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All Journal Jurnal Algoritma Proceedings Konferensi Nasional Sistem dan Informatika (KNS&I) Sinkron : Jurnal dan Penelitian Teknik Informatika Seminar Nasional Teknologi Informasi Komunikasi dan Industri Jurnal Ilmiah Matrik Jurnal Eksplora Informatika JITK (Jurnal Ilmu Pengetahuan dan Komputer) JURNAL TEKNIK INFORMATIKA DAN SISTEM INFORMASI Voice Of Informatics Jurnal Sistem Cerdas CSRID (Computer Science Research and Its Development Journal) SENSITEK E-JURNAL JUSITI : Jurnal Sistem Informasi dan Teknologi Informasi IT (INFORMATIC TECHNIQUE) JOURNAL Aiti: Jurnal Teknologi Informasi Jurnal Scientia Budapest International Research and Critics Institute-Journal (BIRCI-Journal): Humanities and Social Sciences Jurnal Teknik Informatika (JUTIF) Informatics and Digital Expert (INDEX) Jurnal Kewarganegaraan Jurnal PkM MIFTEK Jurnal Algoritma Jurnal Teknologi Informasi International Journal of Applied Information Systems and Informatics
Yoga Handoko Agustin
Institut Teknologi Garut
Religion Aerospace Engineering Arts Humanities Automotive Engineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Mathematics Social Sciences Other

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Analisis Sentimen Opini Publik Menggunakan Algoritma Naive Bayes dan TF-IDF Agustin, Yoga Handoko; Cici Mulyani, Neng; Sindu Prasetya, Wahyu
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.22-2.2671

Abstract

Penelitian ini berfokus pada analisis sentimen masyarakat terhadap kebijakan larangan study tour yang dikeluarkan oleh Gubernur Jawa Barat dengan memanfaatkan data komentar dari media sosial Instagram. Data dikumpulkan melalui teknik web scraping menggunakan ekstensi Instant Data Scraper dengan kata kunci relevan, kemudian diberi label secara otomatis oleh ChatGPT. Untuk menjamin kualitas pelabelan, dilakukan validasi manual terhadap 10% data secara acak, yang menghasilkan tingkat akurasi sebesar 93%. Proses analisis dilakukan menggunakan algoritma Naïve Bayes dengan kerangka kerja SEMMA (Sample, Explore, Modify, Model, Assess). Tantangan distribusi kelas yang tidak seimbang diatasi melalui penerapan SMOTE (Synthetic Minority Over-sampling Technique). Evaluasi model dilakukan menggunakan confusion matrix, accuracy, precision, recall, dan F1-score. Hasil penelitian menunjukkan akurasi model sebesar 80%, dengan F1-score tertinggi pada kategori sentimen positif (82%) dan negatif (81%). Temuan ini membuktikan bahwa kombinasi SEMMA dan algoritma Naïve Bayes efektif untuk memetakan opini publik berbasis data media sosial. Lebih jauh, penelitian ini memberikan kontribusi praktis bagi pemerintah dan pembuat kebijakan, khususnya dalam memonitor persepsi masyarakat secara real-time terhadap kebijakan yang diterapkan. Dengan pendekatan ini, pemerintah dapat lebih cepat mengidentifikasi respons publik, mengantisipasi potensi penolakan, serta menyusun strategi komunikasi yang lebih tepat sasaran. Selain itu, kerangka kerja yang digunakan dapat diadaptasi pada isu kebijakan lainnya, sehingga bermanfaat sebagai model analisis sentimen yang sistematis, terukur, dan mendukung pengambilan keputusan berbasis data.
Rancang Bangun Sistem Pendukung Keputusan Seleksi Penerimaan Tenaga Kerja Dengan Metode Fuzzy Inference System Agustin, Yoga Handoko; Cahya Setia Ningrum, Asni
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.22-2.2818

