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Stock Sentiment Prediction of LQ-45 Based on News Articles Using LSTM Kristina, Kristina; Agus Dwi Suarjaya, I Made; Cahyawan Wiranatha, Anak Agung Ketut Agung
Journal of Applied Informatics and Computing Vol. 9 No. 4 (2025): August 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i4.9699

Abstract

The growth in the number of investors in the financial market indicates that the investment world is currently experiencing rapid development. One of the long-term investment instruments that has experienced significant growth in the financial market is the stock market. Growth data as of September 2024 sourced from the Indonesia Stock Exchange report reveals that the number of stock market investors has reached more than 6 million single investor identification (SID). The share price of a company can be influenced by two main factors, namely internal factors and external factors. Internal factors come from within the company itself, while external factors come from conditions outside the company. Model development uses the Long Short-Term Memory (LSTM) method to predict daily stock sentiment in realtime. Labeling is done based on the history of stock price changes taken from Yahoo Finance. Stock market news data is obtained automatically every day through Really Simple Syndication (RSS) with the help of cronjob. The results of the LSTM model showed good performance, with a macro F1-Score of 0.73, a macro precision of 0.72, and a macro recall of 0.75. When compared to baseline models such as Logistic Regression, Naive Bayes, and Random Forest which only achieve a macro F1-Score of 0.58, 0.54, and 0.65, respectively, it can be concluded that the developed LSTM model has superior performance. This research can provide new considerations to investors, so as to reduce the risk of loss due to errors in choosing companies to invest in.
Esscore: An OCR-Based Android App for Scoring Short Handwritten Answer Using Levenshtein Distance Apriana, Krisna; I Made Agus Dwi Suarjaya; Ni Kadek Dwi Rusjayanthi
Journal of Applied Informatics and Computing Vol. 9 No. 4 (2025): August 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i4.9708

Abstract

Manual evaluation of short answer tests is time-consuming and prone to subjectivity. This study presents Esscore, an Android-based application that automates the scoring of handwritten short answers using EasyOCR and the Levenshtein Distance algorithm. EasyOCR extracts text from student answers image, while Levenshtein Distance measures similarity against predefined answer keys, allowing tolerance for varied correct responses. The system was tested on 350 student’s handwritten answers, achieving 95.7% accuracy. Functional testing using 14 black box scenarios showed all features operated correctly without failure. A usability test conducted with the SUS method produced a score of 76.5, rated “Good” with a grade “B” and an “Acceptable” acceptance level. The Net Promoter Score (NPS) placed the application in the “Passive” category. These results confirm Esscore as a functional, accurate, and user-friendly solution for automated answer scoring in educational environments.
Classification of public complaint report types on social crimes using a chatbot for law enforcement agencies Purwanthi, Luh Putu Ary; Suarjaya, I Made Agus Dwi; Trisna , I Nyoman Prayana
Matrix : Jurnal Manajemen Teknologi dan Informatika Vol. 15 No. 2 (2025): Matrix: Jurnal Manajemen Teknologi dan Informatika
Publisher : Unit Publikasi Ilmiah, P3M, Politeknik Negeri Bali

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31940/matrix.v15i2.50-59

Abstract

Social crime is a complex problem that occurs every day and requires a quick response. The large number of reports with language variations makes the manual classification process difficult. This research aims to develop an AI-based chatbot to classify types of social crime reports automatically using the IndoBERT model. Data was obtained from East Denpasar Police, LAPOR website, and X social media. The initial data set of 250 reports was augmented to 6,250 data using synonym augmentation technique. The data was then divided into 70:20:10 training scenarios to produce the best model. The evaluation showed high performance with accuracy 0.999200, precision 0.999203, recall 0.999200, and F1-score 0.999200. Validation was also done through confusion matrix and accuracy-loss graph. The chatbot is able to receive reports from the public and classify them into five main categories, namely theft, maltreatment, embezzlement, domestic violence, and murder. The results show that IndoBERT is effective in understanding and classifying Indonesian text reports accurately. The system is expected to assist law enforcement agencies in improving efficiency and speed in handling community reports as well as supporting the digitisation of the social crime complaint process.
Android-based multi-IoT fish feeding system: an end-to-end information system approach Adyatma, Putu Nanda Arya; Buana, Putu Wira; Suarjaya, I Made Agus Dwi
Matrix : Jurnal Manajemen Teknologi dan Informatika Vol. 15 No. 2 (2025): Matrix: Jurnal Manajemen Teknologi dan Informatika
Publisher : Unit Publikasi Ilmiah, P3M, Politeknik Negeri Bali

