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All Journal IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Epsilon: Jurnal Matematika Murni dan Terapan Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) Jurnal Teknologi Informasi dan Ilmu Komputer Telematika KLIK (Kumpulan jurnaL Ilmu Komputer) (e-Journal) Sistemasi: Jurnal Sistem Informasi Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Jurnal Komputasi Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Jurnal Sains dan Informatika Journal of Electronics, Electromedical Engineering, and Medical Informatics Jurnal Pengabdian Kepada Masyarakat (Mediteg) Indonesian Journal of Electrical Engineering and Computer Science International Journal of Advances in Data and Information Systems Jurnal Teknik Informatika (JUTIF) JUSTIN (Jurnal Sistem dan Teknologi Informasi) Jurnal Pengabdian Ilung (Inovasi Lahan Basah Unggul) Journal of Data Science and Software Engineering Jurnal Teknik Ilmu dan Aplikasi Jurnal Informatika Polinema (JIP) Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics
Saragih, Triando Hamonangan
Universitas Lambung Mangkurat
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Analysis of Static and Contextual Word Embeddings in Capsule Network for Sentiment Analysis of The Free Nutritious Meal Program on Twitter Raditya, Virgi Atha; Saragih, Triando Hamonangan; Faisal, Mohammad Reza; Abadi, Friska; Muliadi, Muliadi
Jurnal Teknik Informatika (Jutif) Vol. 7 No. 1 (2026): JUTIF Volume 7, Number 1, February 2026
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2026.7.1.5424

Abstract

Public discourse surrounding Indonesia’s Makan Bergizi Gratis (MBG) program reflects diverse opinions that have not yet been systematically examined using computational methods. This study addresses that gap by evaluating the effectiveness of static and contextual word embeddings within a Capsule Network (CapsNet) framework for sentiment analysis of MBG-related tweets on Twitter. A total of 7,133 Indonesian-language tweets were collected through web crawling, preprocessed, and manually labeled into positive, neutral, and negative categories. Four embedding techniques—Word2Vec, FastText, ELMo, and IndoBERT—were tested under two preprocessing settings, raw and stemming. The experimental results show that Word2Vec on raw text achieved the highest accuracy of 96.17%, while FastText obtained the best performance on stemmed data with 94.10%. These findings indicate that morphological normalization benefits static and subword-based embeddings, whereas contextual models maintain stable performance without extensive fine-tuning. Overall, this study demonstrates the potential of combining CapsNet with appropriate embedding strategies for Indonesian-language sentiment analysis and provides evidence that natural language processing can support data-driven evaluation of public programs such as MBG.
Peningkatan Akurasi Model Boosting pada Prediksi Kesehatan Tidur Menggunakan Optuna Mazdadi, Muhammad Itqan; Saragih, Triando Hamonangan; Budiman, Irwan; Anshory, Muhammad Naufal
Jurnal Informatika Polinema Vol. 12 No. 2 (2026): Vol. 12 No. 2 (2026)
Publisher : UPT P2M State Polytechnic of Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33795/jip.v12i2.8878