Abstract

Advances in information technology have driven significant changes in the recruitment process. Many companies still rely on manual selection, which tends to be time-consuming, subjective, and poorly documented. This study aims to design and develop a web-based Decision Support System (DSS) using the Mamdani Fuzzy Inference System (FIS) method to support the employee selection process. The system is designed with assessment criteria such as GPA, work experience, skills, and test results. Candidate data is processed through the stages of fuzzification, inference, and defuzzification to produce recommendations for the best candidate order. Testing was conducted using black box testing to ensure system functionality and usability testing involving HRD and administrators. The results of the usability test using the System Usability Scale (SUS) method showed an average score of 93.13, which means that the system is excellent, easy to use, and supports objective and efficient workforce selection needs. This study concludes that the application of Mamdani FIS can assist companies in making more measurable decisions. Further development recommendations include integration with machine learning to improve assessment accuracy and the development of a mobile application for greater flexibility of use.
SENTIMENT ANALYSIS OF IT WORKERS ON NO CODE AND LOW CODE TRENDS: COMPARISON OF LSTM AND SVM MODELS Agustin, Yoga Handoko; Nabil Nur Afrizal
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 11 No. 2 (2025): JITK Issue November 2025
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v11i2.7166

Abstract

This research explores the sentiment of IT professionals toward the growing trend of No Code and Low Code technologies by comparing the performance of Support Vector Machine (SVM) and Long Short-Term Memory (LSTM) algorithms. Using the SEMMA methodology and automatic labeling with ChatGPT, a total of 4,238 comments were collected from Reddit and Twitter and categorized into positive, neutral, and negative sentiments. The analysis showed that neutral sentiment dominates on both platforms (47.9% on Reddit and 48.8% on Twitter), followed by positive sentiment (41.3% and 43.1%, respectively), indicating cautious but optimistic attitudes toward LCDPs. In terms of model performance, SVM outperformed LSTM with 87% accuracy and a weighted F1-score of 0.87, compared to LSTM’s 80% accuracy and a weighted F1-score of 0.80. These findings confirm that classical machine learning methods remain highly effective for short-text sentiment analysis in social media, particularly when combined with TF-IDF feature representation, SMOTE balancing, and LLM-based automatic labeling, while also offering new insights into IT community perceptions of disruptive technologies
ShopTips: Design and Development of an AI-Based Web Application for Automating E-Commerce Product Marketing Content Yoga Handoko Agustin; Alwan Nugraha Putra
Journal of Applied Information System and Informatic (JAISI) Vol 4, No 1 (2026): MEI 2026
Publisher : Deparment Information System, Siliwangi University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37058/jaisi.v4i1.18221

Abstract

The rapid growth of e-commerce has prompted sellers to produce compelling product descriptions quickly and efficiently. However, many sellers lack the copywriting skills needed to craft persuasive marketing content from raw product specifications. This research aims to design and implement ShopTips, an AI-powered web application that transforms product data into ready-to-use marketing content. The system was developed using a web-based architecture consisting of an HTML/CSS/JavaScript frontend, a Node.js with Express.js backend, MongoDB as the database, and an external AI API for natural language generation. The development methodology followed the Waterfall model, encompassing requirement analysis, system design, coding, testing, and evaluation phases. ShopTips enables users to input product details such as name, category, description, specification, target market, and sales platform. The system then generates persuasive product descriptions, Unique Selling Points (USPs), SEO keywords, call-to-action phrases, social media captions, and a multi-dimensional content quality score encompassing clarity, persuasion, SEO, and emotional dimensions. In addition, the application provides structured feedback to help users refine and improve their content iteratively, making it a practical tool for both novice and experienced sellers. The system is also designed with a simple and user-friendly interface to ensure ease of use and accessibility for micro, small, and medium enterprises (MSMEs). Functional testing using black-box methods showed that all eight primary endpoints operated as intended without critical errors. User acceptance testing with 30 respondents yielded a satisfaction score of 85.6%, indicating high acceptability and usability. The findings demonstrate that the application significantly reduces the time required to produce marketing content while improving overall content quality. Therefore, ShopTips can serve as an effective solution for sellers who lack advanced writing skills and need efficient content generation tools. Future research may explore user authentication, export features, direct marketplace integration, and competitor analysis functionality to further enhance system capabilities and scalability.
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.
Penerapan Algoritma Naïve Bayes Untuk Memprediksi Kondisi Kelahiran Bayi Ripani Vergania; Yoga Handoko Agustin
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.23-1.3247

