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All Journal Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) REKAYASA JURNAL MEDIA INFORMATIKA BUDIDARMA Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Jesya (Jurnal Ekonomi dan Ekonomi Syariah) Buletin Ilmiah Sarjana Teknik Elektro Mobile and Forensics Journal of Robotics and Control (JRC) Jurnal Abdi Insani Humanism : Jurnal Pengabdian Masyarakat International Journal of Robotics and Control Systems International Journal of Social Service and Research Decode: Jurnal Pendidikan Teknologi Informasi SainsTech Innovation Journal IJMST Khazanah Informatika : Jurnal Ilmu Komputer dan Informatika SainsTech Innovation Journal
Asno Azzawagama Firdaus
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Religion Aerospace Engineering Agriculture, Biological Sciences & Forestry Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Mechanical Engineering Neuroscience Physics Public Health Social Sciences Other

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Model Deteksi Jumlah Kendaraan Bermotor Menggunakan Algoritma You Only Look Once (Yolo) V4 Di Parkiran Universitas Qamarul Huda Badaruddin Bagu Ega Silpia Aulia; Syuhada, Fahmi Syuhada; Asno Azzawagama Firdaus
SainsTech Innovation Journal Vol. 7 No. 2 (2024): SIJ VOLUME 7 NOMOR 2 TAHUN 2024
Publisher : LPPM Universitas Qamarul Huda Badaruddin

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37824/sij.v7i2.2024.756

Abstract

Kemajuan teknologi yang pesat telah mendorong berbagai inovasi dalam sistem berbasis Internet of Things (IoT), termasuk pada konsep smart city. Salah satu tantangan di era ini adalah manajemen parkir, terutama dalam mendeteksi keberadaan kendaraan bermotor. Keterbatasan ruang parkir di lingkungan pendidikan, seperti Universitas Qomarul Huda Badaruddin Bagu, sering kali menjadi penyebab kemacetan. Sistem parkir konvensional yang diawasi oleh petugas sering kali tidak efisien dan tidak menyediakan informasi real-time mengenai ketersediaan tempat parkir. Penelitian ini bertujuan untuk mengembangkan model deteksi kendaraan bermotor di area parkir Universitas Qomarul Huda Badaruddin Bagu menggunakan metode YOLO (You Only Look Once) V4. Data yang digunakan berupa gambar parkiran yang diambil dari kamera CCTV di area parkir kampus. Model YOLO diimplementasikan untuk mendeteksi kendaraan, khususnya motor, dan hasil deteksinya dibandingkan dengan perhitungan manual untuk mengevaluasi akurasinya. Program yang dihasilkan diharapkan mampu memberikan solusi yang lebih efektif dalam memantau kapasitas parkir dan memudahkan pengelolaan fasilitas parkir di kampus.
Implementation of Deep Learning for Personal Protective Equipment (PPE) Detection on Workers Using the YOLO Algorithm Soekarta, Rendra; Yusuf, Muhammad; Visman, Javan; Hasa, Muh. Fadli; Firdaus, Asno Azzawagama
Mobile and Forensics Vol. 7 No. 2 (2025)
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/mf.v7i2.13884

Abstract

Occupational accidents represent a major challenge in the construction and manufacturing industries. This study aims to develop a deep learning model for real-time detection of personal protective equipment (PPE) usage using the YOLOv5 algorithm. Utilizing a dataset that includes four classes (hardhat, no hardhat, coverall, and no coverall), the model was trained and evaluated based on precision, recall, and mean Average Precision (mAP) metrics. The results demonstrated that the model achieved a high accuracy level with an mAP of 0.91 and stable performance. The model can also rapidly and effectively detect safety attributes even in complex work environments, such as varied lighting conditions and numerous background objects. Based on usability testing results of 85.35% and satisfactory black box testing, this study produced a prototype web-based application enabling efficient and effective PPE monitoring. The application is designed to support the improvement of workplace safety across various industrial sectors in a more practical and adaptive manner. It is expected to increase PPE compliance, reduce accident risks, and contribute significantly to workplace safety in the industry. The conclusion indicates that the YOLOv5 algorithm holds great potential for implementation in technology-based safety monitoring systems and supports the development of a safer and more modern industry.
Cluster-Based Modeling of Internal Factors and FinTech Influence on Strategy: A Case Study of Bank BNI Aryandani, Aisyah; Firdaus, Asno Azzawagama
Mobile and Forensics Vol. 7 No. 2 (2025)
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/mf.v7i2.14066

