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All Journal Informatika Mulawarman: Jurnal Ilmiah Ilmu Komputer JIKO (Jurnal Informatika dan Komputer) PENDIPA Journal of Science Education Jurnal Teknologi Terapan METIK JURNAL JISKa (Jurnal Informatika Sunan Kalijaga) Journal of Innovation Information Technology and Application (JINITA) PLAKAT : Jurnal Pelayanan Kepada Masyarakat International Journal of Engineering, Science and Information Technology Prosiding Seminar Nasional Rekayasa Teknologi Industri dan Informasi ReTII Adopsi Teknologi dan Sistem Informasi
Gubtha Mahendra Putra
Informatics Department, Engineering Faculty, Mulawarman University, Samarinda, 75123, Indonesia
Aerospace Engineering Humanities Astronomy Automotive Engineering 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 Earth & Planetary Sciences Education Electrical & Electronics Engineering Energy Engineering Environmental Science Industrial & Manufacturing Engineering Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Physics Social Sciences Transportation

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Peningkatan Kemampuan Analisis Statistik Kuantitatif Pada Riset Eksperimen Dengan Metode Workshop Fahrul Agus; Gubtha Mahendra Putra; Zanu Alfandi Kamil; Iswanto Arifin; Okta Ihza Gifari
Plakat : Jurnal Pelayanan Kepada Masyarakat Vol 4, No 2 (2022): Plakat: Jurnal Pelayanan Kepada Masyarakat
Publisher : Fakultas Ilmu Sosial dan Ilmu Politik, Universitas Mulawarman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30872/plakat.v4i2.8954

Abstract

Riset merupakan kegiatan akademik yang wajib dilakukan oleh mahasiswa tugas akhir di Program Studi Informatika, Fakultas Teknik Universitas Mulawarman, Samarinda Kalimantan Timur. Riset eksperimen memerlukan pengetahuan dan pemahaman yang kuat di bidang Statistik. Pelajaran Matematika dan Statistika termasuk materi yang sulit dipahami di kalangan mahasiswa di beberapa perguruan tinggi. Pelatihan atau workshop ini betujuan untuk melakukan penguatan kembali (recharging) serta peningkatan pengetahuan peserta tentang ilmu pengetahuan, konsep dan aplikasi Statistik Kuantitatif untuk riset eksperimen. Pelatihan dilakukan dengan cara pembelajaran secara langsung di dalam kelas. Monitoring pelatihan dilakukan dengan observasi selama kegiatan berlangsung, sedangkan evaluasi dilakukan melalui survey kepada peserta. Bobot penilaian oleh peserta terhadap materi, narasumber, fasilitas pelatihan dan peningkatan kemampuan diukur dengan Skala Likert. Pelatihan dilaksanakan dengan 38 peserta dan observasi monitoring menunjukkan dinamika peserta dan fasilitator yang aktif dan lancar. Hasil evaluasi kegiatan pelatihan menunjukkan materi, narasumber, fasilitas pelatihan dan peningkatan kemampuan telah memenuhi kebutuhan peserta dengan persentase rata-rata sebesar 99.7%. Evaluasi juga menunjukkan bahwa sebesar 64% peserta menyatakan peningkatan pengetahuan yang sangat tinggi, 31% menilai tinggi dan 5% menyatakan sedang. Students in the Department of Informatics at the Faculty of Engineering, Mulawarman University, Samarinda, East Kalimantan, are required to conduct research for their final project. Strong statistical expertise and understanding are necessary for conducting experimental research. Many universities' students report having trouble understanding math and statistics classes. The purpose of this training is to deepen and broaden participants' understanding of scientific principles and the uses of quantitative statistics in experimental research. Direct learning takes place during the training in the classroom. Evaluation of training is done through surveys given to participants, while monitoring is done by observing during the activity. A Likert Scale was used to gauge how seriously the participants took the content, resource people, training facilities, and capacity building. 38 people attended the training, and monitoring observations revealed the dynamics of fluent and active participants and facilitators. According to the findings of the evaluation of training activities, an average percentage of 99.7% of the participants' needs were met by the training materials, resource people, training facilities, and capacity building. Additionally, the evaluation revealed that 64% of participants rated the level of knowledge gain as very high, 31% as high, and 5% as moderate.
Validitas dan Efektivitas Media Karibu Dayak Berbasis Genially pada Materi Klasifikasi Makhluk Hidup SMP Ru'iyah Ru'iyah; Elsje Theodora Maasawet; Ruqqoyah Nasution; Dora Dayu Rahma Turista; Sri Purwati; Gubtha Mahendra Putra; Marwah Ulwatunnisa
PendIPA Journal of Science Education Vol 10 No 2 (2026): April - June
Publisher : UNIB Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33369/pendipa.10.2.576-585

