Article Per Year (5 Year)
p-Index From 2021 - 2026
2.109
P-Index
This Author published in this journals
All Journal JOURNAL OF SCIENCE AND SOCIAL RESEARCH JATI (Jurnal Mahasiswa Teknik Informatika) JISA (Jurnal Informatika dan Sains) Jurnal Abdi Insani Brilliance: Research of Artificial Intelligence Jurnal Informatika: Jurnal Pengembangan IT Jurnal Pengabdian Rekayasa Sistem
Julkarnain, M.
Unknown Affiliation
Agriculture, Biological Sciences & Forestry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mathematics Mechanical Engineering Social Sciences Other

Published : 13 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 13 Documents
Search

 1   2 
Pengembangan Aplikasi Kamus Bahasa Bima-Inggris-Indonesia Menggunakan Rapid Application Development Julkarnain, M.; Mardinata, Erwin
Jurnal Informatika: Jurnal Pengembangan IT Vol 8, No 3 (2023)
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/jpit.v8i3.5692

Abstract

The dictionary is one of the solutions for learning about vocabulary and translating it. The use of dictionaries in the form of books is less effective and efficient, so it is necessary to develop electronic dictionaries in the form of dictionary applications available on smartphones. This study aims to develop an Android-based three-language dictionary application, namely Bima, Indonesian, and English, which includes a speech to text  feature. The software development method uses the Rapid Application Development (RAD) method. This method has four main stages: requirements planning, design, construction, and cutover. Data collection methods in this research include observation, interviews, documentation, and literature study. The data analysis technique used is qualitative data analysis of data resulting from observations, interviews, and literature studies. The application was built using the Flutter and Codeigniter frameworks. In the final stage of dictionary application development, testing was carried out on the application's functionality using the black box method. The results of the test show that the application runs very well; all buttons and features work as they should after fixing bugs and problems found in the final test before launch.
SOSIALISASI DAN PELATIHAN PEMBUATAN PAVING BLOCK DARI LIMBAH SAMPAH PLASTIK DI DESA LABUHAN KABUPATEN SUMBAWA Adiasa, Iksan; Aldrin; Julkarnain, M.; Mashabai, Ismi; Robban, Farisan; Esabella, Shinta
Jurnal Abdi Insani Vol 11 No 2 (2024): Jurnal Abdi Insani
Publisher : Universitas Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/abdiinsani.v11i2.1347

Abstract

Sumbawa is an area that has minimal waste processing, especially plastic. Processing plastic waste needs to be done to prevent freezing, especially on beaches. This is because plastic waste is waste that is difficult to decompose. One of the areas that produces a lot of plastic waste in Sumbawa is Labuhan Village. Therefore, efforts need to be made to minimize the amount of waste in Labuhan Village by processing it into paving blocks. The aim of this service activity is to provide outreach and training on making paving blocks from plastic waste. This is done to improve the human resources of the Village Community in minimizing existing Plastic Waste. The method used is through socialization, delivery of material and direct question and answer discussions to the community and direct training related to making plastic paving blocks. Before carrying out the training, the community was given pretest questions and ended with post-test questions. The results obtained from this activity are an increase in the ability of village residents to process plastic waste into paving block products. This can be seen from the increase in public understanding by 68% as seen from the comparison of the pretest and post-test results. Apart from that, the community is also able to directly instill the process of making paving blocks from plastic waste, so that the community can utilize this understanding by making it independently and making it a side business in minimizing the accumulation of plastic waste. This community service activity is able to provide understanding and abilities to village residents in processing plastic waste into paving blocks.
Application of Information Gain to Select Attributes in Improving Naïve Bayes Accuracy in Predicting Customer's Payment Capability Herfandi, Herfandi; Zaen, Mohammad Taufan Asri; Yuliadi, Yuliadi; Julkarnain, M.; Hamdani, Fahri
JISA(Jurnal Informatika dan Sains) Vol 4, No 2 (2021): JISA(Jurnal Informatika dan Sains)
Publisher : Universitas Trilogi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31326/jisa.v4i2.1044

Abstract

The customer is the main factor in the running of PT. XYZ. A good understanding of customers is very important for predicting the capability of customers to pay. The implementation of credit collectibility is used to determine the quality of customer credit, one of which is the customer's capability to pay interest and principal on time. While manually, it is very difficult to accurately predict the capability of customer credit payments. Data mining techniques with the Naïve Bayes algorithm were chosen to classify customers to be able to find patterns, analyze and predict, because they have good performance, are efficient, and simple. The Naïve Bayes algorithm has a weakness in terms of sensitivity to many attributes, so the accuracy is low. Based on the problem stated, his study will apply the Information Gain method to select the most influential attribute on the label in order to increase the accuracy of the Naïve Bayes algorithm. This research produces a new dataset with seven attributes: TENOR, SALARY, DOWN PAYMENT, INSTALLMENT, APPROVAL, OTR CLASS, AGE with Labels: Status and Id: Id number based on the Information Gain method. The dataset comparison process with 995 data records showed an increase in accuracy, precision, and AUC using the new dataset compared to the old dataset, but in the t-Test test with an alpha value = 0.05 there is a difference but not significant. In the evaluation process, performance experienced a significant increase in the use of new datasets with the following percentages of performance improvement: accuracy = 8%, precision = 18.42%, recall = 17.65% and AUC= 0.057%. The results of this study obtained AUC of 0.876, accuracy of 87.88%, precision of 61.90%, and recall of 76.47%, and classified into good classification. 
Co-Authors Aldrin Andini, Virna Febri Bahtiar, Syamsul Dinola Dzil Ikram, Fadhli Eri Sasmita Susanto Fahri Hamdani Hamdan, Fahri Herfandi Herfandi Hermanto, Koko Ikram, Fadhli Dzil Iksan Adiasa Imam Munandar Ismi Mashabai Juniardi Akhir Putra Mardinata, Erwin Mohammad Taufan Asri Zaen Nawassyarif Nuryadi, Halid Oktavia, Siska Atmawan Putra, M. Fatwa Andi Syah Putraedi, Andri Qifli Ilhamdi, Jizan Robban, Farisan Rodianto, Rodianto Ryanto, Rizky Adi Shinta Esabella Sofya, Nora Dery W, Yunari. Yuliadi Yuliadi Yuliadi