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INDONESIA
Journal of Information Technology and Computer Science
Published by Universitas Brawijaya
ISSN : 25409433     EISSN : 25409824     DOI : -
Core Subject : Science, Engineering,
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering
The Journal of Information Technology and Computer Science (JITeCS) is a peer-reviewed open access journal published by Faculty of Computer Science, Universitas Brawijaya (UB), Indonesia. The journal is an archival journal serving the scientist and engineer involved in all aspects of information technology, computer science, computer engineering, information systems, software engineering and education of information technology. JITeCS publishes original research findings and high quality scientific articles that present cutting-edge approaches including methods, techniques, tools, implementations and applications.
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Articles 11 Documents
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Search results for , issue "Vol. 7 No. 1: April 2022" : 11 Documents clear
The Effect Of Mobile Map With Geospatial Technologies On The Development Of Blind Disabilities’s Spatial Relational Thinking Muhammad Erwin Amrullah Amrullah; Fatwa Ramdani; Herman Tolle
Journal of Information Technology and Computer Science Vol. 7 No. 1: April 2022
Publisher : Faculty of Computer Science (FILKOM) Brawijaya University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jitecs.202271307

Abstract

Orang tunanetra mungkin mengalami masalah saat berkunjung dari satu tempat ke tempat lain. Satu masalah umum yang mereka hadapi adalah mereka kesulitan membiasakan diri dengan tempat-tempat yang mereka kunjungi. Bagi masyarakat pada umumnya, masalah tersebut dapat diatasi dengan menggunakan peta. Kendati demikian, solusi penyandang tunanetra seperti itu mungkin bukan solusi terbaik karena peta yang bisa digunakan oleh tunanetra tersebut adalah peta taktil. Peta taktil memiliki banyak kekurangan, salah satunya adalah sulitnya mendapatkan update ketika terjadi perubahan pada area karena peta taktil belum terdigitalisasi untuk aplikasi mobile. Oleh karena itu, perlu dibuat peta berbasis aplikasi mobile untuk memudahkan penyandang tunanetra. Penelitian ini akan memfokuskan pada peta digital yang dapat diakses melalui aplikasi mobile dengan pendekatan Extreme Programming (XP) serta studi kasus yang dilakukan di Universitas Brawijaya. Peta digital yang dikembangkan nantinya akan diujicobakan kepada responden untuk diuji kelayakannya sehingga dapat membantu meningkatkan pemahaman spasial para penyandang tunanetra khususnya di wilayah Universitas Brawijaya. Lebih penting lagi, diharapkan dapat membantu para penyandang tunanetra di sekitar Universitas Brawijaya untuk membiasakan diri dengan lingkungan sekitarnya.
E-Module Development using the Four-D Model in Computer Systems Subjects for 10th Grade of Computer and Network Engineering Expertise Program at SMKN 3 Malang Vearen Dika Sofirudin; Admaja Dwi Herlambang; Satrio Hadi Wijoyo
Journal of Information Technology and Computer Science Vol. 7 No. 1: April 2022
Publisher : Faculty of Computer Science (FILKOM) Brawijaya University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jitecs.202271311

Abstract

This research aims to explain the results of needs analysis in development e-module using the four-d model in computer systems subjects and explains the results of measuring the success rate of implementing e-modules using the four-d model in computer systems subjects. E-Module is a type of digital learning media that is arranged systematically and attractively by utilizing electronic devices. The method used is research and development or the R&D four-d model. The model research four-d consists of four stages of development, including (1) definition, (2) design, (3) development, and (4) dissemination. The subjects of this study were students of 10th grade majoring in 1st Computer and Network Engineering at Vocational High School 3 Malang, while the research instruments used were validation e-module sheets, pre-test and post-test question sheets, assessment sheets, and student response questionnaires. Based on the results of the study, it was found that: (1) In developing e-module products, researchers have adjusted to the characteristics of students who find it difficult to understand theoretical material, especially in computer systems subjects. So that the presentation of content e-module is in the form of text, images, and videos to make it easier for students to learn the material independently. (2) The results of measuring the success rate of application e-module through the Paired Sample T-Test by comparing the pre-test and post-test results, namely a significance value of 0.000. The acquisition of a significance value that is less than 0.05 means that there are differences in learning outcomes before and after using the e-module.Keywords. development, e-module, computer systems, Four-D models
Recommendations for Selection of New Students at Madrasah Aliyah Negeri Sidoarjo Using the Analytical Hierarchy Process – Weighted Product Muhammad Rizky Abdul Gofur; Ahmad Afif Supianto; Nanang Yudi Setiawan
Journal of Information Technology and Computer Science Vol. 7 No. 1: April 2022
Publisher : Faculty of Computer Science (FILKOM) Brawijaya University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jitecs.202271346

