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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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Rancang Bangun Web Pengaduan Masalah Sampah dan Edukasi Berbasis Gamifikasi Struktural Septiana, Yosep; Kilin, Mochammad Agus Dharma
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.22-2.3045

Abstract

The waste problem in Indonesia, particularly in urban areas, remains a complex and persistent environmental issue. Garut Regency faces serious challenges in waste management, with an average daily production of around 230 tons, of which only 30 percent can be managed optimally. Low public awareness and limited participation in reporting waste-related issues remain major obstacles to effective handling. This study designs and develops a web-based waste complaint and education platform incorporating structural gamification as an effort to increase community engagement. The system design applies the Rational Unified Process (RUP), which involves four phases: Inception, Elaboration, Construction, and Transition. The application was built using the Laravel framework and MySQL as its database, featuring location-based reporting, follow-up management by officers, and environmental education supported by structural gamification elements such as points, levels, badges, and rewards. Testing results show a user satisfaction rate of 90.45 percent in the “strongly agree” category, indicating that the system operates well and meets user needs. Beyond serving as a reporting tool, the system also functions as an educational platform that enhances motivation and community involvement through the application of structural gamification elements for sustainable waste management.
Perbandingan Kinerja Algoritma Machine Learning Deteksi Malware dengan Z-Score Normalization Hasil Terbaik pada Random Forest Gilang Ramadhan, Zaka; Miftahul Ashari, Wahid; Koprawi, Muhammad
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.22-2.3077

Abstract

Malware detection is a major challenge in the world of cybersecurity, especially with the increasing complexity and variety of attacks. Traditional approaches are often unable to identify these new threats, making machine learning (ML) an effective solution. The purpose of this study is to compare the performance of three machine learning algorithms, namely Random Forest, XGBoost, and Support Vector Machine (SVM), in detecting malware in the SOMLAP dataset consisting of Windows executable files. Data processing, the use of the SMOTE technique for class imbalance, and assessment using metrics such as accuracy, precision, recall, and F1 score are all part of the research methodology. This study also applies Z-Score Normalization to reduce the influence of extreme values in the data, which helps the model handle data with different scales. The results show that Random Forest has the best performance with an accuracy of 99.16%, followed by XGBoost and SVM. Random Forest excels in the balance between precision, recall, and accuracy, making it the most effective algorithm for detecting malware. This study suggests further algorithm development using ensemble techniques and other optimizations to improve malware detection accuracy in the future.
Optimasi Penjadwalan Rapat Berbasis Web Untuk Mengurangi Konflik Jadwal Menggunakan Kombinasi Algoritma Greedy dan Decision Tree Ahmad, Tjoet Muty; Lamasitudju, Chairunnisa Ar.
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.22-2.3080

Abstract

The manual meeting scheduling process has a high potential for scheduling conflicts. These conflicts include delays in information, meeting room clashes, or meeting time clashes. These problems are caused by the absence of a system and visualization for managing meeting schedules. To streamline the scheduling process, a website-based meeting scheduling information system was developed for activities that are carried out routinely with high frequency, using a combination of two algorithms that can overcome these problems. The Greedy algorithm is used to detect conflicts, and the rule-based Decision Tree algorithm is used to provide alternative time or room suggestions when schedule conflicts occur. The results of blackbox testing and usability testing prove that the application of these algorithms makes the system more effective and provides the right workflow for this system. This research contributes to the development of an effective meeting scheduling system and integrates two algorithms as a new solution in managing the scheduling process.
Peran Digital Linguistic dalam Optimalisasi Promosi E-Commerce UMKM Kuliner: Pendekatan Analisis Wacana Digital pada Coffee Shop di Garut Qoriah, Desi; Muhammad Akbar, Dioka; Nurmalasari, Mutiana; Hilman Firmansyah, Muhamad; Septiana, Yosep
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.22-2.3082

