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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.
Arjuna Subject : -
Articles 617 Documents
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Assessing the Influence of Mobility Behavior on the Covid-19 Transmission: A Case in the Most Affected City of Indonesia Umar, Najirah; Gani, Hamdan; Zuhriyah, Sitti; Gani, Helmy; Zhipeng, Feng
ILKOM Jurnal Ilmiah Vol 14, No 1 (2022)
Publisher : Prodi Teknik Informatika FIK Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v14i1.1043.17-24

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.
The Implementation of Artificial Neural Network (ANN) on Offline Cursive Handwriting Image Recognition Fitrianingsih, Fitrianingsih; Susetianingtias, Diana Tri; Pernadi, Dody; Patriya, Eka; Arianty, Rini
ILKOM Jurnal Ilmiah Vol 14, No 1 (2022)
Publisher : Prodi Teknik Informatika FIK Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v14i1.1113.63-73

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 Indra, Dolly; Satra, Ramdan; Azis, Huzain; Manga, Abdul Rachman; L, Harlinda
ILKOM Jurnal Ilmiah Vol 14, No 1 (2022)
Publisher : Prodi Teknik Informatika FIK Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v14i1.1054.25-31

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 Nurtanio, Ingrid; Bustamin, Anugrayani; Yohannes, Christoforus; Handoyo, Alif Tri
ILKOM Jurnal Ilmiah Vol 14, No 1 (2022)
Publisher : Prodi Teknik Informatika FIK Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v14i1.1121.80-90

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: TAM Approach Sancoko, Sancoko; Prayogi, Zahra Shalsabilla; Al Aufa, Badra; Yuliawan, Rahmat
ILKOM Jurnal Ilmiah Vol 14, No 1 (2022)
Publisher : Prodi Teknik Informatika FIK Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v14i1.1120.74-79

Abstract

PT Sarana Utama Adimandiri (SUA) which is engaged in the construction sector implements an IMS application in its purchasing activity. This paper aims at describing the evaluation of employee acceptance of the information system at PT SUA using the Technology Acceptance Model (TAM) approach. TAM has two main variables i.e: perceived usefulness and perceived ease of use which function as independent variables, while the dependent variable is acceptance of IT (integrated management system/IMS applications). The population and sample in this study were all employees of PT SUA, which was used to obtain research data through the distribution of structured questionnaires. The instrument was tested using validity and reliability tests, and data was analyzed by using spearman rank test. This study suggests that there is a strong effect of perceived usefulness and perceived ease of use on acceptance of IT.
Classification of reading skills of early childhood with C4.5 algorithm Suherman Suherman; Agus Nursikuwagus; Ahmad Sugiyarta; Indah Komala
ILKOM Jurnal Ilmiah Vol 14, No 2 (2022)
Publisher : Prodi Teknik Informatika FIK Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v14i2.1012.142-149

Abstract

Early age is a golden age for children, which is important to improve their development. The children also need to get a good education according to development to improve their abilities. The education provided is certainly following the developmental aspects, one of which is language. The language skills needed at this age is early reading skills. In the understanding given by the teacher, it was found the problems and the need related to it. There is no determination of the classification of early childhood reading skills. At this moment, the teacher has difficulty providing the achievement of reading skills to students based on the criteria of early reading ability and classifying it and improving learning quality to each student and material for discussion with parents. There is a suggestion given to achieve the goals and resolve it that the classification of early childhood reading skills has been designed and for data processing is carried out using a computer. This study uses a decision tree and Algorithm C4.5 for the method. Algorithm C4.5 is included in the classification based on Decision Tree, which Algorithm C4.5 is a method that can handle features with numeric types. The purpose of this study is to perform classification using Algorithm C4.5 as a method that can find out the level of reading skills in early childhood, based on the criteria, so that classification can be done easily and get the right improvement suggestion. The result in this study is that the Algorithm C4.5 method has a good result in solving and calculating for the classification so that the accuracy value of the Algorithm C4.5 calculation that has been carried out is 77,14%.
Implementation of Data Mining Using K-Means Algorithm for Bicycle Sales Prediction Ivan Anggriawan; Wawan Gunawan
ILKOM Jurnal Ilmiah Vol 14, No 3 (2022)
Publisher : Prodi Teknik Informatika FIK Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v14i3.1291.284-293

Abstract

During the pandemic, to reduce the number of Covid-19 spreads,  the government imposed social distancing and work from home (WFH) to reduce community activities outside the home. This caused people to have irregular patterns or lifestyles which less any physical activity . It surely can lower immunity system in which can increase the risk of being infected by the virus. Therefore, during the pandemic, sports or exercises become one of the activities that regularly carried out by the community to increase their immunity. One of the sports activities that can be done to maintain their immunity is cycling. Cycling itself is a light activity that can be practiced by all ages. This occasion is certainly a good marketing target for bicycle selling companies, but the company sometimes experiences problems regarding bicycle stocks that do not match with the consumer market target. The purpose of this study is to find out what types of bicycles are on demand by predicting bicycle sales and looking at the desired interests of the community. This study uses the K-Means Clustering algorithm. The results of the K-Means Clustering research are divided into three clusters; Cluster 1 with 209 members with the most interest in mountain bikes, Cluster 2 with 787 members with the most interest in folding bicycles, and Cluster 3 with 540 members with bicycle interests. Most of them are city bicycles, from the clustering process above, the Dunn Index validation (Dunn Index) can be obtained with a value of 0.1324532.
Implementation of the prophet model in COVID-19 cases forecast Rodiah Rodiah; Eka Patriya; Diana Tri Susetianingtias; Ety Sutanty
ILKOM Jurnal Ilmiah Vol 14, No 2 (2022)
Publisher : Prodi Teknik Informatika FIK Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v14i2.1219.99-111

