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JISA (Jurnal Informatika dan Sains)
Published by Universitas Trilogi
ISSN : 27763234     EISSN : 26148404     DOI : https://doi.org/10.31326/jisa
Core Subject : Science, Engineering,
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering
JISA (Jurnal Informatika dan Sains) is an electronic publication media which publishes research articles in the field of Informatics and Sciences, which encompasses software engineering, Multimedia, Networking, and soft computing. Journal published by Program Studi Teknik Informatika Universitas Trilogi aims to give knowledge that can be used as a reference for researchers and can be useful for society. Accredited “SINTA 4” by The Ministry of Research-Technology and Higher Education Republic of Indonesia, Free of Charge (Submission,Publishing). JISA (Jurnal Informatika dan Sains) is scheduled for publication in June and December (2 issue a year) This Journal accepts research articles in these following fields: Software Engineering: Web Development, Mobile Apps Development, Database Management System Multimedia: Augmented Reality, Virtual Reality, Game Development Networking: Cloud Computing, Internet of Things, Wireless Sensor Network, Mobile Computing Soft Computing: Data Mining, Data Warehouse, Data Science, Artificial Intelligence, Decision Support System
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 158 Documents
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Grouping of Village Status in West Java Province Using the Manhattan, Euclidean and Chebyshev Methods on the K-Mean Algorithm Gatot Tri Pranoto; Wahyu Hadikristanto; Yoga Religia
JISA(Jurnal Informatika dan Sains) Vol 5, No 1 (2022): JISA(Jurnal Informatika dan Sains)
Publisher : Universitas Trilogi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31326/jisa.v5i1.1097

Abstract

The Ministry of Villages, Development of Disadvantaged Areas and Transmigration (Ministry of Village PDTT) is a ministry within the Indonesian Government in charge of rural and rural development, empowerment of rural communities, accelerated development of disadvantaged areas, and transmigration. Village Potential Data for 2014 (Podes 2014) in West Java Province is data issued by the Central Statistics Agency in collaboration with the Ministry of Village PDTT which is in unsupervised data format, consists of 5319 village data. The Podes 2014 data in West Java Province were made based on the level of village development (village specific) in Indonesia, by making the village as the unit of analysis. Base on the Regulation of the Minister of Villages, Disadvantaged Areas and Transmigration of the Republic of Indonesia number 2 of 2016 concerning the village development index, the Village is classified into 5 village status, namely Very Disadvantaged Village, Disadvantaged Village, Developing Village, Advanced Village and Independent Village based on the ability to manage and increase the potential of social, economic and ecological resources. Village status is in fact inseparable from village development that is under government funding support. However, village development funds have not been distributed effectively and accurately according to the conditions and potential of the village due to the lack of clear information about the status of the village. Therefore, the information regarding the villages priority in term of which villages needs more funding and attention from the government is still lacking. Data mining is a method that can be used to group objects in a data into classes that have the same criteria (clustering). One of the algorithms that can be used for the clustering process is the k-means algorithm. Data grouping using k-means is done by calculating the closest distance from data to a centroid point. In this study, different types of distance calculation in the K-means algorithm are compared. Those types are Manhattan, Euclidean and Chebyshev. Validation tests have been carried out using the execution time and Davies Bouldin index. From this test, the data Village Potential 2014 in West Java province have grouped all the 5 status of the village with the obtained number of villages for each cluster is a cluster village Extremely Backward many as 694 villages, cluster Villages 567 villages, cluster village Evolving as much as 1440 villages, the cluster with Desa Maju1557 villages and the cluster Independent Village for 1061 villages. For distance calculation, Chebyshev has the most efficient accumulation time of 1 second compared to Euclidean 1.6 seconds and Manhattan 2.4 seconds. Meanwhile, the Euclidean method has the value, Davies Index most optimal which is 0.886 compared to the Manhattan method 0.926 and Chebyshev 0.990.
Naive Bayes and Support Vector Machine Algorithm for Sentiment Analysis Opensea Mobile Application Users in Indonesia Laurenzius Julio Anreaja; Norma Nobuala Harefa; Julius Galih Prima Negara; Venantius Nathan Hermanu Pribyantara; Agung Budi Prasetyo
JISA(Jurnal Informatika dan Sains) Vol 5, No 1 (2022): JISA(Jurnal Informatika dan Sains)
Publisher : Universitas Trilogi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31326/jisa.v5i1.1267

