ILKOM Jurnal Ilmiah
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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<![CDATA[Implementation of Data Mining Using K-Means Algorithm for Bicycle Sales Prediction ]]>
<![CDATA[Ivan Anggriawan]]>;
<![CDATA[Wawan Gunawan]]>
<![CDATA[ ILKOM Jurnal Ilmiah Vol 14, No 3 (2022) ]]>
Publisher : <![CDATA[Prodi Teknik Informatika FIK Universitas Muslim Indonesia ]]>
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DOI: 10.33096/ilkom.v14i3.1291.284-293
<![CDATA[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.]]>
<![CDATA[Design of library noise detection tools based on voice pressure parameters ]]>
<![CDATA[Yuda Irawan]]>;
<![CDATA[Refni Wahyuni]]>;
<![CDATA[Hasnor Khotimah]]>;
<![CDATA[Herianto -]]>;
<![CDATA[Bambang Kurniawan]]>;
<![CDATA[Haris Tri Saputra]]>;
<![CDATA[Yulisman Yulisman]]>;
<![CDATA[Abdi Muhaimin]]>;
<![CDATA[Reno Renaldi]]>;
<![CDATA[Rahmaddeni Rahmaddeni]]>
<![CDATA[ ILKOM Jurnal Ilmiah Vol 14, No 3 (2022) ]]>
Publisher : <![CDATA[Prodi Teknik Informatika FIK Universitas Muslim Indonesia ]]>
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DOI: 10.33096/ilkom.v14i3.1191.237-244
<![CDATA[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.]]>
<![CDATA[Association of single nucleotide polymorphism and phenotype in type 2 of diabetes mellitus using Support Vector Regression and Genetic Algorithm ]]>
<![CDATA[Ratu Mutiara Siregar]]>;
<![CDATA[Wisnu Ananta Kusuma]]>;
<![CDATA[Annisa Annisa]]>
<![CDATA[ ILKOM Jurnal Ilmiah Vol 14, No 3 (2022) ]]>
Publisher : <![CDATA[Prodi Teknik Informatika FIK Universitas Muslim Indonesia ]]>
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DOI: 10.33096/ilkom.v14i3.1283.194-202
<![CDATA[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.]]>
<![CDATA[Sentiment analysis of customer satisfaction levels on smartphone products using Ensemble Learning ]]>
<![CDATA[Muhammad Ma’ruf]]>;
<![CDATA[Adam Prayogo Kuncoro]]>;
<![CDATA[Pungkas Subarkah]]>;
<![CDATA[Faridatun Nida]]>
<![CDATA[ ILKOM Jurnal Ilmiah Vol 14, No 3 (2022) ]]>
Publisher : <![CDATA[Prodi Teknik Informatika FIK Universitas Muslim Indonesia ]]>
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DOI: 10.33096/ilkom.v14i3.1377.339-347
<![CDATA[Increasingly sophisticated technological developments create new ways for people to conduct trading business. An example of this technology application is the use of e-commerce. However, there are conditions where the seller cannot measure the level of satisfaction and identify problems experienced by his customers if it is only based on the rating as the case in smartphones transactions. Therefore, a solution is needed to create a system that can filter negative and positive comments. This study offers a solution to address this issue by using machine learning employing the K-Nearest Neighbors, SVM, and Naive Bayes algorithms with hyperparameters from previous studies. This study applied the ensemble learning method with the Voting Classifier technique, which is an algorithm to combine several algorithms that have been made. From the test results, the highest accuracy was obtained by SVM with an accuracy value of 91.18% while the ensemble learning method obtained an accuracy value of 89.22%. The difference in the accuracy of training and testing for SVM and ensemble learning method is 7.1% and 4% respectively. These results indicate that the ensemble learning method can help improve the performance of sentiment analysis algorithms for comments on smartphone products.]]>
<![CDATA[Information technology governance in University of Muhammadiyah Palembang using framework COBIT 5 domain; Evaluate, Direct and Monitor (EDM) ]]>
<![CDATA[Zulhipni Reno Saputra Elsi]]>;
<![CDATA[Karnadi Karnadi]]>;
<![CDATA[Jimmie Jimmie]]>;
<![CDATA[Fajrie Agus Dwino Putra]]>;
<![CDATA[Hartini Hartini]]>;
<![CDATA[Sri Primaini Agustanti]]>
<![CDATA[ ILKOM Jurnal Ilmiah Vol 14, No 3 (2022) ]]>
Publisher : <![CDATA[Prodi Teknik Informatika FIK Universitas Muslim Indonesia ]]>
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DOI: 10.33096/ilkom.v14i3.1136.294-302
<![CDATA[This study aims to find out about Information Technology management at Muhammadiyah University of Palembang and to get right advice in managing Information Technology from the University level to the Study Program. Regarding benchmarks in Information Technology Governance use the Cobit 5 framework with the Evaluate, Direct and Monitor domains. Monitoring and evaluation was carried out using a questionnaire distributed to lecturers and employees at the Muhammadiyah University of Palembang and the researchers did observations on the management of higher education information technology governance. Based on the questionnaire result, the highest gap occurs in sub domain 4, which is 3.65 while the observation result towards the capability level is at level 3 with a value of 56.67%, the sub domain ensuring resource optimization has the highest capability value of 66.67%. Based on the data obtained using the EDM domain, the University of Muhamadiyah Palembang has to set Standard Operating Procedures (SOP) and Work Instructions (IK) so every five processes can run well to create good IT governance.]]>