Abstract

In the era of the digital economy, language plays a strategic role in enhancing the visibility and success of Micro, Small, and Medium Enterprises (MSMEs), particularly in the culinary sector of coffee shops. However, many culinary MSME owners in rural areas such as Garut, West Java, still lack awareness and skills in using language effectively for digital marketing. This study aims to explore the concept of Digital Linguistic Capital by examining how language use on digital platforms influences customer engagement and business performance. This research employs a qualitative method using semi-structured interviews, discourse analysis of online content, and passive observation of several cafés and coffee shops in Garut. The findings indicate that most MSME owners intuitively use trendy and persuasive language, but they do not yet possess formal knowledge of SEO, brand linguistics, or stylistic consistency. MSMEs with strong linguistic strategies—such as emotional diction, persuasive expressions, and a consistent brand voice—demonstrate higher levels of customer interaction and digital visibility. In contrast, MSMEs with inconsistent language use experience low online engagement. The study concludes that digital linguistic capital is an essential yet underdeveloped asset. Strengthening digital communication skills and persuasive copywriting can enhance competitiveness as well as the cultural representation of coffee shop MSMEs in the digital marketplace. This research contributes to the development of a linguistics-based digital communication model for MSMEs.
Penerapan Algoritma K-Nearest Neighbor untuk Analisis Sentimen Ulasan Produk Elektronik pada Platform E-Commerce Octavia, Noer Fotin; Berlilana
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.22-2.3083

Abstract

The study aims to evaluate user sentiment toward Samsung products on the Tokopedia e-commerce platform using the K-Nearest Neighbor (KNN) algorithm. E-commerce plays a crucial role in modern trade, where consumer reviews provide insights into their level of satisfaction. The KNN method is applied to classify reviews into positive, negative, and neutral sentiment categories based on the collected review data. The research procedure includes collecting 2,200 Samsung product reviews from Tokopedia, followed by preprocessing steps such as tokenization, normalization, stopword removal, stemming, and data cleaning. The data is then weighted using TF-IDF before being classified with KNN. The results show that the KNN model achieved the highest accuracy of 91.35 percent at K=3, while K=5 yielded 90.38 percent and K=7 reached 90.14 percent. The model performed exceptionally well in detecting positive sentiment, with 100 percent precision and recall and an F1-score of 96 percent, although its performance was less optimal for negative and neutral sentiments. Overall, KNN proved effective in analyzing sentiment in Tokopedia product reviews, demonstrating higher accuracy than other methods used in previous studies and showing the capability to capture local patterns within a single-brand dataset. Nevertheless, further methodological improvements and enhanced data processing are needed to achieve more precise and balanced performance across all sentiment categories.
Tinjauan Literatur: Dampak Transformasi Digital terhadap Pengambilan Keputusan di Lembaga Pemerintahan Ma'sum, Aziz; Purwanto, Heri; Nugraha, Rikky Wisnu
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.22-2.3091

Abstract

The development of information and communication technology (ICT) has fundamentally changed the way government agencies provide services and make decisions. Digital transformation has now become a strategic necessity for realizing efficient, transparent, and adaptive governance in line with the changing times. This study aims to analyze the impact of digital transformation on decision-making in government institutions through a literature study approach. The method used is a Systematic Literature Review (SLR) by examining 10 relevant national scientific articles published in the period 2020–2025. The analysis was conducted qualitatively using content analysis techniques to identify the main themes, benefits, and challenges of digitization on the public decision-making process. The results show that digital transformation improves bureaucratic efficiency, strengthens transparency, and expands public participation in policy formulation. The use of technologies such as e-Government, artificial intelligence (AI), and big data analytics enables faster, more accurate, and evidence-based decision-making. However, its success is highly dependent on the readiness of human resources, digital infrastructure, and data security. In conclusion, digital transformation is not merely a technological innovation, but also a key driver towards a smart, transparent, and data-driven government system.
Komparasi Model IndoBERT dan IndoGPT untuk Analisis Sentimen pada Produk E-commerce Moch. Habibi, Fajar; Gelar Guntara, Rangga; Nuryadin, Asep
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.22-2.3070

Abstract

The growth of the e-commerce industry in Indonesia has generated a huge volume of user comments, which contain important opinions for business people and NLP researchers. Automatic processing of comments through deep learning models poses a challenge, especially in the context of sentiment classification. This study aims to compare the performance of two transformer-based models, namely IndoBERT and IndoGPT, in sentiment analysis tasks on Indonesian-language e-commerce beauty product comments. The method used is quantitative comparative with testing two hyperparameter scenarios (variations in batch size, learning rate, and epoch), and using evaluation metrics in the form of precision, recall, f1-score, and accuracy. The main contribution of this research is to present a head-to-head evaluation of IndoBERT and IndoGPT on an informal Indonesian-language e-commerce dataset, a context that has not been directly tested in previous literature. The results of the experiment show that IndoBERT consistently provides superior results compared to IndoGPT on all sentiment labels. The highest accuracy was achieved by IndoBERT at 83 percent, surpassing IndoGPT, which only reached 80 percent. These findings indicate that IndoBERT is more effective in handling class imbalance and language complexity in product reviews, making it more suitable for application in automated opinion analysis systems on e-commerce platforms.
Implementasi Algoritma A dalam Sistem Informasi Geografis Pemetaan Rute Rumah Sakit Berbasis Web Menggunakan Leaflet Adyatma Yoransa, Vito; Witriyono, Harry
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.22-2.2397

