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All Journal Journal of Marine Research Syntax Jurnal Informatika Jurnal Informatika dan Teknik Elektro Terapan Scientific Journal of Informatics Jurnal Ilmiah Mahasiswa Pendidikan Jasmani, Kesehatan dan Rekreasi Jurnal Ilmiah Ekonomi Islam BAREKENG: Jurnal Ilmu Matematika dan Terapan JOURNAL OF APPLIED INFORMATICS AND COMPUTING JURNAL ILMIAH INFORMATIKA Kamaya: Jurnal Ilmu Agama Jurnal Teknik Informatika UNIKA Santo Thomas JURNAL AGRIBISAINS Majalah Kedokteran Andalas DIVERSI : Jurnal Hukum Ta'dib: Jurnal Pendidikan Islam dan Isu- isu Sosial JATI (Jurnal Mahasiswa Teknik Informatika) Jurnal Bioindustri Jurnal Keselamatan Transportasi Jalan (Indonesian Journal of Road Safety) Zona Laut : Jurnal Inovasi Sains dan Teknologi Kelautan Jurnal Tunas Agraria DedikasiMU: Journal of Community Service Hasanuddin Journal of Sociology (HJS). Journal for Lesson and Learning Studies ARRUS Journal of Engineering and Technology Society: Jurnal Pengabdian Masyarakat Jurnal Teknik Informatika Unika Santo Thomas (JTIUST) JIPMukjt:Jurnal Ilmu Pendidikan Muhammadiyah Kramat Djati Lamahu: Jurnal Pengabdian Masyarakat Terintegrasi Jurnal Atma Inovasia Bandung Conference Series: Business and Management Indonesia Law Reform Journal (ILREJ) Jurnal Teknik, Komputer, Agroteknologi dan Sains Jurnal Ilmiah Pengabdian dan Inovasi Peradaban Journal of Law and Society Al-Khidmah: Jurnal Pengabdian Kepada Masyarakat OMNICODE Journal (Omnicompetence Community Developement Journal) (Indonesian of Interdisciplinary Journal) At Tahfidz Jurnal Ilmu Al-Qur'an dan Tafsir Jurnal TNI Angkatan Udara Journal of Innovative Food Technology and Agricultural Product Journal of Bioindustry
Iqbal Maulana
Jurusan Teknik Sipil FT. UNDIP Jl. Prof. H. Soedarto SH., Tembalang, Semarang 50275
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Journal : JOURNAL OF APPLIED INFORMATICS AND COMPUTING

 1 
Classification of COVID-19 Aid Recipients in Kasomalang District Using the K-Nearest Neighbor Method Permatasari, Ismi Aprilianti; Dermawan, Budi Arif; Maulana, Iqbal; Kurniawan, Dwi Ely
Journal of Applied Informatics and Computing Vol. 8 No. 1 (2024): July 2024
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v8i1.3279

Abstract

The impact of the Coronavirus, also known as COVID-19, which emerged in 2019, has not only threatened public health but also affected the global economy, including Indonesia. The government has initiated various aid programs to assist the community during the COVID-19 pandemic. These aids are expected to alleviate the economic burden on the affected population. One such aid program is the Direct Cash Assistance (Bantuan Langsung Tunai/BLT) from the Village Fund, which has been distributed since the onset of COVID-19 in Indonesia. However, the distribution of BLT has encountered several issues, including misidentification of recipients and double or excessive distribution beyond the established criteria. To address these issues, data mining for the classification of aid recipients can be employed. This study uses the K-Nearest Neighbor (KNN) method for data mining classification to classify residents' data with new patterns, ensuring aid distribution aligns with the criteria and eliminating double recipients. The application of K-Nearest Neighbor to the population data in Kasomalang District yields optimal performance, with evaluation results showing an accuracy of 96%, precision of 0.98, recall of 0.96, and F1 score of 0.97 using the confusion matrix method.
Implementation of Identity Loss Function on Face Recognition of Low-Resolution Faces With Light CNN Architecture Mufid, Tsaqif Mu'tashim; Adam, Riza Ibnu; Jaman, Jajam Khaeru; Garno, Garno; Maulana, Iqbal
Journal of Applied Informatics and Computing Vol. 8 No. 1 (2024): July 2024
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v8i1.6274

