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All Journal Teknika RABIT: Jurnal Teknologi dan Sistem Informasi Univrab JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Jurnal SOLMA JOURNAL OF APPLIED INFORMATICS AND COMPUTING Jurnal Informatika Kaputama (JIK) Jurnal Ilmiah ILKOMINFO - Ilmu Komputer & Informatika Journal of Computer Science, Information Technology and Telecommunication Engineering (JCoSITTE) JINAV: Journal of Information and Visualization Rambideun : Jurnal Pengabdian Kepada Masyarakat International Journal of Engineering, Science and Information Technology Jurnal Tika jeti Brilliance: Research of Artificial Intelligence Aptekmas : Jurnal Pengabdian Kepada Masyarakat Sisfo: Jurnal Ilmiah Sistem Informasi Jurnal Teknologi Terapan and Sains 4.0 Journal of Mathematics Instruction, Social Research and Opinion Jurnal Pendidikan Sains Indonesia (Indonesian Journal of Science Education) MEUSEURAYA : JURNAL PENGABDIAN MASYARAKAT Journal Of Artificial Intelligence And Software Engineering Jurnal Malikussaleh Mengabdi Journal of Advanced Computer Knowledge and Algorithms Gameology and Multimedia Expert JuTISI (Jurnal Teknik Informatika dan Sistem Informasi) Proceedings of International Conference on Multidisciplinary Engineering (ICOMDEN) Proceedings of Malikussaleh International Conference on Multidisciplinary Studies (MICoMS) Journal of Algorithmic Digital Engineering and Networks Jurnal Pengabdian Masyarakat Teknologi Informasi (PERANTI)
Rini Meiyanti
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Prediksi Jumlah Kebutuhan Pemakaian Air Menggunakan Metode Fuzzy Time Series Pada Perumda Aceh Utara Ayu Ramazani; Wahyu Fuadi; Rini Meiyanti
Jurnal Ilmiah ILKOMINFO - Ilmu Komputer & Informatika Vol 8, No 2 (2025): Juli
Publisher : Akademi Ilmu Komputer Ternate

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47324/ilkominfo.v8i2.360

Abstract

Abstrak: Penelitian ini bertujuan untuk mengembangkan sistem peramalan yang akurat dan efektif menggunakan metode Fuzzy Time Series (FTS) untuk memprediksi kebutuhan konsumsi air pada Perusahaan Umum Daerah Air Minum (PERUMDA) Tirta Pase Aceh Utara. Sistem peramalan ini dibangun dengan memanfaatkan data historis distribusi dan produksi air bersih yang diperoleh dari pihak PERUMDA selama periode 2023 hingga 2024. Dalam pengembangan sistem, digunakan pendekatan model Unified Modeling Language (UML) untuk merancang tahapan pengembangan sistem, dimulai dari pembuatan Use Case Diagram, Sequence Diagram, hingga Activity Diagram. Model-model ini digunakan untuk menggambarkan proses sistem, interaksi pengguna, serta alur data yang mendukung pengolahan informasi dalam sistem peramalan. Metode FTS dipilih karena kemampuannya dalam menangani data deret waktu yang mengandung ketidakpastian, serta kesederhanaannya dalam pengolahan data tanpa memerlukan proses pelatihan yang kompleks. Sistem yang dikembangkan bertujuan untuk memberikan prediksi yang lebih akurat terkait kebutuhan air bersih, sehingga dapat digunakan sebagai alat bantu dalam pengelolaan distribusi air yang lebih efisien. Implementasi sistem ini berhasil memberikan hasil yang dapat diandalkan dalam memprediksi kebutuhan konsumsi air secara lebih tepat waktu. Dengan keberhasilan penerapan sistem ini, PERUMDA Tirta Pase Aceh Utara kini dapat membuat keputusan yang lebih baik dalam merencanakan dan mengelola pasokan air, serta memastikan ketersediaan air yang cukup untuk memenuhi kebutuhan masyarakat di wilayah tersebut. Sistem ini juga dapat diadaptasi dan diterapkan pada wilayah lain yang menghadapi permasalahan serupa dalam pengelolaan distribusi air bersih.Kata kunci: Fuzzy Time Series, Prediksi Kebutuhan Air, Distribusi Air, PERUMDA, UMLAbstract: This study aims to develop an accurate and effective forecasting system using the Fuzzy Time Series (FTS) method to predict water consumption needs at the Perusahaan Umum Daerah Air Minum (PERUMDA) Tirta Pase Aceh Utara. The forecasting system is built by utilizing historical data on water distribution and production obtained from PERUMDA for the period from 2023 to 2024. In the system development process, a Unified Modeling Language (UML) approach is used to design the stages of system development, starting with the creation of Use Case Diagrams, Sequence Diagrams, and Activity Diagrams. These models are used to represent system processes, user interactions, and data flows that support the information processing in the forecasting system. The FTS method was chosen for its ability to handle time-series data containing uncertainty, as well as its simplicity in data processing without requiring complex training processes. The developed system aims to provide more accurate predictions regarding clean water needs, thus serving as a tool for more efficient water distribution management. The implementation of this system has successfully provided reliable results in predicting water consumption needs in a more timely manner. With the successful application of this system, PERUMDA Tirta Pase Aceh Utara can now make better decisions in planning and managing water supply, ensuring sufficient water availability to meet the needs of the community in the region. This system can also be adapted and applied to other areas facing similar challenges in clean water distribution management..Keywords: Fuzzy Time Series, PERUMDA, Water Demand Prediction, Water Distribution, UML
Student Graduation Prediction System In the MBKM Program Using The Mamdani Fuzzy Method Muthiah Riani Harahap; Safwandi; Rini Meiyanti
Proceedings of International Conference on Multidisciplinary Engineering (ICOMDEN) Vol. 2 (2024): Proceedings of International Conference on Multidisciplinary Engineering (ICOMDEN)
Publisher : Faculty of Engineering, Malikussaleh University

