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All Journal Edutik : Jurnal Pendidikan Teknologi Informasi dan Komunikasi
Kebri Kein Mopudy Pajung
Universitas Negeri Manado
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Languange, Linguistic, Communication & Media Other

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Studi Literatur: Penerapan Teknik Pengajaran Pedagogik Berbasis Teknologi Informasi Kebri Kein Mopudy Pajung; Trudi Komansilan; Desriani Dachi; Dwi Putri Natalia Sitanggang; Simon Palendokan; Jestin Jernih Ziliwu; Abdi Santo Laia; Aurelia Relsi Patintingan; Novia Saputri
Edutik : Jurnal Pendidikan Teknologi Informasi dan Komunikasi Vol. 6 No. 2 (2026): EduTIK : April 2026
Publisher : Jurusan PTIK Universitas Negeri Manado

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.67142/edutik.v6i2.432

Abstract

ABSTRAK Penelitian ini bertujuan untuk mengkaji penerapan teknik pengajaran pedagogik yang diintegrasikan dengan teknologi informasi guna meningkatkan kualitas pembelajaran. Melalui pendekatan Systematic Literature Review (SLR), artikel ini menganalisis 15 sumber referensi ilmiah yang diterbitkan antara tahun 2021 hingga 2025. Fokus utama kajian mencakup empat variabel utama: (1) kompetensi pedagogik guru, (2) penguasaan teknologi informasi dan komunikasi (TIK/ICT), (3) motivasi pengembangan profesional, serta (4) kendala dan solusi implementasi di lapangan. Kerangka analisis mengacu pada Technological Pedagogical Content Knowledge (TPACK) yang menjadi fondasi integrasi teknologi dalam pembelajaran. Prosedur penelitian dilaksanakan melalui enam tahapan sistematis: identifikasi masalah, pencarian basis data, seleksi literatur, ekstraksi data, analisis tematik, dan penarikan kesimpulan. Hasil tinjauan menunjukkan bahwa integrasi pedagogik digital dan kompetensi TPACK secara signifikan meningkatkan efektivitas pengajaran dan profesionalisme guru. Keberhasilan ini sangat bergantung pada tingkat literasi digital pendidik, dukungan infrastruktur TIK, dan motivasi internal untuk berinovasi. Kesimpulannya, penguasaan teknologi informasi merupakan kompetensi esensial bukan sekadar alat bantu yang harus dikuasai pendidik abad ke-21. ABSTRACT This study aims to examine the application of pedagogical teaching techniques integrated with information technology to improve learning quality. Through a Systematic Literature Review (SLR) approach, this article analyzes 15 scientific reference sources published between 2021 and 2025. The main focus covers four variables: (1) teacher pedagogical competence, (2) mastery of information and communication technology (ICT), (3) professional development motivation, and (4) implementation constraints and solutions. The analytical framework refers to the Technological Pedagogical Content Knowledge (TPACK) framework. Research procedures were conducted through six systematic stages. Results indicate that integration of digital pedagogy and TPACK competencies significantly enhances teaching effectiveness and teacher professionalism. In conclusion, mastery of information technology is an essential competence not merely a supporting tool that 21st-century educators must master to maximize interactive learning potential.
Analisis Literatur: Transformasi Mutu Pembelajaran melalui Kecerdasan Buatan (Artificial Intelligence [AI]) di Era Digital Kebri Kein Mopudy Pajung; Trudi Komansilan; Aril Jdungguti; Chrystani Sumolang; Ilarius Sasaka; Cian Anggraini Salote; Oni Pinim; Nataniel Christian Kapoh; Futriyani Supu
Edutik : Jurnal Pendidikan Teknologi Informasi dan Komunikasi Vol. 6 No. 2 (2026): EduTIK : April 2026
Publisher : Jurusan PTIK Universitas Negeri Manado

