Prabowo Budi Utomo, Dona Wahyudi, Adimas Ketut Nalendra
{"title":"Implementasi Convolution-Augmented Transfomer Berbasis Kecerdasan Buatan dalam Analisis Sentimen Teks Hasil Konversi Suara ke Teks","authors":"Prabowo Budi Utomo, Dona Wahyudi, Adimas Ketut Nalendra","doi":"10.29407/gj.v8i1.22202","DOIUrl":"https://doi.org/10.29407/gj.v8i1.22202","url":null,"abstract":"Keterbatasan mendapatkan informasi yang dialami penyandang disabilitas menjadikan mereka kurang update terhadap perkembangan yang terjadi sehingga secara tidak langsung mendorong berbagai upaya untuk mendapatkan informasi tanpa mempedulikan sumber dan konteks informasi yang diperoleh, konteks informasi yang diperoleh dalam hal ini berkaitan dengan emosi yang berusaha disampaikan lewat tulisan atau teks informasi, maka perlu dirancang dan diimplementasikan yang mampu mengekstraksi dan menemukan inteprestasi emosi bermuatan positif, negatif atau netral dari teks hasil konversi teknologi Speech to Text, sehingga dapat membantu penderita disabilitas pendengaran dalam memahami konteks dan emosi yang terkandung didalam informasi. Aplikasi Speech to Text yang dikombinasikan dengan metode Conformer berbasis kecerdasan buatan dapat membantu penyandang disabilitas pendengaran untuk memahami sentimen atau emosi dari teks hasil konversi suara. Dengan menggunakan kecerdasan buatan yang tergabung dalam metode Conformer dapat dilakukan klasifikasi sentimen terhadap teks hasil konversi juga dapat dideteksi topik yang disampaikan, sehingga diharapkan dapat dimanfaatkan penyandang disabilitas pendengaran dalam memberikan umpan balik yang tidak menyinggung perasaan dan sesuai topik bahasan.","PeriodicalId":200108,"journal":{"name":"Generation Journal","volume":"43 5","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140250251","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Assyifa Khalif, A. Hasanah, M. Ridwan, Betha Nurina Sari
{"title":"Klasterisasi Tingkat Kemiskinan di Indonesia menggunakan Algoritma K-Means","authors":"Assyifa Khalif, A. Hasanah, M. Ridwan, Betha Nurina Sari","doi":"10.29407/gj.v8i1.21470","DOIUrl":"https://doi.org/10.29407/gj.v8i1.21470","url":null,"abstract":"Poverty is one of the deep social challenges around the world and is a major focus in the global development agenda. This article discusses the role of clustering methods in analyzing and understanding poverty issues. We use data from Statistics Indonesia (BPS) on 34 provinces in Indonesia to classify groups of people who are vulnerable to poverty. Clustering analysis helps us identify characteristics that may be overlooked by conventional approaches, which in turn enables the development of more targeted and effective solutions to poverty. We use the K-Means method in our analysis and present it within the framework of the CRISP-DM methodology. The results show that almost 95% of the poor in Indonesia belong to the 'Poor' group. Therefore, we recommend effective actions based on indicators that are the main factors of poverty, as well as designing specific policies for regions with similar characteristics. This article aims to contribute to the global effort to end poverty and achieve the vision of equitable and inclusive sustainable development.","PeriodicalId":200108,"journal":{"name":"Generation Journal","volume":"2 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140250093","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
N. Nurbaiti, Eka Putra Syarif Hidayat, Khairil Anwar, Dudung Hermawan, Salman Izzuddin
{"title":"Development of AI Models from Mammography Images with CNN for Early Detection of Breast Cancer","authors":"N. Nurbaiti, Eka Putra Syarif Hidayat, Khairil Anwar, Dudung Hermawan, Salman Izzuddin","doi":"10.29407/gj.v8i1.21601","DOIUrl":"https://doi.org/10.29407/gj.v8i1.21601","url":null,"abstract":"Early detection of breast cancer with computer assistance has developed since two decades ago. Artificial intelligence using the convolutional neural network (CNN) method has successfully predicted mammography images with a high level of accuracy similar to human brain learning. The potential of AI models provides opportunities to spot breast cancer cases better. This research aims to develop AI models