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Abstract

This study aims to develop a graduation prediction system for the MBKM Program using the Fuzzy mamdani method. The system is designed to process various academic criteria such as GPA, internship experience, and other supporting documents to provide an accurate projection of graduation probability. The implementation was carried out using data from 61 students of the Informatics Engineering Department at Universitas Malikussaleh. The Fuzzy mamdani method was applied through stages of fuzzification, rule formation, fuzzy inference, and defuzzification to produce the final prediction. The test results show that this method is effective in handling uncertainty and provides a high prediction accuracy, where 67% of students were predicted to graduate, and 33% were not. This system can be used by academic staff to evaluate student performance and provide more precise guidance, as well as to help students plan their studies to achieve graduation in the MBKM Program.
Comparison Of Maximal Marginal Relevance ( MMR) And Textrank Automatic Text Summarization Methods In Jurnal Muhammad Alif Al Fattah; Rizal Rizal; Rini Meiyanti
Proceedings of International Conference on Multidisciplinary Engineering (ICOMDEN) Vol. 2 (2024): Proceedings of International Conference on Multidisciplinary Engineering (ICOMDEN)
Publisher : Faculty of Engineering, Malikussaleh University

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Abstract

The purpose of this study is to find out the application of the Maximal Marginal Relevance (MMR) and TextRank methods in automatic text summarization in journals in viewing the best model value in text automatically. This study can also implement an automatic text summarization application and find out the comparison between MMR and TextRank in the text summarization process in journals. Next research will evaluate the performance of the two models in producing relevant and informative text summaries. The problem of this research is how to overcome the problem of summarizing text with the basic concept of summary in providing the essence or overall content text in a journal. The main focus of this research is to display significant and relevant information in a more organized form and to display values for the efficiency of which model is the best after comparing the two models. The results of this research are a decision support system for determining the quality of poor rice using the fuzzy madm yager model with a value of (MMR) 0.4 and top N(3). The results of the comparison of the Maximal Marginal Relevance (MMR) similarity values are 0.510 and the score is 0.510, while the TextRank similarity is 0.510 and the score is 0.015. Based on testing of the two models, the best value was obtained from the rextrank model with a score of 0.015. Keywords: maximal marginal relevance (mmr), textrank, summary
Development of a Decision Support System for Movie Recommendations Using the Evaluation Based on Distance from Average Solution M. Raiyan Firdaus; Mukhlis Abdul Muthalib; Rini Meiyanti
Proceedings of International Conference on Multidisciplinary Engineering (ICOMDEN) Vol. 2 (2024): Proceedings of International Conference on Multidisciplinary Engineering (ICOMDEN)
Publisher : Faculty of Engineering, Malikussaleh University

