Judul: A Modelling and Implementation of Graph Database for Nusantara Culinary Recommendation System
Abstrak: Sistem rekomendasi kuliner nusantara merupakan perangkat yang memberikan informasi berupa saran mengenai makanan lokal kepada pelanggan sesuai preferensi. Untuk memenuhi kebutuhan basis data dalam sistem rekomendasi, dibutuhkan pemodelan basis data. Pemodelan basis data yang baik penting untuk menjamin konsistensi informasi, menghilangkan data berlebih, menjalankan query yang efisien, dan meningkatkan kinerja basis data. Untuk mengatasi pertumbuhan data yang cepat dan memfasilitasi respons cepat dari permintaan pengguna, basis data graf digunakan untuk menyimpan data dalam sistem kuliner Nusantara. Struktur basis data graf menyederhanakan proses komputasi yang diperlukan untuk menghasilkan rekomendasi, dan juga menyimpan nilai untuk rekomendasi di masa mendatang. Penelitian ini bertujuan untuk memodelkan basis data graf dan implementasinya pada platform Neo4j. Basis data graf yang dimodelkan dapat menyimpan data makanan di restoran dan preferensi pelanggan, riwayat pembelian, dan lokasi pelanggan dan restoran. Ada beberapa skenario yang dapat ditangani oleh basis data, antara lain: pemilihan menu berdasarkan pencarian makanan, lokasi, dan berdasarkan hasil rekomendasi, penyimpanan data transaksi pelanggan, dan pemeliharaan data historis dan preferensi pelanggan untuk kebutuhan perhitungan rekomendasi., Nusantara culinary recommendation system is a tool that provides information in the form of advice on local food to customers according to preferences. To understand database requirements in the recommendation system, need database modeling. Good database modeling is important to ensure consistency of information, eliminate redundant data, run efficient queries, and improve database performance. In order to cope with the fast data growth and facilitate the rapid response of user requests, a graph database is used to store data in the Nusantara culinary system. The graph database structure simplifies the computational process required to generate recommendations, and also stores values for future recommendations. This study aims to model the graph database and its implementation on the Neo4j platform. The modeled graph database can store food data in restaurants and customer preferences, purchase history, and customer and restaurant locations. There are several scenarios that can be handled by the database, including: menu selection based on food search, location, and based on recommendation results, customer transaction data storage, and maintenance of historical data and customer preferences for recommendation calculation needs.
Keyword: graph database, implementation, modelling, Nusantara culinary, recommendation system

Judul: Recommendation System for Selecting Majors Based on Student Personality Using the Similarity Graph Algorithm
Abstrak: Pemilihan jurusan di perguruan tinggi merupakan tahap krusial dalam perjalanan pendidikan seseorang. Dalam menentukan jurusan yang akan diambil, sering kali terjadi kesalahan yang mengakibatkan ketidakcocokan antara jurusan dan siswa yang bersangkutan. Penelitian yang berkaitan dengan sistem rekomendasi untuk pemilihan jurusan bertujuan untuk mengembangkan sistem yang mampu memberikan rekomendasi jurusan kepada siswa sesuai dengan profil mereka. Masa remaja memiliki rentang waktu yang singkat, sekitar 5 tahun, dan memegang peran penting sebagai tonggak atau fondasi kehidupan seseorang. Penelitian ini berusaha menggabungkan teori minat Holland dengan tipe kepribadian dalam membentuk preferensi. Penelitian ini bertujuan untuk merancang sebuah sistem rekomendasi untuk pemilihan jurusan dengan menggunakan algoritma Similarity Graph dengan perhitungan Jaccard Similarity terhadap atribut tes kepribadian dan keminatan yang divisualisasikan menggunakan Neo4J. Dataset yang digunakan dibagi menjadi 2 yaitu data uji yang berisikan sampel acak dan data latih berupa data mahasiswa jurusan tertentu yang dibentuk ke dalam profil jurusan. Berdasarkan evaluasi yang dilakukan, similarity graph memberikan hasil yang cukup baik. Visualisasi yang dilakukan menggunakan Neo4J juga sangat membantu dalam proses evaluasi hasil., Choosing a major in college is a crucial stage in a person's educational journey. In determining which major to take, mistakes often occur which result in a mismatch between the major and the student concerned. Research related to recommendation systems for selecting majors aims to develop a system that is able to provide major recommendations to students according to their profiles. Adolescence has a short time span, around 5 years, and plays an important role as a milestone or foundation of a person's life. This research attempts to combine Holland's interest theory with personality type in forming preferences. This research aims to design a recommendation system for selecting majors using the Similarity Graph algorithm with Jaccard Similarity calculations for personality and interest test attributes which are visualized using Neo4J. The dataset used is divided into 2, namely test data which contains random samples and training data in the form of student data from certain departments which are formed into departmental entities. Based on the evaluation carried out, the similarity graph gives quite good results. Visualization carried out using Neo4J is also very helpful in the results evaluation process.
Keyword: graph, majors, Neo4jJ, Recommendation system, Similarity, Student interest

Judul: Durability of Specific Immunoglobulin (Ig) Y Againts Influenza and Covid-19 in Sunscreen
Abstrak: The benefit of specific immunoglobulin Y (Ig Y) as an active ingredient in personal care products such as sunscreen has not been properly studied. Our study aims to evaluate the reliability of specific IgY against influenza and the COVID-19 virus in a sample sunscreen product. Specific IgY influenza and COVID-19 viruses were inserted in the sample sunscreen at two concentrations of 0.1% and 0.25%. Three treatments were observed: control (C) sunscreen without IgY, treatment 1 (T1) sunscreen with 0.1% IgY, and treatment 2 (T2) sunscreen with 0.25% IgY. Organoleptic observations were carried out to monitor changes in aroma, color, and texture, and sunscreen sampling was done every week for 6 weeks. The durability of IgY was evaluated using an enzyme-linked immunosorbent assay (ELISA) and a neutralization assay. Organoleptic observation from weeks 1 to 6 showed consistency in its aroma, color, and texture. Enzyme-linked immunosorbent assay and neutralization assay (NA) results of the T1 and T2 groups showed negative results except for sample T1 at week 5, which showed a positive result in the Influenza A ELISA. Based on the results, it seems that IgY concentrations of 0.1% or 0.25% were not optimal. However, this negative result might be due to low concentration of the IgY and the presence of beeswax, which inhibits IgY and causes it to dissolve in distilled water, thus preventing it from being detected in the assay.
Keyword: Immunoglobulin Y, Sunscreen, Influenza, COVID-19, Biological Activity