with CNN using the public DDSM dataset with a sample size of 1871, consisting of 1546 images for training and 325 images for testing. These AI models provided prediction results with different accuracy rate. Increasing the accuracy of the AI model can be done by improving the image quality before the modeling process, increasing the number of datasets, or carrying out a more profound iteration process so that the AI model with CNN can have a better level of accuracy.","PeriodicalId":200108,"journal":{"name":"Generation Journal","volume":"30 5","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140248762","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Klasifikasi Kualitas Buah Apel Berdasarkan Warna dan Bentuk Menggunakan Metode KNN","authors":"Cindy Suryanti, Mujibur Rohman","doi":"10.29407/gj.v8i1.21052","DOIUrl":"https://doi.org/10.29407/gj.v8i1.21052","url":null,"abstract":"Buah apel merupakan merupakan salah satu buah-buahan yang memiliki banyak penggemar dengan kandungan buah seperti tinggi serat, vitamin C, dan berbagai macam antioksidan. Satu buah apel diketahui mengandung 95 kalori, yang sebagian besarnya berasal dari kandungan karbohidrat di dalamnya. Pemilihan buah apel untuk dikonsumsi adalah buah dengan kualitas yang bagus yaitu tidak terlalu muda dan tidak busuk. Tujuan dari penelitian ini adalah untuk merancang dan membangun sistem klasifikasi jenis jeruk berdasarkan daun sehingga diketahui kelebihan dan kekurangan metode KNN dan untuk mengetahui tingkat akurasi metode KNN. Aplikasi ini menggunakan metode KNN dan menggunakan eksraksi fitur meanR, meanG, meanB, standR, standG, standB, contras, correlation, energy, homogeneity, perimeter, area, accentricity. Pada penelitian ini untuk menentukan kualitas baik dan buruk, data seluruhnya ada 117 diantara lain data Training 74 dan testing 43 dan penelitian ini memiliki nilai akurasi tertinggi yaitu K5 dengan total sebesar 88.37%. \u0000Apples are one of the fruits that have many fans with fruit content such as high fiber, vitamin C, and various kinds of antioxidants. One apple is known to contain 95 calories, most of which come from the carbohydrate content in it. The selection of apples for consumption is fruit with good quality, which is not too young and not rotten. The purpose of this research is to design and build a citrus type classification system based on leaves so that the advantages and disadvantages of the KNN method are known and to determine the accuracy of the KNN method. This application uses the KNN method and uses feature extraction meanR, meanG, meanB, standR, standG, standB, contrast, correlation, energy, homogeneity, perimeter, area, accentricity. In this study, to determine good and bad quality, there were 117 data in total, including training data 74 and testing 43 and this study had the highest accuracy value, namely K5 with a total of 88.37%. \u0000 ","PeriodicalId":200108,"journal":{"name":"Generation Journal","volume":"120 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140461593","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Mohammad Jadid, Ahmad Sabil Adani, Purnomo Hadi Susilo
{"title":"Implementasi Metode K-Nearest Neighbor Sebagai Sistem Pendeteksi Kualitas Ikan Bandeng","authors":"Mohammad Jadid, Ahmad Sabil Adani, Purnomo Hadi Susilo","doi":"10.29407/gj.v8i1.21131","DOIUrl":"https://doi.org/10.29407/gj.v8i1.21131","url":null,"abstract":"Ikan bandeng (Chanos Chanos) merupakan salah satu ikan konsumsi yang hidup tersebar didaerah tropik Indo Pasifik, Ikan bandeng juga telah menjadi komoditas yang memiliki tingkat konsumsi yang tinggi terutama di daerah desa Bendungan kecamatan Duduksampean kabupaten Gresik, Semakin tingginya minat terhadap ikan bandeng, sehingga kualitas ikan bandeng menjadi sangat penting. Salah satu parameter dari kualitas ikan bandeng adalah kesegaran ikan. Ikan bandeng pada umumnya mudah mengalami penurunan kualitas, bila kesegaran ikan menurun, penurunan kesegaran tersebut berpotensi menjadi basi, Beberapa metode yang digunakan masyarakat untuk mengidentifikasi kesegaran dari ikan bandeng masih secara manual, Kekurangan dari metode di atas yaitu tidak semua pendapat dari masyarakat sama dalam hal menilai kualitas bandeng yang