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Journal : Jurnal Mantik

Web based yogyakarta food recipe application using sdlc waterfall method Annisaa Utami; Dasril Aldo; Yohani Setiya Rafika Nur; Trihastuti Yuniati
Jurnal Mantik Vol. 7 No. 1 (2023): May: Manajemen, Teknologi Informatika dan Komunikasi (Mantik)
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/mantik.v6i4.3621

Abstract

Cooking is the process of processing food ingredients and seasonings to be used as a variety of dishes. Cooking can be done by anyone. When cooking, recipes are needed as a reference to process food ingredients into a dish. A recipe in the modern sense is defined as a set of instructions that tell how to prepare and cook food, including a list of what foods are needed . Yogyakarta has a variety of culinary delights. With the passage of time, Yogyakarta's specialties are displaced by foreign food entering Yogyakarta. The Recipe application contains a variety of recipes for typical Yogyakarta food. This research aims to help the community in making Indonesian culinary food, especially the Yogyakarta food menu. The application is created web-based using PHP programming language and mysql database. For the software development method, which is carried out is using the Software Development Life Cycle (SDLC) Waterfall method. The first stage carried out is planning, design stages, implementation and trials. Based on the test results using a black box, the system created has functional features according to what is expected. The system features login, Manage recipe data, Manage category data and Manage comment data.
Implementation of certainty factor method for the diagnosis of tyla fish diseases Yehezekiel Ramasyah Putra Haloho; Yohani Setiya Rafika Nur; Ummi Athiyah
Jurnal Mantik Vol. 7 No. 3 (2023): November: Manajemen, Teknologi Informatika dan Komunikasi (Mantik)
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/mantik.v7i3.4262

Abstract

Tilapia is one of the most attractive fish for the people of Indonesia because it can be easily found in traditional markets. Obstacles during the fish farming process are disease transmission in tilapia that is farmed so that it can affect sales and will certainly harm fish farmers. Based on this, an expert system was created in order to diagnose diseases in Tilapia. The purpose of the system is to assist fish farmers in diagnosing and treating diseases of tilapia. The method or technique used in this study is the Certainty factor. The results of the study obtained an output in the form of the disease experienced and also a solution for handling tilapia attacked by the disease using the Certainty factor method. The expert system produced by the research, then tested using blackbox testing where all components in the tested system produce the desired results and match their functionality, and the results of accuracy testing with experts obtain an accuracy value of 96% so that the system is declared accurate
Classifying student academic performance: C4.5 and SVM Methods in ITTP's Computer Engineering Program Edelin Gultom; Yohani Setiya Rafika Nur; Rima Dias Ramadhani
Jurnal Mantik Vol. 7 No. 4 (2024): February: Manajemen, Teknologi Informatika dan Komunikasi (Mantik)
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/mantik.v7i4.4686

Abstract

The decline in the number of graduates from the Computer Engineering Program, based on the graduation percentages of the 2017 and 2018 cohorts, coupled with the imbalance between on-time and delayed graduates, poses various challenges. These challenges include suboptimal program accreditation and an excessive number of active students. This research aims to develop a classification model for student performance categorization into On-Time Graduation (LTW) and Delayed Graduation (LTTW) classes using SVM and C4.5 algorithms. The C4.5 algorithm will handle attribute selection, while SVM will be responsible for building the prediction model. The classification results will be visualized on a website using the Flask framework, allowing users to input relevant data. The classification accuracy on the test set, reaching 77.74%, indicates the model's precision in predicting student performance categories
Co-Authors Adanti Wido Paramadini Ade Prasetyo, Ade Adriano, Riftian Dimas Afifatul Fajri, Nabila Ajeng Dyah Kurniawati Al Faiz, M. Hanif Alfonsus Simbolon Alika, Shintia Dwi Amalia Beladinna Arifa Aminatus Sa’adah Andre Citro Febriliyan Lanyak Audrey Hillary Auliya Burhanuddin Azmi, Wifqi Wifakul Bachrul Restu Bagja Bidayatul Masulah Bita Parga Zen Christantie Effendy Christian Tambunan, Gerry Claudio Felle, Roland Dading Qolbu Adi Dasril Aldo Dedi Rahman Habibie Dedy Agung Prabowo Deni Romadan, Muhamad Dwi Putro Wicaksono, Aditya Edelin Gultom Endraswari, Putri Mentari Eryan Ahmad Firdaus Faisal Dharma Adhinata Faiz, M. Hanif Al Fathan, Faizal Burhani Ulil Fau, Andrew Filfimo Yulfiz Ahsanul Hulqi Firmansyah, Muhammad Raafi'u Gusla Nengsih, Yeyi Gusnita Linda Hasan, Faiz Hidayat, Afifah Naurah Imam Ghozali J. Manurung, Barnes Kristanto, Joshua Putra Fesha Lina Fatimah Lishobrina Luqman Wahyudi M Yoka Fathoni Maulana, Ihsan Maulana, ⁠Ihsan Melinda Br Ginting Miftahul Ilmi Muadin, Dika Alim Muhamad Azrino Gustalika Nadia Ayu Isroh Nia Annisa Ferani Tanjung Nur Ghaniaviyanto Ramadhan Nurhaeka Tou Pamuji, Yanuar Ikhsan Paradise Ramadhani, Rima Dias Rania Nur Hikmah Rianto Putra, Frederick Ridho Rahmadi Sa'adah, Aminatus Sahara Sahara Sapta Eka Putra Sulaeman, Gilang Suprapto, Amelia Rut Suryani, Ajeng Ayu Syahputra, Dio Trihastuti Yuniati Ummi Athiyah Usman, Muhammad Lulu Latif Utami, Annisaa Wahyu Adi Prabowo Wanda Ilham Warto Widya Lelisa Army Yasin, Feri Yehezekiel Ramasyah Putra Haloho Yoka Fathoni, M. Yuan Sa'adati Zahirah, Regina Putri Wanda