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Jusikom: Jurnal Sistem Informasi Ilmu Komputer
Published by Universitas Prima Indonesia
ISSN : -     EISSN : 25802879     DOI : 10.34012
Core Subject : Science,
Decision Sciences, Operations Research & Management
This journal is about information systems and computer science.
Arjuna Subject : -
Articles 222 Documents
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THYROID DISEASE CLASSIFICATION ANALYSIS USING XGBOOST MULTICLASS panjaitan, haris samuel pranada; Gulo, Agustinus; Alfi, Ahmad Haikal; Harmaja, Okta Jaya; Indra, Evta
Jurnal Sistem Informasi dan Ilmu Komputer Vol. 6 No. 1 (2022): JURNAL SISTEM INFROMASI DAN ILMU KOMPUTER PRIMA (JUSIKOMP)
Publisher : Fakultas Teknologi dan Ilmu Komputer Universitas Prima Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34012/jurnalsisteminformasidanilmukomputer.v6i1.2831

Abstract

ABSTRAK- Sickness is an unusual condition of the body or mind that causes discomfort, malfunction, or suffering to the sick person. One disorder that occurs due to a lack of health concerns is thyroid disease. The thyroid is a butterfly-shaped endocrine gland near the neck's bottom. The diagnosis of thyroid disease is complicated because the symptoms of thyroid disease can fluctuate based on the rise and fall of thyroid hormones, which increase the utilization of oxygen by the body's cells. In this case, a thyroid examination by a doctor and proper interpretation of clinical data is required to identify thyroid disease. However, the limitations of a doctor due to age and time constraints lead to a lack of interpretation of patient clinical data. Therefore, a study was conducted on the analysis of thyroid disease classification to simplify and speed up the process of diagnosing thyroid disease using the Xgboost Multiclass method, which is expected to get an accuracy value above 90%. Keywords: Classification, Thyroid, Xgboost Multiclass, Machine Learning
COMPARISON OF CLASSIFICATION ALGORITHM IN CLASSIFYING AIRLINE PASSENGER SATISFACTION Indra, Evta; Suwanto, Jacky; Sitompul, Daniel Ryan Hamonangan; Sinurat, Stiven Hamonangan; Situmorang, Andreas; Ruben, Ruben; Ziegel, Dennis Jusuf
Jurnal Sistem Informasi dan Ilmu Komputer Vol. 6 No. 1 (2022): JURNAL SISTEM INFROMASI DAN ILMU KOMPUTER PRIMA (JUSIKOMP)
Publisher : Fakultas Teknologi dan Ilmu Komputer Universitas Prima Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34012/jurnalsisteminformasidanilmukomputer.v6i1.2848

Abstract

In order to revive the airline industry, which is being hit by the current recession, it is essential to restore passenger confidence in airlines by improving the services provided by airlines. With the influence of technology in all industrial fields, airlines can now use Machine Learning to find the essential points that can make passengers feel satisfied with airline services and classify passenger satisfaction. This study presents the making of Machine Learning models starting from Data Acquisition, Data Cleaning, Exploratory Data Analysis, Preprocessing, and Model Building. It is concluded that Random Forest is the best algorithm used in this case study, with an F1 accuracy score of 89.4, ROC-AUC score of 0.90, and a shorter modeling period than other algorithms used in this study.
Laptop Price Prediction with Machine Learning Using Regression Algorithm Siburian, Astri Dahlia; Sitompul, Daniel Ryan Hamonangan; Sinurat, Stiven Hamonangan; Situmorang, Andreas; Ruben, Ruben; Ziegel, Dennis Jusuf; Indra, Evta
Jurnal Sistem Informasi dan Ilmu Komputer Vol. 6 No. 1 (2022): JURNAL SISTEM INFROMASI DAN ILMU KOMPUTER PRIMA (JUSIKOMP)
Publisher : Fakultas Teknologi dan Ilmu Komputer Universitas Prima Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34012/jurnalsisteminformasidanilmukomputer.v6i1.2850

