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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) Gelar : Jurnal Seni Budaya JTP - Jurnal Teknologi Pendidikan Jurnal Karinov TRIDARMA: Pengabdian Kepada Masyarakat (PkM) 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 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 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) Jurnal Riset Rumpun Agama dan Filsafat Jurnal Inovasi Teknologi dan Edukasi Teknik Jurnal Pendidikan Islam IRDH International Journal of Technology, Agriculture and Natural Sciences (IRDH IJTANS)

Syaad Patmanthara

Universitas Negeri Malang

Author-ID : 2590732

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Clustering-Based Adaptive UX in E-Learning Systems: Aligning Microservices with the 4C Framework Belluano, Poetri Lestari Lokapitasari; Patmanthara, Syaad; Ashar, Muhammad; Kurniawan, Fachrul; Kurubacak, Gulsun
Journal of Applied Data Sciences Vol 6, No 4: December 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i4.884

This study introduces a clustering-driven adaptive User Experience (UX) architecture for e-learning systems, aligning machine learning segmentation with the 21st-century 4C educational framework (critical thinking, communication, collaboration, creativity). The objective is to dynamically personalize digital learning interactions through a microservices architecture responsive to users' UX profiles. A quantitative survey was conducted involving 50 active users of Shopee and Tokopedia, whose interaction feedback was mapped using the User Experience Questionnaire (UEQ). Three unsupervised clustering techniques—KMeans, Agglomerative, and DBSCAN—were compared. KMeans outperformed the others with a silhouette score of 0.157, compared to 0.146 for Agglomerative and −0.017 for DBSCAN, identifying three meaningful clusters representing high, medium, and low UX proficiency. A one-way ANOVA test confirmed statistically significant differences (p 0.01) among the clusters in dimensions such as error clarity, support responsiveness, and user confidence. These UX profiles were then mapped to individualized microservices: Cluster 0 received autonomous content with minimal support, Cluster 1 was offered guided prompts, and Cluster 2 was provided with simplified interfaces and proactive assistance. Each cluster was aligned with specific 4C competencies to ensure pedagogical relevance. The proposed architecture, built with gRPC-based microservices, enabled asynchronous, low-latency personalization based on user cluster membership. The novelty of this research lies in its dual alignment—technological (microservices + machine learning) and educational (4C competency mapping)—to construct a scalable and responsive e-learning environment. The system design, although validated through simulation, demonstrates a practical foundation for future deployment in platforms like Moodle or OpenEdX. By linking behavioral UX clustering to pedagogical intervention strategies, this study offers a model for adaptive, data-informed instructional systems that are both scalable and learner-centered.

Revolusi Epistemologi dalam Era Kecerdasan Buatan: Tantangan dan Implikasi Pembelajaran Mendalam dalam Perspektif Filsafat Ilmu Rizal, Muhammad Fatkhur; Patmanthara, Syaad; Rahmawati, Chusnia
Discovery Vol 10 No 1 (2025): March 2025
Publisher : LPPM Universitas Hasyim Asy'ari Lembaga Penelitian dan Pengabdian Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33752/discovery.v10i1.8449

Artikel ini membahas hubungan antara kecerdasan buatan (AI), khususnya dalam konteks pembelajaran mendalam (deep learning), dan perspektif epistemologi. Seiring dengan berkembangnya AI yang berbasis data dan ateoretis, pendekatan ini menantang paradigma epistemologi tradisional yang berfokus pada teori dan justifikasi pengetahuan universal. AI, dengan kemampuannya untuk memberikan personalisasi berdasarkan data yang kontekstual, membawa perubahan signifikan terhadap cara kita memahami dan menghasilkan pengetahuan. Artikel ini mengeksplorasi bagaimana AI menggeser fokus epistemologi dari pencarian kebenaran universal menuju pengetahuan yang lebih dinamis dan terfokus pada kebutuhan individu, serta implikasi filsafat ilmu dalam mengevaluasi konsep-konsep dasar seperti kebenaran, justifikasi, dan teori pengetahuan. Penelitian ini mengajak pembaca untuk merenungkan peran AI sebagai agen epistemik yang tidak hanya memengaruhi teknologi, tetapi juga pemahaman kita tentang pengetahuan di era modern. Keywords: Kecerdasan Buatan (AI), Pembelajaran Mendalam (Deep Learning), Epistemologi, Filsafat Ilmu, Pengetahuan Dinamis.

