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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 6 Documents
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Search results for , issue "Vol 7 No 2 (2024): JBIDAI Desember 2024" : 6 Documents clear
Implementasi K-Means Clustering Untuk Mengelompokan Data Sparepart Alat Berat Rahmawati, Andira; Fadlan, Muhammad; 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.v7i2.37

Abstract

Data mining is a crucial process for extracting valuable information from existing data, which can then be used by companies for quick and accurate decision-making. One of the commonly used methods in data mining is the K-Means Clustering method. In this study, the author applied K-Means Clustering in the retail sector to address the challenges faced by PT. Patria Jaya Mandiri. The author designed an application that can cluster heavy equipment spare parts based on sales data, with the aim of helping the company identify which spare parts are most favored by consumers. This clustering is expected to simplify the process of determining optimal spare part stock, ultimately positively impacting the company’s revenue. The results of this study indicate that heavy equipment spare parts can be categorized into three groups: Most Popular, Popular, and Least Popular. Cluster 1 (Most Popular) consists of 3 data points, Cluster 2 (Popular) consists of 39 data points, and Cluster 3 (Least Popular) consists of 8 data points. This clustering result can serve as a guide for PT. Patria Jaya Mandiri in determining the optimal spare part inventory in the future.
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.
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.
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

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