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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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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

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

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.
Klasifikasi Penyakit Karies Gigi Menggunakan Algoritma Modified K-Nearest Neighbor Kalalo, Arnold; Rosmini, Rosmini; Anto, Anto
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.v7i2.60

Abstract

Dental caries, commonly known as tooth cavities, is a disease where bacteria damage the structure of tooth tissues such as enamel, dentin, and cementum. The primary cause of dental caries is the demineralization of tooth surfaces caused by organic acids from sugary foods. If dental caries is not promptly treated or checked from the beginning, the damage can worsen to the point where the tooth must be extracted. To facilitate identifying the severity of caries, a dental caries classification system was developed using the MKNN (Modified K-Nearest Neighbor) algorithm. The MKNN method is an enhancement of the KNN method, with the main differences being in the calculation of training data validity and the weight voting process. In this study, there are three different classes of dental caries and six symptoms or variables. The stages of the MKNN method used are: distance calculation using Euclidean distance, testing the validity of training data, determining k based on distance calculation, and weight voting calculation in KNN. The test results show that the k value, the number of training data, and the number of test data affect the classification results. The classification results from the test using 20 training data, 10 test data, and k=3 are as follows: 1 patient classified with superficial caries, 5 patients with media caries, and 3 patients with profunda caries. The diagnosis produced by the application is consistent with the expert (doctor) diagnosis.
Implementasi Data Mining Untuk Pengelompokkan Santri Menggunakan Metode K-Medoids Hasrianti, Syamsri; Evi Dianti Bintari; Pangestika, Cindy
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.v7i2.61

Abstract

Clustering merupakan suatu proses pengelompokkan data, observasi atau atau mengelompokkan kelas yang memiliki kesamaan objek. Pondok pesantren daarul ilmi boarding school juga harus melakukan pengelompokkan atau clustering terhadap santri santrinya untuk memaksimalkan proses pengajaran. Pondok pesantren tersebut belum memiliki sebuah aplikasi komputer untuk pengelompokkan santri sehingga  sering terjadi kesalahan dalam menempatkan santri sesuai kriteria yang telah ditetapkan oleh pihak pesantren. Proses pengelompokkan santri dilakukan berdasarkan nilai ahlak, ibadah, rata-rata nilai raport, prestasi dan nilai tahfidz. Proses pemberian nilai yang dilakukan masih bersifat subjektif, artinya pemberian nilai didasarkan atas pemberian nilai dari guru pengampu mata pelajaran tertentu, sehingga penelitian ini bertujuan untuk membantu menentukan keputusan pengelompokkan santri secara objektif. Penelitian ini menggunakan Algoritma K-Medoids atau yang biasa disebut dengan Partitioning around method (PAM). Penelitian ini menggunakan 54 data santri yang aktif pada tahun ajaran 2021/2022 dimana santri akan dikelompokkan dalam kelas murtafi dan mumtaz. Penelitian ini meliputi tahap penentuan jumlah cluster, pemilihan medoid awal secara acak, menghitung jarak masing-masing objek menggunakan manhattan dan minkowski distance serta menghitung total simpangan. Berdasarkan uji coba program yang dilakukan, penelitian ini berhasil mengelompokkan santri ke dalam kelas murtafi dan mumtaz. Berdasarkan perhitungan jarak menggunakan manhattan, 15 santri masuk dalam kelas murtafi dan 39 santri masuk dalam kelas mumtaz, sedangkan jika menggunkan perhitungan jarak minkowski, 14 santri masuk dalam kelas murtafi dan 40 santri masuk dalam kelas mumtaz. Dari evaluasi menggunakan Davies Bouldin Index (DBI) terhadap hasil cluster santri tersebut pengukuran jarak menggunakan manhattan lebih baik dibanding dengan minkowski dimana nilai DBI manhattan sebesar -0,0869.
Implementasi Metode Market Basket Analysis Untuk Menentukan Menu Paket Penjualan Di Bius Café Susilayani, Asrini; Putri, Eviana Tjatur; Hartono, Lies; Ardi, Mohamad
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.v7i2.63

