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All Journal JURNAL MEDIA INFORMATIKA BUDIDARMA JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) Journal of Computer System and Informatics (JoSYC) Jurnal Sistem Komputer dan Informatika (JSON) KLIK: Kajian Ilmiah Informatika dan Komputer
Yusra Yusra
Universitas Islam Negeri Sultan Syarif Kasim Riau, Pekanbaru
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Industrial & Manufacturing Engineering

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Implementasi Metode Moora Pada Sistem Pendukung Keputusan Penilaian Kinerja Karyawan Heri Susanto; Fitra Kurnia; Yusra Yusra; Lola Oktavia
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 6, No 4 (2022): Oktober 2022
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v6i4.4750

Abstract

Employee performance appraisal is needed by an agency or company with the aim of evaluating performance and improving the quality of competent human resources and high loyalty for each employee at work, then an agency or company can give awards to each of its employees such as contract extensions, salary increases , get special promotions, appointments, and allowances, which can motivate every employee. This study aims to facilitate a planner in a company PT. SUPRACO INDONESIA in providing performance appraisals of each employee uses a decision support system using the Multi Objective Optimization On The Basic Of Ratio Analysis (MOORA) method. This employee performance appraisal decision support system uses a sample of 3 employees from 11 employees using the MOORA method of calculation. the final results of the calculations carried out are: for the first rank in alternative 2 with a value of 5.7805, while the second rank in alternative 1 with a value of 5.7736, and third place in alternative 3 with a value of 5.7671. In the tests carried out using Blackbox Testing, for all the features on the system running 100% with very good information and testing using the UAT (User Acceptance Test) method, it showed that the results of system user acceptance were 92%.
Implementasi Data Mining Memprediksi Penjualan Crude Palm Oil Berdasarkan Kapasitas Tangki Menggunakan Multiple Linear Regression Ana Komaria Baskara; Alwis Nazir; Muhammad Irsyad; Yusra Yusra; Fitri Insani
Jurnal Sistem Komputer dan Informatika (JSON) Vol 4, No 3 (2023): Maret 2023
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/json.v4i3.5665

Abstract

Data mining is a process of discovering information from data that can be used to improve business, product development, and other decision-making processes. One application of data mining is in PT. Kerry Sawit Indonesia, which is an agribusiness company in the Wilmar Group that deals with processing crude palm oil (CPO). Sales of CPO are crucial for palm oil plantation companies. To increase efficiency and profitability, palm oil plantation companies can predict CPO sales to optimize sales and CPO inventory. One method that can be used to predict CPO sales is through data mining techniques. In this study, the data mining technique used is multiple linear regression. Multiple linear regression is used to determine the relationship between the tank capacity variable and CPO sales. The data used in this study are CPO production data, CPO sales data, and tank capacity data obtained from palm oil plantation companies over the last five years. The results of the Multiple Linear Regression calculation in this case study show that the coefficient of determination (R-squared) value is 0.9546, indicating that 95.46% of the CPO delivery variability can be explained by the independent variables. Additionally, the MAPE and RMSE tests show that the regression model obtained has good accuracy in predicting CPO deliveries. Therefore, this regression model can be used to predict CPO deliveries in the future, considering the predetermined independent variable values.
Klasifikasi Sentimen Masyarakat di Twitter Terhadap Ancaman Resesi Ekonomi 2023 dengan Metode Naïve Bayes Classifier Dea Ropija Sari; Yusra Yusra; Muhammad Fikry; Febi Yanto; Fitri Insani
Jurnal Sistem Komputer dan Informatika (JSON) Vol 4, No 4 (2023): Juni 2023
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/json.v4i4.6276

Abstract

Economic recession is a condition in which the economic turnover of a country changes to slow or bad that can last for years as a result of the growth of the Gross Domestic Product (GDP) a country decreases over two decades significantly. Early warnings of the emergence of a global recession become a concern for all countries in the world, even global recessions also have a major impact on Indonesia. Such as declining public spending due to decreasing incomes, increasing unemployment, increasing poverty, and many of whom have to accept PHK or salary cuts. Economic strengthening will be important in minimizing these threats, this research needs to be done to see the response of the public to the threat of economic recession. Twitter provides a container to users to comment on the problem of the economy recession 2023 which can be used as sentiment classification information to know positive and negative comments. This research uses the naive bayes classifier algorithm. In this study there are seven main processes, namely data collection, manual labelling, processing, feature weighing (tf-idf), tresholding, naive bayes method classification, testing. From the 1408 comments data on Twitter about the threat of a 2023 economic recession. Based on the results of the classification, using 2 testing models namely data balance and non-balance data obtained the best balance data test results with the highest accuracy result with the process of classification using algortima naïve bayes classifier resulted in accurateness of 78% obtainable by using a comparison of 90% training data and 10% test data.
Klasifikasi Sentimen Masyarakat di Twitter Terhadap Kenaikan Harga BBM dengan Metode Support Vector Machine Siti Nurhaliza; Yusra Yusra; Muhammad Fikry
Jurnal Sistem Komputer dan Informatika (JSON) Vol 4, No 4 (2023): Juni 2023
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/json.v4i4.6322

