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

 2   3   4   5   6 
IMPLEMENTASI ALGORITMA APRIORI PADA ANALISIS POLA PENJUALAN SEPATU Nabila Sofia Az-zahra; Zaehol Fatah
Jurnal Riset Teknik Komputer Vol. 1 No. 4 (2024): Desember : Jurnal Riset Teknik Komputer (JURTIKOM)
Publisher : CV. Denasya Smart Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.69714/krx4n816

Abstract

In a competitive business world, data-driven strategies are key to maintaining business continuity. Local brand shoe sales face challenges in managing increasing sales data, which is often only used for archives without providing added value in strategic decision making. This study aims to utilize data mining techniques, especially the Apriori algorithm, to analyze transaction patterns of local brand shoe sales. The Apriori algorithm was chosen because of its ability to find relevant association patterns from large transaction data. This study includes data pre-processing, pattern mining, and interpretation of results, with a focus on extracting relationships and linkages that can improve marketing strategies. The results of this study are expected to produce valuable information that supports decision making, while providing solutions to the lack of decision support systems in managing shoe sales data. Thus, this study contributes to the development of data-driven marketing strategies to improve the competitiveness of local shoe products.
Implementasi Metode K-Nearest Neighbors (KNN) Untuk Menentukan Jurusan Siswa di SMK Sumber Bunga Komarul Imam; Zaehol Fatah
Gudang Jurnal Multidisiplin Ilmu Vol. 2 No. 12 (2024): GJMI - DESEMBER
Publisher : PT. Gudang Pustaka Cendekia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59435/gjmi.v2i12.1090

Abstract

Penentuan kelas siswa di SMK merupakan proses penting yang dapat mempengaruhi keberhasilan belajar siswa dan karir di masa sebelumnya. Proses ini sering kali membutuhkan pertimbangan berbagai faktor akademik, seperti nilai mata pelajaran utama. Penelitian ini menggunakan algoritma K-Nearest Neighbors (KNN) untuk membantu mengklasifikasikan siswa ke mata pelajaran yang sesuai berdasarkan data nilai mata pelajaran, seperti Bahasa Indonesia, IPA, IPS, dan Matematika. Dengan data siswa SMK Sumber Bunga, model KNN dikembangkan dan dievaluasi untuk menentukan efektivitasnya dalam mengklasifikasi mata pelajaran "Teknologi Komputer dan Jaringan" serta "Multimedia". Hasil evaluasi menunjukkan akurasi model mencapai 97,14%, dengan presisi dan recall yang tinggi pada kedua jurusan. Tingkat keyakinan ( confident ) dari model prediksi juga memberikan gambaran yang jelas tentang keakuratan setiap prediksi. Hasil ini menunjukkan bahwa metode KNN dapat diimplementasikan sebagai alat bantu yang efektif untuk penentuan mata pelajaran, sehingga dapat mendukung keputusan yang lebih objektif dan sesuai dengan kemampuan akademik siswa.
Metode Pengumpulan Data Pada Deteksi Buah Paprika Berdasarkan Citra Digital Menggunakan Teachable Machine Learning Fatma Nur Afifah; Zaehol Fatah
Gudang Jurnal Multidisiplin Ilmu Vol. 2 No. 12 (2024): GJMI - DESEMBER
Publisher : PT. Gudang Pustaka Cendekia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59435/gjmi.v2i12.1110

Abstract

Visi computer yang merupakan cabang kecerdasan buatan yang menggunakan citra digital sebagai input data. Penelitian ini bertujuan untuk mengembangkan metode pengumpulan data dalam deteksi warna buah paprika menggunakan citra digital dan platform Teachable Machine. Metode ini dirancang untuk meningkatkan efisiensi dan akurasi dalam mengidentifikasi variasi warna paprika, yang penting untuk kualitas produk di industri pertanian. Data dikumpulkan melalui pengambilan gambar paprika dalam kondisi pencahayaan yang konsisten, kemudian diproses menggunakan teknik segmentasi warna dan analisis histogram. Model machine learning dilatih menggunakan Teachable Machine, yang memungkinkan klasifikasi warna dengan mudah dan cepat. Hasil evaluasi menunjukkan bahwa model dapat mendeteksi warna paprika dengan akurasi yang memuaskan. Penelitian ini memberikan wawasan penting tentang potensi penerapan teknologi digital dalam pertanian dan membuka peluang untuk pengembangan lebih lanjut dalam deteksi dan analisis kualitas produk pertanian. Dengan demikian, penelitian ini berkontribusi pada peningkatan efisiensi pengelolaan hasil pertanian serta promosi inovasi dalam sektor ini.
Deteksi Keaslian Uang Kertas Berdasarkan Citra Digital Dengan Menggunakan Teachable Machine Learning Yeni nur hasanah; Zaehol Fatah
Gudang Jurnal Multidisiplin Ilmu Vol. 2 No. 12 (2024): GJMI - DESEMBER
Publisher : PT. Gudang Pustaka Cendekia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59435/gjmi.v2i12.1111

Abstract

Penipuan uang palsu merupakan masalah serius yang dapat merugikan perekonomian negara dan masyarakat. Seiring dengan kemajuan teknologi, deteksi keaslian uang kertas kini dapat dilakukan menggunakan metode berbasis citra digital. Penelitian ini bertujuan untuk mengembangkan sistem deteksi keaslian uang kertas menggunakan pendekatan machine learning dengan memanfaatkan platform Teachable Machine. Sistem ini memanfaatkan citra digital uang kertas yang diambil menggunakan kamera digital untuk dianalisis dan diklasifikasikan berdasarkan keaslian uang tersebut. Data citra uang kertas yang digunakan mencakup gambar dari berbagai sisi dan elemen pengaman pada uang, seperti watermark, benang pengaman, dan cetakan mikroteks. Model machine learning yang diterapkan dilatih dengan berbagai gambar uang kertas asli dan palsu untuk menghasilkan model yang dapat mengidentifikasi perbedaan antara keduanya. Hasil penelitian menunjukkan bahwa sistem ini dapat mendeteksi keaslian uang kertas dengan tingkat akurasi yang tinggi, memberikan solusi praktis dan efisien untuk membantu mendeteksi uang palsu secara cepat dan akurat. Implementasi sistem ini berpotensi untuk digunakan di berbagai sektor, seperti perbankan, ritel, dan pemeriksaan keuangan.
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.
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