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Jurnal Ilmiah Multidisiplin Ilmu
Published by CV. Denasya Smart Publisher
ISSN : 30472113     EISSN : 30472121     DOI : 10.69714
Core Subject : Education,
Other
Jurnal Ilmiah Multidisiplin Ilmu (JIMI) dengan e-ISSN : 3047-2121, p-ISSN : 3047-2113, merupakan platform publikasi jurnal Karya suatu hasil penelitian orisinil atau tinjauan Pustaka yang ditulis oleh Dosen, mahasiswa dan atau Peneliti lainnya. Ruang lingkup karya yang diterbitkan mencakup Multidisiplin diantaranya yaitu: Ilmu Sosial Humaniora, Ilmu Hukum, Pertanian, Kesehatan, Peternakan, perikanan, Politik, Pendidikan, Ilmu Teknik, Teknik Elektro dan Informatika, Desain Komunikasi Visual, Manajemen, Ekonomi dan Akuntansi, Kewirausahaan dan Bisnis. Jurnal ini terbit 1 tahun 6 kali (Februari, April, Juni, Agustus, Oktober dan Desember)
Arjuna Subject : Umum - Umum
Articles 239 Documents
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KLASIFIKASI DATA MINING UNTUK MEMPREDIKSI KELULUSAN MAHASISWA MENGGUNAKAN METODE NAIVE BAYES Jefri Jefri; Zaehol Fatah
Jurnal Ilmiah Multidisiplin Ilmu Vol. 2 No. 1 (2025): Februari : Jurnal Ilmiah Multidisiplin Ilmu (JIMI)
Publisher : CV. Denasya Smart Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.69714/mhjq1v85

Abstract

Data mining helps provide precise and careful decisions. Student graduation on time is one of the assessment points in the higher education accreditation process. However, student graduation cannot always be detected quickly, which can reduce the assessment of a university in the accreditation process. This problem arises to find out whether students will be able to graduate on time or not Classification method for predicting student graduates using the Naïve Bayes algorithm. Whether a student graduates on time or not, it is hoped that the results will provide information and input for the university in making future policies. From the results of this test, it was found that by applying the Naïve Bayes algorithm the system can predict student graduation in a timely manner. After comparing several literatures, it can be concluded that this method can be used for this prediction with an accuracy rate of 90%. This literature review is important as a supporting factor for research.
PENGGUNAAN DATA MINING UNTUK MEMPREDIKSI PENJUALAN PADA TOKO PERLENGKAPAN BANGUNAN MENGGUNAKAN METODE APRIORI Ilham Rafi Jawara; Zaehol Fatah
Jurnal Ilmiah Multidisiplin Ilmu Vol. 2 No. 1 (2025): Februari : Jurnal Ilmiah Multidisiplin Ilmu (JIMI)
Publisher : CV. Denasya Smart Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.69714/xwtjdb79

Abstract

This study applies the Apriori method in data mining to analyze sales transaction data in building supply stores, aiming to identify consumer purchasing patterns that support strategic decision-making. The data mining process includes data cleaning, integration, selection, transformation, and the application of the Apriori algorithm to discover significant association rules. The analysis results reveal purchasing patterns, such as product combinations with confidence levels reaching 100%, indicating strong correlations between frequently co-purchased items. These findings are utilized to design strategies such as product bundling, optimizing item placement, and targeted promotions, significantly enhancing operational efficiency and customer satisfaction. This study demonstrates that the implementation of the Apriori algorithm is an effective solution for supporting data-driven management while strengthening the competitive edge of building supply stores in the retail industry.
IMPLEMENTASI DATA MINING MENGGUNAKAN ALGORITMA APRIORI UNTUK MENENTUKAN PERSEDIAAN BARANG Ahmed Arifi Hilman Rahman; Zaehol Fatah
Jurnal Ilmiah Multidisiplin Ilmu Vol. 2 No. 1 (2025): Februari : Jurnal Ilmiah Multidisiplin Ilmu (JIMI)
Publisher : CV. Denasya Smart Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.69714/2rkam171

