Article Per Year (5 Year)
p-Index From 2019 - 2024
1.475
P-Index
This Author published in this journals
All Journal Building of Informatics, Technology and Science 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
Muhammad Fikry
Universitas Islam Negeri Sultan Syarif Kasim Riau, Pekanbaru
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Industrial & Manufacturing Engineering

Published : 10 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 10 Documents
Search

 1 
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 Masyarakat di Twitter terhadap Ganjar Pranowo dengan Metode Naïve Bayes Classifier Sinta Wahyuni Ritonga; Yusra .; Muhammad Fikry; Eka Pandu Cynthia
Building of Informatics, Technology and Science (BITS) Vol 5 No 1 (2023): Juni 2023
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v5i1.3535

Abstract

Indonesia is a country with a Democratic political system. The public is given freedom of speech, collaboration and public criticism. In the modern era, the use of social media is growing rapidly at the community level. One of the social media trends in Indonesia is Twitter which is used to convey aspirations to the government and as a means to convey daily activities, opinions, culture and get the latest information or news from Indonesia and abroad. Public opinion taken from Twitter can be positive, negative and neutral. The number of tweets on Twitter one of the trend topics in Indonesia is Ganjar Pranowo, can be used as a source of data in the assessment of sentiment classification which is processed to produce accuracy values. This study aims to classify public opinion on social media Twitter about Ganjar Pranowo using Naïve Bayes Classifier method. In the classification processing using a dataset of 4000 tweet data with two labeling classes, positive and negative to determine the efficiency of NBC performance combined with TF-IDF weighting, feature selection using supervised learning approach techniques. The results of the test on the classification of public sentiment research on Twitter about Ganjar Pranowo using NBC method using 10% of the test data from the dataset used to produce an accuracy value of 83.0%.
Klasifikasi Sentimen Masyarakat di Media Sosial Twitter terhadap Calon Presiden 2024 Prabowo Subianto dengan Metode K-NN Avaldy Rahmat Rivita; Yusra; Muhammad Fikry
KLIK: Kajian Ilmiah Informatika dan Komputer Vol. 3 No. 6 (2023): Juni 2023
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/klik.v3i6.890

Abstract

The 2024 Republic of Indonesia Presidential Election is a democratic stage to determine the President of the Republic of Indonesia and Vice President of the State of Indonesia for the 2024-2029 period which is scheduled to take place on Wednesday, 14 February 2024. This election is the fifth direct presidential and vice presidential election in Indonesia. Several parties have currently nominated or selected their presidential candidates for the 2024 presidential election. Three presidential candidates have emerged, namely Prabowo Subianto, Ganjar Pranowo, and Anies Baswedan. Based on a survey, Prabowo Subianto is the presidential candidate (capres) with the highest electability compared to other competitors. The society's view of the 2024 presidential candidate, especially Prabowo Subianto, has raised many pros and cons. Society's view can be seen on social media, like one of  this is the Twitter. This study aims to classify public sentiment towards the Presidential Candidate (capres) Prabowo Subianto on Twitter. The amount of data used is 2100 tweets which are collected based on the keywords "Presidential Candidate" and "Prabowo Subianto". The application of the K-Nearest Neighbor (K-NN) method with weighting in the form of TF-IDF and Feature Selection in the form of Threshold will be implemented using Google Colab. Based on the results of testing the K-NN method using the confusion matrix at seven K values, namely (3,5,7,9,11,13,15) with the comparisons used 70:30, 80:20, 90:10 the highest accuracy was obtained at K = 5 at the ratio of training data and test data 80:20.
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 Ancaman Resesi Ekonomi 2023 dengan Metode K-Nearest Neighbor Dimas Ferarizki; Yusra; Muhammad Fikry; Febi Yanto; Fitri Insani
KLIK: Kajian Ilmiah Informatika dan Komputer Vol. 4 No. 2 (2023): Oktober 2023
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/klik.v4i2.1306