Abstract

Kualitas tidur memiliki peran penting dalam menjaga kesehatan fisik maupun mental, sementara gangguan tidur dapat meningkatkan risiko berbagai penyakit kronis. Perkembangan machine learning membuka peluang untuk melakukan prediksi kesehatan tidur secara lebih akurat melalui pemanfaatan data gaya hidup. Penelitian ini berfokus pada penerapan algoritma boosting, yaitu XGBoost, LightGBM, AdaBoost, dan GradientBoosting, dengan dukungan teknik hyperparameter tuning berbasis Optuna untuk meningkatkan akurasi prediksi. Dataset yang digunakan adalah Sleep Health and Lifestyle Dataset yang memuat variabel demografis, kebiasaan hidup, serta kondisi tidur. Tahapan penelitian meliputi praproses data, pembagian data latih dan uji, pelatihan model, optimasi hyperparameter menggunakan Optuna dengan metode Tree-structured Parzen Estimator (TPE), serta evaluasi model menggunakan metrik akurasi. Hasil eksperimen menunjukkan bahwa tuning dengan Optuna memberikan peningkatan akurasi pada beberapa model, khususnya LightGBM dan AdaBoost, dengan nilai akurasi mencapai 93,3% dan 90,7%. Sementara itu, XGBoost dan GradientBoosting menunjukkan performa stabil dengan akurasi tetap tinggi baik sebelum maupun sesudah tuning. Temuan ini menegaskan bahwa efektivitas tuning bergantung pada karakteristik algoritma yang digunakan. Secara keseluruhan, penelitian ini membuktikan bahwa Optuna dapat menjadi solusi efektif dalam meningkatkan kinerja model boosting untuk prediksi kesehatan tidur. Sebagai arah penelitian lanjutan, disarankan penggunaan metrik evaluasi yang lebih beragam, penerapan teknik penyeimbangan data, serta eksplorasi integrasi dengan metode deep learning untuk memperkaya hasil analisis.
Comparasion Of Weather Classification Methods On Weather Images Using GLCM Features With Random Forest And Catboost Algoritms Noorhafizi, Muhammad; Saragih, Triando Hamonangan; Mazdadi, Muhammad Itqan; Muliadi, Muliadi; Herteno, Rudy; Rozaq, Hasri Awal Akbar
International Journal of Advances in Data and Information Systems Vol. 7 No. 1 (2026): April 2026 - International Journal of Advances in Data and Information Systems
Publisher : Indonesian Scientific Journal

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Abstract

Weather image classification is an essential process for improving automated weather information systems. However, most existing studies rely on numerical meteorological data and rarely utilize the textural characteristics embedded in atmospheric imagery. This study addresses that limitation by applying the Gray Level Co-Occurrence Matrix (GLCM) for texture feature extraction combined with Random Forest (RF) and CatBoost algorithms for classification. The dataset, obtained from Kaggle, consists of 1,125 weather images categorized into four classes: cloudy, rain, shine, and sunrise. All images were uniformly normalized and augmented using four rotation angles (0°, 45°, 90°, 135°). GLCM features were extracted with a pixel distance of 1 and gray-level quantization of 8, generating four statistical attributes: contrast, correlation, energy, and homogeneity. Both algorithms were optimized through parameter tuning and evaluated using a 5-fold cross-validation scheme with an 80:20 split ratio. Results show that the Random Forest model (n_estimators = 100, max_depth = 10, random_state = 42) achieved the highest accuracy of 92.43% (±1.12), precision of 92.50%, recall of 92.43%, and F1-score of 92.42%. In comparison, CatBoost (iterations = 100, learning_rate = 0.1, depth = 6) achieved an accuracy of 68.88% (±2.31). The findings demonstrate that GLCM feature extraction combined with Random Forest offers superior stability and accuracy for weather image classification, providing a foundation for efficient and interpretable weather information systems.
Empirical Performance of E2E Frameworks in React-Vue SPAs Using DIA Rezeki, Abdillah; Saputro, Setyo Wahyu; Saragih, Triando Hamonangan; Nugroho, Radityo Adi; Abadi, Friska
International Journal of Advances in Data and Information Systems Vol. 7 No. 1 (2026): April 2026 - International Journal of Advances in Data and Information Systems
Publisher : Indonesian Scientific Journal

