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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Seminar Nasional Aplikasi Teknologi Informasi (SNATI) JOIV : International Journal on Informatics Visualization Jurnal Mantik Penusa JURNAL TEKNIK INFORMATIKA DAN SISTEM INFORMASI Jurnal Sisfokom (Sistem Informasi dan Komputer) INTECOMS: Journal of Information Technology and Computer Science Indonesian Journal of Business Intelligence (IJUBI) INFORMASI (Jurnal Informatika dan Sistem Informasi) JOURNAL OF INFORMATION SYSTEM MANAGEMENT (JOISM) Indonesian Journal of Electrical Engineering and Computer Science Journal of Computer Networks, Architecture and High Performance Computing Jurnal Teknik Informatika (JUTIF) Journal of Applied Data Sciences Information System Journal (INFOS) Journal of Community Development Journal Automation Computer Information System (JACIS) Abdiformatika: Jurnal Pengabdian Masyarakat Informatika Jurnal Dinamika Informatika (JDI) JAMAIKA: Jurnal Abdi Masyarakat The Indonesian Journal of Computer Science Research Jurnal Pengabdian Literasi Digital Indonesia JURNAL MULTIDISIPLIN BHATARA
Sri Ngudi Wahyuni, Sri Ngudi
Universitas Amikom Yogyakarta
Religion Agriculture, Biological Sciences & Forestry Arts Humanities Astronomy Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Nursing Public Health Social Sciences

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Comparative analysis of time series prediction model for forecasting COVID-19 trend Sri Ngudi Wahyuni; Eko Sediono; Irwan Sembiring; Nazmun Nahar Khanom
Indonesian Journal of Electrical Engineering and Computer Science Vol 28, No 1: October 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v28.i1.pp600-610

Abstract

The outbreak of the COVID-19 pandemic occurred some time ago, making the world a pandemic. Based on this condition is important to predict early to prevent the COVID-19 disease if someday pandemic occurs. The aim of the study is to compare the analysis result of cumulative cases of COVID-19 using multiple linear regression (MLR), ridge regression (RR), and long short term memory (LSTM) models for cases study Java and Bali islands. We chose both islands as a case study because they have very dense populations. These three models are the most widely used time series-based prediction models and have relatively high accuracy values.  The predictive variables used are the number of cumulative cases, the daily cases, and population density. The research data was taken from Kaggle and processed using google collabs. Data was taken from January 20, 2020, to August 8, 2020, and data training was carried out for 12 days. The results show the accuracy of LSTM is better than other models. it can be seen in the accuracy value (99.8 %) of the model test result. The testing model uses R2, mean square error (MSE), and root mean square error (RMSE).
Pelatihan Pembuatan Konten Media Sosial Untuk Karang Taruna Sebagai Upaya Peningkatan Pengunjung Desa Agrowisata di Desa Bolu Seyegan Sri Ngudi Wahyuni; Rum Muhammad Andri; Rahma Widyawati; Istiningsih Istiningsih; Anik Sri Widowati; Rosyidah Jayanti Vijaya
Jurnal Pengabdian Literasi Digital Indonesia Vol. 1 No. 2 (2022): December
Publisher : Puslitbang Akademi Relawan TIK Indonesia (ARTIKA)

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (841.456 KB) | DOI: 10.57119/abdimas.v1i2.13

Abstract

Bolu Village is a village located in Seyegan District, Sleman Regency, Special Region of Yogyakarta Province. This village is developing an Agrotourism area as a financial improvement orientation. The use of technology in the form of social media branding is needed as an effort to promote the region. To introduce a new activity, service, or product, branding is an important step that needs to be done so that the wider community is interested in the object. This community service activity was carried out for 2 days involving 10 Karangtaruna members. By utilizing technology and owning a cell phone, this content creator training is able to provide insight for youth in an effort to increase visitors and increase finances. The evaluation results show that 20% of device ownership is still insufficient, and the rest is adequate. On average, all participants understand the basics of material in creating social media content and are able to implement it in the field. Approximately 90% of participants understand the material presented. This training is considered successful.
SISTEM PENUNJANG KEPUTUSAN PEMILIHAN SMARTPHONE BERBASIS WEBSITE DENGAN METODE SIMPLE ADDITIVE WEIGTHING Andika Fakhrizal; Sri Ngudi Wahyuni; Rosyidah Jayanti Vijaya
The Indonesian Journal of Computer Science Research Vol. 2 No. 2 (2023): July
Publisher : Hemispheres Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59095/ijcsr.v2i2.75

