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Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi)
ISSN : 25800760     EISSN : 25800760     DOI : https://doi.org/10.29207/resti.v2i3.606
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) dimaksudkan sebagai media kajian ilmiah hasil penelitian, pemikiran dan kajian analisis-kritis mengenai penelitian Rekayasa Sistem, Teknik Informatika/Teknologi Informasi, Manajemen Informatika dan Sistem Informasi. Sebagai bagian dari semangat menyebarluaskan ilmu pengetahuan hasil dari penelitian dan pemikiran untuk pengabdian pada Masyarakat luas dan sebagai sumber referensi akademisi di bidang Teknologi dan Informasi. Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) menerima artikel ilmiah dengan lingkup penelitian pada: Rekayasa Perangkat Lunak Rekayasa Perangkat Keras Keamanan Informasi Rekayasa Sistem Sistem Pakar Sistem Penunjang Keputusan Data Mining Sistem Kecerdasan Buatan/Artificial Intelligent System Jaringan Komputer Teknik Komputer Pengolahan Citra Algoritma Genetik Sistem Informasi Business Intelligence and Knowledge Management Database System Big Data Internet of Things Enterprise Computing Machine Learning Topik kajian lainnya yang relevan
Articles 25 Documents
Search results for , issue "Vol 4 No 1 (2020): Februari 2020" : 25 Documents clear
Educational Data Mining untuk Prediksi Kelulusan Mahasiswa Menggunakan Algoritme Naïve Bayes Classifier Sutoyo, Edi; Almaarif, Ahmad
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 4 No 1 (2020): Februari 2020
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v4i1.1502

Abstract

The quality of students can be seen from the academic achievements, which are evidence of the efforts made by students. Student academic achievement is evaluated at the end of each semester to determine the learning outcomes that have been achieved. If a student cannot meet certain academic criteria that are stated by fulfilling the requirements to continue his studies, the student may have the potential to not graduate on time or even Drop Out (DO). The high number of students who do not graduate on time or DO in higher education institutions can be minimized by detecting students who are at risk in the early stages of education and is supported by making policies that can direct students to complete their education. Also, if the time for completion of student studies can be predicted then the handling of students will be more effective. One technique for making predictions that can be used is data mining techniques. Therefore, in this study, the Naive Bayes Classifier (NBC) algorithm will be used to predict student graduation at Telkom University. The dataset was obtained from the Information Systems Directorate (SISFO), Telkom University which contained 4000 instance data. The results of this study prove that NBC was successfully implemented to predict student graduation. Prediction of the graduation of these students is able to produce an accuracy of 73,725%, precision 0.742, recall 0.736 and F-measure of 0.735.
Implementasi Metode Perceptron Untuk Pengenalan Pola Jenis-Jenis Cacing Nematoda Usus Erni Rouza; Jufri; Luth Fimawahib
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 4 No 1 (2020): Februari 2020
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v4i1.1662

Abstract

The purpose of pattern recognition is do the process of classifying an object into one particular class based on the pattern it has, so it can be used to recognize patterns of intestinal nematode worm types. One of the methods used in pattern recognition is by utilizing the artificial neural network method, the artificial neural network is able to represent a complex Input-Output relationship. For that the algorithm used is the perceptron algorithm. Perceptron is one method of Artificial Neural Networks. In the introduction of types of intestinal nematode worms, a computer must be trained in advance using training data and test data, this study discusses how a computer can recognize a pattern of types of intestinal nematode worms using the perceptron method. Based on the results of testing trials with input in the form of worm image scan results, based on the results of the perceptron method testing is able to recognize the pattern recognition of the types of intestinal nematode worms and be able to analyze with the right results of 100% for pinworms patterns, hookworm patterns, and 40- 50% for roundworms, by comparing the output value and the target value entered first.
Aplikasi Expert system Pengembangan Karir Menggunakan Inventory Kepribadian Entrepreneurship Resmi Darni; Dony Novaliendry; Ika Parma Dewi
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 4 No 1 (2020): Februari 2020
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v4i1.1626

