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All Journal Jurnal Informatika SMATIKA Jurnal Ilmiah KOMPUTASI Jurnal Pengabdian UntukMu NegeRI Journal of Information Technology and Computer Science Jurnal Obsesi: Jurnal Pendidikan Anak Usia Dini Journal of Information Technology and Computer Science (JOINTECS) Teknika: Engineering and Sains Journal Jurnal Sains dan Informatika Jurnal Ilmiah Soulmath : Jurnal Edukasi Pendidikan Matematika BAREKENG: Jurnal Ilmu Matematika dan Terapan JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) JOISIE (Journal Of Information Systems And Informatics Engineering) JSAI (Journal Scientific and Applied Informatics) Building of Informatics, Technology and Science Jurnal Mantik Jurnal Pengabdian Masyarakat IPTEKS Jurnal ABDINUS : Jurnal Pengabdian Nusantara JATI (Jurnal Mahasiswa Teknik Informatika) Jurnal Tekinkom (Teknik Informasi dan Komputer) Abdimas Galuh: Jurnal Pengabdian Kepada Masyarakat Journal of Computer System and Informatics (JoSYC) Indonesian Journal of Cultural and Community Development Indonesian Journal of Law and Economics Review Jurnal Penelitian Sekolah Tinggi Ilmu Kesehatan Nahdlatul Ulama Tuban Jurnal Teknik Informatika (JUTIF) Community Empowerment JPM: JURNAL PENGABDIAN MASYARAKAT KLIK: Kajian Ilmiah Informatika dan Komputer Proceedings Series on Physical & Formal Sciences Indonesian Journal of Innovation Studies PELS (Procedia of Engineering and Life Science) Procedia of Social Sciences and Humanities Jurnal Algoritma Indonesian Journal of Islamic Studies Publikasi Pengabdian Masyarakat Komputer dan Teknologi (PUNDIMASKOT) JOINCS (Journal of Informatics, Network, and Computer Science) Jurnal Penelitian Innovative Technologica: Methodical Research Journal PEDAMAS (Pengabdian Kepada Masyarakat) SAINTEK Physical Sciences, Life Science and Engineering Indonesian Journal of Applied Technology Journal of Internet and Software Engineering Prosiding Seminar Nasional Unimus Kesatria : Jurnal Penerapan Sistem Informasi (Komputer dan Manajemen) Advances in Cancer Science Journal of Electrical Engineering Manajemen Pelayanan Kesehatan semanTIK IJHCS Smatika Jurnal : STIKI Informatika Jurnal Academia Open
Uce Indahyanti
Informatika, Universitas Muhammadiyah Sidoarjo, Indonesia
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PRIVACY-PRESERVING REAL TIME TRACING SYSTEM FOR COVID-19 PATIENT USING GPS TECHNOLOGY Azizah, Nuril Lutvi; Indahyanti, Uce
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 16 No 1 (2022): BAREKENG: Jurnal Ilmu Matematika dan Terapan
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (711.725 KB) | DOI: 10.30598/barekengvol16iss1pp121-128

Abstract

The new normal condition in Indonesia does not mean that Indonesia is completely free from infection with the Covid 19 virus. Individuals exposed to the Covid 19 virus have symptoms like mild, moderate, to severe condition. Most individuals who have mild symptoms are self isolating at their home until tested negative for the Covid 19 virus. The impact of Covid 19 has led to an increase in the use of gadgets to access all the information needed. The purpose of this study is to provide information regarding patients infected with Covid 19 in a certain area through a tracing application. The application can help public to find out how many individuals are infected with Covid 19 in the surrounding environment by prioritizing privacy-preserving in a real time. The method used in this study is a combination of graph theory and GPS tracing system on a gadget. The initial stage of this study was carried out through tracing Covid 19 patients based on their position of residence. The final stage of the study was carried out using a graph approach based on distance and percentage of transmission. The result of this study obtained privacy-preserving real-time tracing with the predicted precentage of Covid 19 transmission susceptibility within the scope of danger or vulnerability, quite safe, and secure. Furthermore, individuals can take precautions by maintaining a safe distance.
Designing a Web-Based Information System for Educational Decision Support: Merancang Sistem Informasi Berbasis Web untuk Pendukung Pengambilan Keputusan Pendidikan Afidah, Dewi Nur; Astutik, Ika Ratna Indra; Indahyanti, Uce; Azinar, Azmuri Wahyu
Indonesian Journal of Islamic Studies Vol. 13 No. 2 (2025): May
Publisher : Universitas Muhammadiyah Sidoarjo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21070/ijis.v13i2.1813

