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All Journal Jurnal Ilmu Pendidikan Tekno : Jurnal Teknologi Elektro dan Kejuruan Teknologi dan Kejuruan: Jurnal teknologi, Kejuruan dan Pengajarannya Cakrawala Pendidikan JPTK: Jurnal Pendidikan Teknologi dan Kejuruan ELINVO (Electronics, Informatics, and Vocational Education) Jurnal Pendidikan: Teori, Penelitian, dan Pengembangan JOIN (Jurnal Online Informatika) Jurnal Pendidikan (Teori dan Praktik) JOURNAL OF APPLIED INFORMATICS AND COMPUTING SENTIA 2016 Nazhruna: Jurnal Pendidikan Islam Jurnal Basicedu Jurnal Nasional Pendidikan Teknik Informatika (JANAPATI) Journal of Computer Science and Informatics Engineering (J-Cosine) Gelar : Jurnal Seni Budaya JTP - Jurnal Teknologi Pendidikan JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Jurnal Karinov ILKOMNIKA: Journal of Computer Science and Applied Informatics TRIDARMA: Pengabdian Kepada Masyarakat (PkM) Generation Journal Jurnal Teknodik JPP (Jurnal Pendidikan dan Pembelajaran) Discovery : Jurnal Ilmu Pengetahuan Belantika Pendidikan Letters in Information Technology Education (LITE) Indonesian Journal of Instructional Media and Model Jurnal Abdimas Berdaya : Jurnal Pembelajaran, Pemberdayaan dan Pengabdian Masyarakat Didaktika: Jurnal Kependidikan Journal of Applied Data Sciences International Journal of Engineering, Science and Information Technology Journal of Science and Education (JSE) IJORER : International Journal of Recent Educational Research Emerging Information Science and Technology International journal of education and learning Jurnal Integrasi dan Harmoni Inovatif Ilmu-ilmu Sosial Jurnal Basicedu JP (Jurnal Pendidikan) : Teori dan Praktik Jurnal Ilmiah Edutic : Pendidikan dan Informatika Reflection Journal Research and Development in Education (RaDEn) JUSIFOR : Jurnal Sistem Informasi dan Informatika Jurnal Riset Rumpun Agama dan Filsafat Jurnal Inovasi Teknologi dan Edukasi Teknik Jurnal Pendidikan Islam Jurnal Pengabdian Masyarakat dan Riset Pendidikan IRDH International Journal of Technology, Agriculture and Natural Sciences (IRDH IJTANS) Energy: Jurnal Ilmiah Ilmu-ilmu Teknik
Syaad Patmanthara
Universitas Negeri Malang
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DIGITALISASI MATERI DAN MEDIA PEMBELAJARAN “ZAT DAN PERUBAHANNYA” BERBASIS MOBILE APPLICATION UNTUK MEMBENTUK KARAKTER HIDUP BERSIH PADA SISWA PROGRAM KEAHLIAN DESAIN KOMUNIKASI VISUAL Massitta, Massitta; Patmanthara, Syaad; Alfianto, Imam; Nurjanah, Nunung
JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Vol 11, No 1 (2026)
Publisher : STKIP PGRI Tulungagung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29100/jipi.v11i1.9398

Abstract

Penelitian ini bertujuan untuk menghasilkan produk berupa materi dan media pembelajaran yang layak dan efektif pada mata pelajaran Projek Ilmu Pengetahuan Alam dan Sosial (Projek IPAS) dengan topik "Zat dan Perubahannya", yang diintegrasikan secara inheren dengan nilai-nilai karakter hidup bersih. Penelitian ini menggunakan model penelitian dan pengembangan (Research and Development) 4D yang terdiri dari tahap Define, Design, Develop, dan Disseminate. Latar belakang penelitian ini adalah adanya kesenjangan antara tuntutan kompetensi teknis dan pembinaan karakter di Sekolah Menengah Kejuruan (SMK), serta kurangnya relevansi materi ajar sains dengan konteks keseharian siswa pada program keahlian Desain Komunikasi Visual (DKV). Subjek dari kegiatan pengembangan ini adalah siswa kelas X DKV di SMKN 1 Nguling. Produk yang dihasilkan berupa sebuah paket pembelajaran terpadu yang terdiri dari modul mini, video pembelajaran, Lembar Kerja Peserta Didik (LKPD), dan poster edukatif. Hasil validasi ahli dan uji coba terbatas menunjukkan bahwa produk yang dikembangkan sangat layak dan efektif untuk digunakan. Implementasi di kelas menunjukkan peningkatan antusiasme dan partisipasi aktif siswa, serta teramatinya perubahan perilaku positif siswa dalam hal kepedulian terhadap kebersihan lingkungan belajar. Dengan demikian, pengembangan media berbasis kontekstual ini terbukti tidak hanya mampu meningkatkan pemahaman kognitif siswa terhadap materi sains, tetapi juga efektif sebagai sarana internalisasi dan pembentukan karakter positif.
Penerapan Problem-Based Learning dalam Pembelajaran Pemrograman menggunakan Codemonkey untuk Meningkatkan Kemampuan Pemecahan Masalah Siswa Kelas VII: Penelitian Setyawan Aji Samudra; Syaad Patmanthara; Syeh Umar Anggana; Rasif Nidaan Khofia Ahmadah; Putri Alivia Nabila; Rachman Kurniawan
Jurnal Pengabdian Masyarakat dan Riset Pendidikan Vol. 4 No. 4 (2026): Jurnal Pengabdian Masyarakat dan Riset Pendidikan Volume 4 Nomor 4 Tahun 2026
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/jerkin.v4i4.3185

