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Yosep Septiana
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INDONESIA
Jurnal Algoritma
Published by Institut Teknologi Garut
ISSN : 14123622     EISSN : 23027339     DOI : https://doi.org/10.33364/algoritma
Core Subject : Science,
Computer Science & IT
Jurnal Algoritma merupakan jurnal yang digunakan untuk mempublikasikan hasil penelitian dalam bidang Teknologi Informasi (TI), Sistem Informasi (SI), dan Rekayasa Perangkat Lunak (RPL), Multimedia (MM), dan Ilmu Komputer (Computer Science).
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 1,026 Documents
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Klasifikasi Keaslian Uang Kertas Menggunakan Algoritma K-Nearest Neighbor dan Metode Gabor Filter Mulyani, Asri; Nurazizah, Neng Putri
Jurnal Algoritma Vol 22 No 2 (2025): Jurnal Algoritma
Publisher : Institut Teknologi Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33364/algoritma/v.22-2.2375

Abstract

Counterfeiting is currently on the rise in Indonesia. Based on Law No. 7 of 2011 concerning counterfeit rupiah currency, counterfeit currency is defined as any object whose material, size, color, image, and/or design resembles the rupiah that is made, formed, printed, duplicated, circulated, or used as a means of payment illegally. The objectives of this study are: To obtain a paper money classification model by applying the KNN and Gabor Filter algorithms. To improve the evaluation results of the paper money classification model by applying the KNN and Gabor Filter algorithms with Confusion Matrix and ROC-AUC Curve. The results of testing the banknote classification model show the effectiveness of the KNN algorithm model and the Gabor Filter method, as well as the assistance of PCA, producing the best performance with an accuracy value of 97.14%, precision of 95.72%, recall of 95.77%, f1-score of 95.74%, and specificity of 99.62%. The AUC value obtained on the ROC-AUC curve based on the test results produced a banknote classification model with an average AUC performance for all classes of 97.35%, which is classified as excellent in classifying banknotes, so that the model can be implemented into the system.
Optimalisasi Engine Optimalization On-Page untuk Meningkatkan Kinerja Situs Berita Digital Menggunakan Analisis CTR dan UX Martisa Fiorentina, Rinda; Miftahul Ashari, Wahid; Kuswanto, Jeki
Jurnal Algoritma Vol 22 No 2 (2025): Jurnal Algoritma
Publisher : Institut Teknologi Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33364/algoritma/v.22-2.2377

Abstract

This study analyzes the application of Search Engine Optimization (SEO) strategies on the Radar Jogja website to increase its visibility on search engines. With internet penetration in Indonesia reaching 78.19% in 2023, digital platforms have become an important necessity for media companies. Radar Jogja faces the challenge of competing for top positions on Google's search engine results pages (SERPs), where most users only access the first two pages. This study uses a descriptive-analytical method to evaluate key SEO elements such as titles, URLs, internal link structure, and page performance. Test results show that CTR increased from 2% to 4.75% and average position improved from 12th to 6th. In addition, dwell time increased to 2.25 minutes and bounce rate decreased, indicating a significant improvement in user experience. The results of this study contribute to content-based SEO strategies for local media to increase digital competitiveness and gain better visibility on search engines.
Evaluasi Kualitas Klaster Wilayah Rawan Bencana Menggunakan K-Means dengan Silhouette dan Elbow Method Sudrajat, Risqi; Hadiana, Asep Id; Melina, Melina
Jurnal Algoritma Vol 22 No 2 (2025): Jurnal Algoritma
Publisher : Institut Teknologi Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33364/algoritma/v.22-2.2379