Abstract

Since the COVID-19 pandemic, many activities are now carried out in a Work From Home (WFH) manner. According to data from the Central Statistics Agency (BPS) of East Java, in 2021, large and medium-sized enterprises (UMB) who choose to work WFH partially are 32.37%, and overall WFH is 2.24% (BPS East Java, 2021 ). With this percentage of 32.37%, many people need a work device (in this case, a laptop) that can boost their productivity during WFH. WFH players must have laptops with specifications that match their needs to encourage productivity. To prevent buying laptops at overpriced prices, a way to predict laptop prices is needed based on the specified specifications. This study presents a Machine Learning model from data acquisition (Data Acquisition), Data Cleaning, and Feature Engineering for the Pre-Processing, Exploratory Data Analysis stages to modeling based on regression algorithms. After the model is made, the highest accuracy result is 92.77%, namely the XGBoost algorithm. With this high accuracy value, the model created can predict laptop prices with a minimum accuracy above 80%.
APPLICATION OF THE FUZZY TIME SERIES MODEL IN CLOTHING MATERIAL STOCK FORECASTING Ula, Mutammimul; -, Bakhtiar; Yulisda, Desvina; -, Badriana; Bintoro, Andik
Jurnal Sistem Informasi dan Ilmu Komputer Vol. 6 No. 1 (2022): JURNAL SISTEM INFROMASI DAN ILMU KOMPUTER PRIMA (JUSIKOMP)
Publisher : Fakultas Teknologi dan Ilmu Komputer Universitas Prima Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34012/jurnalsisteminformasidanilmukomputer.v6i1.2862

Abstract

The application of fuzzy time series is used to view the stock of clothing materials. As for the problem so far, CV Duta Express does not have a model for seeing the stock of complete materials in the warehouse, so the process is not optimal. This will have an impact on orders that come in at the same time and in large quantities. to avoid stock shortages, which resulted in the company experiencing losses. The purpose of this study is to make it easier to predict the stock of clothing materials and to be able to analyze every stock management at CV Duta Express with a fuzzy time series model. The variables used are stock needs, the amount of stock of school clothes, batik clothes, and pants. The research methodology in data collection consists of product type data from 2018–2021 at CV Duta Express Aceh Utara. Data analysis needs consist of school clothes, school pants, and clothes. Next, the fuzzy time series process determines the actual data is the type of school clothes, and what is forecast is sales for January 2018–2021 at the end of December. For a value of 1108 A2 fuzzification value, 172 A4 fuzzification value for batik clothes, and 894 pants with an A1 fuzzification value, then the value of the universe set used is U = [26, 323]. The value of forming a linguistic set is based on the length of the interval U3 = [111,153], U4 = [153,196], and U5 = [196,238]. The result of the fuzzification value from historical data for the value of 172 fuzzification is A4, for data of 133 fuzzification is A3. The formation of Fuzzy Logic Relationship (FLR) values for the period 1/7/2021 to 1/13/2021 is obtained from the data range A4=174 and range A3=125 in each period to be related. The results of forecasting with fuzzy time series testing at the end of December 2021 are 196 stocks of clothes that must be optimized in the following month. The test results in this study are to see if the error value using the AFER model is 0.4511% while the RMSE test value has an error value of 5.0199. After being calculated for the forecast every month, the average obtained for AFER is 0.50154 % and the RMSE is 9.86518.. Keywords : Forecasting, Fuzzy Time Series, stock
STEGANOGRAPHY CAPACITY ANALYSIS OF TEXT IN TEXT WITH SENTENCE STRUCTURE IN INDONESIAN Siahaan, R. Fanry; Sitohang, Amran; Febrian, Ibnu; Putri, Widia
Jurnal Sistem Informasi dan Ilmu Komputer Vol. 6 No. 1 (2022): JURNAL SISTEM INFROMASI DAN ILMU KOMPUTER PRIMA (JUSIKOMP)
Publisher : Fakultas Teknologi dan Ilmu Komputer Universitas Prima Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34012/jurnalsisteminformasidanilmukomputer.v6i1.2863

