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Jurnal Sistem Komputer dan Informatika (JSON)
Published by Sekolah Tinggi Manajemen Informatika dan Komputer Budi Darma
ISSN : -     EISSN : 2685998X     DOI : https://dx.doi.org/10.30865/json.v1i3.2092
Core Subject : Science, Engineering,
Computer Science & IT Control & Systems Engineering
The Jurnal Sistem Komputer dan Informatika (JSON) is a journal to managed of STMIK Budi Darma, for aims to serve as a medium of information and exchange of scientific articles between practitioners and observers of science in computer. Focus and Scope Jurnal Sistem Komputer dan Informatika (JSON) journal: Embedded System Microcontroller Artificial Neural Networks Decision Support System Computer System Informatics Computer Science Artificial Intelligence Expert System Information System, Management Informatics Data Mining Cryptography Model and Simulation Computer Network Computation Image Processing etc (related to informatics and computer science)
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 755 Documents
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Analisis Sentimen Terhadap Program Kampus Merdeka Menggunakan Naive Bayes Dan Support Vector Machine Irma Putri Rahayu; Ahmad Fauzi; Jamaludin Indra
Jurnal Sistem Komputer dan Informatika (JSON) Vol 4, No 2 (2022): Desember 2022
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/json.v4i2.5381

Abstract

In order to prepare students to face the rapid development of technology, changes in work life and skills, students must be better prepared to face the progress of the times. Universities must be able to carry out innovative learning processes so that students achieve optimal learning outcomes which include aspects of knowledge, skills and attitudes. So the MBKM program was launched to answer these demands. However, MBKM has pros and cons in its implementation, so it is necessary to analyze and evaluate policies to improve performance through feedback from the public by conducting sentiment analysis of MBKM policies on twitter users from 2019 to 2022 with the hashtag #kampusmerdeka. This study used the Naïve Bayes and SVM algorithms to determine accuracy based on sentiment classification. The data used 1118 data with positive sentiment 618 data and negative sentiment 500 data. This study resulted in an accuracy of 86%, precision of 87% and recall of 80% with testing data using the Naïve Bayes algorithm. Then using the linear kernel SVM algorithm with the same testing data resulted in accuracy of 93%, precision of 100% and recall of 84%. Therefore, it is important to conduct studies to improve the MBKM program so that its implementation is clearly in accordance with existing procedures.
Optimasi Akurasi Klasifikasi Pada Prediksi Smokte Detection dengan Menggunakan Algoritma Adaboost Amin Nur Rais; Warjiyono Warjiyono
Jurnal Sistem Komputer dan Informatika (JSON) Vol 4, No 2 (2022): Desember 2022
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/json.v4i2.5154

Abstract

The problem of fire is a threat to nature and the environment. To deal with fire incidents, a smoke detector was created and developed in combination with an IoT device so that incident data can be recorded properly where the recorded data will be used as a reference for increasing the accuracy of early detection. Increasing the accuracy of smoke detectors so that they can be combined with artificial intelligence technology. This research proposes prediction optimization using the adaboost algorithm combined with the naïve Bayes classification algorithm with a measurement matrix based on accuracy, recall, and precision. The results showed that using the adaboost algorithm could increase the resulting accuracy value with a value of 0.987. If you look at the evaluation from the precision side, it also shows that the use of the adaboost algorithm can increase the precision value with a value of 0.971. But the recall evaluation showed that without boost it got a better score with a value of 0.995
Penerapan Feature Selection Pada Algoritma Decision Tree Untuk Menentukan Pola Rekomendasi Dini Konseling Oman Somantri; Wildani Eko Nugroho; Abdul Rohman Supriyono
Jurnal Sistem Komputer dan Informatika (JSON) Vol 4, No 2 (2022): Desember 2022
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/json.v4i2.5267

Abstract

Early detection in providing recommendations for student counseling is very important, therefore you can assess the student's potential, beliefs, and attitude as early as possible. The problem that arises in this case is how to detect a student early so that he or she needs counseling assistance or not so that it can be identified early to minimize the risk of further psychological conditions. This article proposes a data mining model using a decision tree to classify counseling recommendations for students. In addition, to improve the resulting accuracy performance, a feature selection method is proposed using forward selection and genetic algorithms. The stages of the research were carried out by pre-processing the data, implementing algorithms, validating data, and optimizing the model. The experimental results show that the best level of accuracy using the decision tree model is 95.64%. It increases to 96.91% after optimization using the genetic algorithm.
Metode Bidirectional Associative Memory (BAM) Kontinu Pengenalan Pola Karakter Untuk Keamanan Data Andri Yunaldi; Very Karnadi
Jurnal Sistem Komputer dan Informatika (JSON) Vol 4, No 2 (2022): Desember 2022
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/json.v4i2.5339

