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  • 1
    Online Resource
    Online Resource
    Analisa Faktor yang Mempengaruhi Kondisi Kesehatan Menggunakan Algoritma Frequent Pattern Growth
    LP2M Universitas Islam Negeri (UIN) Syarif Hidayatullah Jakarta ; 2018
    In:  Applied Information System and Management (AISM) Vol. 1, No. 1 ( 2018-05-01)
    Details
    In: Applied Information System and Management (AISM), LP2M Universitas Islam Negeri (UIN) Syarif Hidayatullah Jakarta, Vol. 1, No. 1 ( 2018-05-01)
    Abstract: Health is an important factor in human life that have to be guarded, both physically and mentally. This study aimed to analyze the factors that affect health condition using medical check up data. Factors analyzed were consuming alcohol, smoking, exercise, age and gender. The method was the association rule using FPGrowth. The result of this study was factors that affect the health condition is alcohol, exercise and age. This result evidenced by the rules A3→K3, which means that if a person consumes more alcohol than 4 days/week with the amount of alcohol is less than 180ml/day, then health condition was poor with 11% support and 67% confidence. E1→K3, which means that if one rarely exercise then health condition was poor with 24% support and 99% confidence. G2→K3, which means that if a person in middle age group, then the condition of health was poor with 24% support and 99% confidence.
    Type of Medium: Online Resource
    ISSN: 2621-2544 , 2621-2536
    URL: Article
    DOI: 10.15408/aism.v1i1.8646
    Language: Unknown
    Publisher: LP2M Universitas Islam Negeri (UIN) Syarif Hidayatullah Jakarta
    Publication Date: 2018
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  • 2
    Online Resource
    Online Resource
    Pengelompokan pembagian zakat dengan menggunakan metode clustering k-means
    LPPM Universitas Muhammadiyah Riau ; 2023
    In:  Jurnal CoSciTech (Computer Science and Information Technology) Vol. 4, No. 1 ( 2023-04-30), p. 154-163
    Details
    In: Jurnal CoSciTech (Computer Science and Information Technology), LPPM Universitas Muhammadiyah Riau, Vol. 4, No. 1 ( 2023-04-30), p. 154-163
    Abstract: Zakat is a worship that involves property. Zakat is also included in the fourth pillar of Islam which has the aim of purifying the assets of every Muslim by setting aside a portion of his wealth, if it has reached the time and the amount is given to those who are entitled to receive it. The collection and distribution of zakat is usually handled by the Amil Zakat Agency (BAZ) in every region of Indonesia, one of which is in Pekanbaru. In accordance with the regulations, there are two stages in providing assistance to mustahik, namely conducting interviews and field observations, then determining the nominal amount of assistance given to the Mustahik category of recipients of zakat 1, zakat 2, and zakat 3. Problems that are often encountered in determining potential recipients assistance is a way of selecting Mustahik which still uses the manual method, so that it often causes problems such as the length of the selection process and the occurrence of miscalculations so that the results of Mustahik's selection become inaccurate. For that, it is necessary to create analysis that can process data into information. Data mining is a process for processing data into information using statistical techniques, AI, and machine learning. There are many methods in data mining. In this study using the k-means clustering and for testinguse Davies Bouldin Index. based on testing using the davies bouldin index (DBI) cluster 4 is the best cluster with a value of 0.671, where the lower the value, the better the cluster.
