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All Journal RABIT: Jurnal Teknologi dan Sistem Informasi Univrab Jurnal Ilmiah Sinus Jurnal Abdimas PHB : Jurnal Pengabdian Masyarakat Progresif Humanis Brainstorming Community Development Journal: Jurnal Pengabdian Masyarakat Jurnal TIKOMSIN (Teknologi Informasi dan Komunikasi Sinar Nusantara) Ittishal Educational Research Journal JURNAL REKAYASA INFORMASI SWADHARMA (JRIS) Postulat : Jurnal Inovasi Pendidikan Matematika Jurnal Pustaka AI : Pusat Akses Kajian Teknologi Artificial Intelligence Jurnal Sistem Informasi Triguna Dharma (JURSI TGD) Jurnal Hasil Pengabdian Masyarakat (JURIBMAS) Jurnal Pengabdian Kolaborasi dan Inovasi IPTEKS Jurnal Pengabdian Masyarakat Teknologi dan Pendidikan (MANTAP) Systematic Literature Review Journal Journal of Information Technology, Computer Engineering and Artificial Intelligence (ITCEA) Jurnal Ilmu Komputer dan Teknik Informatika Indonesian Applied Research Computing and Informatics
Dwi Remawati
Universitas Tiga Serangkai
Religion Agriculture, Biological Sciences & Forestry Arts Humanities Automotive Engineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Health Professions Languange, Linguistic, Communication & Media Mathematics Mechanical Engineering Nursing Public Health Social Sciences Other

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SISTEM PAKAR DIAGNOSA PENYAKIT JAMUR (CRAYFISH PLAGUE) PADA LOBSTER AIR TAWAR MENGGUNAKAN METODE CERTAINTY FACTOR BERBASIS WEB Vivi Dian Pratiwi; Didik Nugroho; Sri Hariyati Fitriasih; Dwi Remawati
Jurnal Teknologi Informasi dan Komunikasi (TIKomSiN) Vol 13, No 2 (2025): Jurnal Tikomsin, Vol 13, No.2, Oktober 2025
Publisher : STMIK Sinar Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30646/tikomsin.v13i2.1018

Abstract

Crayfish plague, caused by the oomycete Aphanomyces astaci, is a deadly disease in crayfish/freshwater lobsters and poses a threat to lobster cultivation and sustainability. This study designed a web-based expert system to diagnose fungal disease (crayfish plague) in freshwater lobsters using the Certainty Factor (CF) method. Knowledge was gathered from observations at lobster farms and expert interviews. Seven symptoms were used and compiled as a rule base. The system was implemented using PHP and MySQL. The inference mechanism used a combination of expert and user CF, along with functional (black-box) and validity testing. The results showed a combined CF value for fungal diagnosis reached 0.9369 for the observed symptom combination. Seven scenarios tested yielded a 6/7 (85.7%) agreement. The expert system using the CF method is suitable for use as an early diagnosis tool for fungal diseases in freshwater lobsters, especially in local cultivation.
Analysis of the Complexity of Heuristic Algorithms for Permutation Optimization in Large-Scale Computing Fitriasih, Sri Hariyati; Cynthia, Eka Pandu; Cynthia, Maulidania Mediawati; Cynthia, Dessy Nia; Remawati, Dwi
Jurnal Ilmu Komputer dan Teknik Informatika Vol. 2 No. 1 (2026): Januari 2026
Publisher : CV. Raskha Media Group

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64803/juikti.v2i1.79

Abstract

Permutation optimization is a fundamental problem in large-scale computing that arises in various applications such as scheduling, resource allocation, and combinatorial decision-making. As the size of the solution space grows exponentially, conventional optimization methods often struggle to achieve acceptable performance within reasonable computational time. Heuristic and metaheuristic algorithms have therefore become widely adopted due to their flexibility and ability to provide near-optimal solutions for NP-hard problems. However, increasing data scale significantly impacts their computational complexity, making efficiency and scalability critical concerns.This study aims to analyze the computational complexity and performance characteristics of several heuristic algorithms applied to permutation optimization in large-scale computing environments. The research employs a quantitative experimental approach combined with theoretical complexity analysis. Greedy heuristic, simulated annealing, genetic algorithm, and adaptive heuristic methods are evaluated using synthetic permutation datasets with varying sizes. Performance is assessed based on execution time, memory usage, scalability, and solution quality. The results indicate that greedy heuristics offer the fastest execution and lowest memory consumption but tend to produce suboptimal solutions due to their local search strategy. Simulated annealing improves solution quality through probabilistic exploration, while genetic algorithms achieve the highest-quality solutions at the cost of substantial computational and memory overhead. Adaptive heuristic algorithms demonstrate a balanced performance by dynamically adjusting parameters during execution, achieving near-optimal solutions with reduced computational complexity. Overall, this research highlights the trade-offs between efficiency and solution quality among heuristic algorithms and emphasizes the potential of adaptive heuristic approaches for large-scale permutation optimization. The findings provide valuable insights for designing efficient and scalable optimization algorithms suitable for real-world large-scale computing applications.
Development of Personalized Recommendation System for Online Educational Content Based on Machine Learning Dwi Remawati; Khairunnisa; Afril efan Pajri; Kumaratih Sandradewi; Sri Hariyati Fitriasih
Indonesian Applied Research Computing and Informatics Vol. 1 No. 1: July (2025)
Publisher : PT. Teras Digital Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