Abstract

Finding the fastest route to health care facilities is one of the most important needs in emergency situations. This study aims to develop a web-based Geographic Information System (GIS) that can determine the fastest route to the nearest hospital in Bengkulu Province. This system uses the A* algorithm to calculate the shortest route and Leaflet.js to display an interactive map based on OpenStreetMap. The system development method used is the Rational Unified Process (RUP), which consists of four phases, namely inception, elaboration, construction, and transition. The results of the study show that the system is capable of displaying hospital locations, providing a visualization of the shortest route map, and displaying interactive route navigation instructions. Testing using five black-box test scenarios shows that all key features work as expected. The contribution of this study is to provide an interactive WebGIS solution for medical route search with high efficiency that has not been widely implemented in local navigation systems.
Implementation of Long Short-Term Memory Algorithms on Cryptocurrency Price Prediction with High Accuracy on Volatile Assets Nursiana Zasqia, Andi Nirina; Laila, Rahmah; Trezandy Lapatta, Nouval; Yazdi Pusadan, Mohammad; Santi, Dessy; Wirdayanti
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.22-2.2422

Abstract

Cryptocurrencies have emerged as one of the most popular digital assets, characterized by high volatility, which presents a significant challenge in forecasting their price movements accurately. This study aims to implement the Long Short-Term Memory (LSTM) algorithm to predict the prices of selected cryptocurrencies, including Bitcoin (BTC), Binance Coin (BNB), Ethereum (ETH), Dogecoin (DOGE), Solana (SOL), and Shiba Inu (SHIB). The LSTM model is trained using the Adam optimizer and employs early stopping to mitigate overfitting. Model performance is evaluated using Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and the coefficient of determination (R²). The results indicate that the LSTM model achieves strong predictive accuracy for relatively low-volatility assets such as Dogecoin and Solana, with R² scores of 0.9795 and 0.9523, respectively. In contrast, its performance declines when applied to highly volatile assets like Bitcoin and Binance Coin. The findings also suggest that LSTM performs best in short-to-medium-term forecasts (7 to 30 days), but shows limitations in long-term predictions. This study contributes to the field by demonstrating the applicability of LSTM in financial forecasting and highlighting its strengths and constraints across different volatility profiles. Practically, the findings can assist traders and financial analysts in making data-driven decisions by applying LSTM models for more reliable short-term predictions, while emphasizing the need to integrate external market factors to enhance long-term forecast accuracy.
Model Optimalisasi Seleksi Penerimaan Beasiswa Perguruan Tinggi Swasta Menggunakan K-Means dan TOPSIS Al-akbari, Munawir Fikri; Munandar, Muhamad Arief; Triyono, Gandung
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.22-2.2531

Abstract

Ensuring a fair and well-targeted scholarship distribution process remains one of the major challenges faced by private universities. In many cases, scholarship recipient selection is carried out subjectively and lacks support from a systematic approach. This study proposes a hybrid method using K-Means Clustering and the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) to optimize the scholarship selection process. Student data covering academic aspects (GPA), socio-economic factors (parental income and occupation, family dependents), and non-academic components (achievements and organizational activity) were analyzed using the K-Means algorithm to group students with similar characteristics. Silhouette Score validation produced four optimal clusters with a score of 0.1683. Subsequently, the TOPSIS method was applied to rank the clusters based on predetermined criteria. The results show that Cluster 4 achieved the highest ranking with a score of 0.7853, followed by Cluster 3 (0.6359), Cluster 1 (0.6014), and Cluster 2 (0.5807). Attribute contribution analysis revealed that GPA is the dominant factor (48.61%–52.26%), followed by parental income (16.15%–19.59%) and family dependents (11.36%–12.09%). The developed model successfully provides an objective foundation for allocating scholarship quotas based on student group characteristics. This study contributes to the development of a more transparent and accountable scholarship selection system.

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