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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
 82   83   84   85   86 
Sistem Klasifikasi Tingkat Kerusakan Kunci Motor Menggunakan Random Forest dengan Hyperparameter Tuning Jalu Wira Yuda; Hastie Audytra; Nur Mahmudah
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.v12i2.8517

Abstract

Motorcycle key damage is often a problem for users, while the identification process still relies on technicians, which can be time-consuming and subjective. This study develops a classification system for motorcycle key damage levels using the Random Forest method with hyperparameter optimization. The dataset consists of 1,000 samples collected through observation and technician interviews, with data preprocessing using the SMOTE technique to address class imbalance. The model is trained and optimized with Random Forest using GridSearchCV and evaluated based on accuracy, precision, recall, and F1-score. The results show that the optimized Random Forest model achieves an accuracy of 85.5%, an improvement from 82% before tuning, enabling faster and more accurate identification of motorcycle key damage levels. The implementation of this system is expected to improve repair service efficiency and help users take action before the damage worsens.
Penerapan Algoritma Textmining Pada Aplikasi Ujian Berbasis LAN Tingkat Sekolah Dasar Krisnawati Br. Rajagukguk
JURIKOM (Jurnal Riset Komputer) Vol 11, No 2 (2024): April 2024
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v11i2.8451

Abstract

Bantuan sarana sekolah sangat penting untuk sekolah yang belum mempunyai sarana yang memadai. Penentuan penerima bantuan sarana sekolah harusnya tidak bersifat subjektif artinya bebas dari kepentingan pribadi, suku, agama, maupun daaerah, sehingga proses penentuan penerima bantuan sarana akan lebih efektif dan efisien. Pada Dinas Pendidikan Kabupaten Deli Serdang penentuan penerima bantuan sarana sekolah masih terbilang lambat karena pada prosesnya masih menggunakan tulis tangan. Text Mining merupakan proses ekstraksi pola dari sejumlah besar sumber data tak terstruktur. Penambangan teks ini menggunakan proses yang sama dengan penambangan data, namun memiliki masukan yang berbeda. Berdasarkan penjelasan diatas maka penulis ingin membangun suatu aplikasi ujian online pada tingkat sekolah dasar dengan menerapkan algoritma text mining untuk melakukan koreksi secara otomatis. Algoritma text mining dianggap mampu menyelesaikan masalah tersebut dengan cara melakukan membandingkan kata yang streaming kedalam database kamus kata besar, jika kata di-input memiliki pasangan awalan-akhiran” Be-lah” maka langkah stemming selanjutnya adalah 5,3,4,5,6, hilangkan partikel dan kata ganti kepunyaan, hilangkan juga akhiran(:I”,”an”) sesuai dengan model imbuhan. Berdasarkan penelitian yang dilakukan oleh  “Kestrilia Rega Prilianti “  dengan judul “Aplikasi Text Mining untuk Automasi Penentuan Tren Topik Skripsi  dengan Metode K-Means Clustering“ . Dapat disimpulkan bahwa Proses automasi penentuan tren topik skripsi dapat dilakukan dengan menerapkan metode text mining. Dengan metode tersebut analisis tren topik skripsi dapat dilakukan dengan cepat karena meminimalkan keterlibatan dari pengguna. Melalui penelitian ini ditunjukkan bahwa algoritma k-means clustering yang digunakan dalam proses penemuan pola tebukti dapat membantu proses pengelompokan berbagai topik skripsi yang ada sehingga diperoleh informasi yang bermakna dalam menentukan tren penelitian Universitas dari tahun ke tahun.
Rainfall Prediction Using Attention-Based LSTM Architecture Romadhani, Ahmad; Santoso, Irwan Budi; Crysdian, Cahyo
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.v12i3.8727

