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J-SAKTI (Jurnal Sains Komputer dan Informatika)
Published by STIKOM Tunas Pematang Siantar
ISSN : 25489771     EISSN : 25497200     DOI : -
Core Subject : Science,
Computer Science & IT
JSAKTI adalah jurnal yang diterbitkan oleh LPPM STIKOM Tunas Bangsa Pematangsiantar yang bertujuan untuk mewadahi penelitian di bidang Manajemen Informatika. JSAKTI (Jurnal Sains Komputer dan Informatika) adalah wadah informasi berupa hasil penelitian, studi kepustakaan, gagasan, aplikasi teori dan kajian analisis kritis dibidang Ilmu Komputer terbit 2 kali setahun.
Arjuna Subject : -
Articles 499 Documents
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Penerapan Federal Enterprise Architecture Framework Pada Sistem Informasi Taman Kanak-Kanak Septiani, Neng Sri Intan; Saepudin, Sudin
J-SAKTI (Jurnal Sains Komputer dan Informatika) Vol 7, No 1 (2023): EDISI MARET
Publisher : STIKOM Tunas Bangsa Pematangsiantar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/j-sakti.v7i1.607

Abstract

In the world of education, it is necessary to have a computerized system for various purposes. Especially in the Kindergarten Information System at RA Al-Masthuriyah, currently the acceptance of new students to the process of managing student data at RA Al-Masthuriyah is generally still conventional, which means that prospective student parents must come directly to the school to register and the teachers in charge of managing student and education data still use Microsoft Office and agenda books. Both of these are considered inefficient because it wastes time for prospective student guardians in registering and makes it difficult for teachers to manage or search student data. With this background, the need for an information system that can be used as a solution to these problems. In this study, a design of a kindergarten information system was proposed using the Federal Enterprise Architecture Framework (FEAF) model. the goal to be achieved in this research is to be able to assist and facilitate the registration of new students, to the management of student data at RA Al-Masthuriyah.
Penerapan Data Mining Untuk Prediksi Jumlah Total Porduksi Bakpao Pada PT. Estetika Tata Tiara Menggunakan Algoritma Regresi Linier Berganda Wicaksana, Prasetya; Pakereng, Magdalena A. Ineke
J-SAKTI (Jurnal Sains Komputer dan Informatika) Vol 7, No 1 (2023): EDISI MARET
Publisher : STIKOM Tunas Bangsa Pematangsiantar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/j-sakti.v7i1.598

Abstract

Production objective planning is the process of identifying the product to be produced, the required quantity, the completion deadline, and what sources will be needed. The research objective is to determine the total production of Bakpao PT. Estetika Tata Tiara uses multiple linear regression algorithm. At this stage, the data mining technique uses multiple linear regression algorithms. This research was conducted at PT. Aesthetics of Tata Tiara which is a bakpao production company. The results show that the regression equation obtained from the results of multiple linear regression analysis is for the prediction of Bakpao in April 2022 as follows: Y = 473.531 + 0.56 X1 + 0.043 X2. After the analysis, it can be concluded that the variables X1 and X1 affect the prediction of the amount of Bakpao production in 2022. The relationship between sales (X1) and stock (X2), and Bakpao production has a strong positive and unidirectional relationship.
Perbandingan Algoritma Untuk Analisis Sentimen Pada Twitter Transportasi Umum Commuterline Novaneliza, Rizka; Handayani, Fitri; Suhandar, Reja Juniarsah; Surono, Hendro; Azzahra, Nadya Salma; Nadilla, Dya
J-SAKTI (Jurnal Sains Komputer dan Informatika) Vol 7, No 1 (2023): EDISI MARET
Publisher : STIKOM Tunas Bangsa Pematangsiantar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/j-sakti.v7i1.566