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.67142/edutik.v6i2.433

Abstract

ABSTRAK                 Akselerasi teknologi digital telah mendorong transformasi masif di sektor pendidikan, salah satunya melalui integrasi Kecerdasan Buatan atau Artificial Intelligence (AI). Penelitian ini bertujuan menganalisis efektivitas, ragam implementasi, serta tantangan adopsi AI dalam meningkatkan kualitas pembelajaran di era digital di Indonesia. Penelitian menggunakan metode Systematic Literature Review (SLR) dengan pendekatan kualitatif analitis, melalui penelusuran literatur pada basis data Google Scholar dengan kata kunci kombinasi seperti “Kecerdasan Buatan dalam Pendidikan”, “Kualitas Pembelajaran AI”, dan “Etika AI di Era Digital”. Kriteria inklusi meliputi artikel jurnal nasional terakreditasi yang membahas AI dalam konteks pendidikan, diterbitkan pada tahun 2023-2026, sedangkan kriteria eksklusi mencakup artikel opini, prosiding yang tidak bereputasi, dan artikel duplikasi. Dari proses seleksi, diperoleh 14 artikel yang memenuhi kriteria dan disintesis ke dalam 4 klaster tematik. Hasil menunjukkan bahwa penerapan AI seperti adaptive learning, chatbot, big data, dan generative AI secara signifikan meningkatkan motivasi, keterlibatan, dan pemahaman konsep siswa. Secara kuantitatif, ditemukan koefisien regresi yang sangat kuat (r = 0,953) antara pemanfaatan AI dan efektivitas belajar di perguruan tinggi. AI juga terbukti meningkatkan efisiensi kerja guru dalam menyusun bahan ajar dan penilaian otomatis. Namun implementasi AI di Indonesia masih menghadapi tantangan serius: kesenjangan infrastruktur digital antardaerah, rendahnya kompetensi literasi digital guru, serta urgensi regulasi etika AI. Kesimpulannya, AI berpotensi transformasional dalam meningkatkan mutu pendidikan nasional, dengan syarat dukungan kebijakan regulasi, pemerataan fasilitas teknis, dan pelatihan literasi digital berkelanjutan. ABSTRACT                The acceleration of digital technology has driven a massive transformation in the education sector, notably through the integration of Artificial Intelligence (AI). This study aims to analyze the effectiveness, implementation variety, and adoption challenges of AI in enhancing learning quality within the digital era in Indonesia. This study employed a Systematic Literature Review (SLR) methodology with a qualitative-analytical approach, searching the Google Scholar database using combined keywords such as “Artificial Intelligence in Education,” “AI Learning Quality,” and “AI Ethics in the Digital Era.” Inclusion criteria encompassed nationally accredited journal articles discussing AI in educational contexts published between 2023 and 2026, while exclusion criteria covered opinion articles, non-reputable conference proceedings, and duplicate publications. Through a structured selection process, 14 articles met the criteria and were synthesized into 4 thematic clusters. The results indicate that AI applications including adaptive learning systems, chatbots, big data analytics, and generative AI significantly improve student motivation, engagement, and conceptual understanding. Quantitatively, a very strong regression coefficient (r = 0.953) was found between AI utilization and learning effectiveness at the higher education level. AI is also proven to increase teachers’ efficiency in instructional material development and automated assessment. Nevertheless, AI implementation in Indonesia continues to face serious challenges: regional digital infrastructure disparities, insufficient digital literacy competence among teachers, and the urgency of AI ethics regulation. In conclusion, AI holds transformational potential to elevate national education quality, provided it is supported by robust policy regulations, equitable technical facilities, and continuous digital literacy training for educators.
Pengembangan Sistem Presensi Siswa Berbasis Web di SMK Bina Karya Tobelo Remon Pangkalangi; Christine Takarina Meitty Manoppo; Kebri Kein Mopudy Pajung
Edutik : Jurnal Pendidikan Teknologi Informasi dan Komunikasi Vol. 6 No. 2 (2026): EduTIK : April 2026
Publisher : Jurusan PTIK Universitas Negeri Manado

