<?php

require_once 'Controller.php';

class JurnalController extends Controller {
    
    private function getJournals() {
        return [
            [
                'id' => 1,
                'category' => 'SCIENCE',
                'title' => 'Cervical Cancer Detection using Visual Inspection with Acetic Acid (VIA) Test Images through Convolutional Neural Network (CNN) based Image Classification ',
                'author' => 'Zia Shadra Sahasrara',
                'school' => 'Sekolah Murid Merdeka (SMM) Hub',
                'competition' => 'IRPRO JUNI 2025 (SILVER)',
                'abstract' => "Cervical cancer is a form of cancer that grows within the cervix that is caused by a Human Papillomavirus (HPV) infection that results in the formation of a malignant tumor. Out of all women cancer patients in Indonesia as per year 2021, 17.2% suffer from cervical cancer. One of the key factors contributing to the high incidence of cervical cancer in Indonesia is the low coverage of early screening. As of 2021, only 6.82% of women aged 30–50 had undergone Visual Inspection with Acetic Acid (VIA) screening, far below the 70% national target. Early detection with Visual Inspection with Acetic Acid (VIA) is an acceptable, affordable, and safe alternative method in developing countries. This process is relatively cheap and quick since it doesn't need any expensive tools and the results are able to be seen with the naked eye. The results are still determined manually by colposcopy which means that there's still a possibility of a misdiagnosis. Integrating an artificial intelligence (AI) that specializes in image classification using Convolutional Neural Network (CNN) into screening processes could offer faster and more accurate results to improve early detection and outcomes. The built model successfully identifies relevant visual patterns in cervical images, providing reliable predictions in classifying positive VIA and negative VIA cervicals. The proposed image  classification model is proven to be effective in automatically detecting cervical cancer. Which shows the great potential of applying artificial intelligence technology in assisting the medical screening process.",
                'description' => 'Keywords: Cervical Cancer Image Classification Visual Inspection with Acetic Acid (VIA) Convolutional Neural Network (CNN)'
            ],
            [
                'id' => 2,
                'category' => 'SCIENCE',
                'title' => 'Cervical Oncovue',
                'author' => 'Zia Shadra Sahasrara',
                'school' => 'Sekolah Murid Merdeka (SMM)',
                'competition' => 'WICE',
                'abstract' => 'If left untreated, human papillomaviruses (hr-HPV) infection can lead to pre-cancerous lesions and invasive cervical carcinoma (ICC). This infection causes over 90% of all cervical cancer in the world.[1] Timely diagnosis in precancerous lesions followed by medical referral, timely diagnosis, safe treatment and follow-up will be able to prevent cervical cancer at a relatively low cost.[4] Early detection with Visual Inspection with Acetic Acid (VIA) is an acceptable, affordable, and safe alternative method in developing countries. This process is relatively cheap and quick since it doesnt need any expensive tools and the results are able to be seen with the naked eye. The results are still determined manually by colposcopy which means that theres still a possibility of a misdiagnosis. Integrating an artificial intelligence (AI) that specializes in image classification using Convolutional Neural Network (CNN) into screening processes could offer faster and more accurate results to improve early detection and outcomes. The built model successfully identifies relevant visual patterns in cervical images, providing reliable predictions in classifying positive VIA and negative VIA cervicals. The proposed image  classification model is proven to be effective in automatically detecting abnormalities which may lead to cervical cancer. Which shows the great potential of applying artificial intelligence technology in assisting the medical screening process.',
                'description' => 'Keywords: Cervical Abnormalities, Image Classification, Visual Inspection with Acetic Acid (VIA), Convolutional Neural Network (CNN)'
            ],
            [
                'id' => 3,
                'category' => 'SCIENCE',
                'title' => 'BioCount AI Object Detection Microbes',
                'author' => 'Zelda Farah Nurfitri',
                'school' => 'SMP Laboratorium Percontohan UPI',
                'competition' => 'WICE',
                'abstract' => 'Viable Plate Count (VPC) measures the number of bacteria per gram (or milliliter) and is crucial for food safety, water quality, and clinical diagnostics, where accurate colony-forming units (CFU) counts guide decisions. Object detection offers a promising approach for counting and detecting colonies in VPC. By training models to accurately recognize and differentiate individual colonies, object detection systems can significantly enhance both the efficiency and precision of VPC. This research focuses on the development and evaluation of an object detection–based machine learning framework for automating VPC analysis. The aim is to reduce human workload, improve counting accuracy, and provide a scalable solution for high-throughput microbiological testing. Through model training, optimization, and validation, this work seeks to demonstrate a machine learning–driven VPC system. This system can serve as a reliable and cost-effective alternative to manual counting, contributing to faster and more consistent microbiological assessments. Although the accuracy needs to be improved, the AI is working perfectly.',
                'description' => 'Keywords: Object Detection, Machine Learning, Viable Plate Count'
            ],
            [
                'id' => 4,
                'category' => 'SCIENCE',
                'title' => 'Aim-Ball, The Basketball AI',
                'author' => 'Jiro Aliifio Wirjawan And Mahija Bregas Dary Abiyyu',
                'school' => 'SMP Laboratorium Percontohan UPI',
                'competition' => 'WICE',
                'abstract' => ' Traditional basketball training generally requires the presence of a coach to provide guidance and technical corrections. However, not all players have access to professional coaches or adequate training facilities. The use of AI technology in personal basketball training can be an innovative solution to overcome these limitations. The Aim-Ball offers shooting training by teaching shooting form on a personal basis, without a coach. This research focuses on the development and evaluation of a pose classifier to help players. Through collected data and creating a model, the Aim-Ball AI can detect the shooting form successfully. The Aim-Ball AI can play sound differently according to which kind of shooting form we are doing.',