Abstract

Data security issues become very important when the computer has been used as a communication tool on a global network (internet). One method that is quite popular for securing data from irresponsible parties is text-based steganography, where confidential data or information is hidden or inserted into other text media so as not to arouse suspicion from other parties. Based on the input data calculated on the number of words, it has a significant influence on the results of the stegotext. This can be seen from the comparison of the length of the input message which is calculated in the number of words with the length of the output message (stegoteks) which is influenced by the length of the style (pattern) of the sentence structure used. For input data with a word length of 1, the average capacity of the stegotext is 8 to 9 words or 12.4%, for input word length 2, the average capacity of the stegotext is 22 words or 8.85% and the input word length is 4 then the capacity of the stegotext is between 30 to 33 words or equivalent to 12.91%. Keywords :Text Steganography; Sentence Pattern; Payload Balancing Capacity
Analysis of Application of Fuzzy Grid Partition on Mamdani Method Fuzzy Inference System Marbun, Murni; -, Sandhuri; -, Aishwarya
Jurnal Sistem Informasi dan Ilmu Komputer Vol. 6 No. 1 (2022): JURNAL SISTEM INFROMASI DAN ILMU KOMPUTER PRIMA (JUSIKOMP)
Publisher : Fakultas Teknologi dan Ilmu Komputer Universitas Prima Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34012/jurnalsisteminformasidanilmukomputer.v6i1.2890

Abstract

The Mamdani method has been widely used for various control devices, which is the beginning of fuzzy technology and has the advantage of being able to express concepts that are difficult to formulate and the use of fuzzy membership functions in making objective observations of subjective values, making it easier to make decisions that are full of uncertainty. The Mamdani method has also been widely used for various optimization problems. This study produces new findings on the analysis of the application of fuzzy grid partition on the fuzzy inference system of the Mamdani method for the issue of optimizing the amount of production of a food product. The research stage begins with determining the number of partitions that will form a grid structure. The grid formed from the combination of relations between each partition will have the potential to develop rules. Based on the results of the analysis, determining the amount of production using the Mamdani method produces a smaller amount of production but produces stock output that is not within the specified stock limit, while the application of fuzzy grid partition in the Mamdani method has a higher and correct amount of product because it produces stock output that is at the specified stock limit
ANALYSIS OF LINEAR REGRESSION AND TREND MOMENT METHODS IN PREDICTING SALES USING MAPE Adam Suhaidi Batubara, Adam Suhaidi Batubara; Dafitri, Haida; Faisal, Ilham
Jurnal Sistem Informasi dan Ilmu Komputer Vol. 6 No. 1 (2022): JURNAL SISTEM INFROMASI DAN ILMU KOMPUTER PRIMA (JUSIKOMP)
Publisher : Fakultas Teknologi dan Ilmu Komputer Universitas Prima Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34012/jurnalsisteminformasidanilmukomputer.v6i1.2919

Abstract

ABSTRACK- Sales transaction data stored in the database stores a large number of transaction records, causing the amount of data to continue to increase every day. To explore sales transaction data, data mining techniques are used. One of the goals of data mining is prediction. Prediction is basically an assumption or estimate about the occurrence of an event or event in the future. Through prediction, it is expected to minimize the influence of uncertainty from the future, so that getting results that have the least prediction error is the goal of prediction. This shows that prediction is a very important tool in planning effectively and efficiently. The discussion method used to predict sales is the time series method by using a comparison of two types of prediction methods, namely the Linear Regression method and the Trend Moment method. The use of these two methods will be a better basis for making decisions to determine which method is suitable for predicting future sales. The result of a prediction cannot always be verified in absolute 100%. Therefore, the parameters used to determine the better method are based on the smallest error accuracy rate calculated using MAPE. Based on the results of the comparative prediction analysis of the Linear Regression method and the Trend Moment method, the recommended prediction result is to use the Trend Momnet method because the resulting MAPE error value is smaller, namely 0.439845%. Meanwhile, the MAPE error value with the Linear Regression method is 1.511509%..
FORENSIC NETWORK ANALYSIS AND IMPLEMENTATION OF SECURITY ATTACKS ON VIRTUAL PRIVATE SERVERS saragih, Naikson; Panjaitan, Ridho Agus Wery Nanda; Purba, Mufria Jonatan
Jurnal Sistem Informasi dan Ilmu Komputer Vol. 6 No. 2 (2023): JURNAL SISTEM INFROMASI DAN ILMU KOMPUTER PRIMA (JUSIKOMP)
Publisher : Fakultas Teknologi dan Ilmu Komputer Universitas Prima Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34012/jurnalsisteminformasidanilmukomputer.v6i2.2932

