Article Per Year (5 Year)
p-Index From 2021 - 2026
20.689
P-Index
This Author published in this journals
All Journal Jurnal Simantec 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 "JAMASTIKA" Jurnal Mahasiswa Teknik Informatika Gudang Jurnal Multidisiplin Ilmu Jurnal Ilmiah Research Student Jurnal Sains Student Research Jurnal Riset Sistem dan Teknologi Informasi 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
Unknown Affiliation
Religion Aerospace Engineering Agriculture, Biological Sciences & Forestry Arts Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Medicine & Pharmacology Physics Public Health Social Sciences Other

Published : 183 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 183 Documents
Search

 3   4   5   6   7 
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.
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.
Klasifikasi Algoritma Decision Tree Untuk Tingkat Kemiskinan Di Indonesia Mifta Wilda Al -Aluf; Zaehol Fatah
Journal of Computer Science and Technology (JOCSTEC) Vol 3 No 1 (2025): JOCSTEC - Januari
Publisher : PT. Padang Tekno Corp

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59435/jocstec.v3i1.440

Abstract

Kemiskinan merupakan salah satu masalah sosial yang terus menjadi tantangan bagi pemerintah di berbagai negara, termasuk Indonesia. Dalam upaya mengidentifikasi faktor-faktor yang memengaruhi tingkat kemiskinan, analisis data yang tepat diperlukan untuk mendukung pengambilan kebijakan yang efektif. Penelitian ini bertujuan untuk mengklasifikasikan tingkat kemiskinan di Indonesia dengan menggunakan algoritma Decision Tree, salah satu metode pembelajaran mesin yang populer. Data yang digunakan dalam penelitian ini mencakup indikator ekonomi, demografi, dan sosial yang relevan dengan kemiskinan di Indonesia. Dengan menggunakan algoritma Decision Tree, kami dapat mengidentifikasi variabel-variabel kunci yang berperan dalam klasifikasi tingkat kemiskinan serta membangun model prediksi yang dapat membantu dalam pengambilan keputusan. Hasil penelitian menunjukkan bahwa algoritma Decision Tree memiliki kinerja yang baik dalam mengklasifikasikan data kemiskinan dan memberikan wawasan mendalam tentang faktor-faktor yang memengaruhi kemiskinan di Indonesia. Temuan ini diharapkan dapat berkontribusi dalam upaya penanggulangan kemiskinan melalui pendekatan berbasis data.
Penerapan Data Mining Untuk Prediksi Diagnosis Demam Berdarah Dengan Algoritma Decision Tree C4.5 Supandi; Zaehol Fatah
Journal of Computer Science and Technology (JOCSTEC) Vol 3 No 2 (2025): JOCSTEC - Mei
Publisher : PT. Padang Tekno Corp

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59435/jocstec.v3i2.437

Abstract

Prediksi dengan model sistem pendukung keputusan merupakan cara yang tepat sasaran untuk digunakan dalam memecahkan masalah. Demam Berdarah Dengue (DBD) adalah penyakit endemik di Indonesia yang memerlukan penanganan cepat untuk mencegah komplikasi lebih lanjut. Prediksi diagnosis DBD dengan menggunakan algoritma Decision Tree C4.5 memiliki tingkat akurasi 100% dan meyakinkan. Dataset yang digunakan mencakup data medis pasien, seperti gejala klinis yaitu demam, nyeri sendi, mual, hasil laboratorium berupa trombosit, hematokrit, uji NS1, serta riwayat komorbiditas dan durasi gejala. Proses pre-processing dilakukan untuk memastikan data siap digunakan, dengan menangani data yang hilang dan menyesuaikan format data agar konsisten. Model Decision Tree C4.5 dipilih karena kemampuannya mengolah data dengan berbagai format dan hasilnya dapat dengan mudah dipahami. Model C4.5 dievaluasi menggunakan metrik akurasi, presisi, sensitivitas, dan spesifisitas. Dengan performa yang baik, model ini memiliki potensi untuk digunakan dalam sistem pendukung keputusan medis. Implementasinya di lapangan dapat membantu tenaga medis dalam mempercepat diagnosis dan memberikan penanganan yang lebih tepat waktu, yang sangat penting dalam menangani pasien DBD.
PENERAPAN DATA MINING UNTUK PENILAIAN TES HADRAH DI PESANTREN SALAFIYAH SYAFI'IYAH MENGGUNAKAN METODE RANDOM FOREST Citra Nursihah; Zaehol Fatah; Rizki Hidayaturrochman
Jurnal Riset Teknik Komputer Vol. 2 No. 1 (2025): Maret : Jurnal Riset Teknik Komputer (JURTIKOM)
Publisher : CV. Denasya Smart Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.69714/jfe1c445

Abstract

The application of data mining in the evaluation of Hadrah tests at Pesantren Salafiyah Syafi'iyah is explored using the Random Forest algorithm. Hadrah, a form of Islamic artistic performance involving vocal and percussion elements, is integral to the cultural and spiritual life in Islamic boarding schools. The objective of this research is to enhance the accuracy and objectivity of performance assessments in Hadrah, particularly in the context of competition or educational evaluation at Pesantren Salafiyah Syafi'iyah. By utilizing the Random Forest method, which is a robust machine learning technique, the study aims to minimize the subjectivity and inconsistency inherent in traditional evaluation methods. The study leverages secondary data from previous Hadrah tests, applying preprocessing steps to ensure the data is suitable for analysis. The results show that Random Forest provides a high level of precision in classifying participants based on key assessment features such as tempo, consistency, and overall performance. This method contributes significantly to improving the reliability and fairness of the evaluation process, ensuring a more standardized approach to assessing artistic skills in the context of Islamic traditions. The findings suggest that data-driven approaches can play a pivotal role in preserving and promoting Islamic arts while enhancing the educational process.
Co-Authors Abdul Hadi Abdur Rohman Nurut Toyyibin Abrori, Syariful Ach. Zubairi Achmad Fathoni Verdian Afcharina Diniyil Muhlisin Afrizal Rizqy Pratama Ahmad Homaidi Ahmad Syahril Lail 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 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 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 Pratama Maharani Rahmatul Hanani 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 Oka dewata Syaputra Prastika Buya Hakim Putri Anindya Damayanti Qittratul Ameliatus Qurratul Aini 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 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