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All Journal Jurnal Masyarakat Informatika Semantik Techno.Com: Jurnal Teknologi Informasi Jurnal Simetris Jurnal Transformatika Scientific Journal of Informatics JAIS (Journal of Applied Intelligent System) ANDHARUPA Journal of Information System ITEj (Information Technology Engineering Journals) Edu Komputika Journal Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control JOURNAL OF APPLIED INFORMATICS AND COMPUTING JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) Budimas : Jurnal Pengabdian Masyarakat Abdimasku : Jurnal Pengabdian Masyarakat Jurnal: International Journal of Engineering and Computer Science Applications (IJECSA) Bumi: Jurnal Hasil Kegiatan Sosialisasi Pengabdian Kepada Masyarakat
Erika Devi Udayanti
Universitas Dian Nuswantoro, Semarang
Religion Arts Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Mathematics Public Health Social Sciences Other

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Pemanfaatan QuiverVision sebagai Media Pembelajaran Mewarnai dan Pengenalan Suara bagi Anak Usia Dini Udayanti, Erika Devi; Adnan, Fajrian Nur; Karima, Aisyatul
ABDIMASKU : JURNAL PENGABDIAN MASYARAKAT Vol 2, No 2 (2019): Juli 2019
Publisher : LPPM UNIVERSITAS DIAN NUSWANTORO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33633/ja.v2i2.42

Abstract

Generasi saat ini merupakan generasi dengan tingkat peradaban yang sangat tinggi dimana anak-anak sejak lahir sudah dikenalkan dengan teknologi. Perangkat mobile khususnya smartphone menjadi perangkat yang paling banyak digunakan oleh anak- anak karena sifatnya yang mobile. Pembelajaran pada jenjang anak usia dini saat ini sangatlah berbeda dengan pendidikan anak- anak terdahulu. Guru yang notabene pendamping aktivitas belajar anak-anak disekolah harus mampu mengikuti dan menyesuaikan perubahan generasi anak didiknya. solusi yang ditawarkan dalam kegiatan ini mengadopsi aplikasi Quiver for Coloring untuk memberikan pembekalan dan sosialisasi kepada guru paud dan tk tentang peran teknologi sebagai media alternatif bermain dan belajar yang ramah untuk anak usia dini.
IMPLEMENTASI FIREBASE CLOUD MESSAGING PADA EMERGENCY CALL BERBASIS ANDROID Kartikadarma, Etika; Yutriatmansyah, Widi Widayat; Udayanti, Erika Devi; Hafidhoh, Nisa’ul
Simetris: Jurnal Teknik Mesin, Elektro dan Ilmu Komputer Vol 10, No 1 (2019): JURNAL SIMETRIS VOLUME 10 NO 1 TAHUN 2019
Publisher : Fakultas Teknik Universitas Muria Kudus

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24176/simet.v10i1.2778

Abstract

Tindak asusila atau kriminalitas merupakan satu dari banyak faktor serius yang dapat mengancam keselematan atau bahkan nyawa seseorang. Tindak kejahatan atau kriminalitas yang dilakukan baik oleh individu maupun kelompok (komplotan) dapat terjadi dimana saja dan kapan saja. Kebutuhan akan rasa aman menjadi suatu kebutuhan yang sangat penting. Kesadaran masyarakat akan kewaspadaan terhadap tindak kejahatan yang bisa terjadi pun meningkat. Perkembangan perangkat teknologi informasi dan komunikasi meningkat dengan pesat. Penelitian ini mengusulkan solusi berbasis teknologi yang dapat diimplementasikan untuk mengatasi situasi darurat korban kejahatan dengan aplikasi Emergency Call berbasis Android. Penerapan Firebasse Cloud Messaging digunakan untuk menjalankan Push Notification pada Android. Dengan pengembangan aplikasi Emergency Call ini diharapkan dapat mengurangi resiko akibat kejahatan yang lebih serius.
COVID-19 Suspects Monitoring System Based on Symptom recognition using Deep Neural Network Udayanti, Erika Devi; Kartikadharma, Etika; Firdausillah, Fahri; Ikhsan, Nur
International Journal of Engineering and Computer Science Applications (IJECSA) Vol. 2 No. 1 (2023): March 2023
Publisher : Universitas Bumigora Mataram-Lombok

