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All Journal Jurnal Nasional Komputasi dan Teknologi Informasi Informatik : Jurnal Ilmu Komputer Jurnal Ilmiah Sains dan Teknologi (SAINTEK) Jurnal Esensi Infokom : Jurnal Esensi Sistem Komputer dan Informasi
Tyas Setiyorini
Universitas Bina Sarana Informatika
Aerospace Engineering Automotive Engineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering Library & Information Science Neuroscience Physics Transportation

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Perbandingan Neural Network dan K-Fold Cross Validation dengan Neural Network dan Sliding Window Validation untuk Estimasi Kuat Tekan Beton Tyas Setiyorini; Harsih Rianto
Jurnal Nasional Komputasi dan Teknologi Informasi (JNKTI) Vol 8, No 3 (2025): Juni 2025
Publisher : Program Studi Teknik Komputer, Fakultas Teknik. Universitas Serambi Mekkah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32672/jnkti.v8i3.9128

Abstract

Abstrak – Beton menjadi bahan yang penting dan banyak digunakan untuk pembangunan seperti perumahan, gedung, jembatan, jalan dan lain-lain. Beton banyak digunakan karena memiliki kuat tekan yang tinggi dan fleksibel sesuai kebutuhan jenis pembangunannya. Oleh karena itu pengukuran kuat tekan beton menjadi hal yang sangat penting. Namun pengukuran kuat tekan beton saat ini masih dilakukan dengan cara konvensional dan menggunakan rumus standar yang ada dalam ilmu Teknik Sipil. Cara tersebut masih kurang efektif dan kurang akurat karena banyak faktor yang mempengaruhi kuat tekan beton. Oleh karena itu pengukuran kuat tekan beton menjadi hal yang sangat penting dalam dunia Teknik Sipil. Seperti pada penelitian sebelumnya data kuat tekan beton bersifat nonlinear dan memiliki varians yang tinggi. Metode Neural Network sangat cocok untuk mengatasi masalah nonlinear. K-Fold Cross Validation merupakan salah satu metode yang mampu mengurangi varians yang tinggi sehingga dapat meningkatakan akurasi. Pada penelitian ini, diperoleh hasil RMSE sebesar 0,388 pada penerapan Neural Network dan K-Fold Cross Validation, dan RMSE sebesar 0,499 pada penerapan Neural Network dan Sliding Window Validation. Nilai RMSE menunjukkan penggunaan K-Fold Cross Validation lebih rendah dibanding Sliding Window Validation. Hal tersebut membuktikan bahwa K-Fold Cross Validation mampu meningkatkan kinerja yang lebih baik dengan mengurangi varians yang tinggi pada estimasi kuat tekan beton.Kata kunci: Kuat Tekan Beton; Estimation; Neural Network; K-Fold Cross Validation; Sliding Window Validation. Abstract - Concrete is an important material and is widely used for construction such as housing, buildings, bridges, roads and others. Concrete is widely used because it has high compressive strength and is flexible according to the needs of the type of construction. Therefore, measuring the compressive strength of concrete is very important. However, measuring the compressive strength of concrete is currently still done conventionally and using standard formulas in Civil Engineering. This method is still less effective and less accurate because many factors affect the compressive strength of concrete. Therefore, measuring the compressive strength of concrete is very important in the world of Civil Engineering. As in previous studies, concrete compressive strength data is nonlinear and has high variance. The Neural Network method is very suitable for overcoming nonlinear problems. K-Fold Cross Validation is one method that can reduce high variance so that it can increase accuracy. In this study, the RMSE results were obtained of 0.388 in the application of Neural Network and K-Fold Cross Validation, and RMSE of 0.499 in the application of Neural Network and Sliding Window Validation. The RMSE value shows that the use of K-Fold Cross Validation is lower than Sliding Window Validation. This proves that K-Fold Cross Validation is able to improve better performance by reducing high variance in concrete compressive strength estimates.Keywords: Concrete Compressive Strength; Estimation; Neural Network; K-Fold Cross Validation; Sliding Window Validation.
Perancangan Sistem Manajemen Data Barang Berbasis Website Pada FC Computer PIK Jakarta Utara Akbar Fariq Mulia; Jordy Lasmana Putra; Tyas Setiyorini
Jurnal Esensi Infokom : Jurnal Esensi Sistem Informasi dan Sistem Komputer Vol 9 No 2 (2025)
Publisher : Institut Bisnis Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55886/infokom.v9i2.331