Abstract

A recession is a decline in overall economic activity, this is considered a phase of significant and sustainable economic decline in various sectors and economic indicators. The threat of a recession in 2023 has become a topic of discussion in many countries, including Indonesia. This happens because Indonesia is threatened as a country affected by a recession due to weakening economic activity in the real sector. This sentiment classification research aims to analyze public opinion and opinion regarding the issue of recession news in 2023 which is conveyed via the social media platform Twitter. This research aims to understand whether these opinions fall into the category of positive sentiment or negative sentiment. Apart from that, this research also aims to measure the level of accuracy in classifying these sentiments into appropriate classes. This research has several main processes starting from data collection then manual data labeling, text processing, feature weighting (TF-IDF), Thresholding feature selection and K-Nearest Neighbor method classification. Based on the classification results using a testing model from a total of 1000 comment data divided between 596 positive class data and 404 negative class Twitter data regarding the threat of recession in 2023, the highest accuracy results were obtained at 85% at a value of k = 3 using the 90:10 comparison model training and testing data
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%.
Klasifikasi Sentimen Masyarakat di Twitter Terhadap Ganjar Pranowo dengan Metode Support Vector Machine Syaiful Azhar; Yusra; Muhammad Fikry; Surya Agustian; Iis Afrianty
KLIK: Kajian Ilmiah Informatika dan Komputer Vol. 4 No. 3 (2023): Desember 2023
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/klik.v4i3.1537

Abstract

The classification of public sentiment towards Ganjar Pranowo on Twitter can provide insights into his popularity, support, or criticism. This research aims to classify public sentiment towards Ganjar Pranowo on Twitter using the Support Vector Machine (SVM) method. The research data consists of 4000 tweets collected from Twitter. After undergoing preprocessing, these tweets are classified using SVM into positive or negative classes. The classification method is optimized to produce the most optimal model by testing the influence of feature selection stages and SVM parameter tuning. The data is divided into 80% training (TRAIN_SET) and 20% testing (TEST_SET). The optimal model is validated using 10% of the randomly selected TRAIN_SET for validation data. Sixteen experiments are conducted to explore the optimal model, with the highest validation results (top rank 4 models) tested on the TEST_SET, yielding F1-scores of 84.13%, 84.13%, 84.13%, and 84.13% for experiment IDs 1, 7, 14, and 16, respectively. In this research, SVM proves to be sufficiently effective in classifying sentiment-related tweets about Ganjar Pranowo on Twitter
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.
Implementasi Data Mining Untuk Prediksi Stok Penjualan Keramik dengan Metode K-Means Ferdian Arya Dinata; Alwis Nazir; Muhammad Fikry; Iis Afrianty
Journal of Computer System and Informatics (JoSYC) Vol 5 No 3 (2024): May 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josyc.v5i3.5200

Abstract

Ceramics has become one goods that consumers show interest in every year, so many companies are interested in selling ceramics. However, ceramic sales must meet and balance changing customer needs as well as problems found regarding ceramic products and customers, such as a lack of stock of ceramic products which results in customers not placing orders and product sales not meeting targets. So it is necessary to group ceramics to anticipate the risks that the company will accept by utilizing the data mining process using past data. This research uses the K-Means method found in data mining. The objective of this research is to group determine sales of brands that have potential for additional stock in the future and to test the data using the DBI (Davies Bouldin Index) which is carried out by testing the distance values between clusters through a series of experiments. This research uses data for the last 1 year from January 2022 to December 2022 with a total of 156 data using 9 attributes, namely brand, item code (FT, WT) and size (40x40, 25x25, 50x50, 25x40, 60x60, 20x40). The results of the research using the K-Means method, the best-selling brand is cluster 2, the best-selling brand is cluster 1 and the best-selling brand is cluster 0. The best-selling brand is HRM, the best-selling brand is VALENSIA and the best-selling brand is MCC. Test results using the DBI method with a validity of 01.013 show that the best cluster is obtained at k=3 using the elbow method. It is hoped that this research will contribute to related companies as support for decision making.
Co-Authors Abdul Halim Alwis Nazir Avaldy Rahmat Rivita Dea Ropija Sari Dimas Ferarizki Eka Pandu Cynthia Elvia Budianita Febi Yanto Ferdian Arya Dinata Fitri Insani Iis Afrianty Muhammad Irsyad Muhammad Riski Nur Hasanah Hrp Sinta Wahyuni Ritonga Siti Nurhaliza Surya Agustian Syaiful Azhar Yusra Yusra . Yusra Yusra