                'description' => 'Keywords: Basketball, Shooting Form, Artificial Intelligence, Pose Classifier'
            ],
             [
                'id' => 5,
                'category' => 'SCIENCE',
                'title' => 'BioCount AI Optimizing Garlic Antibacterial Assays',
                'author' => 'Zelda Farah Nurfitri And Aisha Rahmayanti Kartawinata',
                'school' => 'SMP Laboratorium Percontohan UPI',
                'competition' => 'AISEEF',
                'abstract' => 'The issue of antibiotic resistance poses a serious challenge in global health, prompting the search for safer and more effective alternative treatments. Garlic (Allium sativum) is known to have antimicrobial properties thanks to its allicin content, an active compound capable of inhibiting the growth of various microorganisms. This study aims to test the effectiveness of garlic as an antibiotic against oral microbes using an Object Detection Machine Learning technology approach with the viable plate count (VPC) method. The results show that higher concentrations of garlic significantly reduce the number of microbial colonies, with the highest effectiveness at a concentration of 50%. The machine learning model using object detection showed high precision (up to 100%) but low recall at a threshold of 20%, and more balanced performance at a threshold of 1% (precision 90.6%, recall 94.1%). These findings indicate that a machine learning-based VPC system can be a reliable and efficient alternative in microbiological analysis, and supports the potential of garlic as a sustainable natural antibiotic.',
                'description' => 'Keywords: Machine Learning, Viable Plate Count, Object Detection, Garlic, Antibiotics.'
            ],
            [
                'id' => 6,
                'category' => 'SCIENCE',
                'title' => 'Senti AI : Is Sentiment Analysis Related to Adolescence Understanding of Disaster Mitigation? a Case Study of BMKG Gowa Vtuber ',
                'author' => 'Zia Shadra Sahasrara And Nur Wahidah And Azra Hasna Kamila',
                'school' => 'Sekolah Murid  Merdeka (SMM)',
                'competition' => 'AISEEF',
                'abstract' => 'This study intends to make a machine learning algorithm to quantify sentiment analysis to examine the effectiveness of using Virtual YouTuber (VTuber) Shinna Faultline, introduced by BMKG Gowa, as an alternative medium for delivering disaster information to adolescents. The AI that included a scraper, which was written in VScode, uses a natural language processing module with an incorporated dataset and data module known as Vader_Lexicon. To measure public understanding, a quantitative approach with a pre-test and post-test design was conducted at a junior high school. The research instrument was a dichotomous questionnaire that included favorable and unfavorable items which were processed with a paired sample t test. In conclusion there is a strong correlation between the comment section sentiment analysis and teenager understanding of disaster mitigation where if the video has a bigger percentage of positive comments, the understanding of the material generally increases amongst the students.',
                'description' => 'Keywords: Sentiment analysis, Machine Learning, VTuber'
            ],
            [
                'id' => 7,
                'category' => 'SCIENCE',
                'title' => 'Cash-Voice, AI Money for the Blind',
                'author' => 'Arkantama Azka Riyadinata',
                'school' => 'SMP Laboratorium Percontohan UPI',
                'competition' => 'WYIIA',
                'abstract' => 'Money is a tangible or electronic commodity that is universally accepted as a means of payment for goods, assets, and services in a particular economy within a specific environment. Money enables the pricing of goods, assets, and services in the economy, making the pricing system more transparent and informative. For people with visual impairments, recognizing money is very difficult due to their limited access to the visual elements of money. This limitation makes people with visual impairments more vulnerable to risks in their daily activities, such as shopping or making transactions. The “Cash-Voice AI Money for the Blind” application is an AI application to address these needs with the ability to detect money through image classification technology to assist people with visual impairments. The Cash-Voice AI Money for the Blind application successfully detects money based on denomination and emits an audio output, thereby enhancing financial security.',
                'description' => 'Keywords: Visual Impairments, Detect Money, Image Classification.'
            ],
            [
                'id' => 8,
                'category' => 'SCIENCE',
                'title' => 'Comparative Effects of Traditional Learning Methods and AI Learning Aids on Senior High School Student Comprehension and Academic Performance',
                'author' => 'Farayyan Wistara Asanov And Feyzan Aqil Rochman And Pasha Darmawan',
                'school' => 'Senior High School',
                'competition' => 'AISEEF',
                'abstract' => 'Artificial intelligence is one of the most discussed issues in Indonesia s education system, where AI learning will be implemented in schools learning systems 10, This research was conducted to determine which one of the learning systems is more efficient in building student understanding, whether by using Artificial Intelligence (AI) or Traditional learning systems. This research uses the quantitative experimental method analyzed using Descriptive (Min, Max, Mean, Modus, Median) and Paired t-tests conducted in SMAN 8 Bandung s ten graders using questionnaires in Google Form. From the research results, we found that our hypothesis was proven correct where traditional learning is more efficient than using artificial intelligence to reach high order thinking skills and improve academic performance.',
                'description' => 'Keywords:Artificial intelligence,Traditional learning systems,Education'
            ],
            
        ];
    }
    
    public function index() {
        $data = [
            'title' => 'Jurnal Exhibition & Hall of Fame',
            'current_page' => 'jurnal',
            'journals' => $this->getJournals()
        ];
        
        $this->view('jurnal/index', $data);
    }
    
    public function detail($id) {
        $journals = $this->getJournals();
        $journal = null;
        
        foreach ($journals as $j) {
            if ($j['id'] == $id) {
                $journal = $j;
                break;
            }
        }
        
        if (!$journal) {
            echo "Jurnal not found!";
            return;
        }
        
        $data = [
            'title' => $journal['title'],
            'journal' => $journal,
            'current_page' => 'jurnal'
        ];
        
        $this->view('jurnal/detail', $data);
    }
}
