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All Journal RABIT: Jurnal Teknologi dan Sistem Informasi Univrab Jurnal Penelitian Pendidikan IPA (JPPIPA) JURNAL PENDIDIKAN TAMBUSAI Progresif: Jurnal Ilmiah Komputer Jurnal Tekinkom (Teknik Informasi dan Komputer) Afeksi: Jurnal Penelitian dan Evaluasi Pendidikan Abdimas Langkanae INTERNATIONAL JOURNAL OF EDUCATION, INFORMATION TECHNOLOGY, AND OTHERS Jurnal Pendidikan dan Pembelajaran Jurnal Sains dan Teknologi Jurnal Pengabdian Masyarakat Bidang Sosial dan Humaniora STORAGE: Jurnal Ilmiah Teknik dan Ilmu Komputer Jurnal Ilmiah Informatika dan Ilmu Komputer Indonesian EFL Journal
Muhammad Fadli
Politeknik Negeri Lampung
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Hybrid XGBoost-SVM Model untuk Sistem Pendukung Keputusan dalam Prediksi Penyakit Diabetes Muhammad Surono; Muhammad Fadli; Dian Sri Purwanti; Erliyan Redy Susanto
INSOLOGI: Jurnal Sains dan Teknologi Vol. 4 No. 3 (2025): Juni 2025
Publisher : Yayasan Literasi Sains Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55123/insologi.v4i3.5410

Abstract

Diabetes is a chronic disease that continues to rise globally each year, requiring early detection for more effective prevention. This study develops an artificial intelligence-based decision support system for diabetes prediction using a Hybrid XGBoost-SVM model. The model combines the Support Vector Machine (SVM), known for its interpretability, with XGBoost (XGB), which enhances accuracy through boosting techniques. The study utilizes the Pima Indians Diabetes Dataset, undergoing preprocessing, normalization, data splitting, and model training. The evaluation compares accuracy, precision, recall, and F1-score across the three models. Experimental results indicate that XGBoost and SVM both achieve an accuracy of 75%. However, the Hybrid XGBoost-SVM model provides consistently improved performance, achieving the highest accuracy (77%), along with increased precision (70%) and F1-score (65%). Although the numerical improvement in accuracy appears relatively small, this enhancement is significant in the medical context, especially due to improved precision and balanced classification. This study concludes that the Hybrid XGBoost-SVM approach offers a more optimal and reliable alternative in decision support systems for diabetes prediction. Future research can explore other model combinations, such as Stacking or Weighted Voting, to enhance predictive performance further.
Deteksi Dini Stroke Menggunakan Machine Learning Kevinda Sari; Muhammad Fadli; Muhammad Fahmi Fudholi; Erliyan Redy Susanto
INSOLOGI: Jurnal Sains dan Teknologi Vol. 4 No. 4 (2025): Agustus 2025
Publisher : Yayasan Literasi Sains Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55123/insologi.v4i4.5590

Abstract

Stroke is one of the leading causes of death and disability worldwide. Early detection of stroke risk is crucial to prevent more severe complications. This study aims to develop a stroke prediction model based on machine learning using an open dataset from Kaggle containing patients' medical and demographic information. Four machine learning algorithms were utilized and compared: AdaBoost, Gradient Boosting, LightGBM, and XGBoost. Data preprocessing steps included missing value imputation, categorical variable encoding, numerical feature normalization, and class balancing using the SMOTEENN method. Additionally, feature selection was performed using the Extra Trees algorithm to enhance model performance. The results showed that the XGBoost model delivered the best performance, achieving an accuracy of 97.16%, an F1-score of 97.49%, and an AUC of 99.75%. This model proved to be effective in detecting stroke cases and holds potential for integration into clinical decision support systems. The study concludes that a combination of modern boosting algorithms and optimal preprocessing techniques can yield a reliable stroke prediction system suitable for implementation in digital healthcare contexts.
Rancang Bangun Modul Kontrol Berbasis PID untuk Pengaturan Kecepatan dan Posisi Motor DC Menggunakan STM32 dan Rotary Encoder Doris Juarsa; Muhammad Fadli; Erliyan Redy Susanto
INSOLOGI: Jurnal Sains dan Teknologi Vol. 4 No. 4 (2025): Agustus 2025
Publisher : Yayasan Literasi Sains Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55123/insologi.v4i4.6081