Abstract

KRL Commuter Line is a common transportation that is in great demand by the public. Affordable rates are the reason why commuter lines are in high demand. With so many users of this transportation service, the KRL Commuter Line must continue to improve services. In the process, there are often obstacles that cause users to make complaints. Users of Commuter Line often make complaints or give their opinions via social media twitter. In this study, sentiment analysis was carried out to Commuter Line users. Sentiment analysis is performed for the classification of tweets or tweets regarding commuter line service into positive and negative sentiments. The focus of this study is to compare the Support Vector Machine (SVM), SVM-Particle Swarm Optimization, Naive Bayes and NB-Adaboost algorithms. The data used was 1001 tweet data on Twiiter @CommuterLine. The comparison results obtained average values for SVM: 78.15%, SVM-PSO: 79.47%, NB: 76.67% and NB-Adaboost: 78.80%. So that it can be seen that the classification of algorithms using optimization methods can increase the average value. In this study, the SVM algorithm with the PSO optimization method is a better classification used compared to the SVM algorithm, Naive Baiyes and Naive Bayes with AdaBoost optimization.
Klasifikasi Teks Mining Terhadap Analisa Isu Kegiatan Tenaga Lapangan Menggunakan Algoritma K-Nearest Neighbor (KNN) Ajijah, Nur; Kurniawan, Adi; Susilawati, S
J-SAKTI (Jurnal Sains Komputer dan Informatika) Vol 7, No 1 (2023): EDISI MARET
Publisher : STIKOM Tunas Bangsa Pematangsiantar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/j-sakti.v7i1.589

Abstract

Information and communication technology at this time is developing very rapidly, including in the easier dissemination of information. With the sophisticated technology in conveying information, it is possible that there is information that is not certain of its truth. The issue that occurs is due to the discrepancy of expectations expected by distributors, kiosks, and farmers.  The amount of issue data obtained greatly affects the efficiency of the results that will be obtained. Therefore, it is necessary to have a text analysis  to find out the issues spread in the field regarding the services of products and services provided by PT XYZ. In this study, it applied the CRISP-DM research stages and the application of the K-Nearest Neighbor (KNN) algorithm  which showed that the resulting accuracy rate was 93.88% with data of 2,500 data. And the highest precission value is obtained by the payment qualification of 98.67%.
Sistem Pendukung Keputusan Pemilihan Mekanik Terbaik Dengan Metode Analytical Hierarchy Process Badrul, Mohammad; Gultom, Ranita
J-SAKTI (Jurnal Sains Komputer dan Informatika) Vol 7, No 1 (2023): EDISI MARET
Publisher : STIKOM Tunas Bangsa Pematangsiantar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/j-sakti.v7i1.580

Abstract

Human resources in a company organization are very important to support the progress and quality of the company in achieving its goals. Human resources are a determining factor for success in achieving a goal. Therefore, a way to improve employee performance is needed, namely by evaluating employee performance. CV. Karya Indah Motor is required to continue to strive to provide the best service to ensure quality, reliability, efficiency and satisfaction to users. To do all of that, the company must have a mechanic whose job is to serve customers who come to perform regular service. Currently there is an assessment process or selection of the best mechanic, but it tends to be subjective based on proximity to his superiors so that it will cause jealousy and eventually mechanics are less motivated to work. Therefore, an appropriate method is needed to calculate the weighting of the criteria used to produce a decision to determine the best mechanic. Analytical Hierarchy Process method can assist in determining the priority of several criteria by conducting a pairwise comparison analysis of each criterion. The criteria determined include discipline, attendance, SOP standards, loyalty, and responsibility. With a decision support system that provides information, modeling and data manipulation that is used to help decision makers in semi-structured situations.
Analisis Perbandingan Manajemen Bandwidth Quality of Service Dengan Menggunakan Metode Simple Queue Dan Queue Tree Pada Telkom University Landmark Tower Rahman, Ilham Auliya; Kurniawan, MT.; Saedudin, Rd. Rohmat
J-SAKTI (Jurnal Sains Komputer dan Informatika) Vol 7, No 2 (2023): EDISI SEPTEMBER
Publisher : STIKOM Tunas Bangsa Pematangsiantar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/j-sakti.v7i2.686

Abstract

The advancement of the internet impacts the flow of information circulating among internet users, which increases yearly. Telkom University has a TULT building that provides internet access services to all internet users in that building. However, the internet service in that building still needs to be improved as internet users still experience internet problems such as sudden disconnection, slow connection, and unstable connection. Therefore, bandwidth management is needed to improve the quality of internet services in the building. There are several methods to perform bandwidth management, but this study uses Simple Queue and Queue Tree methods. The selection of the Simple Queue and Queue Tree methods in this study is due to the difference in configuration between the two. Bandwidth management simulation uses 4 Mikrotik routers and six laptops as clients. The simulation is conducted for 1 hour via video streaming and online meetings with each client. During the simulation, traffic data is also captured using the Wireshark application. Then, from that Wireshark capture, Throughput, Packet Loss, Delay, and Jitter from the Simple Queue and Queue Tree methods, are analyzed. This research concludes that using the Queue Tree method is more appropriate because it has a packet loss value below 1% with the internet network characteristics of the TULT building, which has a packet loss value above 7.80%. Besides that, the working method of the Queue Tree will be beneficial when the TULT building internet network is experiencing peak traffic.
Evaluasi dan Peningkatan Keamanan Pada Sistem Informasi Akademik Universitas XYZ Palembang Fajarino, Aldo; Kunang, Yesi Novaria; Yudha, Hendra Marta; Negara, Edi Surya; Damayanti, Nita Rosa
J-SAKTI (Jurnal Sains Komputer dan Informatika) Vol 7, No 2 (2023): EDISI SEPTEMBER
Publisher : STIKOM Tunas Bangsa Pematangsiantar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/j-sakti.v7i2.702

