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All Journal KACANEGARA Jurnal Pengabdian pada Masyarakat Jurnal Teknologi Informasi dan Terapan (J-TIT) SKANIKA: Sistem Komputer dan Teknik Informatika
Bekti Maryuni Susanto
AMIK BSI Yogyakarta
Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Engineering

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PENERAPAN HORIZONTAL POD AUTOSCALER DAN REDIS CLUSTER BERBASIS KUBERNETES UNTUK MENINGKATKAN PERFORMA WEBSITE ELEARNING Gurohman, Diki Taufik; Susanto, Bekti Maryuni; Hariyanto, Agus; Jullev Atmadji, Ery Setiyawan; Gumilang, Mukhamad Angga; Antika, Elly; Mukhlisoh, Nanik Anita
SKANIKA: Sistem Komputer dan Teknik Informatika Vol 7 No 2 (2024): Jurnal SKANIKA Juli 2024
Publisher : Universitas Budi Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36080/skanika.v7i2.3211

Abstract

Elearning is a very vital tool in learning today. To provide optimal service, elearning servers require quite large computing resources when a large number of users access them simultaneously. However, expensive computing resources such as CPU, memory and disk storage make it difficult for organizations to meet the needs of large users. Previous research compared the performance of two public clouds on a moodle-based learning management system. The results showed that backup and restore times increased by about 10 seconds for every additional 500 MB of data size. This research aims to apply Kubernetes-based horizontal pod autoscaler and Redis cluster on the Moodle elearning server. Moodle is used to run elearning and Redis as database memory which can improve website performance. Horizontal implementation of pod autoscaler and Redis cluster was able to increase the performance of the Moodle e-learning website by 4.3% compared to a monolithic approach. Research shows that implementing Kubernetes and Redis clusters can improve the performance of Moodle e-learning websites. This research also shows that the microservice approach has better performance compared to the monolithic approach..
Community Empowerment karang taruna Kelurahan Sempusari melalui penggunaan Aplikasi Pantau Banjir dan pelatihan evakuasi korban Elisanti, Alinea Dwi; Susanto, Bekti Maryuni; Ardianto, Efri Tri
KACANEGARA Jurnal Pengabdian pada Masyarakat Vol 7, No 2 (2024): Mei
Publisher : Institut Teknologi Dirgantara Adisutjipto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28989/kacanegara.v7i2.1892

Abstract

Banjir menjadi bencana hidrologi yang paling sering terjadi. Di Kelurahan Sempusari Kaliwates Jember, tercatat 3 kali banjir pada tahun 2022 dengan korban meninggal terseret arus sebanyak 3 orang. Pengabdian masyarakat ini bertujuan untuk memberdayakan karang taruna kelurahan Sempusari melalui penggunaan aplikasi pantau banjir sebagai early warning system dan pelatihan evakuasi korban. Kegiatan ini merupakan penerapan IPTEK dosen berupa aplikasi pantau banjir dan pelatihan evakuasi korban. Alat ukur yang digunakan yaitu kuesioner, Teknik observasi dan ceklist. Mitra yang terlibat sejumlah 30 orang, dengan hasil kegiatan berupa aplikasi pantau banjir berbasis android dan web, peningkatan pengetahuan dan keterampilan karang taruna dalam melakukan evakuasi korban banjir. Diketahui 83% mitra mengalami peningkatan pengetahuan, 86,6% mitra cukup terampil dan terampil dalam penggunaan aplikasi pantau banjir, dan 90% mitra memiliki keterampilan cukup baik dan baik dalam evakuasi korban. Luaran kegiatan berupa manual book aplikasi, publikasi pada media massa, koran online serta artikel ilmiah
An Encryption Method of 8-Qubit States Using Unitary Matrix and Permutation Susanto, Bekti Maryuni; Atmoko, Rizky Alfanio; Kartiko, Erik Yohan; Setiyadi, Agung Teguh
Jurnal Teknologi Informasi dan Terapan Vol 11 No 2 (2024): December
Publisher : Jurusan Teknologi Informasi Politeknik Negeri Jember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25047/jtit.v11i2.433

Abstract

The paper explores the methods for encrypting and decrypting an 8-qubit states of quantum system using unitary and permutation matrix. Our approach utilizes a unitary matrix to create a new superpositions of an encrypted 8-qubits states. By applying a permutation matrix, we shuffle the state vectors, adding an additional layer of security. The encryption process will be performed on the encrypted state using the formula , where is the original state vector, is the unitary matrix, and is the permutation matrix. To ensure the total probability remains normalized, we showed that the resulting new 8-qubits state remains normalized. The decryption process is achieved by applying the following operations retrieving the original state. This paper also is showing that the original quantum state can be accurately recovered post-decryption. This highlights the robustness of our approach in maintaining the integrity of quantum information. Furthermore, we aim to create block for different 8-qubits state using a different key in each block from the initial unitary matrix and permutation . In order to implement these methods, we need to generate a new unitary matrix for each block. Either by random pick or using iteration. In fact, we showed how to create the new unitary matrix using iteration for each block. Here we showed that the new generated matrix is also a unitary matrix so that we can use iteration proses to create a new unitary matrix in each block for different 8-qubits state. Here we generate the unitary matrix from as key in block . This result in the encryption of each block for each 8-qubits state using the formula resulting in a more robust security. The encryption/decryption scheme we referenced can theoretically be implemented on modern quantum hardware but verifying operations involving hundreds of qubits would demand rigorous calibration and error correction
Performance Comparison of CNN Transfer Learning Models for Coffee Bean Quality Classification Fadli, Nur Muhammad; Destarianto, Prawidya; Riskiawan, Hendra Yufit; Susanto, Bekti Maryuni; Priyambada, Satrio Adi; Nur, Wawan Hendriawan; Gumilang, Mukhamad Angga
Jurnal Teknologi Informasi dan Terapan Vol 12 No 2 (2025): December
Publisher : Jurusan Teknologi Informasi Politeknik Negeri Jember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25047/jtit.v12i2.457

Abstract

According to SNI Standard No. 01-2907-2008, accurate sorting of coffee beans is crucial for improving export value. Manual sorting is time-consuming, subjective, and error-prone, especially when visual differences are subtle between roast levels. This study proposes and evaluates an automatic, machine-learning based system to support quality assurance in coffee production. We compare three transfer-learning CNN architectures: Xception, MobileNetV2, and EfficientNet-B1 on a publicly available dataset of 1,600 coffee bean images divided into four classes (dark, medium, light, green). All models were trained with the same preprocessing and hyperparameter settings. EfficientNet-B1 achieved the highest test accuracy (100%), followed by Xception (99.5%) and MobileNetV2 (97%). We discuss trade-offs between accuracy and computational efficiency and recommend EfficientNet-B1 for high-accuracy applications and MobileNetV2 for edge/mobile deployment.
Co-Authors Agus Hariyanto Alinea Dwi Elisanti Ardianto, Efri Tri Atmadji, Ery Setiyawan Jullev Atmoko, Rizky Alfanio Brenkman, Willy Laurent Elly Antika, Elly Fadli, Nur Muhammad Gumilang, Mukhamad Angga Gurohman, Diki Taufik Hendra Yufit Riskiawan Jullev Atmadji, Ery Setiyawan Kartiko, Erik Yohan Mukhlisoh, Nanik Anita Nur, Wawan Hendriawan Prawidya Destarianto Priyambada, Satrio Adi Setiyadi, Agung Teguh