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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 Jurnal Abdimas Mahakam Progresif: Jurnal Ilmiah Komputer Techno Xplore : Jurnal Ilmu Komputer dan Teknologi Informasi 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
Ahmad Fauzi
Universitas Buana Perjuangan Karawang
Humanities Automotive Engineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Mechanical Engineering Public Health Social Sciences

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Penerapan Fuzzy Logic Untuk Menentukan Indeks Massa Tubuh (IMT) Berbasis Internet of Things (IoT) Ardi Apriansyah; Ahmad Fauzi; Sutan Faisal
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 7, No 1 (2023): Januari 2023
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v7i1.5470

Abstract

A person's Body Mass Index (BMI) can be determined using the parameters of body weight and height. In manual measurements, to determine BMI, measurements of body weight and height must be carried out and then the BMI results are calculated, so it is felt that it still requires effort and is less effective. Then a tool is made to determine BMI using the Fuzzy Logic method by measuring body weight and height in one step. This Fuzzy Logic method is used to determine the membership status of weight and height. Fuzzy set of weight and height that has been adjusted to the threshold in Indonesia, including weight 35-55 low status, 44-66 normal status, 54-80 heavy status, and height 145-165 low status, 149-176 status normal, 159-190 high status. Software testing results have successfully displayed the results of both sensors, namely, body weight 69.43 Kg with Fuzzi weight status and 161 cm height with normal Fuzzi status. The results of the measurements of the two sensors will also be stored in a database and then displayed on a web containing historical information to find out when the body was last weighed. Then the test results on the LCD Hardware have been able to display a person's weight with a weight of 53 Kg and a height of 171 cm. After testing, these scales have been able to determine the results of a person's body BMI and the two sensors on the scales have been able to determine a person's weight and height.
The Effect of V-Bending Parameters Utilizing Electrolytic Zinc-Coated Steel Sheet (SECC) Material 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.
Klasifikasi Sampah Logam Dan Plastik Berbasis Raspberry Pi Dengan Metode Convolution Neural Network Ahmad Rahman; Ahmad Fauzi; Jamaludin Indra
Scientific Student Journal for Information, Technology and Science Vol. 4 No. 1 (2023): Scientific Student Journal for Information, Technology and Science
Publisher : Scientific Student Journal for Information, Technology and Science

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Abstract

Hasil Susenas menunjukkan hanya 1,2 persen rumah tangga melakukan daur ulang sampah. Permasalahan tersebut dapat diatasi dengan peran teknologi yaitu dengan membuat alat yang dapat mengklasifikasikan jenis sampah. Raspberry pi mengklasifikasikan sampah bekas minuman kemasan logam, plastik dan other. Gambar dari pi camera diproses pada raspberry pi untuk mengetahui jenis sampah logam, plastik dan other. Pada proses klasifikasi terdapat 2 tahapan yaitu train model dan predict. Proses klasifikasi menggunakan metode cnn. Train model adalah proses pelathihan model untuk mengenal sampah. Hasil proses training dengan 20 kali epoch diperoleh hasil nilai akurasi training 0.9866. Dari model yang sudah ditraining dilakukan proses prediksi untuk melakukan klasifikasi sampah. Dari 20 kali percobaan diperoleh rata-rata akurasi pengujian model 81,387 %.
Implementasi Algoritma Fuzzy Logic pada Sistem Kendali Lampu Otomatis dengan Arduino dan Ac Light Dimmer Adie Putra; Ahmad Fauzi; Dwi Kusumaningrum
Scientific Student Journal for Information, Technology and Science Vol. 4 No. 1 (2023): Scientific Student Journal for Information, Technology and Science
Publisher : Scientific Student Journal for Information, Technology and Science

