cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
Yosep Septiana
Contact Email
yseptiana@itg.ac.id
Phone
+6282124588750
Journal Mail Official
algoritma@itg.ac.id
Editorial Address
Jl. Mayor Syamsu No.1, Jayaraga, Kec. Tarogong Kidul, Kabupaten Garut, Jawa Barat 44151
Location
Kab. garut,
Jawa barat
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 1,150 Documents
 108   109   110   111   112 
Kombinasi Decision Tree dan Naïve Bayes dengan Explainable AI untuk Prediksi Dropout Agung Wibowo; Kustiyono; Eko Nur Hermansyah
Jurnal Algoritma Vol 23 No 1 (2026): Jurnal Algoritma
Publisher : Institut Teknologi Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33364/algoritma/v.23-1.3367

Abstract

Predicting student dropout risk is crucial for supporting early intervention and accountable academic decision-making. This study proposes a multi-class classification (Dropout, Enrolled, Graduate) using voting (Naïve Bayes and Decision Tree) and Explainable AI to enhance transparency. The dataset consists of 4,424 records with 36 features. Evaluation was conducted using k-fold stratified cross-validation (k=10) and the F1-macro metric. The results show that model performance is relatively close and stable at k=10, so model selection must consider the trade-off between performance and interpretability. The main contribution of this research is a web-based early warning DSS prototype that integrates Voting (NB+DT) with an XAI module (SHAP–LIME) so that predictions can be explained, audited, and followed up with academic intervention recommendations.
Penerapan Random Forest dan XGBoost untuk Analisis Sentimen pada Ulasan Aplikasi M-Pajak Cherliana; M. Rudi Sanjaya; Dwi Rosa Indah; Dedy Kurniawan
Jurnal Algoritma Vol 23 No 1 (2026): Jurnal Algoritma
Publisher : Institut Teknologi Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33364/algoritma/v.23-1.3370

Abstract

This study aims to evaluate the quality of digital tax services by analyzing the sentiment expressed in user reviews of the M-Pajak app. A dataset of 6,829 reviews was classified into negative, neutral, and positive sentiment, and the study tested the performance of the Random Forest and XGBoost algorithms. Although the test results showed high accuracy rates of 85.21% in the hold-out validation scheme and 90.14% in stratified k-fold cross-validation, an in-depth evaluation using a confusion matrix revealed significant model bias toward the majority class. Key findings indicate that these accuracy figures are misleading because both models completely failed to classify the neutral class, yielding extremely low F1-scores (0.00–0.11). This phenomenon confirms that the primary issue lies not in algorithm selection, but in the extreme data distribution imbalance and the ambiguity of rating-based labeling. The scientific contribution of this research lies in demonstrating that the evaluation of sentiment classification systems must go beyond conventional accuracy metrics. By prioritizing performance stability across each class, the resulting system is expected to provide fairer and more objective evaluation results for public data.
Analisis Sentimen Ulasan Pengguna Pada Aplikasi m.tix – XXI Di Google Play Store Menggunakan Metode Decision Tree Dan Support Vector Machine (SVM) Rahma Ardhia Cahyani; M. Rudi Sanjaya; Dwi Rosa Indah; Dedy Kurniawan
Jurnal Algoritma Vol 23 No 1 (2026): Jurnal Algoritma
Publisher : Institut Teknologi Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33364/algoritma/v.23-1.3371

