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Rudi Heriansyah, Rudi
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Implementasi Single Sign-On Menggunakan Protokol Openid Connect (OIDC) Pada Virtual Private Server (VPS) Sahrin, Muhammad Aditya; Heriansyah, Rudi; Sartika, Dewi
Jurnal Nasional Ilmu Komputer Vol. 5 No. 2 (2024): Jurnal Nasional Ilmu Komputer
Publisher : Training and Research Institute Jeramba Ilmu Sukses (TRI - JIS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47747/jurnalnik.v5i2.1748

Abstract

Single Sign-On (SSO) is an authentication method that allows users to access multiple website services using one account and one login process. OpenID Connect (OIDC) is a protocol based on OAuth 2.0 and provides user identity information through JWT (JSON Web Token) tokens. Virtual Private Server (VPS) is a service that can be configured according to user needs. Internet users have many accounts needed to access various online services such as email, social media, online shopping, and many more. Each online service usually has a different username and password. Users often find it difficult to remember each password needed. In addition, using the same password on each online service is also not recommended because it can increase security risks. Based on this problem, an SSO system is needed that is useful for helping users in logging in. The SSO system will be tested with two Dummy Students and Dummy Elearning clients using Black Box Testing with the Equivalence Partitioning technique. From the test results, it was obtained that the SSO system showed that 83% of the test scenarios were successfully carried out
Klasifikasi Kinerja Teknisi Pada Pt. Telkom Menggunakan Metode K-Nearest Neighbor Amidhan, Muhammad; Heriansyah, Rudi; Permatasari, Indah
Jurnal Nasional Ilmu Komputer Vol. 4 No. 3 (2023): Jurnal Nasional Ilmu Komputer
Publisher : Training and Research Institute Jeramba Ilmu Sukses (TRI - JIS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47747/jurnalnik.v4i3.1751

Abstract

Productivity is expected to support the company's goal of achieving high profits. Technician performance assessment still relies on subjective assessment from the team leader, causing difficulties in objective evaluation. This research aims to improve the appraisal of technician performance at PT Telkom by proposing using the k-nearest Neighbor (k-NN) method as a classification method. The k-NN method was chosen because of its robustness to training data and effectiveness on large training data. The classification process includes finding the distance between two points using the Euclidean equation, sorting the lowest value as the nearest neighbor, calculating the number of neighbors based on the 5 (five) nearest neighbors for the classification of "Dissatisfied", "Quite Satisfied,", "Satisfied", and "Very Satisfied". Based on calculations carried out using a confusion matrix, this algorithm has an accuracy rate of 74%, precision of 80%, and recall of 80%, so it can be recommended to PT Telkom to know the actions to be taken, whether a technician can be retained or not in the company
Pemberdayaan UMKM Kain Tenun Songket dan Kain Jumputan Binaan LPP-PEKKA Yayasan Masjid Agung Palembang untuk Meningkatkan Skala Produksi dan Pewarna Alami S.Si., M.Kom, Dr. Shinta Puspasari,; Setiawan, Herri; Viatra, Aji Windu; Yustini, Tien; Dhamayanti; Heriansyah, Rudi; Alie, Marzuki
Jurnal Abdimas Mandiri Vol. 7 No. 3
Publisher : UNIVERSITAS INDO GLOBAL MANDIRI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36982/jam.v7i3.3477