Abstract

Along with the development of smartphones in Indonesia, people in various circles are very dependent on the use of smartphones. Especially with the current pandemic making people depend on the use of smartphones. With the various brands, types, and specifications of smartphones, people are confused about which smartphones to choose according to their respective needs. From these problems, a website-based decision support system is needed to help and facilitate people in choosing the right smartphone model according to the criteria. The criteria that will be included in this system are price, brand, RAM, storage, camera, battery screen, and features. The purpose of this thesis is to build a system that can help people find smartphones that match the required criteria. This system uses the Simple Additive Weighting (SAW) method which is used to normalize the weights of the inputted criteria and to determine the highest smartphone value as a recommendation option. The results of this study are the creation of a smartphone selection decision support system with the website-based Simple Additive Weighting method that can provide recommendations for smartphone types according to their respective needs.
PEMBUATAN APLIKASI LAPORAN KINERJA ONLINE (LAKON) BERBASIS ANDROID MENGGUNAKAN METODE WATERFALL Majid, Ammar Waly; Istiqomah, Dewi Anisa; Wiratama , Bill Bilal; Guji S. U. , Fitra Jibjaya; Wahyuni, Sri Ngudi; Windarni , Vikky Aprelia
Information System Journal Vol. 7 No. 01 (2024): Information System Journal (INFOS)
Publisher : Universitas Amikom Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24076/infosjournal.2024v7i01.1528

Abstract

Kementerian Agama Kabupaten Klaten dituntut untuk memperbaiki sistem kerja khususnya dalam hal pelaporan kinerja. Sistem pengisian Laporan Capaian Kinerja Harian (LCKH) dan Sasaran Kerja Pegawai (SKP) saat ini masih dilakukan secara manual menggunakan Microsoft Excel. Untuk memperbaiki proses ini, perlu membangun aplikasi Laporan Kinerja Online (LAKON) berbasis Android. Penggunaan aplikasi LAKON dapat menambah fleksibilitas kinerja pegawai. Pegawai dapat mengisi laporan kinerja dari mana saja dan kapan saja. Pembuatan aplikasi LAKON menggunakan metode Waterfall. Alasan pemilihan metode Waterfall dalam pembuatan aplikasi LAKON yaitu tahapan dalam metode Waterfall dilakukan secara bertahap, sehingga kualitas sistem yang dihasilkan akan baik. Kontribusi dari  penelitian ini yaitu menguatkan hasil penelitian sebelumnya. Dari hasil penelitian dapat disimpulkan bahwa penulis berhasil membangun aplikasi LAKON berbasis Android dengan melalui tahapan identifikasi masalah, analisis kebutuhan, perancangan, implementasi, dan pengujian. Aplikasi LAKON dapat melakukan pengisian dan pencarian laporan kinerja secara online serta terintegrasi dengan website monitoring kinerja pegawai.
Optimizing the long short-term memory algorithm to improve the accuracy of infectious diseases prediction Sediyono, Eko; Wahyuni, Sri Ngudi; Sembiring, Irwan
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 3: September 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i3.pp2893-2903

Abstract

This study discusses the implementation of the proposed optimizedlong short-term memory (LSTM) to predict the number of infectious disease cases that spread in Central Java, Indonesia. The proposed model is developed by optimizing the output layer, which affects the output value of the cell state. This study used cases of four infectious diseases in Indonesia's Central Java Province, namely COVID-19, dengue, diarrhea, and hepatitis A. This model was compared to basic LSTM and MinMax schaler LSTM improvement to see the difference in the accuracy of each disease. The results showed a significant difference in the average prediction results with real cases between the three models. The main objectives of this study were: modifying the LSTM algorithm to predict the number of infectious disease cases to get a smaller residual value, comparing the results of the optimization accuracy of the LSTM algorithm with the LSTM algorithm in previous studies, and evaluating the use of spatial variables in applying infectious disease prediction models using the LSTM algorithm. The results found that the performance difference between the proposed optimization algorithm and the model in the previous study was obtained. The proposed LSTM optimization algorithm had an accuracy improvement of about 2% over the previous model.
Implementasi Multipe Linear Regression untuk Prediksi Data Runtun Waktu Pada Penyakit Menular Menggunakan Pendekatan Machine Learning Wahyuni, Sri Ngudi
JATISI Vol 11 No 2 (2024): JATISI (Jurnal Teknik Informatika dan Sistem Informasi)
Publisher : Universitas Multi Data Palembang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35957/jatisi.v11i2.7878