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Abstract

Cahaya begitu penting bagi kehidupan manusia, cahaya juga memberikan penerangan untuk segala aktifitas bagi manusia. Umumnya lampu dikendalikan secara manual melalui saklar, akan tetapi masyarakat Indonesia ketika berpergian keluar kota yang cukup lama lampu dibiarkan menyala sampai pemilik rumah kembali dengan itu tidak efisien dalam penggunaan listrik. Sehingga dibuatkan sistem kendali lampu otomatis untuk mengontrol kecerahan dari lampu tersebut, sistem kendali lampu otomatis ini diatur otomatis oleh sensor cahaya berdasarkan dari sinar matahari. Pada prototipe dan sistem kendali lampu otomatis yang dibuat menggunakan metode logika fuzzy, cahaya dikontrol dengan menggunakan modul ac light dimmer. Penelitian yang dilakukan dengan pengujian selama 10 hari, tanpa menggunakan sistem konsumsi, daya yang dipakai sebesar 1,116 KWh. Sedangkan dengan menggunakan sistem kendali lampu otomatis yang didalamnya menggunakan metode logika fuzzy dengan konsumsi daya sebesar 1,444 KWh. Sistem kendali otomatis sangat cocok untuk dinyalakan selama 24 jam.
Penerapan Algoritma Backpropagation untuk Memprediksi Mahasiwa Baru: (Studi Kasus: UBP Karawang) Agung Rahmat; Ahmad Fauzi; Dwi Kusumaningrum
Scientific Student Journal for Information, Technology and Science Vol. 4 No. 1 (2023): Scientific Student Journal for Information, Technology and Science
Publisher : Scientific Student Journal for Information, Technology and Science

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Abstract

Prediksi adalah proses memperkirakan secara sistematis apa yang paling mungkin terjadi di masa depan berdasarkan informasi masa lalu dan sekarang untuk meminimalkan kesalahan. Universitas Buana Perjuangan Karawang merupakan Universitas yang berada di Karawang jawa barat yang perkembangannya begitu pesat karena adanya peningkatan pendaftaran pada setiap tahunnya. Pada penelitian menggunakan algoritma backpropagation untuk memprediksi jumlah mahasiswa baru pada masa yang akan mendatang. Pada data yang dimasukkan mahasiswa baru tahun 2015 sampai 2021 Universita Buana Perjuangan Karawang. Dari hasil algoritma backpropagation mendapatkan hasil prediksi pada setiap prodi yaitu Teknik Informatika 174, Teknik Industri 315, Sistem Informasi 100, Farmasi 134, Psysikolog 343, PGSD 162, PPKN 362, Akuntansi 64, Manajemen 471, dan Hukum 308 dengan akurasi nilai mean absolut error 88.1 %, Root Mean Square Error 2.28058E-05, dan error 0.458597909941040.
Analisis Sentimen Terhadap Komentar Video Youtube Menggunakan Support Vector Machines Toif Muhayat; Ahmad Fauzi; Jamaludin Indra
Progresif: Jurnal Ilmiah Komputer Vol 19, No 1: Februari 2023
Publisher : STMIK Banjarbaru

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35889/progresif.v19i1.1060

Abstract

Many Youtube users leave comments on the YouTube video content they watch. These comments would be useful if further analysis were carried out. This study aims to analyze Sentiments Toward Youtube Video Comments, to make it easier for creators to find out what types of videos are of interest to viewers, without having to read the comments one by one. The stages carried out in this research are web scrapping, preprocessing, labeling, feature extraction, classification and evaluation. The results of the analysis show that the type of video content with the theme of daily vlogs is more in demand by YouTube users with positive dominant sentiment results. The daily vlog theme has a positive sentiment of 84.0% and a negative sentiment of 16.0%. The use of the SVM (Support Vector Machine) algorithm has an accuracy value of 86%, a precision of 87%, a recall of 99% and an f1-score of 100%.Keywords: Sentiment Analysis; Text Mining; Support Vector Machine; Youtube AbstrakBanyak pengguna Youtube yang meninggalkan komentar pada konten video youtube yang mereka tonton. Komentar-komentar tersebut akan memberikan manfaat jika saja dilakukan analisis lebih lanjut. Penelitian ini bertujuan menganalisis Sentimen Terhadap Komentar Video Youtube, untuk memudahkan kreator mengetahui jenis video yang diminati penonton, tanpa harus membaca komentar secara satu per satu. Tahapan yang dilakukan dalam penelitian ini adalah web scrapping, preprocessing, labelling, ekstraksi fitur, klasifikasi dan evaluasi. Hasil analisis menunjukkan jenis konten video bertema daily vlog lebih banyak diminati oleh pengguna youtube dengan hasil sentimen dominan positif. Tema daily vlog memiliki sentimen positif sebesar 84.0% dan sentimen negatif sebesar 16.0%. Penggunaan algoritma SVM (Support Vector Machine) memiliki nilai akurasi sebesar 86%, presisi sebesar 87%, recall sebesar 99% dan f1-score sebesar 100%.Kata Kunci: Analisis Sentimen; Text Mining; Support Vector Machine; Youtube
Analisis Sentimen Gojek Indonesia Pada Twitter Menggunakan Algoritme Naïve Bayes Dan Support Vector Machine Yusuf Khoiruddin; Ahmad Fauzi; Amril Mutoi Siregar
Progresif: Jurnal Ilmiah Komputer Vol 19, No 1: Februari 2023
Publisher : STMIK Banjarbaru