Abstract

The rapid development of financial technology (FinTech) and shifting organizational dynamics have compelled banking institutions to reassess their internal capabilities and strategic positioning. This study aims to examine the influence of internal factors namely the Core Values of State-Owned Enterprises (AKHLAK), innovation culture, gratitude, employee commitment, and employee performance on the competitive strategy of Bank BNI, while also investigating the moderating role of FinTech. A quantitative research design was employed using a survey method, involving 200 employees of Bank BNI. Data were analyzed using Cluster Analysis and Structural Equation Modeling–Partial Least Squares (SEM–PLS) through WarpPLS software. The results indicate that AKHLAK core values, innovation culture, and gratitude have significant positive effects on employee commitment and performance. Furthermore, both employee commitment and performance significantly enhance the bank’s competitive strategy. FinTech was found to significantly moderate the relationship between employee-related factors and competitive strategy. In conclusion, this study presents an integrated model that highlights the strategic role of internal organizational values and behavior, enhanced by digital technology, in fostering competitive advantage within the banking sector.
Prediction of Presidential Election Results using Sentiment Analysis with Pre and Post Candidate Registration Data Firdaus, Asno Azzawagama; Yudhana, Anton; Riadi, Imam
Khazanah Informatika : Jurnal Ilmu Komputer dan Informatika Vol. 10 No. 1 (2024): April 2024
Publisher : Universitas Muhammadiyah Surakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23917/khif.v10i1.4836

Abstract

Social-media is a solution for politicians as a campaign tool because it can save costs compared to conventional campaigns. The 2024 Indonesian presidential election has attracted public attention, especially among social media users. Twitter, as one of the most widely used social media platforms in Indonesia, has become an effective campaign platform. Sentiment analysis is one approach that can be used to measure public opinion on Indonesian presidential candidates based on Twitter data. The data was collected before the declaration of candidates in March 2023 and shortly after the registration of presidential and vice-presidential candidates in November 2023. The data obtained amounted to 15,000 in March 2023 collection and 11,569 in November 2023 collection and used manual labeling by linguists. After removing duplicated tweets, the data changed to 10,569 data with each candidate having 3,523 data for March 2023 and 4,893 data, with each candidate pair having 1,631 data for November 2023. The sentiment analysis classification model is determined using the Naïve Bayes and Support Vector Machine (SVM) methods with Term Frequency-Inverse Document Frequency (TF-IDF) feature extraction. Based on the data, the highest percentage of positive sentiment for the data obtained in March 2023 is for Ganjar Pranowo data by 77.94% and the highest percentage of negative sentiment is for Anies Baswedan data by 31.39%. Meanwhile, for the data obtained in November 2023, the highest positive sentiment was obtained for the candidate pair Ganjar Pranowo - Mahfud MD by 69.16%, and the highest negative sentiment was found in the data Prabowo Subianto - Gibran Rakabuming Raka by 52.12%. Words that frequently appeared in the positive sentiment for Ganjar Pranowo - Mahfud MD included "strong", "corruption", "support", "appreciation", and others. This research achieved the highest accuracy for SVM method which is 86% and Naive Bayes method which is 79%.
Convolutional Autoencoder for Reconstruction of Historical Document Images: Ancient Manuscript Babad Lombok Syuhada, Fahmi; Firdaus, Asno Azzawagama; Ni'mah, Ana Tsalitsatun; Sa’adatai, Yuan; Tajuddin, Muhammad
Rekayasa Vol 17, No 1: April, 2024
Publisher : Universitas Trunojoyo Madura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21107/rekayasa.v17i1.26101

Abstract

The Babad Lombok is an ancient literary or manuscripts document that generally contains stories about the origins of the people of Lombok. This document is written on a lontar leaf, which in the past was used to write manuscripts, letters, and documents. At present, the Babad Lombok document can be seen in the form of photos or scans, so it can be viewed without having to go to a museum or cultural heritage site where the document is usually exhibited. However, because this document is an ancient artifact that has been around for hundreds of years, it has naturally experienced fading in the original document or its scanned versions. This makes the text inside less clear. This paper proposes to automatically reconstruct/repair the Babad Lombok document using a neural network. The type of neural network used is an Autoencoder or Convolutional Autoencoder (CAE). The CAE model is built sequentially and trained using original images of Babad Lombok as its training data and manually corrected images of Babad Lombok as the target or ground truth data. In the process, the two types of data are iteratively cropped to a size of 64x64 along the original size of the Babad Lombok image. This process results in input and target data for the CAE training process in this research, each consisting of 48,288 images. Testing the trained autoencoder model shows that the Babad images have been successfully repaired, making the text quality clearer before reconstruction. Ultimately, the proposed CAE has achieved training and validation accuracies of 89.09% and 94.57%, with corresponding loss values of 0.0418 and 0.0226.
Sentiment Analysis on Marketplace in Indonesia using Support Vector Machine and Naïve Bayes Method Muhammad Mujahid Dakwah; Asno Azzawagama Firdaus; Furizal Furizal; Rangga Faresta
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol. 10 No. 1 (2024): March
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v10i1.28070

