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All Journal BERKALA FISIKA Jurnal Pendidikan Matematika dan IPA Jurnal Teknik Kimia USU Jurnal Online Mahasiswa (JOM) Bidang Keguruan dan Ilmu Pendidikan Jurnal Transformatika Biology, Medicine, & Natural Product Chemistry MPI (Media Pharmaceutica Indonesiana) Jurnal Penelitian Pembelajaran Fisika Sinkron : Jurnal dan Penelitian Teknik Informatika JIKO (Jurnal Informatika dan Komputer) Jurnal Penelitian Pendidikan IPA (JPPIPA) Bubungan Tinggi: Jurnal Pengabdian Masyarakat Journal of Intelligent Computing and Health Informatics (JICHI) Ideguru: Jurnal Karya Ilmiah Guru Jurnal Program Kemitraan dan Pengabdian Kepada Masyarakat Jurnal Pijar MIPA Jurnal Teknoif Teknik Informatika Institut Teknologi Padang Adopsi Teknologi dan Sistem Informasi Depik Jurnal Ilmu-Ilmu Perairan, Pesisir dan Perikanan Depik Jurnal Ilmu-Ilmu Perairan, Pesisir, dan Perikanan Unnes Science Education Journal Scientific Journal of Informatics SWAGATI: Journal of Community Service JIPFRI (Jurnal Inovasi Pendidikan Fisika dan Riset Ilmiah)
Mega Novita
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Agriculture, Biological Sciences & Forestry Astronomy Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Health Professions Immunology & microbiology Materials Science & Nanotechnology Mathematics Medicine & Pharmacology Physics Public Health Social Sciences Other

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Effect of Modified Kimpul Flour Substitution and Glycerol Monostearate Concentration on The Physicochemical and Sensory Properties of Sweet Bread Setianingsih, Siti Nurlaela; Ujianti, Rizky Muliani Dwi; Muflihati, Iffah; Nurdyansyah, Fafa; Novita, Mega; Paramita, Diva Julia; Nofitasari, Shindi; Anggarini, Dola Mareta; Annajah, Abdillah Fathan Generus
Biology, Medicine, & Natural Product Chemistry Vol 14, No 2 (2025)
Publisher : Sunan Kalijaga State Islamic University & Society for Indonesian Biodiversity

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14421/biomedich.2025.142.679-683

Abstract

Wheat flour is the primary ingredient in sweet bread production, yet its import-dependent supply in countries like Indonesia prompts the need for alternative local ingredients. Kimpul tuber (Xanthosoma sagittifolium), rich in carbohydrates, presents a promising substitute, though its native starch properties are less suitable for baking. This study aimed to evaluate the effect of substituting wheat flour with heat moisture treatment (HMT)-modified kimpul flour and the addition of glycerol monostearate (GMS) on the physicochemical and sensory properties of sweet bread. A factorial completely randomized design was applied using three wheat-to-kimpul flour ratios (3:1, 1:1, and 1:3) and three GMS concentrations (2%, 3%, and 4%). Results showed that higher kimpul flour substitution increased moisture and carbohydrate content but reduced protein and fat levels. Textural properties such as hardness and adhesiveness also increased with kimpul content, but these were mitigated by the addition of GMS, particularly at 3%. The optimal formulation 1:1 wheat-to-kimpul ratio with 3% GMS produced sweet bread with the best overall sensory acceptance. The findings suggest that HMT-modified kimpul flour combined with GMS can serve as a functional and acceptable alternative to wheat flour in bread production. This supports food diversification strategies and promotes the utilization of local tuber-based flours in bakery applications.
Peningkatan Performa Prediksi Survival Pasien Gagal Jantung Menggunakan Stacking Ensemble Learning Salwa, Faiza Rulla; Novita, Mega; Renaldy, Ramadhan
JURNAL INFORMATIKA DAN KOMPUTER Vol 9, No 3 (2025): Oktober 2025
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat - Universitas Teknologi Digital Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26798/jiko.v9i3.2126