masih segar, sehingga kebasian pada ikan bandeng berbeda – beda dan kurang valid. Munculnya permasalahan di atas maka perlu dikembangkan suatu metode untuk identifikasi kualitas dari ikan bandeng agar lebih valid. Oleh karena itu pada penelitian ini diusulkan deteksi kesegaran ikan bandeng menggunakan Image Processing dan menggunakan metode K- Nearest Neighbor ( KNN ) untuk mengetahui kesegaran ikan bandeng, Pada penelitian ini meenggunakan total 195 data diantara lainnya 150 data training dan 45 data testing. Dari penelitian ini memiliki nilai akurasi yang tertinggi pada parameter nilai K1 dengan hasil akurasi 84,44%.","PeriodicalId":200108,"journal":{"name":"Generation Journal","volume":"174 7","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140492739","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Implementasi Algoritma AES Pada Aplikasi Pembelian Voucher Hotspot Berbasis Android","authors":"R. Irawan, Umi Mahdiyah, Rizki Dwi Kurniawan","doi":"10.29407/gj.v8i1.20817","DOIUrl":"https://doi.org/10.29407/gj.v8i1.20817","url":null,"abstract":"Pada era digital yang semakin maju, kebutuhan akan akses internet yang cepat dan aman semakin meningkat. Dalam konteks ini, voucher hotspot menjadi salah satu cara yang populer untuk memperoleh akses internet yang terjangkau dan mudah digunakan. Saat ini, penggunaan voucher masih menggunakan metode cetak voucher ke kertas. Hal ini dapat menimbulkan kekurangan salah satunya yaitu menimbulkan sampah kertas bekas voucher tersebut. Dengan permasalahan tersebut, maka dibuatlah sebuah aplikasi pembelian voucher berbasis android dengan implementasi algoritma enkripsi AES. Enkripsi AES adalah algoritma enkripsi yang terkenal karena kemanannya yang sudah terjamin. Tujuan dari penelitian ini adalah membangun sebuah sistem atau aplikasi pembelian voucher hotspot berbasis android dengan enkripsi AES. Dari pengujian sistem yang telah dilakukan dengan metode blackbox, aplikasi yang dibuat telah berjalan sesuai dengan rancangan dan menjawab permasalahan penelitian","PeriodicalId":200108,"journal":{"name":"Generation Journal","volume":"23 5","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140492985","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Analisis Klasifikasi Penyakit Multiple Sclerosis Menggunakan Algoritma Logistic Regression dan SVM","authors":"I. Laela, Wiga Maulana Baihaqi","doi":"10.29407/gj.v8i1.20646","DOIUrl":"https://doi.org/10.29407/gj.v8i1.20646","url":null,"abstract":"Health is the most important aspect to support daily activities. Of course, by having a healthy body, everyone can carry out various activities comfortably and calmly. Every individual certainly has a strong instinct to live a healthy life and be free from disease, one of which is by increasing the body's immunity. Multiple sclerosis (multiple sclerosis/MS) is a neurodegenerative autoimmune disease that affects the central nervous system. The affliction of MS is characterized by chronic inflammation, demyelination, gliosis, and neuronal death. The symptoms faced by MS patients are unpredictable, so there is a need for a classification related to the disease. Therefore, a classification study was carried out using the logistic regression algorithm and SVM. The method used in this research is a literature study with the Python programming language. The results of this study indicate that the SVM algorithm has a high accuracy rate of 88.33% of the logistic regression algorithm. So it can be concluded from this study that the SVM method has good performance for processing multiple sclerosis datasets.","PeriodicalId":200108,"journal":{"name":"Generation Journal","volume":"129 1-3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140492480","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Virtual Tour 360 Objek Wisata Curug Cimanintin Salopa Kabupaten Tasikmalaya","authors":"Bayu Muhamad Nur, E. Hidayat, Heni Sulastri","doi":"10.29407/gj.v8i1.21477","DOIUrl":"https://doi.org/10.29407/gj.v8i1.21477","url":null,"abstract":"Makalah ini bertujuan untuk merancang dan membangun aplikasi Virtual Tour 360 berbasis android agar dapat memberikan pengalaman pengguna dan memberikan informasi mengenai objek wisata Curug Cimanintin di