Abstract

A café in Tarakan City, Bius Café, is looking for sales strategies due to increasing business competition. One promotional step that a restaurant industry can take is creating package menus that are more affordable than buying items individually, making consumers happier with such menus. Bius Café, a café with a considerable number of patrons, intends to create package menus. These packages will be a combination of food and drink items from the café, taking into account how often these combinations are chosen by customers. The Apriori Algorithm is a data mining algorithm for extracting association rules. It is a type of association rule in data mining, often referred to as affinity analysis or market basket analysis. Based on the analysis results, using the Apriori algorithm to determine sales patterns shows that to form rules, the process starts with the formation of combinations from transaction analysis of 1-itemset, 2-itemset, and 3-itemset combinations that meet the support and confidence values in forming these combinations. Several transactions are selected according to the minimum support and confidence requirements, forming rules that are then ranked with the highest confidence value of 72%, which includes "Mie Goreng" and "Bius Café". Thus, the business owner can create promotional package menus by combining these two items.
Analisis Sentimen Kritik dan Saran Layanan RSUD Akhmad Berahim Tana Tidung menggunakan Metode Lexicon-Based Maulidia, Nurmala; Praseptian M, Dikky
Journal of Big Data Analytic and Artificial Intelligence Vol 8 No 1 (2025): JBIDAI Juni 2025
Publisher : STMIK PPKIA Tarakanita Rahmawati

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.71302/jbidai.v7i2.65

Abstract

Rapidly developing information technology must become a significant component in its use in all human life to simplify work. This matter underlined the research with that title, which the author believes will be an element of service assessment, whether positive, negative, or neutral. Sentiment analysis is required while evaluating a service, particularly in hospitals. The Lexicon-Based method uses a dictionary or lexicon as a language basis. This method classifies a sentiment from each opinion so that a sentiment sentence can be classified as positive, neutral, or negative. The text data will then be calculated using a Lexicon-Based to produce service quality sentiment analysis. The research used 100 data, with a questionnaire distributed of as many as 90 data and a suggestion box of as many as 10 data for sentiment analysis. The research received data of 33 criticisms and 67 suggestions. The Lexicon-Based method also classifies data into positive, negative, and neutral. The designed system can assist hospitals in evaluating services.
Rekayasa Aplikasi Rekomendasi Pencarian Lokasi Dan Analisis Sentimen Menggunakan Penambangan Teks Febriyanti, Eka; Noviyantono, Endyk; Praseptian M, Dikky
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.v7i2.66

Abstract

 This research aims to provide information and assessments about tourist attractions and culinary attractions that are popular on social media (Instagram and Twitter). The research process uses a text mining approach, starting with text processing (case folding, tokenizing, stopword removal, and stemming) to filter comments. Furthermore, weighting is carried out using the TF-IDF method to determine the relevance of words. The process of classifying comments by location name is carried out using the Naïve Bayes algorithm, followed by sentiment analysis to assess positive, negative, or neutral comments. The research application was built using PHP with a MySQL database and utilized a dataset of 73 comments (17 for tourism and 56 for culinary) collected from social media. The results of the study show that the system is able to produce recommendations for tourist and culinary attractions effectively based on data analysis
Implementasi Data Mining Menggunakan Algoritma Apriori Untuk Menentukan Tata Letak Obat Pratiwi, Widya; Fitriana, Ayu; Sinawati, Sinawati; Zulhilmi, Hafizhan
Journal of Big Data Analytic and Artificial Intelligence Vol 8 No 1 (2025): JBIDAI Juni 2025
Publisher : STMIK PPKIA Tarakanita Rahmawati

