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All Journal Techno.Com: Jurnal Teknologi Informasi Sinkron : Jurnal dan Penelitian Teknik Informatika Martabe : Jurnal Pengabdian Kepada Masyarakat Journal Sensi: Strategic of Education in Information System Jurnal Pembelajaran Pemberdayaan Masyarakat (JP2M) International Journal of Basic and Applied Science Jurnal Pengabdian Kepada Masyarakat Patikala Jurnal Abdimas Kartika Wijayakusuma Journal of Natural Science and Technology Adpertisi: Bidang Sains dan Teknologi NUSANTARA: Jurnal Pengabdian Kepada Masyarakat Jurnal Informatika: Jurnal Pengembangan IT ABDIKOM
Nurul Afifah Arifuddin
Universitas Pembangunan Nasional Veteran Jakarta
Agriculture, Biological Sciences & Forestry Arts Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Energy Engineering Languange, Linguistic, Communication & Media Library & Information Science Physics Social Sciences Other

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Komparasi Naïve Bayes dan Support Vector Machine dalam Klasifikasi Jenis Citrus I Wayan Pinastawa; Nurul Afifah Arifuddin
Techno.Com Vol 22, No 2 (2023): Mei 2023
Publisher : LPPM Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33633/tc.v22i2.7777

Abstract

Citrus merupakan pohon berbunga dan tergolong dalam kelompok Rutaceae. Pohon Citrus menghasilkan buah jeruk dengan berbagai jenis buah-buahan. Karena kesamaan spesies sehingga antar jenisnya memiliki kemiripan satu sama lain, dan tidak semua dapat melakukan identifikasi secara jelas setiap jenis buahnya. Cara yang dapat dilakukan untuk melakukan identifikasi dan pengelompokan adalah menggelompokan data sesuai dengan kelas label aslinya menggunakan bantuan data mining. Pendekatan data mining yang dapat diterapkan salah satunya dengan teknik klasifikasi, dengan melakukan pengelompokan berdasarkan kriteria atau kategori tertentu. Pada hal ini, klasifikasi didasarkan pada diameter, dan citra warna Red, Green, Blue atau RGB untuk mendapatkan pengelompokan sesuai dengan kelasnya. Algoritma yang digunakan ada 2 yakni, Support Vector Machine (SVM) dan Naïve Bayes, keduanya akan dilakukan perbandingan dalam melakukan klasifikasi pengelompokan jenis buah citrus. Teknik komparasi dilakukan dengan mengamati hasil akurasi dari setiap algoritma klasifikasi, penelitian ini menyimpulkan bahwa akurasi algoritma Support Vector Machine (SVM) mencapai prosentase sebesar 96,36 % dan algoritma Naïve Bayes memiliki akurasi sebesar 92 %. Algoritma paling optimal dalam penelitian klasifikasi citrus ini adalah algortima Support Vector Machine (SVM).  
PELATIHAN MICROSOFT OFFICE BAGI SISWA UNTUK MENINGKATKAN SOFTSKILLS DI SMKS MANDIRI BOJONGGEDE Zatin Niqotaini; Nurul Afifah Arifuddin; Neny Rosmawarni
Martabe : Jurnal Pengabdian Kepada Masyarakat Vol 6, No 7 (2023): Martabe : Jurnal Pengabdian Kepada Masyarakat
Publisher : Universitas Muhammadiyah Tapanuli Selatan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31604/jpm.v6i7.2423-2429

Abstract

Pemerintah memperhatikan pendidikan vokasi, termasuk sekolah menengah kejuruan (SMK). Beberapa program pemerintah tentang sekolah menengah kejuruan sudah diluncurkan dengan tujuannya adalah menghasilkan lulusan yang mampu memenuhi kebutuhan organisasi. Mengetahui aplikasi yang dipakai oleh perkantoran sangat penting bagi lulusan yang akan mengembangkan karir di dunia industri. Tujuan kegiatan pengabdian masyarakat ini adalah memberikan pelatihan dalam meningkatkan softskills kepada siswa SMKS Mandiri Bojonggede dalam menguasai aplikasi komputer seperti Microsoft Office, yang mencakup Microsoft PowerPoint, Microsoft Word, dan Microsoft Excel. Untuk melaksanakan pengabdian masyarakat ini ada beberapa tahapan seperti observasi, koordinasi dengan mitra, pelatihan, dan evaluasi. Pengabdian masyarakat ini diikuti oleh 40 siswa SMKS Mandiri Bojonggede, dan kegiatan pengabdian dilaksanakan selama 2 hari pada tanggal 25 dan 26 Mei 2023. Hal ini ditunjukkan oleh semangat siswa siswi untuk mengikuti pelatihan Microsoft Office. Dengan bantuan kegiatan pengabdian yang telah dilaksanakan, diharapkan siswa SMKS Mandiri Bojonggede mampu mengoperasikan fungsi – fungsi aplikasi perkantoran serta dapat membantu siswa dalam meningkatkan softskills.
WP Sistem Pendukung Keputusan Penyedia Jasa Asisten Rumah Tangga Menggunakan Metode Weighted Product (WP): WP Anugraha, Nurhajar; Arifuddin, Nurul Afifah; Saputra , Febri Hidayat; Maulidinnawati, Andi; Pangayan, Yulianto
JNSTA ADPERTISI JOURNAL Vol. 3 No. 1 (2023): Januari 2023
Publisher : Aliansi Dosen Perguruan Tinggi Swasta Indonesia (Adpertisi)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62728/jnsta.v3i1.375

