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All Journal Jurnal Informatika Kaputama (JIK) Jurnal Teknologi Informasi dan Multimedia Indo-MathEdu Intellectuals Journal Bakti Sekawan : Jurnal Pengabdian Masyarakat
Zulkipli Zulkipli
Mahasiswa Teknologi Pembelajaran Pascasarjana-Universitas Negeri Malang
Religion Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Health Professions Mathematics Social Sciences Other

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Penerapan Collaborative Learning dalam Pembelajaran White-Box Testing: Studi Kasus pada Pengujian Looping dan Condition Branch Zulkipli, Zulkipli; Putri, Lara Aulia; Aranbi, Muhammad Defa; Purnomo, Reno; Wea, Yohanes Emanuel Dala
Indo-MathEdu Intellectuals Journal Vol. 7 No. 1 (2026): Indo-MathEdu Intellectuals Journal
Publisher : Lembaga Intelektual Muda (LIM) Maluku

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54373/imeij.v7i1.5043

Abstract

Software testing learning, particularly white-box testing, is an important competency in Information Technology Education, but it remains a challenge for students because it requires an understanding of complex program logic. Difficulties arise mainly in the material on testing program control structures, such as looping and condition branches. This study aims to analyse the application of the collaborative learning method in white-box testing learning in this material. The research used a qualitative descriptive approach with a case study design involving students from the Information Technology Education Study Programme. Data were collected through observation of the learning process, analysis of group assignment results, and student reflections. Data analysis was carried out through the stages of data reduction, data presentation, and conclusion drawing. The results of the study indicate that the application of collaborative learning can improve students' conceptual understanding, program logic analysis skills, and cooperation skills in solving source code testing problems. In addition, students showed increased activity in discussions and the ability to explain program logic flows systematically. Thus, the collaborative learning method is effective in white-box testing learning, especially in looping and condition branch material.
Efektivitas Penggunaan Visual Learning Tools dalam Pembelajaran Sequence Diagram bagi Siswa SMK Jurusan Rekayasa Perangkat Lunak Zulkipli, Zulkipli; Syahrian, Ekal; Sauri, Sovian; Rania, Salwa; Amad, Lalu Dafa Al
Indo-MathEdu Intellectuals Journal Vol. 7 No. 1 (2026): Indo-MathEdu Intellectuals Journal
Publisher : Lembaga Intelektual Muda (LIM) Maluku

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54373/imeij.v7i1.5053

Abstract

This study aims to examine the effectiveness of visual learning tools in improving students' understanding of sequence diagram concepts in vocational high schools majoring in Software Engineering (RPL). The study uses a quantitative approach with a quasi-experimental design involving two groups, namely the experimental group who learned using visual learning tools and the control group who used conventional learning methods. Data were collected through objective tests to measure students' cognitive understanding of sequence diagram concepts. Data analysis was performed using a t-test to determine the difference in average learning outcomes between the two groups. The results showed that students in the experimental group obtained significantly higher scores than the control group. These findings indicate that the use of visual learning tools is effective in improving students' understanding of sequence diagram concepts. Graphic and dynamic visual representations help students model object interactions more concretely, thereby reducing cognitive load and supporting the UML learning process more effectively.
Pengembangan Integrated Learning Lab untuk Simulasi Network Function Virtualization (NFV) Menggunakan Openstack Zulkipli, Zulkipli; Aranbi, Muhammad Defa; Putri, Lara Aulia; Purnomo, Renno; Rania, Salwa; Sauri, Sovian
Indo-MathEdu Intellectuals Journal Vol. 7 No. 1 (2026): Indo-MathEdu Intellectuals Journal
Publisher : Lembaga Intelektual Muda (LIM) Maluku

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54373/imeij.v7i1.5136

Abstract

The development of cloud computing-based computer network technology requires flexible, scalable, and efficient infrastructure. This challenge has an impact on computer network learning, which still relies heavily on theoretical approaches and physical laboratories with limited equipment and costs. This study aims to examine the development of an Integrated Learning Lab as a simulation-based network learning medium using Network Function Virtualization (NFV) on the OpenStack platform. The methods used are literature review and conceptual analysis of the concepts of NFV, OpenStack, and integrated learning laboratory models in the context of computer network education. The results of the study show that NFV enables network functions such as routers, firewalls, and load balancers to run virtually as Virtual Network Functions without dependence on special hardware. OpenStack acts as a Virtualized Infrastructure Manager that centrally manages computing and network resources, thereby supporting realistic NFV simulations. The discussion shows that the NFV and OpenStack-based Integrated Learning Lab has the potential to improve students' conceptual understanding and practical skills, while providing a flexible and efficient learning environment. This study concludes that the laboratory model is relevant for application in computer network learning to bridge academic needs and the demands of the modern network industry.
Peningkatan Kemampuan Fungsi TIK Polda NTB Melalui Pelatihan Penerapan AI di Era Digitalisasi Azwar, Muhamad; Hariyadi, I Putu; Ismarmiyati, Ismarmiyati; Anas, Andi Sofyan; Madani, Miftahul; Zulkipli, Zulkipli
Bakti Sekawan : Jurnal Pengabdian Masyarakat Vol. 6 No. 1 (2026): Juni
Publisher : Puslitbang Sekawan Institute Nusa Tenggara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35746/bakwan.v6i1.979