Abstract

One of the steps to understanding this pandemic is to look at the spread of the data by predicting an increase in cases in various countries so that prevention can be carried out as early as possible. One way to see fluctuations in COVID-19 pandemic data is to predict the rate of cases using forecasting methods so that conclusions can be drawn on the spread of COVID-19 pandemic data around the world to be processed using statistical models. This study will implement the use of the Prophet Model in seeing the rate of development of COVID-19 in the world using four features in the forecasting process such as the number of confirmed cases, the number of cases of recovered patients, the number of cases of death, and the number of active cases. The results of this study produce forecasting data on the number of cases of the COVID-19 pandemic that can be viewed daily, weekly, and even monthly. Forecasting results show the first spike at the end of March until the number of cases reached around 10,275,800 million as of June 29, 2020, where the number of cases grew exponentially until June 29, 2020. The case rate of growth in many instances experienced significant growth until the end of October, touching the number in the range of 34,507,150 million as of October 25, 2020. After June 29, 2020, a very high spike was different from the increase in cases in the previous months. Forecasting results show no point decline because historical data on the number of daily confirmed cases of the COVID-19 pandemic has not decreased. The forecasting results in this study are expected to be able to systematically predict events or events that will occur in the COVID-19 pandemic around the world with the help of valid periodic data so that some information can be obtained for preventive measures related to the COVID-19 pandemic.
Design of library noise detection tools based on voice pressure parameters Yuda Irawan; Refni Wahyuni; Hasnor Khotimah; Herianto -; Bambang Kurniawan; Haris Tri Saputra; Yulisman Yulisman; Abdi Muhaimin; Reno Renaldi; Rahmaddeni Rahmaddeni
ILKOM Jurnal Ilmiah Vol 14, No 3 (2022)
Publisher : Prodi Teknik Informatika FIK Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v14i3.1191.237-244

Abstract

A library visitor would want a quiet atmosphere without noise when in the library so that he can concentrate when reading a book. However, not all visitors come to the library to read books; some want to chat and use free Wi-Fi or other, so it disturbs the concentration of other visitors who read books. Therefore, it is necessary to have a tool to detect sound pressure or sound based on the sound level and the sound produced in a library based on the noise level limit in the library, namely 45-55 dB (desible). This tool is designed based on a microcontroller where the definition of a microcontroller is a complete microprocessor system contained in a microcontroller chip which is different from the multi-purpose microprocessor used in a PC because a microcontroller generally already includes the minimum system supporting components of a microprocessor, namely memory, and programming. This tool can help officers monitor the library room for noise that can interfere with the concentration and comfort of library visitors. Based on the results of testing, the overall system is as desired, including the noise detection tool can work in an integrated system, where when the sound sensor detects a noise that exceeds the sound limit, the buzzer will sound, the red led light turns on, the sound module issues a voice message pre-recorded and also the device can be controlled or monitored from the web application.
Association of single nucleotide polymorphism and phenotype in type 2 of diabetes mellitus using Support Vector Regression and Genetic Algorithm Siregar, Ratu Mutiara; Kusuma, Wisnu Ananta; Annisa, Annisa
ILKOM Jurnal Ilmiah Vol 14, No 3 (2022)
Publisher : Prodi Teknik Informatika FIK Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v14i3.1283.194-202

Abstract

Precision Medicine is used to improve proper health care and patients' quality of life, one of which is diabetes. Diabetes Mellitus (DM) is a multifactorial and heterogeneous group of disorders characterized by deficiency or failure to maintain normal glucose homeostasis. About 90% of all DM patients are Type 2 Diabetes Mellitus (T2DM). Biological characteristics and genetic information of T2DM disease were obtained by looking for associations in Single Nucleotide Polymorphism (SNP) which allows for determining the relationship between phenotypic and genotypic information and identifying genes associated with T2DM disease. This research focuses on the Support Vector Regression method and Genetic Algorithm to obtain SNPs that have previously calculated the correlation value using Spearman's rank correlation. Then do association mapping on the SNP results from the SVR-GA selection and check pastasis interaction. The results produced 14 SNP importance. Evaluation of the model using the mean absolute error (MAE) obtained is 0.02807. If the value of MAE is close to zero, then a model can be accepted. The genes generated from the association can be used to assist other researchers in finding the right treatment for T2DM patients according to their genetic profile.

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