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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) JOURNAL OF APPLIED INFORMATICS AND COMPUTING 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 ADMA: Jurnal Pengabdian dan Pemberdayaan 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) Berita Biologi
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 Languange, Linguistic, Communication & Media Materials Science & Nanotechnology Mathematics Medicine & Pharmacology Nursing 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.
ANTIOXIDANT ACTIVITY PROFILING OF RED BAJAKAH TAMPALA (Spatholobus littoralis Hassk) FRACTIONS USING THE DPPH RADICAL SCAVENGING METHOD Amanda, Nathasya Gracya; Sari, Ghani Nurfiana Fadma; Novita, Mega; Marlina, Dian
Berita Biologi Vol 24 No 3 (2025): Berita Biologi
Publisher : BRIN Publishing (Penerbit BRIN)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55981/berita_biologi.2025.11322

Abstract

Indonesia’s rich biodiversity harbors numerous plants with medicinal properties, including Red Bajakah Tampala (Spatholobus littoralis Hassk.), which has long been used in traditional medicine for its therapeutic benefits. Despite its promising ethnomedicinal reputation, scientific validation of its antioxidant potential remains limited. This study aims to evaluate the antioxidant properties of Red Bajakah Tampala and identify key bioactive compounds responsible for its bioactivity. The plant's antioxidant capacity was assessed using the DPPH assay, and the chemical composition was analyzed through phytochemical screening and thin-layer chromatography (TLC). The ethyl acetate fraction exhibited the strongest antioxidant activity, with an IC₅₀ value of 12.87 ppm, followed by the n-hexane fraction (24.08 ppm) and the water fraction (57.23 ppm). Phytochemical screening revealed the presence of flavonoids, tannins, triterpenoids, phenols, and saponins, which contribute to the plant's antioxidant effects. These findings provide scientific evidence supporting the traditional use of Red Bajakah Tampala as a natural antioxidant, with implications for its potential use in therapeutic and cosmetic applications. Further research into the molecular mechanisms and practical applications of Bajakah-based formulations is recommended.
Ultra-Low-Cost Hybrid OCR–LLM Architecture for Production Grade E-KTP Extraction Saputro, Anjar Tiyo; Herlambang, Bambang Agus; Novita, Mega
Scientific Journal of Informatics Vol. 12 No. 4: November 2025
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v12i4.38200

Abstract

Purpose: The purpose of this study is to be able to avoid limitations of inexpensive ID card data extraction services and preserve privacy, which can simultaneously achieve reliable operation even under an environment with minimum infrastructure, in particular if no dependency on GPU-based servers are required. Method: The proposed approach is a microservice pipeline with three stages: (1) local lightweight pre-processing on devices, (2) Tesseract CPU-based OCR. js, (3) fast text tokenization through a small premature external LLM. The system is developed as TypeScript backend utilizing the Hono framework with all image processing taking place locally in order to keeping user data private. Result: The result of the experimental evaluations with real ID card samples is that the system can run stably in low-performance VPS (1 vCPU, 1 GB RAM) with operation cost approximately IDR 2.5047 per extraction process and its accuracy level is acceptable for use in a production environment. Moreover, the results indicate that system latency is dominated by LLM inference at the cloud. Novelty: The main contribution and novelty of this study is that we demonstrate, for the first time, a cost-effective (privacy-preserving) OCR-LLM hybrid pipeline without demanding expensive GPU models at large scale which makes our system suitable under limited storage and resource constraints on-premises or edge environments in small organizations including micro-SaaS services.
Anti-Acne Activity of Robusta Green Coffee Bean Extract against Cutibacterium acnes Putri, Cinthiya Ekwinta; Puspitasari, Ismi; Novita, Mega; Marlina, Dian
MPI (Media Pharmaceutica Indonesiana) Vol. 7 No. 2 (2025): DECEMBER
Publisher : Fakultas Farmasi, Universitas Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24123/mpi.v7i2.7857

