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JURIKOM (Jurnal Riset Komputer)
Published by Sekolah Tinggi Manajemen Informatika dan Komputer Budi Darma
ISSN : 2407389X     EISSN : 27157393     DOI : http://dx.doi.org/10.30865/jurikom.v7i1.1927
Core Subject : Science, Engineering,
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering
JURIKOM (Jurnal Riset Komputer) membahas ilmu dibidang Informatika, Sistem Informasi, Manajemen Informatika, DSS, AI, ES, Jaringan, sebagai wadah dalam menuangkan hasil penelitian baik secara konseptual maupun teknis yang berkaitan dengan Teknologi Informatika dan Komputer. Topik utama yang diterbitkan mencakup: 1. Teknik Informatika 2. Sistem Informasi 3. Sistem Pendukung Keputusan 4. Sistem Pakar 5. Kecerdasan Buatan 6. Manajemen Informasi 7. Data Mining 8. Big Data 9. Jaringan Komputer 10. Dan lain-lain (topik lainnya yang berhubungan dengan Teknologi Informati dan komputer)
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 1,069 Documents
 81   82   83   84   85 
Optimasi Algoritma K-Nearest Neighbors pada Prediksi Penyakit Diabetes Arfiah, Sitti; Wajidi, Farid; Nur, Nahya
JURIKOM (Jurnal Riset Komputer) Vol 12, No 3 (2025): Juni 2025
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v12i3.8615

Abstract

Diabetes mellitus is a chronic disease characterized by high blood sugar levels due to metabolic system disturbances, specifically related to insulin production or effectiveness. If left untreated, it can lead to serious complications. Early and accurate detection is crucial for timely medical intervention. This research aimed to improve the accuracy of a diabetes classification system using the K-Nearest Neighbors (KNN) algorithm. An initial KNN model with imbalanced data (without SMOTE) and no GridSearchCV achieved only 83% accuracy. While seemingly good, its performance for the positive class was low (precision 80%, recall 69%, F1-score 74%), indicating bias towards the negative class due to data imbalance. To address this, several steps were implemented: data preprocessing (handling missing data and feature normalization), hyperparameter optimization using GridSearchCV, and data balancing with SMOTE. After these improvements, the KNN model showed significant performance gains, with accuracy reaching 94%. Performance for the positive class greatly improved (precision 90%, recall 98%, F1-score 94%), and for the negative class (precision 98%, recall 89%, F1-score 93%). These results demonstrate that combining preprocessing, model optimization, and class balancing effectively enhances the KNN algorithm's ability to detect diabetes more accurately and robustly, proving that machine learning with proper data processing can aid in developing medical decision support systems for early diabetes diagnosis.
Implementasi Metode MOORA dengan Pembobotan ROC Dalam Sistem Pendukung Keputusan Pemilihan Guru Wali Kelas Terbaik Sandro Willy
JURIKOM (Jurnal Riset Komputer) Vol 11, No 5 (2024): Oktober 2024
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v11i5.8468

Abstract

Pemilihan guru wali kelas terbaik di SDN 101933 Perbaungan merupakan proses yang penting untuk mendukung kualitas pendidikan. Dalam konteks ini, pengambilan keputusan yang tepat memerlukan metode yang dapat mempertimbangkan berbagai kriteria secara objektif. Metode Multi-Objective Optimization on the Basis of Ratio Analysis (MOORA) dengan pembobotan Rank Order Centroid (ROC) diterapkan untuk membantu dalam pengambilan keputusan yang lebih akurat dan objektif. Namun, proses pemilihan guru wali kelas terbaik seringkali mengalami kendala karena banyaknya kriteria yang harus dipertimbangkan. Tanpa metode yang sistematis dan objektif, keputusan yang diambil dapat menjadi subyektif dan kurang akurat. Selain itu, ketidakjelasan dalam penentuan bobot untuk setiap kriteria menambah kompleksitas dalam proses pengambilan keputusan. Penerapan metode MOORA dengan pembobotan ROC dapat meningkatkan akurasi dan objektivitas dalam proses pemilihan guru wali kelas terbaik di SDN 101933 Perbaungan. Dengan mengimplementasikan metode ini, diharapkan dapat diperoleh keputusan yang lebih adil dan tepat berdasarkan penilaian multi-kriteria yang terstruktur.
Implementasi Deep Neural Network untuk Prediksi Harga Saham PT Bank Central Asia Tbk Muhammad Rakha Almasah; Wahyu Aji Eko Prabowo
JURIKOM (Jurnal Riset Komputer) Vol 12, No 2 (2025): April 2025
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v12i2.8544

