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All Journal 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) JOIN (Jurnal Online Informatika) Jurnal Pendidikan (Teori dan Praktik) JOURNAL OF APPLIED INFORMATICS AND COMPUTING SENTIA 2016 Jurnal Basicedu Jurnal Nasional Pendidikan Teknik Informatika (JANAPATI) Journal of Computer Science and Informatics Engineering (J-Cosine) JTP - Jurnal Teknologi Pendidikan JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Jurnal Karinov JE-Unisla ILKOMNIKA: Journal of Computer Science and Applied Informatics TRIDARMA: Pengabdian Kepada Masyarakat (PkM) Generation Journal Jurnal Teknodik 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 Best : Journal of Applied Electrical, Science and 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 Pembelajaran, Bimbingan, dan Pengelolaan Pendidikan 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 JGK (Jurnal Guru Kita) Jurnal Inovasi Teknologi dan Edukasi Teknik Jurnal Pendidikan Islam Jurnal Pengabdian Masyarakat dan Riset Pendidikan Journal Electric Field 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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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 April - Juni
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.
Towards Intelligent Performance Monitoring for Blockchain-Based Learning Systems: A Multi-Class Classification Approach Aditya Galih Sulaksono; Syaad Patmanthara; Harits Ar Rosyid
International Journal of Engineering, Science and Information Technology Vol 5, No 4 (2025)
Publisher : Malikussaleh University, Aceh, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52088/ijesty.v5i4.1138

Abstract

This study proposes a multi-class classification framework for monitoring blockchain system performance as a step toward integration within blockchain-based learning management systems (LMS). Reliable performance monitoring is essential because smart contracts in educational settings depend on timely and accurate system responses to ensure valid grading and credential issuance. A dataset of 3,081 transactional logs was generated from simulated blockchain testbed, capturing throughput, latency, block size, and send rate. Throughput values were discretized into seven qualitative categories ranging from “Very Poor” to “Very Good” using quantile-based binning. Preprocessing involved data cleaning, categorical encoding, Z-score normalization, and label encoding to ensure model compatibility. Five algorithms: Logistic Regression, Decision Tree, Random Forest, Support Vector Machine (SVM), and K-Nearest Neighbors (KNN) were trained and evaluated using stratified 80–20 partitioning and 5-fold cross-validation with grid search for hyperparameter tuning. Performance metrics included accuracy, macro precision, recall, and F1-score. Random Forest achieved the best results with 91.35% accuracy, 0.910 macro precision, 0.911 recall, and 0.910 F1-score, outperforming other models by handling complex feature interactions and reducing variance. Decision Tree offered strong interpretability (88.32% accuracy), while Logistic Regression (84.97%) and SVM (84.86%) provided stable performance. KNN showed balanced results (87.78%) but incurred high computational costs. The findings demonstrate that multi-class stratification provides more actionable insights than binary methods, supporting low-latency decision-making for smart contract execution in decentralized LMS ecosystems. The novelty of this research lies in applying multi-class classification instead of binary methods, enabling nuanced monitoring. Future work will validate the framework in real blockchain-LMS deployments.
Paradigma Epistemologis Kompresi Data Teks: Huffman, Arithmetic, dan Neural Language Model Luqman Affandi; Didik Dwi Prasetya; Syaad Patmanthara
JUSIFOR : Jurnal Sistem Informasi dan Informatika Vol 4 No 2 (2025): JUSIFOR - Desember 2025
Publisher : Fakultas Sains Dan Teknologi, Universitas Raden Rahmat Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70609/jusifor.v4i2.8384