Abstract

This study aims to analyze review sentiment and compare the performance of the Decision Tree and Support Vector Machine (SVM) algorithms on a dataset of 14,000 reviews from the m.tix – XXI app, which were classified as 57.4% positive, 33.8% negative, and 8.8% neutral. Through the pre-processing stage, 200 data points were deemed invalid, leaving 13,800 data points suitable for analysis. The dataset was then split into two categories: 80% training data (11,040 reviews) and 20% testing data (2,760 reviews), to support more accurate model performance measurement. The main contribution of this study lies in identifying the advantages of SVM in handling review data, with evaluation results showing that SVM achieved an accuracy of 86%. This is evidenced by the significant superiority of SVM’s F1-score across all categories, particularly for positive sentiment (0.93), negative sentiment (0.80), and neutral sentiment (0.08) compared to the Decision Tree’s accuracy of 83%, with F1-scores of 0.92 for positive sentiment, 0.73 for negative sentiment, and 0.03 for neutral sentiment. This research can be utilized by m.tix-XXI management as a foundation for evaluating and improving the quality of the m.tix-XXI application’s services.
Integrasi Express.js dan LLM dalam Pembuatan Soal Otomatis untuk Latihan Anak Disleksia Stevan Andreas; Arni Muarifah Amri; Muhammad Asthi Seta Ari Yuwana; Michael Angello Qadosy Riyadi; Siti Nafiatul Fauziah
Jurnal Algoritma Vol 23 No 1 (2026): Jurnal Algoritma
Publisher : Institut Teknologi Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33364/algoritma/v.23-1.3372

Abstract

Dyslexia is a neurological disorder that affects a child’s ability to read and write, resulting in low literacy levels despite normal intelligence. This study aims to develop a web-based learning system that integrates a Large Language Model and is capable of generating automated practice questions tailored to the needs of children with dyslexia. The system development in this study utilizes a RESTful API architecture using Express.js, MongoDB, and Ollama. The development model employs the ADDIE framework with an Agile approach, while testing was conducted using black-box testing, white-box testing, usability testing, and performance testing. The test results indicate that the system is capable of generating automated practice questions tailored to individual needs, with functionalities operating effectively and a positive user experience. In terms of performance, the system achieved a 100% success rate with 6 concurrent users. However, this study is still limited to a small-scale pilot with low user load, so further optimization is needed for broader implementation. Overall, the integration of Express.js and a local LLM has proven effective in providing a fast, secure, and adaptive learning solution for children with dyslexia.
Analisis Sentimen Ulasan Pengguna Pada Aplikasi M2U ID Menggunakan Metode Random Forest dan Support Vector Machine (SVM) Anadya Nisrina Salsabila; M. Rudi Sanjaya; Ali Ibrahim; Endang Lestari Ruskan
Jurnal Algoritma Vol 23 No 1 (2026): Jurnal Algoritma
Publisher : Institut Teknologi Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33364/algoritma/v.23-1.3375

Abstract

The M2U ID app is the primary digital banking platform of PT Bank Maybank Indonesia Tbk, serving as a crucial tool for supporting customers’ online transactions. The objective of this study is to analyze user review sentiment on the Google Play Store by comparing the performance of the Random Forest and Support Vector Machine (SVM) algorithms. A total of 16,270 review data points were collected via web scraping and processed through preprocessing stages and feature extraction using N-Gram-based TF-IDF with Chi-Square feature selection using the SelectKBest approach. Given the significant imbalance in data distribution, this study applied class weighting techniques as well as hyperparameter optimization using Grid Search and 5-Fold Cross-Validation. Testing on 3,254 test data points indicated that SVM performed more optimally with an accuracy rate of 82% and an F1-Macro score of 0.6344, compared to Random Forest, which yielded an accuracy of 73% and an F1-Macro score of 0.5832. The main contribution of this study is an in-depth analysis of classification errors in the minority (neutral) class, which has a low F1-score (0.16–0.17). The results of the error analysis show that the model’s limitations are caused by the ambiguity of technical features and the overlap of vocabulary in reviews with minimal emotional content.
Analisis Sentimen Ulasan Pengguna Pada Aplikasi Kitabisa: Donasi & Zakat Menggunakan Metode Support Vector Machine (SVM) dan Naive Bayes Nabilah Putri Maharani; M. Rudi Sanjaya; Ali Ibrahim; M. Husni Syahbani
Jurnal Algoritma Vol 23 No 1 (2026): Jurnal Algoritma
Publisher : Institut Teknologi Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33364/algoritma/v.23-1.3376

