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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 1,145 Documents
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Klasifikasi Emosi pada Kalimat Bahasa Indonesia Menggunakan Transformer Rey Aji Darusalam; Ridwan Ilyas; Fatan Kasyidi
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.23-1.2457

Abstract

Text-based emotion classification is a challenging task in natural language processing, particularly for Indonesian, which has flexible sentence structures and varied informal language usage. This study aims to develop an emotion classification model for Indonesian sentences using a Transformer-based approach, specifically leveraging the IndoBERT model. The dataset employed is an adaptation of the SemEval 2025 benchmark, translated into Indonesian, and includes five emotion categories: anger, fear, joy, sadness, and surprise. The research process involves data preprocessing through IndoBERT tokenization, padding, label encoding, and the implementation of three data balancing strategies: Synthetic Minority Oversampling Technique (SMOTE), class weighting, and random oversampling. Fine-tuning of IndoBERT is conducted using the [CLS] token representation as the main feature for classification. Evaluation is performed for all balancing approaches using accuracy, precision, recall, and F1-score metrics. Results indicate that SMOTE achieves the highest accuracy at 58.31%, while the class weight approach yields the highest recall at 48.22%. Random oversampling demonstrates relatively stable performance across all metrics. The surprise emotion category is the most challenging class to recognize across all three approaches, highlighting the need for improvements in data and model design. Additionally, all models exhibit mild overfitting, as evidenced by performance differences between training and validation datasets. These findings demonstrate that IndoBERT can be effectively used for emotion classification in Indonesian sentences, with performance significantly influenced by the data balancing strategy employed. This study provides an initial insight valuable for the development of emotion classification systems that account for the context and characteristics of the Indonesian language.
Analisis Kinerja Perhitungan Jarak Hamming pada Model Klasifikasi Penyakit Paru-Paru Menggunakan Algoritma K-Nearest Neighbor (KNN) Fitri Nuraeni; Siti Luthfiah Khoirotunnisa; Ridwan Setiawan; Muhammad Rikza Nashrulloh
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.23-1.2578

Abstract

Lung diseases are among the leading causes of death worldwide and require early, accurate diagnosis to minimize the risk of complications. In the digital era, developing artificial intelligence–based classification models has become a potential solution to support the diagnostic process, particularly for categorical data that represent symptoms such as coughing, shortness of breath, and smoking history. This study proposes a lung disease classification model using the K-Nearest Neighbor (K-NN) algorithm with a simple categorical distance approach, namely the Hamming distance. The dataset used is imbalanced; therefore, data balancing was performed using the random oversampling method. Model evaluation was carried out using two schemes—data splitting and 10-fold cross-validation—by testing multiple values of parameter k. The best results were obtained at k = 7 with an accuracy of 94.58%, precision of 95.25%, recall of 94.39%, and an F1-score of 94.53%. These findings demonstrate that the combination of the K-NN algorithm, Hamming distance, and oversampling can produce high and stable classification performance for categorical datasets in lung disease prediction.
Integrasi Flowise AI dan LLM Gemini Untuk Chatbot Wisata Berbasis Website dengan Dukungan API Fitri Nuraeni; Muhammad Ghopur; Rinda Cahyana; Leni Fitriani
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.23-1.2590

Abstract

Tourism destination information is often scattered across various unintegrated sources, making it difficult for visitors to obtain quick and accurate answers. This problem can be overcome by implementing chatbot technology that is capable of providing automated responses based on verified data. This study aims to implement a tourism information service chatbot using the open-source Flowise AI platform integrated with Google's Gemini Large Language Model (LLM). The system development method uses a waterfall approach, including analysis, design, implementation, testing, deployment, and maintenance. Tourism information data is converted into vector representations through GoogleGenerativeAI Embeddings and stored in a vector store. The question and answer process is carried out using Conversational Retrieval QA Chain to generate relevant responses based on source documents. Testing results show that the chatbot is capable of providing fast, accurate, and appropriate answers based on available data, covering historical information, facilities, rates, cuisine, routes, accommodations, and tourism regulations. The contribution of this research is to provide an AI-based digital solution that facilitates access to tourism information, enhances user experience, and supports efficient tourism destination promotion.
Optimasi Klasifikasi Citra Kanker Payudara Dengan Kombinasi Ekstraksi Fitur Gabor Dan Deep Learning CNN Raditya Pratika Ramadhan; Sri Rahayu
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.23-1.2648

