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Gedung Universitas Prima Indonesia, Medan Fakultas Teknologi dan Ilmu Komputer Jurusan Sistem Informasi Jl. Sekip Simpang Sikambing
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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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SENTIMENT ANALYSIS OF MYPERTAMINA APPLICATION USING SUPPORT VECTOR MACHINE AND NAÏVE BAYES ALGORITHMS Simbolon, Ongki Sopie; Manullang, Murni Esterlita; Alvarez, Stevin; Brutu, Lolo Frans M.; Indra, Evta
Jurnal Sistem Informasi dan Ilmu Komputer Vol. 7 No. 1 (2023): JUSIKOM: JURNAL SISTEM INFROMASI ILMU KOMPUTER
Publisher : Fakultas Teknologi dan Ilmu Komputer Universitas Prima Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34012/jurnalsisteminformasidanilmukomputer.v7i1.4078

Abstract

In line with the needs of the community and the progress of the times in the advanced field of fintech, cash payments are currently considered insecure as well as ineffective and efficient. To run a non-cash or cashless transaction program currently run by the government, PT. Pertamina invites the public to use E-Payment from the My Pertamina application in collaboration with LinkAja. In this study, the sentiments of MyPertamina application users will be analyzed based on reviews on the Google Play Store. Review data will be analyzed to determine whether the review has positive, negative, or neutral sentiments. The data analysis stage is text preprocessing to change uppercase to lowercase, clearing text, separating text, taking important words, changing essential words, and labeling data into positive, negative, and neutral classes. As well as the classification and evaluation of results. This study used the Support Vector Machine (SVM) and Naïve Bayes classification methods. To evaluate the results, the confusion matrix was used to test the accuracy, precision, recall, and F1 score value. The classification results obtained the highest accuracy value for the Support Vector Machine (SVM) method, which had accuracy (68.50%), precision (70.00%), recall (69.70%), and F1 score (68.46%). Meanwhile, the Naïve Bayes method has performance with accuracy (63.00%), precision (63.90%), recall (61.34%), and F1 score (59.55%).
IMPLEMENTATION OF DATA MINING TO PREDICT THE VALUE OF INDONESIAN OIL AND NON-OIL AND GAS IMPORT EXPORTS USING THE LINEAR REGRESSION METHOD Ompusunggu, Elvis Sastra; Sinaga, Wilson; Siahaan, Mikael; Banjarnahor, Jepri; Winata, Jaspin; Laia, Yonata; Sihombing, Oloan
Jurnal Sistem Informasi dan Ilmu Komputer Vol. 7 No. 1 (2023): JUSIKOM: JURNAL SISTEM INFROMASI ILMU KOMPUTER
Publisher : Fakultas Teknologi dan Ilmu Komputer Universitas Prima Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34012/jurnalsisteminformasidanilmukomputer.v7i1.4081

Abstract

Indonesia's export-import activities in recent years, the value of Indonesia's exports and imports has decreased due to global conditions. The problems that occur are the uncertainty and complexity in estimating the value of international trade in the oil and gas and non-oil and gas sectors, dependence on just one or a few markets, and the problem of unfair competition, unfair competition between business actors can reduce export-import prices. The value of oil and gas and non-oil and gas exports and imports is influenced by several external factors that are difficult to predict, such as fluctuations in oil and gas prices, changes in trade policies, and global economic factors. The prediction results are obtained every month from the export value data using the rapid miner application. From the export data, the value of non-oil and gas exports obtains a very high value compared to the export data of oil and gas values. Then the results from rapid miner using the linear regression algorithm are obtained. The predicted import value of oil and gas and non-oil and gas value data in June is 209,162,268, and the predicted export value of oil and gas and non-oil and gas value data in June is 349,285,781 and non-oil and gas which more are predicted to have the highest value compared to the value of oil and gas in each month.
COMPARATIVE ANALYSIS OF STROKE CLASSIFICATION USING THE K-NEAREST NEIGHBOR DECISION TREE, AND MULTILAYER PERCEPTRON METHODS Barus, Ertina Sabarita; Halim, Jenny Evans; Yessica, Sally
Jurnal Sistem Informasi dan Ilmu Komputer Vol. 7 No. 1 (2023): JUSIKOM: JURNAL SISTEM INFROMASI ILMU KOMPUTER
Publisher : Fakultas Teknologi dan Ilmu Komputer Universitas Prima Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34012/jurnalsisteminformasidanilmukomputer.v7i1.4083