Abstract

Background: Education in the digital era requires effective and efficient information systems to support data management and decision-making processes. Knowledge Gap: Previous studies focused more on technical development without emphasizing usability and data accuracy aspects in educational contexts. Aims: This study aims to design and develop a web-based information system to facilitate effective educational decision-making. Results: The results show that the proposed system successfully improves data accessibility, simplifies processing, and ensures higher accuracy compared to previous manual methods. Novelty: The novelty lies in the integration of multi-user access and real-time reporting features that enhance system efficiency in the education environment. Implications: The findings contribute to improving information management performance in educational institutions and provide a reference for future system development. Highlights:• Development of a web-based educational decision support system• Integration of real-time access and multi-user features• Increased accuracy and efficiency in educational data management Keywords: Web-based System, Education Technology, Decision Support, Data Management, Usability
ANALISIS SENTIMEN PUBLIK ATAS RESPONS PEMERINTAH PADA SERANGAN RANSOMWARE DENGAN PENDEKATAN MACHINE LEARNING DAN SMOTE Prayugah, Indra; Indahyanti, Uce; Ariyanti, Novia
JOISIE (Journal Of Information Systems And Informatics Engineering) Vol 8 No 2 (2024)
Publisher : Institut Bisnis dan Teknologi Pelita Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35145/joisie.v8i2.4764

Abstract

Serangan ransomware pada pusat data nasional Indonesia menjadi topik yang banyak dibicarakan di masyarakat. YouTube menjadi platform utama untuk menyebarkan informasi dan masyarakat beropini. Penelitian ini bertujuan untuk mengidentifikasi sentimen publik mengenai penanganan pemerintah terhadap serangan ransomware melalui analisis komentar di kanal YouTube CNN Indonesia dan MetroTV. Data dikumpulkan menggunakan teknik web scraping dan dimasukkan ke dalam model klasifikasi dengan tiga label yaitu sentimen positif, netral, dan negatif. Tiga model machine learning yang akan digunakan adalah SVM, Random Forest, dan Naïve Bayes, dengan dua skenario pengujian yaitu menggunakan Synthetic Minority Over-sampling Technique (SMOTE) dan tanpa SMOTE. Penerapan SMOTE meningkatkan akurasi model, terutama pada SVM yang mencapai 96%. Hasil penelitian menunjukkan bahwa mayoritas komentar mengungkapkan sentimen negatif terhadap kinerja pemerintah. Penelitian ini diharapkan memberikan pemahaman mengenai persepsi publik terhadap isu keamanan siber di Indonesia dan efektivitas SMOTE dalam analisis sentimen
Prediction Model of Voter Participation Using Naïve Bayes and Village Development Indicators: Model Prediksi Partisipasi Pemilih Menggunakan Naïve Bayes dan Indikator Pembangunan Desa Abidin, Husnul; Fitrani, Arif Senja; Setiawan, Hamzah; Indahyanti, Uce
Indonesian Journal of Cultural and Community Development Vol. 16 No. 2 (2025): June
Publisher : Universitas Muhammadiyah Sidoarjo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21070/ijccd.v16i2.1243

Abstract

Background: Electoral participation reflects the quality of democracy, particularly in rural communities with diverse socioeconomic structures. Specific Background: In Sidoarjo Regency, disparities in participation levels among villages suggest that local development factors play a crucial role. Knowledge Gap: Previous models only used demographic attributes without integrating the multidimensional Village Development Index (IDM) indicators. Aims: This study aims to construct a predictive model of voter participation using the Naïve Bayes classification algorithm based on IDM data. Results: By applying preprocessing, feature selection, and probabilistic classification to 48 attributes of IDM, the model achieved 78.65% accuracy, 79% precision, 76% recall, and 77% F1-score, revealing that education, health, and accessibility variables are key predictors. Novelty: Unlike prior research, this work combines social, economic, and ecological IDM dimensions with an open-source Python-based approach for transparent model validation. Implications: The findings demonstrate the feasibility of data-driven governance tools for mapping electoral participation and can support strategic planning to improve civic engagement in rural Indonesia.Highlights:• Uses IDM indicators to predict election participation• Naïve Bayes model achieves 78.65% accuracy• Supports data-driven democratic planning
Decision Tree Analysis for Predicting Voter Participation Using IDM Data: Analisis Pohon Keputusan untuk Memprediksi Partisipasi Pemilih Menggunakan Data IDM Yuwanto, Mahmud Adi; Fitrani, Arif Senja; Dijaya, Rohman; Indahyanti, Uce
Indonesian Journal of Cultural and Community Development Vol. 16 No. 2 (2025): June
Publisher : Universitas Muhammadiyah Sidoarjo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21070/ijccd.v16i2.1255

