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All Journal Sistemasi: Jurnal Sistem Informasi Journal of Information Technology and Computer Science (JOINTECS) Jurnal CoreIT DoubleClick : Journal of Computer and Information Technology JUKI : Jurnal Komputer dan Informatika Jurnal Teknik Informatika (JUTIF) ABSYARA: Jurnal Pengabdian Pada Masyarakat International Journal Education and Computer Studies Dedikasi: Jurnal Pengabdian Kepada Masyarakat Room of Civil Society Development Room of Civil Social Development
Muhammad Furqan Rasyid
Universitas Dipa Makassar
Religion Agriculture, Biological Sciences & Forestry Arts Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Languange, Linguistic, Communication & Media Library & Information Science Mathematics Social Sciences Other

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Sistem Deteksi Kantuk Pengemudi Mobil Berdasarkan Analisis Rasio Mata Menggunakan Computer Vision Suradi, Andi Asvin Mahersatillah; Alam, Samsu; Mushaf, Mushaf; Rasyid, Muhammad Furqan; Djafar, Imran
JUKI : Jurnal Komputer dan Informatika Vol. 5 No. 2 (2023): JUKI : Jurnal Komputer dan Informatika, Edisi Nopember 2023
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53842/juki.v5i2.269

Abstract

Driver drowsiness is one of the main causes of motor vehicle accidents. According to National Sleep Foundation records, about 32% of drivers have at least one drowsy driving experience per month. About 25% of traffic accidents are caused by drowsiness while driving each year. The purpose of this study is to design a system that can detect driver sleepiness based on the aspect ratio of the eye with certain parameters using a webcam placed in the car's speedometer area. The methods used are Histogram Oriented Gradients (HOG) and Linear SVM which are in the dlib library which includes machine learning algorithms and uses real time applications. A pre-trained facial landmark detector from the dlib library is used to predict the location of the 68 x-y coordinates that map the facial landmarks to the face zones. The results of this study indicate that the system can be used in real time to detect driver drowsiness with the camera position in the speedometer area at a distance of 50 cm with an average accuracy of 90.4%.
HORTICULTURE SMART FARMING FOR ENHANCED EFFICIENCY IN INDUSTRY 4.0 PERFORMANCE Arifin, Nurhikma; Insani, Chairi Nur; Milasari, Milasari; Rasyid, Muhammad Furqan
Jurnal Teknik Informatika (Jutif) Vol. 5 No. 5 (2024): JUTIF Volume 5, Number 5, Oktober 2024
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2024.5.5.2728

Abstract

Chili peppers and papayas are important horticultural commodities in Indonesia with high economic value. To enhance productivity and efficiency in cultivating these crops, the application of Smart Farming technology is crucial. This study evaluates the use of image processing and artificial intelligence in the pre-harvest and post-harvest processes for chili peppers and papayas. For the pre-harvest process, data from 50 images of ripe chili peppers on the plant were used. The counting of ripe chilies was performed using HSV color segmentation with two masking processes, resulting in an average accuracy of 82.58%. In the post-harvest phase, 30 images of papayas, consisting of 10 images for each ripeness category—unripe, half-ripe, and ripe—were used. Papaya ripeness classification was carried out using the Support Vector Machine (SVM) algorithm with a Radial Basis Function (RBF) kernel and parameters C = 10 and γ = 10-3, achieving perfect classification accuracy of 100% for all categories. This study underscores the significant potential of Industry 4.0 technologies in enhancing agricultural practices and efficiency in the horticultural sector, providing important contributions to optimizing chili pepper and papaya production.
Comparison of SVM and Gradient Boosting with PCA for Website Phising Detection Syam, Nur Aini; Arifin, Nurhikma; Firgiawan, Wawan; Rasyid, Muhammad Furqan
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 2 (2025): JUTIF Volume 6, Number 2, April 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.2.4344

Abstract

The increasing use of the internet has led to a rise in phishing attacks, posing a threat to user data security. This study compares the performance of the Support Vector Machine (SVM) and Gradient Boosting algorithms, integrated with Principal Component Analysis (PCA) for dimensionality reduction, in classifying phishing websites. The dataset consists of 11,054 samples classified into two categories: phishing (1) and non-phishing (-1), with three data partition scenarios for training and testing: 70:30, 80:20, and 90:10. Experimental results indicate that SVM outperforms Gradient Boosting in terms of accuracy and recall, particularly in detecting phishing websites. In the 80:20 and 70:30 data partition scenarios, the SVM model achieved an accuracy of 96% to 97% and had a higher recall for phishing websites, making it more sensitive to phishing detection. However, Gradient Boosting demonstrated consistent performance with an accuracy of around 94%, providing a balanced result between precision and recall for both classes. Therefore, the SVM model is superior for phishing detection tasks requiring high sensitivity to phishing websites, while Gradient Boosting remains a viable alternative when a more balanced performance between phishing and non-phishing sites is needed. The study concludes that both algorithms can be effectively used for phishing detection, with potential improvements through further experiments and hyperparameter tuning.
Peningkatan Mutu Pembelajaran Guru Bidang Studi Basis Data dalam Menghadapi Ujian Kompetensi Keahlian (UKK) SMKS Mutiara Ilmu Makassar Mustafa, M. Syukri; Aryasa, Komang; Rasyid, Muhammad Furqan
Room of Civil Society Development Vol. 2 No. 4 (2023): Room of Civil Society Development
Publisher : Lembaga Riset dan Inovasi Masyarakat Madani

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59110/rcsd.191

Abstract

Uji Kompetensi Keahlian (UKK) merupakan penilaian yang diselenggarakan khusus bagi siswa SMK untuk mengukur pencapaian kompetensi peserta didik yang setara dengan kualifikasi jenjang 2 (dua) atau 3 (tiga) pada KKNI. SMK Komputer Mutiara Ilmu sudah mengikutsertakan siswa-siswinya dalam mengikuti UKK tersebut sejak tahun 2005 sampai saat ini. Dalam pelaksanaan UKK tersebut ditemukan adanya ketidak sesuaian antara soal yang diujikan dengan kurikulum yang diajarkan pada beberapa mata pelajaran tertentu. Kegiatan pelatihan pada pengabdian masyarakat ini diukur dengan memberikan Pretest dan Post Test kepada 11 peserta pelatihan. Hasil pengujian statistik non parametrik uji bertanda wilcoxon menunjukkan terdapat perbedaan bermakna antara kelompok PreTest dan PosTtest, atau adanya peningkatan nilai yang signifikan antara PreTest dengan PostTest.
Co-Authors Alam, Samsu Anak Agung Gede Sugianthara Andi Asvin Mahersatillah Suradi Arham Arifin Arham Arifin Arifin, Nurhikma Arwansyah Asmah Akhriana Djafar, Imran Djafar, Imran Erfan Hasmin Firgiawan, Wawan Hadis, Muhammad Sabirin Harlina, Sitti Heriadi Heriadi Insani, Chairi Nur Komang Aryasa Milasari Milasari Muhammad Rizal MUHAMMAD RIZAL Muhammad Syukri Mustafa Muhammad Syukri Mustafa, Muhammad Syukri Mushaf Mushaf Mushaf Mushaf Mushaf, Mushaf Novita Sambo Layuk Nurul Aini Samsie, Indra Santi Santi Sitti Aisa SRI WAHYUNINGSIH PIU Sriwahyuningsi Piu Suci Ramadhani Arifin Sunardi Sunardi Syam, Nur Aini Wilem Musu