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All Journal Scan : Jurnal Teknologi Informasi dan Komunikasi Jurnal ELTIKOM : Jurnal Teknik Elektro, Teknologi Informasi dan Komputer 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 Scientific Journal of Informatics SIGMA TEKNIKA 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
Aerospace Engineering Agriculture, Biological Sciences & Forestry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Mathematics Mechanical Engineering 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.
Classification of Diabetes Mellitus using Decision Trees Hapsari, Rinci Kembang; Salim, Abdullah Harits; Oktavian, Leonardo Fahsi; Fitra, Aldy Ramadhan
Jurnal ELTIKOM : Jurnal Teknik Elektro, Teknologi Informasi dan Komputer Vol. 9 No. 2 (2025)
Publisher : P3M Politeknik Negeri Banjarmasin

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31961/eltikom.v9i2.1461

Abstract

Diabetes Mellitus is a global health concern, with its prevalence and incidence rising sharply world-wide, including in Indonesia. Several factors contribute to the onset of diabetes mellitus, such as heredity, age, weight, and blood pressure. Managing blood sugar levels, maintaining a balanced diet, exercising regularly, and undergoing early screening when necessary are among the key measures to prevent and control this disease. Early diagnosis is essential to reduce both the number of cases and the associated risks. This study aims to detect diabetes mellitus using classification techniques. The method involves several subprocesses within the classification procedure. The first stage, data preprocessing, includes feature selection and data cleaning. The resulting preprocessed data are then used in the classification stage, specifically the learning subprocess, to generate a decision tree model. Model construction employs pruning, followed by training and performance evaluation. The study utilizes a diabetes dataset obtained from kaggle.com, consisting of 768 records. The dataset includes attributes such as Pregnancies, Glucose, Blood Pressure, Skin Thickness, Insulin, Body Mass Index (BMI), Diabetes Pedigree Function, Age, and the label Outcome. Testing was conducted using decision trees with maximum depths of 3, 5, 7, 10, and 15. The results show that the highest accuracy (88.56%) occurred at a maximum depth of 5, while the highest recall (100%) was achieved at a depth of 3. The highest precision (47.37%) and specificity (95.85%) were also obtained at a depth of 3.
Klasifikasi Penderita Penyakit Diabetes Berdasarkan Decision Tree Menggunakan Algoritma C4.5 Hapsari, Rinci Kembang; Wahyu, Bagas Aulifia Riski Putra; Farozi, Achmad Fayi; Mahendra, Caesario Putra
INTEGER: Journal of Information Technology Vol 8, No 1 (2023): Maret
Publisher : Fakultas Teknologi Informasi Institut Teknologi Adhi Tama Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31284/j.integer.2023.v8i1.4423

Abstract

Diabetes is a metabolic disease characterized by high blood sugar levels (hyperglycemia) caused by a lack of insulin or the ineffectiveness of insulin in regulating glucose metabolism. In addition there are other factors that cause diabetes such as heredity, weight, age, blood pressure and so on. It is estimated that the death rate caused by diabetes will continue to increase every year. Treatment of diabetes can be done by controlling blood sugar levels, eating a healthy diet, exercising regularly, and if necessary, carrying out early checks to reduce the risk of developing diabetes. Therefore it is necessary to have an early diagnosis which is expected to reduce diabetes and reduce complications of diabetes in the future. One thing that can be done is to apply the method contained in data mining, namely utilizing the classification method using the C4.5 algorithm which can produce more accuracy. Classification can be used as early treatment of this disease. Algorithm C4.5 is an algorithm that is used to form a decision tree. From the test results, it produces a fairly large accuracy, namely 85% Precision of 92%, and Recall of 85%.
Penerapan Algoritma K-Medoids Clusetering Untuk Rekomendasi Menu dan Strategi Stok Bahan Baku Rinci Kembang Hapsari; M Safi Anwar Anas; Reza Zulkifli Ferdiansyah; Hanif Prasetyo; Mochamad Muhajir
Jurnal Ilmiah Informatika Vol. 9 No. 1 (2024): Jurnal Ilmiah Informatika
Publisher : Department of Science and Technology Ibrahimy University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35316/jimi.v9i1.30-38