Abstract

Precision control of DC motors in industrial and robotics applications is often compromised by external loads and the limitations of open-loop systems, which cause instability in rotational speed and angular position. This study aims to design and build a PID-based intelligent control module for integrated DC motor speed and position control using an STM32F103C8T6 microcontroller and a rotary encoder as feedback. This system is designed as a closed-loop system to reduce the error between the setpoint and the actual value. Tests were conducted under no-load and with-load conditions at various speed setpoints (10–30 RPM) and angular changes (slow and fast). The results show that the system is able to stabilize motor performance with an average speed error of −0.3033 and 0.2766 RPM (no-load) for Motors A and B, and 0.2633 and 3.47 RPM (with-load). For angular position control, the average errors were 0.69° and 0.895° (without load), and 0.475° and 0.335° (with load). These findings demonstrate the effectiveness of the PID-based intelligent control module in improving system accuracy and stability. This system offers a compact and practical solution for industrial automation and robotics applications requiring precise motor control.
Exploring the Relationship between Digital Communication Intensity and Learning Effectiveness in Higher Education: Towards Sustainable Digital Pedagogy (SDG 4) Muhammad Fadli; Ratu Chaterine Fajri; Arifin Arifin; Prisma Yunia Putri; Made Dewi Purnami
Jurnal Penelitian Pendidikan IPA Vol 12 No 7 (2026)
Publisher : Postgraduate, University of Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/jppipa.v12i7.15571

Abstract

This study analyzes the relationship between digital communication intensity and learning effectiveness, aligning with SDG 4 (Quality Education). Employing a quantitative approach, 350 university students participated. Descriptive analysis showed a mean digital communication intensity of 3.00 (SD = 0.75) and learning effectiveness of 3.65 (SD = 0.72). Contrary to linear assumptions, Pearson correlation revealed a non-significant linear relationship (r = 0.12; p = 0.06). However, polynomial regression confirmed a significant inverted-U pattern (β₂ = -0.18; p < 0.001; R² = 0.68), with an optimum point at 3.66, indicating peak effectiveness at moderate intensity. Hierarchical regression showed that learning motivation and internet infrastructure quality significantly controlled the variance, confirming the robustness of the curvilinear model. These findings underscore the need for regulated digital platform usage to prevent cognitive overload
Career English Bootcamp: Pelatihan Bahasa Inggris Berbasis Dunia Kerja Pelayanan bagi Siswa MA Al-Ittihadiyah Dewi Suryanti; Muhammad Fadli; Sitti Suharni; Mc. Fulvia. Hy
Abdimas Langkanae Vol. 6 No. 2 (2026)
Publisher : Pustaka Digital Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53769/jpm.v6i2.916

