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ILKOM Jurnal Ilmiah
Published by Universitas Muslim Indonesia
ISSN : 20871716     EISSN : 25487779     DOI : -
Core Subject : Science,
Computer Science & IT
ILKOM Jurnal Ilmiah is an Indonesian scientific journal published by the Department of Information Technology, Faculty of Computer Science, Universitas Muslim Indonesia. ILKOM Jurnal Ilmiah covers all aspects of the latest outstanding research and developments in the field of Computer science, including Artificial intelligence, Computer architecture and engineering, Computer performance analysis, Computer graphics and visualization, Computer security and cryptography, Computational science, Computer networks, Concurrent, parallel and distributed systems, Databases, Human-computer interaction, Embedded system, and Software engineering.
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Articles 10 Documents
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Search results for , issue "Vol 14, No 1 (2022)" : 10 Documents clear
Comparison of Support Vector Machine and XGBSVM in Analyzing Public Opinion on Covid-19 Vaccination Rahmaddeni Rahmaddeni; M. Khairul Anam; Yuda Irawan; Susanti Susanti; Muhammad Jamaris
ILKOM Jurnal Ilmiah Vol 14, No 1 (2022)
Publisher : Teknik Informatika Fakultas Ilmu Komputer Univeristas Muslim Indonesia

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Abstract

The corona virus has become a global pandemic and has spread almost all over the world, including Indonesia. There are many negative impacts caused by the spread of COVID-19 in Indonesia, so the government takes vaccination measures in order to suppress the spread of COVID-19. The public's response to vaccination was quite diverse on Twitter, some were supportive and some were not. The data used in this study came from Twitter which was taken using the drone emprit portal, using the keyword, namely "vaccination". The classification will be carried out using the SVM and hybrid methods between SVM and XGBoost or what is commonly called XGBSVM. The purpose of this study is to provide an overview to the public whether the Covid-19 vaccination actions carried out tend to be positive, neutral or negative opinions. The results of the sentiment evaluation that have been carried out can be seen that SVM has the highest accuracy of 83% with 90:10 data splitting, then XGBSVM produces 79% accuracy with 90:10 data splitting.
Classification of Coffee Bean Defects Using Gray-Level Co-Occurrence Matrix and K-Nearest Neighbor Mila Jumarlis; Mirfan Mirfan; Abdul Rachman Manga
ILKOM Jurnal Ilmiah Vol 14, No 1 (2022)
Publisher : Teknik Informatika Fakultas Ilmu Komputer Univeristas Muslim Indonesia

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Abstract

Defects in coffee beans can significantly affect the quality of coffee production so that defects in coffee beans can cause a decreasing the level of coffee production. The purpose of this study is to implement the GLCM (gray-level co-occurrence matrix) and the K-NN (k-nearest neighbor) method on a web-based program and provided a website to detect coffee bean defects. This study uses the GLCM algorithm to extract the features of the coffee images and uses the K-NN algorithm to classify the defect level of coffee beans. The system development was built using Unified Modeling Language. The development of this website was utilized the programming structure of PHP, HTML, CSS, Javascript, Mozilla Firefox as a browser for the website and MySql for the database management systems. The results show that the system can provide the output in the form of a classification level of the defect level of the coffee bean images. Then, the accuracy of the coffee bean defect assessment was achieved by 90%. Finally, this study concluded that the proposed system could help the coffee farmers determine the defect level of the coffee beans using images input.
Implementation of Fuzzy Logic in Fish Dryer Design Nur Yanti; Taufik Nur; Randis Randis
ILKOM Jurnal Ilmiah Vol 14, No 1 (2022)
Publisher : Teknik Informatika Fakultas Ilmu Komputer Univeristas Muslim Indonesia

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Abstract

The fish drying process aims to preserve fish, so as to reduce losses due to the spoilage process. There is sunlight, the drying process does not experience obstacles, however if it is raining, it will take a longer time, and give a smell effect that disturbs the surrounding environment for a relatively long time. Fish dryer designed to work automatically, aims to speed up drying time using fuzzy logic, thus minimizing rot and air pollution due to the smell of the fish drying process. The design of the tool used experimental methods through literature study as a source of study, planning and manufacturing of fish drying equipment consists of hardware using the Arduino Mega 2560 microcontroller, temperature sensor of DHT 22, load cell sensor, humidity sensor, fan, heating element and LCD and software using the Fuzzy Mamdani method. The results obtained are the weight of the fish that has undergone a drying process using an automatic drying device, namely 500 grams, indicating that the drying process is 50% of the initial weight of 1000 grams, with a drying time of 4.48 hours, while drying time by drying or manually takes 45 hours. Shows the control system using fuzzy logic on fish drying equipment,  speed up the drying time about 10 hours faster than the drying time by drying in the sun.  So that it can increase the amount of dry fish production, reduce the smell in the environment around the drying, because the fish are in the dryer closed. 
Implementation of Deep Learning for Handwriting Imagery of Sundanese Script Using Convolutional Neural Network Algorithm (CNN) Arif Purnama; Saeful Bahri; Gunawan Gunawan; Taufik Hidayatulloh; Satia Suhada
ILKOM Jurnal Ilmiah Vol 14, No 1 (2022)
Publisher : Teknik Informatika Fakultas Ilmu Komputer Univeristas Muslim Indonesia