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Abstract

Digital transformation has changed how people enjoy media content, including films, through digital platforms like YouTube. Recommendation systems play a vital role in helping viewers find movies that match their preferences, utilizing methods such as Simple Additive Weighting and Collaborative Filtering to enhance recommendation accuracy and relevance. In this study, the Evaluation Based on Distance From Average Solution (EDAS) method is applied to provide more independent and user-focused movie recommendations. EDAS works by analyzing user profiles, which contain keywords or features related to films of interest. Based on an analysis of 300 film alternatives, the results show that Dune: Part Two (A199) ranks highest with a qualitative utility score of 1, followed by Spider-Man: Across the Spider-Verse (A182) with a score of 0.932194, and Furiosa: A Mad Max Saga (A201) with a score of 0.853523. The lowest-ranked alternative is Cobweb (A158) with a qualitative utility score of 0. Through the EDAS approach, this movie recommendation system offers a more relevant and satisfying viewing experience for users.
Stock Prediction Of Single-Use Medicine Using Autoregressive Integrated Moving Average Raisya Kamila; Dahlan Abdullah; Rini Meiyanti
Proceedings of Malikussaleh International Conference on Multidisciplinary Studies (MICoMS) Vol. 4 (2024): Proceedings of Malikussaleh International Conference on Multidisciplinary Studies (MI
Publisher : LPPM Universitas Malikussaleh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29103/micoms.v4i.894

Abstract

Stock Prediction of Single-Use Medicine Using Autoregressive Integrated Moving Average the(3, 1, 3) model, derived from the (p,q,d) model where p is the AR level, d is the process level that makes the data stationary, and q is the MA level. The (3, 1, 3) model used provides quite good results the ARIMA (3, 1, 3) model can be a good tool to predict the need for consumable drug stocks show that the ARIMA (3, 1, 3) model gives good results,log likelihood values and information criteria indicating that the model is reliable. Predictions for the demand for consumable drugs in 2025 show a downward trend, Requires further attention to understand the causes. health centres can plan drug procurement more precisely and efficiently meet patient needs without experiencing overstocks or shortages.
Research Results Boardify A Comprehensive Approach to Academic Task Management with Push Notifications and Scheduling Optimization Faiz Syukri Arta Faiz; Muhammad FIkry; Rini meiyanti
Proceedings of Malikussaleh International Conference on Multidisciplinary Studies (MICoMS) Vol. 4 (2024): Proceedings of Malikussaleh International Conference on Multidisciplinary Studies (MI
Publisher : LPPM Universitas Malikussaleh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29103/micoms.v4i.910

Abstract

Abstract. This study explores the development and implementation of Boardify, an integrated academic task management system designed to enhance the thesis submission process for Informatics students. The system incorporates features such as task management, status tracking, file submission, and seamless communication between students and supervisors, aiming to streamline the entire workflow. By leveraging Firebase Cloud Messaging, Boardify enables real-time push notifications, ensuring timely updates and reducing delays. Furthermore, the implementation of scheduling algorithms optimizes notification timing based on probabilistic factors, enhancing the efficiency of the system. A comparative analysis was conducted between Boardify and similar task management platforms, focusing on aspects such as website load speed, feature functionality, and user experience. The system's performance was further evaluated by measuring the average time taken by supervisors to review student submissions. Results indicate that Boardify significantly improves the efficiency of the thesis submission process, enhancing transparency and facilitating effective communication. The findings underscore Boardify's potential as a powerful tool for academic institutions, offering a promising approach to optimizing educational task management and promoting the advancement of educational technology. Keywords: Boardify. Probability Scheduling, Push Notifications, Informatic, Task Management System
Pendampingan Implementasi dan Pelaksanaan E-office bagi Aparatur Gampong Cot Keumuneng Kabupaten Aceh Utara untuk Mendukung Smart governance Aidilof, Hafizh Al Kautsar; Rosnita, Lidya; Fitria, Rahma; Meiyanti, Rini; Yusdartono, Habib Muharry; Rangkuti, Haris Yunanda; Azwir, Andrea Micola
Jurnal SOLMA Vol. 15 No. 1 (2026)
Publisher : Universitas Muhammadiyah Prof. DR. Hamka (UHAMKA Press)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22236/solma.v15i1.16410