Abstract

Permasalahan yang terjadi adalah banyak keluarga kesulitan mendapatkan informasi tentang asisten rumah tangga karena keterbatasan informasi yang tersebar, sehingga calon pengguna jasa yang ingin mencari dan ingin menggunakan jasa asisten rumah tangga harus bertanya-tanya mengenai informasi asisten rumah tangga tersebut kepada teman atau keluarga. Maka dari itu dibutuhkan sebuah aplikasi sistem yang memudahkan pencarian penyedia jasa asisten rumah tangga. Penelitian ini bertujuan untuk merancang dan membangun sebuah Sistem Pendukung Keputusan Penyedia Jasa Asisten Rumah Tangga Menggunakan Metode Weighted Product. Data ini diperoleh melalui penelitian lapangan, penelitian Pustaka dan wawancara. Metode yang digunakan dalam penelitian ini adalah metode weighted product. Hasil penelitian ini menunjukkan bahwa Sistem Pendukung Keputusan Penyedia Jasa Asisten Rumah Tangga Menggunakan Metode Weighted Product berhasil diimplementasikan dan sangat baik digunakan dengan nilai kuesioner penelitian 85,6%.
Classification of Stroke Opportunities with Neural Network and K-Nearest Neighbor Approaches Arifuddin, Nurul Afifah; Pinastawa, I Wayan Rangga; Anugraha, Nurhajar; Pradana, Musthofa Galih
Sinkron : jurnal dan penelitian teknik informatika Vol. 7 No. 2 (2023): Research Article, Volume 7 Issue 2 April, 2023
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v8i2.12228

Abstract

Stroke is one of the deadly diseases. This is illustrated in stroke deaths in Indonesia which reached a death rate of 131.8 cases. Some of the things that cause a stroke to become a disease with the highest mortality rate are related to transitions in human life in 4 aspects, namely epidemiology, demography, technology, and economics, socio-culture. Of the many influencing aspects, one of the transition points of human life in the technological aspect can be an alternative solution and prevention. Aspects of technology with the utilization of data can be used as a preventive measure for stroke. One approach is to use data mining techniques, which can provide an initial picture regarding the chances of getting a stroke so that it can be used as an early warning for patients. With so many techniques in data mining, this study used a classification or grouping approach using 2 algorithms, namely K-Nearest Neighbor and one of the Neural Network groups, namely Multi-Layer Perceptron. This research will focus on finding the accuracy and best results of the two algorithms in classifying. The final result of this study is that the K-Nearest Neighbor algorithm has a better accuracy of 95% compared to the Multi-Layer Perceptron which produces an accuracy of 88%
A CNN Model for ODOL Truck Detection Arifuddin, Nurul Afifah; Gusti, Kharisma Wiati; Amalia, Rifka Dwi
Sinkron : jurnal dan penelitian teknik informatika Vol. 8 No. 3 (2024): Research Artikel Volume 8 Issue 3, July 2024
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v8i3.13780