Abstract

This study addresses the challenge of accurately predicting rainfall in regions with complex climate dynamics, such as Malang Regency, East Java. It evaluates the performance of a Long Short-Term Memory (LSTM) model enhanced with the Bahdanau Attention Mechanism, comparing it with a Standard LSTM model in forecasting daily rainfall based on historical weather parameters including average temperature, relative humidity, sunshine duration, and wind speed. Using daily data from BMKG covering 2000 to 2023, both models underwent a structured machine learning process including data preprocessing, feature selection, model training, and evaluation. The Attention-Based LSTM consistently outperformed the Standard LSTM, particularly in handling rainfall anomalies, achieving an MSE of 0.00800 and RMSE of 0.08948, compared to 0.00817 and 0.09039 respectively for the Standard LSTM. These results demonstrate that integrating Bahdanau Attention improves the model’s focus on relevant temporal features, enhancing prediction accuracy and robustness. The architecture consisting of two LSTM cells combined with the attention mechanism effectively captures complex sequential patterns that the standard model tends to overlook. This research highlights the potential of attention mechanisms in time series weather prediction, contributing to more reliable early warning systems, adaptive agricultural strategies, and disaster risk reduction frameworks. Future work could explore hybrid models or incorporate additional weather features to further improve performance and generalization.
Analisa Pola Pembelian Konsumen Menggunakan Metode Association Rule Pada Data Transaksi Penjualan Dunia Vape Azhar Radhitya Tarigan; Firahmi Rizky
JURIKOM (Jurnal Riset Komputer) Vol 12, No 1 (2025): Februari 2025
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v12i1.8423

Abstract

Dunia Vape Trading Business is a trading business engaged in the sale of electronic cigarette spare parts. Every consumer who is going to buy a product, the consumer looks at the product shelves one by one to find the product they are looking for. The problem that occurs is that sometimes it is tiring for consumers to search on different shelves or it can also cause consumers not to buy the products they actually need. This is because the products sold are not arranged based on the purchasing patterns that consumers often make. Therefore, a method is needed that can make it easier for consumers to find the desired product and so that UD Dunia Vape can find out consumer purchasing patterns so that it can arrange and provide products according to the usual patterns. Therefore, researchers utilize computer technology to help consumers find the desired product and so that UD Dunia Vape can find out consumer purchasing patterns so that it can arrange and provide products according to the usual patterns. In computer science, one of the methods that can be applied is by using data mining. One of the methods used in determining patterns is the Association Rule Mining Method. Association rule mining is a data mining technique that aims to find association rule patterns between a combination of items. Implementation of the Association Rule Mining Method on sales of goods at the UD Dunia Vape Store is used to provide information on the results of building material sales patterns each month which can be used as parameters in managing UD Dunia Vape's inventory of materials, in addition to providing information on the most popular and most sold building materials at UD Dunia Vape. The results obtained Based on the results and discussion, it can be concluded that the analysis of consumer purchasing patterns using the Association Rule method on Dunia Vape sales transaction data can be applied based on the results of manual method application where the association rule results are the same as data mining application testing, namely if you buy I = LOST VAPE URSA CAP POD KIT JOY PURPLE then Buy N = PODS FRIENDLY ICE PEDIA BLUEBERRY 15MG 30ML (24) with a confidence value of 100% and if you buy N = PODS FRIENDLY ICE PEDIA BLUEBERRY 15MG 30ML (24) then Buy I = LOST VAPE URSA CAP POD KIT JOY PURPLE with a confidence value of 100%.
Klasifikasi Kanker Payudara Berbasis Deep Learning Menggunakan Vision Transformer dengan Teknik Augmentasi Data Citra Ardiyansyah, Muhamad Salman; Umbara, Fajri Rakhmat; Melina, Melina
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.v12i3.8619