Menimbang Objektivitas dan Makna dalam Penilaian Esai Otomatis: Perspektif Filsafat Ilmu Maskur, Maskur; Didik Dwi Prasetya; Patmanthara, Syaad
Discovery Vol 10 No 2 (2025): October 2025
Publisher : LPPM Universitas Hasyim Asy'ari Lembaga Penelitian dan Pengabdian Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33752/discovery.v10i2.10439

Perkembangan teknologi kecerdasan buatan dalam bidang pendidikan telah melahirkan sistem penilaian esai otomatis yang semakin sering digunakan di berbagai perguruan tinggi. Kehadirannya dianggap mampu menjawab tantangan klasik berupa keterbatasan waktu, konsistensi penilaian, beban kerja dosen yang semakin meningkat. Meski demikian, penggunaan sistem ini menimbulkan pertanyaan mendasar yang tidak bisa dilepaskan dari kajian filsafat ilmu. Penilaian dalam pendidikan bukan sekadar aktivitas teknis memberi skor, melainkan juga mencerminkan cara kita memahami pengetahuan, membangun kebenaran, dan menegakkan nilai-nilai pendidikan. Dari sisi ontologi, persoalan yang muncul adalah apa sebenarnya yang dinilai oleh sistem otomatis: apakah hanya bentuk linguistik dan struktur kalimat, atau juga makna, argumentasi, serta refleksi pengetahuan mahasiswa. Dari sisi epistemologi, klaim bahwa algoritma mampu menghadirkan objektivitas perlu dipertanyakan, sebab setiap algoritma dibangun atas dasar data, model, dan asumsi tertentu yang tidak bebas dari bias. Sementara itu, dari perspektif aksiologi, untuk menimbang apakah penilaian otomatis hanya berfungsi sebagai alat efisiensi, atau justru dapat mendukung tujuan pendidikan yang lebih luas, seperti keadilan, keterbukaan, dan pengembangan kemampuan berpikir kritis. Artikel ini menegaskan bahwa penilaian esai otomatis perlu dipahami bukan hanya sebagai inovasi teknologi, melainkan sebagai praktik yang sarat makna filosofis. Dengan meninjau aspek ontologi, epistemologi, dan aksiologi, penilaian diharapkan tidak kehilangan makna hakikinya, yaitu mendukung tercapainya tujuan pendidikan yang lebih adil, bermakna, dan humanis.

ANALISIS KEBERHASILAN SISTEM E-LEARNING SMK NEGERI 1 MALANG Sakkinah, Intan Sulistyaningrum; Patmanthara, Syaad
Elinvo (Electronics, Informatics, and Vocational Education) Vol. 2 No. 1 (2017): May 2017
Publisher : Department of Electronic and Informatic Engineering Education, Faculty of Engineering, UNY

E-learning merupakan salah satu pembelajaran yang saat ini banyak dikembangkan di lembaga pendidikan. SMK Negeri 1 Malang merupakan salah satu sekolah yang telah menggunakan e-learning namun sistem e-learning SMK Negeri 1 Malang belum diukur tingkat keberhasilannya. Oleh sebab itu tujuan dari penelitian ini untuk mengetahui kerhasilan sistem e-learning SMK Negeri 1 Malang dengan menggunakan model analisis DeLone dan McLean. Hasil dari penelitian ini menunjukkan bahwa e-learning SMK Negeri 1 Malang dinyatakan berhasil.

Perbedaan Kemampuan Berpikir Kritis dan Hasil Belajar Kognitif Sistem Komputer antara Model CTL dengan Model Examples Non Examples Kurniawan, Singgih Adie; Patmanthara, Syaad; Soraya, Dila Umnia
Elinvo (Electronics, Informatics, and Vocational Education) Vol. 4 No. 2 (2019): November 2019
Publisher : Department of Electronic and Informatic Engineering Education, Faculty of Engineering, UNY

Penelitian ini bertujuan untuk mengetahui perbedaan pada kemampuan berpikir kritis dan hasil belajar kognitif mata pelajaran sistem komputer, antara kelas eksperimen menggunakan model contextual teaching and learning dan kelas kontrol menggunakan model examples non examples di SMKN 11 Malang. Penelitian ini merupakan penelitian experimental semu (Quasy Experimental Design) dengan pola posttest only control design. Sampel yang dipilih adalah kelas X TKJ 1 sebagai kelas eksperimen dan X TKJ 4 sebagai kelas kontrol. Rata-rata hasil kemampuan akhir siswa di kedua kelas setelah mendapat perlakuan yaitu, kelas TKJ 1 sebesar 89,16 dan kelas TKJ 4 sebesar 83,90. terdapat selisih rata-rata sebesar 5,26 antara kedua kelas. Hasil kemampuan berpikir kritis pada kelas eksperimen menunjukkan 20% kemampuan sangat tinggi, 10% kemampuan tinggi, 42% kemampuan sedang, 6% kemampuan rendah dan 13% kemampuan sangat rendah. Sedangkan untuk kelas kontrol presentase yang didapat 7% memiliki kemampuan sangat tinggi, 20% kemampuan tinggi, 23% kemampuan sedang, 20% kemampuan rendah dan 30% kemampuan sangat rendah. Hasil uji-t juga menunjukkan nilai 0,002 (dimana nilai 0,002 < 0,05). Maka dapat disimpulkan terdapat perbedaan signifikan kemampuan berpikir kritis dan hasil belajar kognitif antara kelas eksperimen dan kelas kontrol.