Abstract

Prediction modeling is one way to get prediction results that are close to their true values. Prediction and machine learning have a relationship in the process-relational approach, where it is used to improve processes, data quality, and model quality. This study aims to implement a Multiple Linear Regression (MLR) model to predict time series data, especially COVID-19 infectious diseases in Indonesia using a Machine Learning approach. This research data was taken from March 2, 2020, to November 8, 2020, and updated by the National Disaster Management Agency (BNPB). The predictive analysis uses parameters of the number of new cases, the number of recovered patients, and the number of deaths. The prediction is carried out over the next 4 days to see the short-term trend of adding new data on COVID-19 patients in Indonesia. The test results show that the R2 value in the MLR model is close to 100%, which is 4.161E+12. So that the Mean Square Error (MAE) value of the MLR model is 1,386E+12 so the MLR accuracy value is 4.1% and the accuracy value is 95.9%.
PEMBUATAN GAME BERBASIS PEMBELAJARAN MENGGUNAKAN RPG MAKER MV Wahyuni, Sri Ngudi; Andiyoko, Cia
Journal of Computer Networks, Architecture and High Performance Computing Vol. 1 No. 1 (2019): Computer Networks, Architecture and High Performance Computing
Publisher : Information Technology and Science (ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/cnapc.v1i1.5

Abstract

The RPG game is one of the games are in great demand by the public because they are able to sharpen their right brain. One method used in game development is GDLC which consists of 6 phases, where each phase is arranged systematically. The aimed of this study is how to develop RPG maker game based learning for children cognitive. The analysis method is used SWOT analysis and testing method used ? and ? testing methods on 36 respondents who were taken by random and the response rate was 99.8%. The testing instrument used a questionnaire with testing parameters (1) Design consisting of music, sound, and color (2) Moral Message (3) Storyline. The test of results were 42.5% of respondents said that the development design was considered good, 44.3% of respondents said the game had a good storyline, and 56.3% of respondents stated that the Rise of the Zokai Clan game using RPG Maker MV had a good moral message.
Pengembangan Sistem Informasi Sppd Kabupaten Dogiyai Berbasis Website Menggunakan Framework Django Arbiansyah, Reynaldi; Triwidodo, Ahmad; Grafvera, Ervira Diva; Wahyuni, Sri Ngudi
Journal Automation Computer Information System Vol. 4 No. 1 (2024): Mei
Publisher : Indonesian Journal Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47134/jacis.v4i1.73

Abstract

Berdasarkan hasil penelitian yang dilakukan dalam membangun Website SPPD Kabupaten Dogiyai maka dapat diambil kesimpulannya adalah: Sistem Informasi SPPD menggunakan bahasa pemrograman python dan menggunakan Framework Django sudah dapat diimplementasikan oleh seluruh ASN di Kabupaen Dogiyai. Aplikasi ini dapat menghitung anggaran yang ingin digunakan bagi pagawai yang ingin melakukan perjalanan dinas secara otomatis,  sistem ini juga dapat digunkan sebelum mencetak surat-surat yang diperlukan dalam perjalanan dinas, dan aplikasi ini dapat mempercepat dalam proses pembuatan dokumen-dokumen perjalanan dinas yang diperlukan, serta penyediaan rekap laporan dari setiap perjalanan dinas. Pengujian sistem menggunakan white dan blakbox dan hasilnya dapat diimplementasikan dengan baik di Kabupaten Dogiyai
Perbandingan Algoritma SVM dan RF pada Analisis Sentimen menggunakan Pendekatan Machine Learning Ariaji, Tristanto; Wahyuni, Sri Ngudi; Ikhsan, Muhammad
Journal Automation Computer Information System Vol. 5 No. 1 (2025): Mei
Publisher : Indonesian Journal Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47134/jacis.v5i1.107

Abstract

Analisis sentimen tentang kelangkaan Bahan Bakar Minyak (BBM) di Indonesia merupakan salah satu cara untuk mengetahui opini masyarakat tentang kelangkaan BBM. Analisis sentimen digunakan sebagai dasar pengambilan keputusan oleh pihak berwenang sebagai upaya penyelesaian masalah, sehingga prediksi sentimen perlu dilakukan. Tujuan penelitian ini adalah melakukan perbandingan akurasi algoritma Support Vector Machine (SVM), dan Random Forest (RF) untuk analisis sentimen. Kontribusi pada penelitian ini adalah penentuan algoritma yang efektif dalam analisis sentimen Bahan Bakar Minyak di Indonesia. Adapun Tools olah data menggunakan Google Colab, dengan bahasa pemrograman Python dan  pendekatan Machine Learning. Data eksperimen menggunakan data Twitter, diambil pada tanggal 1 -30 Juli 2022 dan terkumpul 6602 data dalam bahasa inggris. Hasil eksperimen menunjukkan bahwa hasil uji SVM untuk nilai Pressision, F1-Score dan support sebesar 0.98 lalu 0,97, kemudian 0.98 dan 67, sehingga nilai akurasi secara keseluruhan SVM adalah 0.98. Sedangkan RF memiliki hasil uji nilai Pressision, Recall. F1-Score dan support sebesar 0,86 kemudian 0,99 lalu 0,92 dan 67. Sedangkan nilai akurasi secara keseluruhan RF adalah 0.90. sehingga secara keseluruhan model SVM lebih direkomendasikan untuk pemodelan prediksi khususnya analisis sentimen pada kasus kelangkaan BBM melalui data Twitter.
AMIKOM-RECSYS: Enhancing Movie Recommender System using Large Language Model (ChatGpt), Deep Learning and Probabilistic Matrix Factorization Hanafi, Hanafi; Widowati, Anik Sri; Wahyuni, Sri Ngudi
Journal of Applied Data Sciences Vol 6, No 4: December 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i4.897

