Garuda - Garba Rujukan Digital

PERANCANGAN ALAT UKUR SISTEM MONITORING TERHADAP SUHU DAN KELEMBABAN TANAH BERBASIS IOT (INTERNET OF THINGS) DENGAN MENGGUNAKAN WEB MOBILE DI DESA MUARA KATI KABUPATEN MUSI RAWAS Sobri, Ahmad; Nurdiansyah, Deni; Sunardi, Lukman
Jusikom : Jurnal Sistem Komputer Musirawas Vol 8 No 2 (2023): Jusikom : Jurnal Sistem Komputer Musi Rawas DESEMBER
Publisher : LPPM UNIVERSITAS BINA INSAN

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32767/jusikom.v8i2.2145

The purpose of this study was to determine the level of soil temperature and humidity in the village of Muara Kati, Musi Rawas Regency. The use of pH temperature and soil moisture measuring devices will have an impact on the crop yields of farmers in the Muara Kati sub-district and will also develop the community in cultivating other plantation crops if the soil impacts are good. The measurement tools used are arduino uno, usb cable, arduino IDE, Ethernet Shield, Twisted Pie cable, and a webmobile design system that uses UML, PHP and MySQL which helps this system. to make a temporary design for the system to be made, namely use cases, activity diagrams, sequence diagrams and also class diagrams. This system also displays a page that will detect the flow of the senor on the hardware and will display the level of soil acidity at soil pH and the temperature unstable in soil management and also good moisture content in the soil caused by unstable humidity.

IMPLEMENTASI DEEP LEARNING ALEXNET UNTUK DETEKSI DAN KLASIFIKASI TANDA TANGAN Deni Nurdiansyah; Ahmad Sobri; Lukman Sunardi; Rusdiyanto Rusdiyanto; Budi Santoso
Jurnal Teknologi Informasi Mura (JTI) Vol. 17 No. 2 (2025): Jurnal Teknologi Informasi Mura DESEMBER
Publisher : LPPM UNIVERSITAS BINA INSAN

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32767/jti.v17i2.2897

The problem in this research is that the manual signature verification process is still widely used. However, this method is prone to human error and is highly subjective, so its accuracy in distinguishing genuine and fake signatures is not optimal. The pattern recognition extraction process in signatures uses the Alexnet algorithm. This study uses a digital signature image dataset consisting of two classes, with 90 images per class. Furthermore, the signature pattern recognition extraction process based on digital images can be performed using the Alexnet model. The purpose of this paper is to help classify signature types, which can facilitate the medical treatment process. The analysis uses deep learning with Python tools. Explicitly, the total sample size in Figure "Distribution of Classes in Training, Validation, and Testing Data" (image_98f1fc.png) shows that the number of samples for the 'full_forg' class is fewer than for the 'full_org' class. Although the model performs very well on the minority class, the presence of perfect recall for the 'full_org' class will be interesting to observe.

Pengembangan Model Deteksi Autism Spectrum Disorder (ASD) Dengan Algoritma Facenet Vggface Dan Insightface Marsha Falen Fransisca; Lukman Sunardi; Harma Oktafia LW; Budi Santoso
Bulletin of Information Technology (BIT) Vol 7 No 2 (2026)
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bit.v7i2.2697

Autism Spectrum Disorder (ASD) is a neurodevelopmental condition that affects communication, social interaction, and behavior. Conventional ASD diagnosis relies on clinical observation, which is time-consuming and subjective. Therefore, an automated approach using artificial intelligence is required to support early detection. This study proposes an ASD detection model based on facial image analysis using deep learning approaches, namely FaceNet, VGGFace2, and InsightFace as facial feature extraction methods. The dataset consists of 3,620 facial images categorized into ASD and non-ASD classes. The research process includes preprocessing, feature extraction, model training, and evaluation using accuracy, precision, recall, and F1-score metrics. The results indicate that all models achieved good classification performance, with FaceNet achieving the highest accuracy of 98%, followed by InsightFace with 96%, and VGGFace2 with 95%. These findings demonstrate that face embedding-based models provide superior feature extraction capabilities for ASD detection.