Abstract

Background: Perkembangan teknologi khususnya tekologi informasi telah merubah pola pelayanan masyarakat desa sebagai unit terkecil dari pemerintahan Republik Indonesia, dari yang sebelumnya terbatas pada ruang dan waktu, kini telah fleksibel dan lebih leluasa. Pengabdian kepada masyarakat ini dilakukan untuk mendukung program pemerintah menerapkan konsep smart village dimana dalam pelaksanaan administrasinya menerapkan smart government. Metode: Kegiatan pengabdian kepada masyarakat ini menyasar Aparatur Desa Cot Keumuneng yang berjumlah 15 orang sebagai peserta pelatihan yang nantinya akan menggunakan aplikasi e-office. Setelah pelatihan dan pendampingan dilakukan survey untuk melihat pemahaman aparatur desa dalam menggunakan e-office. Hasil: Kegiatan pengabdian kepada masyarakat ini mendapat hasil positif di kalangan aparatur desa dimana dengan adanya aplikasi ini proses persuratan di kalangan aparatur desa menjadi lebih efektif dan efisien. Dampak lainnya adalah pelayanan kepada masyarakat dapat lebih ditingkatkan karena fleksibilitas yang didapat dengan penerapan teknologi informasi. Kesimpulan: Kegiatan pengabdian kepada masyarakat ini sangat bermanfaat baik bagi internal aparatur desa maupun bagi pelayanan kepada warga desa sendiri dan aparatur desa berharap kegiatan serupa dapat terus dilaksanakan secara berkesinambungan.
A Natural Language Processing-Based Chatbot as a Medium for Consultation and Education on Direct-Contact Infectious Diseases Serlina Serlina; Eva Darnila; Rini Meiyanti
Journal of Mathematics Instruction, Social Research and Opinion Vol. 5 No. 1 (2026): March
Publisher : MASI Mandiri Edukasi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58421/misro.v5i1.984

Abstract

Direct-contact infectious diseases such as influenza, diphtheria, tuberculosis (TB), scabies, varicella, impetigo, herpes simplex, and HIV remain public health threats. Limited access to accurate information encourages the development of chatbots as educational media. This study aims to design and build an NLP-based chatbot named SerMediCare to provide consultation and education on infectious diseases. The Research and Development (R&D) method with an iterative approach was used, including needs analysis, data collection from journals and medical books, and interviews with healthcare workers; system design; model training; and implementation on a web platform. The dataset was prepared in JSON format, including patterns, responses, and tags, and trained with a Transformer-based model to accurately recognize user intent. Evaluation results show that SerMediCare achieves 86% accuracy, indicating its ability to provide relevant responses to user queries. Black box testing confirmed that all features function properly. This chatbot is expected to be an effective digital tool for improving health literacy and facilitating public access to reliable information about infectious diseases.
Application of a Smart Farming Monitoring System to Optimize Vegetable Production in North Aceh Rini Meiyanti; Nunsina Nunsina; Rahma Fitria; Muhammad Muaz Munauwar
Brilliance: Research of Artificial Intelligence Vol. 5 No. 2 (2025): Brilliance: Research of Artificial Intelligence, Article Research November 2025
Publisher : Yayasan Cita Cendekiawan Al Khwarizmi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/brilliance.v5i2.7285

Abstract

This research aims to design and implement a smart farming monitoring system tailored to the local conditions of North Aceh, optimizing the production of leading vegetables and facilitating sustainable agricultural transformation. In line with the national agenda toward the digitalization of the agricultural sector, this research is part of a concrete effort to encourage the adoption of smart farming technology at the local level. North Aceh Regency has great horticultural potential, but it is not yet optimal due to the minimal application of technology. This research supports the development of agriculture based on local potential. The study also promotes a participatory and educative approach to increase farmers' digital literacy and reduce the technology gap between conventional and modern technology-adopting farmers. The Smart Farming monitoring system was successfully implemented using soil moisture, air temperature, soil pH, and light intensity sensors integrated into a web-based dashboard and mobile application. The implementation of this system was able to increase vegetable productivity by 18–22%, especially for mustard greens, chili, and tomatoes, compared to conventional methods. The system also contributed to the efficient use of resources, shown by a 25% savings in irrigation water and a 15% reduction in the use of chemical fertilizers. The farmer response was quite positive, although there are still challenges related to digital literacy among some older farmers. Overall, the implementation of Smart Farming in North Aceh Regency had a real impact on increasing productivity and cost efficiency while supporting sustainable agriculture in line with the SDGs.
Hybrid Ensemble Learning to Improve Prediction Disease Kidney Chronic Fahmi Izhari Izhari; Rini Meiyanti
JADEN : Journal of Algorithmic Digital Engineering and Networks Vol. 1 No. 1 (2025): The Journal of Algorithmic Digital Engineering and Networks
Publisher : Cv. Data Sinergi Digital