Abstract

E-commerce has become one of the most widely used digital applications globally, enabling personalized product discovery and purchasing. To support these services, recommender systems are essential, offering item suggestions based on user preferences. Most recommender systems rely on machine learning algorithms to estimate user-item relevance scores, often utilizing product ratings. However, a persistent challenge in this domain is the issue of data sparsity, where only a small fraction of users provides explicit ratings, leading to reduced accuracy in recommendation results. In this study, we introduce a novel hybrid recommendation algorithm, named AMIKOM-RECSYS, designed to address the sparsity problem and enhance rating prediction. Our model integrates three main components included a Large Language Model (LLM) using ChatGPT, a Transformer-based encoder (BERT), and Probabilistic Matrix Factorization (PMF). The LLM generates descriptive information about movies based on specific prompts, which is then passed to BERT to encode the content into meaningful 2D vector representations. These enriched embeddings are subsequently utilized by the PMF algorithm to predict missing user-item ratings. We evaluate the proposed model on two benchmark datasets, ML-1M and ML-10M using Root Mean Squared Error (RMSE) as the evaluation metric. The AMIKOM-RECSYS model achieved RMSE values of 0.8681 on ML-1M and 0.7791 on ML-10M under a 50:50 data split, outperforming several baseline models including CNN-PMF, LSTM-PMF, and Attention-PMF. These results highlight the effectiveness of integrating LLM and Transformer-based contextual understanding into matrix factorization frameworks. In future work, we plan to extend this framework by incorporating other matrix factorization techniques such as Singular Value Decomposition (SVD) and integrating additional sources of user information, including social media activity, to further improve recommendation performance.
Co-Authors Achmad Fauzi Achmad Fauzi Achmad Fauzi Ahmad Syaebani Alva Hendi Muhammad Andika Fakhrizal Andiyoko, Cia Andiyoko, Cia Anik Sri Widowati Arbiansyah, Reynaldi Ariaji, Tristanto artherio chanifudin Barka Satya Cia Andiyoko Dewi Anisa Istiqomah Dimas Alan Wijaya Dita Via Tyasmala Dwi Nurani Dwi Rahmawati Rahmawati Eko Sediono Eko Sediyono Eny Nurnilawati Erina Dwi Utami Erna Daniati Fendi Setiabudi Fiyan Kurnia Hidayat Galang Setia Budi Grafvera, Ervira Diva Guji S. U. , Fitra Jibjaya Hanafi Hanafi Hayaty, Mardhiya Hendrik Hendrik Irwan Sembiring Istiningsih Istiningsih Istiningsih Istiningsih Istiningsih Khanom, Nazmun Nahar Kurnia Rasyid, Muhammad Rum Andri Lila Garjita M. Hanafi Maemunah, Mei Majid, Ammar Waly Manik Hapsara, P.hD Mei Maemunah Mei Maemunah Mei Maemunah Mei Maemunah Mei Maemunah Maemunah Mei Maimunah Mohammad Aufa Hanif Mohammad Khalis Purwanto Muhammad Ikhsan Mulyatun, Sri Nazmun Nahar Khanom Nuraini Rahma Maulani Rahma Widyawati Rahma Widyawati Rahma Widyawati Widyawati Rosyidah Jayanti Vijaya Rosyidah Jayanti Vijaya, Rosyidah Jayanti Rosyidah Jayanti Viyata Rum Muhammad Andri Santosa Santosa Solehatin, Solehatin Sri Mulyatun Sri Mulyatun Suyatmi Suyatmi Suyatmi Teguh Efriyanto Triwidodo, Ahmad Utha Pradipta Vivi Wulandari Widowati, Anik Sri Widyawati, Rahma Windarni , Vikky Aprelia Wiratama , Bill Bilal Yohanes De Deo Abhinaya Sigit Kumara Yuli Astuti