Abstract

Natural disasters such as floods, earthquakes, and landslides are recurring threats in Cirebon City, West Java. This study aims to classify disaster-prone areas using the K-Means algorithm based on 1,144 incident data from Open Data Jabar. The data were grouped into three clusters, namely safe, moderate, and dangerous. Cluster quality was evaluated using the Silhouette Score and Elbow Method. The results of this study show that the model without normalization produced a score of 0.6804, reflecting good cluster separation. Conversely, the application of MinMaxScaler normalization significantly reduced the model's performance, with a score of 0.3900. The main contribution of this study is to show that data normalization can disrupt the natural pattern of risk distribution, thereby reducing the quality of clustering. Therefore, the selection of pre-processing techniques needs to be adjusted to the characteristics of local data. It is hoped that this study can be the basis for the development of a more adaptive and data-driven disaster mitigation decision support system.
Komparasi Ekstraksi Fitur Nada Gamelan Gangsa Terhadap Performa Klasifikasi Dengan LSTM Budaya, I Gede Bintang Arya; Yusadara, I Gede Putra Mas; Harsemadi, I Gede; Martha, Gede Indra Raditya; Agustino, Dedy Panji
Jurnal Algoritma Vol 22 No 2 (2025): Jurnal Algoritma
Publisher : Institut Teknologi Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33364/algoritma/v.22-2.2382

Abstract

Indonesia has a rich and diverse culture, one aspect of which is traditional musical instruments. Bali, as one of the provinces in Indonesia, has unique instruments such as the Gangsa, which is an important part of the Gamelan ensemble. The main challenge in learning traditional musical instruments is the accurate recognition and classification of notes. This study aims to classify Gangsa notes using a Long Short-Term Memory (LSTM) model based on audio feature extraction. Three feature extraction methods were used: Mel-Frequency Cepstral Coefficients (MFCC), Chroma Features, and Mel-Spectrogram. The dataset consisted of 10 tone classes recorded manually from Gangsa bars. The research stages included audio pre-processing, feature extraction, model training, and performance evaluation using accuracy, precision, recall, and confusion matrix metrics. The results show that the MFCC-based model achieved the highest accuracy of 100%, followed by Chroma Features with 98%, and Mel-Spectrogram with 88%. This study shows that the selection of appropriate audio features has a significant effect on tone classification performance. These findings contribute to the application of Artificial Intelligence (AI) in cultural preservation through digital music education.
Sistem Data Loss Prevention Untuk Deteksi dan Enkripsi pada Dokumen Menggunakan Regex dan Format Preserving Encryption Rahmawati, A Lusi Fitri; Hadiana, Asep Id; Melina
Jurnal Algoritma Vol 22 No 2 (2025): Jurnal Algoritma
Publisher : Institut Teknologi Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33364/algoritma/v.22-2.2387

Abstract

In today’s digital era, the leakage of sensitive information has become a serious threat for both individuals and organizations, especially when data is not adequately protected. To address this issue, a system is required that not only detects the presence of sensitive data but also protects it effectively. This study develops a Data Loss Prevention (DLP) system that integrates sensitive data pattern detection using regular expressions (regex) with Format-Preserving Encryption (FPE) techniques to safeguard sensitive information in digital documents. The system is designed to identify data patterns such as national ID numbers (NIK), tax identification numbers (NPWP), phone numbers, email addresses, and bank account numbers using regex, and then encrypt the detected data without altering its original format. The test data used in this research consists of synthetic datasets that resemble real-world data. The encryption process employs the FF3 algorithm with a deterministic approach tailored to each data type to maintain system compatibility. The evaluation covers detection effectiveness using precision, recall, and F1-score metrics, as well as encryption efficiency and security through processing time measurements and entropy values. The evaluation results indicate a detection accuracy of 94.1%, precision of 100%, recall of 88.8%, and an F1-score of 94.1%. The average encryption time per document is only 0.15 milliseconds, while the encryption process successfully increases the document entropy by 0.0645 bits. This system demonstrates stable and reliable performance in detecting and protecting sensitive information without disrupting data structure or operational processes.
Implementasi Algoritma XGBoost Untuk Prediksi Status Gizi Balita Berbasis Website Pangestu, Andi; Mujiyono, Sri
Jurnal Algoritma Vol 22 No 2 (2025): Jurnal Algoritma
Publisher : Institut Teknologi Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33364/algoritma/v.22-2.2390

