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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) 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 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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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.
CNN Implementation in Progressive Web App for Automatic Garbage Classification using TensorFlow.js Eka Setyabudi; Noora Qotrun Nada; Mega Novita
Jurnal Teknologi Informasi dan Terapan Vol 12 No 2 (2025): December
Publisher : Jurusan Teknologi Informasi Politeknik Negeri Jember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25047/jtit.v12i2.458

Abstract

The substantial and continuously increasing volume of global waste has become a critical environmental challenge, exacerbating the inherent inefficiency of conventional manual sorting techniques. This research addresses this problem by developing and evaluating an automated waste classification system using Convolutional Neural Networks (CNN), specifically the VGG16 architecture, integrated into a Progressive Web App (PWA) to enhance accessibility and sorting efficiency. Our primary goal is to deliver an intelligent, lightweight, and cross-platform solution capable of performing client-side inference on diverse devices. The VGG16 model was retrained using transfer learning on a validated public dataset of 10,365 images, comprising two classes (organic and inorganic waste). The trained model was converted to a browser-compatible format, TensorFlow.js, and deployed within the PWA framework which utilizes Service Workers for offline capabilities. Despite the significant challenge posed by the VGG16 model's large size, the system successfully performed client-side inference by prioritizing GPU acceleration and achieved 0.94 overall accuracy on the test dataset2. This result, supported by high F1-scores for both waste categories, confirms that deploying high-accuracy CNN models at the edge using PWA and TensorFlow.js is a feasible and promising strategy for practical, technology-based waste management and environmental education.
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.
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 Arwan Mangli Bambang Agus Herlambang, Bambang Burhanuddin, Aan Desi Purwaningsih Dwi Saputro, Nugroho Eka Setyabudi 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 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 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