Abstract

Prediksi kelangsungan hidup pasien gagal jantung merupakan aspek penting dalam mendukung pengambilan keputusan medis secara dini dan tepat. Penelitian ini bertujuan untuk meningkatkan akurasi prediksi kelangsungan hidup pasien gagal jantung dengan menerapkan metode Stacking Ensemble Learning yang menggabungkan tiga base learners, yaitu Decision Tree, Naive Bayes, dan K-Nearest Neighbor, serta menggunakan Support Vector Machine sebagai meta-learner. Dataset yang digunakan adalah Heart Failure Clinical Records dari UCI Machine Learning Repository yang telah melalui proses pra-pemrosesan berupa standardisasi numerik dan pembagian data menggunakan stratified sampling dengan rasio 80:20. Eksperimen dilakukan menggunakan validasi silang (5-fold cross-validation) dan tuning hyperparameter pada meta-learner menggunakan GridSearchCV untuk menemukan kombinasi terbaik dari parameter C dan gamma. Hasil evaluasi menunjukkan bahwa model stacking mampu mencapai akurasi sebesar 98,7% dan F1-score 0,9791, mengungguli semua model tunggal. Keberhasilan ini menunjukkan bahwa strategi penggabungan beberapa model ringan mampu meningkatkan kinerja sistem prediktif secara signifikan, tanpa menambah kompleksitas yang berlebihan. Oleh karena itu, pendekatan ini sangat potensial untuk diterapkan pada sistem pendukung keputusan klinis berbasis data, khususnya dalam konteks prediksi penyakit kronis.
SISTEM INFORMASI GEOGRAFIS PEMETAAN JENIS KEKERASAN TERHADAP PEREMPUAN DI JAWA TENGAH MENGGUNAKAN METODE K-MEANS CLUSTERING Maulana, Novan; Harjanta, Aris Tri Jaka; Novita, Mega
Jurnal Teknoif Teknik Informatika Institut Teknologi Padang Vol 13 No 2 (2025): TEKNOIF OKTOBER 2025
Publisher : ITP Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21063/jtif.2025.V13.2.77-86

Abstract

Violence against women is a social issue with widespread impacts and remains highly prevalent in Indonesia, including in Central Java Province. The forms of violence include physical, psychological, and sexual abuse, exploitation, neglect, and others. Presenting data in a general form without spatial mapping often makes it difficult to identify regions with high levels of vulnerability. This study aims to cluster regencies/municipalities in Central Java based on types of violence against women by integrating the K-Means Clustering method with Geographic Information Systems (GIS). The data used are records of violence against women in 2024 from 35 regencies/municipalities. The K-Means method was applied iteratively until reaching a convergent condition, resulting in three main clusters. The clustering results were visualized using QGIS software in the form of thematic maps, facilitating the interpretation of spatial patterns. The evaluation shows that spatial classification was successfully applied with a spatial match rate of 100%, and a Silhouette Score of 0.577, indicating a moderately good cluster quality. The majority of regions are included in the low cluster, while only one region is in the high cluster. This study concludes that the combination of K-Means and GIS is effective in detecting and visualizing regional vulnerability to violence against women and has the potential to serve as a basis for developing more targeted and evidence-based protection policies. It is recommended that future research expand the dataset, include additional risk variables, and explore alternative clustering methods or advanced spatial analyses to improve the accuracy and understanding of violence patterns.
Comparative Analysis of Express and Hono Framework Performance in Simple Registration Application Saputro, Anjar Tiyo; Novita, Mega
Sinkron : jurnal dan penelitian teknik informatika Vol. 9 No. 1 (2025): Research Article, January 2025
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v9i1.14333

Abstract

This research evaluates the performance of two Node.js frameworks, Express and Hono, in developing a simple registration application. This application serves as a backend to store user registration data into a PostgreSQL database using the pg client of the node package manager (npm). The purpose of this performance comparison is to identify the framework that is superior in executing 1 million requests in this scenario. The analysis shows that Express has an average execution time of 26.85% faster than Hono. However, it is inversely proportional to the resource usage, where Hono shows better efficiency with lower CPU and memory usage of 29.29% and 19.97%. These findings provide important insights for developers in choosing a suitable framework based on performance and resource efficiency requirements.
Mental Health Chatbot Application on Artificial Intelligence (AI) for Student Stress Detection Using Mobile-Based Naïve Bayes Algorithm Mariyana, Ekanata Desi Sagita; Novita, Mega; Nur Latifah Dwi Mutiara Sari
Scientific Journal of Informatics Vol. 12 No. 2: May 2025
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v12i2.24307

