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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
 68   69   70   71   72 
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 : Universitas 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.
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 : Universitas 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.
Sistem Administrasi Pelayanan Rukun Tetangga Dengan Framework PIECES Untuk Kepuasan Pengguna Berbasis Android Rima Tamara Aldisa; Puspa Ayu Soleha
Jurnal Sistem Komputer dan Informatika (JSON) Vol. 4 No. 2 (2022): Desember 2022
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/json.v4i2.5288

Abstract

This research aims to build a pillar-saving administration system aimed at helping residents around the Jagakarsa area, South Jakarta. With this system, it can make it easier for residents to be more efficient by knowing how satisfied and comfortable residents are. This system was created using the PIECES Framework with measurements from 6 aspects, namely from the aspects of Performance, Information, Economics, Control, Efficiency, Service from the results of the Pieces Analysis showing that the measurement of the level of satisfaction of system users. This system is intended for residents who want to make monthly contribution payments, find out about the activities being carried out, provide criticism and suggestions, find out announcements, request letters if needed to analyze user satisfaction with deductions which results in an acquisition score of 3,610 which is the average and gets the final score which is satisfying. So it was concluded that users, namely citizens, can use the application that has been designed.
Penerapan Algoritma K-Means dan Decision Tree Dalam Analisis Prestasi Siswa Sekolah Menengah Kejuruan Muhammad Bari Abdul Majid; Yusup Mad Cani; Ultach Enri
Jurnal Sistem Komputer dan Informatika (JSON) Vol. 4 No. 2 (2022): Desember 2022
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/json.v4i2.5299

Abstract

This study aims to determine how much influence parents' income and the distance from a student's home to school have on student achievement at SMK Tarbiyatul Ulum. Because if you know the factors that influence student achievement, steps or actions can be taken to improve student achievement. The method used in this study includes the use of clustering using K-Means and then classifying it using the Decision Tree method with a total of 157 datasets used as research material. After conducting the classification modeling using the decision tree algorithm, it was found that parents' income did not affect student achievement, but the distance from home to school did affect student achievement. Then at the evaluation stage, the decision tree algorithm is not suitable for use in predicting student achievement, because the accuracy and AUC values of this algorithm are 68% and 0.561, where these values fall into the failure category.
Sistem Pendukung Keputusan Seleksi Tenaga Fasilitator Lapangan BSPS Menggunakan Metode Multi Factor Evaluation Process Nur Oktavin Idris; A. Mulawati Mas Pratama; Muliati Badaruddin
Jurnal Sistem Komputer dan Informatika (JSON) Vol. 4 No. 2 (2022): Desember 2022
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/json.v4i2.5303

Abstract

Field facilitator workers shall guide the Self-Help Housing Stimulant Assistance Program (BSPS) recipients intensively, either in technical matters or the fund use realization process during construction terms. Guidance is given from planning, implementing a construction, and forming an assistance recipient community group that can construct decent housing independently. Accordingly, field facilitator workers are imperative to guide the BSPS recipient community. Public Works and Spatial Planning Office in Gorontalo is still selecting field facilitator workers manually, bringing on assessment errors in the selection process, e.g., selecting field facilitator workers that do not fulfill the requirement. Additionally, the sheer number of applicants overwhelms staff as they have to face off piling files of applicants. It brings about a longer selection process. As such, a systematic selection process based on the determined criteria is important. Later, it can act as a reference. It is called a decision-supporting system. Our decision-supporting system aims to help make decisions to select field facilitator workers guiding the BSPS recipient community using the Multi-Factor Evaluation Process (MFEP) method. The results point out Bambang achieved the highest final score of 87.675. Hence, MFEP in the supporting system is a recommended method to make decisions to select field facilitator workers guiding the BSPS recipient community in building decent housing.
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 : Universitas 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.
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 : Universitas 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.
Budidaya Tanaman Sayuran Menggunakan Sistem Pendukung Keputusan Dengan Metode Profile Matching Dhella Amelia; Revi Gusriva
Jurnal Sistem Komputer dan Informatika (JSON) Vol. 4 No. 2 (2022): Desember 2022
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/json.v4i2.5338

Abstract

Cultivating vegetable plants is very important for farmers. In the cultivation of vegetable plants, there are problems that are often faced by farmers, one of which is the lack of information about natural conditions and limited knowledge of farmers. so it is necessary to have an auxiliary media in the form of a system that can provide solution information at any time. Selection of plants that are not suitable will get unsatisfactory results. Farmers must know that what needs to be considered in cultivating vegetable crops is compatibility with natural conditions. This research builds a web-based decision support system software regarding vegetable cultivation that meets the criteria using the profile matching method as a tool to facilitate the decision-making process with several comparison criteria, namely: temperature, sunlight, rainfall and humidity, altitude, harvest time, marketability, susceptibility to disease, and level of care. This application is made web-based using PHP as the programming language and MySQL as the data storage. The purpose of this application is to provide effective and efficient information to farmers in making decisions and alternatives that are in accordance with the natural conditions of the area. The result of making this system is that farmers make the right decisions when cultivating vegetables based on the criteria.
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 : Universitas 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.
Analisis Sentimen Tindakan Pemerintah Indonesia Dalam Penanganan Covid-19 Menggunakan Metode Support Vector Machine Ariesta Damayanti; Helda Ludya Safitri; Rudy Cahyadi
Jurnal Sistem Komputer dan Informatika (JSON) Vol. 4 No. 2 (2022): Desember 2022
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/json.v4i2.5341

Abstract

Corona Virus Disease 2019 (Covid-19) which has hit the world including Indonesia since the beginning of 2020 is an outbreak that has become a serious threat to world health. The Indonesian government is taking various actions to deal with this problem, while the public, with the existence of social media, has provided many responses to these government policies. Twitter is one of the social media that is widely used by the public to convey comments in the form of responses, suggestions, to criticism of the government regarding the handling of Covid-19. The comments that appear should be used by the government as part of the reference in evaluating a policy or action taken in handling Covid-19. So that one way that can be used to deal with this is one of the methods that exist in the domain of text mining, namely sentiment analysis. This research was conducted by analyzing sentiment using the Support Vector Machine (SVM) method with the Kernel Radial Basis Function (RBF). Tweets will be classified into positive, negative and neutral sentiments, so that the percentage of each opinion category can be known. This study uses data of 600 tweets obtained from the results of scraping using a Twitter scraper. The result of this study is that the training accuracy rate is 77% in classifying positive, negative, and neutral sentiments. From the results of the data classification, it was found that most of the tweets consisted of negative sentiments.

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