Abstract

Opensea is an NFT buying and selling application-based platform that is booming in the community. One way to find out the public's perception of the Opensea application is by sentiment analysis, as done in this study. Data that is used is user review data for the Opensea application in the Indonesian play store. The sentiment analysis technique used is the Naïve Bayes Classifier and the Support Vector Machine (SVM) method. Both are used to compare public responses from sentiment analysis of reviewed data labeled as positive, negative, and neutral. Based on this study, it was found that the Naive Bayes algorithm gives the results that class precision is 87.31%, class recall is 71.02%, and accuracy is 89.81%. While the SVM algorithm gives the results that class precision is 94.23%, class recall 71.96%, and Accuracy 90.78%. It is concluded that the SVM algorithm has a better performance than the Naive Bayes algorithm.  
Application of Data Mining to Determine Promotion Strategy Using Algorithm Clustering at SMK Yadika 1 Jerry Watulangkouw
JISA(Jurnal Informatika dan Sains) Vol 5, No 1 (2022): JISA(Jurnal Informatika dan Sains)
Publisher : Universitas Trilogi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31326/jisa.v5i1.1107

Abstract

The Promotion Strategy is very important to achieve the desired target, in determining the School Promotion Strategy for the results of new student admissions and in recommending the right promotion that can be used to overcome the problems faced by SMK Yadika  which experienced a decrease in the number of new students from the 2017/2018 class entering 267 new student, then experiencing difficulties in determining promotion strategies, and promotion decisions taken by the school are sometimes not right on target, even though the position of SMK Yadika has a very strategic environment or place that can produce and get a lot of students. This study aims to apply the K-Means algorithm in the Promotion Strategy grouping which produces seven clusters based on the K Optimal Davies Bouldin Index so that it can be used to determine the right promotion strategy and develop an information system prototype to assist schools in compiling and deciding the right promotion. The results of this research, schools can carry out promotions based on the origin of the student's school, promotions based on the field of study of interest, promotions based on the study program expertise, promotions based on competency skills, and promotions based on the district where the student lives or domicile. With the results of clustering using the K-Means methodology, Cluster 1 (17.71%), cluster 2 (32.67%), cluster 3 (10.43%), cluster 4 (5.7%), cluster 5 (4.55 %), cluster 6 (3.34%), and cluster 7 (25.78%).
Music Genre Recommendations Based on Spectrogram Analysis Using Convolutional Neural Network Algorithm with RESNET-50 and VGG-16 Architecture nyoman purnama
JISA(Jurnal Informatika dan Sains) Vol 5, No 1 (2022): JISA(Jurnal Informatika dan Sains)
Publisher : Universitas Trilogi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31326/jisa.v5i1.1270

Abstract

Recommendations are a very useful tool in many industries. Recommendations provide the best selection of what the user wants and provide satisfaction compared to ordinary searches. In the music industry, recommendations are used to provide songs that have similarities in terms of genre or theme. There are various kinds of genres in the world of music, including pop, classic, reggae and others. With genre, the difference between one song and another can be heard clearly. This genre can be analyzed by spectrogram analysis. In this study, a spectrogram analysis was developed which will be the input feature for the Convolutional Neural Network. CNN will classify and provide song recommendations according to what the user wants. In addition, testing was carried out with two different architectures from CCN, namely VGG-16 and RESNET-50. From the results of the study obtained, the best accuracy results were obtained by the VGG-16 model with 20 epochs with accuracy 60%, compared to the RESNET-50 model with more than 20 epochs. The results of the recommendations generated on the test data obtained a good similarity value for VGG-16 compared to RESNET-50.
Implementation of the AHP-SMARTER Method in the Decision Support System for Giving Sanctions for Violation of Student Disciplines Sofiansyah Fadli; Maulana Ashari; Hasyim Asyari; Ahmad Susan Pardiansyah
JISA(Jurnal Informatika dan Sains) Vol 5, No 1 (2022): JISA(Jurnal Informatika dan Sains)
Publisher : Universitas Trilogi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31326/jisa.v5i1.1049