Abstract

Madrasah Aliyah Negeri (MAN) Sidoarjo is the only Madrasah Aliyah in Sidoarjo Regency under the Ministry of Religion. Many applicants always fill every year MAN Sidoarjo compared to the number of admission quotas, so it takes the suitable method in selecting new students. However, until now, MAN Sidoarjo has not found the correct method in determining new students, and the school has not used the data of new students as best as possible. In solving the problem, the methods used are AHP (Analytical Hierarchy Process) and Weighted Product. The AHP method is used in determining the weight of the criteria used, while the Weighted Product method is used to assess the quality of the recommendation data. The data used in this study is the data of admission of new students man Sidoarjo school year 2020/2021 with a total of 875 data. The expected result is 267 prospective students who qualified, 40 candidates, declare in reserve, and 568 candidates did not qualify. The results conduct test agreement using the coefficient kappa generated value 0.837 (excellent). Data-limited visualizations do with Google Data Studio in a dashboard of graphs and cards. Usability testing conduct using the System Usability Scale with a value of 82.5 shows the dashboard successfully deploy in the registration selection program of new students of MAN Sidoarjo.
Business Prospects Prediction for Waqf Lands Using Naïve Bayes And Apriori Algorithm Amiq Fahmi; Edi Sugiarto; Agus Winarno
Journal of Information Technology and Computer Science Vol. 7 No. 1: April 2022
Publisher : Faculty of Computer Science (FILKOM) Brawijaya University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jitecs.202271351

Abstract

Waqf is a donation activity of an own property for charity and the general welfare under sharia. The productive waqf empowerment in perspective economic changes the use of waqf from consumptive to productive. Lands are one form of waqf, and they are strategic assets for productive waqf empowerment. This research aims to build a classifier to predict waqf lands as productive or not productive assets for business prospects. The classification used Naïve Bayes with attributes summarised from administrative data of waqf lands. A new method was proposed to improving the classification accuracy using a modified Apriori algorithm. A threshold value defined based on a mean value from the classification process by the Naïve Bayes was used to select classification results with a deviation of posterior value, and the value which was below to be reclassified using the Apriori algorithm. The proposed method used can improve prediction accuracy better than using only one Naïve Bayes classifier.
Comparing Data Mining Models in Loan Default Prediction: A Framework and a Demonstration Cuong Nguyen; Liang Chen
Journal of Information Technology and Computer Science Vol. 7 No. 1: April 2022
Publisher : Faculty of Computer Science (FILKOM) Brawijaya University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jitecs.202271352

Abstract

In the banking sector, credit risk assessment is an important process to ensure that loans could be paid on time, and that banks could maintain their credit performance effectively. Despite restless business efforts allocated to credit scoring yearly, high percentage of loan defaulting remains a major issue. With the availability of tremendous banking data and advanced analytics tools, data mining algorithms can be applied to develop a platform of credit scoring, and to resolve the loan defaulting problem. This paper puts forward a framework to compare four classification algorithms, including logistic regression, decision tree, neural network, and Xgboost, using a public dataset. Confusion matrix and Monte Carlo simulation benchmarks are used to evaluate their performance. We find that the XGboost outperforms the other three traditional models. We also offer practial recommendation and future research.
Comparing and Analysis of Geospatial Interpolation Prediction Algorithm: Case Study The Quality of Education of Malang and Batu City, Indonesia Erik Yohan Kartiko; Fatwa Ramdani; Fitra Abdurrachman Bachtiar
Journal of Information Technology and Computer Science Vol. 7 No. 1: April 2022
Publisher : Faculty of Computer Science (FILKOM) Brawijaya University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jitecs.202271373

Abstract

Abstract. The number of schools in Indonesia continues to grow. This must also be balanced with improving the quality of education in accordance with the objectives of the 4 SDGs, which as a whole are to improve the quality of education that is inclusive, equitable and provides lifelong learning opportunities. However, until now it is very difficult to determine differences in the quality of education in an area. From the problem of education quality and education equity, it is necessary to have a regional analysis of the quality of education. This analysis can be performed using various geospatial interpolation methods. Geospatial Interpolation is a technique to find the value of a missing variable in a known data range in an area. The data used for the Geospatial interpolation process in this study are School Quality data taken through research questionnaires, as well as school accreditation data at the junior high school level. The geospatial interpolation method used in this study is the Inverse Distance Weighted, Spline, Kriging and Natural Neighbor methods. The use of different interpolation methods can indicate the best method for this research case study. Measurement validation results from each geospatial interpolation method using RMSE. From the results of this accuracy validation, the most accurate method will be obtained in determining the quality of education contained in an area.
Geometrial: Development of Educational Digital Game for Combined Two-Dimensional Figure Learning Ahmad Fairuzabadi; Herman Tolle; Fitra A Bachtiar; Ahmad Afif Supianto
Journal of Information Technology and Computer Science Vol. 7 No. 1: April 2022
Publisher : Faculty of Computer Science (FILKOM) Brawijaya University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jitecs.202271339