    Type of Medium: Online Resource
    ISSN: 2723-5661 , 2723-567X
    URL: Issue
    URL: Article
    DOI: 10.37859/coscitech.v4i1
    DOI: 10.37859/coscitech.v4i1.4804
    Language: Unknown
    Publisher: LPPM Universitas Muhammadiyah Riau
    Publication Date: 2023
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  • 3
    Online Resource
    Online Resource
    Identification of Suicide-Related Events Through Network Analysis of Adverse Event Reports
    Springer Science and Business Media LLC ; 2014
    In:  Drug Safety Vol. 37, No. 8 ( 2014-8), p. 609-616
    Details
    In: Drug Safety, Springer Science and Business Media LLC, Vol. 37, No. 8 ( 2014-8), p. 609-616
    Type of Medium: Online Resource
    ISSN: 0114-5916 , 1179-1942
    URL: Article
    DOI: 10.1007/s40264-014-0195-2
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2014
    detail.hit.zdb_id: 2023894-0
    SSG: 15,3
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  • 4
    Online Resource
    Online Resource
    Variable Selection to Determine Majors of Student using K-Nearest Neighbor and Naïve Bayes Classifier Algorithm
    IOP Publishing ; 2019
    In:  Journal of Physics: Conference Series Vol. 1363, No. 1 ( 2019-11-01), p. 012057-
    Details
    In: Journal of Physics: Conference Series, IOP Publishing, Vol. 1363, No. 1 ( 2019-11-01), p. 012057-
    Abstract: Appropriate student’s major placement in high school can help students to better improve their academic achievement. There are many variables which must be considered to determine the student’s majors, such as: Gender, Interests, Intelligence Quotient (IQ); Four subjects in Junior High School (JHS), average junior high school grades, matriculation score of four subjects, and average rate of matriculation. The number of variables used in the selection, causes some weaknesses among them, such as the complexity of variable, the inefficiency of variable and the existence of some variables which is only as an addition without having a significant contribution. This study aims to reduce the number of these variables so it will become easier to analyze and to be applied. The Process of reduction was done by combining experiments with Predefined attributes. A total of ten combinations were attempted using K-Nearest Neighbor (K-NN) and Naïve Bayes Classifier (NBC) which then was measured by Confusion Matrix accuracy. The experimental result showed that the combination of variables which produce the best accuracy were the 9th and 10th experiment with variable matriculation, interest, and IQ, and an accuracy of 96.77% from K-NN also 98.38% from NBC. By combining both algorithms, 99.87% of maximum accuracy was obtained from those three variables. New information which can be extracted from this research is that there are only three important variables to determine major placement in Senior High School, Average Scores of Matriculation, Interests and IQ followed by four supporting variables such as the scores of Mathematic, Physics, English and Economics in Matriculation.
    Type of Medium: Online Resource
    ISSN: 1742-6588 , 1742-6596
    URL: Article
    DOI: 10.1088/1742-6596/1363/1/012057
    Language: Unknown
    Publisher: IOP Publishing
    Publication Date: 2019
    detail.hit.zdb_id: 2166409-2
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  • 5
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    Online Resource
    Data Warehouse Design For Sales Transactions on CV. Sumber Tirta Anugerah
    Universitas Islam Negeri Sultan Syarif Kasim Riau ; 2022
    In:  Jurnal CoreIT: Jurnal Hasil Penelitian Ilmu Komputer dan Teknologi Informasi Vol. 8, No. 2 ( 2022-12-12), p. 88-
    Details
    In: Jurnal CoreIT: Jurnal Hasil Penelitian Ilmu Komputer dan Teknologi Informasi, Universitas Islam Negeri Sultan Syarif Kasim Riau, Vol. 8, No. 2 ( 2022-12-12), p. 88-
    Abstract: Many data warehouses are implemented in companies engaged in retail, CV. Sumber Tirta Anugerah is one of the paint product retail companies that has not implemented it yet. As time goes by, the sales transaction data is getting more and more difficult to process because it is still stored in Microsoft Excel. This is a serious problem in utilizing historical data to assist in making a decision. It is difficult to store sales data because the data is quite large and a lot. Based on the above problems, a data warehouse design is needed for sales transaction data. This data warehouse design uses Kimball's nine-steps method and star schema. To perform the ETL process (extract, transform, and load) using Pentaho software. In this data warehouse design, Tableau software is used to visualize the processed data into a graph and dashboard report. The result of this research is a data warehouse design using nine steps and a star schema which gets a transformation response time of 4048 MS. 
