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All Journal Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) Jurnal Teknologi International Journal of Artificial Intelligence Research JURNAL MEDIA INFORMATIKA BUDIDARMA Progresif: Jurnal Ilmiah Komputer Techno Xplore : Jurnal Ilmu Komputer dan Teknologi Informasi Generation Journal Jurnal Asiimetrik: Jurnal Ilmiah Rekayasa Dan Inovasi Jurnal Tekinkom (Teknik Informasi dan Komputer) Jurnal Sistem Komputer dan Informatika (JSON) Scientific Student Journal for Information, Technology and Science Malcom: Indonesian Journal of Machine Learning and Computer Science Jurnal Pengabdian Masyarakat Nasional Jurnal Polimesin Jurnal Abdimas Mahakam
Ahmad Fauzi
Universitas Buana Perjuangan Karawang
Automotive Engineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Materials Science & Nanotechnology Mechanical Engineering Other

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Performance Comparison of Support Vector Machine Algorithm and Logistic Regression Algorithm Hanny Hikmayanti; Anis Fitri Nurmasruriyah; Ahmad Fauzi; Nunung Nurjanah; Arphilia Nur Rani
International Journal of Artificial Intelligence Research Vol 7, No 1.1 (2023)
Publisher : STMIK Dharma Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29099/ijair.v7i1.1.1114

Abstract

According to the World Health Organization (WHO), there are around 7 million breast cancer patients each year, with about 5 million of them dying. Based on Globocan 2018 data, the death rate from breast cancer averages 17 per 100,000 people with incidents of 2.1 per 100,000 people attacking women in Indonesia. Hence breast cancer causes spread genetic mutations in the DNA of breast epithelial cells that radiate to the ducts. The purpose of this study was to classify the type of cancer (benign or malignant) that was suffered. The difference between previous research and this research is in the algorithm testing method chosen. In this study the algorithm used is SVM and Logistic Regression by applying the SMOTE technique. The K-fold cross validation method is used in testing this research. The accuracy results obtained are 1.0, precision 1.0 and recall 1.0.While the highest evaluation results for the model without SMOTE were Accuracy 0.97, precision 1.0 and recall 0.90 with the LR method. So based on the results of the comparison, it shows that the evaluation of models using SMOTE tends to be higher than models without SMOTE
PENERAPAN ALGORITMA NAÏVE BAYES UNTUK PREDIKSI PENERIMAAN KARYAWAN Intan Murni Pratiwi; Ahmad Fauzi; Santi Arum Puspita Lestari; Yana Cahyana
Jurnal Tekinkom (Teknik Informasi dan Komputer) Vol 7 No 1 (2024)
Publisher : Politeknik Bisnis Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37600/tekinkom.v7i1.1282

Abstract

The number of job seekers keeps growing, as does the quantity of companies that open job vacancies and offer opportunities to prospective employees. In terms of recruiting new employees, companies are very selective.. Companies are very selective in accepting prospective workers, where prospective workers must have qualifications that are in accordance with the positions needed in the company, because employees are an important asset in the growth and development of the company. because employees are an important factor in the growth and development of the company. Quality companies need good employees. This research uses employee recruitment data from PT Atma Darma Apta. The data has 372 rows and 8 attributes. The Naïve Bayes algorithm and the assessment techniques Mean Squared Error, Root Mean Squared Error, and R2 Score are used in this study. The results showed that the algorithm obtained good results by using a 90 to 10 data division resulting in a large accuracy value of 97.14%. In addition, the MSE, RMSE, and R2 Score values have quite good results, which are 2.86, 16.90, and 1.00. The 70 to 30 data division produces poor values with error values of 152.80 and 123.60, but the accuracy and R2 Score values are quite large at 96.15% and 0.95. With these results, this research can be continued into an application that can predict employee selection results.
The Effect of V-Bending Parameters Utilizing Electrolytic Zinc-Coated Steel Sheet (SECC) Material: Pengaruh Variasi V-Dies Bending Angle pada Material Electrolytic zinc-coated Steel Sheet (SECC) Dodi Mulyadi; Khoirudin; Sukarman; Mohamad Rizkiyanto; Nana Rahdiana; Ade Suhara; Ahmad Fauzi; Sumanto
Jurnal Asiimetrik: Jurnal Ilmiah Rekayasa Dan Inovasi Volume 5 Nomor 1 Tahun 2023
Publisher : Fakultas Teknik Universitas Pancasila