Abstract

This study explores text data compression as an epistemological paradigm through a comparative analysis of three fundamental approaches: traditional methods (Huffman Coding + LZW), bit-based methods (Arithmetic Coding), and machine learning approaches (Neural Language Models). Using the Project Gutenberg dataset comprising 15,000 classical literary works with a total size of 8.5 GB and 2.1-billion-word tokens, the evaluation is conducted based on compression ratio, execution time, and memory usage. The results reveal fundamental trade-offs among the paradigms. Traditional methods achieve the fastest execution (8.3 seconds/GB, 482 MB/s, 52 MB) with a compression ratio of 3.2:1. Arithmetic coding attains near-optimal performance (99.5% of the Shannon bound) with a compression ratio of 3.8:1. Neural language models yield the highest compression ratio of 4.6:1 but require substantially higher execution time and memory. The epistemological analysis highlights distinct conceptions of information—mechanistic, mathematically optimal, and semantic-aware—and provides a conceptual framework for developing adaptive compression systems.
Enhancing Cluster-Based SMOTE with kNN-Based Post-Oversampling Cleaning for Robust Health Risk Prediction Ilham, Mohamad; Solikin, Akhmad; Patmanthara, Syaad
BEST Vol 8 No 1 (2026): BEST
Publisher : Universitas PGRI Adi Buana Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36456/a9k8xq74

Abstract

Class imbalance is a common problem in health datasets and often leads to poor recognition of the minority (disease) class. NR-Clustering-SMOTE is a cluster-based oversampling method that combines noise reduction, K-Means clustering, and SMOTE with a modified distance metric to improve classification performance on imbalanced health data. However, the original method only performs noise reduction before SMOTE, so noisy synthetic samples generated around borderline or highly overlapped regions may still degrade classifier performance and introduce epistemological bias in the learned decision rules. This paper proposes a lightweight extension called NR-CluSMOTE-KNC (NR-Clustering-SMOTE with Post-SMOTE k-NN Cleaning). After the standard NR-Clustering-SMOTE pipeline, a k-nearest neighbour filter is applied solely to synthetic minority samples; synthetic points that are surrounded predominantly by majority neighbours are identified as extreme noise and removed. On the Pima Indians Diabetes dataset using Random Forest, the proposed method improves accuracy from 0.8481 (baseline NR-Clustering-SMOTE) to 0.8589 with NR-CluSMOTE-KNC, accompanied by consistent gains in G-Mean and AUC. These results indicate that a simple post-SMOTE cleaning step can epistemologically refine the representation of minority concepts in the data, producing more reliable and fair predictive models for health decision support.
MENINGKATKAN MOTIVASI BELAJAR KELOMPOK DENGAN MEDIA WORD WALL MATA PELAJARAN INFORMATIKA KELAS X Manik Hidayat; Manik Hidayat; Syaad Patmanthara
Jurnal Pembelajaran, Bimbingan, dan Pengelolaan Pendidikan Vol. 4 No. 8 (2024)
Publisher : Universitas Negeri Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.17977/um065.v4.i8.2024.12

Abstract

Abstract: This study aims to explore the effectiveness of the Word Wall media in enhancinggroup learning motivation in the subject of Computer Science at the high schoollevel, specifically for tenth-grade students. The Word Wall method is implementedas a collaborative approach that combines interaction between tea chers andstudents with the use of key word visualizations. The study involved X tenth-gradestudents as the sample. The research method employed was classroom actionresearch, utilizing observation, questionnaires, and interviews as data collectioninstruments. The results of the study indicate a significant increase in group learningmotivation following the implementation of the Word Wall media. Studentsdemonstrated more active participation in the learning process, enhancing theirunderstanding of the material, and developing collaborative skills. These findingssuggest that the Word Wall media has the potential to significantly enhance studentlearning motivation in the context of group learning in the Computer Sciencesubject. Practical implications and recommendations for further research arediscussed in the abstract
Meningkatkan Motivasi Belajar Informatika Kelas VII dengan Notion: Media Pembelajaran Berbasis Website : Penelitian Rasif Nidaan Khofia Ahmadah; Syaad Patmanthara; Putri Alivia Nabila; Rahmadita Sugma Ryanti; Lailatus Sa’adah; Rachman Kurniawa
Jurnal Pengabdian Masyarakat dan Riset Pendidikan Vol. 5 No. 1 (2026): Jurnal Pengabdian Masyarakat dan Riset Pendidikan Volume 5 Nomor 1 (Juli 2026 -
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/jerkin.v5i1.3355

