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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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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 Energy 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

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ANALISIS KECANDUAN SMARTPHONE PADA MAHASISWA MENGGUNAKAN METODE K-NEARST NEIGHBORS (K-NN) Ivana Dwikartika Sari; Zaehol Fatah
Jurnal Ilmiah Multidisiplin Ilmu Vol. 1 No. 5 (2024): Oktober : Jurnal Ilmiah Multidisiplin Ilmu (JIMI)
Publisher : CV. Denasya Smart Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.69714/623bk437

Abstract

Smartphone are a tecnology that is widely used among teenagers. Smartphone have a negative impact on teenagers, one of which is that amsrtphone addiction can interfere with various activities in teenagers’ real lives.this writing aims to understand and describe various aspects including health aspects, psychological aspects, academic aspects, social aspects and financial aspects. Classification is carried out to support decision making regarding smartphone addiction problems. K-Nearest Neighbors (KNN) is a machine learning classification method used in this research. The research results show that the best method for classifying smartphone addiction is KNN with attribute selection using Linear Regression based on weight correlation.
KLASTERISASI PENDIDIKAN SD UNTUK MENGETAHUI DAERAH DENGAN PENDIDIKAN TERENDAH MENGGUNAKAN ALGORITMA K-MEANS Kevin Riyas Robbani; Zaehol Fatah
Jurnal Ilmiah Multidisiplin Ilmu Vol. 1 No. 5 (2024): Oktober : Jurnal Ilmiah Multidisiplin Ilmu (JIMI)
Publisher : CV. Denasya Smart Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.69714/jgmf7903

Abstract

Elementary education serves as the foundational stage in efforts to improve the overall quality of education in Indonesia. Identifying regions with the lowest levels of elementary education is essential for effectively targeting initiatives to enhance education quality. The K-Means clustering algorithm is employed to group regions based on specific indicators, such as the number of students, dropout rates, classrooms, teaching staff, school principals, and others. The objective of this method is to identify regions with the lowest levels of elementary education by pinpointing clusters of areas that require the most support and development. K-Means clustering operates by dividing data into several clusters based on the similarity of feature patterns. This process facilitates the identification of regional groups with varying priorities for support and development. The clustering analysis results reveal that from 39 datasets related to elementary education across various regions in Indonesia, three clusters were formed. Cluster 0 consists of 34 data points, Cluster 1 contains only 1 data point, and Cluster 2 comprises 4 data points.
IMPLEMENTASI ALGORITMA CLUSTERING K-MEANS PADA PENGGUNA WARTEL DI PONDOK PESANTREN SALAFIYAH SYAFI'IYAH SUKOREJO Irfansyah, Khairullah; Zaehol Fatah
Jurnal Ilmiah Multidisiplin Ilmu Vol. 1 No. 5 (2024): Oktober : Jurnal Ilmiah Multidisiplin Ilmu (JIMI)
Publisher : CV. Denasya Smart Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.69714/55xet429

Abstract

This research discusses the application of the K-Means Clustering algorithm to analyze the usage patterns of wartel services at the Salafiyah Syafi'iyah Sukorejo Islamic Boarding School. The purpose of this research is to group users into several clusters based on call duration, frequency of use, and total call cost. User data was analyzed using the stages in the SEMMA method (Sample, Explore, Modify, Model, Assess) to ensure systematic and structured data processing. The results showed that the K-Means algorithm was able to form three main clusters, namely users with low, medium, and high intensity. The majority of users belong to the low-intensity cluster with short average call duration and minimal expenditure, while the high-intensity cluster consists of users who make long calls with high costs. Further analysis shows that the highest usage time is at night (19.00-21.00). Based on these results, it is recommended that wartel managers optimize operating hours and provide promotional call packages according to the needs of each user cluster. In addition, diversification of services such as cheap internet access can also increase the attractiveness of wartel in the digital era. This research uses clustering methods to assist data-based strategic decision-making, as outlined by Han and supported by the application of SEMMA from SAS Institute (1998).
KLASIFIKASI PENYAKIT DIABETES MENGGUNAKAN  METODE K-NEAREST NEIGHBORS (KNN) Luluk Nuril Mukarromah; Zaehol Fatah; Irma Yunita
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/jgq41610

Abstract

Diabetes is a chronic disease caused by impaired insulin production, which causes an increase in blood sugar levels and has the potential to cause serious complications. Early detection of this disease is very important to prevent the risk of complications in patients. This research aims to implement a data mining method with the K-Nearest Neighbors (KNN) algorithm in the classification of diabetes, using attributes such as blood pressure, age, obesity and family history as variables. The KNN method is used to identify patterns in data that are relevant to potential diabetes, with stages of model learning and performance evaluation. The analysis results show that the KNN algorithm is able to classify data with a fairly good level of accuracy, showing its effectiveness in detecting possible diabetes in patients. The implementation of this algorithm shows potential as a supporting tool in the early diagnosis of diabetes.
KLASIFIKASI KELULUSAN MAHASISWA MENGGUNAKAN METODE DECISION TREE MENGGUNAKAN APLIKASI RAPIDMINER Qittratul Ameliatus; 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/em8qnw54

Abstract

Data mining helps to make predictions and helps to provide precise and careful decisions. Classification of student graduation is an important process in the education system. By using classification methods, information can be obtained about the possibility of student graduation based on related variables. This research aims to analyze the classification of student graduation using the Decision Tree method with the RapidMiner application. The data used is student graduation data from 100 students consisting of 50 male students and 50 female students. The variables used are age, gender, grade, course, UTS, UAS, and graduation. The results showed that the Decision Tree method can be used for student graduation classification with a high accuracy of 99.00%. The most influential variables in the classification of student graduation are grades and UTS and UAS.
SISTEM INFORMASI ANTRIAN LOKET PELAYANAN PT. POS INDONESIA CABANG BONDOWOSO BERBASIS WEB Mutmainnah Ilmiatul Faidah; 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/fvf8ey13

Abstract

This research focuses on the development of a web-based information system designed to manage queues at the service counter of the PT Pos Indonesia Bondowoso Branch. It addresses common issues such as customer confusion, delays in service processes, and potential queue violations through an innovative digital solution. The system was implemented using a Waterfall model approach within the System Development Life Cycle (SDLC) framework and features automatic queue number retrieval, digital calling, live queue status updates, as well as daily and monthly queue recording functionalities.Employing technologies such as PHP, MySQL, and XAMPP, along with an interface designed in Adobe XD, this system provides a user-friendly and integrated solution. The implementation results demonstrate enhanced operational efficiency, streamlined processes, and reduced customer wait times. Overall, this system positively impacts counter services and facilitates better evaluation of service management. at the post office.  
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.
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