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All Journal Bulletin of Electrical Engineering and Informatics Building of Informatics, Technology and Science Jurnal Manajemen Teknologi dan Sistem Informasi PROCEEDING OF INTERNATIONAL CONFERENCE ON BUSINESS MANAGEMENT AND ACCOUNTING
Istoningtyas, Marrylinteri
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Humanities Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Social Sciences

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Analisis Kualitas Layanan Website Pengadilan Negeri Jambi Dengan Metode Webqual 4.0 Donzon, Allbet; Istoningtyas, Marrylinteri
Jurnal Manajemen Teknologi Dan Sistem Informasi (JMS) Vol 4 No 2 (2024): JMS Vol 4 No 2 September 2024
Publisher : LPPM STIKOM Dinamika Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33998/jms.2024.4.2.1959

Abstract

The Jambi District Court is a first-level judicial institution within the judicial system of Indonesia. The Jambi District Court's website provides various information, but it is possible that there are still issues or shortcomings, such as the website's less appealing design, the complaint service being difficult to understand, incomplete or insufficient information provided to users, the lack of responsiveness regarding complaints through external platforms (social media), and the limited information and discrepancies in the scheduled court hearings. Based on this background, this study aims to analyze user satisfaction levels with the Jambi District Court’s website using the WebQual 4.0 method. The study employs four variables: Usability Quality, Information Quality, Service Interaction Quality, and User Satisfaction. A total of 315 respondents who have used the Jambi District Court’s website participated in this study. The data was processed using SPSS 2.5 software. The results show that only one variable, Service Interaction, has a significant effect on satisfaction, while the other two variables, Usability Quality and Information Quality, do not have a significant impact on satisfaction.
Analisis Kepuasan Pengguna Aplikasi ChatGPT Menggunakan Metode E-Serqual (Studi Kasus : Mahasiswa/I Universitas Dinamika Bangsa) Mulyanto, Ade; Istoningtyas, Marrylinteri
Jurnal Manajemen Teknologi Dan Sistem Informasi (JMS) Vol 4 No 2 (2024): JMS Vol 4 No 2 September 2024
Publisher : LPPM STIKOM Dinamika Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33998/jms.2024.4.2.1965

Abstract

The ChatGPT application provides natural language processing (NLP) technology capable of responding to human questions in the form of text (referred to as Prompts) typed into the application. The author conducted this research to determine the extent of the impact of user satisfaction on the ChatGPT application. The method used is the E-SERVQUAL method, with a sample of 360 respondents and data analysis performed using SPSS software. The results of the validity and reliability tests indicated that all data in this study were valid and reliable. The research findings show that out of the seven E-SERVQUAL variables, only five—Fulfillment, System Availability, Responsiveness, Compensation, and Contact—significantly affect user satisfaction with the ChatGPT application.
Reduksi False Positive Pada Klasifikasi Job Placement dengan Hybrid Random Forest dan Auto Encoder Pahlevi, M. Riza; Rasywir, Errissya; Pratama, Yovi; Istoningtyas, Marrylinteri; Fachruddin, Fachruddin; Yaasin, Muhammad
Building of Informatics, Technology and Science (BITS) Vol 5 No 4 (2024): March 2024
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v5i4.4864

Abstract

The False Positive (FP) interpretation shows a negative prediction result and is a type 1 error answer with an incorrect positive prediction result. Based on this, we try to reduce type 1 errors to increase the accuracy value of the classification results. A low FP rate is critical for the use of Computer Aided Detection (CAD) systems. In this research proposal, to reduce FP, we use a Random Forest (RF) evaluation result design which will be reinterpreted by the Auto Encoder (AE) algorithm. The RF algorithm was chosen because it is a type of ensemble learning that can optimize accuracy in parallel. RF was chosen because it performs bagging on all Decision Tree (DT) outputs used. To suppress TP reduction more strongly, we use the Auto Encoder (AE) algorithm to reprocess the class bagging results from RF into input in the AE layer. AE uses reconstruction errors, which in this case is Job Placement classification. From the test results, it was found that combining the use of a random forest using C4.5 as a decision tree with an Autoencoder can increase accuracy in the Job Placement Classification task by a difference of 0.004652 better than without combining it with an autoencoder. Apart from that, in testing using a combination of RF and AE, fewer False Positive (FP) values ​​were produced, namely 11 items in the Cross Validation-5 (CV-5) Test, then 13 items in the Cross Validation-10 (CV-10) test and in testing split training data of 60%, the FP was only 12. This value is less than the false positives produced by testing without Autoencoder, namely 12 items on CV-5, 15 items on CV-10, and 13 on split training data
JMO App User Behavior Analysis with Theory of Planned Behavior (Case Study: Jambi City Community) Salwa, Shakira; Rambe, Lio Nauli; Yani, Serli Putri Andri; Borroek, Maria Rosario; Istoningtyas, Marrylinteri
International Conference on Business Management and Accounting Vol 2 No 1 (2023): Proceeding of International Conference on Business Management and Accounting (Nov
Publisher : Institut Bisnis dan Teknologi Pelita Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35145/icobima.v2i1.3526

Abstract

Jamsostek Mobile application is an application as an information service feature for Employment BPJS policies which can be accessed online, one of its features is a simulation of JHT and Pension Guarantee (JP) balances. The purpose of this study is to analyze user behavior in using JMO – Jamsostek Mobile application and to find out which variables have the most influence and have the highest value compared to other variables. The method used in this study is Theory of Planned Behavior (User Behavior Theory) which is an extension of Theory of Reasoned Action (TRA) aims to look at user behavior in JMO – Jamsostek Mobile application which focuses on 5 variables, in the form of 3 independent variables namely attitude toward behavior, subjective norm, perceived behavioral control) and 2 dependent variables namely behavioral intention and behavior. The population were the people of Jambi City where the number of respondents obtained from distributing questionnaires via Google Form was 384 respondents and this data analysis technique used SmartPLS. the results of hypothesis testing have a strong level of relationship that the independent variable has an influence on the dependent variable.
Public complaint tweet data feature analysis for sentiment classification Rasywir, Errissya; Pratama, Yovi; Irawan, Irawan; Istoningtyas, Marrylinteri
Bulletin of Electrical Engineering and Informatics Vol 13, No 6: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i6.7172

Abstract

The perception of the public regarding a government's performance significantly impacts a city's advancement. This research involved analyzing complaint tweets from Jambi City residents directed at the government to gauge sentiment. In the testing phase, 500 Twitter accounts were examined to categorize sentiment as positive, negative, or neutral. Training data was prepared by extracting tokens through feature selection techniques such as information gain (IG) and mutual information (MI). For testing, all tokens are entered as data in the input layer in the recurrent neural network (RNN). From the tests carried out, the average use of feature selection can achieve a good value compared to no feature selection. But more specifically the use of IG produces better accuracy compared to the use of MI. From the research conducted, Twitter data is classified using a RNN and several tests by adding feature selection to produce differences. The results are proven to improve classification performance. With a recall value of 92.243%, it shows the system's success rate in sentiment classification and a precision of 92% indicates a level of accuracy that is sufficient to support the government's sentiment assessment.
Co-Authors Borroek, Maria Rosario Donzon, Allbet Errissya Rasywir Fachruddin Fachruddin Irawan Irawan Mulyanto, Ade Pahlevi, M. Riza Rambe, Lio Nauli Salwa, Shakira Yaasin, Muhammad Yani, Serli Putri Andri Yovi Pratama