Abstract

This research addresses the challenges of marketplace customer feedback, which is an important aspect in today's era of online transactions. Marketplaces often receive many unsatisfactory comments from their customers through social media platforms. One approach that can be used to address this is sentiment analysis. This research contributes new insights as recommendations for marketplaces based on customer opinions on available services and delivery. The sentiment analysis methods used are Naive Bayes and Support Vector Machine because they are considered the best methods in training text-based classification models. Before being classified, the data goes through preprocessing stages such as cleaning, case folding, filtering, stemming, and tokenizing, as well as feature extraction stages using Term Frequency - Inverse Document Frequency (TF-IDF). The objects analyzed are divided into several well-known marketplaces in Indonesia such as Tokopedia, Lazada, and Shopee in discussing services and delivery of goods. The data used in this study comes from Twitter (X) social media accessed on August 27, 2023, using crawling techniques and successfully obtained as much as 2057 Tweet data. The best accuracy is obtained in the SVM method when compared to the Naive Bayes method. Words obtained based on service talks include price, service, application service, feedback, independence, and others. As for the delivery of goods, common words such as COD, delivery, package, courier, cheap, price, and others appear. Both methods used have good accuracy and can be recommended for use in similar research.
Analisis Sentimen Pada Proyeksi Pemilihan Presiden 2024 Menggunakan Metode Support Vector Machine Asno Azzawagama Firdaus; Anton Yudhana; Imam Riadi
Decode: Jurnal Pendidikan Teknologi Informasi Vol. 3 No. 2: SEPTEMBER 2023
Publisher : Program Studi Pendidikan Teknologi Infromasi UMK

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51454/decode.v3i2.172

Abstract

Indonesia menganut sistem demokrasi dan Pemilihan Umum sebagai penerapan dari sistem tersebut. Pemilihan Presiden dan Wakil Presiden dilaksanakan tahun 2024 dan isu tersebut menjadi fokus perbincangan publik. Calon-calon dan koalisi pengusung terus melakukan kampanye politik secara tradisional maupun melalui media sosial. Twitter menjadi platform media sosial yang banyak digunakan oleh masyarakat untuk membicarakan isu Pemilihan Presiden. Keberpihakan masyarakat dapat diketahui pada diskusi yang ada di Twitter, namun diperlukan pembelajaran komputer yang mampu mengklasifikasi sentimen tersebut. Analisis sentimen digunakan sebagai salah satu teknik untuk mengklasifikasi sentimen masyarakat di Twitter tentang isu Pemilihan Presiden. Metode yang digunakan yaitu Support Vector Machine (SVM) untuk klasifikasi teks. Didapatkan hasil sentimen berdasarkan tiga dataset kandidat yang dipilih, yaitu anies baswedan 65,62%, ganjar pranowo 73,58%, dan prabowo subianto 66,34%. Hasil akurasi metode yang dimiliki oleh ketiga dataset yaitu anies baswedan 73%, ganjar pranowo 79% dan prabowo subianto 79%. Berdasarkan wordcloud popularitas kata yang muncul di Twitter dengan pembahasan Presiden 2024 secara berturut-turut adalah “prabowo subianto”, “presiden ri”, “calon presiden”, “ganjar pranowo”, hingga “anies baswedan”.
Co-Authors Abao , Anrom G. Abdul Fadlil Abdul Fadlil Adipta, Marazaenal Afriansyah, M. Ahmad Fatoni Dwi Putra Aldi Bastiatul Fawait Fawait Alfian Ma’arif Ana Tsalitsatun Ni'mah Anggarista, Randa Anton Yudhana Anton Yudhana Anwar, Miftahul Ariska Fitriyana Ningrum Arqam, Mhd Lailan Aryandani, Aisyah Atmojo, Ahmad Muslih B. Talirongan, Florence Jean Daniel Santoso Dianda Rifaldi Ega Silpia Aulia Elmalky , Ahmed Mahmoud Zaki Fahmi Syuhada Faresta, Rangga Alif Feri Adriyanto Furizal Furizal Furizal Furizal Furizal, Furizal Habibi , Ahmad Rizky Nusantara Hasanah, Rakyatol Imam Riadi Imam Riadi Insyroh, Nazaruddin Joko Slamet Saputro Joni Saputra Kariyamin, Kariyamin Khairanis Yulita Kumoro, Danang Tejo Maghfiroh, Hari Mahsun Mahsun Maulana , Adrian Maulana, Adrian Muh. Fadli Hasa Muhajir Yunus Muhamad Yamin Muhammad Ilham Muhammad Mujahid Dakwah Muhammad Yusuf Muis, Alwas Munzir, Misbahul Murinto Muzakkir Pangri Nakib, Arman Mohammad Palahuddin, Palahuddin Pramesti, Retta Farah Pratama, Ramadhana Agung Rangga Faresta Sani SR, Sahrul Sa’adatai, Yuan Sherly da Costa, Apolonia Diana Soekarta, Rendra SULISTIANI Sulton, Chaerus Suwarno, Iswanto Syahrani Lonang Syuhada, Fahmi Syuhada, Fahmi Syuhada Tajuddin, Muhammad Talirongan , Hidear Thoyyibah, Mazraatin Visman, Javan Wahyu Rahmaniar Wijaya, Setiawan Ardi