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35889/progresif.v19i1.1173

Abstract

Transportation is an important element in everyday life and serves as a means of connecting between places. Online motorcycle taxi services such as Gojek have penetrated all regions, enabling users to order transportation services such as motorcycle taxis, taxis and cars online through applications. With the large number of content tweeted by Twitter users related to the use of the Gojek application, therefore sentiment analysis is needed to determine user perceptions of a topic or event. This study aims to analyze people's responses to online transportation through data collected from tweets. The data is then classified into two sentiment classes, namely positive and negative. The classification results using the Naive Bayes algorithm show an accuracy of 91%, while the use of the SVM (Support Vector Machine) algorithm produces a higher accuracy, which is equal to 99%. This indicates that the SVM algorithm is more effective in producing higher classification accuracy than the Naive Bayes algorithm.Keywords: Sentiment analysis; Online transportation; Naive bayes; Support Vector Machine AbstrakTransportasi merupakan elemen penting dalam kehidupan sehari-hari dan berfungsi sebagai sarana untuk menghubungkan antar tempat. Layanan ojek online seperti Gojek telah merambah di seluruh wilayah, memungkinkan pengguna untuk memesan layanan transportasi seperti ojek, taksi, dan mobil secara online melalui aplikasi. Dengan banyaknya isi tweet pengguna twitter terkait dengan penggunaan aplikasi Gojek, oleh karena itu diperlukan analisis sentimen untuk mengetahui persepsi pengguna terhadap suatu topik atau peristiwa. Penelitian ini bertujuan untuk melakukan analisis respons masyarakat terhadap transportasi online melalui data yang terkumpul dari tweet. Data tersebut kemudian diklasifikasikan ke dalam dua kelas sentimen yaitu positif dan negatif. Hasil klasifikasi menggunakan algoritme Naive Bayes menunjukkan akurasi sebesar 91%, sedangkan penggunaan algoritme SVM (Support Vector Machine) menghasilkan akurasi yang lebih tinggi, yaitu sebesar 99%. Hal ini mengindikasikan bahwa algoritme SVM lebih efektif dalam menghasilkan akurasi klasifikasi yang lebih tinggi dibandingkan dengan algoritme Naive Bayes.Kata kunci: Analisis sentimen; Transportasi online; Naive bayes; Support Vector Machine
Optimization of Machine Learning Models with Segmentation to Determine the Pose of Cattle Amril Mutoi Siregar; Sony Hartono Wijaya; Ahmad Fauzi; Tjong Wan Sen; Sutan Faisal; Tukino Tukino; Yana Cahyana
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol 9, No 3 (2023): September
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v9i3.26750