Abstract

Artificial Neural Networks have a paradigm for processing information with the concept of working using biology in processing information similar to how the human brain works. Artificial neural networks solve many problems using the concept of uncertainty such as the discussion in the material, namely the introduction of Continuous BAM Character Patterns. The problem in this research is the lack of data security in maintaining information so that a lot of data can be accessed by people who do not have authority. The main objective of this research is to maintain data security using the Continuous BAM concept. The BAM Kontine method is a method that has the ability to have associative memory or content addressable memory that can be called by using the part stored in the memory itself. The Continuous BAM method will change input to output more smoothly with values that lie in the range [0,1]. The activation function used is the sigmoid function. The results obtained from x1 = [7,-11], x2= [7,13] and x3=[-1,9], All Signs can be recognized by the Continuous BAM algorithm. All Sign Patterns have the same target but have different values.
Komparasi Algoritma Naïve Bayes Dan Support Vector Machine (SVM) Pada Analisis Sentimen Spotify Ayu Sri Rahayu; Ahmad Fauzi; Rahmat Rahmat
Jurnal Sistem Komputer dan Informatika (JSON) Vol 4, No 2 (2022): Desember 2022
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/json.v4i2.5398

Abstract

The Spotify app is a subject of interest to social networking communities with significant disagreements or sentiments. Sentiment Analysis is a solution to automatically categorize opinions or ratings into negative or positive opinions. The techniques used in this research are Support Vector Machines (SVM) and Naïve Baye. The advantages of Naïve Bayes are simple, fast and high accuracy. SVM, on the other hand, can identify different hyperplanes that maximize the margin between two different classes. The classification results of this study have two category labels, namely negative and positive. The resulting accuracy value indicates the best test model for sentiment classification cases. Accuracy is measured by the confusion matrix and the results show that the accuracy value of the SVM algorithm is 84% while the accuracy value of the Naïve Bayes algorithm is higher than SVM which is 86.4%.
Personality Detection of Twitter Social Media Users using the Support Vector Machine Method Salsabila Anza Salasa; Warih Maharani
Jurnal Sistem Komputer dan Informatika (JSON) Vol 4, No 2 (2022): Desember 2022
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/json.v4i2.5345

Abstract

 Personality is a person's psychological tendency to carry out certain social behaviors, whether in the form of feelings, thoughts, attitudes and will or actions. Big Five is the most popular and widely used personality model, therefore this proposal uses the Big Five Personality model. In this technological era, humans interact using social media. One of the fastest growing social media is Twitter. Twitter is a social media used by all groups. Every human being has a different personality. Personality detectors are needed for employee recruitment to dig up information about the personality of prospective employees. So personality detection or BigFive Personality can be done through tweets that are shared on Twitter. With this, it is necessary to detect personality using the Support Vector Machine (SVM) method. From the results of the study, it was found that the maximum performance parameter combination in detecting personality on Twitter users was a combination of Linear parameters and C = 10 which obtained an accuracy of 0.979. The data used is the result of crawling on the Twitter site with 146 user usernames and 38853 tweets.
Sistem Pengambilan Keputusan Penentuan Jurusan Pada Jenjang Sekolah Menengah Atas Menggunakan Model Yager Made Leo Radhitya; I Gede Iwan Sudipa; I Putu Hery Setiawan; I Putu Hendika Permana; I Nyoman Tri Anindia Putra
Jurnal Sistem Komputer dan Informatika (JSON) Vol 4, No 2 (2022): Desember 2022
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/json.v4i2.5274