Strategic Resource Relationship Model, Adaptability to Environmental Dynamics, Implementation of School-Based Management and Vocational School Competitiveness Jamil, Amidatus Sholihat; Kamdi, Waras; Hadi, Syamsul; Patmanthara, Syaad
JTP - Jurnal Teknologi Pendidikan Vol. 25 No. 3 (2023): Jurnal Teknologi Pendidikan
Publisher : Lembaga Penelitian dan Pengabdian kepada Masyarakat, Universitas Negeri Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21009/jtp.v25i3.48677

The aim of this research is to obtain the best relationship model between strategic resources, adaptability to environmental dynamics, and the implementation of school-based management on competitiveness in vocational schools which is thought to have an indirect relationship through existing mediating variables, without ignoring the direct relationship of each variable. This research is included in explanatory research with the statistical analysis technique used, namely Structural Equation Modeling (SEM). The population and sample in this study were 397 public and private vocational school teachers in East Java Province. Research data was collected through the distribution of questionnaires conducted online and offline. The results of hypothesis testing show that: (1) strategic resources have a positive and significant effect on environmental dynamics; (2) strategic resources have a positive and significant effect on the implementation of SBM; (3) adaptability to environmental dynamics has a positive and significant effect on SBM implementation; (4) strategic resources have a positive and significant effect on competitiveness; (5) SBM implementation has a positive and significant effect on competitiveness; (6) environmental dynamics have a positive and significant effect on competitiveness through the implementation of SBM; (7) strategic resources have a positive and significant effect on competitiveness through environmental dynamics and SBM implementation; (8) strategic resources have a positive and significant effect on competitiveness through the implementation of MBS; and (9) strategic resources have a positive and significant effect on the implementation of SBM through environmental dynamics.

AI, Agriculture, and Decolonial Perspectives: Recognizing Local Knowledge for Sustainability Julfikar Mawansyah; Mokh. Sholihul Hadi; Syaad Patmanthara
Jurnal Riset Rumpun Agama dan Filsafat Vol. 4 No. 3 (2025): Desember: Jurnal Riset Rumpun Agama dan Filsafat
Publisher : Pusat Riset dan Inovasi Nasional

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55606/jurrafi.v4i3.7250

This study explores the intersection of Artificial Intelligence (AI), agriculture, and decolonial philosophy, emphasizing the role of local knowledge as the foundation for sustainable agricultural technology in Indonesia. The research investigates how AI can be developed not as a tool of technological domination but as a dialogical partner that recognizes the epistemic value of indigenous wisdom. Using a mixed-method approach, the study combines algorithmic experiments applying lightweight Convolutional Neural Networks (CNN) with Explainable AI (XAI) methods such as SHAP and LIME with participatory interviews involving farmers in Bima District. Empirical findings show that models integrated with localized visualization and community-based interpretability improved user trust by 84% and reduced computational energy by 28% without compromising accuracy. More importantly, the interaction between AI and farmers revealed a form of epistemic integration where algorithmic logic aligns with traditional indicators, such as soil texture, humidity, and seasonal signs known to local farmers. Philosophically, this research asserts that sustainable AI should emerge from ecological and cultural contexts rather than imposing external frameworks. In the decolonial sense, it positions local farmers not as passive users but as active epistemic agents shaping the meaning of technology. Thus, AI becomes not only a technical instrument but a site of ethical and epistemic liberation that reaffirms human responsibility toward knowledge, culture, and the earth.

Abstract Syntax Tree Model for Minimizing False Negative in Semantic Evaluation of Python Fill-in-the-Blank Nurhasan, Usman; Prasetya, Didik Dwi; Patmanthara, Syaad
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.v9i6.11090