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/ijecsa.v2i1.2073

Abstract

The outbreak of the Corona virus or COVID-19 was still a global concern even though it has been declared an endemic in several countries in the world, including Indonesia. However, with the emergence of new variants of this virus, preventive efforts continue to be made to prevent its spread. To prevent the spread of this virus, early detection was important, especially in knowing prospective clients who are positive and reactive to this virus, thus enabling early isolation measures for prospective patients who are taking action. This identification can be carried out in public areas that are the center of community activities. In this study, an intelligent system will be developed that can detect people suspected of COVID-19 through fever and breathing problem symptoms that can provide solutions to prevent the spread of this virus. Identify these symptoms through thermography-based image processing sourced from thermal camera sensors and then look for the possibility of suspected and reactive COVID19. Furthermore, the AI model was used by the early detection system of people suspected of being positive and reactive for COVID-19 using the Deep Neural Network method. This study aims to identify symptoms of fever and respiratory infection through image processing sourced from thermal camera sensors and further diagnose prospective patients who are suspected of being positive and reactive for COVID19 using the CNN method as an intelligent system for early detection of suspected positive and reactive COVID19 patientsIn the process of testing the classification training model, the performance results in the CNN classification process have an accuracy value of more than 88%. Furthermore, a comparison was made between the CNN classification and other classifications, such as SVM, Naive Bayes and Multi-Layer Perceptron (MLP). The results obtained from this comparison have an average percentage of accuracy above 80%. MLP has the lowest accuracy among its classification methods of 83.56%. CNN has the highest accuracy value compared to other methods of 88.68%. Therefore, CNN can be chosen to be the right one for use in the COVID-19 suspect detection system through the recognition of symptoms and respiratory disorders. Based on these performance measurements, the process of detecting COVID19 suspects indicated by health symptoms can be applied to real data.
Expert System for Detection of Diseases in Layers Using Forward Chaining and Certainty Factor Methods Kevin Febrianto; Erika Devi Udayanti; Bonifacius Vicky Indriyono; Wildan Mahmud; Iqlima Zahari
Jurnal Masyarakat Informatika Vol 14, No 2 (2023): JURNAL MASYARAKAT INFORMATIKA
Publisher : Department of Informatics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/jmasif.14.2.52266

Abstract

Inaccuracies in the process of diagnosing a type of disease result in errors in handling so that it will pose a risk of death. Accurate diagnostic process results require a high level of confidence so that the results are truly convincing. Current technological developments are making more and more mindsets for the development of information technology in the field of computerization born. One of them is an expert system. This expert system is often used to analyze disease in laying hens. The deficiency in previous research is that there is no degree of confidence so what happens is that the diagnosis often only uses the value of the expert. The role of the system user is only to select the available symptoms without giving the weighted value of the selected symptoms. This study aims to build an expert system capable of detecting symptoms in laying hens by assigning a degree of confidence to each symptom. The system is built with a combination of forward chaining techniques with a certainty factor, the weight value is based on a combination of the weight of symptoms from users and experts to anticipate conditions that are not ideal. Several stages in the research include data collection, knowledge base modeling, implementation into applications and testing. The conclusion that can be drawn from the trial results is that the system can show a maximum validity value of up to 100% when compared to manual calculations.
Analisis Perbandingan Algoritma Naive Bayes Classifier dan Support Vector Machine untuk Klasifikasi Berita Hoax pada Berita Online Indonesia Ramadhan Rakhmat Sani; Yunita Ayu Pratiwi; Sri Winarno; Erika Devi Udayanti; Farrikh Alzami
Jurnal Masyarakat Informatika Vol 13, No 2 (2022): JURNAL MASYARAKAT INFORMATIKA
Publisher : Department of Informatics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/jmasif.13.2.47983