Abstract

Face recognition in low-resolution images has seen significant advancements over the past few decades. Although extensive research has been conducted to improve accuracy in these conditions, one of the main challenges remains the difficulty in identifying unique facial features in low-resolution images, leading to high error rates in identification. The use of Deep Convolutional Neural Networks (DCNN) for low-resolution face recognition is still limited. However, employing super-resolution models like REAL-ESRGAN can enhance recognition accuracy in low-resolution images. This study utilizes the Light CNN architecture and applies the margin-based identity loss function AdaFace on low-resolution datasets. The model is trained using the Casia-WebFace dataset and evaluated using the LFW and TinyFace test datasets. Based on the evaluation results on the LFW test data, the best model is Light CNN9-AdaFace, achieving the highest accuracy of 97.78% at 128x128 resolution. For images with the lowest resolution of 16x16, an accuracy of 83.37% was achieved using super-resolution techniques. On the TinyFace test data, the use of super-resolution resulted in performance metrics with a Rank-1 accuracy of 47.26%, Rank-5 accuracy of 55.25%, Rank-10 accuracy of 58.61%, and Rank-20 accuracy of 61.90% using the Light CNN9-AdaFace architecture.
Analisis Sentimen Ulasan pada Aplikasi E-Commerce dengan Menggunakan Algoritma Naive Bayes Ramadhan, Bintang Zulfikar; Adam, Riza Ibnu; Maulana, Iqbal
Journal of Applied Informatics and Computing Vol. 6 No. 2 (2022): December 2022
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v6i2.4725

Abstract

The rapid development of E-commerce has given rise to many marketplaces in Indonesia such as Tokopedia, Shopee, Lazada. Tokopedia, Shopee and Lazada applications are applications that help sellers and buyers to make sales and purchase transactions for goods and services. Until now, of the three major E-Commerce applications, around 100 million users have downloaded the three E-Commerce applications. With the launch of some of these applications, it has caused a lot of opinions and criticisms from the public. Based on this, a sentiment analysis of the Naive Bayes algorithm was carried out to find out how the sentiment of users compares to the E-Commerce application on the Google Play Store. This research uses the Knowledge Discovery in Database (KDD) method which consists of 5 stages, namely data selection, preprocessing, transformation, data mining, and evaluation. The data used is a review of 500 E-Commerce applications per each application. At the data mining stage, it is carried out with 3 scenarios data sharing is 80:20, 70:30 and 60:40. The best results were obtained in scenario 1 (80:20) on the Shopee application using the Naive Bayes algorithm which resulted in an accuracy of 92%, precision of 92.13%, recall of 98.8% and f1-score of 95.35%.
Analisis Sentimen Pengguna Twitter Terhadap Grup Musik BTS Menggunakan Algoritma Support Vector Machine Safitri, Tiara; Umaidah, Yuyun; Maulana, Iqbal
Journal of Applied Informatics and Computing Vol. 7 No. 1 (2023): July 2023
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v7i1.5039