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35814/asiimetrik.v5i1.3937

Abstract

This study discusses the phenomenon of spring-back and spring-go in the bending kinematic forming using V-bending dies process and Electrolytic zinc-coated steel sheet (SECC/JIS G 3313) material. The zinc layer on the galvanized steel surface should not be damaged during the material forming process. The zinc layer on the galvanized steel sheet will affect the metal forming process. This study uses an experimental design with four input parameters, namely v-die opening L (mm), punch angle (degree), punch speed (mm/minutes), and bending force (kN). The smallest spring-back was obtained in the 4th test sample: the v-die opening of 35 mm, the punch angle of 40o, the punch speed of 30 mm/minute, and the bending force of 7.50 kN. The minor spring-back degree was 1.67o. Meanwhile, the smallest spring-go obtained in the second sample, namely the v-die opening of 30 mm, the punch angle of 50o, the punch speed of 40 mm/minute, and the bending force of 7.00 kN, the minor spring-go degree of 0.92o was obtained. These results show that the best spring-back degree for SECC/JIS G 3313 material is obtained when the bending process is performed with the v-die bending parameter of 30 mm, punch angle of 50o, punch speed of 40 mm/minute, and bending force of 7.00 kN.
Analisis Sentimen Terhadap Program Kampus Merdeka Menggunakan Naive Bayes Dan Support Vector Machine Irma Putri Rahayu; Ahmad Fauzi; Jamaludin Indra
Jurnal Sistem Komputer dan Informatika (JSON) Vol. 4 No. 2 (2022): Desember 2022
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/json.v4i2.5381

Abstract

In order to prepare students to face the rapid development of technology, changes in work life and skills, students must be better prepared to face the progress of the times. Universities must be able to carry out innovative learning processes so that students achieve optimal learning outcomes which include aspects of knowledge, skills and attitudes. So the MBKM program was launched to answer these demands. However, MBKM has pros and cons in its implementation, so it is necessary to analyze and evaluate policies to improve performance through feedback from the public by conducting sentiment analysis of MBKM policies on twitter users from 2019 to 2022 with the hashtag #kampusmerdeka. This study used the Naïve Bayes and SVM algorithms to determine accuracy based on sentiment classification. The data used 1118 data with positive sentiment 618 data and negative sentiment 500 data. This study resulted in an accuracy of 86%, precision of 87% and recall of 80% with testing data using the Naïve Bayes algorithm. Then using the linear kernel SVM algorithm with the same testing data resulted in accuracy of 93%, precision of 100% and recall of 84%. Therefore, it is important to conduct studies to improve the MBKM program so that its implementation is clearly in accordance with existing procedures.
Komparasi Algoritma Naïve Bayes Dan Support Vector Machine (SVM) Pada Analisis Sentimen Spotify Ayu Sri Rahayu; Ahmad Fauzi; Rahmat Rahmat
Jurnal Sistem Komputer dan Informatika (JSON) Vol. 4 No. 2 (2022): Desember 2022
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/json.v4i2.5398

Abstract

The Spotify app is a subject of interest to social networking communities with significant disagreements or sentiments. Sentiment Analysis is a solution to automatically categorize opinions or ratings into negative or positive opinions. The techniques used in this research are Support Vector Machines (SVM) and Naïve Baye. The advantages of Naïve Bayes are simple, fast and high accuracy. SVM, on the other hand, can identify different hyperplanes that maximize the margin between two different classes. The classification results of this study have two category labels, namely negative and positive. The resulting accuracy value indicates the best test model for sentiment classification cases. Accuracy is measured by the confusion matrix and the results show that the accuracy value of the SVM algorithm is 84% while the accuracy value of the Naïve Bayes algorithm is higher than SVM which is 86.4%.
Literasi Teknologi untuk Budidaya Jamur Ahmad Fauzi; Jamaludin Indra; April Hananto; Elfina Novalia; Aviv Yuniar Rahman
Jurnal Abdimas Mahakam Vol. 6 No. 02 (2022): Juli
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24903/jam.v6i02.1513

Abstract

Kabupaten Karawang memiliki lahan pertanian yang dapat mendukung budidaya jamur. Pendapatan budidaya jamur yang menjanjikan maka perlu adanya sosialiasi pemanfaatan teknologi. Pengkondisian ruangan budidaya jamur dilakukan menggunakan mikrokontroller dengan pengaturan standar ruangan budidaya jamur. Budidaya jamur befokus pada dua jenis jamur yaitu Jamur Tiram (Pleurotus Ostreatus) dan Jamur Merang yang merupakan salah satu komoditas pertanian yang memiliki nilai gizi sangat baik dan memiliki potensi yang baik untuk dikembangkan. Kegiatan dilakukan dengan penerapan teknologi mikrokontroller dan IoT dalam kumbung jamur untuk budidaya jamur merang. Literasi dilakukan kepada petani melalui sosialisasi penerapan tekologi tersebut sesuai dengan potensi manfaat Industri 4.0 mengenai perbaikan kecepatan fleksibilitas produksi. Peralatan teknologi yang diterapkan terdiri atas sensor dan actuator. Monitoring ruangan dapat terlihat melalui display LED yang menggambarkan kondisi ruang kumbung. Hasil yang diperoleh selama masa tanam 35 hari yaitu warna jamur lebih cerah, ukuran jamur lebih besar dan hasil panen lebih banyak. Kata Kunci: Budidaya jamur, Literasi teknologi, mikrokontroller, IoT, Industri 4.0.
Pengembangan Usaha Ikan Pindang sebagai Salah Satu Produk Unggulan Daerah Dedi Mulyadi; Ahmad Fauzi; Afif Hakim
Jurnal Abdimas Mahakam Vol. 9 No. 01 (2025): Januari
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24903/jam.v9i01.3253