Abstract

This study aims to investigate the improvement of students’ learning motivation through cycles of Classroom Action Research (CAR) by integrating Notion as an interactive learning medium, particularly in the context of data analysis learning. The background of this research stems from the low motivation observed among students in learning data analysis, thereby necessitating an appropriate solution involving the use of digital media, effective teaching methods, and suitable instructional approaches. The method employed in this study follows a classroom action research design, with data analyzed quantitatively. The findings reveal that the use of Notion as a learning tool, when combined with the appropriate pedagogical strategies, significantly enhances students’ motivation to learn. Furthermore, the implementation of this media has the potential to be expanded for broader classroom applications. In conclusion, this study contributes to the development of innovative learning practices and may serve as a valuable reference for future educational research.
Epistemological and Axiological Analysis of ResNet18-Based Dysgraphia Classification Kartika Candra Kirana; Anik Nur Handayani; Syaad Patmanthara; Nur Eva
Generation Journal Vol 10 No 1 (2026): Generation Journal
Publisher : Universitas Nusantara PGRI Kediri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29407/gj.v10i1.27419

Abstract

Based on an ontological perspective, there is a gap in feature representation and in binary dysgraphia classification using ResNet18, an area that has not been explored simultaneously. Thus, our contribution is an analysis of research on dysgraphia classification using ResNet18 that employs epistemological and axiological approaches. ResNet18 was chosen as the backbone of the proposed framework because it has shortcut connections that can degrade residues into useless features. As a representation of new knowledge, ResNet18 was pre-trained on ImageNet. Classification was tested on challenging word assignments, comprising 145 dysgraphia images and 188 non-dysgraphia images. Epoch trials were conducted to find the best architecture. The results showed that ResNet18 at epoch 10 achieved the best performance in binary classification, with a recall of up to 93.55%. This indicates that ResNet18 is sensitive to recognizing dysgraphia classes. Challenges outlined in this study serve as a foundation for further research.
Development of a CNN-Based Knowledge System for Rupiah Currency Authenticity Detection and Nominal Classification Ahmad Sahru Romadhon; Syaad Patmanthara; Anik Nur Handayani
Generation Journal Vol 10 No 1 (2026): Generation Journal
Publisher : Universitas Nusantara PGRI Kediri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29407/gj.v10i1.27464

Abstract

The circulation of counterfeit money in Indonesia inflicts substantial losses on the public and financial institutions. Manual verification of money is inefficient and error-prone, especially during high transaction volumes, because counterfeit bills exhibit physical characteristics nearly identical to genuine currency. To uncover counterfeit notes, an ultraviolet lamp exposes invisible ink. This research employs the Convolutional Neural Network (CNN) to detect authenticity and classify Indonesian rupiah banknotes. The CNN is trained using images of authentic banknotes captured with a camera and ultraviolet light across various denominations. The system stores the images and trains the model to identify authenticity and denomination features. Experimental results demonstrate that the proposed approach achieves high classification accuracy in distinguishing genuine and counterfeit Rupiah banknotes, as well as in recognising their respective denominations. The testing phase introduces real notes exposed to ultraviolet light, producing images that reveal invisible ink patterns. The authenticity detection achieved a 100% success rate, while the denomination recognition rates were 70% for Rp. 5,000 notes, 80% for Rp. 10,000 and Rp. 20,000 notes, and 90% for Rp. 50,000 and Rp. 100,000 notes. The system’s overall success rate is 82%.
An Epistemological Analysis of Metaheuristic MPPT Performance for Photovoltaic Systems under Partial Shading Conditions Khusnul Hidayat; Arif Nur Afandi; Syaad Patmanthara
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.
Integration of Human Organisational Technological Factors and Information Quality Metrics for Assessing Hospital Information System Performance Rumambi, Frendy Rocky; Prasetya, Didik Dwi; Patmanthara, Syaad
Emerging Information Science and Technology Vol. 6 No. 2 (2025)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/eist.v6i2.29172