Abstract

Masyarakat mampu mengkonsumsi tiap informasi yang tersebar di internet dengan cepat dan terkadang informasi yang beredar tidak selalu memberikan kebenaran yang sesuai dengan kenyataannya (hoax). Demi mendapatkan keuntungan dan mencapai tujuan pribadi, hoax seringkali sengaja dibuat dan dibagikan. Informasi yang didapatkan dari hoax tentunya dapat mempengaruhi masyarakat karena menimbulkan keraguan dan kebingungan terhadap informasi yang diterima Oleh karena itu, penelitian ini membahas tentang bagaimana mengklasifikasikan berita hoax berbahasa Indonesia mengenai isu kesehatan menggunakan TF-IDF serta algoritma Naïve Bayes Classifier dan Support Vector Machine dengan 4 model yang berbeda sehingga mampu memprediksi sebuah berita hoax atau valid. Pada penelitian ini dataset yang dikumpulkan sebanyak 287 diantaranya 200 valid dan 87 hoax. Hasil evaluasi model penelitian ini dengan menggunakan 4 model berbeda pada masing-masing algoritma, diperoleh nilai classification report terbesar untuk algoritma NBC pada model Complement Naïve Bayes dengan hasil precision 95.4%, recall 95.4%, f1-score 95.4% dan accuracy 93.1%. Sedangkan nilai classification report terbesar untuk algoritma SVM pada kernel Sigmoid dengan hasil precision 95.6%, recall 100%, f1-score 97.7% dan accuracy 96.5%. Sehingga dapat disimpulkan bahwa hasil performa rata-rata dari algoritma SVM memiliki kinerja yang lebih baik jika dibandingkan dengan algoritma NBC dalam melakukan klasifikasi berita hoax mengenai isu kesehatan.
Optimizing Bankruptcy Prediction on Imbalanced Data using XGBoost with Random Oversampling and Chi-Square Suyatno, Revalina; Udayanti, Erika Devi; Dewi, Ika Novita
Journal of Applied Informatics and Computing Vol. 10 No. 1 (2026): February 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v10i1.11841

Abstract

In the midst of modern financial dynamics, the ability to predict corporate bankruptcy holds strategic significance, as it directly affects economic stability and investor confidence. However, the development of a reliable predictive model is often hindered by the complex nature of financial data, particularly the class imbalance between bankrupt and non-bankrupt companies. This imbalance causes models to become biased toward the majority class, thereby reducing their sensitivity in detecting bankruptcy cases which are, in fact, the most critical for financial decision-making. This research aims to construct a more balanced and sensitive bankruptcy prediction model by specifically addressing the issue of data imbalance. The proposed approach integrates the Random Oversampling (ROS) technique to equalize class distribution, Chi-Square feature selection to identify the most informative financial variables, and the Extreme Gradient Boosting (XGBoost) algorithm as the core predictive model. The dataset used is the UCI Taiwanese Bankruptcy Prediction dataset, consisting of 6,819 observations and 96 financial ratio variables. Experimental results show that the Chi-Square method successfully identified 20 influential variables, including Per Share Net Profit Before, Debt Ratio, and ROA(B) Before Interest and Depreciation After Tax. The proposed XGBoost model achieved an overall accuracy of 0.9648 and an F1-score of 0.4286, demonstrating superior performance. These findings confirm that the combination of ROS, Chi-Square, and XGBoost effectively enhances data balance and prediction sensitivity for the bankruptcy class. This research is expected to serve as a foundation for developing financial decision-support systems capable of providing early warnings of potential corporate bankruptcy.
English English Karimah, Sofia Rizkal; Udayanti, Erika Devi
Journal of Applied Informatics and Computing Vol. 10 No. 1 (2026): February 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v10i1.11921

Abstract

This research aims to compare the performance of the Apriori and FP-Growth algorithms in the process of data mining association patterns in the online sales transaction data of a bookstore. The dataset used consists of 74.090 transactions resulting from data cleaning from the period January-June 2025. The analysis was conducted through the stages of data collection, followed by data preparation consisting of data cleaning and data transformation, and then continued to the modeling stage of the two algorithms. The results of the experiment show that Apriori tends to be faster on small-scale datasets with simple transaction patterns, while FP-Growth has more stable memory usage and shows more efficient processing time on parameters that analyze larger data. Both algorithms produce identical numbers and contents of association rules for each parameter variation, indicating that the significant difference lies in performance efficiency, and not in the knowledge patterns produced. Rules with the highest lift values, such as the association between the books "Rumah Kaca" and "Jejak Langkah" (lift: 183,306 & confidence 0,903) and between the books "Namaku Alam" and "Pulang" (lift: 34,062 & confidence: 0,51) indicate strong purchasing patterns between titles with the same author and theme. These findings have the potential to support cross-selling strategies and product recommendations in online sales systems. This research is still limited to a relatively small and homogeneous dataset, so further using a broader data coverage is recommended to test the algorithm's performance more comprehensively.
Multivariate LSTM-Based Intraday Gold Price Prediction with Rolling Time Series Validation Arif, Mohammad; Alzami, Farrikh; Fahmi, Amiq; Udayanti, Erika Devi; Naufal, Muhammad; Winarno, Sri; Malim, Nurul Hashimah Ahmad Hassain; Yosep Teguh Sulistyono, Marcelinus
Jurnal Masyarakat Informatika Vol 17, No 1 (2026): May 2026 (Ongoing)
Publisher : Department of Informatics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/jmasif.17.1.78091