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Abstract

Aksara Sunda becomes one of the cultures of sundanese land that needs to be preserved. Currently, not all people know Aksara Sunda because of the shift in cultural values and there is a presumption that Aksara Sunda is difficult to learn because it has a unique and complicated shape. The use of deep learning has been widely used, especially in the field of computer vision to classify images, one of the commonly used algorithms is the Convolutional Neural Network (CNN). The application of The Convolutional Neural Network (CNN) algorithm on sundanese handwriting imagery can make it easier for people to learn Sundanese script, this study aims to find out how accurate the neural network convolutional algorithm is in classifying Aksara Sunda imagery. Data collection techniques are done by distributing questionnaires to respondents. System testing using accuracy tests, testing on CNN models using data testing get 97.5% accuracy and model testing using applications get 98% accuracy. So based on the results of the trial, the implementation of deep learning methods using neural network convolution algorithms was able to classify the handwriting image of Aksara Sunda well.
Neural Network Method Based on Particle Swarm Optimization for Predicting Satisfaction of Recipients of Internet Data Support from the Ministry of Education and Culture Annahl Riadi; Irvan Muzakkir; Marniyati H. Botutihe
ILKOM Jurnal Ilmiah Vol 14, No 1 (2022)
Publisher : Teknik Informatika Fakultas Ilmu Komputer Univeristas Muslim Indonesia

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Abstract

The free quota assistance program for students and lecturers is an assistance program carried out by the ministry of education and culture, this program has been implemented since the impact of the covid-19 pandemic in all regions of Indonesia, this assistance is expected to help students and lecturers in carrying out online learning caused by the pandemic covid-19, the purpose of this study is to measure the level of visitor satisfaction through predictions of satisfaction so that it can help the government in advancing the world of education., data processing is carried out using the rapid miner application and using the neural network method with particle swarm optimization, from the results of data processing the results obtained are Values the accuracy for the neural network algorithm model is 42.44% and the accuracy value for the PSO-based neural network algorithm model is 91.86%.
Assessing the Influence of Mobility Behavior on the Covid-19 Transmission: A Case in the Most Affected City of Indonesia Najirah Umar; Hamdan Gani; Sitti Zuhriyah; Helmy Gani; Feng Zhipeng
ILKOM Jurnal Ilmiah Vol 14, No 1 (2022)
Publisher : Teknik Informatika Fakultas Ilmu Komputer Univeristas Muslim Indonesia

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Abstract

An emerging outbreak of Covid-19 has now been detected across the globe. Given this pandemic condition, the robust estimation reports are urgently needed. Therefore, this study aims to analyze the impacts of community mobility (before, during, and after the lockdown period) on the spread of the Covid-19 in Jakarta, Indonesia. The secondary data was derived from surveillance data for Covid-19 daily cases from the Health Office of DKI Jakarta Province and the Ministry of Health. The community mobility indicators were retrieved from the Google website. Our results showed that in the pre-lockdown period, the Covid-19 daily cases rapidly increased, while community mobility significantly dropped. The increasing number of Covid-19 daily cases was significantly affected by the number of Covid-19 tests per day rather than community mobility. During the restriction period, the number of Covid-19 tests per day, and community mobility statistically affected the decreasing number of Covid-19 daily cases. Meanwhile, after the lockdown period, the number of Covid-19 daily cases rapidly increased, which significantly has a direct relationship with the increasing level of community mobility. Overall, community mobility and the number of tests per day are the essential variables that explain the number of Covid-19 daily cases in Jakarta, Indonesia. Additionally, this study did not observe any impact of average air temperature and air pollution on the spread of Covid-19. This study figures out that community mobility could potentially explain the progression of Covid-19.
Implementation of Artificial Neural Network (ANN) in the Image Recognition of Offline Cursive Handwriting Fitrianingsih Fitrianingsih; Diana Tri Susetianingtias; Dody Pernadi; Eka Patriya; Rini Arianty
ILKOM Jurnal Ilmiah Vol 14, No 1 (2022)
Publisher : Teknik Informatika Fakultas Ilmu Komputer Univeristas Muslim Indonesia

