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Journal of Big Data Analytic and Artificial Intelligence
Published by Sekolah Tinggi Ilmu Manajemen Informatika dan Komputer Tarakanita Rahmawati
ISSN : 25979604     EISSN : 27223256     DOI : https://doi.org/10.71302
Core Subject : Science,
Computer Science & IT
JBIDAI adalah jurnal nasional berbahasa Indonesia versi online yang dikelola oleh Prodi Sistem Informasi STMIK PPKIA Tarakanita Rahmawati. Jurnal ini memuat hasil-hasil penelitian dengan cakupan fokus penelitian meliputi : Artificial Intelligence, Big Data, Data Mining, Information Retrieval, Knowledge Doscovering in Database dan bidang-bidang lainnya yang termasuk ke dalam rumpun ilmu tersebut.
Arjuna Subject : Ilmu Komputer (semua kategori) - Kecerdasan Buatan
Articles 5 Documents
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Search results for , issue "Vol 5 No 1 (2019): JBIDAI Juni 2019" : 5 Documents clear
Analisa Tingkat Kepuasan Pelanggan Menggunakan Metode Servqual Dan Lexicon Based Ahmad Syar; Nurhalizah Noor; Aida Indriani; Anto, Anto
Journal of Big Data Analytic and Artificial Intelligence Vol 5 No 1 (2019): JBIDAI Juni 2019
Publisher : STMIK PPKIA Tarakanita Rahmawati

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Abstract

Customer satisfaction can be achieved through delivering optimal service performance to customers. Excellent service is realized when a business can maintain and develop customer loyalty while continuously improving the quality of services provided. In the case of Trijaya Computer Store, there is currently no evaluation framework in place to assess service quality, making it difficult to gauge customer satisfaction. In this study, two methods are employed: the SERVQUAL method and the Lexicon-Based method. The SERVQUAL method is used to measure service quality and customer satisfaction based on the services provided, while the lexicon-based method identifies which variables within the service quality dimensions need improvement by analyzing the three highest negative opinion scores. The analysis using the SERVQUAL method reveals that the largest service quality gap occurs in the Tangibles dimension, with a gap score of -0.44, indicating that customer expectations in this dimension are significantly unmet, leading to dissatisfaction. In the lexicon-based sentiment analysis, the highest negative opinion scores are 21, 17, and 16, corresponding to variables V3, V6, and V2, respectively. These variables represent specific aspects of the service that need improvement at Trijaya Computer. The variable V6, which states that "Trijaya Computer employees can resolve issues in a timely manner," is particularly important, as it appears in the results of both methods, indicating that timely problem resolution is a key concern for customers and should be prioritized for improvement. By addressing these issues, Trijaya Computer can enhance its service quality and improve customer satisfaction.
Implementasi Moora Pada Penilaian K3 Pemerintah Kota Tarakan Romadan; Rusmin; Yusni Amaliah; Anto, Anto
Journal of Big Data Analytic and Artificial Intelligence Vol 5 No 1 (2019): JBIDAI Juni 2019
Publisher : STMIK PPKIA Tarakanita Rahmawati

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Abstract

The Tarakan City Government, specifically the Organization Division of the Regional Secretariat (Setda), has struggled to maintain clean, organized, and aesthetically pleasing office environments, leading to numerous public complaints. These issues have negatively impacted the government's image and service quality. To address this, a decision support system is needed to help the Regional Secretary identify which government offices meet the standards for being comfortable and welcoming, based on Occupational Health and Safety (OHS) criteria outlined in Perwali No. 14 of 2017. Currently, Setda lacks an integrated information system to assess which offices meet these standards and identify which criteria require improvement. The existing evaluation method, done through Microsoft Excel, is inefficient, making the decision-making process less effective. In response, this study proposes a system that can categorize offices based on OHS standards and highlight criteria for improvement using the MOORA method. The study evaluates 16 government offices as alternatives, with data collected from interviews and the 2017 OHS evaluation sheets, covering eight criteria (seven benefit and one cost criterion) and 24 sub-criteria. The MOORA method is applied to generate final scores that provide rankings, categories, and improvement criteria. The OHS categories are defined as Green (scores between 70 and 90, indicating a high level of OHS), Yellow (scores between 50 and 70, indicating a moderate level of OHS), and Red (scores between 30 and 50, indicating a low level of OHS). The results show that the system is effective, informative, and efficient in displaying the OHS status of the offices. Out of the 16 offices, 11 are classified in the Green category, while 5 fall into the Yellow category. The Green category scores range from 70.78 to 79.63, and the Yellow category scores range from 66.26 to 69.09. The study identifies the need for improvements, particularly in Waste Management and Policies/Innovations by the Heads of Offices. This MOORA-based decision support system enables the government to better evaluate and improve the OHS performance of its offices, contributing to enhanced service quality and government reputation.
Analisis Sentimen pada Hasil Angket Penilaian Sarana dan Prasarana Laboratorium Menggunakan Metode Holistic Lexicon Based Yunus Langan; Evi Dianti Bintari; Gusmana, Roman
Journal of Big Data Analytic and Artificial Intelligence Vol 5 No 1 (2019): JBIDAI Juni 2019
Publisher : STMIK PPKIA Tarakanita Rahmawati

