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All Journal Jurnal Nasional Komputasi dan Teknologi Informasi Journal of Big Data Analytic and Artificial Intelligence
Gusmana, Roman
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Aerospace Engineering Automotive Engineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering Neuroscience Transportation

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Journal : Journal of Big Data Analytic and Artificial Intelligence

 1 
Sistem Pakar Diagnosa Kerusakan Printer Menggunakan Metode Teorema Bayes Ellisa Harini; Indra Tri Saputra; Gusmana, Roman
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.v6i1.34

Abstract

Trijaya Computer performs computer sales and repair services located on Jl. Panglima Batur, Markoni. Printer repair demands quite a lot. Its massive usage is for printing reports, documents, and other things. Extensive use or even non-use of the printer can damage it. The research applies Bayes' Theorem analysis to an expert system to help admins diagnose printer damage. The discoverer of Theorem by Reverend Thomas Bayes (1701-1761). In general, it is to calculate the probability truth value of evidence. It can also interpret to calculate data uncertainty into definitive data by comparing "yes" or "no" data. The study result was an expert system for diagnosing printer damage as a support system or admins assistant with 80% accuracy. Eight of ten test data were equal to the expert's result.
Application of K-Means Clustering for Student Class Division System Tri Martuti; Eviana Tjatur Putri; Gusmana, Roman
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.v6i2.35

Abstract

SMP Negeri 2 Malinau Utara is a junior high school in Desa Putat, Malinau Utara, Malinau, Kalimantan Utara and has 127 students. Currently, the class division process is inefficient and random. On the other hand, the clustering process' class division must be able to provide each class a balanced number of students. This study proposes the grades of Indonesian and English languages, Mathematics, and Natural Sciences for the clustering. K-means is applied to evenly group students based on predetermined value criteria to achieve the expected class formation. K-Means Clustering is an algorithm in data analysis to group a set of data into several groups based on their similar characteristics. In the clustering process, the distance between the data and the Centroid was calculated using the Euclidean Distance. Initial centroid determination and data distance calculation with the initial centroid were performed until the centroid member remains unchanged. The initial centroid was determined using a combination of 1,081 times obtained from 47 data combinations for two clusters. This research has been successfully applied to classify students using the K-Means Clustering method and select a balanced number of students between one class and another. Next, combine some students in each cluster with other clusters, so that each class has different levels of learning ability. With the combination of two clusters in one class, it is expected that students can help each other during the learning process.
Perbandingan Metode Klasifikasi Naïve Bayes dan C4.5 untuk Menentukan Potensi Nasabah Pada NSC Finance Marhaeni; Eviana Tjatur Putri; Gusmana, Roman
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.v6i1.36

Abstract

NSC Finance is a service business that provides loans to the public to meet their needs. However, NSC Finance does not use customer data to obtain necessary information. This research classifies customer data to gain information about promising customers, considered customers, and unpromising customers to loan re-offer. This study compares Naïve Bayes and C4.5 to help customer classification systems be more accurate by measuring accuracy using recall precision. These methods' comparative analyses are to investigate which methods have the highest classification accuracy. Therefore, the company can discover the highest accuracy rate of the classification results of these two methods. Results revealed that the classification patterns of 80 training data and 20 test data make it possible that data still have classification differences from the original data. Methods comparison indicated that the Naïve Bayes classification is better, with 85% accuracy, 94.44% precision, and 89.47% recall.
Perbandingan Metode Euclidean Probability dan Teorema Bayes untuk Diagnosa Penyakit Gigi Natalia Cangera; Yusni Amaliah; Gusmana, Roman
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.v6i1.42

Abstract

Dental disease is a disease that interferes with the normal function of the teeth. Dental disease has almost similar symptoms, so it requires an expert system of dental disease diagnosis for the proper treatment before the disease becomes more serious. The research employs Euclidean probability and Bayes' Theorem. Euclidean probability is a case approach for measuring probability based on causes, while Bayes' Theorem is a mathematical formula for determining conditional probability. Both of these methods determine the disease percentage based on the input symptoms. Their differences reflect in the calculation. Research shows that the Bayesian analysis is better than Euclidean probability, as evidenced by the similarity in the systems diagnostic with experts of 80% accuracy, while Euclidean probability is 40%.
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.
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.
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.
Co-Authors Adimulya Dyas Wibisono Asmah Damayanti, Nabela Devaus, Lea Ellisa Harini Evi Dianti Bintari Evi Marliyani Eviana Tjatur Putri Haryansyah Haryansyah, Haryansyah Indra Tri Saputra Marhaeni Moh. Masduki Syahlan Muhammad Sya’bani Natalia Cangera Obert Qolbiah Fitri Tri Martuti Yunus Langan Yusni Amaliah Yusni Amaliah