Abstract

This study aims to describe the process of designing expert system applications in career development using entrepreneurship personality inventory. The reason for conducting this research is a) the limited number of instruments that are able to measure the entrepreneurship personality of vocational students, b) the high costs incurred in conducting personality tests in career development of vocational students, c) The need for an application that is able to speed up the process of identifying personality and provide recommendations careers for vocational students. The research methodology used is Research and Development. There are four stages that must be carried out: Define, Design, Develop and Disseminate. The instrument used to measure entrepreneurial personality is inventory (non-test) with four indicators of entrepreneurship personality, namely Extroverted, Leader, Moderate Risk Taker, Ambisious, and tested fit using Confirmatory Factor Analysis. The research sample was 30 vocational high school students in the field of Information Technology and Computers. Bayes Method is used for the process of transferring knowledge from experts to the system and decision making, after Based on the results of data analysis, it was found that a) Career development applications based on Entrepreneurship Personality are valid with a percentage (0.887) and very practical (91.11), the results of product effectiveness are 82.47 (effective). Therefore this application is declared valid, practical and effective in measuring the personality of vocational student entrepreneurship.
English Edugame Application for Childhood base on Android Dony Novaliendry; Andriani, Sisca
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 4 No 1 (2020): Februari 2020
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v4i1.1622

Abstract

apply arithmetic with play compared to without playing. Play and child is an inseparable unity. Play activities carried out children and children’s activities always indicate play activities. In childhood the comprehension is very high, more activities a child does, the ability to remember will also be higher. There are several factors make it difficult for children to recite and remember the object, color around them, it is less interested in the media used. Childhood always follows the rhyme of its development. at an early age, language development is very important, because at this age it is a sensitive period for children. The English language is the International language. English language for children starts from introducing vocabulary that is closest to the child, the goals to make it easier for children to remember. One way to maximal comprehension by giving learning fun involves learning with the game. The research resulted in an English game application based on android. In this application, there are education and game features. The education features an exciting good and gets the sound from the lesson that is available. Game features available are guessed pictures, guess sounds, and puzzles. The purpose of English edugame application to teach children vocabulary of English and make children not bored to learn.
Evaluasi Usability Sistem Pelaporan Publikasi Penelitian Dosen Berbasis Android Fransiskus Panca Juniawan; Laurentinus; Dwi Yuny Sylfania
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 4 No 1 (2020): Februari 2020
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v4i1.1555

Abstract

The research publication reporting system of the STMIK Atma Luhur lecturer is an android-based system used by the STMIK Atma Luhur lecturer to report their research publications to the LPPM Research Section. To find out whether the system used is running well, an evaluation is carried out. The evaluation focuses on the value of Usability which consists of five categories as independent variables, namely Efficiency (X1); Learnability (X2); Satisfaction (X3); Errors (X4); and Memorability (X5). In addition, the dependent variable is Overall Impression Usability (Y). We used 37 lecturers as respondents. The test method used is a validation test consisting of a correlation test and a reliability test; simple linear regression analysis test, and the comparison of the Significance value with the Alpha value used is 0.05. The tests conducted using SPSS version 25. From the validity test conducted, taken from the value of Pearson Correlation and Corrected Item - Total Correlation values ​​that have values ​​above 0.05 and the results are that all variables have values ​​above 0.05. From the reliability testing, all questions proved to be reliable from one to another according to Cronbach’s Alpha values ​​above 0.60. From the simple linear regression test, the results show that the Efficiency (X1), Learnability (X2), Satisfaction (X3), and Memorability (X5) variables have a significant effect on Overall Impression (Y). In addition, the Errors (X4) variable does not have a significant effect on the Overall Impressions (Y) variable.
Analisis Fasilitas Pariwisata Menggunakan Prosedur Pengambilan Keputusan N-Soft Set Fatia Fatimah; Andriyansah
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 4 No 1 (2020): Februari 2020
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v4i1.1536

Abstract

Conflict resolution plays an important role in organizations such as in business, government, politics, etc. Some management systems make a conflict as an effort to explore the ability of their employees in rational thinking. Unfortunately, conflict situations tend to be uncertain. It is very difficult to make decisions in rational manner. In this paper, we used -soft sets approach to handle such conflict problems. The proposed algorithm was applied to the real tourism data. The -soft sets algorithm for conflict analysis is effective for decision making problems. The results showed that public transportations and place of worships are very good in Gili Trawangan, Senggigi, Sembalun, and Pantai Kuta, West Nusa Tenggara.
Sistem Rekomendasi Pemilihan Peminatan Menggunakan Density Canopy K-Means Ananda, Ridho; Muhammad Zidny Naf’an; Amalia Beladinna Arifa; Auliya Burhanuddin
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 4 No 1 (2020): Februari 2020
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v4i1.1531