PENDEKATAN COMPUTER VISION BERBASIS FUSION CNN DAN GRAD-CAM UNTUK IDENTIFIKASI PENYAKIT DAUN CABAI Andri Anto Tri Susilo; Lukman Sunardi; Budi Santoso
Jurnal Teknologi Informasi Mura (JTI) Vol. 18 No. 2 (2026): Jurnal Teknologi Informasi Mura JUNI
Publisher : LPPM UNIVERSITAS BINA INSAN

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32767/jti.v18i2.3033

The rapid advancement of Artificial Intelligence (AI) technology has created new opportunities for modernizing the agricultural sector, particularly in the early detection and classification of plant diseases based on digital images. A Computer Vision-based approach has emerged as an effective solution, as it enables the automation of visual analysis that was previously reliant on manual observation. In this study, a method based on Fusion Convolutional Neural Networks (CNN) is proposed, combining the strengths of ResNet and DenseNet architectures to produce more robust and discriminative feature representations. In addition, this research integrates an Explainable Artificial Intelligence (XAI) technique using Grad-CAM to provide visual interpretations of the model’s decisions, thereby enhancing user trust in the developed system. The dataset used consists of three main classes of chili leaf conditions: Bacterial Spot, Curl Virus, and Healthy. Experimental results demonstrate that the proposed model achieves excellent performance, with an accuracy of 98%. Further analysis through the classification report indicates that the Healthy class attains perfect performance, with precision, recall, and f1-score all reaching 1.00. Meanwhile, the Bacterial Spot class achieves a recall of 1.00 and an f1-score of 0.97, indicating the model’s capability to correctly identify all samples in this class. The Curl Virus class also shows strong performance, with a precision of 1.00, recall of 0.95, and f1-score of 0.97. Overall, the macro average and weighted average f1-scores both reach 0.98, reflecting the model’s stability and consistency across all classes. Furthermore, the implementation of Grad-CAM is able to highlight specific regions on chili leaves that contribute to the model’s predictions, providing deeper insight into the disease patterns recognized by the model. This not only enhances interpretability but also supports visual validation by users. Therefore, this study demonstrates that the combination of Fusion CNN and Grad-CAM is not only effective in improving classification accuracy but also ensures transparency in the decision-making process, making it highly suitable for intelligent decision-support systems in precision agriculture

IMPLEMENTASI DEEP LEARNING ALEXNET UNTUK DETEKSI DAN KLASIFIKASI TANDA TANGAN Deni Nurdiansyah; Ahmad Sobri; Lukman Sunardi; Rusdiyanto Rusdiyanto; Budi Santoso
Jurnal Teknologi Informasi Mura (JTI) Vol. 17 No. 2 (2025): Jurnal Teknologi Informasi Mura DESEMBER
Publisher : LPPM UNIVERSITAS BINA INSAN

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32767/jti.v17i2.2897

The problem in this research is that the manual signature verification process is still widely used. However, this method is prone to human error and is highly subjective, so its accuracy in distinguishing genuine and fake signatures is not optimal. The pattern recognition extraction process in signatures uses the Alexnet algorithm. This study uses a digital signature image dataset consisting of two classes, with 90 images per class. Furthermore, the signature pattern recognition extraction process based on digital images can be performed using the Alexnet model. The purpose of this paper is to help classify signature types, which can facilitate the medical treatment process. The analysis uses deep learning with Python tools. Explicitly, the total sample size in Figure "Distribution of Classes in Training, Validation, and Testing Data" (image_98f1fc.png) shows that the number of samples for the 'full_forg' class is fewer than for the 'full_org' class. Although the model performs very well on the minority class, the presence of perfect recall for the 'full_org' class will be interesting to observe.