Abstract

Acne vulgaris, a common chronic skin disorder, is primarily caused by the overgrowth of Cutibacterium acnes and often treated with synthetic agents that may cause side effects and resistance. The increasing demand for therapeutics with improved safety profiles and natural alternatives has encouraged the exploration of herbal remedies, including robusta green coffee beans (Coffea canephora). This study aimed to evaluate the anti-acne activity of robusta coffee bean ethanol extract against C. acnes. The extract was prepared through maceration and subsequently evaluated using to phytochemical screening, thin layer chromatography, and in vivo testing on New Zealand rabbits. Results confirmed the presence of flavonoids, alkaloids, tannins, steroids, and triterpenoids. In vivo assays demonstrated that the 75% extract concentration achieved a 96.69% reduction of acne lesions, comparable to the positive control at 99.35%, while lower concentrations showed moderate activity. These findings highlight the potential of robusta coffee bean extract as a promising natural anti-acne agent. The study implies that robusta extract can be further developed into herbal-based dermatological formulations, although future research should focus on isolating active compounds and conducting clinical trials for broader application.
Implementation and Comparative Analysis of CNN and Transfer Learning Models (EfficientNetB0, MobileNetV2, and ResNet50) for Rice Leaf Disease Detection Based on Digital Images Utami, Tri Wahyu; Novita, Mega; Latifa, Khoiriya
Journal of Applied Informatics and Computing Vol. 9 No. 6 (2025): December 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i6.11616

Abstract

Rice leaf diseases significantly reduce agricultural productivity, making early and accurate detection essential, particularly in rice-producing regions such as Indonesia. This study proposes an automated rice leaf disease detection system based on Convolutional Neural Networks (CNN) and transfer learning. The dataset, obtained from the Mendeley Data Repository, consists of 6,889 images classified into eight categories: Bacterial Leaf Blight, Brown Spot, Healthy Rice Leaf, Leaf Blast, Leaf Scald, Narrow Brown Leaf Spot, Rice Hispa, and Sheath Blight. The dataset was divided into 70% training, 15% validation, and 15% testing. A baseline CNN model and three pre-trained models—EfficientNetB0, MobileNetV2, and ResNet50—were evaluated using accuracy, precision, recall, F1-score, and confusion matrix analysis. The baseline CNN achieved a test accuracy of 48.26%, while EfficientNetB0 achieved 58.41%. In contrast, MobileNetV2 and ResNet50 demonstrated significantly better performance, with test accuracies of 79.98% and 76.60%, respectively. MobileNetV2 exhibited the most balanced performance across all classes, showing superior generalization capability and computational efficiency. The best-performing model was integrated into a Streamlit-based application, enabling real-time rice leaf disease detection through image upload. The results confirm that transfer learning substantially improves classification accuracy and robustness compared to conventional CNNs. This study highlights the potential of lightweight deep learning models for practical implementation in smart agriculture systems and provides a reliable solution for automated rice disease detection in real-world conditions.
Co-Authors AA Sudharmawan, AA Amanda, Nathasya Gracya 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 Ghani Nurfiana Fadma Sari Hayat, M. Syaipul Hermana, Rifki Hukmi Hukmi, Hukmi Iffah Muflihati, Iffah Ingsan, Roies Nur Intan Indiati Jubhar Mangimbulude Karyawan Kusumo, Haryo Latifa, Khoiriya 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 Puspitasari, Ismi Putri, Cinthiya Ekwinta Renaldy, Ramadhan Rukmi, Pramastuti Adiar Salwa, Faiza Rulla Saputro, Anjar Tiyo Setianingsih, Siti Nurlaela Setyoningsih Wibowo Siti Patonah Siwi, Andini Prabandaru Sri Wahyuni Sutomo Sutomo Ujianti, Rizky Muliani Dwi Utami, Tri Wahyu Wafa, Alfan Najihil Waliyansyah, Rahmat Robi Wibowo, Aries Setyo Yunitha, Emmellia