Abstract

Kedai kopi yang merupakan sebuah tempat yang menyediakan minuman kopi maupun minuman panas lainnya. Banyak pelanggan terutama anak muda yang berkunjung ke kedai kopi untuk menikmati makanan dan minuman sambil bersantai. Seiring pertumbuhan kedai kopi yang semakin meningkat dikarenakan tempat yang modern serta harga makanan dan minumannya yang terjangkau. Dalam berkompetisi service kedai kopi kepada pelanggan, banyak kedai kopi yang mengembangkan varian/ jenis minimum dan makanan yang dijual di kedai kopi. Banyaknya varian makanan dan minuman membuat pelanggan memerlukan waktu yang agak lama dalam memilih menu, dan juga membuat kesulitan bagian pembelian pada saat menyediakan stok bahan baku. Sehingga pada penelitian ini bertujuan untuk mengelompokkan menu yang ada di kedai kopi menjadi 2 cluster, yaitu cluster menu yang laku dan cluster menu cukup laku. Dalam penelitian ini dilakukan proses klasterisasi terhadap data penjualan menu kedai kopi dengan mengimplementasikan algoritma k-medoids. Dan dapat mengetahui setiap anggota dari cluster 1 dan setiap anggota dari cluster 2. Dari pengujian yang telah dilakukan, dapat membantu para pelanggan dan pengusaha kedai kopi untuk mendukung strategi pembelian. Dengan melihat menu cluster 1, dapat dijadikan sebagai informasi rekomendasi menu sehingga konsumen lebih mudah dalam memilih menu minuman dan makanan di kesai kopi. Selain itu juga dapat dijadikan sebagai dasar untuk melakukan pembelian bahan baku makanan dan minuman.
PENGEMBANGAN MODEL PENGENDALIAN KUALITAS PRODUKSI PIPA PVC MENGGUNAKAN SIX SIGMA, SEVEN TOOLS, DAN STATISTICAL PROCESS CONTROL (SPC) MENUJU ZERO DEFECT MANUFACTURING Arief andika Putra; Dimas Akmarul Putera; Nellya Wahyuning Sri Gunarti; Muhammad Yusuf Hidayat; Rinci Kembang Hapsari
SIGMA TEKNIKA Vol 9, No 1 (2026): SIGMATEKNIKA, VOL.9, N0. 1, JUNI 2026
Publisher : Fakultas Teknik, Universitas Riau Kepulauan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33373/sigmateknika.v9i1.9100

Abstract

The purpose of this study is to improve the quality control of PVC pipe products at PT.XYZ by using Six Sigma , Seven Tools, and Statistical Process Control (SPC) methods to achieve zero defect manufacturing. This study applied a quantitative method with the DMAIC approach. The results showed a total production of 89,315.81 kg with defects of 1,521.30 kg, a DPMO value of 3,406.56, and a sigma level of 4.2 sigma. Pareto analysis identified machine M.13 as the main contributor to defects, while the Fishbone diagram revealed that defects were caused by human, machine, material, method, and environmental factors. Improvements were carried out using the 5W+1H method, and P-Chart analysis showed that the production process was still within statistical control limits. The results indicate that the implementation of Six Sigma , SPC, and Seven Tools can improve product quality toward zero defect manufacturing
Co-Authors Adi, Danu Septi Aditya, Andre Fransisco Aji, Rega Wahyu Alamsyah, Mochamad Tegar Bagas Arief andika Putra Arma Adzhana Mahran Audrey, Talitha Naifa Azmuri Wahyu Azinar Chanafi, Moch Irfan Dian Puspita Hapsari Dimas Akmarul Putera Farozi, Achmad Fayi Fikry, Muhamad Agus Fitra, Aldy Ramadhan Gutierres, Ego Prasetyo Ho H, Luky Agus Hadad, Heksa Bustomi Hakim, Permana Faddyahsari Hanif Prasetyo Hendro Nugroho Hendro Nugroho Hendro Nugroho, Hendro Irmayanti, Sivana Amelia Islamiyah, Mitha Lande, Gamaliel Virani Fofid Limanjaya, Rafi Oktaviano Eka Luky Agus Hermanto Luky Agus Hermanto, Luky Agus M Safi Anwar Anas Maftahatul Hakimah Maftahatul Hakimah, Maftahatul Mahatma, Adhitya Arie Mahendra, Caesario Putra Mochamad Muhajir Muhammad Yusuf Hidayat Navaro, Kamal Fauzan Nellya Wahyuning Sri Gunarti Nur Sulaiman NUR SULAIMAN Oktavian, Leonardo Fahsi Putri, Chatarina Natassya Rewina, Anggita Eka Reza Zulkifli Ferdiansyah Salim, Abdullah Harits Siti Maimunah Siti Maimunah Sugiyanto - Sugiyanto, Sugiyanto Titus Kristanto Tukadi Tukadi, Tukadi Tutuk Indriyani Ubaidillah, M Ravi Vandaningtyas, Andriany Vinda Setia Nita Vinda Setia Nita, Vinda Setia Wahyu, Bagas Aulifia Riski Putra Wardana, Septiyawan Rosetya Wardhana, Septiyawan Rosetya Widodo, Muhammad Amiruddin Wijaya, Abdul Hamid