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INDONESIA
Sistemasi: Jurnal Sistem Informasi
Published by Universitas Islam Indragiri
ISSN : 23028149     EISSN : 25409719     DOI : -
Core Subject : Science, Engineering,
Computer Science & IT Electrical & Electronics Engineering
Sistemasi adalah nama terbitan jurnal ilmiah dalam bidang ilmu sains komputer program studi Sistem Informasi Universitas Islam Indragiri, Tembilahan Riau. Jurnal Sistemasi Terbit 3x setahun yaitu bulan Januari, Mei dan September,Focus dan Scope Umum dari Sistemasi yaitu Bidang Sistem Informasi, Teknologi Informasi,Computer Science,Rekayasa Perangkat Lunak,Teknik Informatika
Arjuna Subject : -
Articles 1,011 Documents
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Application of Text File Steganography on Video using Least Bit Significant (LSB) Method Mellynda, Nabila; Nasution, Yusuf Ramadhan
Sistemasi: Jurnal Sistem Informasi Vol 13, No 2 (2024): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v13i2.3933

Abstract

In the era of rapid development of information technology, data security is a crucial aspect that must be considered. The transfer and storage of information involves various media, including text and video. Unfortunately, most data sent or stored tends to be vulnerable to security threats. The purpose of this research is to implement text file steganography on video using the Least Bit Significant (LSB) method that can be used to hide secret messages. The method used in this research are LSB and Waterfall development method. The results of the study concluded that the LSB algorithm can be used in the process of insertion and extraction of text messages on video, text messages in this study are text contained in a text file with the txt extension. Videos that have been inserted text messages will have an extension of mp4 and cannot be played on video file playback media, The application produced in this study can be run through a web browser application, The application in this study was built using Visual Studio Code software using the HTML and Javascript programming languages.
Implementation of Certainty Factor Method for Identification of Pest Types on Dendrobium Based on Expert Systems Muhammad Innuddin; Hairani Hairani; Ida Putu Andika
Sistemasi: Jurnal Sistem Informasi Vol 12, No 2 (2023): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v12i2.2469

Abstract

Orchid is an ornamental plant that has high aesthetic value with a variety of attractive colors on its flowers and has high economic value. One of the problems in the cultivation of orchids is the problem of pests that can inhibit growth and reduce the aesthetics of orchid plants. Not only that, the shortage of orchid plant experts can be a trigger for delays in identifying the types of pests on orchids, resulting in poor growth quality and even crop failure. Early identification is needed so that handling is fast so that the quality of growth is good. The solution offered by this research is the implementation of the certainty factor method for identifying web-based types of pests on dendrobium orchids. The stages of this research consist of knowledge acquisition, knowledge modeling, implementation, and accuracy testing. Based on the test results of 32 data, the certainty factor method can identify exactly 29 data and the rest are identified incorrectly, resulting in an accuracy of 90.6%. Thus, the certainty factor method can be used to identify the type of pest on orchids because it has very good accuracy.
Mapping the Distribution of Covid-19 Information using a Web-based Information System Ibrahim, Ali; Okllilas, Ahmad Fali; Azhar, Iman Saladin B.; Utama, Yadi; Zahran, Ahmad Hafizh
Sistemasi: Jurnal Sistem Informasi Vol 13, No 2 (2024): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v13i2.3773

Abstract

Based on information from several official government websites and national television media, at the beginning of November 2021, there was a decrease in the spread of the COVID-19 virus. At the end of November 2022, a new type of virus was discovered, namely SARS-CoV-2, also known as the Omicron variant, resulting in additional cases. Therefore, researchers conducted research with the aim of providing information about the spread of the virus with a mapping system down to the RT level, so that the public gets detailed and real-time information about the area based on mapping. The urgency of this research is that, with the results of this research, the public can take better care of and be able to provide self-awareness regarding health protocols. The public can have access to detailed information and mapping about the spread of the virus. This research adopts changes in the design science research methodology proposed by Hevner. The results of the research are an Information System for Mapping the Distribution of COVID-19 Cases and Their Danger Level with 5 calculation categories, namely class I with a range of 66–80 has very high danger level criteria, class II with a range of 51–65 has high danger level criteria, class III with a range of 36–50 has medium level criteria, class IV with a range of 36–50 has criteria for a low level of danger, and class V with a range of 5–20 has a very low level of danger.
Comparison of k-Nearest Neighbor and Support Vector Machine using Binary Dragonfly Algorithm Optimization Nugroho, Andi; Khomeini, Muhammad Imam; Heraldi, Rifan
Sistemasi: Jurnal Sistem Informasi Vol 13, No 1 (2024): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v13i1.2953