Abstract

Stock price prediction is one of the main challenges in financial market analysis. This study develops a Deep Neural Network (DNN) model using TensorFlow to predict the stock price of PT Bank Central Asia Tbk (BBCA) based on historical stock price data, the Jakarta Composite Index (IHSG), and the USD/IDR exchange rate. The model is optimized through hyperparameter tuning using KerasTuner with the Hyperband method, allowing for more efficient exploration of hyperparameter combinations. The tuning process yielded the best model configuration with a learning rate of 0.001269, four hidden layers, and the application of Batch Normalization, L2 kernel regularization, and dropout in each hidden layer.The model was evaluated using various batch sizes (4, 16, 32, 64, and 128) with Mean Squared Error (MSE) and Mean Absolute Error (MAE) as performance metrics. The results show that batch size 128 provides the best performance, with MSE of 0.0094 and MAE of 0.0490 on the test data, indicating high accuracy and good generalization on unseen data. The best model was then implemented as an API using Flask, deployed to Google Cloud Run, and integrated with a Flutter-based mobile application. Testing confirmed that the API can handle requests quickly using TensorFlow and produce accurate predictions.Thus, this study successfully developed a DNN-based BBCA stock price prediction system that can be applied to data-driven investment decision-making.
Analisis Algoritma Elias Delta Code Dengan Algoritma Elias Gamma Code Pada Kompresi File Text Helena Wisarani Nazara
JURIKOM (Jurnal Riset Komputer) Vol 11, No 3 (2024): Juni 2024
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v11i3.8461

Abstract

Dalam melakukan proses mengubah sekumpulan data menjadi bentuk kode dengan tujuan untuk mengurangi ukuran file sebelum menyimpan atau memindahkan data tersebut ke dalam media penyimpanan merupakan kompresi file. Jika sebuah data disimpan pada media penyimpanan semakin bertambah atau memiliki ukuran yang besar, maka membuat media penyimpanan tidak dapat menyimpan data tersebut melebihi dari kapasitasnya. Data yang memiliki ukuran besar memerlukan waktu transfer yang lebih lama dibandingkan dengan data yang ukurannya lebih kecil. Adapun solusi untuk mengatasi masalah tersebut adalah dengan melakukan kompresi. Hal ini bertujuan agar ukuran file teks menjadi jauh lebih kecil sehingga proses transfer file teks lebih cepat serta dapat menghemat ruang penyimpanan. Ada beberapa algoritma dalam mengkompresi file teks, namun penelitian ini menggunakan algoritma Elias Delta Code dan algoritma Elias Gamma Code. Setelah melakukan kompresi pada kedua algoritma tersebut, selanjutnya melakukan analisa hasil kompresi dari kedua algoritma tersebut yang berpatokan dengan beberapa parameter seperti Ratio of Compression (RC), Compression Ratio (CR) dan Space Saving (SS). Hasil dari analisa hasil kompresi algoritma Elias Delta Code dan Elias Gamma Code maka dapat mengetahui algoritma yang lebih baik dalam melakukan kompresi pada file teks.
Penerapan Word2Vec dan SVM dengan Hyperparameter Tuning untuk Deteksi Phishing Wicaksana, Hilman Singgih; Huda, Khairul
JURIKOM (Jurnal Riset Komputer) Vol 12, No 3 (2025): Juni 2025
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v12i3.8729

Abstract

The advancement of information technology in today’s digital age takes place very rapidly from one time to another. This phenomenon is accompanied by increasing cybersecurity threats like phishing. Phishing links are often designed with uniform resource locator (URL) structures that appear convincing and are difficult to distinguish from genuine links. This research proposes a word-to-vector (Word2Vec) and Support Vector Machine (SVM) approach with hyperparameter tuning where Word2Vec is a word embedding technique used to create a word vector representation of a particular URL, SVM is used as a machine learning (ML) approach used in this research, and hyperparameter tuning is used as a technique to find the best combination of parameters to produce an optimal SVM model in detecting phishing. The purpose of this research is to compare the performance between SVM and XGBoost models that have been optimized and deploy ML models into a prediction system using the Streamlit framework to detect phishing based on input made by users in the form of certain URLs. The findings of this study indicated that the SVM model performed very well compared to the XGBoost model, with precision, recall, f1-score, and accuracy values of about 99.84% for SVM. On the other hand, the XGBoost model recorded precision, recall, f1-score, and accuracy values of about 99.70% each. Thus, the SVM model is the optimal model to detect phishing precisely and accurately.
Klasifikasi Berita Televisi Menggunakan Metode K-NN, Naïve Bayes dan SVM Tri Wuryantoro; Muljono Muljono; Pujiono Pujiono
JURIKOM (Jurnal Riset Komputer) Vol 11, No 6 (2024): Desember 2024
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v11i6.8420