Abstract

ABSTRACT-PT Kodinglab Integrasi Indonesia's Virtual Private Server (VPS) product requires good quality standards, including security. The challenge that arises is still frequent disruptions to the protection of PT Kodinglab's VPS customers, where it is difficult to identify the source of the attack. Network forensics in the form of dead forensics and live forensics using the NIST method with the stages of collection, examination, Analysis, and reporting are used to find the source of the attack. Data for dead forensics comes from snort tools, and data for live forensics comes from capture Wireshark. The collection stage involves collecting attack data from snort logs and wireshark for life forensics. While the examination dataset stages are further analyzed and mapped. Advanced check on the server via syslog snort. From the attack testing carried out to obtain information in the form of the attacker's IP address, destination IP address, date of the attack, server time, and type of attack from testing the TCP Flooding and UDP Flooding attacks, all attacks on the customer's VPS can be identified. The information obtained regarding the attacker is in the form of the date and time the attack occurred, the attacker's IP address and the victim's IP address, and the protocol used. Kata kunci : Network Forensic, Dead Forensic, Live Forensic, Virtual Private Server, DDos, TCP  Flooding, UDP Flooding.
WEB-BASED SPP PAYMENT INFORMATION SYSTEM WITH MIDTRANS PAYMENT GATEWAY Aditya, Mulyadi; Sulistyowati, Daning Nur
Jurnal Sistem Informasi dan Ilmu Komputer Vol. 6 No. 2 (2023): JURNAL SISTEM INFROMASI DAN ILMU KOMPUTER PRIMA (JUSIKOMP)
Publisher : Fakultas Teknologi dan Ilmu Komputer Universitas Prima Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34012/jurnalsisteminformasidanilmukomputer.v6i2.2942

Abstract

ABSTRACT- The tuition payment system at Taufiq Islamic Middle School is still manually using ledger media in transaction activities and data recap. The system still needs to be revised and is prone to errors in calculating incoming payments and reporting payment data. This research was conducted to facilitate the school in the payment management process so that the recording error rate no longer occurs. The system development method used in this study is the Waterfall model and uses the Midtrans payment gateway in the online payment process. The results of this study are in the form of a tuition payment website that can facilitate all parties, both the school and students. This system makes the payment and recording process better and more efficient. Keywords: information system, waterfall, payment gateway, Midtrans.
EARLY DETECTION OF BREAST CANCER USING THE K-NEAREST NEIGHBOUR (K-NN) ALGORITHM Panggabean, Refli Tiarma Ariani; Octavia, Ledy; Dwi, Noormala; -, Aripin
Jurnal Sistem Informasi dan Ilmu Komputer Vol. 6 No. 2 (2023): JURNAL SISTEM INFROMASI DAN ILMU KOMPUTER PRIMA (JUSIKOMP)
Publisher : Fakultas Teknologi dan Ilmu Komputer Universitas Prima Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34012/jurnalsisteminformasidanilmukomputer.v6i2.3194

Abstract

ABSTRACT- Cancer is one of the Non-Communicable Disease groups whose growth and development are high-speed. One type of cancer is breast cancer (carcinoma mammae). Breast cancer is the leading cause of death for women. The first breast cancer cells can grow into tumors as large as 1 cm, spanning 8-12 years. The prevalence rate of breast cancer in Indonesia is 50 per 100,000 female population. The method used in this study uses the K-Nearest Neighbor (K-NN) algorithm by comparing k values, namely 3, 5, and 7. The dataset used was obtained from the UCI Machine Learning Repository with the Number of datasets after preprocessing, namely 653 data with a class consisting of benign tumors (benign) and malignant tumors (malignant). The variables used in this study take into account the variables of clump thickness, cell size uniformity, cell shape uniformity, marginal adhesion, single epithelial cell size, cell nucleus size, chromatin, normal cell nucleus, and mitosis. The results of the most influential classification for training and testing are using k = 3 with an accuracy of training and testing at a proportion of 70:30 of 83.8074% and 75%; the ratio of 80:20 is 84.6743% and 74.8092%; the percentage of 90:10 is 84.0136% and 84.6154%. Using the value of k = 3, the resulting gap between training and testing is similar.

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