Abstract

Twitter is often used as a source of public opinion and sentiment data for analysis, where the data can be used to understand public opinion about a topic. Sentiment analysis is widely used in various fields, one of which is in the marketing field. a company can carry out a sentiment analysis of the public figures they want to make Brand Ambassadors (BA), which later these sentiments can be taken into consideration for them to be able to determine the BA of their products. Sentiment analysis can also be used to distinguish the attitude of customers, users or followers towards a brand, topic, or product with the help of their reviews. Based on this, this study will analyze the sentiments of Twitter users towards music group BTS, using the Knowledge Discovery Database (KDD) research methodology, with 5 stages namely Data Selection, Data Preprocessing, Data Transformation, Text Mining and Evaluation. By using the Support Vector Machine (SVM) algorithm with a linear kernel, this study will do 3 scenarios with the distribution of training data and testing data 90:10 in scenario 1, 80:20 in scenario 2, and 70:30 in scenario 3. Confusion Matrix is used to evaluate the performance of the algorithm used and the results show that the best performance of the model formed is in scenario 1 and scenario 2.
Co-Authors Abhinaya, Reswara Faza ade, Nofri Afrilian, Mochammad Agim, Agim Agustin, Heny Ainun Safitri Airin Liemanto Akbar, Muhibudin Aldair, Muhammad Diva Alfian, Iqbal Amaliyah, Cinta Ayu Aminuddin Irfani, Aminuddin Ammellia Putri, Zahra Amrih, Dewi Andi Rosa Andni, Riyan Anggellica, Silviana Apriade Voutama Ariana Salsabila, Nazwa Aries Suharso Arif Prambudiarto, Benny Arifin Pahlawan, Ilham Astuti, Elsi Aulia, Nazi Ratul Ayu E., Chinta Kartika Ayunaning, Kholidia Bahary, Achmad Rizal Basri, Ahmad Hasanul Betha Nurina Sari Briliyanti, Rahma Dita Budi Arif Dermawan Budiarti, Puspita Daud, Azzam Hasan Dewi Nurhanifah, Dewi Dikriyah Dwi Agustiar, Fajar Dwi Ely Kurniawan E Haodudin Nurkifli Edi Sofyan, Edi Elfrida Ratnawati Enri, Ultach erdiansyah erdiansyah, erdiansyah FADLI, MOH Fajar Alamsyah, Indra Farah Putri Wenang Lusianingrum Fauzan, Miftahul Febrianti, Amanda Fifa Latifah, Umi Finisica Dwijayati Patrikha Fiqri Faturrian, Muhammad Firdausiah, Salsabila Firmansyah, Faiz Agil Fitri Kurniawati Fitriana Fitriana Gantara, Gerald Dewa garno, Garno Ghufron, Khairul Hanifah, Ayu Nur’aliyah Herlinda Herlinda, Herlinda Herlindah, Herlindah Hidayat Intan Purnamasari Iqron Muhammad, Seno Irwani Irwani Ismiasih, Ismiasih Iwan Permadi Izzati, Nuril Khoirunisa Jaman, Jajam Haerul Jannah, Erana Misbahul Julaiha, Juli Karimuddin, Karimuddin Kholilur Rahman, Moh. Nur Khowwas, Aliyul Komarudin, Oman Kurnia Abdullah, Kunaifi Lejap, Theodorus Yoseph Tatabuang Lestari, Arfena Deah Lubis, Zulfahmi Luthfi, Amar Marlinda, Gusta Maulana, Asyifa Mayasari, Rini Medianti, Vebyola Dwi Meirany, Jasisca Miftahussalamah, Dwi Moh. Jufriyanto Mufid, Tsaqif Mu'tashim Muhammad Indrawan Jatmika Muhammad Jafar, Muhammad Muhammad Manaqib, Muhammad Mukhlidin Mukholad Fauzi, Wildan Mumtaz Muizza, Muhammad Ahmad Muslikhun, Alfin Norkhaliza, Fitria Novalia, Elfina Nugraha, Ihsan Satya Adi Nugroho, Trio Nur Qomariyah Nur, Asrul Ibrahim Nurina Sari, Betha Onny Medaline Padilah, Tesa Nur Pamungkas, Mochammad Fitra Permata Ningtyas, Alviani Hesthi Permatasari, Ismi Aprilianti Pradipta, Aditya Arya Pramudya, Aditya Pratama, Elvin Alan Primaya, Aji Purwanto, Purwanto Purwantoro Putri, Windy Anissa Amilia Rahmanto, Ludi Rahmayani, Fenny Ramadhan, Bintang Zulfikar Riliandhita, Riliandhita Riza Ibnu Adam, Riza Ibnu Rizal, Adhi Rohman, Ahmad Maulana Rowiyani Rozi, Achmad Safitri, Tiara Sahri, Agus Salminawati Sandra, Mela Sentot Purboseno Setiabudi, David Wahyu Sholeh, Khoerul Shonaliya, Indra Putra Siti Halimah Sri Redjeki Sulistiyowati, Anjar Sundari, Ariefah Susilo Yuda Irawan, Agung Syafiih, M Syarifah, Atika Nur Syawal Karo-Karo, Muhammad Umaidah, Yuyun Vincent, Roland Wati, Helmalia Wulandari, Adinda Yopi Hutomo Bhakti Yusuf Rismanda Gaja, Muhammad Yusup, Dadang ZAHWA, ST Zaini Dahlan Zidane, M Yazid