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All Journal Jurnal Simantec Jurnal Riset Informatika Seminar Nasional Teknologi Informasi Komunikasi dan Administrasi [SEMINASTIKA] GANDRUNG: Jurnal Pengabdian Kepada Masyarakat Jurnal Mahasiswa Sistem Informasi (JMSI) Jurnal Aplikasi Teknologi Informasi dan Manajemen (JATIM) Jurnal Penelitian Rumpun Ilmu Teknik JUSTIFY : Jurnal Sistem Informasi Ibrahimy Jurnal Pengabdian Kolaborasi dan Inovasi IPTEKS Journal Of Computer Science And Technology JURNAL ILMIAH SAINS TEKNOLOGI DAN INFORMASI "JAMASTIKA" Jurnal Mahasiswa Teknik Informatika Jurnal Ilmiah Multidisiplin Nusantara Gudang Jurnal Multidisiplin Ilmu Jurnal Ilmiah Research Student Jurnal Sains Student Research JURNAL ILMIAH PENELITIAN MAHASISWA Jurnal Imiah Pengabdian Pada Masyarakat (JIPM) Jurnal Ilmiah Multidisiplin Ilmu Jurnal Riset Teknik Komputer Jurnal Pengabdian Masyarakat Jurnal Riset Sistem Informasi Jurnal Padamu Negeri Jurnal Pendidikan Sains dan Teknologi Terapan Jurnal Sistem Informasi Karya Anak Bangsa Jurnal Teras Pengabdian Masyarakat Jurnal Pengabdian Indonesia Abhakte
Zaehol Fatah
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IMPLEMENTASI K-MEANS CLUSTERING DALAM PENGELOMPOKAN DATA KUNJUNGAN WISATAWAN ASING DI INDONESIA Miftahul Arif Aldi; 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: 10.69714/3hhfj353

Abstract

Clustering is a data mining technique used for grouping data based on specific similarities. This study implements K-Means Clustering to analyze foreign tourist visit data in Indonesia in 2024. Using the Knowledge Discovery in Database (KDD) methodology, the research involves five stages: Data Selection, preprocessing, Transformation, data mining, and Evaluation. Data Clustering was conducted using RapidMiner software, experimenting with different cluster counts (k=2 to k=7) to determine the optimal number of clusters. Results indicate that three clusters (k=3) with the smallest Davies-Bouldin Index (DBI) value were optimal. This Clustering approach categorizes tourists into low, medium, and high visit groups, assisting policymakers in strategic tourism development. The findings support capacity planning and seasonal marketing strategies to optimize Indonesia's tourism sector.
PENERAPAN ALGORITMA DECISION TREE UNTUK KLASIFIKASI KONSUMSI ENERGI LISTRIK RUMAH TANGGA DENGAN PENGGUNAAN RAPIDMINER Ubeitul Maltuf; 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: 10.69714/0hmk8712

Abstract

The research aims to explore and understand energy consumption patterns in households. By using the Decision Tree algorithm, to classify the level of electrical energy consumption. And data on household electrical energy consumption can be obtained from various sources, such as Household electricity meter. Survey or questionnaire filled out by homeowners regarding the use of electrical appliances. Based on the image above, the application of the Decision Tree algorithm in analyzing risk factors for  The classification of household electrical energy consumption produces an accuracy value of 100.00%. From the displayed confusion matrix, we can see the distribution of predicted and actual values for various classes. For example, in the class "true 110 25," there are 17052 correct predictions. The evaluation results also show the precision and recall values for each class. The highest precision was achieved in the "true 2205" class with 100% recall, while the precision was found in the "true 122.5" class of 100.00%.
PENGUNAAN DATA MINIG UNTUK MENGIDENTIFIKASI PELANGGAN BERESIKO TINGGI DALAM PENJUALAN MENGUNAKAN ALGORITMA DECITION TREE C4.5 Nur Saputra, Zuhrian; 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/s91z1k09