Abstract

FC Computer PIK Jakarta Utara merupakan toko yang bergerak di bidang penjualan dan layanan jasa teknologi, yang masih menggunakan sistem semi-manual berbasis Excel dan Dropbox dalam pengelolaan stok barang. Sistem tersebut menimbulkan sejumlah kendala, seperti konflik file, potensi human error, keterbatasan kolaborasi, dan tingkat keamanan yang rendah. Penelitian ini bertujuan merancang sistem manajemen data barang berbasis website guna meningkatkan efisiensi, akurasi, dan keamanan dalam pengelolaan stok. Metode yang digunakan adalah Waterfall, dengan tahapan analisis kebutuhan, perancangan, implementasi, hingga pengujian sistem. Hasilnya adalah sebuah aplikasi berbasis web yang memungkinkan pencatatan barang masuk dan keluar secara real-time, pengelolaan data pengguna, pembuatan laporan, serta fitur login dengan hak akses berbeda antara admin dan owner. Sistem ini dibangun menggunakan PHP dan MySQL dan telah diuji menggunakan metode blackbox, yang menunjukkan bahwa seluruh fungsi utama berjalan sesuai dengan kebutuhan. Sistem ini diharapkan mampu menjadi solusi efektif dalam mengatasi permasalahan stok barang di FC Computer PIK, sekaligus mendukung pengambilan keputusan yang lebih baik. Kata Kunci: Website, Waterfall, PHP, MySQL, FC Computer PIK.
SISTEM INFORMASI ABSENSI PESERTA MAGANG BERBASIS WEB DI KANTOR PUSAT PT.PEGADAIAN (PERSERO) Savira Nur Rahmadini; Jordy Lasmana Putra; Tyas Setiyorini
Jurnal Ilmiah Sains dan Teknologi Vol. 10 No. 2 (2026): Jurnal Ilmiah Sains dan Teknologi
Publisher : Teknik Informatika Fakultas Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47080/saintek.v10i2.4606

Abstract

The internship program at the Head Office of PT Pegadaian (Persero) still faces several issues, such as manual attendance recording for interns, lack of data integration, and difficulties in monitoring performance and generating reports efficiently. This study aims to develop a web-based application called InTrack (Intern Tracker) to assist in managing intern data, recording attendance, and generating digital reports. The system was developed using the Waterfall methodology. The application utilizes the CodeIgniter framework for the backend and MySQL as the database system. It was tested through black box testing and User Acceptance Testing (UAT) to ensure it meets user requirements. The test results indicate that the system functions well and improves efficiency and accuracy in managing intern data at PT Pegadaian. Therefore, the InTrack application can serve as an effective digital solution to support the administrative and evaluation processes of internships in a real-time and integrated manner
Clustering Social Media Addiction Levels Among Students Using the K-Means Clustering Algorithm Muhamad Sandi; Jordy Lasmana Putra; Tyas Setiyorini
Informatik : Jurnal Ilmu Komputer Vol 22 No 2 (2026): August 2026
Publisher : Fakultas Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52958/iftk.v22i2.12605

Abstract

Social media has become an integral part of students’ lives, yet excessive use often leads to symptoms of addiction that negatively affect mental health and academic performance. This study aims to cluster the levels of social media addiction among students and university students using the K-Means Clustering algorithm as an unsupervised learning approach. The dataset was obtained from the Kaggle platform, containing variables such as daily usage duration, access frequency, sleep disturbance, and psychological impact. The Elbow Method was employed to determine the optimal number of clusters, while Principal Component Analysis (PCA) was used for visualization. The results grouped respondents into three categories: mild addiction (46.8%), moderate addiction (22.6%), and severe addiction (30.6%). A strong correlation was observed between high access frequency and symptoms such as sleep disruption and decreased concentration. These findings highlight the importance of designing data-driven prevention strategies within educational environments and provide a foundation for further institutional interventions to maintain digital balance among youth.
Co-Authors Akbar Fariq Mulia Harsih Rianto Jordy Lasmana Putra Muhamad Sandi Savira Nur Rahmadini