Abstract

Breast cancer ranks among the leading causes of death in women worldwide. Early detection through mammographic image analysis plays a crucial role in increasing survival rates. However, manual interpretation of mammograms requires expert knowledge and is prone to errors. This study aims to develop a breast cancer classification model using mammography images based on the Vision Transformer (ViT) architecture without employing transfer learning. The dataset used is the Digital Database for Screening Mammography (DDSM), consisting of two categories: benign and malignant. To address class imbalance, undersampling and data augmentation techniques (flipping, rotation, cropping, and noise injection) were applied. All images were normalized and resized to 224×224 pixels to match the ViT input requirements. The model was trained for five epochs with a batch size of 16. Evaluation on the test data was conducted using seven metrics: accuracy, precision, recall, F1-score, Matthews Correlation Coefficient (MCC), Cohen’s Kappa Score, and Area Under the Curve (AUC). The results show that the model achieved an accuracy of 92.50%, precision of 90.48%, recall of 95.00%, F1-score of 92.68%, MCC of 85.11%, Kappa Score of 85.00%, and AUC of 95.75%. These findings indicate that the Vision Transformer is highly effective for mammographic image classification and holds potential as a reliable tool for automated breast cancer diagnosis support.
Perbandingan Algortima Boldi-Vigna Codes Dan Algoritma Taboo Codes Dalam Kompresi File Audio (WAV) Joshua Feliex D. Sirait; Surya Darma Nasution; Saidi Ramadan Siregar
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.v11i5.8472

Abstract

Penelitian ini bertujuan untuk melakukan analisis perbandingan antara dua algoritma kompresi, yaitu Boldi-Vigna Codes dan Taboo Codes, dalam konteks pengompresan file audio format WAV. Masalah yang dihadapi adalah kebutuhan akan teknik kompresi yang mampu mengurangi ukuran file audio tanpa mengorbankan kualitas suara, mengingat file audio berformat WAV cenderung memiliki ukuran besar dan membutuhkan ruang penyimpanan yang signifikan. Seiring dengan berkembangnya teknologi audio dan meningkatnya permintaan akan kualitas suara yang tinggi, penting untuk menemukan solusi kompresi yang tidak hanya efektif dalam mengurangi ukuran file tetapi juga mampu mempertahankan integritas dan kualitas audio secara keseluruhan. Untuk mengatasi masalah tersebut, penelitian ini mengimplementasikan dan menguji kinerja kedua algoritma kompresi pada sejumlah file audio WAV. Evaluasi performa dilakukan dengan mengukur tingkat kompresi yang dihasilkan serta kualitas audio yang dipertahankan setelah proses kompresi. Metode eksperimen ini dirancang untuk memberikan pemahaman yang lebih mendalam mengenai efisiensi dan efektivitas masing-masing algoritma dalam konteks kompresi file audio. Hasil penelitian menunjukkan bahwa baik boldi-vigna codes maupun taboo codes mampu mengompresi file audio WAV dengan efektif. Namun, terdapat perbedaan signifikan dalam tingkat kompresi dan kualitas audio yang dihasilkan oleh masing-masing algoritma. Hasil ini memberikan wawasan lebih lanjut tentang keefektifan dan stabilitas kedua algoritma dalam kompresi file audio, serta potensinya dalam meningkatkan efisiensi penggunaan penyimpanan dan pengiriman file audio.
Sentiment Analysis of Youtube Comments on Indonesian Presidential Candidates in 2024 using Naïve Bayes Classifier Nurbaiti Mahfudza; Muhammad Ihksan
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.v12i2.8538

Abstract

The 2024 Indonesian presidential election is one of the most talked about topics on various social media platforms, including YouTube. The comments that appear on political-themed videos can reflect public opinion towards presidential candidates. This research aims to conduct sentiment analysis of YouTube comments related to Indonesian presidential candidates in 2024 using the Naïve Bayes Classifier method. This method was chosen due to its ability to classify text data effectively and efficiently. Data was collected from a number of relevant Kompas tv videos on YouTube, then text preprocessing stages such as data cleaning, tokenization, and stemming were performed. Next, the data was classified into three sentiment categories, namely positive, negative, and neutral. The research shows that the Naïve Bayes model is able to classify sentiment with sufficient accuracy.  This finding can provide an overview of public perceptions of each presidential candidate as well as input for interested parties in the fields of politics and public communication. The results of this study show that the naïve bayes classifier algorithm can analyze with an accuracy of 61 % in the evaluation process using confusion matrix. The results of this study indicate that the naïve bayes classifier algorithm can be an effective alternative for analyzing the sentiment of YouTube comments on presidential candidates.
Implementasi Algoritma Levenstein Dan Algoritma Rice Codes Dalam Mengkompresi File Video Sendi Pratama
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.v11i3.8462