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Abstract

Modern web applications increasingly adopt Single-Page Application (SPA) architectures to enhance the user experience through client-side rendering and dynamic content loading. However, these characteristics introduce significant challenges for automated end-to-end (E2E) testing, including asynchronous DOM manipulation, complex state management, and timing synchronization issues. This study presents a comprehensive empirical comparison of three prominent E2E testing frameworks—Selenium WebDriver, Cypress, and Playwright—across React and Vue-based SPAs. Using a quantitative experimental approach, 25 standardized test cases were executed 15 times each across Chrome, Firefox, and Edge, for a total of 270 testing sessions. Performance evaluation focused on four key metrics: execution time, success rate, CPU usage, and memory consumption. Results demonstrate that Playwright achieved the fastest execution time (56.25 seconds on React-Chrome), while Selenium exhibited superior resource efficiency with the lowest memory consumption (196.59 MB on Vue-Chrome). The Distance to Ideal Alternative (DIA) multi-criteria decision analysis method identified Playwright-Chrome as optimal for React applications (DIA score: 0.886715) and Selenium-Chrome for Vue applications (DIA score: 0.908237), indicating that framework selection should be context-dependent based on application characteristics and deployment requirements. This research supports the conclusion that no universal "best" testing framework exists, underscoring the importance of evidence-based, application-specific tool selection in software quality assurance.
Co-Authors AA Sudharmawan, AA Abadi, Friska Abdul Latief Abadi Abdullayev, Vugar Achmad Rizal Adawiyah, Laila Afifa, Ridha Ahmad Rusadi Arrahimi - Universitas Lambung Mangkurat) Ahmad Rusadi Arrahimi - Universitas Lambung Mangkurat) Ahmad Tajali Aida, Nor Ajwa Helisa Al Ghifari, Muhammad Akmal Alamudin, Muhammad Faiq Alfita Rakhmandasari Amelia Aditya Santika Andi Farmadi Andi Farmadi Anshari, Muhammad Ridha Anshory, Muhammad Naufal Ansyari, Muhammad Ridho Athavale, Vijay Anant Athavale, Vijay Annant Bachtiar, Adam Mukharil Bachtiar, Adam Mukharil Difa Fitria Dina Arifah Diny Melsye Nurul Fajri Diny Melsye Nurul Fajri Dodon Turianto Nugrahadi Dwi Kartini Dwi Kartini, Dwi Dzira Naufia Jawza Erdi, Muhammad Erlianita, Noor Faisal, Mohammad Reza Fatma Indriani Fatma Indriani Fayyadh, Muhammad Naufaldi Febrian, Muhamad Michael Friska Abadi Haekal, Muhammad Haekal, Muhammad Hafizah, Rini Hermiati, Arya Syifa Herteno, Rudy Huynh, Phuoc-Hai Ichwan Dwi Nugraha Indriani, Fatma Irwan Budiman Irwan Budiman Itqan Mazdadi, Muhammad Ivan Sitohang Jumadi Mabe Parenreng Keswani, Ryan Rhiveldi Lilies Handayani Lumbanraja, Favorisen R M. Khairul Rezki Mafazy, Muhammad Meftah Mariana Dewi Muhamad Fawwaz Akbar Muhammad Al Ichsan Nur Rizqi Said Muhammad Alkaff Muhammad Darmadi Muhammad Fauzan Nafiz Muhammad Haekal Muhammad Haekal Muhammad Ikhwan Rizki Muhammad Itqan Mazdadi Muhammad Mursyidan Amini Muhammad Nadim Mubaarok Muhammad Reza Faisal, Muhammad Reza Muhammad Rofiq Muliadi Muliadi Muliadi Muliadi Muliadi Muliadi Muliadi Muliadi Muliadi Musyaffa, Muhammad Hafizh Nafiz, Muhammad Fauzan Noorhafizi, Muhammad Noryasminda Nugraha, Muhammad Amir Nurcahyati, Ica Nurlatifah Amini Okta Muthia Sari Purwoko, Agus Putra, Aditya Maulana Perdana Raditya, Virgi Atha Radityo Adi Nugroho Rahmat Ramadhani Rahmat Ramadhani Rahmatullah, Satrio Wibowo Rahmayanti Rahmayanti Ramadhan, Mita Azzahra Ramadhani, Rahmat Ratna Septia Devi Regina Reza Faisal, Mohammad Rezeki, Abdillah Rizki, M. Alfi Rozaq, Hasri Akbar Awal Rozaq, Hasri Awal Akbar Rudy Herteno Rudy Herteno Safitri, Yasmin Dwi Said, Muhammad Al Ichsan Nur Rizqi SALLY LUTFIANI Salsha Farahdiba Saputro, Setyo Wahyu Siena, Laifansan Siti Aisyah Solechah Siti Napi'ah Suci Permata Sari Sulastri Norindah Sari Tajali, Ahmad Totok Wianto Vivi Nur Wijayaningrum Wahyu Caesarendra Wayan Firdaus Mahmudy Winda Agustina Yanche Kurniawan Mangalik YILDIZ, Oktay Yusuf Priyo Anggodo Zamzam, Yra Fatria