Abstract

The carelessly selection of specialization course leaves some students with difficulty. Therefore, it is needed a recommendation system to solve it. Several approaches could be used to build the system, one of them was K-Means. K-Means required the number of initial centroid at random, so its result was not yet optimal. To determine the optimal initial centroid, Density Canopy (DC) algorithms had been proposed. In this research, DC and K-Means (DCKM) was implemented to build the recommendation system in the problem. The alpha criterion was also proposed to improve the performance of DCKM. The academic quality dataset in the 2018 informatics programs students of ITTP was used. There were three main stages in the system, namely determination of the weight of the course in dataset, implementation of DCKM, and determination of specialization recommendations. The results showed that the system by using DCKM has good quality based on the Silhouette results (at least 0.655). The system also used standar valuation scale in ITTP and silhouette index in the process of system. The results showed that 176 (65.91%) students were recommended in IT specialization, 25 (9.36%) students were recommended in MM specialization and 66 (24.7%) students were recommended in SC specialization.
Model Manajemen Big Data Komoditas Beras untuk Kebijakan Pangan Nasional Tosida, Eneng Tita; Wihartiko, Fajar Delli; Hermadi, Irman; Yani Nurhadryani; Feriadi
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 4 No 1 (2020): Februari 2020
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v4i1.1520

Abstract

Rice is the main commodity in Indonesia, both for consumption and production. Rice production data are available at the Badan Pusat Statistika and at Kementrian Pertanian. The data is used to build a large data management model for Indonesia's rice trade. The model development strategy is done through analyzing agriculture big data analytic that is equipped with descriptive analysis, evaluation, predictive and prescriptive. The models and designs that are built discuss business processes, stakeholder networks and network management. Descriptive analysis results in the form of grouping and visualization of rice data. The results of the diagnostic process using classification approach produce a decision tree to see the results of the level of production in a province. In the predictive process produces a linear regression model to predict the results of the following year's production as well as in the analysis.
Komparasi Metode ELECTRE, SMART dan ARAS Dalam Penentuan Prioritas RENAKSI Pasca Bencana Alam Agusta Praba Ristadi Pinem; Titis Handayani; Lenny Margaretta Huizen
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 4 No 1 (2020): Februari 2020
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v4i1.1526

Abstract

Each organization must collect data as a result of the use of information technology. Over time the data is processed into information. The information collected is used as a basis for decision making. But not all information can be directly used for the decision making process. Necessary methods and weighting in the process of getting information. One model in a decision support system is Multi Criteria Decision Making (MCDM). The MCDM model makes it possible to provide the best choice of information from several choices of the many criteria and alternatives used. This study compares the MCDM model, namely the ELECTRE (Elimination Et Choix Traduisant la Realite) method, SMART (Simple Multi-Attribute Rating Technique), ARAS (Additive Ratio Assessment) as a priority determination for the handling of areas affected by natural disasters which must be addressed first in the RENAKSI (Reconstruction and Rehabilitation Action Planning), in this case earthquake natural disasters. The ELECTRE method has a different algorithmic process than SMART and ARAS. The validation test method ELECTRE, SMART and ARAS against dataset occurrence of the earthquake is become the results of this research. Spearman rank correlation values ​​for the three methods amounted to 0.96. And another correlation method value of 0.85 for the ARAS method and 0.82 for the ELECTRE and SMART methods.
Analisis Pengaruh Data Scaling Terhadap Performa Algoritma Machine Learning untuk Identifikasi Tanaman Ambarwari, Agus; Jafar Adrian, Qadhli; Herdiyeni, Yeni
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 4 No 1 (2020): Februari 2020
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v4i1.1517

Abstract

Data scaling has an important role in preprocessing data that has an impact on the performance of machine learning algorithms. This study aims to analyze the effect of min-max normalization techniques and standardization (zero-mean normalization) on the performance of machine learning algorithms. The stages carried out in this study included data normalization on the data of leaf venation features. The results of the normalized dataset, then tested to four machine learning algorithms include KNN, Naïve Bayesian, ANN, SVM with RBF kernels and linear kernels. The analysis was carried out on the results of model evaluations using 10-fold cross-validation, and validation using test data. The results obtained show that Naïve Bayesian has the most stable performance against the use of min-max normalization techniques as well as standardization. The KNN algorithm is quite stable compared to SVM and ANN. However, the combination of the min-max normalization technique with SVM that uses the RBF kernel can provide the best performance results. On the other hand, SVM with a linear kernel, the best performance is obtained when applying standardization techniques (zero-mean normalization). While the ANN algorithm, it is necessary to do a number of trials to find out the best data normalization techniques that match the algorithm.

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