Abstract

Perkembangan teknologi digital menuntut Kepolisian Daerah Nusa Tenggara Barat (Polda NTB) untuk memperkuat ekosistem Teknologi Informasi dan Komunikasi (TIK) guna mendukung program Satu Data Polri. Namun, pemanfaatan kecerdasan buatan (AI) dalam tugas administratif dan operasional harian masih perlu ditingkatkan. Oleh karena itu, kegiatan pengabdian kepada masyarakat ini bertujuan untuk memberikan bimbingan teknis terkait penerapan AI sederhana bagi personel Bid TIK dan Satuan Operasional Polda NTB. Metode pelaksanaan menggunakan pendekatan Participatory Action Learning (PAL) melalui pelatihan interaktif dan demonstrasi praktis yang diselenggarakan dalam agenda Rapat Kerja Teknis pada tanggal 6 November 2025 di Mataram. Hasil evaluasi cepat (rapid assessment) sebelum dan sesudah kegiatan menunjukkan adanya peningkatan pemahaman dan keterampilan peserta yang signifikan. Pemahaman konsep dasar AI generatif meningkat dari 40% menjadi 90%, kemampuan menyusun perintah (prompt) yang efektif melonjak dari 25% menjadi 85%, dan kesadaran terkait keamanan data sensitif instansi pada penggunaan AI publik naik dari 50% menjadi 95%. Selain itu, peserta menunjukkan antusiasme tinggi dalam mempraktikkan AI untuk menyusun draf dokumen dan merangkum regulasi. Kesimpulannya, pengenalan dan pelatihan AI sederhana terbukti sangat efektif dalam meningkatkan literasi digital serta kapasitas sumber daya manusia di lingkungan Polda NTB. Pemanfaatan teknologi ini diharapkan berlanjut untuk menciptakan efisiensi kerja yang terukur, sekaligus memperkokoh kesiapan kepolisian dalam menghadapi tantangan operasional di era modern yang terdigitalisasi.
Optimization of Support Vector Machine Using SMOTE and Grid Search for Kidney Health Data Classification Muhammad Maulana; Zulkipli Zulkipli; Tanwir Tanwir; Dading Oktaviadi Resmiranta; Naufal Hanif; Raisul Azhar
Jurnal Teknologi Informasi dan Multimedia Vol. 8 No. 2 (2026): May
Publisher : Sekawan Institut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35746/jtim.v8i2.993

Abstract

Kidney disease is a highly prevalent health problem that can seriously impact the quality of life of those affected. To improve diagnostic accuracy, machine learning methods are widely used to classify patient data. Class imbalance (imbalanced data) is one of the problems that often occurs in the classification process and can affect the performance of machine learning models, especially in detecting minority classes. This study aims to improve the performance of the Support Vector Machine (SVM) algorithm by applying the SMOTE (Synthetic Minority Over-sampling Tech-nique) and Grid Search methods in the data classification process. SMOTE is used to balance the class distribution by adding synthetic data to the minority class, while Grid Search is used to ob-tain optimal model parameters. The results show that the SVM model without handling data im-balance produces relatively low performance with an accuracy value of 51%, precision 17%, re-call 33%, and F1-score 23%. After applying the SMOTE method, the model performance increases significantly to 81% accuracy, 81% precision, 80% recall, and 81% F1-score. Furthermore, the ap-plication of Grid Search to the SVM + SMOTE model provides the best results with an accuracy of 84%, precision 82%, recall 81%, and F1-score 81% with an AUC value of 0,92. The findings of this study indicate that the combination of SMOTE and Grid Search is effective in improving the per-formance of the SVM algorithm in data classification. The novelty of this study demonstrates that data imbalance management and hyperparameter optimization play a crucial role in producing more accurate and optimal classification models.
Co-Authors Adrian, M. Reza Ahmat Adil Alfian, Khaerul Amad, Lalu Dafa Al Amri, Haerul Andi Sofyan Anas Anthony Anggrawan Aranbi, Muhammad Defa Ariq, Tomy Augustin, Kartarina Azhar, Lalu Zazuli Azhar, Raisul Dading Oktaviadi Resmiranta Daindo Milla Darma, I Made Yadi Eko Saputra Elyakim Nova Supriyedi Patty, Elyakim Nova Supriyedi Hariyadi, I Putu Irawaty Irawaty Ismarmiyati, Ismarmiyati Isnain, M. Firdaus Lilik Widyawati Lutfie, Muhammad Hilal Mumtaz Miftahul Madani Mohammad Efendi Mudawil Qulub Muhamad Azwar, Muhamad Muhammad Maulana Muhammad Tahir Muhammad Tahir Mujamil, Ahmad Naufal Hanif Nulkamil, Putra Hasan Ondi Asroni Prasetyo, Bimo Purnomo, Renno Purnomo, Reno Putri, Lara Aulia Rania, Salwa Reliubun, Abdurrahman Shaleh Riskila, Aprila Kusuma Risla, Syaif Al Feiqo Sahar, Adnan Santoso, Heroe Sauri, Sovian Sihkabuden Sihkabuden Supriantono, Herman Sutrisno, Shendy Emilia Putri Syahrian, Ekal Syahrir, Moch. Syaikhurrahman, Muh Tanwir Tanwir v, Sovian Wea, Yohanes Emanuel Dala