Abstract

The ability to solve problems is a crucial 21st-century skill that should be developed from an early age, including through Informatics lessons at the junior high school level. However, traditional methods of teaching programming are often less effective in stimulating active participation and student understanding. The main focus of this study is to evaluate the effectiveness of implementing a Problem-Based Learning (PBL) strategy combined with the CodeMonkey platform as an effort to develop problem-solving skills among seventh-grade students at SMP Negeri 8 Malang. The method applied in this study is Classroom Action Research (CAR) based on the Kemmis and McTaggart model, carried out over two cycles. Data were obtained through student learning outcome evaluations and analyzed using quantitative methods. Results from the first cycle showed a mastery level of 20% with an average score of 60. After implementing PBL in the second cycle, mastery increased to 83,33% with an average score of 85. The application of problem-based learning proved effective in promoting the improvement of students’ cognitive and social skills through group collaboration, discussions, and mastery of contextual challenges. Therefore, the PBL model combined with interactive media such as CodeMonkey can serve as an innovative and relevant alternative learning strategy to enhance programming skills among junior high school students.
A Conceptual Framework for Human AI Collaboration: Ontological and Epistemological Perspectives Apriyani, Meyti Eka; Patmanthara, Syaad
ENERGY: JURNAL ILMIAH ILMU-ILMU TEKNIK 2025: ENERGY: JURNAL ILMIAH ILMU-ILMU TEKNIK (Special Issue on Engineering Paradigm 2025 Edition)
Publisher : Universitas Panca Marga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51747/energy.si2025.251

Abstract

Collaboration between humans and artificial intelligence (AI) has become a pivotal phenomenon in the evolution of information systems, yet its philosophical foundations remain underexplored. This study develops an integrative conceptual framework that combines ontological and epistemological perspectives to examine how human–AI collaboration shapes knowledge creation and decision-making within sociotechnical contexts. The proposed framework identifies five ontological levels of AI agency and four epistemological processes underlying hybrid knowledge formation. It further integrates six interrelated dimensions—ontological, epistemological, technical, ethical, social, and organizational—that collectively define the dynamics of human–AI collaboration. The findings contribute to the theoretical discourse by introducing the constructs of quasi-epistemic entities and hybrid epistemology, which reconceptualize AI not merely as a computational artifact but as a participant in epistemic processes, thereby extending existing theories of distributed cognition and epistemic accountability beyond instrumental human–machine models. Practically, the framework informs the design of transparent, adaptive, and ethically aligned human–AI systems within information-intensive environments.
From THD to Causality: Epistemology of Artificial Intelligence-Based Harmonic Analysis in Hybrid Microgrids Achadiyah, Ana Nuril; Afandi, Arif Nur; Patmanthara, Syaad
ENERGY: JURNAL ILMIAH ILMU-ILMU TEKNIK 2025: ENERGY: JURNAL ILMIAH ILMU-ILMU TEKNIK (Special Issue on Engineering Paradigm 2025 Edition)
Publisher : Universitas Panca Marga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51747/energy.si2025.252