Abstract

Digital transformation in health care necessitates HISs that can efficiently integrate people, organisations, and technology to deliver best performance in service delivery. This paper proposes a HIS performance evaluation model solution based on humanorganisation– technology fit (HOT-Fit) and information quality metrics (IQM) concept to evaluate system success technically. This model modifies the HOT-Fit model by incorporating an information quality metrics dimension with four constructs: accuracy, completeness, timeliness, and relevance. Research Methodology: The study follows quantitative approach and the survey questionnaires were filled by 150 active SIRS users from which the data was analysed using SEM. The findings suggest that the technology quality and information quality constructs significantly influence both user satisfaction and system use. Information quality is established as a mediating variable in the relationship between technological factors and user satisfaction. From a practical perspective, this integrated model has implications for understanding that achieving SIRS success depends not only on how well humans, organisations, and technology fit together but on the quality of the data and information produced. This research is a step towards the advancement of data metric based information system evaluation specifically in the domain of Health Infor matics, and also serves as a conceptual framework for enhancing HIS governance in Indonesia.
Co-Authors A.N. Afandi Achmad Imam Agung Aditya Galih Sulaksono Agung Bella Putra Utama Ahmad Baihaqi Mas Ahmad Sahru Romadhon Aji Prasetya Wibawa Akhmad Solikin Al Mukafi, Muhammad Hamdan Alfarid Hendro Yuwono Amidatus Sholihat jamil Amrullah, Ahmad Khakim Angga Achmad Cholid Anidia Wulan Sari 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 Carolina Sisca Djunaidi Daniar Wahyu Akbar Oktaviando Dendy Dewa Widjaya Putra Dhega Febiharsa Didik Dwi Prasetya Didik Nurhadi Dila Umnia Soraya Djoko Kustono Dyah Lestari Eddy Sutadji Eddy Triswanto Setyoadi Eddy Triswanto Setyoadi Ega Putriatama Eko Setiawan Eko Setiawan Ekohariadi Ekohariadi Elfia Najib Kholifiatin 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 Fitri Indra Kusumaningtyas 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 Lailatus Sa’adah Lokapitasari Belluano, Poetri Lestari Luqman Affandi M Ibrahim Ashari M. Djunaidi Ghony M. Zainal Arifin M. Zainal Arifin Mahali, Mahali Manik Hidayat Manik Hidayat Marji Marji Maskur Maskur Massitta Massitta Masyfa, Faiz Hilmawan MAULA, PUTRINDA INAYATUL Meidy, Ria Devita Meidy, Ria Devita Mentari, Febiana Putri Moh. Afifullah Mohamad Ilham Mokh Sholihul Hadi Mubarok, Sulton Muhamad Syamsu Iqbal Muhammad Auva Romadhon Muhammad Hamdan Al Mukafi Muhammad Hudan Rahmat Muhammad Maulana Akbar Mukhamad Angga Gumilang Muladi Nafi Isbadrianingtyas Naimu Shudur Naurah Septi Anggraini Nia Arlika Nidhom, Ahmad Mursyidun Ningrum, Gres Dyah Kusuma Nunung Nurjanah Nur Aini Susanti Nur Eva 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 Kurniawa Rachman Kurniawan Rachman, Tegar Fatur Rahajeng Kartika Sari Rahmadita Sugma Ryanti Rahmawati, Chusnia Ramadiani, Nanda Resta Rasif Nidaan Khofia Ahmadah Resti Pranata Putri Ria Devita Meidy Rinjani Alega Dio Saputra Rizal, Muhammad Fatkhur Rokhimatul Wakhidah Rudi Irmawanto Rumambi, Frendy Rocky Sakkinah, Intan Sulistyaningrum Sari, Heni Vidia Sari, Rahajeng Kartika Setyawan Aji Samudra Shofiyah Al Idrus Siti Munawaroh Siti Sendari Slamet Wibawanto Soenar Soekopitojo 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