Abstract

BDA is an adaptation of Dragonfly Algorithm (DA) that optimizes computation for single-objective, discrete, and multi-objective problems. Combining BDA optimization algorithm with KNN and SVM classification algorithms aims to improve the performance of the prediction model. This research compares and tests accuracy of KNN and SVM algorithms on the diabetes dataset used in research to find out the best algorithm in predicting diabetes. This research uses the BDA optimization algorithm to select the best features in the dataset, then the KNN and SVM classification algorithms, in classifying data, predicting, and comparing the accuracy of the accuracy of the two algorithms on the diabetes dataset. Medical record data from people with diabetes is processed using the KNN and SVM algorithms, which will then produce an accuracy level that can be used in predicting diabetes. Previous research has conducted a comparison between classification algorithms in predicting diabetes. In the previous research above, no one has combined BDA with classification algorithms, because BDA itself is a relatively new method and has not been widely studied, so researchers use this optimization algorithm. The results of the research conducted obtained the highest accuracy results in the BDA + KNN algorithm with a Precision value of 96.10%, Recall 79.36%, F-1 Score 86.93% and Accuracy 85.55%.
Rice Classification with K-Nearest Neighbor based on Color Feature Extraction and Invariant Moment Hapsari S, Santika Tri; Widadi, Rahmat; Permatasari, Indah
Sistemasi: Jurnal Sistem Informasi Vol 12, No 3 (2023): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v12i3.2683

Abstract

Rice is the staple food of Indonesians which comes from rice plants. Rice plants often experience crop failure due to disease. Of course this will affect the yield. Therefore, in this era of technological advances, digital images can be used to help farmers classify rice leaf diseases so they can be controlled. One of the classifications uses K-Nearest Neighbor (KNN) which is sourced from learning data information with the closest distance. Research requires color feature extraction and invariant moment methods in order to obtain information on the distinguishing characteristics of an object from other objects. Data comes from the UCI Machine Learning Repository totaling 120 images which are divided into 3 types of bacterial disease leaf blight, brown spot, and leaf smut with each class having 40 images. The color features used by HSV are Hue, Saturation, and Value. Meanwhile, the invariant moment uses the seven features H1 to H7 introduced by Hu. Feature selection is carried out after the feature extraction process to get the highest accuracy value. In addition, variations in the number of neighbors (k) in KNN are also varied from k=1 to k=10. The best accuracy results are obtained from the use of features, namely hue, saturation, value, h2, h3, and h7 and the value of the number of neighbors in KNN k=1 with an accuracy 81.66%.
User Experience Testing on JoinGeek Admin using a User Experience Questionnaire and Usability Testing Kusuma Dewi, Fatimah Azzahra
Sistemasi: Jurnal Sistem Informasi Vol 13, No 3 (2024): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v13i3.4084

Abstract

Job vacancy portals are becoming increasingly popular as a primary tool for job searching. As a result, developing and improving the quality of job vacancy portals is a suitable response to these changes. As job vacancy portals evolve, it is also vital to pay attention to adjustments to the dashboard that HR uses to process applications. As a technology firm, Geekgarden understands the importance of this development and is committed to improving JoinGeek Admin, their HR dashboard, to keep it relevant to this issue. This study compares the user experience on JoinGeek Admin before and after the redesign to determine the success of the new design. The methods employed are the User Experience Questionnaire (UEQ) and usability testing. The UEQ technique measures six dimensions: attractiveness, perspicuity, efficiency, dependability, stimulation, and novelty. The evaluation results reveal considerable improvements in all areas of the system before and after the redesign, which increase in each dimension: attractiveness (+2.166), perspicuity (+1.666), efficiency (+2.416), dependability (+2), stimulation (+2.5), and novelty (+0.834). Usability testing evaluates the system after redesign in terms of success rate, efficiency, and error rate. The test results reveal a success rate of 94%, an efficiency of 92.5%, and a low error rate of 3.8%. Thus, the evaluation findings show that the JoinGeek Admin redesign was successful in improving the user experience in all areas.
Using a Partition System to Improve the Performance of the Apriori Algorithm in Speeding Up Itemset Frequency Search Process Syahrir, Moch; Hammad, Rifqi; Abd. Latif, Kurniadin; Rosanensi, Melati
Sistemasi: Jurnal Sistem Informasi Vol 13, No 1 (2024): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v13i1.3610