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.67142/edutik.v6i2.443

Abstract

ABSTRAK  Proses presensi siswa di SMK Bina Karya Tobelo masih dilakukan secara manual sehingga menyebabkan proses pencatatan kehadiran memerlukan waktu yang lama, rentan kesalahan, dan sulit dipantau oleh pihak sekolah maupun orang tua. Selain itu, penyampaian informasi ketidakhadiran siswa kepada orang tua belum dilakukan secara cepat dan terstruktur. Penelitian ini bertujuan untuk mengembangkan sistem presensi siswa berbasis web menggunakan QR Code dinamis, validasi lokasi GPS, validasi perangkat, dan notifikasi WhatsApp kepada orang tua agar proses presensi menjadi lebih efektif dan terpantau. Penelitian menggunakan metode Research and Development (R&D) dengan model Prototyping yang meliputi pengumpulan kebutuhan, perancangan prototipe, pengkodean, pengujian, evaluasi, dan implementasi sistem. Sistem dikembangkan menggunakan framework Laravel dengan fitur QR Code dinamis, validasi lokasi GPS, validasi perangkat, dan notifikasi WhatsApp kepada orang tua. Pengujian sistem dilakukan menggunakan Black Box Testing dan menunjukkan seluruh fungsi sistem berjalan dengan baik. Evaluasi ahli sistem memperoleh persentase 100,00% dengan kategori sangat layak, ahli media memperoleh 100,00% dengan kategori sangat layak, dan evaluasi pengguna memperoleh 96,49% dengan kategori sangat layak. Hasil wawancara orang tua/wali menunjukkan bahwa notifikasi WhatsApp mudah dipahami, tepat waktu, dan membantu memantau kehadiran siswa. Dengan demikian, dapat disimpulkan bahwa sistem presensi siswa berbasis web yang dikembangkan dinyatakan sangat layak dan dapat digunakan untuk mendukung proses presensi siswa di SMK Bina Karya Tobelo. ABSTRACT  The student attendance process at SMK Bina Karya Tobelo is still conducted manually, causing the attendance recording process to require a long time, be prone to errors, and be difficult to monitor by the school and parents. In addition, the delivery of information regarding student absences to parents has not been carried out quickly and systematically. This study aims to develop a web-based student attendance system using dynamic QR Codes, GPS location validation, device validation, and WhatsApp notifications to parents so that the attendance process becomes more effective and monitored. The research uses the Research and Development (R&D) method with the Prototyping model, which includes requirements gathering, prototype design, coding, testing, evaluation, and system implementation. The system was developed using the Laravel framework with dynamic QR Code features, GPS location validation, device validation, and WhatsApp notifications to parents. System testing was carried out using Black Box Testing and showed that all system functions ran well. The system expert evaluation obtained a percentage of 100.00% with a very feasible category, the media expert evaluation obtained 100.00% with a very feasible category, and the user evaluation obtained 96.49% with a very feasible category. The results of interviews with parents/guardians showed that WhatsApp notifications were easy to understand, timely, and helped monitor student attendance. Thus, it can be concluded that the developed web-based student attendance system is declared very feasible and can be used to support the student attendance process at SMK Bina Karya Tobelo.
Klasifikasi Perilaku Belajar Mahasiswa Menggunakan Random Forest Classifier Berbasis Learning Behavior Questionnaire Kebri Kein Mopudy Pajung; Trudi Komansilan; Genevieve Josevhine Cecilia Kawilarang; Marsela Juwita Thomas; Renalda Gracia Pingkan Nonga; Eirene Alexandria Sumeke; Gemi Kereh; Meiron Kiwo
Edutik : Jurnal Pendidikan Teknologi Informasi dan Komunikasi Vol. 6 No. 2 (2026): EduTIK : April 2026
Publisher : Jurusan PTIK Universitas Negeri Manado

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.67142/edutik.v6i2.466

Abstract

ABSTRAK              Perilaku belajar mahasiswa berperan penting dalam menentukan performa akademik dan keberhasilan studi. Namun, identifikasi perilaku belajar masih banyak dilakukan secara manual sehingga kurang efektif untuk mendukung deteksi dini pada populasi mahasiswa yang besar. Penelitian ini bertujuan membangun model klasifikasi perilaku belajar mahasiswa menggunakan Random Forest Classifier (RFC) berbasis Learning Behavior Questionnaire (LBQ) dengan pendekatan CRISP-DM. Data dikumpulkan dari 400 mahasiswa menggunakan instrumen LBQ yang terdiri atas 28 item dalam tiga dimensi, yaitu Learning Behavior in Classroom, Learning Motivation, dan Social Interaction Outside Classroom. Instrumen memiliki validitas dan reliabilitas yang baik dengan nilai korelasi item 0,620–0,731 dan Cronbach’s Alpha 0,88–0,95. Untuk meningkatkan performa model, penelitian menerapkan SMOTE untuk mengatasi ketidakseimbangan kelas, Optuna untuk optimasi hiperparameter, dan SHAP untuk interpretasi model. Hasil pengujian menunjukkan bahwa RFC mencapai akurasi 98,33%, F1-Macro 0,9861, dan ROC-AUC 1,0000. Analisis SHAP mengidentifikasi IPK, motivasi intrinsik belajar (B8, B10, B9), serta perilaku belajar di kelas (A2, A5) sebagai prediktor paling berpengaruh. Temuan ini menunjukkan bahwa integrasi CRISP-DM, SMOTE, Optuna, dan SHAP menghasilkan model yang akurat, interpretatif, dan bebas data leakage, sehingga berpotensi digunakan sebagai sistem deteksi dini perilaku belajar mahasiswa di perguruan tinggi. ABSTRACT              Student learning behavior plays an important role in academic performance and study success. However, learning behavior identification is often conducted manually, limiting its effectiveness for early detection in large student populations. This study aims to develop a student learning behavior classification model using a Random Forest Classifier (RFC) based on a Learning Behavior Questionnaire (LBQ) within the CRISP-DM framework. Data were collected from 400 undergraduate students using a 28-item LBQ covering three dimensions: Learning Behavior in Classroom, Learning Motivation, and Social Interaction Outside Classroom. The instrument demonstrated satisfactory validity and reliability, with item correlation coefficients ranging from 0.620 to 0.731 and Cronbach’s Alpha values between 0.88 and 0.95. The study employed SMOTE to address class imbalance, Optuna for hyperparameter optimization, and SHAP for model interpretability. The RFC model achieved 98.33% accuracy, 0.9861 macro F1-score, and 1.0000 ROC-AUC on the test set. SHAP analysis identified GPA, intrinsic learning motivation items (B8, B10, B9), and classroom behavior items (A2, A5) as the most influential predictors. These findings demonstrate that integrating CRISP-DM, SMOTE, Optuna, and SHAP produces an accurate, interpretable, and leakage-free model that can support early detection of student learning behavior in higher education.
Analisis Penerapan Confusion Matrix untuk Pengujian Model Certainty Factor dalam Sistem Rekomendasi Jurusan SMK Kebri Kein Mopudy Pajung; Trudi Komansilan; Alfian Mokodongan; Danil R Gopay; Joel Gabriel Doga; Marselia Rambing; Sevan A Rumondor; Thomas Liando; Yemima M Dalita
Edutik : Jurnal Pendidikan Teknologi Informasi dan Komunikasi Vol. 6 No. 2 (2026): EduTIK : April 2026
Publisher : Jurusan PTIK Universitas Negeri Manado