Abstract

The condition of a baby's birth is an important indicator in assessing the health risks of mothers and children. This study aims to develop a model for predicting the risks of childbirth using the Naïve Bayes algorithm with the Cross Industry Standard Process for Data Mining (CRISP-DM) approach, which includes the stages of Business Understanding, Data Understanding, Data Preparation, Modeling, Evaluation, and Deployment. The dataset used consists of 634 pregnant women data obtained from the Bungbulang Community Health Center, Garut Regency. This study tested three variations of data preprocessing, namely One Hot Encoding, Label Encoding, and Min-Max Scaling. The evaluation results show that all Naïve Bayes models perform well with an accuracy above 94%. Among the three, the model with Min-Max Scaling produces the most optimal performance with an accuracy of 95.4%, precision of 95.7%, recall of 94.5%, F1-Score of 95.0%, and AUC reaching 100%. These findings indicate that the application of Min-Max Scaling to the Naïve Bayes algorithm is effective in improving prediction performance while providing balance in evaluation metrics. The results of this study are expected to support early identification of birth risks and contribute to decision-making in maternal and child health services.
Co-Authors Ade Sutedi Adha, Sherly Nabila Afifah, Via Nur Alwan Nugraha Putra Andi Fikri Nugraha Andi Sanjaya Andyarini, Ervina Dwi Anggi Rihadisha Anisa Devisa Putri Arbi Yuan Aspahany Asep Sugiharto Asgara, Zidan Asri Mulyani Aulia, Husni Ayu Latifah B. Balilo Jr , Benedicto Baswardono, Wiyoga Cahya Setia Ningrum, Asni Cici Mulyani, Neng Dani Rohpandi Dede Kurniadi Dendi Ramdani Deni Heryanto Ditdit Putuwenda Egi Badar Sambani, Egi Badar Eni Suryeni, Eni Eri Satria Evi Dewi Sri Mulyani Fahmi Fadlillah Falah Insan Pratama Fauzi, Bayu Muhammad Firmanto, Alam Fitri Nuraeni Hari Ilham Nur Akbar HELFY SUSLAWATI Ibrahim, Roby Ida Farida Imas Dewi Ariyanti Indri Tri Julianto Intan Hartanti Rahman Ningsih Iwan Setiawan Jungjunan, Aditya Rahma Kusrini, Kusrini Kustiana, Ruli M Leni Fitriani Leni Fitriani, Leni Luthfi, Emha Taufiq M.Nabil Naufal Nasrullah Marlina, Rina Miftahul Hidayat, Miftahul Mohamad Fikri Haekal Muhammad Farhan Muhammad Ramdan Rahmatillah Muhammad Rikza Nashrulloh Multajam, Sri Intan Nabil Nur Afrizal Nasrulloh, Anas Nensi Mardhiani Surgawi Nisa, Ziadatun Khoirun Nugraha, Insan Satia Nur Faisal, Ridwan Nur'aeni, Irma Oktapiani, Vini Pratama, Fajri Rahayu, Raden Erwin Gunadi Raisman Raisman Reza Ruswanda Ridwan Setiawan Ridwan Setiawan Ridwan Setiawan Rika Lestari Ripani Vergania Shinta Siti Sundari Sidiq, Repi Fahmi Sindu Prasetya, Wahyu Siti Nurendah Siti Nursifa, Fadia Sopandi, Pendi Sri Fitrya Kamellia Sri Rahayu Sri Sulastri Srihermaning, Nova _ Susanto Susanto Wahyu Sindu Prasetya Wildan Nugraha Wiyoga Baswardono Yosep Septiana Yuli Nurfitria, Yuli Yusuf Abdul Fatah