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All Journal Jurnal Ilmiah Sinus Jurnal Kridatama Sains dan Teknologi Budimas : Jurnal Pengabdian Masyarakat Jurnal TIKOMSIN (Teknologi Informasi dan Komunikasi Sinar Nusantara)
Ridhwanullah, Dziky
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Religion Agriculture, Biological Sciences & Forestry Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Economics, Econometrics & Finance Education Social Sciences Other

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Content-Based Filtering pada Sistem Rekomendasi Buku Informatika Ridhwanullah, Dziky; Kumarahadi, Yovita Kinanti; Raharja, Bayu Dwi
Jurnal Ilmiah SINUS Vol 22, No 2 (2024): Vol. 22 No. 2, Juli 2024
Publisher : STMIK Sinar Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30646/sinus.v22i2.840

Abstract

There are changes taking place in Indonesia's educational system, particularly in universities. The learning approach used in the transformation program is student-centered. A firm foundation in literacy is required for the application of this learning. A library is one of the amenities that students require. The library is an ideal resource for pupils to enhance their critical thinking skills. It's not always simple to find books in the library, though. Students could find it challenging to locate the books they seek because there are so many collections already in existence. One method for doing book searches is through the use of a recommendation system. Using content based filtering is one recommendation system algorithm. This study suggests a content based filtering algorithm-based book recommendation system to facilitate students' search for informatics book titles. TF-IDF and Cosine Similarity are used in a similarity search to find phrases and assign weights to them. The content-based filtering algorithm's research findings might suggest books depending on user-specified parameters. 90% accuracy is the average for this method.Keywords: recommendation system, content based filtering, TF-IDF, Cosime Similarity
Sistem Rekomendasi Makanan Kucing Menggunakan Metode Content-Based Filtering Kumarahadi, Brigitta Melati; Kumarahadi, Yovita Kinanti; Ridhwanullah, Dziky
Jurnal Kridatama Sains dan Teknologi Vol 7 No 01 (2025): Jurnal Kridatama Sains dan Teknologi
Publisher : Universitas Ma'arif Nahdlatul Ulama Kebumen

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53863/kst.v7i01.1471

Abstract

Cats are popular pets because they have cute behavior and adorable physical appearance. Caring for cats does require extra attention, especially when feeding them. Providing food that suits their needs is very important for optimal growth and preventing various health problems. The cat food recommendation system is the right solution for cat owners to choose food that suits their cat's needs. A recommendation system is a system designed to help users get recommendations for items that are relevant and useful. Content based filtering is a recommendation system that provides suggestions based on user preferences for several items, including age, variant, brand, taste, size and price. The data used is cat food products with the brands Whiskas, Cat Choize, Me-O, and Royal Canin at the Pet Shop Colomadu. The recommendation value is calculated based on the cosine similarity value between two items. System testing is carried out using functionality testing (blackbox) and validity testing. The results of functionality testing show that the system can function well. The results of validity testing show that the system is valid and can be used appropriately. It can be concluded that the cat food recommendation system using the content-based filtering method can be used to recommend the right cat food
Pengabdian Sebagai Dewan Juri Lomba Kompetensi Siswa (LKS) Web Technologies & IT Software Tingkat Kota Surakarta Muqorobin, Muqorobin; Yudanto, Bramasto Wiryawan; Ridhwanullah, Dziky
BUDIMAS : JURNAL PENGABDIAN MASYARAKAT Vol. 4 No. 1 (2022): BUDIMAS : VOL. 04 NO. 01, 2022
Publisher : LPPM ITB AAS Indonesia Surakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Lomba Kompetensi Siswa adalah kompetisi tahunan antar siswa pada jenjang SMK sesuai bidang keahlian yang diajarkan pada SMK peserta. LKS ini setara dengan OSN (Olimpiade Sains Nasional) yang diadakan di SMP/SMA. Kegiatan ini merupakan salah satu bagian dari rangkaian seleksi untuk mendapatkan siswa-siswi terbaik dari seluruh Indonesia yang akan dibimbing lebih lanjut oleh tim bidang kompetisi masing-masing dan akan diikutsertakan pada kompetisi keahlian tingkat internasional. Tujuan kegiatan pengabdian masyarakat yang dilakukan melalui kegiatan lomba LKS ini adalah Tim Pengadian berkontribusi sebagai dewan juri dalam mata lomba web teknologi dan IT software. peran dewan juri sebagai pusat penentu lomba LKS tingkat kota, sehingga hasil seleksi siswa terbaik akan mewakili kota surakarta untuk masuk lomba di tingkat provinsi jawa tengah. Hasil akhir dari kegiatan pengabdian masyarakat ini adalah suatu keputusan dari dewan atas proses penilaian lomba dalam menentukan pemenang lomba LKS tingkat kota surakarta.
PENGEMBANGAN SAVORYAI: SISTEM REKOMENDASI RESEP MASAKAN BERBASIS BAHAN DAN PREFERENSI KALORI MENGGUNAKAN CONTENT-BASED FILTERING DAN OPENAI API Irfan Faiz, Ahmad; Ridhwanullah, Dziky
Jurnal Teknologi Informasi dan Komunikasi (TIKomSiN) Vol 13, No 2 (2025): Jurnal Tikomsin, Vol 13, No.2, Oktober 2025
Publisher : STMIK Sinar Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30646/tikomsin.v13i2.1023

Abstract

The high volume of leftover cooking ingredients in households often leads to food waste due to a lack of ideas or references for processing them. This research aims to develop SavoryAI, an intelligent web-based recipe recommendation system that suggests relevan Indonesian dishes based on user-inputted ingredients and calorie preferences. The system integrates Content-Based Filtering (CBF) with cosine similarity and TF-IDF weighting to match user-selected ingredients with recipes in the database. Additionally, OpenAI’s GPT-4o model is utilized to identify food ingredients from uploaded images. The system is implemented using Laravel, Livewire, and TailwindCSS, with data gathered through interviews with household actors and literature reviews. Evaluation was conducted through functional testing (black-box), validity testing, and confusion matrix analysis, using response from household users to determine ground truth. The results show a high accuracy in generating relevant recipe recommendations, with a precision of 1.00, recall of 0.83, and F1-score of 0.91. The results show a high accuracy in generating relevant recipe recommendations. The integration of AI image recognition further enhances usability by enabling automatic ingredient input. The finding highlight the system’s effectiveness in reducing food waste and supporting sustainable cooking practices through personalized recipe suggestions.
Co-Authors Bramasto Wiryawan Yudanto Irfan Faiz, Ahmad Kumarahadi, Brigitta Melati Kumarahadi, Yovita Kinanti Muqorobin, Muqorobin Raharja, Bayu Dwi