Abstract

This study was motivated by students' difficulties in understanding the classification of living things, monotonous science learning, and the limited use of learning media at SMP Negeri 2 Samarinda. The study aimed to determine the validity, practicality, and effectiveness of Genially-based learning media integrated with Dayak Kenyah local wisdom for seventh-grade students. The research employed the Research and Development (R&D) method using the 4D model (Define, Design, Develop, and Disseminate). Data were collected through validation sheets, practicality questionnaires, activity observation sheets, and learning outcome tests. The results showed that the learning media achieved a very valid category with an Aiken’s V value of 0.897, a very practical category with an Aiken’s V value of 0.876, and a very feasible category with a percentage of 86.6%. The media was also proven effective, as indicated by an average student activity score of 92%, a paired sample t-test significance value of 0.000, and an N-Gain score of 0.41, which falls into the moderate category. Therefore, the Genially-based learning media integrated with Dayak Kenyah local wisdom is suitable for use in science learning on the topic of the classification of living things.
Deep Learning Methods for Pneumonia Detection Using ConvNeXt Architecture Akhmad Irsyad; Muhammad Bambang Firdaus; Gubtha Mahendra Putra; Putut Pamilih Widagdo; Hario Jati Setiady; Muhammad Fawaz Saputra; Muhammad Abdillah Rahmat
International Journal of Engineering, Science and Information Technology Vol 6, No 1 (2026)
Publisher : Malikussaleh University, Aceh, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52088/ijesty.v6i1.1797

Abstract

Pneumonia remains a major respiratory infection with high mortality rates, especially in regions with limited access to medical specialists. Early and accurate diagnosis plays a critical role in reducing fatal outcomes and improving patient management. Chest X ray imaging is widely used as a primary diagnostic modality, yet interpretation relies heavily on experienced radiologists, whose availability is often insufficient to meet clinical demand. This condition motivates the development of automated pneumonia detection systems based on artificial intelligence. This study investigates the application of deep learning for pneumonia classification using chest X ray images, with a focus on the ConvNeXt architecture. ConvNeXt represents a modern neural network design that integrates structural advantages from Vision Transformers with the efficiency of traditional Convolutional Neural Networks, enabling strong feature extraction while maintaining computational efficiency. The research evaluates multiple ConvNeXt variants, including Tiny, Small, Base, and Large, to analyze the relationship between model complexity and classification performance. ResNet50 is employed as a baseline model to provide a fair comparative assessment against a widely used convolutional architecture. Model evaluation uses accuracy, precision, recall, and F measure to ensure balanced measurement across different classification outcomes and class distributions. Experimental results indicate that ConvNeXt Tiny achieves the highest overall performance, reaching an accuracy of 97.69 percent while using a relatively low number of parameters. This outcome highlights the efficiency of lightweight architectures for medical image analysis tasks. The findings demonstrate that ConvNeXt Tiny delivers strong discriminative capability with reduced computational requirements, making it suitable for deployment in resource constrained clinical environments. This study contributes evidence supporting the effectiveness of modern deep learning architectures for automated pneumonia detection and provides insight into model selection for practical medical imaging applications.
Co-Authors Anam, M Khairul Anton Prafanto Arinda Mulawardani Kustiawan Awang Harsa Kridalaksana Budiman, Edy Dary Daris Abdurrahman Dedi Cahyadi Elsje Theodora Maasawet Fahrul Agus Fahrul Agus Fandy Hario Jati Setiady Haviluddin Haviluddin Herman Santoso Pakpahan Indah Fitri Astuti Irsyad, Akhmad Iswanto Arifin Marwah Ulwatunnisa Medi Taruk Muhammad Abdillah Rahmat Muhammad Bambang Firdaus Muhammad Bambang Firdaus Muhammad Fawaz Saputra Muhammad Wisdan Pratama Putra Nariza Wanti Wulan Sari Novianti Puspitasari Okta Ihza Gifari Puspitasari, Novianti Putut Pamilih Widagdo Rayner Alfred Reza Wardhana Rima Yustika Hasnida Rosmasari Rosmasari Ru'iyah Ru'iyah Ruqqoyah Nasution Setyadi, Hario Jati Solly Aryza Sri Purwati Suryani Junita Patandianan Taghfirul Azhima Yoga Siswa Turista, Dora Dayu Rahma Yumami, Eva Zanu Alfandi Kamil