Abstract

Purpose: This study aims to design and evaluate a chatbot-based artificial intelligence system to identify stress levels in students using the Naïve Bayes classification method. With increasing mental health concerns among students, early stress detection is considered crucial for timely intervention Methods: This study proposes an AI-based chatbot system to detect student stress levels using a comparative approach between Naïve Bayes and Support Vector Machine (SVM) algorithms. A Kaggle dataset with 15 psychological and academic indicators was preprocessed and balanced using SMOTE. Naïve Bayes showed higher accuracy (90%) than SVM (89%). The trained model was deployed via Flask with Ngrok tunneling and integrated into a Flutter mobile app connected to the Gemini AI API for real-time stress screening. This research offers a practical and scalable solution for early mental health detection in students through intelligent chatbot interaction. Result: The findings show that the Naïve Bayes model achieves a classification accuracy of 90%, slightly surpassing the SVM model, which records an accuracy of 89%. Evaluation through ROC and AUC metrics supports the reliability of Naïve Bayes in detecting stress levels. The integrated chatbot offers a responsive and engaging platform for preliminary mental health assessments. Novelty: This research presents a unique contribution by combining AI-driven stress detection with a real-time chatbot interface, offering an accessible and scalable approach to student mental health support. The integration of machine learning models with conversational AI provides an innovative solution for early intervention. Future developments may involve deep learning and more diverse psychological inputs to further improve accuracy and effectiveness.
Profile of Problem Solving and Student Learning Motivation in Science Learning Material on Motion and Force at SMPN 5 Randublatung Satu Atap Karyawan; Patonah, Siti; Novita, Mega
Jurnal Penelitian Pendidikan IPA Vol 11 No 1 (2025): January
Publisher : Postgraduate, University of Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/jppipa.v11i1.9807

Abstract

This research aims to determine students' problem-solving abilities and learning motivation on Motion and Force material. The method in this research uses a qualitative descriptive approach with the subjects used being 18 grade 7 students of SMPN 5 Randublatung Satu Atap, totaling 18 people. To measure students' learning motivation and ability to solve problems. The research results show that the level of student motivation in learning movement and style still varies (high, medium and low). As many as 53% of students have a high level of motivation (scores 54-65); as many as 37% of students have a moderate level of motivation (scores 35-53); and as many as 10% of students have a low level of motivation (score 24-34). The ability to solve problems obtained results in the form of the lowest score of 15, the highest of 45, and the average in one class was 38. The research results showed that students did not understand the material of motion and force. This can be seen from the results of the questionnaire data and questions.
Co-Authors AA Sudharmawan, AA Anggarini, Dola Mareta Anggraeni, Cerly Nurlita Anggraini, Natasya Dila Putri Annajah, Abdillah Fathan Generus Aris Tri Jaka Harjanta, Aris Tri Bambang Agus Herlambang, Bambang Burhanuddin, Aan Desi Purwaningsih Dwi Saputro, Nugroho fafa nurdyansyah, fafa Febrian Murti Dewanto Febrihastatiwi, Dinda Fenny Roshayanti Ferdy Semuel Rondonuwu Hayat, M. Syaipul Hukmi Hukmi, Hukmi Iffah Muflihati, Iffah Ingsan, Roies Nur Intan Indiati Jubhar Mangimbulude Karyawan Kusumo, Haryo Lukman Anugrah Agung Mardikaningrum, Tri Ambarwati Mariyana, Ekanata Desi Sagita Marlina, Dian Maulana, Novan Mawardi, Dimas Putra Muhammad Syaipul Hayat Muzakki, Tsaqif Nilawati, Anita Nofitasari, Shindi Noora Qotrun Nada, Noora Qotrun Nugroho Dwi Saputro Nugroho Dwi Saputro, Nugroho Dwi Nur Khoiri Nur Latifah Dwi Mutiara Sari Paramita, Diva Julia Pramukantoro, Ganet Eko Priyanto, Widodo Renaldy, Ramadhan Rukmi, Pramastuti Adiar Salwa, Faiza Rulla Saputro, Anjar Tiyo Setianingsih, Siti Nurlaela Setyoningsih Wibowo Siti Patonah Siwi, Andini Prabandaru Sri Wahyuni Ujianti, Rizky Muliani Dwi Wafa, Alfan Najihil Waliyansyah, Rahmat Robi Yunitha, Emmellia