Abstract

Malnutrition among toddlers remains a serious public health issue in Indonesia, with a stunting prevalence of 21.6% in 2022—still above the WHO standard, which sets the maximum threshold at 20%. Traditional methods for assessing nutritional status are time-consuming and prone to human error, highlighting the need for a more efficient and accurate approach. This study aims to develop a system for predicting toddler nutritional status using the XGBoost algorithm, implemented in a web-based application utilizing anthropometric data. A quantitative approach with applied research methods was used, analyzing 5,489 anthropometric records of children from RSUD DR. Gondo Suwarno during the 2017–2023 period, selected through purposive sampling. The dataset included parameters such as age, sex, height, weight, arm circumference, and head circumference of children aged 0–59 months. After data cleaning, 5,169 high-quality samples were retained and divided into 80% training and 20% testing sets with balanced class distribution. The XGBoost model was optimized using Grid Search with 3-fold cross-validation to achieve the best hyperparameter configuration. Results showed that the XGBoost model achieved an accuracy of 97.17%, precision of 97.16%, recall of 97.17%, and F1-score of 97.16% in classifying three nutritional status categories: Normal, Overnutrition, and Undernutrition. Feature importance analysis revealed that weight was the strongest predictor, contributing 42.52%, followed by age (16.79%) and height (15.49%). The system was successfully implemented in a user-friendly web application that allows the input of anthropometric data and provides real-time prediction results. This research produced an effective screening tool for early detection of toddler malnutrition, improving healthcare service efficiency and supporting government programs aimed at reducing stunting rates.
Pemantauan Daya Listrik Real-Time Menggunakan IoT untuk Efisiensi Energi Rumah Tangga Munir, Misbahul; Setiawan Wibisono, Iwan
Jurnal Algoritma Vol 22 No 2 (2025): Jurnal Algoritma
Publisher : Institut Teknologi Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33364/algoritma/v.22-2.2391

Abstract

Teknologi Internet of Things (IoT) hadir sebagai penggerak utama dalam era transformasi digital yang memungkinkan perangkat saling terhubung dan beroperasi secara otomatis. Penggunaan daya listrik yang tidak terkontrol dapat menyebabkan pemborosan energi dan peningkatan biaya operasional. Penelitian ini merancang sebuah sistem monitoring pengelolaan konsumsi daya listrik berbasis IoT (Internet of Things), sehingga memungkinkan pengguna untuk memantau tingkat konsumsi energi secara real-time. Sistem ini memanfaatkan sensor daya PZEM004T yang terhubung ke platform smartphone berbasis aplikasi Blynk melalui mikrokontroler NodeMCU ESP8266. Metode yang diterapkan dalam penelitian ini adalah Research and Development, yang mencakup tahapan perencanaan, pengembangan, serta evaluasi sistem. Hasil pengujian menunjukkan bahwa sistem mampu menurunkan konsumsi daya listrik hingga 20%, meningkatkan akurasi sensor sebesar 3%, serta menurunkan latensi transmisi data hingga 75%. Temuan ini menunjukkan bahwa sistem mampu meningkatkan kesadaran pengguna terhadap pola konsumsi energi dan mendorong perubahan perilaku ke arah yang lebih hemat energi. Selain memberikan solusi praktis untuk pengendalian energi rumah tangga, sistem ini juga menawarkan potensi pengembangan lebih lanjut, seperti integrasi kecerdasan buatan (AI) dan energi terbarukan. Penelitian ini memberikan kontribusi penting terhadap pengembangan sistem IoT di bidang efisiensi energi dengan menghadirkan pendekatan yang aplikatif, hemat biaya, serta ramah lingkungan, sekaligus memperkaya khazanah penelitian sebelumnya di bidang monitoring konsumsi energi berbasis IoT yang belum banyak mengeksplorasi integrasi sistem dengan aplikasi mobile secara langsung dan real-time.
Penerapan Association Rule Mining untuk Rekomendasi Promo Bundling dalam Sistem CRM Berbasis Online Herdian, Gentala Virgiawan; Sulianta, Fery
Jurnal Algoritma Vol 22 No 2 (2025): Jurnal Algoritma
Publisher : Institut Teknologi Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33364/algoritma/v.22-2.2398