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.71302/jbidai.v8i1.62

Abstract

The role of information systems has expanded to other layers of life, especially business sectors operating in the health sector. Improving service and performance productivity in the health sector, one of which is pharmacies. This study aims to identify drug dispensing patterns at 24-hour pharmacies in Tarakan. A 24 hour ready pharmacy is a business place that operates in the field of selling medicines, the sales are still manual and the data is stored as an archive, so there is no application that can help the pharmacy owner in knowing the combination of medicines purchased and in determining the layout of medicines that are still not in accordance with the requirements. alphabetical order, based on tablet drug category. This research aims to determine the relationship between drugs purchased by consumers and how to implement data mining to determine the combination of drug sales at 24 Hour Ready Pharmacies. This research was carried out in several stages. The first stage creates tabular transaction data. The second stage is to determine minimum support and minimum confidence. The third stage is to calculate itemset combinations. The fourth stage is calculating confidence and the fifth stage is making a rule or decision rule..
Optimalisasi Pengelompokkan Konsumen dengan Multi Internal Metric Validation dan Boxplot Analysis Fitriyanto, Rachmad; Nurindah, Nurindah
Journal of Big Data Analytic and Artificial Intelligence Vol 8 No 1 (2025): JBIDAI Juni 2025
Publisher : STMIK PPKIA Tarakanita Rahmawati

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.71302/jbidai.v8i1.67

Abstract

The simultaneous use of multiple internal validation metrics to determine the optimal number of clusters in K-Means Clustering often results in differing K values, which can confuse data practitioners when extracting insights, such as identifying customer characteristics. This study aims to develop an evaluation framework to address the ambiguity arising from varying K values produced by different internal validation metrics. The proposed K evaluation framework consists of two stages. In the first stage, five internal validation metrics—Davies-Bouldin Index (DBI), Silhouette Score, Elbow Method, Dunn Index, and Calinski-Harabasz Index—are used as filters to generate up to five top K candidates. The second stage involves boxplot analysis, interquartile range (IQR), and elbow visualization to explore the cohesiveness and stability of the resulting clusters. The first-stage evaluation yielded four potential cluster counts: K = 2, 5, 7, and 10. In the second stage, based on the elbow graph of the average interquartile range, K = 5 was identified as the most optimal number of clusters compared to the other candidates. These results indicate that using a larger number of internal validation metrics may increase the likelihood of producing multiple K values. However, a higher number of clusters does not necessarily guarantee better quality. The implications of this research highlight the importance of a layered evaluation approach in determining the optimal number of clusters, especially when employing multiple internal validation metrics. The proposed framework can assist data practitioners in making more informed decisions and reducing ambiguity in the clustering process. In the future, this framework can be extended by incorporating external validation metrics or adapted to other clustering algorithms.
Penerapan Algoritma Hash Based untuk Analisa Pola Penjualan Obat pada Apotek Permata Tarakan Sahira Reggina Putri; Fitria, Fitria; Risma Sakila
Journal of Big Data Analytic and Artificial Intelligence Vol 8 No 1 (2025): JBIDAI Juni 2025
Publisher : STMIK PPKIA Tarakanita Rahmawati

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.71302/jbidai.v8i1.68

Abstract

In an era of increasingly intense business competition, entrepreneurs in the healthcare sector, such as pharmacies, are required to optimize the use of transaction data. Apotek Permata Sejahtera has experienced a daily increase in sales transaction volume, resulting in an accumulation of data without further analysis. Therefore, a method is needed to identify associations between pharmaceutical products that are frequently purchased together by customers to support sales strategies and product arrangement. This study applies the Hash-Based algorithm to discover association patterns from drug sales transaction data between February and September 2024. The research stages include tabular data construction, determination of minimum support, hash address computation, calculation of minimum confidence, confidence evaluation, and formulation of association rules. The results show that the maximum itemset combination meeting the minimum support threshold of 40% reaches only up to the 5-itemset level, with a single final combination. From the 41 combinations that met the support criteria, 37 rules were identified with a minimum confidence of 80%, indicating strong relationships among pharmaceutical products. These findings offer practical contributions to sales strategy planning, inventory management, and product layout optimization in pharmacies to enhance operational efficiency and customer satisfaction

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