Abstract

Education is very important in supporting the intelligence of each individual, from an early age it starts to recognize many things in each level of education. Knowledge and abilities continue to develop until determining interest in the right major according to the values, abilities, desires and character of each individual student. In reality, the process of determining majors, especially at the high school level, is carried out in grade X, but this process can be done when students register, for example at Dharma Praja Denpasar High School. There are assessment criteria used in the process of determining students' major interests, namely the Average Report Card Score (C1), Science Test Score (C2), Social Science Test Score (C3) and Psychological Test Score. Applying the Yager model so that the process of determining the weight of the criteria can be done with the concept of a pairwise comparison matrix, another advantage is the process of calculating the intersection of alternative values on each alternative so that it can produce suggestions for majoring interests. The study used 5 alternative students with suggestions for majoring in science and social studies. The results showed that the Yager model could provide recommendations for the best majoring options for 5 alternatives, namely alternative A1 for science majors with a value of 2.19067, while alternatives A2, A3, A4 and A5 obtained recommendations for social studies majors. Features of the web-based major determination decision support system produce the ability to manage alternative data, criteria, alternative values, majoring processes, final results and there are test features that students can do on the system, making it easier for students to make majors and schools to recapitulate the process of determining majors. The results of blackbox testing for a total of 8 scenarios show that the system functionality is running well.
Implementasi Jaringan Syaraf Tiruan Backpropagation Pada Klasifikasi Grade Teh Hitam Muhammad Ikhsan; Armansyah Armansyah; Anggara AlFaridzi Tamba
Jurnal Sistem Komputer dan Informatika (JSON) Vol 4, No 2 (2022): Desember 2022
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/json.v4i2.5312

Abstract

Black tea is the most widely produced type of tea in Indonesia, where Indonesia itself is the 5th largest black tea exporter in the world. According to the provisions of SNI-1902-2016, the quality requirements of black tea through appearance include the shape, size and weight (density), and the color of the black tea particles themselves. This study aims to determine the workings of the backpropagation method and the implementation of python on black tea grade classification, and to determine the level MSE of accuracy in the results of black tea grade classification using backpropagation. The model used in this study uses 4 input layers, 5 hidden layers, and 3 output layers. In the input layer, 4 input variables are used, namely shape, size, density, and color. The results of the classification using backpropagation with a number of iterations of 1000 iterations on the training data obtained an error of 0.096.
Text Mining dan Klasifikasi Sentimen Berbasis Naïve Bayes Pada Opini Masyarakat terhadap Makanan Tradisional Sunneng Sandino Berutu
Jurnal Sistem Komputer dan Informatika (JSON) Vol 4, No 2 (2022): Desember 2022
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/json.v4i2.5138

Abstract

Indonesia has several famous traditional foods and is available in some cities. In addition, several international foods also are interesting to Indonesian. This article analyzes the netizen sentiment for these food categories where the data source is Twitter. The foods are rendang, sate, gudeg, pizza, hamburger, and spaghetti. The text mining approach is adopted to process data. The research steps are data crawling, cleaning, filtering, translating, and splitting. Furthermore, the classifier model based on the Naïve Bayes algorithm is developed. The analysis result shows that the gudeg food reaches a high percentage of positive sentiment with 57,9.  Then, the high rate of negative sentiment is achieved by the rendang food with 21,9 %. Moreover, hamburger food obtains a high percentage of neutral sentiment. Meanwhile, the evaluation of classifier model performance shows that the model with the hamburger dataset achieves a high score for accuracy, precision, and recall parameters with 0.72, 0.72, and 0.68 sequentially. 
Penerapan Business Intelligence Untuk Menganalisa Data Gempa Bumi di Indonesia Menggunakan Tableau Public Diana Fitri Lessy; Arry Avorizano; Firman Noor Hasan
Jurnal Sistem Komputer dan Informatika (JSON) Vol 4, No 2 (2022): Desember 2022
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/json.v4i2.5316

Abstract

One of the natural disasters that often occurs in Indonesia is an earthquake. This is because Indonesia's geological position is between 3 important lithospheric plates, namely the Pacific, Eurasian and Indo-Australian plates. The forces between the plates are constantly changing, dampening disturbances both on land and at sea. This study aims to focus on visualizing earthquake data in Indonesia and implementing Business Intelligence to display earthquake area data, depth and magnitude. The method of this study uses the Tableau Public platform to process earthquake datasets in Indonesia obtained through www.kaggle.com for the period 01 January 2018 to 30 September 2022. This research produces a report in the form of a dashboard that contains data visualization for the earthquake area, depth and magnitude from various regions in Indonesia that can be used to assist in decisions to be taken. Various designs for dashboards can be used in Tableau to make data easier to read and understand.

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