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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
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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Rancang Bangun Sistem Manajemen Proyek Perangkat Lunak Berbasis Web Menggunakan Metode Rational Unified Process Agustin, Yoga Handoko; Kustiana, Ruli M
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.22-2.2577

Abstract

The success of companies in the technology sector greatly depends on effective project management. At PT. Artivisi Intermedia Jakarta, the software project management process faces several challenges, including schedule delays, inadequate team coordination, and difficulties in tracking project progress due to the use of semi-manual systems based on Microsoft Excel. This research focuses on the design and implementation of a web-based project management system to address these issues, with an emphasis on optimizing time management and human resources. The development framework adopted is the Rational Unified Process (RUP), chosen for its iterative and structured approach, which covers four main phases: inception, elaboration, construction, and transition. Unified Modeling Language (UML) was employed for system modeling, while system functionality was tested using the Black Box Testing method, and usability was measured using the System Usability Scale (SUS). The results show that the system was successfully developed in its entirety, with Black Box testing confirming that all core functions were “Valid.” Furthermore, usability testing involving 20 respondents yielded a SUS score of 72.8, indicating that the system is classified as “Good” and “Acceptable” to users. Therefore, the developed system provides a functional solution to improve efficiency and transparency in project management at PT. Artivisi Intermedia Jakarta. The contribution of this study lies in presenting a validated systemic solution for technology companies facing similar challenges, with practical implications for optimizing project execution.
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
Co-Authors Ade Sutedi Adha, Sherly Nabila Afifah, Via Nur 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 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 Ridwan Setiawan Ridwan Setiawan Ridwan Setiawan Rika Lestari Shinta Siti Sundari Sidiq, Repi Fahmi Sindu Prasetya, Wahyu 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