The rapid growth of online educational platforms has increased the demand for intelligent recommendation systems that can personalize learning content to match individual learner needs. However, traditional methods such as Content-Based Filtering (CBF) and Collaborative Filtering (CF) often struggle with issues like data sparsity, limited adaptability, and cold-start problems. This study aims to develop a personalized recommendation system for online educational content by integrating Singular Value Decomposition (SVD) with an adaptive feedback loop to improve recommendation relevance and learner engagement. The proposed machine learning-based method captures latent user-item interactions and dynamically updates recommendations based on real-time user feedback. Experimental evaluation using a dataset of simulated learner interactions demonstrates that the proposed model significantly outperforms baseline methods, achieving higher scores in Precision (0.57), Recall (0.53), F1-Score (0.55), Mean Reciprocal Rank (MRR: 0.52), and Engagement Rate (72.1%). These results suggest that combining matrix factorization with adaptive learning can substantially enhance the performance of educational recommender systems, leading to more accurate, timely, and engaging content delivery.
PERBANDINGAN METODE SUPPORT VECTOR MACHINE DAN K-NEAREST NEIGHBOR DALAM ANALISIS SENTIMEN ULASAN APLIKASI MyXL Nugroho, Raka Aji; Remawati, Dwi; Susyanto, Teguh; Saptomo, Wawan Laksito Yuly
Jurnal Teknologi Informasi dan Komunikasi (TIKomSiN) Vol 14, No 1 (2026): Jurnal Tikomsin, Vol 14, No.1, April 2026
Publisher : STMIK Sinar Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30646/tikomsin.v14i1.1062

Abstract

Sentiment analysis is an essential tool for understanding user perceptions of mobile applications, especially when reviews are unstructured text. This study aims to analyze user reviews of the MyXL application using Support Vector Machine (SVM) and K-Nearest Neighbor (KNN) and to compare their performance in classifying sentiments into positive, negative, and neutral categories. The dataset was obtained from Google Play Store via Kaggle and underwent text preprocessing, including case folding, removal of numbers and punctuation, tokenization, stopword removal, normalization, and stemming. Features were transformed into numerical representations using TF-IDF, and the data was split into training (70%) and testing (30%) sets. Evaluation using accuracy, precision, recall, and F1-score showed that SVM outperformed KNN with an accuracy of 0.743 versus 0.64, particularly in classifying neutral reviews. KNN exhibited higher misclassification in positive and negative classes, while SVM was more stable but tended to be biased toward the neutral class. These results provide insights for application developers to better understand user satisfaction and guide service improvement and feature development.
MENINGKATKAN LITERASI DINI ANAK MELALUI METODE BERMAIN SAMBIL BELAJAR DI PAUD KENANGA DESA BANARAN KECAMATAN GROGOL KABUPATEN SUKOHARJO JAWA TENGAH Dwi Remawati; Sri Hariyati Fitriasih; Kumaratih Sandradewi; Tri Irawati
Community Development Journal : Jurnal Pengabdian Masyarakat Vol. 6 No. 4 (2025): Volume 6 No 4 Tahun 2025
Publisher : Universitas Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/cdj.v6i4.48201