Abstract

This study developed a Convolutional Neural Network (CNN) model as one of artificial intelligence method to detect trucks experiencing over-dimension and over-loading (ODOL). The primary goal of this research is to enhance the efficiency of truck monitoring, reduce road infrastructure damage, and support the sustainability of transportation using artificial intelligence approaches. The model was trained using a dataset consisting of ODOL and non-ODOL truck images, and successfully achieved a testing accuracy of 94.23%. The confusion matrix analysis demonstrated the model's ability to classify trucks with high precision.  Additional testing on truck images not included in the training or testing dataset showed the model's potential for good generalization.
DIGITALISASI SISTEM KEUANGAN UMKM: PELATIHAN PRAKTIS PENCATATAN DAN PELAPORAN KEUANGAN DIGITAL UNTUK PELAKU USAHA SERAT AGEL DI DAERAH ISTIMEWA YOGYAKARTA Ridwan, Muhamad; Pradana, Musthofa Galih; Nyamiati, Retno Dwi; Pinastawa, I Wayan Rangga; Arifuddin, Nurul Afifah; Adrezo, Muhammad; Maulana, Nurhuda
Jurnal Pengabdian Kepada Masyarakat Patikala Vol. 4 No. 4 (2025): Jurnal PkM PATIKALA
Publisher : Education and Talent Development Center of Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51574/patikala.v4i4.3043

Abstract

The digitalization of financial management has become an essential requirement for micro, small, and medium enterprises (MSMEs) to ensure their sustainability and growth in the modern economic landscape. Nevertheless, a significant proportion of MSME actors continue to encounter difficulties in systematically preparing financial statements and recording business transactions. This community engagement initiative was designed to enhance digital financial literacy among MSME artisans engaged in agel fiber crafts in the Sentolo area, Kulon Progo Regency, Special Region of Yogyakarta. The activity employed an interactive training method with a hands-on, practical approach, incorporating both the introduction and application of the SIAPIK platform, a digital tool aimed at facilitating transaction recording and the preparation of financial reports. The results of this activity demonstrate an improvement in participants' understanding of managing business finances through digital platforms. Additionally, participants also experienced an increase in motivation to implement technology-based financial recording. These findings also indicate their initial readiness to adopt digital transformation within their business environments.
Optimizing Road Safety with MobileNet-Based Classification of Over-dimensioned Trucks Arifuddin, Nurul Afifah; Capri, Hary; Setiawan, Deni; Amalia, Rifka Dwi; Gusti, Kharisma Wiati
Jurnal Informatika: Jurnal Pengembangan IT Vol 10, No 2 (2025)
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/jpit.v10i2.8239

Abstract

This study aims to automatically detect overdimension trucks using a lightweight and efficient deep learning model based on MobileNet. Overdimension trucks pose serious threats to road infrastructure, traffic safety, and contribute to increased economic costs due to road damage and congestion. The developed model utilizes MobileNet as a feature extractor without the standard fully connected layers, and is equipped with additional layers including Flatten, Batch Normalization, Dense with Leaky ReLU activation, and Dropout to enhance training stability and prevent overfitting. The dataset consists of two classes—normal trucks and overdimension trucks—with images sized 128×128 pixels, collected from internet sources and field photos. The training process employs binary crossentropy loss, the Adam optimizer with an initial learning rate of 0.0001, and an Early Stopping mechanism. Fine-tuning is performed by unfreezing layers from the 100th layer upward and lowering the learning rate to 0.00001. Evaluation results show an accuracy of 97.92%, with consistent loss and accuracy visualization, demonstrating the model's capability in classifying overdimension trucks to support automatic traffic monitoring systems. This model has the potential to be implemented in toll gate systems to automatically deny access to overdimension vehicles. Furthermore, integration with roadside CCTV allows real-time monitoring of vehicle dimension violations across various traffic checkpoints.
Pengembangan Prototipe untuk Prediksi Tingkat Penyeduhan Kopi Menggunakan Data Spektroskopi dan Deep Learning Prananto, Muhammad Teguh; Raafi'udin, Ridwan; Adrezo, Muhammad; Pradana, Musthofa Galih; Arifuddin, Nurul Afifah
Jurnal Informatika: Jurnal Pengembangan IT Vol 10, No 3 (2025)
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/jpit.v10i3.8710

Abstract

Consistency in coffee flavor is a crucial factor for coffee enthusiasts, thus requiring a method capable of objectively measuring the coffee brewing level in accordance with the standard brewing chart. This study utilizes the AS7265X spectroscopy sensor to capture the characteristics of coffee based on the resulting light spectrum. The spectral data is then used in a deep learning model using the Convolutional Neural Network (CNN) algorithm to classify the coffee brewing level into five distinct classes. A total of 150 data samples were used in the training and testing process. Initial results show that the model achieved a very high average accuracy of approximately 97%. After hyperparameter tuning using the Random Search method, the model's accuracy further improved, reaching a very high accuracy. However, this performance improvement resulted in a trade-off in computational time, with execution time increasing from 15 seconds to 1 minute and 43 seconds. This research is expected to contribute to ensuring consistent coffee brew quality and to open opportunities for further studies that combine sensor technology and artificial intelligence in the food and beverage sector.
Pelatihan Microsoft Office kepada Siswa SMKS Mandiri Bojonggede Bogor Niqotaini, Zatin; Arifuddin, Nurul Afifah; Rosmawarni, Neny
Jurnal Abdimas Kartika Wijayakusuma Vol 4 No 2 (2023): Jurnal Abdimas Kartika Wijayakusuma
Publisher : LPPM Universitas Jenderal Achmad Yani