Abstract

Program Pembinaan Usaha Mikro Kecil dan Menengah (UMKM) Berbasis Kemitraan ini memiliki tujuan pembinaan, pemberdayaan, dan pemanfaatan bahan alami pewarna bahan kain di LPP-PEKKA Yayasan Masjid Agung Palembang. Program yang dirancang meliputi pelatihan Digital Marketing, Pelatihan Branding dan Packaging Produk, Pelatihan Manajemen Keuangan, dan Pelatihan Ketrampilan Produksi Pewarna Alami yang diikuti oleh 50 peserta UMKM LPP-PEKKA yang merupakan para pengerajin kain jumputan dan kain tenun songket Palembang. Metode pelatihan berupa penyampaian materi oleh narasumber, praktek langsung oleh para peserta, dan evaluasi kegiatan dengan pengisian kuesioner oleh peserta pelatihan. Hasil kegiatan menunjukkan adanya peningkatan pemberdayaan mitra yang mengalami peningkatan pengetahuan terkait materi pelatihan yang diberikan. Skor rata-rata peningkatan pengetahuan peserta sebesar 10%. Capaian luaran kegiatan PkM juga diukur dari peningkatan penerapan Iptek yang mendukung mitra UMKM LPP-PEKKA untuk memasarkan produk secara digital. Program ini diharapkan dapat meningkatkan skala produksi dan pewarna alami kain tenun songket dan kain Jumputan sebagai produk budaya tradisional Palembang sekaligus sebagai usaha untuk pelestarian budaya daerah. Dengan peningkatan pengelolaan keuangan dan peluang pasar produk hasil UMKM melalui pemasaran digital yang mampu menjangkau pasar global diharapkan dapat berdampak pada peningkatan perekonomian anggota UMKM LPP-PEKKA serta kelestarian lingkungan dengan pemanfataan pewarna alami yang ramah lingkungan dan aman bagi masyarakat
Improving the Accuracy of Concrete Mix Type Recognition with ANN and GLCM Features Based on Image Resolution Gasim, Gasim; Heriansyah, Rudi; Puspasari, Shinta; Irfani, Muhammad Haviz; Purnamasari, Evi; Permatasari, Indah; Samsuryadi, Samsuryadi
JURNAL INFOTEL Vol 17 No 1 (2025): February 2025
Publisher : LPPM INSTITUT TEKNOLOGI TELKOM PURWOKERTO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20895/infotel.v17i1.1201

Abstract

Concrete is an essential construction material that is often used due to its strength and durability, but its mix type identification often relies on conventional methods that are less efficient and accurate. This research aims to evaluate the effect of image resolution on the accuracy of concrete mix type recognition using Artificial Neural Network (ANN) and Gray-Level Co-Occurrence Matrix (GLCM) features. The method used involves analysing concrete images at various resolutions: 200 x 200, 300 x 300, 400 x 400, 500 x 500, 600 x 600, and 700 x 700 pixels. The experimental results show that higher image resolutions tend to improve recognition accuracy. all types of image sizes using 1,250 training data and 250 test data. Image sizes of 200 x 200 and 300 x 300 pixels give low accuracy of 42% and 45% respectively, while sizes of 400 x 400 and 500 x 500 pixels show an increase in accuracy to 60.5% and 62.5%. The higher resolutions of 600 x 600 and 700 x 700 pixels produced the highest accuracy of 68% and 70%, respectively. These results indicate that larger image resolutions are able to capture more details and characteristics required for more accurate concrete mix type recognition. This research has implications for improving efficiency and consistency in concrete inspection in the construction industry through the use of AI-based image recognition methods.
Co-Authors Aji Windu Viatra Alie, Marzuki Amidhan, Muhammad Budi Setiawan DEWI SARTIKA Dhamayanti Dwi Kurniawan, Budi Džakula, Nebojša Bačanin Fadly Fadly Fajri Munawar, Muhammad Ferano, Dwi Asa gasim, Gasim Herri Setiawan Imelda Saluza, Imelda Indah Permatasari Irfani, Muhammad Hafiz JABAR, Tri Ahmad Jabar Jambak, Muhammad Ikhwan Kanaka, Nayaka Al Syahreal Lestari, Sylvia Dwi Muhammad Haviz Irfani Pameka, Aplea Purnamasari, Evi Ramadhan, Mustafa Remustin, Rivky Riyanti, Rizki S.Si., M.Kom, Dr. Shinta Puspasari, Sahrin, Muhammad Aditya Samsuryadi Samsuryadi Shinta Puspasari Tien Yustini, Tien Widya Astuti, Lastri