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All Journal MEANS (Media Informasi Analisa dan Sistem) Journal of Computer Networks, Architecture and High Performance Computing Digital Transformation Technology (Digitech) ROUTERS: Jurnal Sistem dan Teknologi Informasi
Jinan, Abwabul
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Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Library & Information Science

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Comparing Neural Networks, Support Vector Machines, and Naïve Bayes Algorhythms for Classifying Banana Types Jinan, Abwabul; Siregar, Manutur; Rolanda, Vicky; Suryani, Dede Fika; Muis, Abdul
Journal of Computer Networks, Architecture and High Performance Computing Vol. 6 No. 1 (2024): Article Research Volume 6 Issue 1, January 2024
Publisher : Information Technology and Science (ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/cnahpc.v6i1.3381

Abstract

One of the most significant fruits for human consumption is the banana. Fruit consumption not only promotes health but also lowers the risk of heart disease, stroke, digestive issues, hypertension, some cancers, cataracts in the eyes, skin ailments, cholesterol reduction, and, perhaps most importantly, boosts immunity.The study included secondary data, which is information gathered from online resources like Kaggle. Ten categories of bananas will be identified from the 531 total varieties of bananas used as a train dataset: Ambon bananas, Stone bananas, Cavendish bananas, Kepok bananas, Mas bananas, Red bananas, plantains, Milk bananas, Horn bananas, and Varigata bananas. The development of information technology for image object recognition has become a very intriguing topic along with the rapid advancement of society, and it is undoubtedly directly tied to information data. In order to examine Naive Bayes, Support Vector Machine, and Neural Network techniques for classifying banana types, researchers will use the SqueezeNet Deep Learning model to extract features from photos. The study's findings will provide empirical evidence for the distinctions between each algorithm's accuracy, recall, and precision. Based on the collected results, the Neural Network (NN) method is the best in terms of classification, with accuracy of 72.3%, precision of 72.1%, and recall of 72.3%.
Desain dan Rancang Bangun Sistem E-Learning Menggunakan Framework Laravel Berbasis WEB Jinan, Abwabul; Siregar, Manutur Pandapotan; Suryani, Dede Fika; Rolanda, Vicky; Muis, Abdul
ROUTERS: Jurnal Sistem dan Teknologi Informasi Vol. 3 No. 2, Juli 2025 (In Progress)
Publisher : Program Studi Teknologi Rekayasa Internet, Politeknik Negeri Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25181/rt.v3i2.4182

Abstract

The design and development of a web-based E-Learning system using the Laravel framework aims to provide an effective and structured digital learning solution. This system is developed to address the limitations of face-to-face learning time in traditional classrooms and to leverage technological advancements in order to enhance educational quality. Utilizing Laravel as the primary development framework, the system is built with PHP, HTML, CSS, and JavaScript technologies, and MySQL as the database engine. The E-Learning platform features core functionalities such as instructional material management, class administration, structured user accounts (admin, teacher, and student roles), as well as support for material download and task submission. Testing results indicate that the system performs effectively and supports flexible and efficient teaching and learning processes. It is expected that this system will serve as a reliable and sustainable learning medium to support technology-based academic activities.
Penerapan Decision Tree Algoritma C4.5 Dalam Penentuan Izin Pembongkaran Muatan Kapal Kusuma, Jaka; Jinan, Abwabul; Situmorang, Zakarias
MEANS (Media Informasi Analisa dan Sistem) Volume 7 Nomor 1
Publisher : LPPM UNIKA Santo Thomas Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (595.022 KB) | DOI: 10.54367/means.v7i1.1632

Abstract

Along with the increasing number of bulk cargoes that are dismantled every year at belawan port and for the creation of services in accordance with expectations, it is necessary to develop services in support of indonesia's logistics improvement readiness, especially in terms of demolition. Utilization of machine learning using the C4.5 algorithm can make it easier to conduct selection and classification of the feasibility of ships that get permission for demolition activities. The use of the C4.5 algorithm will produce a decision tree that can equalize the results of data mining, so that the information obtained from the data will be easier to identify in testing methods using the Orange Data Mining tool. The results obtained by the C4.5 algorithm in the form of a decision tree with an accuracy value of 84%, 90% precision and 84% recall.
Optimasi Implementasi Kriptografi Kunci Publik dalam Meningkatkan Keamanan dan Otentikasi Data pada Sistem Informasi Kampus Jinan, Abwabul; Siregar, Manutur Pandapotan; Suryani, Dede Fika; Muis, Abdul; Gunung, Tar Muhammad Raja
Digital Transformation Technology Vol. 5 No. 2 (2025): Periode September 2025
Publisher : Information Technology and Science(ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/digitech.v5i2.6425

Abstract

Perkembangan teknologi digital dan kebutuhan akan keamanan sistem informasi menjadi semakin penting, terutama di lingkungan universitas. Penelitian ini berfokus pada optimalisasi implementasi kriptografi kunci publik untuk meningkatkan keamanan data dan otentikasi dalam sistem informasi kampus. Algoritma yang digunakan antara lain RSA (Rivest-Shamir-Adleman) untuk enkripsi data dan DSA (Digital Signature Algorithm) untuk otentikasi digital. Hasil penelitian menunjukkan bahwa kombinasi RSA dan DSA efektif dalam melindungi kerahasiaan dan integritas data, serta memastikan bahwa informasi tetap otentik selama transmisi. Implementasi sistem diuji berdasarkan kecepatan enkripsi, akurasi otentikasi, dan efektivitas keamanan. Selain itu, analisis pengoptimalan dilakukan untuk menyeimbangkan kinerja dan tingkat keamanan dengan menyesuaikan ukuran kunci dan proses enkripsi selektif. Penelitian ini menyimpulkan bahwa penerapan kriptografi kunci publik di lingkungan universitas tidak hanya meningkatkan keamanan tetapi juga meningkatkan kepercayaan pengguna dalam menggunakan sistem informasi akademik.
Co-Authors Abdul Muis Abdul Muis Gunung, Tar Muhammad Raja Kusuma, Jaka Manutur Siregar Rolanda, Vicky Siregar, Manutur Pandapotan Suryani, Dede Fika Zakarias Situmorang