Abstract

Entrepreneurs engaged in the shopping sector have promising prospects because they can serve the lower and upper middle classes and provide convenience for people to buy everyday goods without having to go to supermarkets or convenience stores. However, if the availability of goods or materials needed is not optimally guaranteed, there may be a shortage of goods or materials needed. This also happens in some stores, where customers often run out of stock of various products and equipment they are looking for, but this is due to the lack of inventory management habits in the store. In this case, it is about finding out what products and needs are needed by store customers. This dataset uses several variables such as transaction date, product name, and sales or purchase amount by applying the apriori algorithm. The apriori algorithm is a type of association rule in data mining that is used to analyze and find correlation patterns. The data used in this study is a sample of 100 sales transaction data. The final association rule obtained from the transaction data is "If consumers buy Flour, they will buy Oil and Eggs" with a support percentage of 54% and a confidence of 96%. These results provide data on the names of the best-selling products, which can be used as an inventory estimate to avoid empty seats that can result in customer disappointment.
ANALISIS DATA MINING MENGGUNAKAN METODE CLUSTERING TERHADAP PRESTASI SISWA I'DADIYAH SUKOREJO Abdur Rohman Nurut Toyyibin; Zaehol Fatah
Jurnal Ilmiah Multidisiplin Ilmu Vol. 2 No. 1 (2025): Februari : Jurnal Ilmiah Multidisiplin Ilmu (JIMI)
Publisher : CV. Denasya Smart Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.69714/remqnx91

Abstract

This study analyzes the performance patterns of students at Madrasah I’dadiyah Sukorejo using data mining methods, specifically clustering. The analyzed factors include exam scores and participation in extracurricular activities, as both are considered to significantly influence academic performance. Exam scores reflect mastery of subjects, while extracurricular activities often positively impact students' social skills and learning motivation.[1] The K-Means algorithm was selected to classify students into three main groups: high-performing, average-performing, and low-performing students. The clustering results are expected to provide strategic guidance for the school to improve the quality of education. Low-performing students can receive additional guidance or motivational training, while average-performing students can be encouraged to participate more actively in extracurricular activities to enhance interpersonal skills. Understanding these performance patterns helps the school design more effective programs to maximize students’ academic potential based on their needs. This study also opens opportunities for further exploration of other factors affecting academic performance, such as family conditions and the home learning environment. Thus, this approach becomes an essential step in creating a more inclusive and high-quality education system.
PENGELOMPOKAN PENDERITA GANGGUAN TIDUR BERDASARKAN GAYA HIDUP MENGGUNAKAN ALGORITMA K-MEANS CLUSTERING Bagas Wira Yuda; Zaehol Fatah
Jurnal Ilmiah Multidisiplin Ilmu Vol. 2 No. 1 (2025): Februari : Jurnal Ilmiah Multidisiplin Ilmu (JIMI)
Publisher : CV. Denasya Smart Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.69714/3eps2496

Abstract

Sleep disorders, including insomnia, can be influenced by various lifestyle factors, such as sleep duration, sleep quality, physical activity, and individual health conditions. This study aims to categorize the risk level of insomnia based on lifestyle using the K-Means clustering algorithm. The data used include sleep duration, sleep quality, heart rate, and daily step count. Through the implementation of the K-Means algorithm, the data is analyzed to group individuals into several categories based on existing lifestyle patterns. The results of the study show a correlation between a healthy lifestyle and better sleep quality. In addition, the resulting clusters provide insight into lifestyle characteristics that affect the risk of insomnia, so that they can be the basis for recommendations for more targeted health interventions. This study is expected to contribute to the development of data-based sleep disorder management strategies by utilizing machine learning methods, especially the K-Means algorithm, to support efforts to improve the quality of life of the community.
PENGELOMPOKKAN HASIL BELAJAR SISWA SDN 3 ARDIREJO DENGAN METODE CLUSTERING K-MEANS Iqbal Ainul Yaqin; Zaehol Fatah
Jurnal Ilmiah Multidisiplin Ilmu Vol. 2 No. 1 (2025): Februari : Jurnal Ilmiah Multidisiplin Ilmu (JIMI)
Publisher : CV. Denasya Smart Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.69714/t57xvh88

Abstract

Grouping student learning outcomes is a strategic step to improve the quality of learning by understanding student achievement patterns in more depth. This study aims to analyze student learning outcomes at SDN 3 Ardirejo by applying the K-Means clustering method, which is designed to group data based on similarities in academic value characteristics from various subjects during one semester. The clustering results show the effectiveness of this algorithm in dividing students into high, medium, and low achievement clusters, making it easier for teachers to design adaptive learning strategies that suit the needs of each group. In addition, the information generated provides valuable insights for planning intervention programs, such as remedial learning for low-achieving students or enrichment materials for high-achieving students. This study contributes to a more systematic management of educational data at the elementary school level and is expected to be a reference for more effective decision-making, both at the school level and by educational stakeholders.
PENERAPAN ALGORITMA K-MEANS PADA SMA PROVINSI DKI JAKARTA UNTUK MENENTUKAN SEKOLAH TERBAIK BERDASARKAN NILAI UN Muhammad Hasan; Zaehol Fatah
Jurnal Ilmiah Multidisiplin Ilmu Vol. 2 No. 1 (2025): Februari : Jurnal Ilmiah Multidisiplin Ilmu (JIMI)
Publisher : CV. Denasya Smart Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.69714/bncwkr16