Abstract

Image pattern recognition poses numerous challenges, particularly in feature recognition, making it a complex problem for machine learning algorithms. This study focuses on the problem of cow pose detection, involving the classification of cow images into categories like front, right, left, and others. With the increasing popularity of image-based applications, such as object recognition in smartphone technologies, there is a growing need for accurate and efficient classification algorithms based on shape and color. In this paper, we propose a machine learning approach utilizing Support Vector Machine (SVM) and Random Forest (RF) algorithms for cow pose detection. To achieve an optimal model, we employ data augmentation techniques, including Gaussian blur, brightness adjustments, and segmentation. The proposed segmentation methods used are Canny and Kmeans. We compare several machine learning algorithms to identify the optimal approach in terms of accuracy. The success of our method is measured by accuracy and Receiver Operating Characteristic (ROC) analysis. The results indicate that using the Canny segmentation, SVM achieved 74.31% accuracy with a testing ratio of 90:10, while RF achieved 99.60% accuracy with the same testing ratio. Furthermore, testing with SVM and K-means segmentation reached an accuracy of 98.61% with a test ratio of 80:20. The study demonstrates the effectiveness of SVM and Random Forest algorithms in cow pose detection, with Kmeans segmentation yielding highly accurate results. These findings hold promising implications for real-world applications in image-based recognition systems. Based on the results of the model obtained, it is very important in pattern recognition to use segmentation based on color even though shape recognition.
Penerapan Technique for Order Performance by Similarity to Ideal Solution (TOPSIS) untuk Mendiagnosa Penyakit Bercak Daun Cabai: Application of Technique for Order Performance by Similarity to Ideal Solution (TOPSIS) for Diagnosing Chili Leaf Spot Disease Yudo Devianto; Saruni Dwiasnati; Bambang Sukowo; Ahmad Fauzi; Kiki Ahmad Baihaqi
MALCOM: Indonesian Journal of Machine Learning and Computer Science Vol. 3 No. 2 (2023): MALCOM October 2023
Publisher : Institut Riset dan Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57152/malcom.v3i2.850

Abstract

Produktivitas  tanaman  cabai  bergantung  kepada  iklim,  lingkungan,  serta hama  dan  penyakit. Petani yang baru dalam budidaya cabai merah menghadapi beberapa kesulitan karena memiliki sedikit pengalaman dalam budidaya, sehingga sulit untuk mengidentifikasi jenis penyakit dan hama yang menyerang. Hal ini menyebabkan penurunan produktivitas. Selain itu, petani juga belum memiliki pengalaman dalam menangani masalah yang timbul pada tanaman yang mereka budidayakan. Dalam rangka membantu petani mengatasi masalah ini, SPK berbasis web telah dikembangkan. Sistem ini menggunakan metode Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). Kriteria yang digunakan dalam metode ini adalah daun mudah mengkerut dengan warna mosaic kekuningan, daun mengkerut hingga menjadi ukuran kecil dan lebih tebal, bercak bulat berwarna coklat pada daun yang mengering, terdapat lubang pada bercak tua, dan pucuk daun yang berubah menjadi kuning jelas. Alternatif yang dipertimbangkan dalam penelitian ini  Daun Cabai " Penyakit Layu Bakteri ", Daun Cabai " Layu Pusertium ", Daun Cabai " Penyakit Virus Kuning ", dan Daun Cabai “Penyakit Bercak Daun”. Perhitungan dengan metode TOPSIS, Daun Cabai " Penyakit Virus Kuning" mendapatkan peringkat dengan nilai preferensi sebesar 2.0118. Penelitian ini disimpulkan bahwa metode TOPSIS dapat digunakan untuk mengetahui daun cabai dengan kriteria apa yang terbaik berdasarkan kriteria
PEMANFAATAN TEKNOLOGI UNTUK MENDUKUNG PEMBELAJARAN SANTRI PADA PONDOK PESANTREN Kiki Ahmad Baihaqi; Ahmad Fauzi; Jamaludin Indra
Jurnal Pengabdian Masyarakat Nasional Vol 3, No 2 (2023)
Publisher : Universitas Mercu Buana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/pemanas.v3i2.24777

Abstract

Technology has penetrated all fields, the technology in question is learning media and learning support applications. Especially in the world of Islamic boarding schools, which is religion-based education, so it balances religious education and technological education. With the pandemic, everything in education has changed, namely, what was previously only conventional and face-to-face-based, education that can be done in any condition that breaks the barriers of time and space. So training is needed to optimize the use of online-based learning support media. The training results from 30 out of 35 people successfully completed the training stages and the remaining 5 passed with improvements.
Co-Authors Ade Suhara Adie Putra 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 Dodi Mulyadi Dwi Kusumaningrum 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 Khoiruddin