Abstract

General Background: Voter participation serves as a core indicator of democratic quality and civic awareness. Specific Background: In East Java’s Mataraman region, significant disparities in electoral participation highlight socioeconomic influences measurable through the Village Development Index (IDM). Knowledge Gap: No prior research integrates IDM-based indicators with machine learning methods for voter behavior prediction. Aims: This study develops a classification model using C4.5, Naïve Bayes, and SVM algorithms to predict voter participation based on IDM attributes. Results: The Decision Tree C4.5 algorithm achieved the highest accuracy (80.87%) and F1-score (0.88) compared to Naïve Bayes and SVM, identifying education and healthcare access as primary determinants of high participation. Novelty: The integration of IDM and C4.5 classification introduces a novel framework for data-driven political participation analysis. Implications: The model can assist policymakers and electoral bodies in targeting civic engagement initiatives within underrepresented regions. Highlights: C4.5 algorithm effectively predicts voter engagement. Education and health access influence participation. Data-driven policy enhances democratic quality.
Implementasi Aplikasi Perpustakaan Mini Mandiri At-Taqwa Urangagung Sidoarjo Rahmawati, Yunianita; Findawati, Yulian; Indahyanti, Uce; Fitroni, Arif Senja
Jurnal Pengabdian UntukMu NegeRI Vol. 7 No. 1 (2023): Pengabdian Untuk Mu negeRI
Publisher : LPPM UMRI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37859/jpumri.v7i1.4830

Abstract

Perpustakaan Mini Mandiri At-Taqwa didirikan untuk memberikan bahan bacaan pada warga perumahan Bhayangkara pada khususnya dan warga sekitar perumahan pada umumnya. Pendataan buku dan anggota dilakukan secara manual sehingga dibutuhkan suatu aplikasi pendataan buku secara otomatis sehingga dibuatlah aplikasi Perpustakaan Mini Mandiri At-Taqwa. Fitur aplikasi ini diantaranya Input Kategori Buku, Input Data Buku, Input Data Anggota, Input Data Petugas, Cari Data Buku, dan Laporan Buku. Aplikasi ini dapat membantu pencatatan dan pencarian data buku, anggota, dan petugas secara otomatis.
TOPIC MODELING IN COVID-19 VACCINATION REFUSAL CASES USING LATENT DIRICHLET ALLOCATION AND LATENT SEMANTIC ANALYSIS Malihatin S, Ulfah; Findawati, Yulian; Indahyanti, Uce
Jurnal Teknik Informatika (Jutif) Vol. 4 No. 5 (2023): JUTIF Volume 4, Number 5, October 2023
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2023.4.5.951