Abstract

In the competitive world of business, identifying high-risk customers is critical to minimizing churn rates and increasing profitability. This research uses data mining techniques using the C4.5 decision tree algorithm to classify customers based on their churn risk. The research stages include data collection, cleaning, data processing, as well as dividing the data into training and testing sets. The implementation of this algorithm was carried out using RapidMiner software, which facilitates customer clustering and predicting behavior based on historical attributes. The evaluation results show the model has an accuracy of 74.59%, with precision and recall indicating the model's ability to identify high-risk customers. Thus, the Decision Tree C4.5 algorithm is proven to be effective in supporting decision making for customer churn risk mitigation strategies.
PENERAPAN DECISION TREE C4.5 DALAM MEMPREDIKSI PREDIKAT TERBAIK DI MADRASAH TA'HILIYAH IBRAHIMY Huday, Ahmad; 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/be4q6n31

Abstract

To improve the evaluation process in assessing student progress, predicting the best grades plays a crucial role in enhancing the quality of education. By identifying the top-performing students, educational institutions can refine their teaching methods and create targeted strategies to foster better learning outcomes. This step is vital for ensuring that the learning process aligns with the institution's goals to produce highly skilled and knowledgeable students. In this research, we focused on utilizing the C4.5 algorithm, a widely recognized decision tree method in data mining, to predict student achievements. The C4.5 algorithm is known for its ability to classify and uncover hidden patterns within datasets, making it a powerful tool for educational data analysis. Through this approach, we aim to analyze the factors influencing student success and provide actionable insights for educators and administrators. The study was conducted on students from Madrasah Ta’hiliyah Ibrahimy, where we applied the decision tree algorithm to predict the best grades based on historical academic data. The experiment resulted in three distinct rules or patterns derived from the data, with an overall accuracy of 74.17%. These findings demonstrate the potential of data-driven approaches in supporting academic decision-making and guiding future interventions to further enhance student performance.
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.
Co-Authors Abdul Hadi Abdur Rohman Nurut Toyyibin Abrori, Syariful Ach. Zubairi Achmad Fathoni Verdian Afcharina Diniyil Muhlisin Afrizal Rizqy Pratama Ahmad Homaidi Ahmad Muflih Wafir Ahmad Syahril Lail Ahmad Wahyu Fernando Ahmed Arifi Hilman Rahman Ahsin Ilallah Ainul Fadil Aisyah Putri Sabrina Akhlis Munazilin Alfan Jamil Alfi Fahira Salsabila Alfi Khairunnisa Alfina Damayanti Alfiyah Aurella Alifan Ibrohim Alifia Rosa Firdausiah Alviatur Rizqiyah Amelia Ismatul Hawa Ammar Farisi Anang Maulana Zulfa Angeli Dwiyanti Nur’azizah Anisa Anisa Anwar Anas Anzori Arif Ferdiansyah audiatul jinan Auliya Apriliana Aviatus Sholiha Bagas Wira Yuda Basmalia Bina Cahya Pamungkas, ihya16092002 Citra Nursihah Danil Bahroni Della Natasya Diana Uzlifatul Khairu Ummah Dila Puspita Dewi Diva Maulana Dwi Alya Putri Arifany Dzakwan Rohmatul Hanif Elvi Nazulia Rahma Elvina Eldiavani