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All Journal Jurnal Jurusan Pendidikan Teknik Elektro Jurnal Pendidikan dan Pembelajaran Khatulistiwa (JPPK) Jurnal Plastik Rekonstruksi Technologia: Jurnal Ilmiah Jurnal Sisfotek Global Jurnal Computer Science and Information Technology (CoSciTech) Digital Transformation Technology (Digitech) Jurnal Sains Sistem Informasi Jurnal Abdimas Dosma
Sanjaya, Hendra
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Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Social Sciences Other

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PENGENALAN TEKNOLOGI MOBIL LISTRIK PADA SISWA SMA NEGERI 2 KOTABARU Andie; Hasanuddin; Sanjaya, Hendra
JURNAL ABDIMAS DOSMA (JAD) Vol. 5 No. 1 (2026): Januari
Publisher : IKATAN ALUMNI DOSEN MAGANG KEMENRISTEKDIKTI TAHUN ANGKATAN 2017

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Electric vehicles (EVs) are environmentally friendly vehicles powered by an electric motor and battery, serving to reduce greenhouse gas emissions. However, public understanding of the technology, advantages, and disadvantages of electric cars is still limited. A Community Service activity was conducted at SMA Negeri 2 Kotabaru, where students faced challenges in assembling an electric car, specifically with its battery, electrical systems, and software. Furthermore, myths among the teachers created apprehension about using electric vehicles. This outreach program aimed to introduce EV technology, educate on its pros and cons, and increase public awareness and acceptance. The methods used included lectures, discussions, and demonstrations. The expected outcome of this activity is an increase in public knowledge and acceptance of electric cars as an eco-friendly technology. Therefore, broader and more intensive outreach is necessary to promote the general use of environmentally friendly technologies.
Komparasi Support Vector Machine dan Random Forest dengan Optimasi GridSearchCV untuk Klasifikasi Kematangan Buah Berbasis Fitur Warna dan Tekstur Hasbi, Muhammad; Sanjaya, Hendra
Jurnal Sains Sistem Informasi Vol 4, No 2 (2026): JSSI (Mei)
Publisher : Universitas Islam Kalimantan Muhammad Arsyad Al Banjari

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31602/jssi.v4i2.23710

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@font-face {font-family:"Cambria Math"; panose-1:2 4 5 3 5 4 6 3 2 4; mso-font-charset:0; mso-generic-font-family:roman; mso-font-pitch:variable; mso-font-signature:-536870145 1107305727 0 0 415 0;}p.MsoNormal, li.MsoNormal, div.MsoNormal {mso-style-unhide:no; mso-style-qformat:yes; mso-style-parent:""; margin:0cm; mso-pagination:widow-orphan; font-size:11.0pt; font-family:"Times New Roman",serif; mso-fareast-font-family:"Times New Roman";}.MsoChpDefault {mso-style-type:export-only; mso-default-props:yes; font-size:11.0pt; mso-ansi-font-size:11.0pt; mso-bidi-font-size:11.0pt; mso-font-kerning:0pt; mso-ligatures:none;}div.WordSection1 {page:WordSection1;} Klasifikasi kematangan buah secara otomatis merupakan permasalahan penting dalam industri pertanian dan pascapanen. Penelitian ini membandingkan performa algoritma Support Vector Machine (SVM) dengan kernel RBF dan Random Forest (RF) dalam mengklasifikasikan kematangan buah tropis — meliputi apel, pisang, dan jeruk — ke dalam enam kelas: segar dan busuk. Dataset Fruits Fresh and Rotten for Classification dari Kaggle digunakan dengan 2.813 sampel valid. Setiap gambar diekstraksi menjadi 26 fitur warna dan tekstur, mencakup statistik HSV (mean, standar deviasi, skewness), statistik RGB, fitur tekstur (contrast, energy, homogeneity), dan histogram hue 8-bin. Seleksi fitur menggunakan ANOVA F-Test menghasilkan 15 fitur terbaik dengan fitur saturasi sebagai yang paling diskriminatif. Optimasi hyperparameter dilakukan dengan GridSearchCV 5-Fold, dan evaluasi akhir menggunakan 10-Fold Stratified Cross-Validation. Hasil menunjukkan SVM dengan C=100 dan gamma=scale menghasilkan akurasi 90,23%, precision 90,36%, recall 90,23%, dan F1-score 90,24%, mengungguli Random Forest yang memperoleh akurasi 82,42%. SVM terbukti lebih unggul untuk klasifikasi kematangan buah berbasis fitur warna dan tekstur pada dataset berukuran sedang.
Co-Authors Agus Adiarta,ST,MT . Agus Budiman Agusli, Rahmat Alfah, Rina Andie Andie, Andie Hamid, Agus Roy Hasanuddin Hasanuddin Hasanuddin I Wayan Niryana LOELITA MARCELIA LUMINTANG, LOELITA MARCELIA Madeten, Sisilya Saman Mahalisa, Galih Martiningsih, Agus MUHAMMAD HASBI Nyoman Santiyadnya Safarina, Gita Ayu Zaenuddin Zaenuddin, Zaenuddin