Abstract

The apriori algorithm uses minimum support and minimum confidence to determine appropriate itemset rules for decision making. The problem faced in this research is how to improve the performance of the a priori algorithm in the process of searching for itemset frequencies using data partition techniques, and be able to produce optimal and consistent rules. To overcome this problem, the author implemented the a priori method and partition system to improve the performance of the a priori algorithm for the itemset frequency search process by taking public data in the form of supermarket transaction data. In this research, the performance of the a priori algorithm was tested with and without a partition system. The data used in this research consists of 350 transaction data from 1784 records with a 4-itemset pattern, minimum support value of 20% and minimum confidence of 0.5 with the best standard rules for determining minimum confidence of 0.8. Based on this research carried out, the research results obtained are that for comparison of time and memory usage the apriori algorithm with a partition system is much faster than the apriori algorithm without a partition system, while memory usage is relatively less for the apriori algorithm with the system than the apriori algorithm without a partition system.
Classification of West Java Batik Motifs Using Convolutional Neural Network Firman Yosep Tember; Ina Najiyah
Sistemasi: Jurnal Sistem Informasi Vol 12, No 2 (2023): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v12i2.2259

Abstract

The difference in the types of batik in West Java Province for the general public will not be seen significantly, because all the motifs at first glance look the same. Classification of batik motifs needs to be done to overcome the difficulties of different types of batik, in order to provide information and make it easier to distinguish the name of a batik motif and can tell the name of a batik motif for ordinary people who do not know the name of a batik motif with a fairly accurate level of accuracy. Classification of batik needs to be done to determine the type of batik from each region to make it easier to distinguish motifs from each region. The method used in this study is the classification of batik types in West Java Province using the Convolutional Neural Network (CNN) method. The results carried out for the classification of West Java batik image types using the Convolutional Neural Network (CNN) method that the feature extraction process can be carried out outside the process contained in the CNN algorithm or using feature learning depending on the needs of the research itself, and the results of the classification at 20 epochs and a learning rate value of 0.001 obtained an accuracy of 90% with a precision of 90% and a recall of 90%. This result is quite good considering the quality and amount of data obtained is not so good and the amount is not much.
WASPAS and ROC Algorithms for Faculty Senate Member Recruitment System Santoso, Adinda Afriliya; Putri, Raissa Amanda
Sistemasi: Jurnal Sistem Informasi Vol 13, No 2 (2024): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v13i2.3966

Abstract

The Faculty Student Senate is a student organization that holds a control function as well as a legislative body and the highest representative of students on campus. The Faculty Student Senate also recruits several new members in accordance with predetermined criteria, the number of students who register to become SEMAF members is very large. If decision making is done manually, it is feared that the selection team for new members does not select objectively or nepotism occurs in the selection process. The purpose of this research is to Apply the WASPAS and ROC Methods to facilitate the selection of new members using the WASPAS method and ROC Weighting and Design and build a Website-based Decision Support System. This research uses the RnD Research and Development method. Based on the research results, the highest value is 0.9850, namely A23 and the lowest value is 0.4641, namely A30. By creating a WEB-based system using the ROC and WASPAS methods can produce an optimum value. Then the value can be used to determine the ranking on the SEMAF new member recruitment system
Analysis of the k-Means Method in Clustering Acceptance of PKH Aid in Pulau Rakyat Tua Village Utami, Dwi Kurnia; Irawati, Novica; Sumantri, Sumantri
Sistemasi: Jurnal Sistem Informasi Vol 12, No 3 (2023): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v12i3.3236

Abstract

The Family Hope Program (PKH) is a program that provides cash assistance to Very Poor Households (RSTM) which are required to fulfill requirements related to efforts to improve the quality of human resources. In selecting residents to be recipients of the Family Hope Program (PKH) in Pulau Rakyat Tua Village, the problem that often arises is that the provision of Family Hope Program assistance is often considered not to be on target. In addition, errors often occur because the selection is still done manually and requires a long time in selecting participants, which can be influenced by the objective assessment of PKH companions. The research objective is to apply the k-means clustering algorithm in selecting prospective beneficiaries of the Family Hope Program (PKH). The method used uses the application of data mining with the k-means clustering algorithm. Based on the results of applying the k-means clustering algorithm, the results of the system being built can make it easier to select potential recipients of Family Program assistance. The results of the k-means clustering algorithm test produced Cluster 1 in the Eligible category totaling 29 PKH beneficiary data and Cluster 2 in the Ineligible category totaling 1 PKH beneficiary data.

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