Abstract

The increasing penetration of PV, wind turbines, battery storage (BESS), and electric vehicle charging stations (EVCS) in hybrid microgrids complicates the harmonic landscape. Common practices rely on FFT-based measurements and THD/TDD indices, but source attribution and causality assignment are often uncertain. We map how epistemological positions shape how we measure, explain, and justify technical claims about harmonics. We then propose an Epistemically-Informed Harmonic AI (EPI-HAI) framework that combines standardized measurements (IEC/IEEE), physics-constrained AI modeling (KCL/KVL, impedance), XAI (SHAP/Grad-CAM), and uncertainty management to strengthen epistemic trust. A vignette of a PV–BESS–EVCS microgrid demonstrates that triangulation of evidence (n-order patterns, operating logs, line impedance) is more valid than mere spectral correlation. The three main contributions of this article are, the compilation of a map of the relationship between epistemology and methodology in harmonic analysis, the formulation of transparent and accountable physics-based artificial intelligence (AI) design principles and a discussion of pedagogical implications that can be applied in the development of power engineering curricula.
An Epistemological Approach to Explainable Automated Assessment of Open Concept Map Propositions Using SHAP Ciptaningrum, Mega Satya; Patmanthara, Syaad; Prasetya, Didik Dwi
ENERGY: JURNAL ILMIAH ILMU-ILMU TEKNIK 2025: ENERGY: JURNAL ILMIAH ILMU-ILMU TEKNIK (Special Issue on Engineering Paradigm 2025 Edition)
Publisher : Universitas Panca Marga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51747/energy.si2025.255

Abstract

Concept mapping is widely recognized as an effective method for supporting meaningful learning and critical thinking because it allows teachers to assess students’ underlying knowledge structures. However, evaluating concept maps and providing feedback remain challenging, as these processes are time-consuming, increase teachers’ workload, and can reduce instructional efficiency. To address this issue, this study applies Transformer-based architectures, which rely on large-scale pre-training and task-specific fine-tuning, to develop an automated assessment system for concept maps. In addition, Explainable Artificial Intelligence (XAI) is integrated through the SHAP (SHapley Additive exPlanations) framework to generate interpretable explanations of the model’s scoring decisions. Using Transformer models such as BERT and DeBERTa, SHAP values are computed at the token level to show how individual words within each proposition contribute to the final score. The results indicate that tokens with positive SHAP values increase scores in line with correct rubric indicators, whereas negative values reduce them. Tokens that consistently show positive contributions in high-scoring outputs reflect stable and faithful model reasoning. Overall, the findings demonstrate that combining Transformer-based assessment with SHAP explanations improves epistemic transparency by aligning the model’s internal reasoning with expert evaluation criteria, thereby supporting more reliable, interpretable, and trustworthy automated feedback in concept mapping-based learning.
The Application of Machine Learning in Liver Disease Diagnosis: Analysis of Algorithm Performance and Axiological Implications Utami, Sri Farida; Patmanthara, Syaad
ENERGY: JURNAL ILMIAH ILMU-ILMU TEKNIK 2025: ENERGY: JURNAL ILMIAH ILMU-ILMU TEKNIK (Special Issue on Engineering Paradigm 2025 Edition)
Publisher : Universitas Panca Marga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51747/energy.si2025.253

Abstract

Liver disease remains a significant global health challenge, requiring accurate and timely diagnosis to improve patient outcomes and reduce healthcare costs. This study investigates the application of four machine learning classification algorithms—Decision Tree, Random Forest, Naïve Bayes, and K-Nearest Neighbors (KNN)—to predict the presence of liver disease using a dataset sourced from Kaggle. These algorithms were evaluated based on performance metrics such as accuracy, precision, recall, and F1 score. Both Decision Tree and Random Forest achieved the highest accuracy rate of 72.41%, demonstrating their robustness in classifying liver disease cases. However, these models showed some limitations in identifying patients without liver disease. Naïve Bayes, with an accuracy of 60.34%, exhibited an impressive recall rate of 96.97%, indicating its potential in detecting liver disease cases, though at the cost of lower precision. KNN, with an accuracy of 70.69%, proved to be a competitive option in the classification task. Beyond technical performance, the study also explores the ethical and axiological implications of using machine learning in healthcare, emphasizing the importance of fairness, transparency, and human oversight. The research highlights the need for responsible deployment of machine learning technologies, ensuring they are aligned with ethical standards to avoid biases and enhance healthcare outcomes. This study demonstrates that machine learning can significantly support liver disease diagnosis, though it must be integrated with a comprehensive ethical framework to ensure equitable and transparent decision-making in clinical practice.
Solar Powered Street Lighting in Rural Areas: A Value-Use Analysis of Green Technology Axiology Riyanto, Didik; Patmanthara, Syaad; Afandi, Arif Nur
ENERGY: JURNAL ILMIAH ILMU-ILMU TEKNIK 2025: ENERGY: JURNAL ILMIAH ILMU-ILMU TEKNIK (Special Issue on Engineering Paradigm 2025 Edition)
Publisher : Universitas Panca Marga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51747/energy.si2025.254

