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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 41 Documents
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Implementasi Logika Fuzzy Mamdani dalam Menentukan Jumlah Pemesanan Produk pada PT Forisa Nusapersada Area Tarakan: Implementation of Mamdani's Fuzzy Logic in Determining the Number of Product Orders at PT Forisa Nusapersada Tarakan Rita Tri Wulandari; Ummi Syafiqoh; M. Hafid
Journal of Big Data Analytic and Artificial Intelligence Vol 6 No 1 (2023): JBIDAI Juni 2023
Publisher : STMIK PPKIA Tarakanita Rahmawati

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.71302/jbidai.v6i1.44

Abstract

PT Forisa Nusapersada, Tarakan is an industrial company engaged in the distribution and marketing of products. When ordering products from the head office, since the customer's product demand sometimes increases or decreases every month, it is sometimes necessary to repeat the order within a month, thus affecting the product inventory in the warehouse. This study applied Fuzzy Mamdani to determine the number of orders based on sales data, goods stock and incoming goods from March to August 2021. This method has 5 stages, fuzzy set formation, rule formation, implication function application, rule composition and defuzzification. The input variables were demand and supply, while the output was orders. The results showed that the determination of the value of the fuzzy set domain can affect the final results differently for each product variant. In this study, the value of the fuzzy set domain was from the minimum and maximum values of sales data, goods stock and incoming goods.
Analisis Sentimen pada Ulasan Penyedia Layanan Menggunakan Algoritma C4.5 Miske Marcillia; Muhammad Fadlan; Hartono, Lies
Journal of Big Data Analytic and Artificial Intelligence Vol 6 No 2 (2023): JBIDAI Desember 2023
Publisher : STMIK PPKIA Tarakanita Rahmawati

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.71302/jbidai.v6i2.45

Abstract

Sentiment analysis is essential for understanding and processing textual data to derive meaningful insights. PT. XYZ, a company operating in the GSM cellular telecommunications sector, faces a challenge due to the lack of a specific application for analyzing visitor reviews on their services. This gap impedes their ability to gain detailed insights into consumer feedback, hindering efforts to improve service quality. This research addresses this issue by developing an application that utilizes the C4.5 algorithm to analyze PT. XYZ's reviews. The study uses 140 consumer reviews collected from Google Maps. The C4.5 algorithm, which creates decision trees to find relationships among variables, is employed for classifying and predicting service review sentiments. The research involves several stages: data crawling, text preprocessing, term frequency (TF) calculation, and applying the C4.5 algorithm for classification.The results demonstrate the effectiveness of this approach. With 126 training data samples and 14 test samples, the model achieved an accuracy of 78.57%, precision of 83.33%, and recall of 90.91%. These findings indicate that increasing the amount of training data enhances pattern recognition and accuracy. The study successfully meets its objectives, proving that sentiment analysis using the C4.5 algorithm can effectively predict service review sentiments and aid in improving service quality.
Analisa Metode Association Rule Untuk Penjualan Skincare Menggunakan Algoritma Pincer Serach Nadia Indah Tarakanita; Yusni Amaliah; Anto, Anto
Journal of Big Data Analytic and Artificial Intelligence Vol 6 No 2 (2023): JBIDAI Desember 2023
Publisher : STMIK PPKIA Tarakanita Rahmawati

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.71302/jbidai.v6i2.46

Abstract

Rumah Masker Tarakan is a business entity in the field of selling cosmetic and skincare products. A common problem faced by Rumah Masker is the placement of products, which often leads to difficulties in finding similar products. Therefore, a method is needed to organize the layout of skincare products that are frequently purchased together, making it easier for customers to select the products they want. The Pincer Search Algorithm, also known as the Two-Way Search, uses two approaches: Top-Down and Bottom-Up. In its process, the primary direction of the Pincer Search is Bottom-Up. The Maximum Frequent Set is a collection of maximal itemsets that are classified as frequent. The purpose of the Maximum Frequent Set is to reduce (prune) the number of candidate Frequent Itemsets that need to be examined in the Bottom-Up process. Based on research conducted using the Pincer Search Algorithm with sales data from January to June 2022, involving a total of 148 data points, it was found that 72 transactions yielded a value of 0.028% for a candidate of 3 itemsets. This demonstrates that the best sales performance occurs with a combination of 3 itemsets.
Studi Efektivitas Metode Sistem Pakar untuk Mendiagnosa Penyakit pada Hewan Ternak Sapi Damayanti, Nabela; Amaliah, Yusni; Gusmana, Roman
Journal of Big Data Analytic and Artificial Intelligence Vol 7 No 2 (2024): JBIDAI Desember 2024
Publisher : STMIK PPKIA Tarakanita Rahmawati