<![CDATA[Factor analysis satisfaction levels of users toward the JKN mobile application in the COVID-19 Era using the PIECES framework ]]>
<![CDATA[Randa Gustiawan]]>;
<![CDATA[Ulung Pribadi]]>
<![CDATA[ ILKOM Jurnal Ilmiah Vol 14, No 3 (2022) ]]>
Publisher : <![CDATA[Prodi Teknik Informatika FIK Universitas Muslim Indonesia ]]>
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DOI: 10.33096/ilkom.v14i3.1280.245-254
<![CDATA[This study aimed to prove the researcher's hypothesis regarding users’ factor analysis satisfaction of the Mobile JKN application in the Covid-19 era in Sungai Penuh City using the PIECES framework. The measurement variables of the PIECES framework were performance, information, economy, control, efficiency, and service. In this study, researchers used quantitative descriptive methods with data sources from questionnaires via google form with 101 respondents, and data processing was carried out using SEM-pls. The results of this study indicated the value of R square was 0.732. It can be concluded that the interpretation of the users’ satisfaction level of the application was 73.2%, which R-square identifies in the Strong/Good category. Several PIECES variables that has a significant effect on people's satisfaction with the JKN mobile application were efficiency and performance variables with P values of 0.004 and 0.033 while variables that did not have significant effect were control, economy, information and services.]]>
<![CDATA[Classification of Dog and Cat Images using the CNN Method ]]>
<![CDATA[Teguh Adriyanto]]>;
<![CDATA[risky aswi ramadhani]]>;
<![CDATA[Risa Helilintar]]>;
<![CDATA[Aidina Ristyawan]]>
<![CDATA[ ILKOM Jurnal Ilmiah Vol 14, No 3 (2022) ]]>
Publisher : <![CDATA[Prodi Teknik Informatika FIK Universitas Muslim Indonesia ]]>
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DOI: 10.33096/ilkom.v14i3.1116.203-208
<![CDATA[Blind people can be defined as those people who are unable to see objects or pictures around them with their eyes. This inability becomes an issue for them when dealing with objects or images in front of them. These problems lead to the novelty of this study that is to recognize objects or images around blind people with the CNN algorithm. Dogs and cats were used as objects in this study. These object recognitions used Deep Learning, a relatively new science in the field of machine learning. Deep learning works like the human brain's ability to recognize an object. In this study, the objects that were used were pictures of a dog and a cat. This study used 3 types of data, namely training, validation, and testing data. The data training consisted of dog data with a total of 1000 images and cat data with a total of 1000 images. Data validation consisted of 500 dog data and 500 cat data. The CCN architecture employed 3 convolution layers. The layer was convolution 1 using 16 filters of kernel size 3x3, the second convolution using 32 filters of kernel size 3x3 and the third using 64 filters of kernel size 3x3. While the data testing consisted of 51dog data and 27 cat data. The method used to analyze the image was CNN. The input was an image with a size of 150x150 pixels with 3 channels, namely R, G, and B. This classification went through a performance test with the Confusion Matrix and it obtained 45% precision, 45% recall and 45% f1-score. From these results it can be concluded that the accuracy values should be improved.]]>
<![CDATA[Sentiment analysis of Indonesian reviews using fine-tuning IndoBERT and R-CNN ]]>
<![CDATA[Herlina Jayadianti]]>;
<![CDATA[Wilis Kaswidjanti]]>;
<![CDATA[Agung Tri Utomo]]>;
<![CDATA[Shoffan Saifullah]]>;
<![CDATA[Felix Andika Dwiyanto]]>;
<![CDATA[Rafal Drezewski]]>
<![CDATA[ ILKOM Jurnal Ilmiah Vol 14, No 3 (2022) ]]>
Publisher : <![CDATA[Prodi Teknik Informatika FIK Universitas Muslim Indonesia ]]>
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DOI: 10.33096/ilkom.v14i3.1505.348-354