Abstract

This study aims to analyze the utility value and axiological implications of the application of green technology, namely Solar Powered Street Lighting (PSL), in Duri Village, Slahung District, Ponorogo Regency. The main problem in the village is the lack of a public street lighting system due to the limited PLN electricity network on the connecting roads between villages. Through an axiological review, this solar power plant technology is analyzed not only from a technical aspect, but also from its beneficial value for community life. The research method includes field studies, planning, implementation of independent Public Street Lighting technology equipped with automatic sensors, implementation testing, and mentoring. The results of the implementation of one Public Street Lighting unit using solar electricity using Smart Bright Solar cell technology with 4000 lm lighting show that this technology provides an independent lighting solution for the general public, improves security, and supports environmental sustainability. The application of solar power plant on Public Street Lighting in rural areas realizes the axiological value of science as a means to improve the quality of life and create energy independence in remote areas.
Sentiment Analysis of YouTube Comments Using the K-Nearest Neighbors (KNN) Method from an Axiological Perspective Lestandy, Merinda; Patmanthara, Syaad
ENERGY: JURNAL ILMIAH ILMU-ILMU TEKNIK 2025: ENERGY: JURNAL ILMIAH ILMU-ILMU TEKNIK (Special Issue on Engineering Paradigm 2025 Edition)
Publisher : Universitas Panca Marga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51747/energy.si2025.257

Abstract

The rapid development of social media as a space for digital interaction has increased the need for sentiment analysis to understand public opinion in a systematic and measurable way. This study analyzes YouTube comment sentiment using the K-Nearest Neighbor (K-NN) method while also examining the axiological value of applying this technology in support of a more ethical digital ecosystem. The dataset consists of 8,200 YouTube comments obtained from Kaggle without predefined sentiment labels. The data were preprocessed through case folding, tokenization, stopword removal, stemming, and normalization. Initial sentiment labels were generated automatically using K-Means clustering to form two classes—positive and negative—and were partially verified manually. The labeled data were split into training and testing sets with ratios of 50:50, 60:40, 70:30, and 80:20, and evaluated using K-NN with k values of 3, 5, 7, and 9. Model performance was assessed using a confusion matrix with accuracy, precision, recall, and F1-score metrics. The results show that accuracy ranges from 0.95 to 0.96, with the best performance achieved at a 70:30 split and an optimal k value yielding 0.96 accuracy. Beyond technical contributions, this study highlights the ethical and practical value of sentiment analysis for interpreting public opinion, supporting fairer content moderation, and improving communication quality in social media environments.
An Epistemological Analysis of Metaheuristic MPPT Performance for Photovoltaic Systems under Partial Shading Conditions Hidayat, Khusnul; Afandi, Arif Nur; Patmanthara, Syaad
ENERGY: JURNAL ILMIAH ILMU-ILMU TEKNIK 2025: ENERGY: JURNAL ILMIAH ILMU-ILMU TEKNIK (Special Issue on Engineering Paradigm 2025 Edition)
Publisher : Universitas Panca Marga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51747/energy.si2025.258