Abstract

Kegiatan pengabdian kepada masyarakat ini bertujuan untuk meningkatkan literasi dini anak-anak melalui metode bermain sambil belajar di PAUD Kenanga, Desa Banaran. Literasi dini merupakan salah satu kemampuan penting yang harus ditanamkan sejak usia prasekolah, namun pelaksanaannya seringkali kurang menarik sehingga anak-anak mudah bosan. Oleh karena itu, tim pengabdian merancang serangkaian kegiatan edukatif berbasis permainan yang menyenangkan dan sesuai dengan perkembangan usia anak. Metode yang digunakan adalah partisipatif dan demonstratif, dengan langkah-langkah berupa observasi awal, koordinasi dengan guru, pelaksanaan permainan edukatif (mengenal huruf dengan kartu, berhitung dengan balok angka, bermain peran sederhana, membaca buku cerita bergambar, dan membentuk clay), serta evaluasi melalui pengamatan perkembangan anak. Hasil kegiatan menunjukkan bahwa anak-anak lebih aktif, antusias, dan berani berpartisipasi dalam aktivitas literasi. Guru-guru juga menyambut baik metode yang diterapkan dan berencana melanjutkannya secara rutin. Kegiatan ini diharapkan dapat menjadi model pembelajaran alternatif yang menarik untuk meningkatkan literasi anak sejak usia dini.
SISTEM REKOMENDASI KEDAI KOPI DI KOTA SURAKARTA DENGAN KATEGORI FASILITAS MENGGUNAKAN ITEM-BASED COLLABORATIVE FILTERING Bagus Cahyo Putro; Dwi Remawati; Sri Hariyati Fitriasih; Kumaratih Sandradewi
JRIS : Jurnal Rekayasa Informasi Swadharma Vol 6, No 1 (2026): JURNAL JRIS EDISI JANUARI 2026
Publisher : Institut Teknologi dan Bisnis (ITB) Swadharma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56486/jris.vol6no1.988

Abstract

The surge in the number of coffee shops in Surakarta City has raised the need for a recommendation sistem that can present choices according to user preferences, especially related to the category of facilities (such as smoking rooms, meeting rooms, outdoor spaces, working-friendly cafes (WFC), omakase, and specialty coffee) and building concepts (modern, vintage, and industrial). This research aims to develop and implement a web-based coffee shop recommendation sistem to help users find coffee shops based on their preferences. The method used in this study is Item-Based Collaborative filtering with cosine similarity. Coffee shop data is collected manually from Google Maps, then processed and stored in a database for recommendation calculation purposes. The sistem was developed using Flutter for the frontend, Laravel for the backend, and MySQL for the database. The study’s results show that the built recommendation sistem can produce a list of relevant coffee shops based on user preferences. The validity test using a confusion matrix in the Working-friendly cafe (WFC) preference scenario with a modern building concept yielded an accuracy of 85.7%, precision of 60%, recall of 75%, and an F1-score of 67%. These results show that the recommended sistem performs well and can serve as a decision-making tool for users when choosing a coffee shop in Surakarta City.Lonjakan jumlah coffee shop di Kota Surakarta memunculkan kebutuhan akan sistem rekomendasi yang mampu menyajikan pilihan sesuai preferensi pengguna, terutama terkait kategori fasilitas (seperti smoking room, meeting room, outdoor space, working-friendly cafe (WFC), omakase, dan specialty coffee) serta konsep bangunan (modern, vintage, dan industrial). Penelitian ini bertujuan untuk mengembangkan dan mengimplementasikan sistem rekomendasi kedai kopi berbasis web yang dapat membantu pengguna menemukan kedai kopi sesuai dengan preferensi mereka. Metode yang digunakan dalam penelitian ini adalah Item-Based Collaborative filtering dengan pendekatan cosine similarity. Data kedai kopi dikumpulkan secara manual dari Google Maps, kemudian diproses dan disimpan dalam basis data untuk kebutuhan perhitungan rekomendasi. Sistem dikembangkan menggunakan Flutter sebagai frontend, Laravel sebagai backend, dan MySQL sebagai sistem manajemen basis data. Hasil penelitian menunjukkan bahwa sistem rekomendasi yang dibangun mampu menghasilkan daftar kedai kopi yang relevan sesuai dengan preferensi pengguna. Uji validitas menggunakan confusion matrix pada skenario preferensi Working-friendly cafe (WFC) dengan konsep bangunan modern menghasilkan nilai akurasi sebesar 85,7%, precision sebesar 60%, recall sebesar 75%, dan F1-score sebesar 67%. Hasil tersebut menunjukkan bahwa sistem rekomendasi yang dikembangkan memiliki performa yang cukup baik dan dapat dijadikan sebagai alat bantu pengambilan keputusan bagi pengguna dalam memilih kedai kopi di Kota Surakarta.
SENTIMENT ANALYSIS OF MOSQUITO-BORNE DISEASES ON SOCIAL MEDIA BASED ON BERT Dziky Ridhwanullah; Dwi Remawati; Ahmad Muhariya
Rabit : Jurnal Teknologi dan Sistem Informasi Univrab Vol 11 No 2 (2026): Juli
Publisher : LPPM Universitas Abdurrab