Abstract

News through television media is still one of the media that is widely used by the public in obtaining the latest information. The Central Java TVRI Public Broadcasting Institution has a news program called Berita Jawa Tengah which airs every day and  doesn’t have a classification system. This research was carried out in several stages, in the initial stage preprocessing was carried out which included: data collection, cleaning, case folding, tokenizing, normalization, stopword removal, stemming, then continued with word weighting (TF-IDF) and finally applying the K-Nearest Neighbor classification method (K-NN), Naïve Bayes and Support Vector Machine (SVM). The results of the classification carried out show that the K-NN classification method has higher results compared to other methods, namely an Accuracy value of 0.94, Precision 0.92, Recall 0.94 and f1-score 0.93, so it can be concluded that Television news classification using the K-NN method is the method that provides the most accurate results.
Implementasi Item-Based Collaborative Filtering Dalam Sistem Pemesanan Online Pada UMKM Berbasis Website Ikhsan, Ramadhani Al; Harahap, Aninda Muliani
JURIKOM (Jurnal Riset Komputer) Vol 12, No 3 (2025): Juni 2025
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v12i3.8568

Abstract

This study designs a web-based online menu ordering system for the UMKM Solo Fried Chicken (SFC), located on Jl. Binjai KM.10.5, Paya Geli, Sunggal District, Deli Serdang Regency. The system is built using PHP and MySQL, with a responsive design to allow customers to place orders anytime and anywhere. The applied technology aims to address several issues previously faced by UMKM SFC, such as slow and inefficient manual ordering processes, long queues, and order recording errors that affect service quality. Additional problems include the lack of available menu information, the absence of a recommendation system to assist customers in choosing menus, and the unavailability of a digital system for recording transactions and sales reports. The main problem addressed in this study is how to build a web-based online ordering system that not only simplifies transactions but also accurately recommends menus based on customer preferences. As a solution, this research implements the Item-Based Collaborative Filtering method to recommend menus based on the purchasing patterns of other customers with similar preferences. Based on the calculations, the system recommends the top three most relevant menus for each main menu item, such as Combo Original Paha for Ayam Paha, and Kidz 3, which frequently appears as a recommendation due to its similarity with many customers' preferences. This system is expected to improve operational efficiency, reduce errors, accelerate service, and provide a more personalized ordering experience. Key features developed in the system include online ordering, menu recommendations, sales reports, and transaction recording, which are visualized through a Use Case Diagram and Flowmap
Analisa Usability Aplikasi Halodoc Dengan Menggunakan Metode Cobit 5.0 Musida Halawa
JURIKOM (Jurnal Riset Komputer) Vol 11, No 6 (2024): Desember 2024
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v11i6.8481

Abstract

Pentingnya usability untuk mengukur kualitas kelayakan suatu sistem yang mengarah pada beberapa metode pengujian. aplikasi Halodoc mendapatkan feedback pengguna dimana didapatkan permasalahan kesulitan dalam mencari informasi dan merasa kurang nyaman dengan tampilan gambar yang dirasa kurang representatif, ada pula keluhan mengenai tampilan pada beberapa menunya. Maka dibutuhkan metode evaluasi yang terstruktur dalam rangka meningkatkan usability aplikasi. Pentingnya metode evaluasi yang terstruktur dalam rangka meningkatkan usability aplikasi Halodoc, diperlukan pendekatan yang terstruktur dan terukur dalam mengevaluasi kepuasan pengguna serta identifikasi area-area yang perlu diperbaiki dengan menggunakan pendekatan model domain DSS pada metode COBIT 5.0. Hasil penerapan  menunjukan  tingkat pengembangan aplikasi hallodoc guna meningkatkan usability aplikasi yang berkaitan dengan DSS01 (Manage Operations) membutuhkan pengembangan 3%. DSS02 (Manage Service Requests and Incidents) membutuhkan pengembangan 0% artinya tetap pada kondisi saat ini, DSS03 (Manage Problems) membutuhkan pengembangan 0% artinya tetap pada kondisi saat ini, DSS04 (Manage Continuity) membutuhkan pengembangan 2%. DSS05 (Manage Security Services) membutuhkan pengembangan 0% artinya tetap pada kondisi saat ini. DSS06 (Manage Business Process Controls) mendapatkan peningkatan 3%.
Peramalan Penjualan Semen Menggunakan Metode Single Moving Average dan Double Moving Average Yuliana, Nur Lutfi; Santi, Nirma Ceisa; Mahmudah, Nur
JURIKOM (Jurnal Riset Komputer) Vol 12, No 3 (2025): Juni 2025
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v12i3.8537