    Type of Medium: Online Resource
    ISSN: 2599-3321 , 2460-738X
    URL: Article
    DOI: 10.24014/coreit.v8i2.19800
    Language: Unknown
    Publisher: Universitas Islam Negeri Sultan Syarif Kasim Riau
    Publication Date: 2022
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  • 6
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    Implementasi Data Mining Untuk Menemukan Pola Asosiasi Data Tracer Study Menggunakan Algoritma Apriori
    Universitas Serambi Mekkah ; 2022
    In:  Jurnal Nasional Komputasi dan Teknologi Informasi (JNKTI) Vol. 5, No. 3 ( 2022-06-28), p. 383-390
    Details
    In: Jurnal Nasional Komputasi dan Teknologi Informasi (JNKTI), Universitas Serambi Mekkah, Vol. 5, No. 3 ( 2022-06-28), p. 383-390
    Abstract: Abstrak -  Mengetahui sebaran alumni dari suatu perguruan tinggi sangat bermanfaat sebagai bahan evaluasi dan tolak ukur aktivitas belajar mengajar di perguruan tinggi terkait. Salah satu cara untuk mendapatkan sebaran alumni adalah dengan melaksanakan tracer study. Pada penelitian ini akan dilakukan tracer study dan selanjutnya data tersebut akan diolah dengan teknik data mining menggunakan algoritma apriori. Hasil data berupa pola hubungan antar atribut akan memudahkan para pemegang keputusan di perguruan tinggi dalam mendapatkan pengetahuan baru tentang lulusan dan dapat digunakaan untuk meningkatkan dan menjamin kualitas pendidikan tinggi tersebut. Tracer study yang dilakukan berfokus pada mahasiswa Teknik Informatika UIN Suska Riau yang lulus pada tahun 2019 dan 2020. Hasil penelitian ini memperoleh pengetahuan baru seperti pekerjaan pertama lulusan dengan masa tunggu kurang dari 6 bulan adalah sebagai pegawai kontrak atau honorer dengan gaji antara 3-5jt dan memiliki ipk antara 3-3,5.Kata kunci: data mining, pola asosiasi, tracer study, apriori Abstract - Knowing the distribution of alumni from a university is very useful as an evaluation and a benchmark for teaching and learning activities in related universities. One way to get the distribution of alumni is to carry out a tracer study. In this research, a tracer study will be carried out and then the data will be processed using the apriori algorithm data mining techniques. The results of the data in the form of relationship patterns between attributes will make it easier for decision makers in higher education to gain new knowledge about graduates and it can be used to improve and guarantee the quality of these higher education institutions. The tracer study carried out was focused on Informatics Engineering students at UIN Suska Riau who graduated in 2019 and 2020. The results of this study are in the form of new knowledge, such as graduates with a waiting period are fewer than 6 months getting their first job as a contract or honorary employees with salaries of between 3-5 million and a GPA between 3-3.5.Keywords: data mining, association rule, tracer study, apriori algorithm
    Type of Medium: Online Resource
    ISSN: 2621-3052 , 2620-8342
    URL: Issue
    URL: Article
    DOI: 10.32672/jnkti.v5i3
    DOI: 10.32672/jnkti.v5i3.4412
    Language: Unknown
    Publisher: Universitas Serambi Mekkah
    Publication Date: 2022
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  • 7
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    Penerapan Algoritma K-Medoids Clustering Untuk Menentukan Srategi Promosi Pada Data Mahasiswa (Studi Kasus : Stikes Perintis Padang)
    Universitas Serambi Mekkah ; 2022
    In:  Jurnal Nasional Komputasi dan Teknologi Informasi (JNKTI) Vol. 5, No. 3 ( 2022-06-30), p. 556-564
    Details
    In: Jurnal Nasional Komputasi dan Teknologi Informasi (JNKTI), Universitas Serambi Mekkah, Vol. 5, No. 3 ( 2022-06-30), p. 556-564
    Abstract: Abstrak - STIKES Perintis Padang merupakan salah satu perguruan tinggi di padang, dimana setiap tahunnya ada kegiatan promosi yang akan dilakukan kesetiap daerah-daerah. Kegiatan promosi memakan biaya yang tidak sedikit karena banyaknya sekolah yang di kunjungi. Untuk mendapatkan hasil promosi yang maksimal maka diperlukan suatu srategi dan cara yang tepat. Pada penelitian ini menggunakan konsep data mining yaitu metode yang dapat menggali dan mengekstak data mahasiswa menjadi suatu informasi yang berharga dengan menggunakan teknik klustering algoritma K-Medoid dan Bahasa pemrograman Python. Tujuan pengelompokkan ini untuk mendapatkan informasi dari penggalian data mahasiswa sebagai rekomendasi bagi STIKES Perintis Padang dalam melakukan promosi. Data mahasiswa yang digunakan adalah data mahasiswa tahun 2014-2018 yang telah lulus dengan atribut NIM, Kota Asal Sekolah, Asal Sekolah, Masa Studi, Program Studi, dan IPK. Hasil penelitian dari Clustering yaitu terbentuknya 4 kluster dengan Cluster pertama 221 mahasiswa dengan asal sekolah tertinggi daerah Kerinci, latar belakang asal sekolah