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36341/rabit.v11i2.7733

Abstract

Mosquito-borne diseases, including malaria, dengue hemorrhagic fever (DHF), and chikungunya, remain significant public health challenges in Indonesia. With the extensive use of the internet, social media platforms such as X (Twitter) and Instagram have emerged as vital sources for capturing public sentiment in real-time. This study aims to identify and analyze Indonesian public sentiment regarding these diseases using a Deep Learning approach. The dataset comprises 1,800 records collected between 2024 and 2025 via web scraping. The methodology utilizes the BERT (Bidirectional Encoder Representations from Transformers) model for contextual feature extraction, evaluated through three Recurrent Neural Network (RNN) architectures: LSTM, GRU, and BiLSTM. The results reveal a predominant negative sentiment (56.99%), followed by neutral (32.99%) and positive (10.01%). While all models achieved high training accuracy (approx. 96%), the Gated Recurrent Unit (GRU) demonstrated superior generalization with the highest average validation accuracy of 94.24%. This study concludes that the GRU model is the most stable and effective architecture for classifying Indonesian public health sentiments on social media.
KLASIFIKASI PRESTASI AKADEMIK MAHASISWA MENGGUNAKAN METODE RANDOM FOREST Serly Sustiana Saputri; Dwi Remawati; Teguh Susyanto; Wawan Laksito Yuly Saptomo
JRIS : Jurnal Rekayasa Informasi Swadharma Vol 6, No 2 (2026): JURNAL JRIS EDISI JULI 2026
Publisher : Institut Teknologi dan Bisnis (ITB) Swadharma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56486/jris.vol6no2.1179

Abstract

This study aims to classify student academic achievement using the Random forest algorithm, utilizing the Student Performance dataset from Kaggle. The main attributes used as predictors include attendance rate, weekly study duration, and class engagement. The research methodology included data preprocessing with label encoding, an 80:20 split between training and test sets, and standardized model evaluation using accuracy, precision, recall, and F1-score. The results showed an accuracy of 31.03%. This low accuracy is due to the complexity of multi-class classification and imbalanced data distribution. This research contributes to mapping student learning behavior patterns and serves as a reference for developing more optimal academic prediction models.Penelitian ini bertujuan untuk mengklasifikasikan prestasi akademik mahasiswa menggunakan algoritma Random forest dengan memanfaatkan dataset Students Performance dari Kaggle. Atribut utama yang digunakan sebagai prediktor meliputi tingkat kehadiran, durasi belajar mingguan, dan keterlibatan di kelas. Metodologi penelitian mencakup pra-pemrosesan data dengan label encoding, pembagian data latih dan uji (80:20), serta evaluasi model yang diseragamkan menggunakan metrik akurasi, precision, recall, dan F1-score. Hasil penelitian menunjukkan nilai akurasi sebesar 31,03%. Rendahnya akurasi tersebut merupakan dampak dari kompleksitas klasifikasi multi-kelas dan distribusi data yang tidak seimbang (imbalanced data). Penelitian ini memberikan kontribusi dalam memetakan pola perilaku belajar mahasiswa serta menjadi referensi bagi pengembangan model prediksi akademik yang lebih optimal.
Metode Naïve Bayes Untuk Prediksi Waktu Produksi Mebel di UD. Wali Barokah Kartasura Sukoharjo Jawa Tengah RM Abdul Dhohir Surya Kusuma; Dwi Remawati; Kumaratih Sandradewi
Journal of Information Technology, Computer Engineering and Artificial Intelligence (ITCEA) Vol. 1 No. 1 (2024): Journal of Information Technology, Computer Engineering and Artificial Intellig
Publisher : Redtech Putra Benua

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Wali Barokah is one of the industrial furniture craftsmen (furniture) with the main material using teak wood. The development of the times has made many furniture entrepreneurs appear, making the competition between furniture craftsmen increasingly tight. One way for customers not to be disappointed is that business voters must serve customers according to the specified time when transacting. The attributes that will be used in classifying the production time are the name of the item, the number of orders, the difficulty, the equipment, the number of workers. The method that will be used is the the method Naïve Bayes Classifier. Based on the results of the confusion matrix test on the nave Bayes method of the dataset that has been taken on the object of research, an accuracy rate of 80% is obtained or is included in the category Good. Meanwhile, Precision is 83% and Recall is 88%.
Analisis Perilaku Penggunaan Smartphone dan Prediksi Kualitas Tidur Menggunakan Metode Statistik dan Machine Learning Adam Candra Kirana Wardana; Dwi Remawati; Teguh Susyanto
Journal of Information Technology, Computer Engineering and Artificial Intelligence (ITCEA) Vol. 3 No. 1 (2026): Journal of Information Technology, Computer Engineering and Artificial Intellig
Publisher : Redtech Putra Benua

