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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 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 Jurnal Teknologi Informasi dan Terapan (J-TIT) Infotekmesin 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 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 Mechanical Engineering Medicine & Pharmacology Nursing Physics Public Health Social Sciences Other

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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.
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.
Transformasi UMKM Desa Doplang melalui Koperasi Digital, Produk Wellness, IoT Smart Watering, dan Branding Wisata Novita, Mega; Senowarsito, Senowarsito; Hermana, Rifki; Sutomo, Sutomo
ADMA : Jurnal Pengabdian dan Pemberdayaan Masyarakat Vol. 6 No. 2 (2025): ADMA: Jurnal Pengabdian dan Pemberdayaan Mayarakat: In-Progress
Publisher : LPPM Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/adma.v6i2.5726

Abstract

Desa Doplang di Kabupaten Semarang memiliki potensi besar pada komoditas bunga sedap malam, namun pemanfaatannya selama ini terbatas pada produk hias dengan nilai ekonomi rendah. UMKM setempat menghadapi kendala inovasi produk, akses pasar, dan kelembagaan ekonomi yang lemah. Program pengabdian multi-tahun sebelumnya (2023–2024) telah menghasilkan pelatihan dasar dan diversifikasi produk awal, tetapi aspek keberlanjutan kelembagaan, penerapan teknologi hemat sumber daya, dan strategi branding desa wisata belum terwujud optimal. Artikel ini bertujuan mendeskripsikan transformasi UMKM Desa Doplang pada tahun 2025 sebagai puncak program pemberdayaan melalui penguatan koperasi digital, pengembangan produk wellness, dan inovasi IoT smart watering. Kegiatan dilaksanakan dengan pendekatan partisipatif melibatkan UMKM, PKK, Karang Taruna, Pokdarwis, dan BumDes, melalui tahapan sosialisasi, pelatihan, penerapan teknologi, pendampingan, dan evaluasi. Hasil kegiatan menunjukkan terbentuknya koperasi digital SIRATIH dengan anggota awal pelaku UMKM desa sebagai wadah distribusi dan transaksi kolektif, peluncuran tiga produk wellness baru (lilin aromaterapi, body butter, dan lulur) yang memperluas portofolio UMKM, serta implementasi prototipe IoT smart watering yang mampu mengurangi penggunaan air hingga sekitar 20% pada lahan uji coba. Selain itu, booklet promosi wisata Jendela Doplang diterbitkan untuk memperkuat citra desa sebagai destinasi wisata berbasis wellness. Program ini membuktikan bahwa integrasi kelembagaan digital, inovasi produk, dan penerapan teknologi tepat guna mampu memperkuat daya saing UMKM sekaligus mendorong keberlanjutan ekonomi lokal. Implikasi lebih luas dari program ini adalah perlunya dukungan lintas sektor untuk mempercepat sertifikasi produk dan replikasi model ke desa lain yang memiliki potensi serupa.
ANALYSIS OF HIGH SCHOOL STUDENTS’ CRITICAL AND COMPUTATIONAL THINKING SKILLS IN THERMOCHEMISTRY Wibowo, Aries Setyo; Patonah, Siti; Novita, Mega
Jurnal Pendidikan Matematika dan IPA Vol 17, No 1 (2026): January 2026
Publisher : Universitas Tanjungpura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26418/jpmipa.v17i1.104115

Abstract

Critical and computational thinking skills are two essential 21st-century competencies that are highly relevant in chemistry education, particularly in thermochemistry topics that require deep conceptual understanding and scientific reasoning. This study aims to provide an in-depth analysis of the profile of high school students’ critical and computational thinking skills in thermochemistry. A descriptive quantitative method was employed, involving 36 eleventh-grade students from SMAN 1 Kedungwuni. The instruments consisted of diagnostic tests based on Facione’s and Brennan & Resnick’s indicators, classroom observations, semi-structured interviews with teachers and students, and student perception questionnaires. The analysis revealed that students’ critical thinking skills were at a moderate level, with the highest score in interpretation (65.3%) and the lowest in evaluation (58.1%) and explanation (59.4%). Similarly, computational thinking skills were also in the moderate category, with the highest score in decomposition (63.2%) and the lowest in abstraction (57.3%). Observational and interview data indicated that learning was still dominated by conventional lecture methods with limited exploratory activities that promote higher-order thinking. However, students expressed strong motivation toward the use of more interactive learning media, such as websites or Android-based applications. In conclusion, these findings underscore the need for innovative, contextual, and technology-based learning media to enhance students’ critical and computational thinking skills and better align chemistry education with 21st-century learning demand.
Prediksi Risiko Depresi Berdasarkan Data Demografis dan Psikososial menggunakan Metode Ensemble Learning dengan Pendekatan Stacking Arwan Mangli; Noora Qotrun Nada; Mega Novita
Infotekmesin Vol 17 No 1 (2026): Infotekmesin: Januari 2026
Publisher : P3M Politeknik Negeri Cilacap

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35970/infotekmesin.v17i1.3102

Abstract

Depression is a mental health problem with high prevalence that requires accurate and reliable computational-based prediction systems to support early detection. This study proposes a depression risk prediction architecture based on a stacking ensemble approach incorporating an out-of-fold (OOF) mechanism to prevent data leakage during meta-feature generation. The model combines Support Vector Machine and XGBoost as base learners, with Logistic Regression employed as the meta-learner. A public Depression Professional Dataset is processed using a stratified split strategy, class balancing on the training data through SMOTE, and feature standardization to enhance training stability. Experimental results demonstrate that the proposed approach achieves superior performance with an accuracy of 0.99, precision of 0.91, recall of 1.00, and an F1-score of 0.95, along with consistent detection capability for the minority class. These findings confirm that the systematic integration of OOF stacking and SMOTE improves model sensitivity while reducing false negative errors, making it suitable for the development of artificial intelligence–based mental health screening systems.
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 Amanda, Nathasya Gracya Anggraeni, Cerly Nurlita Anggraini, Natasya Dila Putri Aris Tri Jaka Harjanta, Aris Tri Arwan Mangli Bambang Agus Herlambang, Bambang Burhanuddin, Aan Desi Purwaningsih Dwi Saputro, Nugroho 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 Noora Qotrun Nada Noora Qotrun Nada, Noora Qotrun Nugroho Dwi Saputro Nugroho Dwi Saputro, Nugroho Dwi Nur Khoiri Nur Latifah Dwi Mutiara Sari Pramukantoro, Ganet Eko Priyanto, Widodo Puspitasari, Ismi Putri, Cinthiya Ekwinta Renaldy, Ramadhan Rukmi, Pramastuti Adiar Salwa, Faiza Rulla Saputro, Anjar Tiyo Setyabudi, Eka 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