Abstract

UMKM Israfood memiliki produk pindang ikan sebagai makanan olahan dengan jenis pindang ikan bandeng dan ikan tuna. Produk olahan ikan desa Jayamukti kecamatan Banyusari ini telah dikenal di masyarakat Karawang dan sekitarnya. Dalam usahanya, UMKM Israfood menemui kendala pada proses produksi untuk pemotongan ikan besar yang masih menggunakan peralatan seadanya yang dimiliki sehingga memakan waktu yang lama. Keinginan pemasaran yang lebih luas juga masih menemui kendala alat penyimpanan, legalitas usaha dan cara penjualan online yang lebih efektif. Legalitas usaha yang belum dimiliki yaitu izin merek, sertifikat halal, dan izin edar. Kegiatan pengabdian dilakukan terdiri atas 5 tahapan yaitu sosialisasi, pelatihan, penerapan teknologi, pendampingan dan evaluasi serta keberlanjutan program. Sosialisasi dilakukan dengan menyampaikan rencana kegiatan dan identifikasi kebutuhan kepada Bapak Cain pemilik UMKM Israfood. Pelatihan diberikan untuk meningkatkan wawasan kewirausahaan, pengurusan izin usaha dan pemanfaatan teknologi untuk membantu pembuatan merek serta bermedia sosial. UMKM Israfood berhasil mengembangkan usaha pindang dengan peningkatan produksi dengan alat pemotong ikan besar dan frezer. Perluasan wilayah pemasaran juga dilakukan dengan produk yang memiliki legalitas usaha, kemasan menarik dan tahan lama sehingga dapat memenuhi pemesanan melalui reseller dan pemesanan online. Hasil evaluasi menunjukkan penerimaan yang sangat baik terhadap kegiatan pengabdian pada masyarakat dan telah membantu ekonomi UMKM Israfood dan masyarakat pekerja usaha pindang.
Classification Of Cirebon Typical Batik Motifs Using The Convolutional Neural Network (CNN) Algorithm Firmansyah Maulana; Ahmad Fauzi; Yusuf Eka Wicaksana
Generation Journal Vol 10 No 2 (2026): Generation Journal
Publisher : Universitas Nusantara PGRI Kediri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29407/gj.v10i2.28290

Abstract

The visual identification of traditional Cirebon batik motifs frequently relies on subjective observation, leading to inconsistent recognition results. To resolve this issue, this study implements a Convolutional Neural Network (CNN) with a four-layer convolutional architecture as an automated classification system. The dataset used in this research contains 1,492 images of Cirebon batik motifs, which are partitioned into a scheme of 80% for training and 20% for validation. Data augmentation is applied during the preprocessing phase to improve the variety and quality of the information processed by the model. The results show that the CNN model achieves an overall accuracy of 92%. Furthermore, the Area Under the Curve (AUC) values ranging from 0.98 to 1.00 confirm the model's strong capability in distinguishing between different motif classes, even though minor challenges persist in identifying motifs with high visual similarities, such as Singa Barong and Paksi Naga Liman.  
Co-Authors Ade Suhara Adie Putra Afif Hakim Agung Rahmat Ahmad Rahman Amelia Pratiwi Amri Abdulah Anis Fitri Nurmasruriyah Anwar Ilmar Ramadhan April Hananto Ardi Apriansyah Arphilia Nur Rani Aviv Yuniar Rahman Ayu Sri Rahayu Baihaqi, Kiki Ahmad Bambang Sukowo Candra Zonyfar Dedi Mulyadi Dodi Mulyadi Dwi Kusumaningrum Firmansyah Maulana Hanny Hikmayanti Handayani Indra, Jamaludin Intan Murni Pratiwi Irma Putri Rahayu Khoirudin Khoirudin Khoirudin Lestari, Santi Arum Puspita Mohamad Rizkiyanto Murtalim Nana Rahdiana Novalia, Elfina Nunung Nurjanah Rahmat Rahmat Randy Kurniadi Ricko Valderama Ridwan Maulana Saruni Dwiasnati Siregar, Amril Mutoi Sony Hartono Wijaya Sriyanto Sriyanto Sukarman Sukarman Sumanto Sutan Faisal Sutan Faisal Tjong Wan Sen Toif Muhayat Tukino Tukino Yana Cahyana Yudo Devianto Yusuf Eka Wicaksana Yusuf Khoiruddin