Abstract

The increase in the price of fuel oil (BBM) in Indonesia has always been a controversy which can be seen from online media such as Twitter which has an effect on the Indonesian economy, with this problem it has a change in the impact of cost instability due to an increase in fuel prices which will also affect the rate of increase in transportation costs and the rate of inflation. The effect of these changes leads to many different public opinions so as to produce pros and cons of these changes, with the existence of the problems above, the classification process is needed. This study uses 3000 tweet data obtained from the crawling process. This study obtains an accuracy of 85% at a ratio of 90:10, for a precision value of 85%, 99% recall and 91% f1-score for negative sentiment, while 83% precision value, 19% recall, 30% f1-score for positive sentiment. Then in the 80:20 comparison experiment, an accuracy of 83% was obtained, for a precision value of 83%, a recall of 99% and an f1-score of 91% for negative sentiment, while a precision value of 82%, a recall of 16%, an f1-score of 26% for positive sentiment.
Klasifikasi Sentimen Ulasan Aplikasi WhatsApp di Play Store Menggunakan Metode K-Nearest Neighbor Muhammad Riski; Muhammad Fikry; Yusra Yusra
KLIK: Kajian Ilmiah Informatika dan Komputer Vol. 4 No. 1 (2023): Agustus 2023
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/klik.v4i1.1050

Abstract

Every app has strengths and weaknesses that can influence various responses from users, including levels of satisfaction and disappointment that are often expressed through reviews on the Google Play Store. On this platform, the ratings and reviews feature allows users to give their opinions and experiences on the apps they use. One example of an application that is popular among the public is WhatsApp. The purpose of this research is to measure users' opinions and views on the WhatsApp application using the K-Nearest Neighbor algorithm. The data used in this study includes 1000 data, with 669 positive opinions and 331 negative opinions on the application. The process of dividing training data and test data was carried out through several experiments with three different ratios, namely 70:30, 80:20, and 90:10. From the results of this test, the best model was obtained in the scenario of dividing training data and test data with a ratio of 90:10 resulting in accuracy reaching 84%, precision value of 87.65%, recall of 92.21%, and f1-score of 89.87% for the positive class. While in the negative class, the precision value reached 68.42%, recall reached 56.52%, and f1-score reached 61.90% at K = 14 and Threshold = 20.
Klasifikasi Sentimen Masyarakat Di Twitter Terhadap Prabowo Subianto Sebagai Bakal Calon Presiden 2024 Menggunakan M-KNN Abdul Halim; Yusra Yusra; Muhammad Fikry; Muhammad Irsyad; Elvia Budianita
Journal of Information System Research (JOSH) Vol 5 No 1 (2023): Oktober 2023
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v5i1.4054

Abstract

Presidential elections are held every five years and each presidential candidate will get support from several political parties to run for candidacy in the election. In a multi-party system, the number of parties participating in the election is very large, so that the perspectives of voters on political actors, including presidential candidates who will advance in the 2024 elections, are varied. The survey results from Polling Indonesia (SPIN) conducted from 7 to 16 October 2022 show that Prabowo Subianto has the highest electability with a score of 31.6%, based on a national leadership survey. In this study, a test was carried out by classifying tweet data from the public collected on the Twitter application from January to December 2022 using the Modified k-Nearest Neighbor method to analyze public sentiment regarding the upcoming election. Data collected as many as 2,100 data with positive and negative categories related to "Presidential Candidate" and "Prabowo Subianto" and the implementation of the Modified k-Nearest Neighbor classification was carried out using Google Colab. Based on the results of the confusion matrix test from the Modified k-Nearest Neighbor classification with three comparisons made (ie comparisons 70%:30%, 80%:20% dan 90%:10%) and using K=3, 5, 7, 9, 11 when testing a comparison of 90:10 at K=3 the highest accuracy results were obtained with a value of 93,3%.
Algoritma Stemming Teks Bahasa Batak Angkola Berbasis Aturan Tata Bahasa Nur Hasanah Hrp; Muhammad Fikry; Yusra Yusra
Journal of Computer System and Informatics (JoSYC) Vol 4 No 3 (2023): May 2023
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josyc.v4i3.3458

Abstract

The Angkola Batak language is a variety of Batak languages, to be precise in the southern Tapanuli area, which is still used and maintained as an everyday language. Until now, the resources of the Angkola Batak language are not yet available in digital form that can be used by researchers in the analytical stages of human natural language processing. Natural language processing (NLP Taks) for the Angkola Batak language must follow the stages of text processing starting from tokenization, lexical analysis, syntax, semantics, and phragmatics. This study conducted natural language processing in the first stage, namely lexical analysis. At the lexical analysis stage, one of the most important NLP tasks is stemming. Stemming is the process of determining root words from affixed words. In this research, an analysis and design of the Angkola Batak stemming algorithm have been carried out based on grammar rules. The stages in this research are starting from collecting the grammar rules of the Angkola Batak language, collecting basic words in the Angkola Batak language as a database dictionary, and removing affixes from root words. The output of this research is the stemmer of the Angkola Batak language in the form of PHP. Based on tests conducted on 450 words originating from the Batak Angkola folklore, 448 test words were correct (99.56%) and 2 test words were wrong (0.44%). The wrong test word is obtained because the root word is not found in the dictionary.
Co-Authors Abdul Halim Alwis Nazir Ana Komaria Baskara Dea Ropija Sari Elvia Budianita Febi Yanto Fitra Kurnia Fitri Insani Heri Susanto Lola Oktavia Muhammad Fikry Muhammad Irsyad Muhammad Riski Nur Hasanah Hrp Siti Nurhaliza