Abstract

As one of the universities in Palembang City, XYZ University has its own web server that functions as an information system in the academic and financial activities of its users. Testing of security systems on information systems needs to be done, web server security is very important to avoid destruction, data theft, data manipulation, and so on. In this study, the OWASP framework and the ISSAF framework were used and then the two methods were compared. The results of this study found several security holes that have been recommended to developers and successfully repaired. There needs to be a comprehensive improvement starting from server configuration, sanitization improvement of character input filters from users, installation of Intrusion Detection System and Intrusion Prevention System.
Penggunaan Factory Method Design Pattern Pada Framework Flask di Dalam Aplikasi Dashboard Sumbaluwu, Devin William; Somya, Ramos
J-SAKTI (Jurnal Sains Komputer dan Informatika) Vol 7, No 2 (2023): EDISI SEPTEMBER
Publisher : STIKOM Tunas Bangsa Pematangsiantar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/j-sakti.v7i2.677

Abstract

Problems related to the use of an object of Flask for various environments are often caused by application designs that are less effective and efficient. An object of Flask is used to run a Flask application based on the states or application environments. Each state or environment has different configuration requirements. The factory method design pattern solves the design problem by separating functions that return an object of Flask based on another object of the configuration class. The implementation of the factory method design pattern will be realized in a dashboard website. It will be used on the backend so that there will be a unit test code to prove the app can be run in a different state or environment
Implementasi Algoritma K-Means Untuk Klasterisasi Data Obat Puskesmas Kotabaru Kurniawan, Muhamad Dicky; Priyatna, Bayu; Nurapriani, Fitria
J-SAKTI (Jurnal Sains Komputer dan Informatika) Vol 7, No 2 (2023): EDISI SEPTEMBER
Publisher : STIKOM Tunas Bangsa Pematangsiantar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/j-sakti.v7i2.693

Abstract

Drug management is one of the things needed to manage drug supplies. Proper planning of drug needs makes drug procurement efficient and effective so that drugs are available in sufficient types and quantities as needed and easily obtained when needed. The purpose of this study was to classify drug data at the Kotabaru Health Center which can be used as a reference in making decisions in planning and controlling drug needs at the Health Center. The data used in this study are the Kotabaru Health Center annual report data from 2019 to 2021. Data processing in this study uses the K-means clustering method with rapidminer tools which is a data grouping technique by dividing the existing data into one or two forms. more clusters. The results of this study divide the drug data into 4 clusters, namely the first cluster (C0) with very low usage consisting of 27 drugs, the second cluster (C2) with low usage consisting of 6 drugs, the third cluster (C3) with high usage consisting of 1 drug, and the fourth (C2) with the highest usage consisting of 1 drug.
Analisis Perbandingan Algoritma Supervised Learning untuk Prediksi Kasus Covid-19 di Jakarta Septhiani, Angeline; Hendry, H
J-SAKTI (Jurnal Sains Komputer dan Informatika) Vol 7, No 2 (2023): EDISI SEPTEMBER
Publisher : STIKOM Tunas Bangsa Pematangsiantar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/j-sakti.v7i2.668

Abstract

Coronavirus disease or called COVID-19 is a pandemic according to World Health Organization (WHO) in February. The virus gives several symptoms, such as cough, asthma, and fever. The data and information are the important part of making a good decision. Those data need to be processed and analyzed to be useful information. In this research, the data will be used to predict the COVID-19 issue in Jakarta, using several supervised learning algorithm models, such as K-Nearest Neighbors, Neural Network, Linear Regression, Support Vector Machine, and Random Forest. Using 10 Fold Cross Validation in model testing and T-Test to get the model with the best accuracy. According to this research, the algorithm that has the best accuracy is K-Nearest Neighbors with the lowest RMSE, 1096.188 +/- 365.077 (micro average: 1149.601 +/- 0.000).

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