Abstract

Mathematics is one of the subjects that is often considered difficult and boring for students. This is evidenced by the poor scores obtained by the students. One of the chapters that are considered difficult is the geometry chapter, especially on the topic of combined two-dimensional figures studied by students at the Vocational High School (SMK) level. Spatial skills are needed for students to be able to solve combined two-dimensional figures questions, which to learn will be very difficult without using assisted learning media. While the learning so far is still using the conventional learning approach which is considered boring for students. This is due to the absence of learning media that is fun and can be easily accessed by students. This study tries to present a solution to this problem in the form of a mobile-based educational digital game design that can be accessed by all students. This digital educational game is called Geometry. This study uses Research & Development (R&D) combined with the Agile-Extreme Programming method to develop this educational digital game. Tests were carried out using an expert validation approach to game prototypes. This study uses a questionnaire that adapts the Computer System Usability Questionnaire (CSUQ) to assess the usability aspect of the game system built. Meanwhile, to assess this game from the point of view of educational media, this study used an evaluation questionnaire of material experts and media experts. The development process occurs in 3 iterations of development which includes the ideation, conceptualization, and prototyping stages. The results obtained from the assessment of material experts are 88.9% in the aspect of material suitability, and 85.7% in the suitability aspect of learning evaluation. While the results of the assessment obtained from media experts were 77.1% on the software engineering aspect, 76.3% on the visual design aspect, 80% on the media design aspect, and 77.6% on the system usability aspect (CSUQ).
The Influence of Word Vectorization for Kawi Language to Indonesian Language Neural Machine Translation I Gede Bintang Arya Budaya; Made Windu Antara Kesiman; I Made Gede Sunarya
Journal of Information Technology and Computer Science Vol. 7 No. 1: April 2022
Publisher : Faculty of Computer Science (FILKOM) Brawijaya University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jitecs.202271387

Abstract

People relatively use machine translation to learn any textual knowledge beyond their native language. There is already robust machine translation such as Google translate. However, the language list has only covered the high resource language such as English, France, etc., but not for Kawi Language as one of the local languages used in Bali's old works of literature. Therefore, it is necessary to study the development of machine translation from the Kawi language to the more active user language such as the Indonesian language to make easier learning access for the young learner. The research developed the neural machine translation (NMT) using recurrent neural network (RNN) based neural models and analyzed the influence of word vectorization using Word2Vec for the machine translation performance based on BLEU scores. The result shows that word vectorization indeed significantly increases the NMT models performance, and Long-Short Term Memory (LSTM) with attention mechanism has the highest BLEU scores equal to 20.86. The NMT models still could not achieve the BLEU scores on par with those human experts and high resource language machine translation. On the other hand, this initial study could be the reference for the future development of Kawi to Indonesian NMT.
Post-Pandemic Hotel Decision Criteria Analysis Using Decision Making Methods Prima Melati Sukma; Cutifa Safitri
Journal of Information Technology and Computer Science Vol. 7 No. 1: April 2022
Publisher : Faculty of Computer Science (FILKOM) Brawijaya University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jitecs.202271388

Abstract

This paper proposes an optimized hybrid process of the Analytic Hierarchy Process with the Simple Additive Weighting method for hotel decision-making. This study is important as many sectors including tourism are striving in the post-pandemic era. The proposed hypothesis is proven through a study case of hotel selection which included four factors for the criteria in decision-making, namely price, facilities, class, and location. The supported literature review on the topic described both methods are still widely used for the decision-making process. This study critically analyzed the importance of the selected factors. The superiority of this approach is to measure the validity by considering the depreciated value. To validate our findings, a group of sampling is done by performing the hybrid methods. Calculated results revealed the proposed methods achieve the decision-making process, the hybrid AHP – SAW model was found to be an effective method for assessing the hotel selection process.
Cancer Classification Based on the Features of Itemset Sequence Pattern of TP53 Protein Code Using Deep Miden - KNN Marji Marji; Imam Cholissodin; Dian Eka Ratnawati; Edy Santoso; Nurul Hidayat
Journal of Information Technology and Computer Science Vol. 7 No. 1: April 2022
Publisher : Faculty of Computer Science (FILKOM) Brawijaya University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jitecs.202271401

Abstract

Cancer is a disease that is still difficult to identify up to today. One of the causes of cancer is genetic modification that because of mutations in p53 gene. Healthy cells have a p53 wild type protein (normal) that is able to manage DNA separation. If DNA mutates, it will be difficult to detect cancer because the composition of the protein has changed. Bioinformatics is a combination of biology and information engineering (TI) that is utilized to manage data. One of the applications of data mining in bioinformatics is the development of pharmaceutical and medical industries. Data mining classification can use variety of methods including K-Nearest Neighbor (KNN), C45, ID3, and several other methods. One of the most reliable data classification methods is KNN. In this study, the development used two algorithms. The first was with the modification of the k-fold method, which divided two data into training data and test data, in which test-1 data and test-2 data were made into slices. The second was by a method for selecting an itemset sequence pattern that had the largest Gain Information, either 2 itemsets, 3 itemsets, and so on (Deep Miden). The best accuracy result of 96.00% was obtained through the process of computation testing in the server based on variations in terms of the number of patterns of Deep Miden itemset sequences and several k values on KNN classification method.

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