PENDEKATAN COMPUTER VISION BERBASIS FUSION CNN DAN GRAD-CAM UNTUK IDENTIFIKASI PENYAKIT DAUN CABAI Andri Anto Tri Susilo; Lukman Sunardi; Budi Santoso
Jurnal Teknologi Informasi Mura (JTI) Vol. 18 No. 2 (2026): Jurnal Teknologi Informasi Mura JUNI
Publisher : LPPM UNIVERSITAS BINA INSAN

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32767/jti.v18i2.3033

The rapid advancement of Artificial Intelligence (AI) technology has created new opportunities for modernizing the agricultural sector, particularly in the early detection and classification of plant diseases based on digital images. A Computer Vision-based approach has emerged as an effective solution, as it enables the automation of visual analysis that was previously reliant on manual observation. In this study, a method based on Fusion Convolutional Neural Networks (CNN) is proposed, combining the strengths of ResNet and DenseNet architectures to produce more robust and discriminative feature representations. In addition, this research integrates an Explainable Artificial Intelligence (XAI) technique using Grad-CAM to provide visual interpretations of the model’s decisions, thereby enhancing user trust in the developed system. The dataset used consists of three main classes of chili leaf conditions: Bacterial Spot, Curl Virus, and Healthy. Experimental results demonstrate that the proposed model achieves excellent performance, with an accuracy of 98%. Further analysis through the classification report indicates that the Healthy class attains perfect performance, with precision, recall, and f1-score all reaching 1.00. Meanwhile, the Bacterial Spot class achieves a recall of 1.00 and an f1-score of 0.97, indicating the model’s capability to correctly identify all samples in this class. The Curl Virus class also shows strong performance, with a precision of 1.00, recall of 0.95, and f1-score of 0.97. Overall, the macro average and weighted average f1-scores both reach 0.98, reflecting the model’s stability and consistency across all classes. Furthermore, the implementation of Grad-CAM is able to highlight specific regions on chili leaves that contribute to the model’s predictions, providing deeper insight into the disease patterns recognized by the model. This not only enhances interpretability but also supports visual validation by users. Therefore, this study demonstrates that the combination of Fusion CNN and Grad-CAM is not only effective in improving classification accuracy but also ensures transparency in the decision-making process, making it highly suitable for intelligent decision-support systems in precision agriculture

PEMBERDAYAAN GENERASI MUDA MELALUI PELATIHAN KECAKAPAN WIRAUSAHA (PKW) TAHUN 2026 UNTUK MENUMBUHKAN JIWA CREATIVEPRENEURSHIP Fido Rizki; Nopalia Nopalia; Ahmad Sobri; Lukman Sunardi; Ahmad Marsehan
JURNAL UNIV.BI MENGABDI Vol 5 No 1 (2026): Jurnal UNIV.BI Mengabdi : Juni
Publisher : LPPM UNIVERSITAS BINA INSAN

Show Abstract | Download Original | Original Source | Check in Google Scholar

Pelatihan Kecakapan Wirausaha (PKW) merupakan program strategis nasional untuk menekan angka pengangguran melalui pembekalan kompetensi bisnis bagi generasi muda yang belum memiliki pekerjaan. Tujuan pengabdian ini adalah membekali para peserta PKW Tahun 2026 dengan fondasi kewirausahaan yang kokoh, kapasitas kreativitas dan inovasi, pemahaman kualitas diri, etos kerja yang tinggi, serta mitigasi risiko terhadap faktor-faktor merugikan dalam usaha. Metode pelaksanaan yang digunakan meliputi ceramah interaktif, diskusi kelompok (Forum Group Discussion), studi kasus, serta simulasi penyusunan ide bisnis. Kegiatan dilaksanakan mulai tanggal 23 Mei 2026 hingga 13 Juli 2026 yang diikuti oleh 20 orang peserta yang merupakan pemuda produktif dengan status belum bekerja di wilayah Kota Lubuk Linggau dan sekitarnya. Hasil dari pengabdian masyarakat ini menunjukkan peningkatan pemahaman teoretis dan praktis para peserta sebesar 55% berdasarkan perbandingan nilai rata-rata pre-test (33,75%) dan post-test (88,75%). Dampak nyata dari kegiatan ini adalah tumbuhnya motivasi berwirausaha secara mandiri, yang dibuktikan dengan lahirnya 5 draf rintisan ide bisnis kreatif berbasis potensi lokal yang dirancang oleh para peserta setelah periode pelatihan usai

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