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All Journal Scan : Jurnal Teknologi Informasi dan Komunikasi INTEGER: Journal of Information Technology Network Engineering Research Operation [NERO] Zeta - Math Journal JPP IPTEK (Jurnal Pengabdian dan Penerapan IPTEK) Prosiding Seminar Nasional Sains dan Teknologi Terapan Jurnal Informatika dan Rekayasa Perangkat Lunak Jurnal Nasional Teknologi Terapan KERNEL: Jurnal Riset Inovasi Bidang Informatika dan Pendidikan Informatika Prosiding Seminar Nasional Teknik Elektro, Sistem Informasi, dan Teknik Informatika (SNESTIK) JuTISI (Jurnal Teknik Informatika dan Sistem Informasi)
Rinci Kembang Hapsari
Institut Teknologi Adhi Tama Surabaya
Agriculture, Biological Sciences & Forestry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Mathematics Other

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Sistem Rekomendasi Film Dengan Menggunakan Sentiment Analysis dan Collaborative Filtering Fikry, Muhamad Agus; Wardhana, Septiyawan Rosetya; Hapsari, Rinci Kembang
KERNEL: Jurnal Riset Inovasi Bidang Informatika dan Pendidikan Informatika Vol 5, No 2 (2024)
Publisher : Institut Teknologi Adhi Tama Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31284/j.kernel.2024.v5i2.7635

Abstract

Seiring pesatnya perkembangan digital pada saat ini, zaman semakin maju berbagai macam file bisa diakses dari internet. Begitu juga dengan film yang sering kita tonton dari TV sekarang bisa diakses dari internet dengan mudah. Banyak peminat film yang kadang masih bingung ketika ingin menonton film. Mengacu pada uraian tersebut, dalam penelitian ini, membangun sebuah sistem yang dapat memberikan rekomendasi film. Collaborative filtering adalah metode yang sering digunakan dalam hal rekomendasi dan Sentiment analysis digunakan untuk menentukan pola sentiment dari user serta menggunakan bahasa pemrograman python 3 untuk menghitung proses-proses pada sistem yang akan dibuat. Dari acuan dan juga metode tersebut tujuan dari penelitian ini adalah membangun sistem rekomendasi menggunakan metode Collaborative Filtering dan Sentiment Analysis terhadap ulasan pada film. Pengujian dilakukan sebanyak 5 kali uji coba, dimana hasil belum bisa memenuhi harapan dalam merekomendasikan, karena rata-rata nilai rekomendasi masih 32%.
Rancang Bangun Aplikasi Resto Berbasis Mobile Menggunakan Metode Personal Extreme Programming Hadad, Heksa Bustomi; Hapsari, Rinci Kembang; Hakim, Permana Faddyahsari
Prosiding Seminar Nasional Teknik Elektro, Sistem Informasi, dan Teknik Informatika (SNESTIK) 2025: SNESTIK V
Publisher : Institut Teknologi Adhi Tama Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31284/p.snestik.2025.7623

Abstract

In the current era of globalization, technological developments are taking place very rapidly, including the development of information and communication technology; one of the steps in the development of information and communication technology is the development of telecommunications technology, especially smartphones. The development of smartphone technology has influenced various fields, including the culinary field. Kebon Kota Tropical Resto is a company in the culinary field. Currently, Kebon Kota Tropical Resto still uses a manual ordering method to order food and drinks, and it takes a long time to deliver consumer orders because of the long distance between kitchens, illegible handwritten orders, order slips, forgotten orders, and long queues. Therefore, an application is needed that makes it easier for customers to order food and drink menus. In developing this restaurant application, one of the agile development models has been used, namely the personal extreme programming model. In the personal extreme programming model, there are various stages: requirements, planning, iteration initialization, design, implementation, system testing, and retrospective. Based on the ISO 9126 evaluation with 50 respondents, the value of each criterion was obtained. Namely, the Usability value was 86.72%, the Functionality value was 86%, the Efficiency value was 86.53%, and the overall value of the application quality was 86.16%. Based on these values, the Kebon Kota Tropical Resto application is outstanding.
Implementasi Algoritma K-Nearest Neighbor Dalam Prediksi Penyakit Jantung Ardiansyah, Arif; Juan; Sirri, Latiful; Hapsari, Rinci Kembang; Santoso, Syahrul Riza Andi
Jurnal Informatika dan Rekayasa Perangkat Lunak Vol. 7 No. 2 (2025): September
Publisher : Universitas Wahid Hasyim

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

Abstract

Heart failure is a serious and pressing health problem that affects millions of people worldwide. Several factors influence the occurrence of heart failure, such as age, type of pain, blood pressure, cholesterol levels, and other risk factors associated with heart disease. With current technological developments, data mining and machine learning can be used to predict patient health conditions. Therefore, the problem of this research is how to implement data mining techniques for identifying heart disease. The goal of the study is to identify heart disease and prevent heart failure. This study utilises the K-Nearest Neighbour (k-NN) algorithm to estimate the likelihood of patients experiencing heart failure based on available data features. The data used is taken from the kaggle.com site, which includes information from patients diagnosed with heart failure and those who do not suffer from heart failure. The analysis process involves data processing steps, such as normalisation, feature grouping, and selecting the optimal K parameter for the k-NN algorithm. Evaluation is carried out by calculating the accuracy, precision, recall, and F1-score values. Testing is carried out on a dataset with 299 patient data, which is divided into training data and test data with a ratio of 80:20. The results of this study indicate that the k-NN algorithm has an accuracy of 87% in predicting kidney failure. This result indicates that the k-Nearest Neighbour algorithm can effectively predict heart failure.
Co-Authors Adi, Danu Septi Aditya, Andre Fransisco Aji, Rega Wahyu Alamsyah, Mochamad Tegar Bagas Ardiansyah, Arif Arma Adzhana Mahran Audrey, Talitha Naifa Azmuri Wahyu Azinar Chanafi, Moch Irfan Dian Puspita Hapsari Fikry, Muhamad Agus Gutierres, Ego Prasetyo Ho H, Luky Agus Hadad, Heksa Bustomi Hakim, Permana Faddyahsari Hendro Nugroho Hendro Nugroho Hendro Nugroho, Hendro Irmayanti, Sivana Amelia Islamiyah, Mitha Juan Lande, Gamaliel Virani Fofid Limanjaya, Rafi Oktaviano Eka Luky Agus Hermanto Luky Agus Hermanto, Luky Agus Maftahatul Hakimah Maftahatul Hakimah, Maftahatul Mahatma, Adhitya Arie Navaro, Kamal Fauzan Nur Sulaiman NUR SULAIMAN Putri, Chatarina Natassya Rewina, Anggita Eka Santoso, Syahrul Riza Andi Sirri, Latiful Siti Maimunah Siti Maimunah Sugiyanto - Sugiyanto Sugiyanto Sugiyanto Sugiyanto Sugiyanto, Sugiyanto Titus Kristanto Tukadi Tukadi, Tukadi Tutuk Indriyani Ubaidillah, M Ravi Vandaningtyas, Andriany Vinda Setia Nita Vinda Setia Nita, Vinda Setia Wardana, Septiyawan Rosetya Wardhana, Septiyawan Rosetya Widodo, Muhammad Amiruddin Wijaya, Abdul Hamid