Abstract

Violations of school rules are often carried out by students, including lack of respect for teachers, students who are not on time, often late for class, skipping classes, jumping fences, smoking and not paying attention to the rules and other regulations in school. This study aims to build a decision support system for sanctions for violations of student discipline that has the ability to analyze each of the criteria and sub-criteria that have been determined by the school. In this case, students who violate school rules will be punished and given sanctions so as to provide an output value of priority intensity which results in a system that provides an assessment of violations against students. The method used in building this decision support system is by combining the Analytical Hierarchy Process (AHP) method and the Simple Multi Attribute Rating Technique Exploiting Rank (SMARTER) method. Weighting criteria using the AHP method and for ranking using the SMARTER method. The system created can be used to assist in processing data on violations of school rules. With this decision support system, it is hoped that policy makers will have no difficulty in determining what types of actions and sanctions will be given to students who violate school rules.
Online Monitoring and Analysis of Lube Oil Degradation for Gas Turbine Engine using Recurrent Neural Network (RNN) Febrianto Nugroho; Rusdianto Roestam
JISA(Jurnal Informatika dan Sains) Vol 5, No 1 (2022): JISA(Jurnal Informatika dan Sains)
Publisher : Universitas Trilogi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31326/jisa.v5i1.1108

Abstract

Lubrication is one of the important aspects of the engine that will impact the overall performance of the gas turbine engine. Degradation of oil is usually known by offline analysis that use oil sample to check some properties and contaminant. The offline analysis will take a longer time, as needed to collect the sample, send it to the laboratory, analyze the sample and create the report. The purpose of this research is to analyze oil parameters in real-time so can predict oil degradation. Sensors and transducers installed on the lube oil system can read some parameters of the oil then transmit easily to the server. The method that will use in this paper is Recurrent Neural Network (RNN) with multi-step Long Short Term Memory (LSTM). The result of this paper will predict oil degradation on the future operation of gas turbine engine.
The Classification of Anxiety, Depression, and Stress on Facebook Users Using the Support Vector Machine Tsania Maulidia Wijiasih; Rona Nisa Sofia Amriza; Dedy Agung Prabowo
JISA(Jurnal Informatika dan Sains) Vol 5, No 1 (2022): JISA(Jurnal Informatika dan Sains)
Publisher : Universitas Trilogi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31326/jisa.v5i1.1273

Abstract

Social media remains an essential platform for connecting people with friends, family, and the world around them. However, when events spread on social media are primarily negative, it will cause depression, anxiety, and stress that tend to increase. This study aims to classify depression, anxiety, and stress using the Support Vector Machine. The data in this study were obtained from active Facebook users using the Depression Anxiety Stress Scale (DASS 21) questionnaire. This study adopted the Knowledge Discover Database process. The result of this study is an evaluation of the performance of the Support Vector Machine classification of depression, anxiety, and stress. The accuracy of the Support Vector Machine in this study is 98.96%.
Analysis Manipulation Copy-Move on Image Digital using SIFT Method and Histogram Color RGB Muhamad Masjun Efendi; Salman Salman; Moh. Subli
JISA(Jurnal Informatika dan Sains) Vol 5, No 2 (2022): JISA(Jurnal Informatika dan Sains)
Publisher : Universitas Trilogi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31326/jisa.v5i2.1334