This study develops and evaluates an automated assessment model using Abstract Syntax Trees (AST) with a view to overcoming the limitations of string-matching techniques in the assessment of Fill-in-the-Blank (FIB) programming answers. Traditional string-matching techniques have a relatively high False Negative Rate (FNR) of 21.5% within the context of detecting semantic equivalence. The current model uses semantic structural triangulation to ascertain the semantic similarity of student answers. Technical assessment shows that the AST approach markedly reduces the FNR to 4.5%. The model demonstrates high reliability (ϰ = 0.83) with high classification accuracy (F1 Score = 0.966) which attests to its inferential validity. From a pedagogical perspective, system implementation leads to substantial learning gains, evidenced by a large effect size (Cohen’s d = 1.82) and a high normalized gain (Normalized Gain = 0.90). Multiple regression analysis confirms that semantic accuracy is the primary causal factor driving improved student comprehension. Ontologically, while AST is valid as a partial representation, its limitations—particularly tree isomorphism in recursive structures—highlight the need for further exploration of graph isomorphism approaches. Control Flow Graphs (CFG) and Data Flow Graphs (DFG) offer more expressive relational models for capturing control and data dependencies. The model demonstrates functional feasibility with a System Usability Scale (SUS) score of 76.47. Overall, the AST Triangulation Model is validated as pedagogically effective, inferentially robust, and supportive of evaluative transparency. Future research recommends validating the model on more complex tasks and releasing it as open-source to support reproducibility.

Ontology-Based Recommender Systems for E-Learning and Multimedia: A Systematic Literature Review Across Domains Riska, Suastika Yulia; Patmanthara, Syaad; Widiyaningtyas, Triyanna
Indonesian Journal of Instructional Media and Model Vol 7 No 2 (2025): Indonesian Journal of Instructional Media and Model
Publisher : Universitas Veteran Bangun Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32585/ijimm.v7i2.7405

The rapid expansion of digital content across various sectors has led to an overwhelming influx of data, highlighting the need for advanced recommendation systems. Traditional methods such as Collaborative Filtering (CF) and Content-Based Filtering (CBF) face limitations like data sparsity and the Cold Start problem, which affect the accuracy of recommendations. This study explores the use of ontologies in enhancing recommendation systems, aiming to overcome these challenges by providing a semantic framework for better item and user representation. A Systematic Literature Review (SLR) methodology was employed to analyze research from 2021 to 2025, focusing on the application of ontologies in e-commerce, healthcare, education, and employment. The findings demonstrate that ontologies improve recommendation relevance, diversity, and explainability, especially in addressing the Cold Start problem. However, challenges in implementation and interpretation remain. This research contributes to the field by emphasizing the potential of integrating ontologies with Knowledge Graphs (KG) and Graph Neural Networks (GNN) to create hybrid models that enhance the accuracy and transparency of recommendations, guiding future advancements in recommendation systems.

Towards Intelligent Performance Monitoring for Blockchain-Based Learning Systems: A Multi-Class Classification Approach Sulaksono, Aditya Galih; Patmanthara, Syaad; Rosyid, Harits Ar
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

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.

Co-Authors Achmad Imam Agung Aditya Galih Sulaksono, Aditya Galih Agung Bella Putra Utama Aji Prasetya Wibawa Al Mukafi, Muhammad Hamdan Alfarid Hendro Yuwono Amidatus Sholihat jamil Amrullah, Ahmad Khakim Angga Achmad Cholid 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 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 Evania Kurniawati Fachrul Kurniawan Fadli Hidayat, M. Noer Fahmi Efendi Yusuf Fandi Akhmad Kurniawan Fatmawati, Hefi Ferdiansyah, Dodik Septian Fikha Rizky Aullia Firman Syahputra, Yohanes Dhimas Gülsün Kurubacak Hakkun Elmunsyah 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 Indraswari, Martha Devi Isnandar Jayadi, Puguh Joumil Aidil Saifuddin Julfikar Mawansyah Karaman, Jamilah Kholiqin, Sabrina Nabila Kurniawan, Rivan Adi Kurniawan, Singgih Adie Kurubacak, Gulsun Lokapitasari Belluano, Poetri Lestari M Ibrahim Ashari M. Zainal Arifin M. Zainal Arifin Mahali, Mahali Marji Marji Maskur Maskur MAULA, PUTRINDA INAYATUL Meidy, Ria Devita Meidy, Ria Devita Mentari, Febiana Putri Moh. Afifullah Mokh Sholihul Hadi Mubarok, Sulton 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 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 R. Mahmud Sugandi Rahajeng Kartika Sari Rahmawati, Chusnia Ramadiani, Nanda Resta Ratnasari, Novia Resti Pranata Putri Ria Devita Meidy Rizal, Muhammad Fatkhur Rokhimatul Wakhidah Sakkinah, Intan Sulistyaningrum Sari, Rahajeng Kartika Shofiyah Al Idrus Siti Munawaroh Slamet Wibawanto Soenar Soekopitojo Suastika Yulia Riska Suci Lestari Sunu Jatmika, Sunu Suparji Suparji Sutapa, Yohanes Gatot Syamsul Hadi Titasari Rahmawati Tiya Nurul Khusna Tri Atmadji Sutikno Tri Wrahatnolo Triyana Widiyaningtyas Triyanna Widiyaningtyas Triyanna Widiyaningtyas 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

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