Abstract

COVID -19 vaccination is a program provided by the Indonesian government to minimize the spread of the virus. The COVID-19 vaccination program in Indonesia goes hand in hand with issues that are circulating, causing controversy and rejection of vaccination on social media, especially Twitter. There are many factors that influence vaccine rejection on Twitter, to summarize frequently discussed topics and find out hidden topics, this study uses the Latent Dirichlet Allocation (LDA) and Latent Semantic Analysis (LSA) methods from 1797 Twitter scrapping data. Both models require a set of words that have been converted into a matrix, so before conducting LDA topic modeling, the dataset will undergo a bag of word (BOW) calculation. Meanwhile, in LSA topic modeling, the existing dataset will undergo word weighting of frequently occurring words using Term Frequency - Inverse Document Frequency (TF-IDF). This study was conducted to find and summarize hidden information in the form of frequently discussed topics, thus understanding public opinions related to the COVID -19 vaccination refusal case. LDA and LSA methods will display topics based on the probability and mathematical calculations of word occurrences in each topic in the document. The topics that appear will be further analyzed through coherence score by applying a limit of 20 topics to display the best value. Further modeling experiments are carried out to display topics through LDA and LSA models, this study takes 6 topics with the highest coherence values including the right of individuals to choose whether to be vaccinated or not (0.484607), the Ribka Tjiptaning controversy (0.473368), rejection of the COVID-19 vaccine by groups represented by public figures (0.463631), punishment for non-compliance in the form of fines (0.324924), and halal certification (0.312521).
Sarcasm Detection in News Headline Dataset with Ensemble Deep Learning Method: Deteksi Sarkasme Pada Dataset News Headline Dengan Metode Ensemble Deep Learning Mochamad Alfan Rosid; Siti Nur Haliza; Yulian Findawati; Uce Indahyanti
JOINCS (Journal of Informatics, Network, and Computer Science) Vol. 6 No. 2 (2023): November
Publisher : Universitas Muhammadiyah Sidoarjo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21070/joincs.v6i2.1628

Abstract

Sarcasm, a prevalent linguistic device, is frequently used in public discourse, often causing offence and distress to the listener. The complexity inherent in detecting sarcasm is a significant and ongoing challenge in the field of sentiment analysis research. The widespread use of this phenomenon in diverse conversational contexts further complicates its identification in data sets full of human interactions. Deficiencies in methodologies for distinguishing such statements adversely affect the performance of sentiment analysis, especially in distinguishing negative, positive or neutral sentiments. Inaccuracies in sarcasm detection can affect the classification results of sentiment analysis. Therefore, sentiment analysis seeks to categorise sarcastic sentences that, despite appearing positive, actually contain negative meanings. This research aims to build a deep learning ensemble stack model. The basic deep learning methods used are Bidirectional Gated Recurrent Unit (BiGRU) and Convolutional Neural Network (CNN). LightGBM is used to perform stack ensemble of deep learning methods. The dataset used comes from the Kaggle website and consists of English headlines. The findings show that the stack ensemble method outperforms BiGRU and CNN, evidenced by an accuracy rate of 91.2% and an F1 score of 90.2%. Therefore, from the above discussion, it can be concluded that the LightGBM method emerges as the optimal solution for sarcasm detection
Analysis of the Predicted Number of HIV/AIDS Spreads in Sidoarjo Regency using Multiple Linear Regression Method Azizah, Risma Nur; Indahyanti, Uce
Advances in Cancer Science Vol. 1 No. 1 (2024): April
Publisher : Indonesian Journal Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47134/acsc.v1i1.3

Abstract

The Office of Communication and Information of East Java Province, which includes Sidoarjo Regency, is one of the data sources used to determine the fourth-highest number of HIV/AIDS cases in Indonesia. The aim of this study is to forecast an annual increase in the number of HIV/AIDS patients in Sidoarjo Regency. The Sidoarjo District Health Office is the private source of data used in this study. The information utilized spans the years 2020–2022, and it contains attributes with projected results of positive HIV/AIDS patients. The estimated number for 2023 is 795,667; for 2024, it is 934,167; and for 2025, it is 1072,667. Because there are numerous attribute data sets obtained, quick miner processes the data using multiple linear regression as the chosen approach. The root mean squared error (RSME) validation test yielded a performance score of 0.816 for the multiple linear regression model, indicating that a smaller prediction result indicates greater validity.
Web-Based Student Violation Management with Real-Time Notifications Transforms School Discipline: Manajemen Pelanggaran Siswa Berbasis Web dengan Notifikasi Waktu Nyata Mengubah Disiplin Sekolah Krisfianto, Moh Ifan; Indahyanti, Uce
Indonesian Journal of Law and Economics Review Vol. 19 No. 3 (2024): August
Publisher : Universitas Muhammadiyah Sidoarjo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21070/ijler.v19i3.1196