Abstract

UD. Kurnia Makmur is a shop that sells various building materials including cement. Previously, the shop UD. Kurnia Makmur still had difficulty in determining the amount of cement stock that should be provided, UD. Kurnia Makmur often also experiences shortages or excess stock due to the rise and fall of inconsistent market demand. Therefore, a forecasting method is needed that can help make better decisions in estimating the amount of cement stock that must be provided. The method used to predict cement stock in this study is the single moving average and double moving average methods. The purpose of this forecasting study is for the company to know the amount that must be provided according to consumer demand for cement sales and to know the accuracy between the single moving average and double moving average methods. Because the previous journal showed that both methods produced a MAPE of less than 10% where, it can be interpreted that if the MAPE is less than 10% then the forecast is very good. In calculating the accuracy of this study using MAD (Mean Absolute Percentage Error) and MAPE (Mean Absolute Percentage Error) using Microsoft Excel as the calculation tool. After being calculated using Microsoft Excel, the results obtained in the study were MAD of 30.72 and MAPE of 2.0% for the single moving average, while the double moving average produced MAD of 19.0 and MAPE of 1.24%.
Sistem Pendukung Keputusan dengan Menerapkan Metode WASPAS dan Pembobotan ROC dalam Penilaian Kinerja Karyawan Aditya Maulana Rahman Maulana Rahman
JURIKOM (Jurnal Riset Komputer) Vol 11, No 4 (2024): Augustus 2024
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v11i4.8471

Abstract

Lion Parcel Alifs Travel&Courier salah satu kantor yang berkembang di kota medan, dimana kantor tersebut berkaitan dengan layanan pengiriman barang dari satu kota ke kota lain bahkan sampai ke seluruh indonesia. Layanan pengiriman barang ini sudah sangat dibutuhkan banyak orang yang bertujuan untuk mengirim barang yang mereka pesan melalui online. Maka dengan itu salah satu kantor tempat layanan pengiriman barang yaitu Lion ParcelAlifs Travel & Courier sangat membutuhkan karyawan yang memiliki kulitas terhadap pemahaman mengenai pengiriman barang agar barang tersebut tetap terjaga dengan aman dan baik. Dalam penilaian kinerja karyawan pada kantor tersebut sering mengalami kendala dalam hal kecepatan dan ketepatan, serta jumlah karyawan yang banyak dapat mempengaruhi waktu penilaian dan objektivitasnya. Untuk mengatasi permasalahan ini, penelitian ini mengusulkan penggunaan Sistem Pendukung Keputusan dengan metode Weight Aggregated Sum Product Assessment (WASPAS) dan pembobotan Rank Order Centroid (ROC). Kombinasi metode WASPAS dan ROC diharapkan menghasilkan nilai perangkingan dan bobot yang akurat untuk penilaian kinerja karyawan. Melalui penelitian ini, diharapkan Kantor Lion Parcel dapat memanfaatkan SPK dengan metode WASPAS dan ROC untuk meningkatkan efisiensi dan objektivitas dalam penilaian kinerja karyawan, sehingga dapat memberikan dampak positif pada kemajuan kantor dan pelayanan kepada masyarakat. Maka dari hasil perhitungan dengan menggunakan metode WASPAS dan Pembobotan ROC akan menghasilkan perangkingan yang tinggi dalam penilaian kinerja pegawai tetap dan nilai yang tertinggi yaitu Raifa Ginting dengan total yang dihasilkan sebesar 0,965.

Page 83 of 107 | Total Record : 1069

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