Abstract

The application of the SIFT (Scale Invariant feature transform) algorithm and the RGB color histogram in Matlab can detect the suitability of objects in digital images and perform tests accurately. In this study, we discuss the implementation to obtain object compatibility on digital images that have been manipulated using the SIFT Algorithm method on the Matlab source, namely by comparing the original image with the manipulated image. The suitability of objects in digital images is obtained from the large number of keypoints obtained, other additional parameters, namely comparing the number of pixels in the analyzed image, as well as changes in the histogram in RGB color in each analyzed image. The purpose of this research is how to apply the SIFT (Scale Invariant feature transform) Algorithm and RGB color histogram to detect the suitability of objects in digital images and perform tests accurately. This study discusses the implementation to obtain object compatibility in digital images that have been manipulated using the SIFT Algorithm method on Matlab sources, namely by comparing the original image with the manipulated image. The suitability of objects in digital images is obtained from the large number of keypoints obtained, other additional parameters, namely comparing the number of pixels in the analyzed image, as well as changes in the histogram in RGB color in each analyzed image
The Influence of Social Media and Marketplace on the interest in Buying Bima Woven Fabric Products Miftahul Jannah
JISA(Jurnal Informatika dan Sains) Vol 5, No 2 (2022): JISA(Jurnal Informatika dan Sains)
Publisher : Universitas Trilogi

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

The globe has become borderless as a result of the rapid development of information and communication technologies. People even use the internet as a means of buying and selling products and services online, which was subsequently referred to as the online market. The internet has evolved to adapt to the demands of the community and people even use the internet as a method of doing so. The Bima weaving company group use web marketing to supplement their sales efforts. The objective of this research is to investigate the role that social media and online marketplaces have in shaping consumer interest in purchasing bima weaving items. The data were acquired via the use of an online questionnaire, and there were a total of one hundred people that responded to the survey. sampling carried out utilizing the method of purposive sampling Excel 2013 and SPSS 22 for Windows are the programs that are used to handle the data. Quantitative research methodologies and multiple linear regression analysis are used in this work. This demonstrates that there is a beneficial influence that social media has on purchase interest. It is possible to draw the conclusion that there is a tight association between the independent and dependent variables as a result of the fact that the correlation value between the social media and marketplace variables from the purchasing interest variable is 75%. According to the findings of the t-test, the desire to purchase bima weaving items is significantly impacted by the use of social media. The marketplace is a crucial factor in determining the degree of purchaser interest in bima weaving items. According to the findings of the F test, the combination of the market and social media has a substantial impact on the amount of interest buyers have in purchasing Bima woven items.
Development of Knowledge Management System to Improve the Performance of the New Student Admission System for Higher Education M. Khairul Anam; Triyani Arita Fitri; Fransiskus Zoromi; Junadhi Junadhi; Nu'man Nu'man
JISA(Jurnal Informatika dan Sains) Vol 5, No 2 (2022): JISA(Jurnal Informatika dan Sains)
Publisher : Universitas Trilogi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31326/jisa.v5i2.1443

Abstract

The New Student Admission System (PMB) is the main door or core business of the University and requires a good management system. Every Academic Year STMIK Amik Riau forms a committee to carry out this PMB activity. The PBM committee consists of several parts, namely the promotion section, the registration section and the selection section.  Each section carries out knowledge sharing or knowledge transfer in carrying out its duties. This knowledge sharing is only limited to informal or formal communication through meetings so that the knowledge sharing process has not been carried out optimally. The purpose of this study was (1) to measure the readiness of human resources in the application of knowledge sharing in terms of the dimensions of knowledge, culture, technology and dimensions and (2) to develop knowledge sharing features in the PMB system to support decision making quickly to increase the business value of the institution. The stages used in this KMS were The 10-Step Knowledge Management Roadmap while the evaluation of the application of KMS used the SECI model. The results obtained in this study are a system that helps new PMB officers learn the STMIK Amik Riau PMB system. so that the new PMB officer does not ask the old officer again.

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