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.71302/jbidai.v7i2.48

Abstract

Cattle often face the risk of diseases that can lead to significant losses for farmers. Limited knowledge about diseases in cattle often makes farmers rely on livestock experts or seek assistance from veterinarians. Therefore, this research aims to compare the effectiveness of two expert system methods, namely Euclidean Probability and Bayes' Theorem, in detecting diseases in cattle. Euclidean Probability is used as a case-based approach technique to measure the likelihood or certainty of conclusions based on the causes that occur. On the other hand, Bayes' Theorem is a method for calculating the probability of hypotheses based on previous data. Both methods have similar goals, which are to determine the presentation of diseases based on the symptoms experienced by cattle, with the main difference lying in the calculation processes they employ. The application of the expert system resulting from this research can assist clinic personnel in detecting diseases in cattle. The effectiveness of the method from the program trial for six different diseases resulted in an accuracy of 83% for the Euclidean Probability method, while the Bayes' Theorem method resulted in 50% accuracy. This concludes that the Euclidean Probability method is more effective than the Bayes' Theorem method in diagnosing diseases in cattle.
Implementasi Clustering K-Means Untuk Pengelompokan Perkembangan Anak Didik Indra Dwi Cahya; Andi Tenri Puji; Fitria
Journal of Big Data Analytic and Artificial Intelligence Vol 6 No 2 (2023): JBIDAI Desember 2023
Publisher : STMIK PPKIA Tarakanita Rahmawati

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.71302/jbidai.v6i2.49

Abstract

TK PAUD KB Kasih Ibu Tulin Onsoi is an early childhood education institution independently established by the Sebuku Village Government. This institution evaluates the child’s progress every semester and provides the progress information through report cards. The teacher assesses the development based on the report card value. There is no student clustering to decide which children will be monitored further by emphasizing learning from students who have not yet developed. One method to implement is K-Means clustering. This study used report card data for one semester. Then, analyze the data using K-Means with 4 clusters, namely Not Developed (BB), Starting to Develop (MB), Developing As Expected (BSH) and Very Well Developed (BSB). The implementation of child clustering has succeeded in becoming several clusters with almost the same similarity values. Clustering 38 children's data shows that 13 children are developing very well (BSB), 4 are developing according to expectations (BSH), 8 starting to develop (MB), and 13 are not yet developing (BB).
Sistem Pakar Dalam Penentuan Minat dan Bakat Anak Usia Taman Kanak-Kanak Menggunakan Metode Dempster Shafer Devaus, Lea; Amaliah, Yusni; Gusmana, Roman
Journal of Big Data Analytic and Artificial Intelligence Vol 7 No 1 (2024): JBIDAI Juni 2024
Publisher : STMIK PPKIA Tarakanita Rahmawati

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.71302/jbidai.v7i1.53

Abstract

Early childhood education relates to the teaching of children from birth up to the age of six, which prepares children for further education by providing educational stimulation to aid physical and spiritual growth. Thus, it is necessary a system to help parents learn their children's interests and talents to direct and develop their potential, especially in the arts. Dempster Shafer is a mathematical theory of evidence based on belief function and plausible reasoning to combine discrete pieces of information to calculate the probability of an event. The results show that children's interest and talent in art at Paud Pelangi Desa Sedulun consists of 5 types: Mosaic Art, Acting Art, Music Art, Montage Art, and Painting Art. Manual calculations and computerized tests produce the highest percentage value and the final conclusion. Determining children's interests and talents from the input of behavioral criteria generates a final assessment that helps children's development in the arts.
KLASIFIKASI KELAYAKAN MENERIMA BANTUAN SOSIAL MENGGUNAKAN METODE K-NEAREST NEIGHBOR Melpin, Melpin; Praseptian M, Dikky; Obert, Obert
Journal of Big Data Analytic and Artificial Intelligence Vol 7 No 1 (2024): JBIDAI Juni 2024
Publisher : STMIK PPKIA Tarakanita Rahmawati

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.71302/jbidai.v7i1.54

Abstract

Data classification is the process of grouping data based on attributes (Congregation Employment, Congregation Dependents, Congregation Home Status, and Congregation Income). The problem currently occurring is that data collection on congregational social assistance recipients is often not on target, so that if social assistance enters the church, it is given to congregations who are actually less well off, but it is transferred to congregations who are well off, giving rise to confusion between one congregation and another. In this research the author used the K-Nearest Neighbor method or what is usually called KNN and measured algorithm performance using a confusion matrix to calculate accuracy, precision and recall. This researcher used 50 data that had been input via Google Form and then filled in the congregation from 50 data divided into 35 training data and 15 testing data. After the data has been input it will go through several stages, the first step is initialization where in the process of this initialization stage it changes the category value, the second stage is the process of dividing the value by the largest value in the attribute and the third stage is calculating the distance to then calculate the confusion matrix to determine accuracy, precision and recall. This research produces an application that can automatically determine which congregations are and are not worthy of receiving social assistance. From trials of 15 testing data, accuracy was 88.89%, precision 100% and recall 75%
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

Show Abstract | Download Original | Original Source | Check in Google Scholar

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

Show Abstract | Download Original | Original Source | Check in Google Scholar

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

Show Abstract | Download Original | Original Source | Check in Google Scholar

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.

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