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31940/matrix.v15i2.87-101

Abstract

The ornamental fish industry in Indonesia has experienced significant growth, positioning the country as the second-largest global exporter of ornamental fish in 2020. However, fish shop owners still face operational challenges, especially in managing consistent and timely feeding across multiple aquariums. Manual feeding practices often lead to inefficiencies and can compromise fish health and water quality. This study presents an end-to-end fish feeding information system integrated with an Android mobile application, designed to address these challenges. System development in this study employs waterfall method. The system supports automated fish feeding routines, device management, and multi-user access with token-based authentication, enabling fish shop owners to operate multiple feeders under a single account. Communication between IoT devices and the backend server utilizes MQTT, ensuring independent control of each feeder through unique topics. The system introduces a novel architecture that supports multi-user, multi-device operations in an end-to-end feeding workflow, improving scalability and efficiency compared to existing single-device systems. System testing, including black box and load testing, demonstrated robust performance, with all test scenarios passing successfully and an error rate of 0.00% during high-load simulations involving up to 100 virtual users. These results indicate that the system effectively addresses existing limitations in fish feeding management and is capable of supporting multiple users and fish feeder devices simultaneously. Further development is recommended to enhance infrastructure, security, and scalability for real-world deployment.
Co-Authors A.A. Ketut Agung Cahyawan W Aditama, I Putu Dede Raditya Adyatma, Putu Nanda Arya Agus Kerta Nugraha, I Wayan Anak Agung Ketut Agung Cahyawan Wiranatha Anak Agung Ketut Agung Cahyawan Wiranatha Apriana, Krisna Astuti, Ni Nyoman Indri Wika Ayu Krisnasari Ni Komang Ayu Wirdiani Ayu Wirdiani Bakkara, Kevin Christopher Bhagaskara, I Made Bagita Bouchra, Ferhati Cahyawan Wiranatha, Anak Agung Ketut Agung Candra, I Putu Wijaya Adi Danito, Philip Datar, Fandy Kusumaraditya Dewa Gede Kesuma Yoga Dextiro, Kadek Deksy Dharmawan, I Putu Yogi Prasetya Diatmika, Nyoman Gede Rayka Sedana Dwi Putra Githa Dwi Rusjayanthi, Dwi Efraim William Solang Eva Martina Sitorus G M Arya Sasmita Gede Widya Dharma Geovaldo, I Putu Hendra Gusti Agung Ayu Putri Gusti Agung Mayun Kukuh Jaluwana I Gusti Ngurah Bagus Picessa Kresna Mandala I Ketut Adi Purnawan I ketut Gede Darma Putra I Made Adhiarta Wikantyasa I Made Sukarsa I Made Sunia Raharja I Made Sunia Raharja, I Made Sunia I Nyoman Piarsa I Putu Agung Bayupati I Putu Agus Eka Pratama I Putu Arya Dharmaadi I Putu Wira Cahaya Pratama Yudha Ida Bagus Gde Dwipermana Sidhi Ida Bagus Kade Taruna Ida Bagus Nyoman Yoga Ligia Prapta Johan Tamin Kadek Suar Wibawa Ketut Mediana Ayu Candrayani Komang Arta Wibawa Krisnadinatha, I Gede Arya Kristina Kristina Luh Kade Devi Dwiyani Made Andika Verdiana Mahadiputra, Putu Gede Krisna Mahaputra, Putu Andre Mahayana, I Putu Gede Panji Badra Nalista, Ni Made Naila Narayana, I Putu Kevin Ari Ngeo Goa, Mario Valentino Ngurah Indra Purnayasa Ni Luh Ketut Inggitarahayu Anggasemara Ni Made Ika Marini Mandenni Ni Putu Ayu Widiari Ni Putu Viona Viandari Novenrodumetasa, Nathania nugraha, gemara adiyasa parahita Nugraha, Made Adhi Satrya Pande Nengah Purnawan Permana, Kadek Arya Putra Prabhaswara, Ilham Yoga Pratama , I Putu Agus Eka Pratama, I Putu Yoga pramesia Purwanthi, Luh Putu Ary Putu Adhika Dharmesta Putu Ratih Wulandari Putu Wira Buana Putu Yudha Yarcana Rahaditya Kusuma, Nyoman Tri Reyhan Todo Noer Yamin Ridho Hisbi Sulaiman Rusjayanthi, Ni Kadek Dwi Sadhaka, Anak Agung Istri Prabhaisvari Salsabila, Archels Ramadhany Saputra, Putu Alta Sari, Ni Kadek Ratna Sasmita, Gusti Made Arya Satriya, Rizki Dwi Savitri, Putu Rheya Ananda Setiawati, Putu Ayulia Shevira, Sheila Solang, Efraim William Susila, A.A Ngurah Hary Trisna , I Nyoman Prayana Trisna, I Nyoman Prayana Vidya Chandradev Wayan Oger Vihikan Wayan Oger Vihikan, Wayan Oger Whurapsari, Gusti Ayu Wahyu Wiartha, I Gusti Made Diva Widia Widhiasih, Ni Putu Nirmala Dewi Wiranatha, A.A. Ketut Agung Cahyawan Wiranatha, Anak Agung Ketut Agung Cahyawan Wiranatha, Anak Agung Ketut Cahyawan Wiratama, Bayu Adhya Yanisa Putri, Komang Sri Zebedeus Cheyso