Abstract

Education is an important aspect in human resource development in Indonesia. One indicator to measure the quality of education is the National Examination (UN) score. However, parents or students often have difficulty choosing the best school in DKI Jakarta Province because of the many choices available. This research aims to apply the K-Means algorithm to group high school schools in DKI Jakarta Province based on National Examination scores. By using the clustering method, it is hoped that groups of schools with the best achievements can be found, making it easier to select schools based on these criteria. In this research, the data used are high school National Examination scores in DKI Jakarta obtained from the Ministry of Education and Culture. The results of this research show that the K-Means algorithm can be effectively used to group schools based on National Examination scores, thereby providing a clearer picture for the public in determining quality schools.
KLASTERING DATA PEGAWAI STATUS PANGKAT DAN JABATAN PADA DINAS PERHUBUNGAN BANYUWANGI MENGGUNAKAN METODE K-MEANS Akmaluddin Akmaluddin; Zaehol Falah
Jurnal Ilmiah Multidisiplin Ilmu Vol. 2 No. 1 (2025): Februari : Jurnal Ilmiah Multidisiplin Ilmu (JIMI)
Publisher : CV. Denasya Smart Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.69714/25yt4342

Abstract

Complex personnel data management is a strategic challenge for organizations, especially in government agencies such as the Banyuwangi Transportation Agency. This study aims to provide a solution to this problem by using the K-Means Clustering method. This technique allows grouping employee data based on key attributes, namely rank, position, and length of service. The research data was obtained from the Banyuwangi Transportation Agency personnel documents and processed using RapidMiner software to ensure the accuracy of the clustering results. The results of the study show that employee data can be grouped into two main clusters. These clusters reflect employee distribution patterns based on the characteristics of rank, position, and length of service, which can then be used to support strategic decision making, such as the preparation of employee training, promotion, and rotation policies. This study proves that the K-Means method is effective in analyzing complex personnel data and makes a significant contribution to increasing the efficiency of human resource management in government agencies.
IMPLEMENTASI DATA MINING UNTUK MENENTUKAN POLA PENJUALAN DI RAHAYU MART MENGGUNAKAN ALGORITMA APRIORI Bina Cahya Pamungkas, ihya16092002; Zaehol Fatah
Jurnal Ilmiah Multidisiplin Ilmu Vol. 2 No. 1 (2025): Februari : Jurnal Ilmiah Multidisiplin Ilmu (JIMI)
Publisher : CV. Denasya Smart Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.69714/vbsxqq63

Abstract

The Apriori algorithm is a method used to discover patterns among a set of items. By analyzing all recorded sales transactions, it helps in determining and developing more accurate and targeted promotions. Rahayu Mart faces a challenge in understanding what customers want and need, as well as how they shop. Identifying frequently purchased items by customers can assist in making appropriate business decisions and serve as a consideration for sales strategies. By applying the Apriori algorithm, consumer purchasing patterns and the influence between items can be uncovered, enabling more effective business insights.
PENINGKATAN EFISIENSI PEMANTAUAN KEHADIRAN SISWA MENGGUNAKAN CLASTERING K-MEANS PADA MADRASAH I'DADIYAH SALAFIYAH SYAFI'IYAH Mohamad Faezal Fauzan Nanda; Zaehol Fatah
Jurnal Ilmiah Multidisiplin Ilmu Vol. 2 No. 1 (2025): Februari : Jurnal Ilmiah Multidisiplin Ilmu (JIMI)
Publisher : CV. Denasya Smart Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.69714/87vcvz50

Abstract

This research aims to increase efficiency in monitoring student attendance at Madrasah I'dadiyah Salafiyah Syafi'iyah by utilizing the K-Means Clustering analysis method. Monitoring student attendance is still carried out conventionally, so it often takes time and is less effective in identifying overall student attendance patterns. For this reason, in this research, student attendance data collected from the madrasa attendance system was analyzed using K-Means Clustering, a machine learning technique that can group students based on their attendance patterns. This process produces several groups which make it easier for the madrasah to identify students who frequently attend, rarely attend, or frequently do not attend. In this way, madrasas can take more appropriate steps in dealing with attendance problems, such as paying special attention to students who are often absent. The results of this research indicate that the application of K-Means Clustering can increase the efficiency of attendance monitoring and provide a stronger basis for decision making to improve the attendance system at the I'dadiyah Salafiyah Syafi'iyah madrasah.

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