Abstract

Early detection of breast cancer is crucial to improve survival rates and reduce mortality. However, the process of classifying histopathological images often faces challenges due to limitations in visual features and data imbalance between classes. This study aims to improve the accuracy of breast cancer image classification by combining texture feature extraction using Gabor Filters and a Convolutional Neural Network (CNN) classification model. The dataset consists of 50×50 pixel images extracted using Gabor filters with varying orientations and frequencies to capture tissue texture characteristics. To address class imbalance, the Synthetic Minority Over-sampling Technique (SMOTE) was used prior to model training. The CNN model was trained using a batch size of 32 and optimized using the Adam algorithm. The evaluation results show that the model is capable of achieving an accuracy of 97.06%, precision of 81.25%, recall of 86.67%, and an F1-score of 83.87%. The high recall and F1-score values indicate the model's ability to detect cancer cases effectively and evenly. Thus, the combination of Gabor Filter, SMOTE, and CNN has great potential to be implemented as a breast cancer diagnosis support system based on histopathological images.
Analisis Sentimen Terhadap Pariwisata Di Kabupaten Garut Menggunakan Pendekatan Machine Learning Rinda Cahyana; Dea Puspitasari
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.23-1.2705

Abstract

Pariwisata merupakan sektor penting yang memberikan kontribusi besar terhadap perekonomian daerah. Kabupaten Garut sebagai salah satu daerah tujuan wisata di Jawa Barat mengalami penurunan jumlah kunjungan wisatawan, yang mendorong perlunya evaluasi berbasis data mengenai persepsi wisatawan. Penelitian ini bertujuan untuk mengetahui sentimen wisatawan terhadap pariwisata di Kabupaten Garut serta mengevaluasi performa algoritma Naïve Bayes dan Support Vector Machine (SVM) dalam mengklasifikasikan sentimen tersebut. Metode yang digunakan mengacu pada tahapan CRISP-DM, meliputi pengumpulan data ulasan wisata dari platform Google Maps, dilanjutkan dengan tahap preprocessing teks seperti pembersihan data, normalisasi, tokenisasi, stopword removal, dan stemming. Data dibagi menjadi data latih dan data uji dengan rasio 80:20. Kedua algoritma, Naïve Bayes dan SVM, diterapkan dan dievaluasi menggunakan confusion matrix dan classification report. Hasil analisis menunjukkan bahwa dari total data ulasan yang dianalisis, sebanyak 81.2% merupakan sentimen positif, dan 18.8% merupakan sentimen negatif. Pada tahap evaluasi model, algoritma Naïve Bayes memperoleh akurasi sebesar 91.62%, precision sebesar 94.69%, recall sebesar 88.19%, dan F1-score sebesar 91.32%. Sementara itu, algoritma SVM menunjukkan performa lebih tinggi dengan akurasi sebesar 95.74%, precision sebesar 99.12%, recall sebesar 92.31%, dan F1-score sebesar 95.59%. Berdasarkan hasil tersebut, dapat disimpulkan bahwa mayoritas ulasan wisatawan terhadap pariwisata di Kabupaten Garut bernada positif, dan algoritma SVM terbukti memberikan performa klasifikasi sentimen yang lebih baik dibandingkan Naïve Bayes.
Redesain UI dan UX Website Suara Garut: Pendekatan Design Thinking Untuk Meningkatkan Kenyamanan Akses Berita Lokal Fauzan Romi Juliansyah; Ridwan Setiawan
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.23-1.2735

Abstract

This study aims to redesign the User Interface and User Experience on the Suara Garut website using the Design Thinking approach. The background of this study is the challenges faced by the existing website, such as unattractive visual design, confusing navigation, and suboptimal information structure, which impact user comfort. The design process was carried out through five systematic stages, namely Empathize, Define, Ideate, Prototype, and Test. Data collection was conducted through interviews with the management and distribution of questionnaires to 50 users to identify their needs. The design solution was realized in the form of an interactive high-fidelity prototype using Figma. The evaluation was conducted using the System Usability Scale (SUS), involving 15 respondents. The test results showed an average score of 83.4, which falls into the “Excellent” category with an “Acceptable” level of acceptance, indicating that the interface design was very well received. This study successfully produced a design that addresses the real needs of Suara Garut website users.
Rancang Bangun Virtual Tour 360° Yayasan Ar-Robbaniyah Berbasis Website Asep Deddy Supriatna; Dika
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.23-1.2787