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Abstract

Identifying a writing is an easy thing to do for human, but this does not apply to computers, in particular if it is handwriting. Handwriting recognition, especially cursive handwriting is a research in the area of image processing and pattern matching that is challenging to complete, following the different characteristics of each person's cursive handwriting style. In this study, the use of the ANN model will be implemented in performing offline handwriting image recognition. The cursive handwriting image that has been obtained is then preprocessed and segmented using bounding box rectangle and contour techniques. Evaluation of system performance using global performance metrics in this study resulted in a percentage of 93% where the bounding box and contour succeeded in determining the segmentation point correctly, so that the ANN model worked optimally.
Detection System of Strawberry Ripeness Using K-Means Dolly Indra; Ramdan Satra; Huzain Azis; Abdul Rachman Manga; Harlinda L
ILKOM Jurnal Ilmiah Vol 14, No 1 (2022)
Publisher : Teknik Informatika Fakultas Ilmu Komputer Univeristas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v14i1.1054.33-39

Abstract

Strawberry is one type of fruit that is favored by the people of Indonesia. The detection process to identify strawberries can be done by utilizing advances in computer technology, One of them is in the field of digital image processing. In this study, we made a strawberry ripeness detection system using the values of Red, Green and Blue as the reference values, while for identification in determining the type of classification using the K-Means algorithm that uses the Euclidean distance difference as the reference. Based on the results of testing using the K-Means algorithm on 51 strawberry images consisting of ripe, semi ripe and raw fruit yielding an accuracy rate of 82.14%, we also conducted tests other than strawberry images as many as 8 images yielded an accuracy rate of 100%.
Multi Classification of Bacterial Microscopic Images Using Inception V3 Ingrid Nurtanio; Anugrayani Bustamin; Christoforus Yohannes; Alif Tri Handoyo
ILKOM Jurnal Ilmiah Vol 14, No 1 (2022)
Publisher : Teknik Informatika Fakultas Ilmu Komputer Univeristas Muslim Indonesia

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Abstract

Microorganisms such as bacteria are the main cause of various infectious diseases such as cholera, botulism, gonorrhea, Lyme disease, sore throat, tuberculosis and so on. Therefore, identification and classification of bacteria is very important in the world of medicine to help experts diagnose diseases suffered by patients. However, manual identification and classification of bacteria takes a long time and a professional individual. With the help of artificial intelligence, we can effectively and efficiently classify bacteria and save a lot of time and human labor. In this study, a system was created to classify bacteria from microscopic image samples. This system uses deep learning with the transfer learning method. Inception V3 architecture was modified and retained using 108 image samples labeled with five types of bacteria, namely Acinetobacter baumanii, Escherichia coli, Neisseria gonorrhoeae, Propionibacterium acnes and Veionella. The data is then divided into training and validation using the k-fold cross validation method. Furthermore, the features that have been extracted by the model are trained with the configuration of minibatchsize 5, maxepoch 5, initiallearnrate 0.0001, and validation frequency 3. The model is then tested with data validation by conducting ten experiments and getting an average accuracy value of 94.42%.
Evaluation of Employee Acceptance of the IMS Application at PT Sarana Utama Adimandiri with the TAM Approach Sancoko Sancoko; Zahra Shalsabilla Prayogi; Badra Al Aufa; Rahmat Yuliawan
ILKOM Jurnal Ilmiah Vol 14, No 1 (2022)
Publisher : Teknik Informatika Fakultas Ilmu Komputer Univeristas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v14i1.1120.%p

Abstract

Today work patterns are strongly influenced by technology so that they are able to change conventional jobs into modern jobs based on information technology by using the internet. The influence of information technology has also penetrated into PT Sarana Utama Adimandiri which is engaged in the construction sector. PT Sarana Utama Adimandiri have an IMS application that is used to make activity purchasing. This paper explain the relationship between independent variable and dependent variable, using the concept developed by Davis regarding the Technology Acceptance Model (TAM). Two independent variables are perceived usefulness and perceived ease of use, while the dependent variable is acceptance of IT (integrated management system/IMS applications). This research method uses a quantitative approach, which is to obtain data by distributing questionnaires. After the data is collected, the authors perform statistical analysis using SPSS for multiple regression. Based on the results of the study, it can be concluded that there is a significant effect between perceived usefulness and perceived ease of use on acceptance of  IT.

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