Abstract

Stroke has become a serious health problem; the main cause of stroke is usually a blood clot in the arteries that supply blood to the brain. Strokes can also be caused by bleeding when blood vessels burst and blood leaks into the brain. In one year, about 12.2 million people will have their first stroke, and 6.5 million people will die from a stroke. More than 110 million people worldwide have had a stroke. Handling that is done quickly can minimize the level of brain damage and the potential adverse effects. Therefore, it is very important to predict whether a patient has the potential to experience a stroke. The K-Nearest Neighbor, Decision Tree, and Multilayer Perceptron algorithms are applied as a classification method to identify symptoms in patients and achieve an optimal accuracy level. The results of making the three algorithms are quite good, where K-Nearest Neighbor (K-NN) has an accuracy value of 93.84%, Decision Tree is 93.97%, and Multilayer Perceptron (MLP) is 93.91%. The best accuracy value is the Decision Tree algorithm with an accuracy difference of no more than 0.10% with the two algorithms used.
EXPLORATORY DATA ANALYSIS OF CLINICAL HEART FAILURE USING A SUPPORT VECTOR MACHINE Sinaga, Putri tua; Purba, Salda Sari; Wiranto, David; Maharja, Okta Jaya; Indra, Evta
Jurnal Sistem Informasi dan Ilmu Komputer Vol. 7 No. 1 (2023): JUSIKOM: JURNAL SISTEM INFROMASI ILMU KOMPUTER
Publisher : Fakultas Teknologi dan Ilmu Komputer Universitas Prima Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34012/jurnalsisteminformasidanilmukomputer.v7i1.4100

Abstract

This study aims to explore the clinical data of patients diagnosed with heart failure using the Support Vector Machine (SVM) algorithm as a classification method. Clinical data from verified patients has been collected and analyzed to identify patterns, associations, and risk factors contributing to heart failure risk. The exploratory data analysis results reveal essential clinical data characteristics and provide initial insight into patient profiles and clinical variables that can influence heart failure risk. The SVM model was built to predict the risk of heart failure based on clinical data. This model is evaluated using classification metrics such as F1-Score and accuracy. Evaluation results show good performance with an F1-Score reaching 0.83, which indicates a reasonable degree of accuracy and balance in predicting the risk of heart failure. The conclusion of this study shows the potential of the classification model as a tool in managing heart failure patients. This model can help medical personnel identify high-risk patients and provide appropriate treatment to prevent disease progression and improve prognosis. However, these results need further verification with more in-depth analysis and validation using broader data. This model can help medical personnel identify high-risk patients and provide appropriate treatment to prevent disease progression and improve prognosis. However, these results need further verification with more in-depth analysis and validation using broader data. This model can help medical personnel identify high-risk patients and provide appropriate treatment to prevent disease progression and improve prognosis. However, these results need further verification with more in-depth analysis and validation using broader data.   Keywords: Exploratory Data Analysis, Heart Failure, Classification, Python, Support Vector Machine
DECISION SUPPORT SYSTEM FOR TEACHER PERFORMANCE APPRAISAL WITH SIMPLE ADDITIVE WEIGHTING METHOD Sagala, Jijon Raphita; Hasugian, Penda Sudarto
Jurnal Sistem Informasi dan Ilmu Komputer Vol. 7 No. 1 (2023): JUSIKOM: JURNAL SISTEM INFROMASI ILMU KOMPUTER
Publisher : Fakultas Teknologi dan Ilmu Komputer Universitas Prima Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34012/jurnalsisteminformasidanilmukomputer.v7i1.4122

