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Komputasi: Jurnal Ilmiah Ilmu Komputer dan Matematika
Published by Universitas Pakuan
ISSN : 16937554     EISSN : 26543990     DOI : 10.33751
Core Subject : Science, Engineering,
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management
Scientific Journal of Computer and Mathematical Science (Jurnal Ilmiah Ilmu Komputer dan Matematika) is initiated and organized by Department of Computer Science, Faculty of Mathematics and Science, Pakuan University (Unpak), Bogor, Indonesia to accommodate the writing of research results for the academics and institutions other. Komputasi journal was originally launched in 1992, and published online since 2007 with ISSN version p-ISSN: 1693-7554 and version of the daring of e-ISSN: 2654-3990 in 2018 (SK No. 0005.26543990/JI.3.1/SK.ISSN/2018.10-15 October 2018 (starting Vol. 16, No. 1, January 2019). The journal is a publication media for original manuscripsts related information technology development and science written in Bahasa Indonesia which is published twice times a year (January and July).
Arjuna Subject : -
Articles 217 Documents
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SENTIMENT ANALYSIS OF ONLINE LOANS ON TWITTER USING LEXICON BASED METHODS AND SUPPORT VECTOR MACHINE (SVM) Saputri, Cita Suci; Qur'ania, Arie; Anggraeni, Irma
Komputasi: Jurnal Ilmiah Ilmu Komputer dan Matematika Vol 21, No 2 (2024): Komputasi: Jurnal Ilmiah Ilmu Komputer dan Matematika
Publisher : Ilmu Komputer, FMIPA, Universitas Pakuan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33751/komputasi.v21i2.10125

Abstract

Technological developments are increasingly rapid and moving towards digital, which in the end technology can also help people who are experiencing economic problems, namely with online loan services. Even though there are many conveniences provided by online loan services, of course not all people give positive comments because there are quite a few negative comments about this service.One of the social media that is widely used by the public to provide comments about online loans is Twitter. Sentiment analysis is a data processing process to obtain information about whether an opinion sentence tends to be positive, negative or even neutral. This research contains sentiment analysis towards Online Loans on Twitter using the Lexicon Based and Support Vector Machine methods. From the results of this research, the accuracy for SVM was 82.36%. From these results it can be concluded that the use of the Lexicon Based and Support Vector Machine methods is considered quite good and effective for classifying sentiment
Spatial Clustering Using Generalized LASSO on the Gender and Human Development Index in Papua Island in 2022 Mutaqin, Ahdan Darul; Rahardiantoro, Septian; Masjkur, Mohammad
Komputasi: Jurnal Ilmiah Ilmu Komputer dan Matematika Vol 21, No 1 (2024): Komputasi: Jurnal Ilmiah Ilmu Komputer dan Matematika
Publisher : Ilmu Komputer, FMIPA, Universitas Pakuan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33751/komputasi.v21i1.9268

Abstract

Equitable development from a gender perspective needs attention. Based on data from the World Economic Forum (WEF), gender equality in Indonesia has increased. Even so, the island of Papua is still very low on gender equality. It can be seen from the Gender Development Index (IPG) from the Central Bureau of Statistics (BPS), there is a considerable gap between the Papua Island IPG and the National. IPG is a comparison between the Human Development Index (IPM) for Men and Women. Based on these conditions, this study aims to classify GPI, Male IPM, and Female IPM by region using the spatial clustering method in 2022. One of the analytical methods that can overcome these conditions is Generalized LASSO. Generalized LASSO can be used on data that only has a response variable (y) for clustering. Generalized LASSO clustering uses a penalty matrix D. The formation of the D matrix is formed by giving values -1 and 1 for areas that intersect or are adjacent and a value of 0 for other areas. The best clustering for IPG uses KNN with K = 3 and the number of clusters formed is 2 clusters. The best clustering for male HDI uses KNN with K = 2 and the number of clusters formed is 8. The best clustering for female HDI uses KNN with K = 2 and the number of clusters formed is 10 clusters.
Analysis of Heartbeat Signals to Detect Sleep Disorders Using Artificial Neural Network Methods Aripin, Moch; Hardhienata, Soewarto; Negara, Teguh Puja
Komputasi: Jurnal Ilmiah Ilmu Komputer dan Matematika Vol 21, No 2 (2024): Komputasi: Jurnal Ilmiah Ilmu Komputer dan Matematika
Publisher : Ilmu Komputer, FMIPA, Universitas Pakuan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33751/komputasi.v21i2.10222

Abstract

A human sleep disorder detection system has been designed using an AD8232 Electrocardiogram sensor module integrated with a microcontroller and internet connection through ESP 32. The heartbeat signals from the sensor are analyzed using Artificial Neural Network (ANN) methods to determine normal conditions, Obstructive Sleep Apnea (OSA), or Central Sleep Apnea (CSA). The sensor's accuracy was measured over 10 measurements, resulting in 96.85%. Testing with 30 training data samples achieved an accuracy of 93.33%, and testing with 20 training data samples achieved an accuracy of 80%. The system displays output values through the Internet of Things (IoT) with an average computation time of around 7.6 ms.
Fake News Detection in the 2024 Indonesian General Election Using Bidirectional Long Short-Term Memory (BI-LSTM) Algorithm Arkaan, Shabiq Ghazi; Atmadja, Aldy Rialdy; Firdaus, Muhammad Deden
Komputasi: Jurnal Ilmiah Ilmu Komputer dan Matematika Vol 21, No 2 (2024): Komputasi: Jurnal Ilmiah Ilmu Komputer dan Matematika
Publisher : Ilmu Komputer, FMIPA, Universitas Pakuan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33751/komputasi.v21i2.9987