SMA, masa studi 4 tahun, dan rata-rata IPK 3,30, Cluster kedua 121 mahasiswa, dengan kota asal sekolah tertinggi Padang, latar belakang asal sekolah SMA, masa studi 4 tahun dan rata-rata IPK 3,31, Cluster ketiga 162 mahasiswa, dengan kota asal sekolah tertinggi agam, latar belakang SMA, masa studi 4 tahun, dengan rata-rata IPK 3,32 dan cluster keempat 220 mahasiswa, dengan kota asal sekolah Bukittinggi, dengan latar belakang asal sekolah SMA, masa studi 4 tahun, dengan rata-rata IPK 3,30.Kata kunci: Promosi, Strategi, Data Mining, Clustering, K-Medoid. Phyton                                         Abstract - STIKES Perintis Padang is one of the universities in Padang, where every year there are promotional activities that will be carried out in each region. Promotional activities cost a lot of money because of the large number of schools visited. To get maximum promotion results, we need the right strategy and method. This study uses the concept of data mining, which is a method that can extract and extract student data into valuable information by using the K-Medoid algorithm clustering technique and the Python programming language. The purpose of this grouping is to obtain information from student data mining as a recommendation for STIKES Perintis Padang in conducting promotions. The student data used is student data from the 2014-2018 class who have graduated with the attributes of NIM, City of Origin, School of Origin, Study Period, Study Program, and GPA. The results of the clustering research are the formation of 4 clusters with the first cluster of 221 students with the highest school origin being in the Kerinci area, high school background, 4 years of study period, and an average GPA of 3.30, the second group 121 students, with the city of school origin the highest is Padang, high school background, study period of 4 years and average GPA 3.31, third group 162 students, with the city of school origin the highest is Agam,  high school background, study period 4 years , with an average GPA of 3.32 and the fourth group of 220 students, Bukittinggi city school, with high school background, study period of 4 years, with an average GPA of 3.30.Keywords: Promotion, Strategy, Data Mining, Clustering, K-Medoid. Phyton 
    Type of Medium: Online Resource
    ISSN: 2621-3052 , 2620-8342
    URL: Issue
    URL: Article
    DOI: 10.32672/jnkti.v5i3
    DOI: 10.32672/jnkti.v5i3.4355
    Language: Unknown
    Publisher: Universitas Serambi Mekkah
    Publication Date: 2022
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  • 8
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    Penerapan Data Mining Pada Hasil Tracer Study Alumni Untuk Menemukan Pola Asosiasi Dengan Algoritma Fp-Growth
    Universitas Serambi Mekkah ; 2022
    In:  Jurnal Nasional Komputasi dan Teknologi Informasi (JNKTI) Vol. 5, No. 3 ( 2022-06-29), p. 464-473
    Details
    In: Jurnal Nasional Komputasi dan Teknologi Informasi (JNKTI), Universitas Serambi Mekkah, Vol. 5, No. 3 ( 2022-06-29), p. 464-473
    Abstract: Abstrak - Tracer study adalah studi terhadap lulusan lembaga pendidikan tinggi, yang juga merupakan proses untuk  mendapatkan informasi terkait transformasi alumni dari masa pendidikan ke dunia industri. Hal ini dilakukan untuk menentukan kelayakan pekerjaan lulusan dan juga penilaian retrospektif, seperti apa pelayanan yang diperoleh selama masa studi. Penelitian ini akan melaksanakan  tracer study  pada tingkat program studi di Teknik Informatika di UIN Suska Riau dan kemudian hasil studi lanjutan ini akan diolah untuk mendapatkan pola korelasi atau hubungan yang bermakna. Pengolahan data akan menggunakan teknologi data mining dan algoritma fp-growth.  Berdasarkan hasil analisa pengujian dan interpretasi dari total data bersih 129 alumni teknik informatika UIN Suska Riau yaitu pola yang dihasilkan dari proses mining dengan minimum support 15%  dan minimum confidence 40% adalah sebanyak 608 pola. Peneliti mengambil beberapa pola dengan antecedents berupa ipk, masa tunggu, bidang pekerjaan, gaji,  pekerjaan pertama, jenis kelamin untuk diinterpretasikan.Kata kunci: Data Mining, Tracer study, Fp-growth Abstract - Tracer study is a study of graduates of higher education institutions, which is also a process to obtain information related to the transformation of alumni from their education period to the industrial world. This is done to determine the employability of graduates as well as a retrospective assessment, such as what services were obtained during the study period. This research will carry out a tracer study at the study program level in Informatics Engineering at UIN Suska Riau and then the results of this follow-up study will be processed to obtain a significant correlation pattern or relationship. Data processing will use data mining technology and the fp-growth algorithm. Based on the results of the analysis of testing and interpretation of the total net data of 129 alumni of informatics engineering UIN Suska Riau, the patterns generated from the mining process with a minimum support of 15% and a minimum confidence of 40% are 608 patterns. Researchers took several patterns with antecedents in the form of GPA, waiting period, field of work, salary, first job, gender to be interpreted.Keywords: Data Mining, Tracer study, Fp-growth 