Abstract

The online ordering system is an important strategy in improving service efficiency and customer loyalty, especially in micro businesses such as Coffee Kane. This study applies the Association Rule Mining (Apriori) algorithm within the CRISP-DM framework to identify customer purchasing patterns and design bundling promotions based on Customer Relationship Management (CRM). The data used is transaction history from the last two months. The analysis results produced a number of significant association rules, such as product combinations with the highest lift value of 31.50. These rules were implemented into the Laravel-based ordering system and automatically displayed to customers. This study shows that this data-driven approach not only improves the effectiveness of promotions but also strengthens customer engagement through an adaptive and personally relevant system.
Implementasi Metode Wavelet Transform dengan ARIMA untuk memprediksi Kebutuhan Bahan Baku Obat di PT. Seikyo Indochem: Studi Kasus Pendekatan Hybrid Time Series pada Industri Farmasi Lolowang, Juan Marten Daniel; Zakiah, Azizah
Jurnal Algoritma Vol 22 No 2 (2025): Jurnal Algoritma
Publisher : Institut Teknologi Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33364/algoritma/v.22-2.2399

Abstract

The optimal availability of pharmaceutical raw materials is a vital aspect in ensuring the continuity of production within the pharmaceutical industry. PT. Seikyo Indochem faces challenges in accurately forecasting raw material requirements due to the fluctuating and complex nature of the data. This study implements the Wavelet Transform method combined with ARIMA (Auto-Regressive Integrated Moving Average) to enhance the accuracy of demand forecasting. Wavelet Transform is utilized to decompose historical data into low- and high-frequency components, enabling a more in-depth analysis of seasonal patterns and trends. The low-frequency component is analyzed using ARIMA to predict long-term patterns, while the high-frequency component is used to capture short-term fluctuations. The results show that this hybrid approach reduces the prediction error (Mean Absolute Percentage Error) by 15 percent compared to using ARIMA alone. This model provides a more reliable predictive solution to support efficient inventory management of pharmaceutical raw materials.
Implementasi Kombinasi Metode Inferensi Forward Chaining dan Certainty Factor untuk Diagnosa Penyakit Tanaman Mawar Nuraeni, Fitri; Putri, Puput
Jurnal Algoritma Vol 22 No 2 (2025): Jurnal Algoritma
Publisher : Institut Teknologi Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33364/algoritma/v.22-2.2412

Abstract

Tanaman mawar merupakan salah satu komoditas unggulan dalam sektor agrowisata di Kabupaten Garut. Namun, budidaya tanaman ini menghadapi kendala serius akibat serangan penyakit yang dapat menurunkan kualitas dan kuantitas produksi. Selain itu, keterbatasan tenaga ahli dalam memberikan edukasi mengenai diagnosis penyakit menjadi tantangan tersendiri. Penelitian ini bertujuan untuk merancang sistem pakar diagnosa penyakit tanaman mawar dengan pendekatan metode inferensi Forward Chaining dan Certainty Factor. Sistem ini diharapkan mampu membantu petani dan pelaku agrowisata dalam mengidentifikasi penyakit secara mandiri dan akurat berdasarkan gejala yang diamati. Metode yang digunakan dalam pengembangan sistem adalah Expert System Development Life Cycle (ESDLC) yang terdiri dari tahap assessment, knowledge acquisition, design, testing, documentation, dan maintenance. Pengetahuan dalam sistem diperoleh dari pakar tanaman mawar di UPTD BBH Cisurupan serta referensi ilmiah terkait. Sistem dibangun menggunakan bahasa pemrograman PHP dan basis data MySQL, dengan desain pemodelan berbasis UML. Hasil penelitian menunjukkan bahwa sistem mampu mendiagnosa 10 jenis penyakit tanaman mawar berdasarkan 25 gejala yang diinput pengguna, dengan output berupa nama penyakit, tingkat kepercayaan diagnosis, dan saran penanganan penyakit. Validasi oleh pakar menunjukkan tingkat akurasi sistem sebesar 96%, mendekati hasil diagnosis manual pakar. Implikasi dari penelitian ini adalah bahwa sistem pakar ini dapat menjadi alat bantu dalam menjaga kualitas tanaman mawar serta mendukung pengembangan agrowisata di Garut secara berkelanjutan.

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