Abstract

The advancement of information technology provides convenience, but it also brings about problems. One area affected by this is the election process in Indonesia, which has seen a rise in fake news often used to discredit political opponents. Fake news misleads the public into believing incorrect information related to the election. To address this issue, a system is needed to detect fake news in the 2024 election to help the public differentiate between true and false information. This system is developed using an artificial intelligence and deep learning approach trained to do text classification on fake news detection. The training data consists of 1999 entries obtained from the Global Fact-Check Database from turnbackhoax.id, detik.com, and cnnindonesia.com. The machine learning model is built using the Bidirectional Long Short-Term Memory (BI-LSTM) algorithm, which is suitable for processing text data. This study compares two types of feature representations: TF-IDF and contextual embeddings with the IndoBERT model. The study results in the best model for text classification with an accuracy of 92% and a loss of 42.92%, achieved by the model using TF-IDF feature representation. The implementation of this system aims to enhance the integrity of the election process by minimizing the spread of misinformation. Future work will focus on refining the model and expanding the dataset to include more diverse sources for improved accuracy and robustness.
Comparative Analysis Accuracy ID3 Algorithm and C4.5 Algorithm in Selection of Candidates Basic Physics Laboratory Assistant Supriyatin, Wahyu; Rianto, Yasman
Komputasi: Jurnal Ilmiah Ilmu Komputer dan Matematika Vol 21, No 1 (2024): Komputasi: Jurnal Ilmiah Ilmu Komputer dan Matematika
Publisher : Ilmu Komputer, FMIPA, Universitas Pakuan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33751/komputasi.v21i1.9198

Abstract

Basic Physics Laboratory is one of the supporting laboratories at Gunadarma University. Each practical activity in the laboratory is supervised by respective assistants. Therefore, a support system is needed as a basis for decision-making in determining assistant candidates. This decision-making process is processed using data mining techniques, specifically classification algorithms. The criteria or attributes used in the decision-making process include written test scores, practical test scores, presentation scores, equipment usage abilities, and interviews. The classification algorithms used in this research are ID3 and C4.5 algorithms. The tools used to implement these algorithms are RapidMiner Studio 9.10. These algorithms will generate decision trees that can be used as decision support. The aim of this research is to conduct an accuracy comparison analysis for the ID3 and C4.5 algorithms. The highest accuracy obtained will be used as a reference for determining whether assistant candidates are accepted or not. The accuracy results show that the C4.5 algorithm has the highest accuracy, precision, and recall compared to the ID3 algorithm. The determination of the highest value is done using the k-fold cross-validation model for values 2, 4, 6, 8, and 10. The C4.5 algorithm has the highest accuracy of 96.67% at k-fold value = 2.
EXPERT SYSTEM DIAGNOSES VARICOCHEL DISEASE USING MAMDANI'S FUZZY LOGIC ALGORITHM METHOD Rizal, Farhan Syah; Karlitasari, Lita; Sadiah, Halimah Tus
Komputasi: Jurnal Ilmiah Ilmu Komputer dan Matematika Vol 21, No 2 (2024): Komputasi: Jurnal Ilmiah Ilmu Komputer dan Matematika
Publisher : Ilmu Komputer, FMIPA, Universitas Pakuan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33751/komputasi.v21i2.9810

Abstract

Varicoceles form when valves in the veins that run along the spermatic cord (the structure that suspends the testicles in the scrotum) prevent blood from flowing properly. Most varicocele cases occur during puberty, from the age of 15 to 25 years. If there are symptoms that are not prolonged, treatment is not necessary. However, if a varicocele causes pain, shrinkage of the testicles, impaired fertility, or swelling, surgery will be performed. Symptoms of this disease are similar to hemorrhoids and bladder stones, so it takes an expert. In the research conducted on the Expert System Application for Diagnosing Hyperthyroid Disease with the Mamdani Fuzzy Logic Inference Method, it can be concluded that the expert system can store expert knowledge from experts in solving problems diagnosing hyperthyroid disease while Mamdani fuzzy inference is used for knowledge processing in order to obtain a more accurate diagnosis conclusion. definitely with accuracy . Making the application is expected to make it easier for people to get information without having to wait for the presence of a doctor/expert for varicocele disease, and is expected to reduce or even solve existing problems.
Application of the Naive Bayes Classifier Method and Fuzzy Analytical Hierarchy Process in Determining Books Eligible for Publishing Irwansyah, Mochamad Denny; Negara, Teguh Puja; -, Erniyati; Citra, Puspa
Komputasi: Jurnal Ilmiah Ilmu Komputer dan Matematika Vol 21, No 1 (2024): Komputasi: Jurnal Ilmiah Ilmu Komputer dan Matematika
Publisher : Ilmu Komputer, FMIPA, Universitas Pakuan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33751/komputasi.v21i1.6677

Abstract

The manuscript selection process is the process of assessing manuscripts worthy of publication. The Editor's job is to provide an evaluation of each manuscript based on the assessment criteria and sub-criteria. By using a decision support system, it can make it easier for policymakers to determine the suitability of a manuscript. In this research, a decision support system is applied to select papers that are worthy of publication, namely the Fuzzy Analytical Hierarchy Process (F-AHP) method for selecting the suitability of manuscripts using subjective criteria and the Naïve Bayes method for classifying books based on their genre. The test results using the F-AHP method produced an accuracy rate of 83.33% using 30 books out of 150 books and using the Naïve Bayes method produced an accuracy rate of 80% using 30 books from the internet. This system uses the Visual Studi Code IDE, Firebase, and Pythonanywhere as its database with an Android display. 

Page 22 of 22 | Total Record : 217

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