kecamatan Salopa kabupaten Tasikmalaya. Tantangan utama dalam penelitian ini adalah bagaimana merancang dan membangun aplikasi virtual tour 360 sebagai aplikasi untuk pengenalan objek wisata tersebut, serta bagaimana mengukur tingkat usability aplikasi virtual tour 360 yang dibuat. Proses pembuatan dan pengembangan aplikasi menggunakan metode MDLC (Multimedia Development Life Cycle) versi Luther. Pengujian dilakukan dengan metode black box untuk menguji fungsionalitas dari aplikasi yang dibuat dan pengujian dengan metode survei menggunakan pendekatan SUS (System Usability Scale) untuk menguji tingkat kebergunaan aplikasi oleh pengguna. Hasil pengujian menunjukan bahwa secara fungsionalitas aplikasi dapat berjalan sesuai fungsinya dan didapat skor rata-rata SUS sebesar 75,50. Nilai tersebut berarti aplikasi dapat diterima (Acceptable) oleh pengguna dengan kategori baik (Good) dan memperoleh Grade ‘C’.","PeriodicalId":200108,"journal":{"name":"Generation Journal","volume":"70 1-2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140491674","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Perbandingan Performa Algoritma KNN dan SVM dalam Klasifikasi Kelayakan Air Minum","authors":"Sopiatul Ulum, Rizal Fahmi Alifa, Putri Rizkika, Chaerur Rozikin","doi":"10.29407/gj.v7i2.20270","DOIUrl":"https://doi.org/10.29407/gj.v7i2.20270","url":null,"abstract":"Air menjadi kebutuhan mendasar bagi kelangsungan makhluk hidup dan pembangunan. Saat ini, kesadaran masyarakat terhadap pola konsumsi air yang berkualitas dan bermutu semakin tinggi sehingga diperlukan penelitian terhadap kelayakan air. Dalam penelitian air tersebut menggunakan metode klasifikasi objek. Pada penelitian ini membahas perbandingan antara 2 metode Machine Learning yaitu K-Nearest Neighbors (K-NN) dengan Support Vector Machine (SVM) berdasarkan parameter yang telah ditentukan. Penelitian ini menghasilkan tingkat akurasi algoritme K-Nearest Neighbors (K-NN) sebesar 65,341% dan algoritme Support Vector Machine (SVM) menghasilkan akurasi sebesar 69,764%. Dari hasil tersebut, dapat disimpulkan bahwa algoritme Support Vector Machine (SVM) memiliki akurasi lebih tinggi daripada algoritme K-Nearest Neighbors (K-NN).","PeriodicalId":200108,"journal":{"name":"Generation Journal","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115862932","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Penerapan Teknologi Virtual Tour untuk Pengembangan Media Promosi Kampus Berbasis Web","authors":"Rafika Akhsani, Ismanto Ismanto, Moch. Kholil","doi":"10.29407/gj.v7i2.20069","DOIUrl":"https://doi.org/10.29407/gj.v7i2.20069","url":null,"abstract":"Pandemi covid-19 telah menjadikan sebuah era perubahan di mana segala aktifitas dilakukan secara terbatas dan mematuhi protokol kesehatan. Covid 19 merupakan sebuah penyakit yang disebabkan oleh infeksi virus severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Oleh karena itu, kita harus mematuhi protokol kesehatan sesuai himbauan dari pemerintah supaya kita terhindar dari covid 19. Sebagai perguruan tinggi yang baru tentunya Akademi Komunitas Negeri Putra Sang Fajar Blitar (AKB) memiliki beberapa keterbatasan. Capaian target mahasiswa saat ini masih belum optimal. Salah satu cara untuk meningkatkan jumlah mahasiswa baru adalah dengan melakukan promosi. Virtual tour merupakan salah satu alternatif yang dapat menjadi alternatif dalam promosi kampus AKB yaitu dengan mengenalkan fasilitas kampus dan lingkungan kampus kepada calon mahasiswa. Calon mahasiswa nantinya dapat melihat semua fasilitas kampus baik ruangan, laboratorium, parkir, dan lain sebagainya melalui dunia virtual atau dunia maya. Penelitian ini bertujuan untuk mengimplementasikan teknologi virtual tour berbasis website sebagai media pengenalan fasilitas kampus AKB atau media promosi kampus AKB. Penelitian ini menggunakan metode MDLC dengan tahapain yaitu Concept, Design, Material Collecting, Assembly, Testing, dan Distribution. Berdasarkan hasil pengujian, semua menu dan hotspot yang telah dibuat berjalan sesuai dengan skenario.","PeriodicalId":200108,"journal":{"name":"Generation Journal","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127160891","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}