Epariani Erinia Dzikrotul Kharimah Fahrillah Fahrillah Faqih Nur Rahman Fatimah Isa Auliya Fatma Nur Afifah Faza Qori Aina Fikri Rostina Firda Wati Husaini Kulsum Fitri Elvi Karisma Fitria Ayu Ulandari Hafidz, M. Fajar Hasna Ruhmaniatin Herlinatus Safira Muasolli Hermanto , Hijrah Hijriah Holida Izzatilla Holil Asy’ari Huday, Ahmad Ifan Farimulyadi Ifan Prasetyariansyah Ifqy Ahmad Fahrizal iin, Nur Inayah Ika Indah Khasanah ila, Sufatun Aila Ilham Rafi Jawara Ilham Rafiqi Imam Nawawi Imelda Valentina Octavia Indah Novita Sari Iqbal Ainul Yaqin Irfansyah, Khairullah Irham, Muhammad Nazril Irma Yunita Islamiyatul Addewiyah Ismawati Ismawati Ismawati Ivana Dwikartika Sari j-sika Jarot Dwi Jarot Dwi Prasetyo Jefri Jefri Jesika Maya Nur Islami Kayyisah Fakhirah Kevin Riyas Robbani Khairul Anam Khozaimah Dian Islami Komarul Imam Laila Devi Sari LAILATUL FITRIYAH Lailatul Risqia Lailatus Syarifah Lailatussyarifah Lina Sosiana Lisa Novia Ramdani Lubebetun Nafisa Lukman Fakih Lukman Fakih Lidimilah Luluk Nuril Mukarromah Lutfiana , Nurisma Lutfiyatul F Anas Lu’luul Maulidya Nova M. Andrik Muqorrobin P M. Andrik Muqorrobin Pratama M. Fazlur Rahman Assauqi Maharani Rahmatul Hanani Mahmudi Mahmudi Mamluatur Rizkiyatun Nafiah Manda Nuria Suhailatin Najwa Maruf Ubaidillah Maryana Mashuri, Ahmad Meliana Khamisah Mifta Wilda Al -Aluf Miftahul Arif Aldi Milka Afifah Rahmatillah Mochammad Rofi Mochammad Syukron Ramadani Moh. Agus Efendi Moh. Baha’Uddin Moh. Syahrul Iskandar Moh. Zaini Romly Mohamad Faezal Fauzan Nanda Mohammad Alfian Husni Mubarok Mohammad Farhan Fatah Muchammad Atfal Nur Afil Muflihatul Hasanah Muftiyah Zakiyah Muhamad Auliya Muhamad Ilhan mansiz Muhammad Al Madany Muhammad Faidhurrahman Wahid Muhammad Hanif Zaky Ubaidillah Muhammad Hasan Muhammad Robitul Umam Muhammad Trisnawadi Ismardani Mutmainnah Ilmiatul Faidah Muyessiroh Muzayyana, Muzayyana Mu’tashim Billah Rahman Nabila Khansa Nabila Sofia Az-zahra Nadia Selvi Ramadhani Nafisatul Insiyah Naqibuzzahidin Naufal Arif Maulana Nur Aida NUR AINI Nur Azise Nur Dina Kamelia Nur Laili Mukarromah Nur Rizatul Mufidah Nur Sahila Chapsah Nur Saputra, Zuhrian Nurin Naimah Nurisma Lutfiana Prastika Buya Hakim Putri Anindya Damayanti Qittratul Ameliatus Qurratul Aini Rafi Jawara, Ilham Raihan Asriel Afandi Ratu Maulidia Anggraini Regina Izza Aofkarina Riatul Jannah Rifki Dwi Saputra Risma Alfiatul Karima Risqiatus Syarifah Risqiyati Amilia Ningsih Rita Irawati rizka, Rizka Aprilia Ningsih Rizki Hidayaturrochman Rosita Natania Maulani Rudi Ananta Al Hidayah Ruqoyyatul Widad Ruwaida Khollatil Widat Safitri Nurul Qomariyah Sagita Maesarah Septi Camelia Ulfa Sidra Al Zahro Sinta Bella Sinta Dewi Anggraeni Siti Aysatin Rodia Siti Imroatul Jannah Siti Kholifah Siti Maghfiroh Siti Nabilatul Hoiroh Siti Nur Azizah Siti Romlah Siti Sulaiha Sitti Ainur Rofiqotul Anisa Sofi Naila Nuriyazih Sofyan, Moh Sofyan Alfandi SU'AYDI, AHMAD SU'AYDI Suci Mulianingsih Sukiman Eki Putra Sulistia Wardani Supri Arrohman Syaiful Hasan Abdullah Syirva Nada Fidya Tadzkirotul Latifah Taufik Saleh Ubeitul Maltuf Ulvi Munawaroh Ummi Fadlilatuz Zakiyah Ummil Mahfudoh Ummul Khoirun Fitriyah Uny Khafifah USWATUN HASANAH Wafi Riga Ramadhani Wafi, Wafi Wardatul Gufronia Wildatul Hasanah Winda Yanti Umami Wiwik Handayani Wulan Shelfiana Kamil Yeni nur hasanah Yua Isman Islam Yulina Sari Zahrafil Jannah Zainur Rahman Zakiyatus Solehah