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.67142/edutik.v6i2.469

Abstract

ABSTRAK Pemilihan kompetensi keahlian yang tidak tepat di SMK seringkali memicu kendala akademik dan ketidaksesuaian minat bagi siswa baru. Penelitian ini bertujuan untuk menguji tingkat akurasi model Certainty Factor (CF) pada sistem pakar rekomendasi jurusan SMK guna mengatasi ketidakpastian dalam penilaian minat dan bakat. Mengingat keterbatasan akses data lapangan, penelitian ini menerapkan pendekatan studi literatur dengan basis pengetahuan sistem yang diakuisisi dari regulasi kurikulum resmi Kemendikbudristek dan pedoman bimbingan konseling. Evaluasi performa klasifikasi model diuji secara ketat menggunakan metode Confusion Matrix terhadap 30 skenario data uji sintetis yang dirancang secara sistematis dari berbagai variasi nilai akademik serta indikator minat. Hasil pengujian menunjukkan bahwa model Certainty Factor mampu mengimplementasikan logika penjurusan dari literatur dengan sangat baik, menghasilkan tingkat Akurasi 93.3%, rata-rata presisi 93.3%, dan rata-rata recall 93.3%. Kesimpulan dari penelitian ini membuktikan bahwa pengujian menggunakan Confusion Matrix berhasil memvalidasi konsistensi kode program dan rumus matematika CF dalam meniru aturan penjurusan normatif secara presisi. Sistem pakar ini dinyatakan valid dan layak dikembangkan lebih lanjut sebagai instrumen bantu (decision support tool) bagi guru bimbingan konseling maupun calon siswa baru, meskipun proses evaluasinya berbasis pada skenario sintetis.  ABSTRACT  Inappropriate selection of vocational competencies in vocational high schools (SMK) often triggers academic difficulties and interest mismatches among new students. This study aims to evaluate the accuracy level of the Certainty Factor (CF) model within an expert system for recommending SMK majors to address uncertainty in interest and talent assessment. Due to limited access to field data, this research employs a literature review approach, where the system's knowledge base is acquired from the official curriculum regulations of the Ministry of Education, Culture, Research, and Technology (Kemendikbudristek) and guidance counseling manuals. The classification performance of the model is rigorously evaluated using the Confusion Matrix method against 30 synthetic test data scenarios systematically designed from various academic grades and interest indicators. The evaluation results indicate that the Certainty Factor model successfully implements the majoring logic derived from the literature, yielding an accuracy rate of 93.3%, a macro average precision of 93.3%, and a macro average recall of 93.3%. In conclusion, this study demonstrates that the Confusion Matrix evaluation successfully validates the consistency of the program code and the CF mathematical formula in precisely replicating normative majoring rules. This expert system is deemed valid and viable for further development as a decision support tool for guidance counseling teachers and prospective students, despite the evaluation process being reliant on synthetic scenarios.
Co-Authors Abdi Santo Laia Alfian Mokodongan Aril Jdungguti Aurelia Relsi Patintingan Christine Takarina Meitty Manoppo Chrystani Sumolang Cian Anggraini Salote Danil R Gopay Desriani Dachi Dwi Putri Natalia Sitanggang Eirene Alexandria Sumeke Futriyani Supu Gemi Kereh Genevieve Josevhine Cecilia Kawilarang Ilarius Sasaka Jestin Jernih Ziliwu Joel Gabriel Doga Marsela Juwita Thomas Marselia Rambing Meiron Kiwo Nataniel Christian Kapoh Novia Saputri Oni Pinim Remon Pangkalangi Renalda Gracia Pingkan Nonga Sevan A Rumondor Simon Palendokan Thomas Liando Trudi Komansilan Yemima M Dalita