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Abstract

This study aims to classify sentiments from the laboratory facilities and infrastructure assessment questionnaire into three main categories, namely positive, negative, and neutral sentiments. The data used comes from a questionnaire containing opinions, comments, and suggestions about laboratory conditions. The method applied is Holistic Lexicon Based, which conducts sentiment analysis based on a sentiment dictionary to determine the orientation of each opinion word. Before the classification process, data from the questionnaire is processed through a preprocessing stage to improve the quality of the analysis results. After that, sentiment classification is carried out by assessing the orientation of the words in the opinion. The test results show that the Holistic Lexicon Based method is able to identify opinion sentences and classify sentiments with an average accuracy of 81.18%. This level of accuracy is influenced by the number of opinion sentences identified and the suitability of the sentiment dictionary used in the analysis process. This study is expected to help organizations in making decisions based on the results of sentiment analysis from the questionnaire that has been processed.
Desain Aplikasi Pencarian Kontrakan Kota Tarakan Berbasis Mobile Menggunakan Metode Algoritma Djikstra Evi Marliyani; Moh. Masduki Syahlan; Obert; Gusmana, Roman
Journal of Big Data Analytic and Artificial Intelligence Vol 5 No 1 (2019): JBIDAI Juni 2019
Publisher : STMIK PPKIA Tarakanita Rahmawati

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Abstract

Nowadays, searching for rented accommodation in Tarakan city for students, employees, and the general public, in their search currently most still use word of mouth and social media. With this system it will be difficult to find information such as, in the process of searching for the address it also takes a very long time in searching for rented accommodation in Tarakan city usually students, employees, and the general public usually only listen to or know the information conveyed from one community to another so that the information obtained is not accurate. Dijkstra's algorithm is an algorithm for determining routes with short distances. It is assumed that all distances traveled are positive. The idea of ​​this algorithm is based on the fact that each minimum distance has more than one, but in fact there is only one distance to travel. This happens because all distances are positive. According to the results of the analysis obtained by the author in conducting research on, the Dijkstra Algorithm Method is that the method used is still very inefficient in determining the shortest route because this method does not calculate from all existing paths but only calculates the closest node from the starting point and will calculate when the node has branches and will choose the smallest value from the node that has branches.
Sistem Pendukung Keputusan Seleksi Penerimaan Beasiswa Bidikmisi Menggunakan Teorema Bayes Qolbiah Fitri; Muhammad Sya’bani; Asmah; Gusmana, Roman
Journal of Big Data Analytic and Artificial Intelligence Vol 5 No 1 (2019): JBIDAI Juni 2019
Publisher : STMIK PPKIA Tarakanita Rahmawati

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Abstract

Bidikmisi Tuition Fee Assistance Program is an education fee assistance program for prospective students who are economically disadvantaged and have good academic potential. STMIK PPKIA is one of the universities that also organizes the bidikmisi scholarship program. In the selection process, STMIK PPKIA still uses manual calculations with Microsoft Excel without using methods and applications. For this reason, a system is needed that can help the bidikmisi scholarship selection process at STMIK PPKIA by designing a decision support system to help rank the eligibility of prospective bidikmisi scholarship recipients. This decision support system uses Bayes' Theorem, by taking a sample of bidikmisi applicant data in 2016. In Bayes' Theorem, each probability of being accepted and the probability of not being accepted are calculated which are interrelated. Based on the results of the Analysis using Bayes' Theorem, out of 25 applicants, students who are eligible for the bidikmisi scholarship are 60% = 15 students and the number of students who are not eligible for the bidikmisi scholarship is 40% = 10 students.

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