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26874/jakw.v4i2.320

Abstract

Pendidikan vokasi, termasuk sekolah menengah kejuruan (SMK), mendapat perhatian pemerintah. Tujuan dari berbagai program yang sudah diluncurkan oleh pemerintah adalah untuk menghasilkan lulusan yang mampu memenuhi kebutuhan industri. Bagi lulusan yang ingin mengembangkan karir mereka di industri perkantoran, sangat penting untuk memahami aplikasi yang digunakan di perkantoran. Tujuan dari kegiatan ini adalah untuk memberikan pelatihan dan bantuan kepada siswa SMKS Mandiri Bojonggede dalam menguasai aplikasi Microsoft Office, seperti Microsoft PowerPoint, Microsoft Word, dan Microsoft Excel. Untuk mencapai tujuan ini, observasi, koordinasi dengan mitra, pelatihan, dan evaluasi digunakan. Hal ini dibuktikan oleh keinginan siswa untuk mengikuti pelatihan. Kegiatan ini membantu siswa SMKS Mandiri Bojonggede mengenal dan menggunakan teknologi. Hasil survei yang melibatkan siswa sekolah menunjukkan bahwa 87% siswa menganggap pelatihan ini sangat bermanfaat dalam memahami dan menggunakan Microsoft Office sebagai aplikasi perkantoran.
Pelatihan Google Workspace sebagai Sistem Manajemen Pengetahuan di TK Islam At-Tin Rifka Dwi Amalia; Nurul Afifah Arifuddin; Radinal Setyadinsa
Nusantara: Jurnal Pengabdian kepada Masyarakat Vol. 5 No. 3 (2025): Agustus : NUSANTARA Jurnal Pengabdian Kepada Masyarakat
Publisher : Pusat Riset dan Inovasi Nasional

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55606/nusantara.v5i3.6451

Abstract

This community service program is designed to address the need for digitalization in administrative management and improve digital literacy in schools. The main focus of the activity is training on the use of Google Workspace as an integrated knowledge management system to improve work efficiency and collaboration among staff. Problems faced by schools include the continued use of manual administrative systems and a low understanding of how to use digital platforms. The training was conducted as a workshop with a learning-by-doing approach, allowing participants to learn directly and contextually. The training material covered the operation of various Google Workspace features, such as Google Forms, Sheets, Calendar, Docs, Drive, and Meet. This activity was designed so that participants not only understand the function of each application but also are able to integrate them into daily administrative activities. The training evaluation was conducted through pre- and post-tests, which showed a significant increase in participants' technical understanding. In addition to improving digital competency, this training also encouraged a shift in work culture towards a more collaborative, efficient, and data-driven one. The program's success demonstrates that digital transformation in educational environments can be achieved through an educational, participatory approach tailored to local needs. With positive results, this activity has the potential to be replicated in other educational institutions as a sustainable strategy for cloud-based administrative management. This training is proof that adopting digital technology in schools can strengthen administrative governance comprehensively and sustainably.
Co-Authors Alrasyid, Wildan Budi Arif Dermawan Budi, Ryan Setya Capri, Hary DENI SETIAWAN Fadhli Suko Wiryanto I Wayan Rangga Pinastawa Junaedy, Sintya Paula Kharisma Wiati Gusti Lahinta, Fitria Claudya Marson James Budiman Maulidinnawati, Andi Muhamad Ridwan Muhammad Adrezo Musthofa Galih Pradana Neny Rosmawarni Nindy Irzavika Niqotaini, Zatin Nurhajar Anugraha Nurhuda Maulana Nyamiati, Retno Dwi Pangayan, Yulianto Prananto, Muhammad Teguh Radinal Setyadinsa Rasenda, Rasenda Ridwan Raafi'udin Rifka Dwi Amalia Rizky A, Galih Prakoso Rumokoy, Steven N. Saputra , Febri Hidayat