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.65853/jaden.v1i1.103

Abstract

Chronic Kidney Disease (CKD) is a major global health issue with a steadily increasing prevalence and high mortality rates. Early detection remains challenging due to non-specific clinical symptoms, often leading to late diagnosis and severe complications such as kidney failure. Machine learning (ML) offers significant opportunities to support early detection and prediction through clinical and laboratory data analysis. However, single models such as Random Forest (RF), Gradient Boosting (GBM), and Support Vector Machine (SVM) still face limitations in generalization and stability when applied to complex and imbalanced datasets. This study proposes a Hybrid Ensemble Learning approach that combines bagging, boosting, and stacking strategies to improve predictive accuracy and robustness. Experimental results using the CKD dataset demonstrate that the Hybrid Stacking model achieves the best performance, with 99% accuracy, 1.0 precision, 0.983 recall, and an AUC-ROC of 0.992. These findings highlight that Hybrid Ensemble Learning, particularly stacking, significantly enhances model sensitivity and reliability, making it a promising tool for supporting clinical decision-making in CKD prediction.
Co-Authors Agam Muarif Ahmad Junaidi Aidilof, Hafizh Al Kautsar Andri Alfitra Angga Pratama Anggara, Aji Ar Razi Arief Rahman Armelia Dafrina Asrianda Asrianda Ayu Ramazani Azmi, Win Azwir, Andrea Micola Bagaswara, Faris Bustami Bustami Chaliza Nur, Wan Amalia Cut Agusniar Cut Lika Mestika Sandy Cut Lika Mestika Sandy Dahlan Abdullah Dahlan Abdullah Eva Darnila Eva Darnila Fahmi Izhari Izhari Faiz Syukri Arta Faiz Fasdarsyah Fasdarsyah Fatayati, Nufus Fitri*, Zahratul Fuadi, Wahyu Fuzna Febriani Habib Muharry Yusdartono Hafidh Rafif, Teuku Muhammad Hamsi, Widia Harahap, Ilham Taruna Harahap, Lina Mardiana Hasan Dalimunthe, Amir Kamaruzzaman, Hilda Zulfira Kautsar, Al Khairul Anshar Lidya Rosnita Lina Mardiana Harahap M. Raiyan Firdaus Mamat, Rizalman Bin Maryana Maryana Maryana Maryana Mey Suci Br Pardosi Mirza Mirza Muchlis Abdul Muthalib Muhammad Muhammad Alif Al Fattah Muhammad Faisal Muhammad Fikry Muhammad Ikhwani Muhammad Muaz Munauwar Muhammad Muaz Munauwar Muhammad Muhammad Mulyawan, Rizka Munirul Ula Muthalib, Muchlis Abd Muthiah Riani Harahap Mutia Zahara Na'syakban, Irvan Nunsina Nunsina Nunsina Nunsina Nunsina, Nunsina Nurdin Nurdin Rahma Fitria Rahma Fitria Rahma Fitria, Rahma Raisya Kamila Ramadhani, Putri Yesi Rangkuti, Haris Yunanda Rara Audia Utami Rizal Rizal Rizal, Reyhan Achmad Rizki Suwanda Rizkya, Ghinni Ruzanna, Arina Safriana Safriana Safwandi Safwandi Safwandi Said Fadlan Anshari Sandy, Cut Lika Mestika Serlina Serlina Suci Khairani Sujacka Retno Sukiman, T. Sukma Achriadi Syibral Malasyi, Syibral Wahyu Fuadi Yesy Afrillia Zahratul Fitri, Zahratul Zainuddin Ginting Zalfie Ardian Zara Yunizar