Abstract

The rapid advancement of digital technology has spurred the emergence of online philanthropy platforms like Kitabisa, which collect a large volume of user reviews. Reviews on the Google Play Store reflect both satisfaction levels and service issues, but their unstructured nature makes manual analysis difficult. This study evaluates user sentiment on the Kitabisa platform by comparing the Support Vector Machine (SVM) and Naive Bayes models. A dataset of 11,887 reviews was processed through preprocessing and word weighting using the TF-IDF approach. The evaluation results show that the Support Vector Machine outperformed Naive Bayes with an accuracy of 84.05% and an F1-score of 0.93, while Naive Bayes achieved an accuracy of 81.73% and an F1-score of 0.92. Theoretically, this study reinforces the literature regarding the superiority of Support Vector Machines for unstructured text data. Additionally, the results of this research produce an automated evaluation framework that can be used by application developers as a basis for improving service quality in accordance with user perceptions accurately.
Sistem Informasi Reminder Payment kepada Penyewa Apartemen melalui Kanal Informasi Steven Winata; Francka Sakti Lee
Jurnal Algoritma Vol 23 No 1 (2026): Jurnal Algoritma
Publisher : Institut Teknologi Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33364/algoritma/v.23-1.3377

Abstract

The manual management of rent payments often leads to delays in sending billing information and difficulties in monitoring tenants’ payment status. This study designs a Payment Reminder Information System for apartment tenants via a web-based information channel, assisting in the monitoring of bills and payments and providing payment reminders in a more structured manner, using the Waterfall method, which includes the stages of planning, analysis, design, implementation, and testing. Unified Modeling Language (UML) was used in the system’s design. The developed system offers features for invoice management, payment information delivery, payment proof upload, payment validation, and the sending of payment reminders via channels such as WhatsApp, email, and a web-based information system. The research results indicate that the system can help administrators monitor tenant payments in a more structured manner and facilitate the delivery of payment information, thereby reducing payment delays.
Analisis Pengelompokan Desa Berdasarkan Indikator Sosial Ekonomi Menggunakan Metode K-Means Sigit Nugraha; Fauriatun Helmiah; Abdul Karim Syahputra
Jurnal Algoritma Vol 23 No 1 (2026): Jurnal Algoritma
Publisher : Institut Teknologi Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33364/algoritma/v.23-1.3378

Abstract

This study aims to cluster villages in Rahuning Subdistrict, Asahan Regency, based on socioeconomic indicators to support targeted development planning. The data used covers ten villages with four main criteria that have been normalized into numerical values. The method used is K-Means Clustering with three clusters (K=3). The analysis process includes determining the initial centroids, calculating distances using Euclidean Distance, and iterating until convergence is achieved. The results of the study show the formation of three groups: the high cluster, consisting of Rahuning Village, Rahuning III Village, Perbaungan Village, and Aek Songsongan Village; the medium cluster, consisting of Rahuning I Village, Batu Anam Village, Gunung Melayu Village, and Sei Piring Village; and the low cluster, consisting of Rahuning II Village and Air Batu Village. An evaluation using the Silhouette Score method yielded a value of 0.57, indicating that the quality of the clustering falls into the “fairly good” category with relatively clear separation between clusters. This study contributes a data-driven approach to identifying socioeconomic disparities among village areas, thereby serving as a foundation for local governments to formulate more effective, targeted, and equitable development policies.
Implementasi Association Rule Mining Menggunakan Algoritma Apriori Untuk Rekomendasi Cross-Selling Produk Ritel Septianingsih; Angga Bayu Santoso
Jurnal Algoritma Vol 23 No 1 (2026): Jurnal Algoritma
Publisher : Institut Teknologi Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33364/algoritma/v.23-1.3381