Abstract

Teachers are the most influential component in creating quality educational processes and outcomes. Therefore, improvement efforts are made to improve the quality of education. Schools will not make a significant contribution without the support of professional and qualified teachers. Teachers are really required to have high performance. To improve the quality of teacher performance, the 064022 State Elementary School conducts teacher performance appraisals every 3 months but the mechanism is less effective due to lack of transparency. Research on teacher performance appraisal using the system is applied for efficiency and transparency. The system was developed using the Simple Additive Weighting method. The Simple Additive Weighting method is part of the Decision Support System (DSS) used to help determine decisions based on alternative data and criteria data. Keywords: Decision Support System, Teacher Performance, Simple Addictive Weighting.
COMPARISON OF SUPPORT VECTOR REGRESSION AND RANDOM FOREST REGRESSION ALGORITHMS ON GOLD PRICE PREDICTIONS Hutagalung, Samuel Valentino; Yennimar, Yennimar; Rumapea, Erikson Roni; Hia, Michael Justin Gesitera; Sembiring, Terkelin; Manday, Dhanny Rukmana
Jurnal Sistem Informasi dan Ilmu Komputer Vol. 7 No. 1 (2023): JUSIKOM: JURNAL SISTEM INFROMASI ILMU KOMPUTER
Publisher : Fakultas Teknologi dan Ilmu Komputer Universitas Prima Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34012/jurnalsisteminformasidanilmukomputer.v7i1.4125

Abstract

This research was conducted to test how the Support Vector Regression and Random Forest Regression algorithms predict gold futures prices. The data used in this research was taken from the Investing.com website which will later be processed into a prediction model by comparing the SVR and RVR algorithms. The Support Vector Regression and Random Forest Regression algorithms will be tested to see the performance of each prediction model. The test results show that the Support Vector Regression model is superior in terms of accuracy with a value of 83%. However, the Random Forest Regression algorithm is superior with a smaller error rate, namely with an MSE value of 270.85 and an MAE value of 12.53. Keyword: Comparison, Prediction, Support Vector Regression, Random Forest Regression.
LIVER DISEASE CLASSIFICATION ANALYSIS USING THE XGBOOST METHOD Sitinjak, Yadi; -, Muhaymin; Nababan, Marlince
Jurnal Sistem Informasi dan Ilmu Komputer Vol. 7 No. 1 (2023): JUSIKOM: JURNAL SISTEM INFROMASI ILMU KOMPUTER
Publisher : Fakultas Teknologi dan Ilmu Komputer Universitas Prima Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34012/jurnalsisteminformasidanilmukomputer.v7i1.4130

Abstract

Liver disease is a severe pathological condition that can cause liver inflammation due to viral infection, toxic agents, or bacterial invasion, interfering with normal liver function. The death rate from this disease reaches 1.2 million people annually in Southeast Asia and Africa. Liver disease can cause damage to the liver and negatively affect overall body function. To reduce disease progression, it is critical to facilitate early diagnosis, thereby enabling rapid initiation of treatment for affected individuals. Classification methods are widely used to make decisions based on new information from previous data processing through calculation algorithms. This study uses the XGBoost classification method to build a predictive model for liver disease. The results of this study confirm that the XGBoost model is a robust and efficient choice for liver disease classification based on patient data. The use of the XGBoost approach has proven its success in the category of liver disease with an accuracy of up to 95% and an accuracy balance of 95%, demonstrating the effectiveness and efficiency of this method in overcoming class imbalances in liver disease classification data.   Keywords: Xgboost, Liver, Classification, Disease
ANALYSIS OF CLASSIFICATION OF LUNG CANCER USING THE DECISION TREE CLASSIFIER METHOD Setiawan, Wendy; Banjarnahor, Jepri; Shandika , Muhammad Faja; -, Amalia; Radhi, Muhammad
Jurnal Sistem Informasi dan Ilmu Komputer Vol. 7 No. 1 (2023): JUSIKOM: JURNAL SISTEM INFROMASI ILMU KOMPUTER
Publisher : Fakultas Teknologi dan Ilmu Komputer Universitas Prima Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34012/jurnalsisteminformasidanilmukomputer.v7i1.4136