<![CDATA[Reviews are a form of user experience information on a product or service that can be used as a reference for potential consumers’ preferences to buy, use, or consume a product. They can be also used by business entities to find out public opinion about their product or the performance of their business products. It will be very difficult to process the review data manually and it will take a long time. Therefore, sentiment analysis automation can be used to get polarity information from existing reviews. In this study, IndoBERT with Recurrent Convolutional Neural Network (RCNN) was used to automate sentiment analysis of Indonesian reviews. The data used was a sentiment analysis dataset obtained from IndoNLU with sentiment consisting of negative sentiment, neutral sentiment, and positive sentiment. The results of the test showed that IndoBERT with the Recurrent Convolutional Neural Network (RCNN) had better results than the IndoBERT base. IndoBERT with Recurrent Convolutional Neural Network (RCNN) obtained 95.16% accuracy, 94.05% precision, 92.74% recall and 93.27% f1 score.]]>
<![CDATA[Multiplayer mechanism design for soil tillage serious game ]]>
<![CDATA[Anang Kukuh Adisusilo]]>;
<![CDATA[Emmy Wahyuningtyas]]>;
<![CDATA[Nia Saurina]]>;
<![CDATA[Radi Radi]]>
<![CDATA[ ILKOM Jurnal Ilmiah Vol 14, No 3 (2022) ]]>
Publisher : <![CDATA[Prodi Teknik Informatika FIK Universitas Muslim Indonesia ]]>
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DOI: 10.33096/ilkom.v14i3.1432.303-313
<![CDATA[The primary goal of Serious Games is not only for fun but also for lesson. In learning the first stage of soil tillage which using the mouldboard plow, a proper understanding is needed so that the soil tillage process will follow the needs of plant growth. The use of serious games as a study instrument for soil tillage is under the concept of digital game-based learning (DGBL). The problem of players when playing serious games is less motivated to play because the serious game system and scenario are less challenging. That challenges accelerate the shape of knowledge and experience when playing the games (user experience). By referring to the Learning Mechanics Gaming Mechanics (LM-GM) model, which is based on multiplayer in serious games, hopefully the learning process of land management using the mouldboard plow can be optimized. This process can increase learning motivation and elevate the user experience. This research results a design concept of a learning mechanism and a game mechanism for a serious multiplayer game of soil tillage with a mouldboard plow. There are three types of learning mechanisms in conceptual and concrete components, also six types of game mechanisms that can be used as a reference for the formation of multiplayer serious games and the increase player motivation.]]>
<![CDATA[Comparative analysis of Fuzzy Tsukamoto's membership functions for determining irrigated rice field feasibility status ]]>
<![CDATA[Ummi Syafiqoh]]>;
<![CDATA[Anton Yudhana]]>;
<![CDATA[Sunardi Sunardi]]>
<![CDATA[ ILKOM Jurnal Ilmiah Vol 14, No 3 (2022) ]]>
Publisher : <![CDATA[Prodi Teknik Informatika FIK Universitas Muslim Indonesia ]]>
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DOI: 10.33096/ilkom.v14i3.1156.255-263
<![CDATA[The representation of the fuzzy set membership curve consisting of trapezoidal, triangular, and linear shapes, has an important role in the fuzzy logic system. The selection of the curve's shapes determines the useable membership function and affects the fuzzy output value. Previous studies generally used curves that had been employed in predecessors or other studies that did not explain the reason for choosing a fuzzy member curve. This condition became problem because there was not a guide in selecting the appropriate membership function model for the parameters used in the fuzzy process so that most researchers only use membership functions that are commonly used in previous studies or in the same case as their research. The purpose of this study was to determine the effect of selecting trapezoidal and triangular curves on the performance of Tsukamoto's fuzzy logic for determining the rice-fields suitability status. The research methodology comprised 3 main stages. The first stage was data collecting, to collect soil pH values, soil moisture, and air temperature in rice fields. The second stage was the implementation of the Tsukamoto fuzzy. At this stage, two membership function curves were used. The third stage was a comparative analysis of Tsukamoto's fuzzy's output of trapezoidal and triangular curves. The results obtained indicate that there is no significant performance difference between the two different membership functions. The results of the research with the trapezoidal membership function have a better accuracy rate of 93% while the triangular membership function has an accuracy rate of 90%.]]>
