Article Per Year (5 Year)
p-Index From 2021 - 2026
0.817
P-Index
This Author published in this journals
All Journal G-Tech : Jurnal Teknologi Terapan Infotek : Jurnal Informatika dan Teknologi ABSYARA: Jurnal Pengabdian Pada Masyarakat Jurnal Teknologi Informasi untuk Masyarakat (Jurnal Teknokrat)
Taufik Akbar
Universitas Hamzanwadi
Agriculture, Biological Sciences & Forestry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Education Energy Engineering Environmental Science Languange, Linguistic, Communication & Media Library & Information Science Mathematics Other

Published : 5 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 5 Documents
Search

 1 
PROTOTIPE SISTEM MONITORING TEGANGAN PANEL SURYA (SOLAR CELL) PADA LAMPU PENERANG JALAN BERBASIS WEB APLIKASI Gunawan, Indra; Akbar, Taufik
Infotek : Jurnal Informatika dan Teknologi Vol 2, No 2 (2019): Infotek : Jurnal Informatika dan Teknologi
Publisher : Fakultas Teknik Universitas Hamzanwadi

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (610.03 KB)

Abstract

Panel surya begitu populer dikalangan pemerintah daerah sebagai alternatif untuk mengurangi biaya operasional khususnya biaya listrik untuk lampu penerang jalan raya. Tapi panel surya memiliki beberapa kelemahan yakni, dalam segi pemiliharan dan perawatan membutuhkan kinerja ekstra dan waktu yang cukup lama, misalnya dalam proses pengecekan tegangan masih kurang efisien karna petugas harus mengecek langsung ke panel surya yang berada di atas tiang atau lampu penerang jalan untuk mengetahui berfungsi atau tidaknya alatnya yang terpasang dengan cara petugas naik meriksanya menggunakan mobil khusus. Hasil penelitian ini menggunakan komunikasi antara web dengan mikrokontroler yang dilakukan menggunakan ethernet shield, dalam web aplikasi monitoring tegangan terdapat fitur hasil tegangan, grafik dan laporan perhari secara realtime dan sistem monitoring tegangan bekerja dengan baik dimana petugas bisa mengecek besar tegangan setiap saat secara realtime walaupun masih menggunakan webserver local sebagai servernyaDOI : 10.29408/jit.v2i2.1452
Channel Assignment Method for Maximizing Throughput in the Internet of Things System Ahmad Sony Alfathani; Ahwan Ahmadi; Taufik Akbar; M Nuzuluddin; Fahmy Rinanda Saputri
G-Tech: Jurnal Teknologi Terapan Vol 7 No 4 (2023): G-Tech, Vol. 7 No. 4 Oktober 2023
Publisher : Universitas Islam Raden Rahmat, Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33379/gtech.v7i4.3155

Abstract

The growth of the number of interconnected wireless devices such as the Internet of Things (IoT) is continuously increasing across various sectors, including smart buildings, smart offices, smart cities, and others. According to estimates, by the year 2030, there will be at least 50 billion devices interconnected through networks. The escalating number of uncontrolled wireless devices can lead to various issues such as interference, collisions, and data loss, resulting in an overall decline in network system performance. This study aims to propose a scenario as an alternative solution to optimize the overall network performance in the system by assigning channels to each interconnected wireless pair to reduce the impact of interference. This research indicates that the proposed method successfully enhances the system throughput performance by 39.75% compared to the condition where all wireless pairs operate on the same channel, thereby outperforming several other comparative methods.
Digitalisasi Sistem Kehadiran Pegawai melalui Penerapan Absensi Online Berbasis IoT di Kantor Desa Perian Taufik Akbar; M.Julianto Maulana Putra; Dwi Rahayu; Ramli Ahmad; Hizbul Izzi
Jurnal Teknologi Informasi untuk Masyarakat Vol. 4 No. 1 (2026): Jurnal Teknologi Informasi untuk Masyarakat (Teknokrat)
Publisher : Universitas Hamzanwadi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29408/jt.v4i1.35276