Abstract

Metaheuristic-based maximum power point tracking algorithms are widely used in photovoltaic systems to address nonlinear and multi-peak characteristics under partial shading conditions. However, many reported performance claims rely mainly on numerical simulation and therefore require cautious interpretation. This study presents a simulation-based comparative and epistemological analysis of Particle Swarm Optimization and Differential Evolution for photovoltaic maximum power point tracking. Both algorithms are implemented in an identical buck converter-based photovoltaic framework to ensure fair comparison. Performance is evaluated under uniform irradiance and partial shading conditions using convergence time and tracked power as evaluation metrics. The results show that under uniform irradiance, both algorithms reliably converge to the maximum power point with similar steady-state accuracy, while Particle Swarm Optimization converges faster. Under partial shading conditions, Particle Swarm Optimization consistently tracks the global maximum power point, whereas Differential Evolution shows occasional convergence failure or suboptimal tracking. From an epistemological standpoint, these findings constitute coherent and pragmatically useful model-based knowledge, while remaining provisional due to the absence of experimental validation.
Co-Authors A.N. Afandi Achadiyah, Ana Nuril Achmad Imam Agung Aditya Galih Sulaksono, Aditya Galih Agung Bella Putra Utama Ahmad Sahru Romadhon Aji Prasetya Wibawa Al Mukafi, Muhammad Hamdan Alfarid Hendro Yuwono Amidatus Sholihat jamil Amrullah, Ahmad Khakim Angga Achmad Cholid Anik Nur Handayani Anwar, Akhmad Syaiful Arie Wardhono Aripriharta - Arizia Aulia Aziiza Arsyillah, Nazhiroh Tahta Asfani, Khoirudin Ashar, Muhammad Ashar, Muhammad Asih Setiani Aya Sofia Mufti Ayuningtyas Kurniawati Azhar Ahmad Smaragdina Benti Gandisa Bramastya, Rista Ciptaningrum, Mega Satya Daniar Wahyu Akbar Oktaviando Dendy Dewa Widjaya Putra Dhega Febiharsa Didik Dwi Prasetya Didik Nurhadi Dila Umnia Soraya Djoko Kustono Djunaidi Ghany Dyah Lestari Eddy Sutadji Eddy Triswanto Setyoadi Eddy Triswanto Setyoadi Ega Putriatama Eko Setiawan Eko Setiawan Ekohariadi Ekohariadi Elfia Najib Kholifiatin Eva, Nur Evania Kurniawati Fachrul Kurniawan Fadli Hidayat, M. Noer Fahmi Efendi Yusuf Fandi Akhmad Kurniawan Fatmawati, Hefi Fawaidul Badri Ferdiansyah, Dodik Septian Fikha Rizky Aullia Firman Syahputra, Yohanes Dhimas Giri Wahyu Wiriasto Gülsün Kurubacak Hakkun Elmunsyah Hanifah, Nida Hanna Zakiyya Hari Putranto Harits Ar Rosyid Harmanto Harmanto Hartarto Junaedi Hary Suswanto Hermansyah, Winda Adelia Heru Wahyu Herwanto Hidayat, Manik I Made Sudana I Made Wirawan Ikhwan Arif Ilmam, Thirafi Imam Alfianto Indraswari, Martha Devi Isnandar Jayadi, Puguh Joumil Aidil Saifuddin Julfikar Mawansyah Karaman, Jamilah Kartika Candra Kirana Kholiqin, Sabrina Nabila Khusnul Hidayat Kurniawan, Rivan Adi Kurniawan, Singgih Adie Kurubacak, Gulsun Lokapitasari Belluano, Poetri Lestari Luqman Affandi M Ibrahim Ashari M. Zainal Arifin M. Zainal Arifin Mahali, Mahali Marji Marji Maskur Maskur Massitta, Massitta Masyfa, Faiz Hilmawan MAULA, PUTRINDA INAYATUL Meidy, Ria Devita Meidy, Ria Devita Mentari, Febiana Putri Merinda Lestandy Moh. Afifullah Mokh Sholihul Hadi Mubarok, Sulton Muhamad Syamsu Iqbal Muhammad Auva Romadhon Muhammad Hamdan Al Mukafi Muhammad Hudan Rahmat Mukhamad Angga Gumilang Muladi Nafi Isbadrianingtyas Naurah Septi Anggraini Nia Arlika Nidhom, Ahmad Mursyidun Ningrum, Gres Dyah Kusuma Nunung Nurjanah Nur Aini Susanti Nur Hidayat, Wahyu Nur Hikmah Nurul Hidayati Odhitya Desta Oki Dwi Yuliana Perdana Putra, Muhammad Ricky Prasetyo, Wiji Dwi Prayoga, Adie Purnomo, Purnomo Putri Alivia Nabila R. Mahmud Sugandi Rachman Kurniawan Rachman, Tegar Fatur Rahajeng Kartika Sari Rahmawati, Chusnia Ramadiani, Nanda Resta Rasif Nidaan Khofia Ahmadah Ratnasari, Novia Resti Pranata Putri Ria Devita Meidy Riyanto, Didik Rizal, Muhammad Fatkhur Rokhimatul Wakhidah Sakkinah, Intan Sulistyaningrum Sari, Heni Vidia Sari, Rahajeng Kartika Setyawan Aji Samudra Shofiyah Al Idrus Siti Munawaroh Siti Sendari Slamet Wibawanto Soenar Soekopitojo Sri Farida Utami Sri Sumanti, Endang Suastika Yulia Riska Suci Lestari Sunu Jatmika, Sunu Suparji Suparji Sutapa, Yohanes Gatot Syamsul Hadi Syeh Umar Anggana Taufik Hidayat Titasari Rahmawati Tiya Nurul Khusna Tri Atmadji Sutikno Tri Wrahatnolo Triyana Widiyaningtyas Triyanna Widiyaningtyas Triyanna Widiyaningtyas Tuwoso Usman Nurhasan Wahyu Sakti Gunawan Irianto Waras Waras Yuli Sutoto Nugroho Yuliana, Oki Dwi Yuniardi, Gigih Dwi Yussi Anggraini Zaeni, Ilham Ari Elbaith Zulfikar, Nizam Muchammad