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Abstract

The rapid growth of smartphone and social media usage has reshaped daily digital behavior and raised increasing concerns regarding its potential impact on sleep patterns. This study investigates the relationship between digital usage behavior, psychological factors, and sleep outcomes using an integrated data science approach. A publicly available Social Media Mental Health Indicators dataset from Kaggle was utilized, comprising 5,000 observations that capture screen time, social media activity, digital interactions, psychological conditions, and sleep duration. Data analysis was conducted through a structured pipeline involving data preprocessing, exploratory data analysis, clustering, and supervised machine learning for classification and regression tasks. Exploratory analysis indicates consistent negative associations between screen-related variables and sleep duration. Clustering analysis reveals distinct behavioral groups characterized by different levels of digital engagement and sleep patterns. Furthermore, Random Forest models demonstrate reliable performance in both sleep quality classification and sleep duration prediction, highlighting their effectiveness in modeling complex and non-linear relationships. Feature importance analysis identifies screen time, social media intensity, and negative digital interactions as dominant contributors to sleep-related outcomes. These findings emphasize the value of combining statistical exploration and machine learning techniques to obtain a comprehensive understanding of how digital behavior relates to sleep, providing empirical support for data-driven evaluation of healthier digital habits.
Co-Authors Abdullah Ahmad Ma'shum Adam Candra Kirana Wardana Aditya Bagas Pratama Afnan May Kandung Utomo Afril Efan Pajri Ahmad Muhariya Ajeng Dhanty Yoshima Aji Pratama Wisnu Wardana Amin Joko Susilo Amin Sulistiyowati Aninda Dyah Hayu P P Aninda Dyah Hayu Pinasti Putri Bagus Cahyo Putro Baktiar Setiyaji Bayu Dwi Raharja Bebas Widada Bramasto Wiryawan Yudanto Bramastyo Wiryawan Yudhanto Charles Parningotan Haratua Simanjuntak Cynthia, Dessy Nia Cynthia, Maulidania Mediawati Dicky Jordan Aji Putra Didik Nugroho Didik Nugroho, Didik Dziky Ridhwanullah Effendi, Rohmat Irvan Eka Pandu Cynthia Fionarizoca, Avierry Firman Dwi Wahyudi Fitriani, Putri Asiska Nur Haqiqi, Arif Naofal Hendro Wijayanto Hendro Wijayanto Hendro Wijayanto Heriyanto Atmojo Heru Budi Kusumo Ina Putri Pertiwi Iwan Ady Prabowo Khairunnisa Khoirul Ahyar Kriswantoro Budi Satrio Kumaratih Sandradewi Kumaratih Sandradewi Kumaratih Sandradewi Kumaratih Sandradewi Kustanto Kustanto Kustanto Kustanto Lutfiyah Muhammad Hasbi Muhammad Ilyas Arrayyan Nirma Mujirahayu Meyliana Nugroho, Raka Aji Paulus Harsadi paulus harsadi Prasastya Dwi Pamungkas Prasetyo, Danar Aji Prihanto Puspa Ningrum, Okky R Arie Febrianto Raharja, Bayu Dwi Retno Tri Vulandari Retno Tri Vulandari RM Abdul Dhohir Surya Kusuma Ruvyanto Dwi Nugroho Salim Salim Salim Salim Saptomo, Wawan Laksito Yuly Septiana, Anisa Dwi Serly Sustiana Saputri Setiyowati Sri Hariyati Fitriasih Sri Hariyati Fitriasih Sri Hariyati Fitriasih Sri Hariyati Fitriasih Sri Hariyati Fitriasih, Sri Hariyati Sutrisno Teguh Susyanto Teguh Susyanto Tri Irawati Tri Irawati Tri Irawati Tri Irawati Tri Irawati Tri Irawati, Tri Tulus Wahyuno Usman Sabila Firdos Vivi Dian Pratiwi Wawan Laksito Yuly Saptomo Widada, Bebas Widianto, Septian Cahyo Widya Restu Utami Wijayanto, Hendro Yustina Retno Wahyu Utami Yusuf Cahyo Nugroho nugroho Zannuba Anugrah Indah Lestari