Abstract

Attendance recording is an important aspect in supporting administrative effectiveness and public services within village government institutions. However, conventional attendance systems still face several limitations, including recording errors, delayed data recapitulation, and difficulties in conducting real-time attendance monitoring. This community service activity aimed to implement an Internet of Things (IoT)-based online attendance system at Perian Village Office, Montong Gading District, East Lombok Regency. The system was developed using an ESP32 microcontroller, a fingerprint sensor for user authentication, a Real Time Clock (RTC) module for timestamp recording, an OLED display for information output, and Google Sheets as a cloud-based data storage platform. The implementation method consisted of needs assessment, hardware and software design, system deployment, user training, testing, and system assistance. The results showed that the developed system was able to identify users through fingerprint authentication, automatically record attendance time, and transmit attendance data to Google Sheets in real time. All main system functions, including user enrollment, attendance recording, online data storage, and administrator management features, operated successfully according to user requirements. The implementation of this system improved the effectiveness of attendance management and supported the digital transformation of village administration through a more transparent, accurate, and efficient attendance recording process
Pelatihan Troubleshooting Laptop Alumni SMK Se-Lombok Timur Taufik Akbar; Intan Komala Dewi; Alimudin Alimudin; Ihwan Ahmadi
ABSYARA: Jurnal Pengabdian Pada Masayarakat Vol 3 No 1 (2022): ABSYARA: Jurnal Pengabdian Pada Masyarakat
Publisher : Universitas Hamzanwadi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29408/ab.v3i1.5724

Abstract

With the rapid development of technology, especially the development of computers, both hardware and software, users must add more knowledge to running computers. Not only operating it but also installing and troubleshooting so that later it will be more accessible when faced with obstacles. SMK alumni mainly contribute to unemployment due to very few industry-standard job opportunities. Additional skills/competencies are needed to equip the alumni, one of which is by providing Troubleshooting knowledge. Troubleshooting is solving problems with damage to Computer/Laptop hardware. This training purpose is to increase the competence of alumni to be independent or prepare for the world of work. The training method applied in this training is the lecture and practice method. The activity took place with 30 registrants and was selected based on terms and conditions; the results were that only 5 participants from SMK 3 Selong and SMK 1 Pringgabaya passed. This activity was initially carried out in June at the Lombok Center IT. The result of this training activity is that participants can assemble and install laptops and install open laptop mainboard components. Participants can measure the signal on the laptop to know the damage
Analisa Komparatif Klasifikasi Citra Sayuran dengan Algoritma Support Vector Machine dan Convolutional Neural Network Ida Wahidah; Hadian Mandala Putra; Suhartini; Taufik Akbar
Infotek: Jurnal Informatika dan Teknologi Vol. 9 No. 1 (2026): Infotek : Jurnal Informatika dan Teknologi
Publisher : Fakultas Teknik Universitas Hamzanwadi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29408/jit.v9i1.32791

Abstract

This study presents a comparative analysis between the Support Vector Machine (SVM) algorithm, representing machine learning techniques, and the Convolutional Neural Network (CNN) algorithm, representing deep learning techniques, for vegetable image classification. The research adopts a quantitative approach by conducting multiple experimental schemes involving various SVM feature extraction methods and CNN architectures. The objective of this study is to evaluate and compare the performance, effectiveness, and computational efficiency of SVM and CNN algorithms. The dataset used in this study is a publicly available vegetable image dataset obtained from the Kaggle platform, consisting of 21.000 images categorized into 15 classes. The experimental results indicate that CNN significantly outperforms SVM in terms of accuracy, precision, recall, and F1-score. Moreover, CNN demonstrates superior generalization capability in predicting unseen image data. The best performance of the SVM algorithm was achieved using the Color Histogram feature extraction method, yielding an accuracy of 93%. In contrast, CNN models employing pre-trained architectures achieved higher accuracy, with VGG16 and MobileNetV2 obtaining accuracies of 98% and 100%, respectively. Based on the comparative results, CNN provides higher classification accuracy than SVM; therefore, this study can serve as a scientific reference for the development of image classification systems in digital agriculture and other applications requiring high accuracy and efficient computational performance.
Co-Authors Ahmad Sony Alfathani Ahmadi, Ahwan Alimudin Alimudin Dwi Rahayu Fahmy Rinanda Saputri Gunawan, Indra Hadian Mandala Putra Hizbul Izzi Ida Wahidah Ihwan Ahmadi Intan Komala Dewi M. Nuzuluddin M.Julianto Maulana Putra Ramli Ahmad Suhartini, Suhartini