Abstract

Projecting XAUUSD (gold vs. US dollar) prices on a one-hour interval is particularly challenging due to the market's dynamic and nuanced character. To address short-term financial forecasting, an advanced deep learning methodology utilizing Long Short-Term Memory (LSTM) models was employed. Historical XAUUSD data for 2024 was resampled to hourly intervals and supplemented with SMA, RSI, MACD, and Bollinger Bands to understand the market structure better. An LSTM model was developed using open, high, low, and close prices as inputs, with the close price designated as the output target. Data normalization was performed via MinMaxScaler. The model was validated using Time Series Cross-Validation (TSCV) with a rolling origin expanding window over five splits—a sophisticated method for evaluating performance. The results demonstrated the LSTM model's capability, showcasing a mean RMSE of 9.9574, a mean MAE of 7.4411, an R² score of 0.9535, and a remarkably low MAPE of 0.3009%. These findings indicate the advanced model effectively predicts intraday prices, even while grappling with complex and nonlinear patterns, offering a powerful instrument for trading professionals and researchers to cut through market noise.
Co-Authors Affandy Affandy Afida, Dita Ahmad MAULANA Aisyatul Karima ALI MUQODDAS Ali Muqoddas Aloysius Soerjowardhana Alzami, Farrikh Andriana, Wiwin Anggadiva, Rifky Anwarri, Kenza Amalia Putri Arika Norma Wahyu Dorroty Aritonang, Ivana Junita Bonifacius Vicky Indriyono Bonifacius Vicky Indriyono Bonifacius Vicky Indriyono, Bonifacius Vicky Candra Irawan Chornelius Aneba Moza Ikratama Christiawan Yosua Hertinando Christy Atika Sari Comara, Maulana Muhammadin Dian Restu Adji Dibyo Adi Wibowo Djuniadi Djuniadi Doheir, Mohamed Dwi Puji Prabowo, Dwi Puji Erba Lutfina Erwin Yudi Hidayat Yudi Hidayat Ery Mintorini Etika Kartikadarma Etika Kartikadharma Fahmi Amiq Fahri Firdausillah Fajar Agung Nugroho Fajar Agung Nugroho Fajar Agung Nugroho Fajrian Nur Adnan Farah Syadza Mufidah Florentina Esti Nilawati Gery Gadman Rachmad Hafidhoh, Nisa'ul Hafidhoh, Nisa?ul Hafidhoh, Nisa’ul Hafidhoh, Nisa’ul Hafidhoh, Nisa’ul Ika Novita Dewi Ikhsan, Nur Iqlima Zahari Karimah, Sofia Rizkal Karmila Karmila Kartikadharma, Etika Kevin Febrianto Lutfina, Erba Malim, Nurul Hashimah Ahmad Hassain Megantara, Rama Aria Mellati, Pita Mohammad Arif Muhammad Agus Muljanto Muhammad Hafidz Muhammad Naufal, Muhammad Muna, Mohamad Sirojul Natalinda Pamungkas Natalinda Pamungkas Nisa'ul Hafidhoh Nur Ikhsan Nur Iksan Putra, Yogi Pratama Raden Arief Nugroho Ramadhan Rakhmat Sani Sanina Quamila Putri Sanjaya, Yusuf Yudha Soerjowardhana, Aloysius Sri Mulatsih Sri Winarno Sri Winarno Suyatno, Revalina Syafira Putri Yuanita Valentina Widya Suryaningtyas, Valentina Widya Widayat Yutriatmansyah, Widi Widi Widayat Yutriatmansyah Wildan Mahmud Wisnumurti, Reza Yosep Teguh Sulistyono, Marcelinus Yuni Lestari Yunita Ayu Pratiwi Yutriatmansyah, Widi Widayat Yutriatmansyah, Widi Widayat  Ignasius Yoga Puji Hascaryo