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All Journal JUTIM (Jurnal Teknik Informatika Musirawas) Jurnal Teknologi Informasi MURA Jurnal Media Computer Science Jurnal Teknologi Informasi Mura
Rusdiyanto Rusdiyanto
Universitas Bina Insan
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Engineering Other

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APLIKASI MUSI RAWAS SMART REGENCY BERBASIS ANDROID Rusdiyanto rusdiyanto
Jurnal Teknologi Informasi Mura Vol 13 No 1 (2021): Jurnal Teknologi Informasi Mura Juni
Publisher : LPPM UNIVERSITAS BINA INSAN

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32767/jti.v13i1.1304

Abstract

Saat ini pemerintah Kabupaten Musi Rawas belum memiliki aplikasi khusus yang menyajikan informasi terkait Kabupaten Musi Rawas, Adapun informasi-informasi terkait Kabupaten Musi Rawas masih tersebar di beberapa Situs sehingga masyarakat masih sedikit kesulitan mencari informasi tersebut. Maka dengan permasalahan tersebut diperlukanlah sebuah aplikasi berbasis Android yang Mulus Musirawas Smart Regency yang dapat menampung berbagai informasi terkait Kabupaten Musirawas sehingga masyarakat tidak perlu lagi mencari di Google Search
APLIKASI MUSI RAWAS SMART REGENCY BERBASIS ANDROID Rusdiyanto rusdiyanto
Jurnal Teknologi Informasi Mura Vol 13 No 1 (2021): Jurnal Teknologi Informasi Mura Juni
Publisher : LPPM UNIVERSITAS BINA INSAN

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32767/jti.v13i1.1304

Abstract

Saat ini pemerintah Kabupaten Musi Rawas belum memiliki aplikasi khusus yang menyajikan informasi terkait Kabupaten Musi Rawas, Adapun informasi-informasi terkait Kabupaten Musi Rawas masih tersebar di beberapa Situs sehingga masyarakat masih sedikit kesulitan mencari informasi tersebut. Maka dengan permasalahan tersebut diperlukanlah sebuah aplikasi berbasis Android yang Mulus Musirawas Smart Regency yang dapat menampung berbagai informasi terkait Kabupaten Musirawas sehingga masyarakat tidak perlu lagi mencari di Google Search
PERANCANGAN SISTEM INFORMASI PELAYANAN KEPENDUDUKAN BERBASIS ANDROID KOTA LUBUKLINGGAU A. Taqwa Martadinata; Rusdiyanto Rusdiyanto; Iski Iski; Agustina Heryati
JUTIM (Jurnal Teknik Informatika Musirawas) Vol 8 No 1 (2023): JUTIM (Jurnal Teknik Informatika Musirawas) JUNI
Publisher : LPPM UNIVERSITAS BINA INSAN

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

Abstract

Permasalahan dalam penilitian ini adalah pada pelayanan kependudukan catatan sipilyang mana pada kondisi pandemi meminta upaya pelayanan publik agar tetap optimal sertamemberikan kemudahan bagi masyarakat dalam mengakses setiap layanan yang tersedia.Sistem pelayanan yang masih konvensional membuat dibutuhkan sedikit sentuhan teknologialam memudahkan proses pelayanan yang ada. Sehingga dengan adanya pandemi kali ini,diharapkan dapat mengubah sistem pelayanan yang manual dan konvensional menjadi lebihterotomatisasi dan digital. Selain itu, metode penelitian ini adalah deskriptif denganmenggunakan metode waterfall yang terdiri dari beberapa tahap: analisis, perancangan,pengkodean dan pengujian. Selanjutnya, dalam mengumpulkan data, peneliti menggunakan dataprimer dan data sekunder. Data primer meliputi observasi, wawancara dan dokumentasi. Di sisilain, data sekunder termasuk referensi, jurnal, artikel dan dokumentasi. Nantinya pada front endakan menggunakan android serta back end admin dapat diakses dengan menggunakan sisteminformasi website. Akhirnya dengan adanya perencanaan sistem informasi pelayanankependudukan berbasis android dapat memberikan kemudahan dalam proses pelayanan yangada pada dinas kependudukan dan catatan sipil kota Lubuklinggau.
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

Abstract

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.
Densenet201 Feature Extraction With Soft Voting Ensemble For Accurate Rice Leaf Disease Classification Nelly Khairani Daulay; Novi Lestari; Rusdiyanto Rusdiyanto
Jurnal Media Computer Science Vol 5 No 3 (2026): Juli
Publisher : LPPJPHKI Universitas Dehasen Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37676/jmcs.v5i3.11876

Abstract

Rice leaf diseases are one of the major factors contributing to reduced agricultural productivity and economic losses for farmers. Manual disease identification generally requires expert knowledge and is often difficult to perform efficiently in field conditions. Therefore, this study aims to develop a rice leaf disease classification system by combining DenseNet201 as a feature extractor and a Voting Ensemble approach as the classifier. The dataset consisted of 1,470 rice leaf images categorized into five classes: Bacterial Leaf Blight, Brown Spot, Healthy Leaf, Leaf Blast, and Tungro. The dataset was divided using a stratified split strategy into 80% training data, 10% validation data, and 10% testing data. Image augmentation was applied only to the training set, increasing the number of training samples to 7,056 images. DenseNet201 was employed to extract image features into 1,920-dimensional feature vectors, which were subsequently classified using Logistic Regression, Support Vector Machine (SVM), Hard Voting, and Soft Voting. Experimental results showed that Logistic Regression achieved an accuracy of 95.24%, while SVM achieved 95.92%. Hard Voting obtained an accuracy of 95.24%, whereas Soft Voting achieved the best performance with an accuracy of 95.92%, precision of 95.75%, recall of 95.70%, F1-score of 95.71%, and ROC-AUC of 99.76%. Furthermore, the best-performing model was deployed in a Streamlit-based application for automatic rice leaf disease identification. The results demonstrate that the combination of DenseNet201 and Soft Voting provides an accurate and effective approach for rice leaf disease classification and has strong potential as an early disease detection tool in 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

Abstract

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.
Co-Authors Agustina Heryati Ahmad Sobri Budi Santoso Deni Nurdiansyah Iski Iski Martadinata, A. Taqwa Nelly Khairani Daulay Novi Lestari Sunardi, Lukman