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All Journal International Journal of Advances in Intelligent Informatics JOURNAL OF APPLIED INFORMATICS AND COMPUTING JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) CCIT (Creative Communication and Innovative Technology) Journal PROSISKO : Jurnal Pengembangan Riset dan observasi Rekayasa Sistem Komputer The Indonesian Journal of Computer Science Bomba: Jurnal Pembangunan Daerah
Laila, Rahma
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Agriculture, Biological Sciences & Forestry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Mechanical Engineering Public Health Social Sciences Transportation Veterinary Other

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Journal : CCIT (Creative Communication and Innovative Technology) Journal

 1 
Usability Analysis of The Student Service Information System at The Faculty of Engineering Tadulako University Using Heuristic Evolution and The System Usability Scale (SUS) Noviantika, Noviantika; Syahrullah, Syahrullah; Laila, Rahma; Lapatta, Nouval Trezandy; Wirdayanti, Wirdayanti
CCIT (Creative Communication and Innovative Technology) Journal Vol 18 No 1 (2025): CCIT JOURNAL
Publisher : Universitas Raharja

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/ccit.v18i1.3532

Abstract

The Student Service Information System is defined as a system designed to assist students, faculty, and administration at Tadulako University's Faculty of Engineering in managing various academic aspects. This research identifies issues or obstacles users face while utilizing the website. The usability evaluation employs the Heuristic Evaluation method and the System Usability Scale (SUS). Based on the research conducted with 100 respondents, all active students at Tadulako University, the usability measurements revealed significant findings. According to the Heuristic Evaluation method, which utilizes 10 principles, areas requiring improvement were identified in qualifications B1.2, B3.3, B8.2, B10.2, and B10.3. Recommendations for enhancements include adding a notification feature, ensuring the search function is easily discoverable, selecting appropriate color combinations, and improving the placement of the user guide. The System Usability Scale yielded a score of 50, categorizing the website as "OK" on the adjective scale, "D" on the grade scale, and "Marginal" on the acceptability scale, with a net promoter score indicating a "Passive" reception.
CNN Algorithm for Herbal Leaf Classification Using MobileNetV2 and ResNet50V2 Pagiu, Harry T.; Kasim, Anita Ahmad; Lapatta, Nouval Trezandy; Pratama, Septiano Anggun; Laila, Rahma
CCIT (Creative Communication and Innovative Technology) Journal Vol 18 No 2 (2025): CCIT JOURNAL
Publisher : Universitas Raharja

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/ccit.v18i2.3776

Abstract

Indonesia is home to over 30,000 types of herbal plants, with approximately 1,200 species utilized as raw materials for alternative and traditional medicine. Leaves play a crucial role in herbal medicine preparation. However, many people struggle to identify different herbal leaves due to their similar appearances, making classification difficult. Each leaf possesses unique characteristics such as shape, size, midrib, stalk, blade, and type, which can be used for differentiation. To assist in identifying herbal leaves, a classification system based on image recognition is essential. Convolutional Neural Networks (CNN) are deep learning algorithms designed for processing two-dimensional image data. Model performance can be enhanced through transfer learning, with MobileNetV2 and ResNet50V2 being widely used architectures. These pretrained models have been trained to recognize images with high accuracy. This study focuses on classifying herbal plants based on leaf shape using CNN architectures from MobileNetV2 and ResNet50V2. The evaluation results show that the MobileNetV2 architecture, with a 90%:10% data split, achieved an accuracy of 98.51%, precision of 98.92%, recall of 98.51%, and an F1-score of 98.56%. These findings indicate that CNN with transfer learning can effectively classify herbal leaves with high accuracy.
UI/UX Design of Jepun Bali Store Product Ordering Application Using Design Thinking Method Widiani, Ni Nengah; Syahrullah, Syahrullah; Laila, Rahma; Lamasitudju, Chairunnisa Ar; Angreni, Dwi Shinta
CCIT (Creative Communication and Innovative Technology) Journal Vol 19 No 1 (2026): CCIT JOURNAL
Publisher : Universitas Raharja

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/ccit.v19i1.3965

Abstract

The internet as a form of technological advancement continues to develop every year and has a great influence on human activities, including in terms of sales. The Jepun Bali Store, which sells products typical of Hinduism and Balinese customs, markets its products online with Instagram, Facebook, and WhatsApp. Instagram and Facebook are used to display the catalog, while WhatsApp is used for order communication. However, this system is considered less efficient because customers have to switch applications to view products, ask questions, and order. Stock and price information is not available in real-time, and the ordering process is still done manually, making it difficult for customers. From the manager's side, manual order recording risks creating errors, while admins are often overwhelmed with handling queries across multiple platforms, which impacts customer satisfaction. This research aims to simplify the transaction process, speed up services, and increase efficiency by applying the Design Thinking method. This method helps in understanding the needs of the user, structuring problems, and producing solutions through systematic stages. The results of the design test using the System Usability Scale (SUS) method with 30 respondents obtained a score of 88.5833 out of 100, included in category A (Excellent) and considered acceptable.
Co-Authors Alamsyah - Anita Ahmad Kasim Chairunnisa Ar. Lamasitudju Dwi Shinta Angreni Fahmi, Moh. Fajar, Moh Hajra Rasmita Ngemba Harani, Makruf Magfirah, Magfirah Maulana, Muhammad Syahputra Miftah Miftah, Miftah Mohammad Yazdi Pusadan Nouval Trezandy Lapatta Noviantika, Noviantika Pagiu, Harry T. Rinianty, Rinianty Ryfial Azhar, Ryfial Sani, Ilham Abdillah Sasuwuk, James Rio Septiano Anggun Pratama Siti Fitriana Syahrullah Syahrullah Syaiful Hendra Ulhaq, Muhammad Naufal Daffa Widiani, Ni Nengah Wirdayanti Yusuf Anshori