetti Kalaway; Pingky Alfa Ray Leo Lede
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 16, No 3 (2022): July
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.70810

Abstract

Intelligence cannot solely be measured in terms of intellectual intelligence. There are various types of intelligence in children, which cause teachers and parents require time to determine the type of intelligence of children. Quick and easy decision-making can be achieved using a decision support system. One method that can be adopted in the decision-making process of a decision support system is the weighted product method. This study aims to measure the level of accuracy of the weighted product method in determining the type of multiple intelligence of children. The decision support system determines the type of intelligence of children in early childhood (ages 4-6 years) using Garner's [1] eight types of multiple intelligences as decision-making criteria. The data was collected using interviews and questionnaires to the teachers of Mutiara State Kindergarten. The study found that a decision support system using the weighted product method can determine the type of children's multiple intelligences with an accuracy rate of 96%. Based on the result of analysis and calculation using the weighted product method from test questionnaire data of 55 children, compared to the results of identification by the teacher, it was found that the compatibility of 53 children.
Ontology-based Complementary Breastfeeding Search Model Astrid Noviana Paradhita; Anny Kartika Sari; Agus Sihabuddin
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 16, No 3 (2022): July
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.71963

Abstract

Children's nutritional requirements differ from those of adults. The health ministry's Indonesian data shows that in 2017, there were 17.8% of malnourished children under five years old (toddlers), one of which was related to complementary breastfeeding problems. Complementary breastfeeding is given to babies starting at 6–24 months of age. This research aims to build a complementary breastfeeding search model and be able to present it as a treatment for malnourished babies. A search model is built to understand natural language input given by a user. Also, it can do reasoning by applying a set of rules to obtain implicit knowledge about the complementary breastfeeding menu recommended for babies. The methods used in this research are data collection, designing a search model, building an ontology model, building SWRL, natural language processing, and usability testing by users and nutritionists. This research succeeded in building an ontology-based complementary breastfeeding search model in the form of a semantic web. The testing result shows that the web can provide an alternative complementary breastfeeding menu according to the baby’s nutritional needs and has a high usability capability of 4.01 on a scale of 1 to 5.
Increasing Performance of Multiclass Ensemble Gradient Boost uses Newton-Raphson Parameter in Physical Activity Classifying Supriyadi La Wungo; Firman Aziz
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 16, No 3 (2022): July
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.73179

Abstract

The sophistication of smartphones with various sensors they have can be used to recognize human physical activity by placing the smartphone on the human body. Classification of human activities, the best performance is obtained when using machine learning methods, while statistical methods such as logistic regression give poor results. However, the weakness of the logistic regression method in classifying human activities is corrected by using the ensemble technique. This paper proposes to apply the Multiclass Ensemble Gradient Boost technique to improve the performance of the Logistic Regression classification in classifying human activities such as walking, running, climbing stairs, and descending stairs. The results show that the Multiclass Ensemble Gradient Boost Classifier by Estimating the Newton-Raphson Parameter succeeded in improving the performance of logistic regression in terms of accuracy by 29.11%.

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