Abstract

The development of digital transactions in the retail industry has generated large volumes of data containing valuable information regarding consumer purchasing patterns. This study aims to identify association rules among products using the association rule mining method with the Apriori algorithm to support cross-selling strategies and product arrangement optimization. The dataset consists of 3,898 transactions with 167 unique products and exhibits sparse market basket characteristics with a density of 5.95%, indicating that most product combinations rarely occur together. The research employs the CRISP-DM framework, which includes data understanding, data preparation, modeling, and evaluation stages. In the modeling stage, the Apriori algorithm was applied with a minimum support threshold of 0.01 and a minimum confidence threshold of 0.40, resulting in 3,016 frequent itemsets and 3,398 association rules. After filtering using the criteria of lift > 1.0 and confidence >= 0.40, a total of 2,228 rules met the quality standards. Validation using the Chi-Square test showed that 74% of the rules were statistically significant at a 95% confidence level (p-value < 0.05). One of the best-performing rules indicates that the purchase of Other Vegetables, Rolls/Buns, and Yogurt has a strong relationship with the purchase of Whole Milk, with a support value of 0.0344, confidence of 65.69%, and lift of 1.434. This study contributes through the implementation of multi-metric evaluation and statistical validation to improve the reliability of association rules. The findings can be utilized by the retail industry for shelf arrangement strategies, bundling promotions, and data-driven cross-selling optimization.
Web-Based CRM pada Mr.Syafii Bakery Terhadap Peningkatan Pelayanan dan Retensi Pelanggan Wiwin Dila; Novica Irawati; Maulana Dwi Sena
Jurnal Algoritma Vol 23 No 1 (2026): Jurnal Algoritma
Publisher : Institut Teknologi Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33364/algoritma/v.23-1.3384

Abstract

Advances in information technology have encouraged food business operators to adopt digital systems to improve customer service and retention. However, Mr. Syafii Bakery still faces challenges in managing customer data and transactions, which are currently handled manually. This study aims to develop a web-based Customer Relationship Management (CRM) system to improve service and customer retention. The method used is systems engineering with the System Development Life Cycle (SDLC) Waterfall approach. System evaluation was conducted using Black Box Testing and the System Usability Scale (SUS). The results show that the system functions well and achieved a SUS score of 78 (good category). Additionally, customer retention increased from 37.5% to 52.3%, or by approximately 14.8%, following the system’s implementation. The contribution of this research lies in the development of an integrated CRM system that combines customer data management, interactions, and promotional strategies into a single platform to support improved service and customer retention for culinary SMEs.

Page 110 of 115 | Total Record : 1150

 108   109   110   111   112 

Filter by Year

2012 2026


Reset
Filter By Issues
All Issue Vol 23 No 1 (2026): Jurnal Algoritma Vol 22 No 2 (2025): Jurnal Algoritma Vol 22 No 1 (2025): Jurnal Algoritma Vol 21 No 2 (2024): Jurnal Algoritma Vol 21 No 1 (2024): Jurnal Algoritma Vol 20 No 2 (2023): Jurnal Algoritma Vol 20 No 1 (2023): Jurnal Algoritma Vol 19 No 2 (2022): Jurnal Algoritma Vol 19 No 1 (2022): Jurnal Algoritma Vol 18 No 2 (2021): Jurnal Algoritma Vol 18 No 1 (2021): Jurnal Algoritma Vol 17 No 2 (2020): Jurnal Algoritma Vol 17 No 1 (2020): Jurnal Algoritma Vol 16 No 2 (2019): Jurnal Algoritma Vol 16 No 1 (2019): Jurnal Algoritma Vol 15 No 2 (2018): Jurnal Algoritma Vol 15 No 1 (2018): Jurnal Algoritma Vol 14 No 2 (2017): Jurnal Algoritma Vol 14 No 1 (2017): Jurnal Algoritma Vol 13 No 2 (2016): Jurnal Algoritma Vol 13 No 1 (2016): Jurnal Algoritma Vol 12 No 2 (2015): Jurnal Algoritma Vol 12 No 1 (2015): Jurnal Algoritma Vol 11 No 2 (2014): Jurnal Algoritma Vol 11 No 1 (2014): Jurnal Algoritma Vol 10 No 2 (2013): Jurnal Algoritma Vol 10 No 1 (2013): Jurnal Algoritma Vol 9 No 2 (2012): Jurnal Algoritma Vol 9 No 1 (2012): Jurnal Algoritma More Issue