    Type of Medium: Online Resource
    ISSN: 2621-3052 , 2620-8342
    URL: Issue
    URL: Article
    DOI: 10.32672/jnkti.v5i3
    DOI: 10.32672/jnkti.v5i3.4407
    Language: Unknown
    Publisher: Universitas Serambi Mekkah
    Publication Date: 2022
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  • 9
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    KLASIFIKASI STATUS GIZI BAYI POSYANDU KECAMATAN BANGUN PURBA MENGGUNAKAN ALGORITMA SUPPORT VECTOR MACHINE (SVM)
    Universitas Banten Jaya ; 2022
    In:  Jurnal Sistem Informasi dan Informatika (Simika) Vol. 5, No. 2 ( 2022-08-28), p. 143-150
    Details
    In: Jurnal Sistem Informasi dan Informatika (Simika), Universitas Banten Jaya, Vol. 5, No. 2 ( 2022-08-28), p. 143-150
    Abstract: This research was conducted to apply the Support Vector Machine algorithm in the process of classifying the nutritional status of infants under five. The nutritional status of early childhood can determine what kind of human resources as successors of a nation in the future. Good nutritional status plays an important role in determining the success or failure of efforts to increase human resources, so that data on the nutritional status of toddlers such as at the Posyandu, Bangun Purba District can be classified using Data Mining techniques using the Support Vector Machine algorithm. The results of this study using 80% of the data as training data and 20% of the data as training data are f1 score 0.865, accuracy 0.876, precision score 0.871, and recall score 0.876. The results showed that from a total of 347 data on the nutritional status of infants, there were 284 infants with good nutrition, 15 infants with poor nutrition, 23 infants with less nutrition, 8 infants with excess nutrition, 6 infants with obesity, and 11 infants at risk of overnutrition. Based on these results, there were 304 baby nutrition data that were classified correctly from a total of 347 baby data that were used as testing data. From this research, it can be concluded that the Support Vector Machine algorithm can classify infant nutrition data at the Posyandu, Bangun Purba District, well.
    Type of Medium: Online Resource
    ISSN: 2622-6375 , 2622-6901
    URL: Article
    DOI: 10.47080/simika.v5i2.2185
    Language: Unknown
    Publisher: Universitas Banten Jaya
    Publication Date: 2022
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  • 10
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    Analisa sentimen terhadap kenaikan bbm di twitter (x) menggunakan naive bayes classifier
    LPPM Universitas Muhammadiyah Riau ; 2024
    In:  Jurnal CoSciTech (Computer Science and Information Technology) Vol. 5, No. 1 ( 2024-05-01), p. 65-74
    Details
    In: Jurnal CoSciTech (Computer Science and Information Technology), LPPM Universitas Muhammadiyah Riau, Vol. 5, No. 1 ( 2024-05-01), p. 65-74
    Abstract: Pada awal September 2022 digemparkan oleh berita naiknya BBM. Pemerintah memutuskan menaikkan harga BBM karena melonjaknya harga minyak dunia. PT Pertamina (Persero) resmi menaikkan harga Bahan Bakar Minyak (BBM) pertiga September 2022, jam 14:30 WIB. Sejak keputusan tersebut akan menimbulkan opini dari masyarakat. Masyarakat banyak memberikan tanggapannya melalui sosial media Twitter, baik berupa tanggapan positif maupun tanggapan negatif. Sehingga hal tersebut masyarakat berikan sentimen positif dan negatif. Data yang digunakan sebanyak 3.000 data tweet dengan kata kunci “KENAIKAN BBM,” data diambil pada 1 November 2022 hingga 1 Desember 2022. Penelitian ini menggunakan metode Naive Bayes Classifier, dilakukan dengan tiga perbandingan dengan threshold (0.001 hingga 0.007). Percobaan ini akan dilakukan dengan tiga pengujian data yaitu data opini, data campuran (opini-non opini) dan data seimbang. Berikut hasil pengujian pada data opini mendapatkan akurasi tertinggi sebesar 80% dengan perbandingan 90:10, data campuran mendapatkan akurasi 67.7% dengan perbandingan 70:30 dan data seimbang mendapatkan akurasi 63.6%, dengan perbandingan 90:10.
    Type of Medium: Online Resource
    ISSN: 2723-5661 , 2723-567X
    URL: Issue
    URL: Article
    DOI: 10.37859/coscitech.v5i1
    DOI: 10.37859/coscitech.v5i1.6954
    Language: Unknown
    Publisher: LPPM Universitas Muhammadiyah Riau
    Publication Date: 2024
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