Abstract

Kegiatan Pengabdian kepada Masyarakat (PKM) ini dilatarbelakangi oleh hasil focus group discussion (FGD) dengan pihak sekolah dan wawancara mendalam dengan guru Bahasa Inggris MA Al-Ittihadiyah, Way Sulan, yang mengungkapkan bahwa siswa masih lemah dalam penguasaan kosakata dan keterampilan berbicara, sementara kurikulum yang digunakan masih bersifat test-based dan minim praktik. Kegiatan ini bertujuan mengintervensi kendala tersebut melalui program Career English Bootcamp. Metode pelaksanaan mencakup identifikasi kebutuhan melalui FGD dan wawancara mendalam, pemberian materi kosakata kontekstual berbasis dunia kerja, praktik role-play sebagai office receptionist, serta evaluasi kualitatif melalui observasi dan wawancara terhadap 20 siswa kelas XI MA Al-Ittihadiyah, Way Sulan, menggunakan pertanyaan umum dan follow-up question. Hasil kegiatan menunjukkan bahwa siswa menjadi lebih memahami cara membangun kosakata. secara kontekstual sesuai bidang karier yang mereka minati, sehingga proses penguasaan kosakata tidak lagi terasa overwhelming. Kegiatan ini juga turut membantu kesiapan kerja (employability readiness) siswa dengan membekali mereka kemampuan komunikasi Bahasa Inggris yang aplikatif untuk memasuki dunia kerja. Sebagai tindak lanjut, dosen Program Studi Bispro berperan sebagai coach bagi guru bahasa Inggris setempat untuk memperkuat pembelajaran berbasis praktik. PKM ini menegaskan bahwa pelatihan bahasa Inggris kontekstual dapat menjadi solusi atas kesenjangan antara kurikulum berbasis tes dan kebutuhan komunikasi dunia kerja
Peningkatan Kompetensi Guru dalam Pengembangan Modul Ajar Berbasis Gemini AI melalui Teknik Prompting Rio Eka Nugraha; Muhammad Fadli; Dian Windriani; Retno Anggraini
ABDISOSHUM: Jurnal Pengabdian Masyarakat Bidang Sosial dan Humaniora Vol. 5 No. 3 (2026): September 2026
Publisher : Yayasan Literasi Sains Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55123/abdisoshum.v5i3.9319

Abstract

The implementation of the Merdeka Curriculum requires contextual teaching modules, but administrative burdens and low AI literacy hinder the optimization of teaching devices at SMKS Swadhipa Natar. This activity aims to improve teacher competence in developing Gemini AI based teaching modules through prompting techniques. The method used is Participatory Action Research involving twenty vocational teachers through intensive workshops, participatory observation, and collaborative reflection. The results show cognitive restructuring among teachers, shifting the AI paradigm from an omniscient entity to a reliable pedagogical partner. Teachers successfully mastered the RCTF prompting framework to produce layered teaching modules responsive to the psychology and industrial context of vocational students. This directly impacts the relevance of teaching materials with industry needs. It is concluded that this andragogy based training effectively increases teacher confidence, reduces cognitive load, and transforms teachers into critical instructional architects in the artificial intelligence era, significantly improving overall vocational learning quality in a highly sustainable and meaningful way for future education today.
Co-Authors Amarudin Amarudin Amarudin Ani Novitasari Arifin Arifin Budi Rahman Budiman, Ega David Kurniawan Dewantoro, Mahendra Dewi Suryanti Dian Puspita Sari Dian Sri Purwanti Dian Windriani Dimas Eko Putro Doris Juarsa Ega Budiman Erliyan Redy Susanto Fajri, Ratu Chaterine Gita Hilmi Prakoso Hadori Rosadi Helidatasa Utami Helma Nopijani Heidy Icha Winadya Permadani Ival Sanjaya Izza Maulida Santoso Kevinda Sari Made Dewi Purnami Mahendra Dewantoro Marbudi, Marbudi Maruly, Riyan Mc. Fulvia. Hy Mc. Fulvya.Hy Meidasari, Reski Mugi Prasetio Muhammad Fahmi Fudholi Muhammad Surono Prisma Yunia Putri Purwanti, Dian Sri Raka Sulistyo Ratu Chaterine Fajri Ratu Sinar Sari Tanjung Refdi Akmal, Refdi Retno Anggraini Retno Anggraini Rifka Simbolon Rio Eka Nugraha Riyan Maruly Ryan Randy Suryono Siska Fitriani Sitti Suharni Suffah, Faila Surono, Muhammad Suryanti, Dewi Susanto, Erliyan Redy Syaifuddin Muhammad Mirza Tina Nurzachra Latifah Rizki Liana Tria Setyani Valdi Mughni Budiman Windia Hanifah Windriani, Dian Yulia Indriani Yunita Fitri Yanti