Abstract

The Ar-Robbaniyah Foundation still uses banners as a medium of introduction, which are less effective in conveying visual information. This study aims to design and build a web-based Virtual Tour as a medium of information for the foundation using the Multimedia Development Life Cycle (MDLC) method, which consists of six stages: concept, design, material collecting, assembly, testing, and distribution. The results of the study show that the Virtual Tour application was successfully built with interactive 360° panoramas, complete with information on each facility, and can be easily accessed via a web device without the need for special tools. Testing using the black-box alpha testing method showed that all functions worked as planned, and users felt more assisted in understanding the facilities and atmosphere of the foundation virtually. The main finding of this research is the creation of innovative information media that is more effective than conventional methods such as banners or brochures. The evaluation of the results shows that the Virtual Tour provides an interactive experience, increases the appeal of the foundation, and expands the reach of information without geographical limitations. The significance of this research solution lies in its contribution to supporting the transparency and information of the foundation in a modern way, as well as being a relevant alternative digital communication strategy for educational institutions.
Penerapan Virtual Reality Tour sebagai Media Informasi pada Yayasan Pendidikan Yoga Handoko Agustin; Siti Nurendah
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.23-1.2796

Abstract

The development of information technology has driven innovation in information delivery, one of which is through Virtual Reality Tours (VR Tours). This study aims to implement a VR Tour as an information medium at the Assalafiyah II Educational Foundation in Cibiuk, Garut, so that the public, prospective students, and parents can explore the foundation’s environment interactively without having to visit the location directly. The method used is the Multimedia Development Life Cycle (MDLC), which consists of the stages of concept, design, material collecting, assembly, testing, and distribution. The data were obtained through 360° panoramic photographs, text, and supporting multimedia, which were then integrated using 3DVista software. The results show that panoramic navigation, information on educational units, galleries, location maps, and social media links function properly. The resulting application is web-based and can be accessed via computers and smartphones. With the implementation of this VR Tour application, the Assalafiyah II Foundation can expand its information reach while enhancing its appeal to prospective students and the general public.
Rancang Bangun Visualisasi SIG Berbasis Web Untuk Pemetaan Kondisi Ruas Jalan Yoga Handoko Agustin; M.Nabil Naufal Nasrullah
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.23-1.2797

Abstract

Road infrastructure is a vital component in supporting community mobility and welfare, including in Garut Regency, which has an extensive road network with a high level of damage. The absence of a web-based spatial visualization system has led to limited transparency and efficiency in conveying information on road conditions. This study aims to design and develop a web-based geographic information system (WebGIS) to map road conditions and damage points in Garut Regency. This system utilizes official survey data from the Public Works and Public Housing Agency, which has been processed using QGIS and compiled in GeoJSON format. The system was developed using the Waterfall method, which consists of the stages of communication, planning, modeling, construction, and deployment. This system was built with open-source technologies such as Laravel, Leaflet.js, and PostgreSQL, and includes interactive visualization features for road segments, damage points, and road condition statistics. Testing results show that the system performs as expected with a System Usability Scale (SUS) score of 76.3, which falls into the “Good” category. This research contributes to strengthening the transparency and efficiency of spatial-based road condition information delivery in Garut Regency and provides a basis for further WebGIS development in delivering information that supports priority road repair planning.
Implementasi Gemini API Pada Aplikasi AI Course Generator Untuk Pembelajaran Personal Muhammad Fahmi Assidiq; Ridwan Setiawan
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.23-1.2803

Abstract

The trend of self-directed learning continues to rise, yet students still face challenges such as limited time, difficulty structuring learning materials, and a lack of systematic learning pathways. This study aims to design and develop a web-based AI Course Generator application by integrating the Gemini API to generate automated learning materials, the YouTube API to recommend educational videos, and an interactive chatbot to provide personalized learning assistance.The development method uses the Rational Unified Process (RUP), consisting of four phases: inception, elaboration, construction, and transition, supported by Unified Modeling Language (UML) modeling. The implementation results show that the application can automatically generate course outlines and learning materials, recommend learning videos, and assist users through the chatbot.Testing was performed using black box testing and usability testing with the USE Questionnaire, yielding a user satisfaction score of 82.09% (strongly agree category). This study demonstrates the effectiveness of integrating the Gemini API and YouTube API in building an adaptive and efficient self-learning application that supports the Merdeka Belajar concept.

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