Abstract

Video merupakan salah satu media hiburan yang sering dan paling banyak diminati oleh para kalangan remaja saat ini. Namun video yang biasanya memiliki ukuran yang relatif cukup besar dan ini tentu akan menjadi masalah dan dapat menyebabkan penyimpanan akan menjadi cepat penuh. Minimnya ruang penyimpanan pada suatu perangkat, seperti handphone tentu akan menjadi masalah, apalagi jika terlalu banyak menyimpan file seperti video. Kompresi merupakan suatu teknik yang dapat digunakan untuk memperkecil ukuran dari suatu data. Algoritma Levenstein dan algoritma rice codes merupakan algoritma dari kompresi. Kedua algoritma tersebut dapat digunakan untuk mengkompresi suatu file, terlebih file video. Algoritma levenstein merupakan pengkodean universal untuk bilangan bulat non-negatif, sedangkan algoritma rice codes merupakan algoritma yang menggunakan teknik golomb coding dimana dapat menghasilkan prefix yang lebih mudah. Dari kedua algoritma tersebut tentunya memiliki nilai plus dan minus-nya masing-masing. Dalam pengimplementasiannya, memanfaatkan teknik double kompresi dengan menggunakan algoritma levenstein dan algoritma rice codes dalam mengompresi file video dinilai sangat efektif karena dapat menghasilkan nilai dari compression ratio (CR) sebesar 37,5%.
Implementasi Model ARIMA untuk Peramalan Reorder Point dalam Supply Chain Management Alexandra, Andrea Cellista; Hartomo, Kristoko Dwi
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.v12i3.8639

Abstract

This research analyzes the patterns and trends of reorder points in inventory management over a two-year period (2023-2024), utilizing weekly time series data generated from daily data resampling. The ARIMA (Autoregressive Integrated Moving Average) method was applied to forecast future reorder point values. An Augmented Dickey-Fuller (ADF) stationarity test revealed that the initial data was non-stationary but became stationary after a single differencing operation. Parameter identification using Autocorrelation Function (ACF) and Partial Autocorrelation Function (PACF) plots indicated that the ARIMA(1,1,1) model was the best choice, based on the lowest Akaike Information Criterion (AIC). Model accuracy was evaluated using Mean Absolute Percentage Error (MAPE), yielding a value of 0.02%, signifying an excellent level of prediction accuracy. Consequently, the ARIMA model is demonstrated to be reliable for forecasting reorder points, supporting more precise decision-making in inventory management.
Implementasi Time Series Forecasting dengan Algoritma LSTM untuk Pemantauan dan Prediksi Produktivitas Kelapa Sawit Berdasarkan Hasil Panen Ardian Pramana Putra; Aninda Muliani Harahap
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.v12i2.8495

Abstract

Palm oil productivity is a key factor in maintaining the stability and sustainability of Indonesia's agribusiness industry. The fluctuation in yield at PTPN IV Kebun Bah Birung Ulu, which increased from 43,308 tons in 2020 to 44,028 tons in 2022 and then decreased to 34,643 tons in 2024, highlights the need for a more accurate monitoring system. These fluctuations are influenced by weather, fertilizer usage, plant infections, and plant age. Manual record-keeping without digital system support also increases the risk of errors and complicates production monitoring. This study aims to develop a web-based palm oil productivity prediction system using the Long Short-Term Memory (LSTM) algorithm. Five years of daily historical data, including plant age, fertilizer usage, rainfall, infection rates, and harvest results per afdeling, were used as model input. The research process includes data collection, preprocessing with Min-Max normalization, data splitting into 80% training and 20% testing, and training the LSTM model with two LSTM layers, two dropout layers, and one Dense layer. Model evaluation using Root Mean Squared Error (RMSE) shows that the model can predict productivity with good accuracy, with the best RMSE for each target variable achieved at different epochs. The 2025 prediction results indicate a stable or declining trend influenced by plant age, fertilizer application, rainfall, and infection rates. The developed web-based system features real-time monitoring and data visualization, providing a more efficient solution for production management and strategic decision-making in palm oil plantations.

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