Abstract

General background: The digital age has significantly impacted education, posing challenges in managing student behavior and addressing rule violations, necessitating more efficient solutions. Specific background: Educational institutions are exploring the use of web-based systems with real-time notifications to improve efficiency in recording and communicating student violations. Knowledge gap: Despite the advancements in technology, many schools have yet to fully adopt integrated digital systems for managing student violations, and the effectiveness of real-time notification systems has not been fully explored. Aims: This study aims to design and develop a web-based application for managing student violations, integrated with WhatsApp notifications, to improve efficiency, accuracy, and communication between schools and parents. Results: Using the Extreme Programming (XP) methodology, the system was developed iteratively, allowing for rapid adjustments based on user feedback. The system successfully reduced errors in violation recording and accelerated communication with parents, facilitating faster responses to student misconduct. Novelty: The web-based violation management system incorporates real-time WhatsApp notifications, offering a more efficient and trustworthy communication channel between schools and parents. Implications: The system enhances both the management of student behavior and the engagement of parents, and it serves as an innovative model for other educational institutions to adopt for more effective administration and improved educational outcomes. Highlights: Efficiency: Reduces errors in managing student violations. Communication: Enhances parent-school connection via real-time WhatsApp notifications. Adaptability: XP methodology supports rapid adjustments and iterative development. Keywords: Web-based system, student violations, real-time notifications, WhatsApp integration, Extreme Programming
Co-Authors Abidin, Husnul Ade Eviyanti Adiffanani Ramdansyah Aditya, M. Fahrul Rizki Afidah, Dewi Nur Agung Izulhaq Ahmad Angga Handoko Aisha Hanif Alfinda Ayu Hadikasari Alfitra Oktavian Anis Farihah Ariansyah, Achmad Arif Senja Fitrani Arif Senja Fitroni Aris Hendra Prayoga Awalludin, Krisna Azizah, Risma Nur Azmuri Wahyu Azinar Berlian Putri Pertiwi Brigide Tirenia Loresta Cecep Kusmana Cholifah, Cholifah Cholifah, Cholifah Cindy Cahyaning Astuti Dafit Setiawan Jaya Damasta, Ifanda Reza Deby Kurniawan Armananda Dewi Komala Sari Diah Krisnaningsih Edi Widodo, Edi Eko Agus Suprayitno Eriyanto, Sandi Eko Evi Rinata Fahmawati, Zaki Nur Fahmi, M. Yusril Fery Febbyanto Firdausi Usqi Salsabilah Firmansah, Noval Fitri Nur Latifah Fitroni, Arif Senja Hadikasari, Alfinda Ayu Hamzah Setiawan Ika Ratna Indra Astutik Indah Suci Purnamasari Irwan A. Kautsar Irwan Alnarus Alkautsar Irwan Alnarus Kautsar Khubro, Jamaluddin Jumadil Krisfianto, Moh Ifan Kurniawan, Wildan Lely Ika Mariyati Lily Puspa Dewi M Cholis Afandi Maghfiroh, Alfiah Mahelda Asri Sudarsono Malihatin S, Ulfah Metatia Intan Mauliana Moch. Aji Bagus Firmansyah Mochamad Alfan Rosid Mochamad Alfan Rosid Mochammad Donni Kurniawan Mochammad Septa Sandy mochammad zien rifqi zien Muhammad Arsyad Dhani Muhammad Sulthon Abiyyu Muhammad Syamsuddin Novia Ariyanti Nuril Lutvi Azizah Nuril Lutvi Azizah Pertiwi, Berlian Putri Prayugah, Indra Putra, Rolando Jordan Permana Rafiiqa, Tasya Ratih Puspitasari Rizka Hadiwiyanti Rohman Dijaya Rolando Jordan Permana Putra Saesar Joko Pramono Siti Nur Haliza Suhendro Busono Sukarjadi Sukarjadi Sukma Aji Sumadyo, Sasmito Bagus Sumarno Sumarno Sumarno Sumarno Suryani, Siti Dwi Suseno Ardiansyah Syahrul Ibnu Rafi Tasya Rafiiqa Taurusta, Cindy Tutut Anjarsari Umi Khoirun Nisak Usabili, Syaikhina Veros Ariferdinand Vevy Liansari via nabila banda Wahyu Santoso Yahya Anugerah Dwi Khurrota A'yunan Yoyok Supriyono Yulian Findawati Yulius Hari Yunianita Rahmawati Yuwanto, Mahmud Adi Zamorano, Ifan