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All Journal Jurnal Ilmiah Kursor JURIKOM (Jurnal Riset Komputer) SEMAR Jurnal Computer Science and Information Technology (CoSciTech) Sistem Pendukung Keputusan dengan Aplikasi Jurnal Informatika Medis (J-INFORMED) IJENSET
Muhammad Shodiq
Universitas Muhammadiyah, Lamongan
Humanities Automotive Engineering Biochemistry, Genetics & Molecular Biology Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Mechanical Engineering Nursing Public Health Social Sciences Veterinary

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STUDI PERBANDINGAN ALGORITMA MACHINE LEARNING : SUPPORT VECTOR MACHINE, DECISION TREE DAN RANDOM FOREST DALAM KLASIFIKASI PENYAKIT DIABETES Muhammad Shodiq; Agus Priyono; Neni Purwati
Jurnal Informatika Medis (J-INFORMED) Vol. 4 No. 1 (2026): Jurnal Informatika Medis (J-INFORMED)
Publisher : LPPM Universitas Muhammadiyah Muara Bungo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52060/im.v4i1.4221

Abstract

Hyperglycemia, or elevated blood glucose levels, is a primary indicator of diabetes mellitus, a chronic metabolic disorder whose prevalence continues to rise globally according to reports from the World Health Organization (WHO). Early detection of diabetes risk is crucial for preventing severe long-term complications. This study aims to evaluate and compare the performance of three machine learning algorithms Support Vector Machine (SVM), Decision Tree, and Random Forest in classifying diabetes based on health indicators and lifestyle patterns. The dataset used was obtained from Kaggle, with preprocessing stages including handling missing values and normalization. Model performance was assessed using accuracy, precision, recall, and F1-score. The experimental results show that the SVM algorithm achieved the highest accuracy at 74.89%, followed by Decision Tree with 73.39%, and Random Forest with 72.41%. This research is expected to serve as a reference for developing early medical screening systems to intelligently and accurately detect diabetes risk.
Sistem Pendukung Keputusan untuk Memilih Program Kesehatan Sekolah Menggunakan COPRAS M. Ari Prayogo; Muhammad Labib Jundillah; Febri Ramanda; Muhammad Shodiq; Riendy Riendy
Sistem Pendukung Keputusan dengan Aplikasi Vol 4 No 2 (2025)
Publisher : Ali Institute or Research and Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55537/spk.v4i2.1304

Abstract

The School Health Program is a strategic initiative aimed at improving students’ health within the educational environment. However, selecting the most appropriate program often requires consideration of multiple complex criteria. This study develops a Decision Support System (DSS) using the COmplex PRoportional ASsessment (COPRAS) method to assist schools in determining the best health program. The alternative programs analyzed include Reproductive Health, Healthy School Cleanliness Competition, Smoke-Free School Area, Prevention of Drug Abuse (NAPZA), and Disease Control. The evaluation was conducted based on six main criteria: Implementation Cost, Student Participation, Program Effectiveness, Long-Term Health Impact, Relevance to School Needs, and Ease of Implementation. The results indicate that the third alternative (A3), namely the Smoke-Free School Area, is the most suitable school health program, achieving a utility value (Ui) of 100% among the five alternatives considered. This system is expected to make the decision-making process more objective, efficient, and supportive of fostering a healthier, more productive, and sustainable school environment.
Penerapan Irigasi Tetes Bertenaga Surya dengan Sistem Otomatis Mikrokontroler Pada Kelompok Tani Kabupaten Lamongan Heri Ardiansyah; Muhammad Shodiq; M Ainul Mahbubillah; Rohmatin Agustina; Abdullah Hakim Gymnastiar; Muhammad Rizky Rahmadani; Fatahillah Akrom; Ahmad Faris Rachmad Putra; Hanif Azhar Ramadhan; Zufar Faiil Haq
SEMAR (Jurnal Ilmu Pengetahuan, Teknologi, dan Seni bagi Masyarakat) Vol 14, No 1 (2025): Mei
Publisher : LPPM UNS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20961/semar.v14i1.93488

Abstract

Keterbatasan sumber daya air di lahan tadah hujan menjadi tantangan utama bagi petani, terutama selama musim kemarau, yang membatasi aktivitas budidaya dan berdampak pada pendapatan rumah tangga. Program pengabdian masyarakat ini bertujuan untuk mengatasi masalah tersebut melalui penerapan teknologi irigasi tetes bertenaga surya dengan sistem kecerdasan buatan menggunakan sensor kelembaban dan suhu tanah di Kelompok Tani Kabupaten Lamongan. Metode pelaksanaan meliputi perancangan dan instalasi sistem irigasi otomatis, pelatihan penggunaan teknologi kepada petani, serta pendampingan budidaya cabai selama musim kemarau. Hasil program menunjukkan bahwa lebih dari 90% petani mengakui manfaat dari irigasi otomatis ini dan berencana untuk menerapkannya, baik secara mandiri (25%) maupun melalui kelompok tani (32,5%). Selain itu, 35% petani memilih untuk mengombinasikan sistem irigasi otomatis dengan metode konvensional untuk menyesuaikan kebutuhan air sesuai musim. Penerapan teknologi ini tidak hanya memungkinkan petani untuk tetap menanam di musim kemarau, tetapi juga meningkatkan pendapatan mereka secara signifikan. Kesimpulannya, teknologi irigasi tetes bertenaga surya yang dilengkapi kecerdasan buatan terbukti efektif dalam meningkatkan efisiensi penggunaan air dan kesejahteraan petani, serta dapat direplikasi untuk mendukung ketahanan pangan dan produktivitas pertanian di wilayah lain.
Co-Authors Abdullah Hakim Gymnastiar Agus Priyono Agus Priyono Agus Priyono Agustina, Rohmatin Ahmad Faris Rachmad Putra Ahmad Syaifuddin Alvinsyah, Dwi Putra Ayuningsih, Ekatri Bagus Dwi Saputra Budi Warsito Eko Handoyo Fatahillah Akrom Febri Ramanda Hanif Azhar Ramadhan Heri Ardiansyah Hidayah, Septina Alfiani M Ainul Mahbubillah M. Ari Prayogo M. Cahyo Kriswantoro Muhammad Labib Jundillah Muhammad Rizky Rahmadani Neni Purwati Nur, Delyardi Pratama, Ahmad Bagus Putra Rachmat Gernowo Ramanda, Febri Riendy Riendy SAPUTRA, BAGUS DWI zu fahim, wahyu Zufar Faiil Haq