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All Journal Journal of Computer Networks, Architecture and High Performance Computing Djtechno: Jurnal Teknologi Informasi ROUTERS: Jurnal Sistem dan Teknologi Informasi
Rolanda, Vicky
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management 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.
SISTEM PAKAR PEMILIHAN SMARTPHONE BERDASARKAN KEBUTUHAN DAN PREFERENSI USER MENGGUNAKAN METODE CASE BASED REASONING Gunung, Tar Muhammad Raja; Egani Sitepu, Sengli; Pandapotan Siregar, Manutur; Muis, Abdul; Rolanda, Vicky
Djtechno: Jurnal Teknologi Informasi Vol 6, No 2 (2025): Agustus
Publisher : Universitas Dharmawangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46576/djtechno.v6i2.7167

Abstract

Pemilihan smartphone yang sesuai dengan kebutuhan pengguna sering kali menjadi permasalahan tersendiri, terutama karena banyaknya pilihan produk dengan spesifikasi yang bervariasi. Penelitian ini bertujuan untuk menerapkan metode Case Based Reasoning (CBR) dalam proses rekomendasi smartphone berdasarkan preferensi pengguna. CBR bekerja dengan membandingkan kasus baru, yaitu kebutuhan dan kriteria pengguna, dengan kasus-kasus sebelumnya yang telah tersimpan dalam basis data untuk menentukan tingkat kemiripan. Pada penelitian ini, digunakan empat tahapan utama dalam metode CBR yaitu: Retrieve, Reuse, Revise, dan Retain.Hasil pengujian menunjukkan bahwa dari 13 alternatif smartphone yang dianalisis, Xiaomi Poco X5 Pro mendapatkan nilai kemiripan sebesar 100%, sedangkan perangkat lainnya seperti Realme Narzo 60x, Infinix Smart 8, Vivo V27, Samsung Galaxy A54, dan lainnya memperoleh nilai kemiripan 0%. Hal ini menunjukkan bahwa Xiaomi Poco X5 Pro merupakan pilihan paling sesuai dengan kebutuhan pengguna dalam studi kasus ini. Dengan demikian, metode CBR terbukti mampu memberikan rekomendasi yang tepat dan terukur, serta dapat menjadi dasar pengembangan sistem pakar atau sistem pendukung keputusan di masa mendatang.
Co-Authors Abdul Muis Abdul Muis Egani Sitepu, Sengli Jinan, Abwabul Manutur Siregar Pandapotan Siregar, Manutur Siregar, Manutur Pandapotan Suryani, Dede Fika Tar Muhammad Raja Gunung