Abstract

The International Agency for Research on Cancer (IARC) revealed staggering figures, with 19.3 million global cancer cases and 10 million related deaths in that year. Cancer, characterized by abnormal cell growth, can potentially be dangerous with the ability to metastasize. Notably, lung cancer is often detected in an advanced stage due to a lack of awareness and comprehensive medical assessment. Lung cancer usually presents with a late-stage diagnosis. From 60% to 85% of individuals diagnosed with lung cancer show a lack of awareness about their condition. Early diagnosis using an accurate classification method can significantly increase the success of lung cancer diagnosis. To improve predictions, Decision Tree Classifier method was used in lung cancer classification, resulting in a significant increase in accuracy. This study achieved a good level of accuracy, with an accuracy value of 95.16% at a max_depth model depth of 15, and tested in 40 experimental iterations. These results are expected to provide hope for progress in the classification of lung cancer.   Keywords: Lung, Cancer, Classification, Decision Tree
OBJECT-ORIENTED PARTS INVENTORY INFORMATION SYSTEM MODELING USING UNIFIED MODELING LANGUAGE Asrin, Fauzan
Jurnal Sistem Informasi dan Ilmu Komputer Vol. 7 No. 1 (2023): JUSIKOM: JURNAL SISTEM INFROMASI ILMU KOMPUTER
Publisher : Fakultas Teknologi dan Ilmu Komputer Universitas Prima Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34012/jurnalsisteminformasidanilmukomputer.v7i1.4147

Abstract

The availability of spare parts in a vehicle service workshop needs to be correctly recorded and recorded, making it easier for workshop owners to ensure the complete availability of vehicle spare parts. From the results of the author's observations, the Rizki Prima workshop has several obstacles in recording the availability of spare parts where the business processes being carried out are still a hassle for the employees working in the workshop. The problem often occurs when recording is the incompleteness of data related to incoming and outgoing goods. So the recording made in the ledger needs to be more accurate and requires a relatively long time to search one by one in the register if the workshop owner needs it. As a result of poor recording of goods, it is difficult for the owner to order goods again, worried that duplicating data will record incoming goods. The above problems are the basis for the authors to conduct research by utilizing information systems and modeling with an object-oriented approach using a unified modeling language to design a spare parts inventory information system according to user needs. To provide an overview of the Rizki Prima workshop transforming from conventional spare parts data recording to digital spare parts recording. The above problems are the basis for the authors to conduct research by utilizing information systems and modeling with an object-oriented approach using a unified modeling language to design a spare parts inventory information system according to user needs. To provide an overview of the Rizki Prima workshop transforming from conventional spare parts data recording to digital spare parts recording. The above problems are the basis for the authors to conduct research by utilizing information systems and modeling with an object-oriented approach using a unified modeling language to design a spare parts inventory information system according to user needs. To provide an overview of the Rizki Prima workshop transforming from conventional spare parts data recording to digital spare parts recording.   KEYWORDS: Modeling, Information Systems, Spare Parts Inventory, Availability, Unified Modeling Language
REDESIGN THE UI/UX OF THE PT MNO COMPANY PROFILE WEBSITE USING THE THINKING DESIGN METHOD Putera, Ihsan; Wati, Emma Nor Kholida; Natasia, Sri Rahayu; Laia, Yonata
Jurnal Sistem Informasi dan Ilmu Komputer Vol. 7 No. 2 (2024): JUSIKOM: JURNAL SISTEM INFROMASI ILMU KOMPUTER
Publisher : Fakultas Teknologi dan Ilmu Komputer Universitas Prima Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34012/jurnalsisteminformasidanilmukomputer.v7i2.4198

Abstract

In the current era of globalization, large and small companies must develop strategies for using websites to improve business branding to the general public. Based on the results of problem identification, there are several UI/UX problems on the PT MNO website, including an unattractive appearance, messy and overlapping fonts, and a messy layout. This research aims to redesign the PT MNO website to improve its UI/UX to make it more informative, clear, and easy to use. The solution can be implemented by redesigning the website design for PT MNO utilizing the design thinking methodology. This method has five phases: Empathize, define, imagine, prototype, and test. Based on observations and interviews, three categories of problems in the current website category were found: content, navigation, and features. Then, five pages were created for the design results or mockup solutions, namely the home page, services, portfolio, programs, and about us. Testing used a usability metric, SEQ (Single Ease Question). Based on SEQ theory, if the results given by respondents are more than 5.5, then the task or scenario is considered successful or easy to do. So, the five functions in terms of convenience were easy for the five respondents to complete. Keywords: Design Thinking, Redesign, UI/UX, Website.

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