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All Journal AMIKOM ICT AWARD 2010 Jurnal Simetris TEKNOLOGI: Jurnal Ilmiah Sistem Informasi Jurnal Buana Informatika Jurnal Edukasi dan Penelitian Informatika (JEPIN) Proceeding Seminar LPPM UMP Tahun 2014 Sinkron : Jurnal dan Penelitian Teknik Informatika Jurnal Informatika dan Multimedia Jurnal Pilar Nusa Mandiri SINTECH (Science and Information Technology) Journal Applied Information System and Management Journal of Innovation in Business and Economics EDUMATIC: Jurnal Pendidikan Informatika Building of Informatics, Technology and Science Journal of Information Systems and Informatics Journal of Robotics and Control (JRC) IJECA (International Journal of Education and Curriculum Application) Community Development Journal: Jurnal Pengabdian Masyarakat Jurnal Teknik Informatika (JUTIF) Journal of Applied Data Sciences Jurnal AbdiMas Nusa Mandiri Journal of Soft Computing Exploration Jurnal Pendidikan dan Teknologi Indonesia Jurnal Pengabdian Masyarakat : Pemberdayaan, Inovasi dan Perubahan Jurnal Ilmiah IT CIDA : Diseminasi Teknologi Informasi Jurnal Pengabdian Masyarakat Jurnal Ilmu Komputer dan Teknologi (IKOMTI) Journal of Practical Computer Science (JPCS) Journal of Artificial Intelligence and Digital Business Inspiration: Jurnal Teknologi Informasi dan Komunikasi Jurnal Abdimastek (Pengabdian Masyarakat Berbasis Teknologi) Journal of Information Technology and Cyber Security Jurnal Manajemen Informatika, Sistem Informasi dan Teknologi Komputer (JUMISTIK) Journal of Informatics and Interactive Technology (JIITE) Jurnal Teknologi Riset Terapan
Dhanar Intan Surya Saputra
Program Studi Teknik Informatika, STMIK AMIKOM Purwokerto Jl. Letjen Pol Sumarto Watumas Purwanegara Purwokerto, Banyumas 53123
Religion Aerospace Engineering Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Mechanical Engineering Physics Public Health Social Sciences Other

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Journal : Jurnal Teknik Informatika (JUTIF)

 1 
COMPARISON OF LOGISTIC REGRESSION AND RANDOM FOREST IN SENTIMENT ANALYSIS OF DISDUKCAPIL APPLICATION REVIEWS Junianto, Haris; Saputro, Rujianto Eko; Kusuma, Bagus Adhi; Saputra, Dhanar Intan Surya
Jurnal Teknik Informatika (Jutif) Vol. 5 No. 6 (2024): JUTIF Volume 5, Number 6, Desember 2024
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2024.5.6.1802

Abstract

Civil registration administration institutions such as Disdukcapil have an important role in carrying out government functions, in supporting the smooth running of administrative services the Government presents the Disdukcapil Mobile Application platform which aims to provide efficient and fast services to the community regarding various population administration needs. Sentiment analysis of user reviews on the Play Store for the Disdukcapil application is needed to understand user perceptions and needs, as well as to improve service quality and application development. In this study, researchers conducted sentiment analysis using 2 algorithms, namely: Logistic Regression and Random Forest, which after comparing by testing the two algorithms with test data of 18810 user review data from PlayStore, obtained the performance results of each algorithm as follows: 90% accuracy, 91% precision, 89% recall, and f1 90% for the performance results of the Logistic Regression algorithm, while for the performance results of the Random Forest algorithm accuracy 89%, precision 92%, recall 86% and f1-score 89%. From these results the Logical Regression algorithm has better performance than the Random Forest algorithm.
Comparative Analysis of Data Balancing Techniques for Machine Learning Classification on Imbalanced Student Perception Datasets Saekhu, Ahmad; Berlilana, Berlilana; Saputra, Dhanar Intan Surya
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 2 (2025): JUTIF Volume 6, Number 2, April 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.2.4286

Abstract

Class imbalance is a common challenge in machine learning classification tasks, often leading to biased predictions toward the majority class. This study evaluates the effectiveness of various machine learning algorithms combined with advanced data balancing techniques in addressing class imbalance in a dataset collected from Class XI students of SMK Ma'arif 1 Kebumen. The dataset, comprising 300 instances and 36 features, includes textual attributes, demographic information, and sentiment labels categorized as Positive, Neutral, and Negative. Preprocessing steps included text cleaning, target encoding, handling missing data, and vectorization. Four sampling techniques—SMOTE, SMOTE + Tomek Links, ADASYN, and SMOTE + ENN—were applied to the training data to create balanced datasets. Nine machine learning algorithms, including CatBoost, Extra Trees, Random Forest, Gradient Boosting, and others, were evaluated using four train-test splits (60:40, 70:30, 80:20, and 90:10). Model performance was assessed using metrics such as accuracy, precision, recall, F1-score, and AUC- ROC. The results demonstrate that SMOTE + Tomek Links is the most effective balancing technique, achieving the highest accuracy when paired with ensemble algorithms like Extra Trees and Random Forest. CatBoost also delivered competitive performance, showcasing its adaptability in imbalanced scenarios. The 90:10 train-test split consistently yielded the best results, emphasizing the importance of adequate training data for model generalization. This study highlights the critical role of data balancing techniques and robust algorithms in optimizing classification performance for imbalanced datasets and provides a framework for future research in similar contexts.
Labeling Optimization and Hybrid CNN Model in Sentiment Analysis of Movie Reviews with Slang Handling Saputra, Alfin Nur Aziz; Saputro, Rujianto Eko; Saputra, Dhanar Intan Surya
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 6 (2025): JUTIF Volume 6, Number 6, Desember 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.6.4465

Abstract

This research focuses on the development of a hybrid Convolutional Neural Network (CNN) model for sentiment analysis of movie comments, specifically designed to overcome the challenges of handling nonstandard language and slang. Slang is often an obstacle in sentiment analysis due to its non-standard nature and is difficult to recognize by traditional algorithms. By utilizing an kamusalay as a data preprocessing step, this research successfully converts slang words into standardized forms, thus improving the quality of data used in modeling. The data was collected through YouTube Data API on the comments of the movie “Pengabdi Setan 2: Communion” and processed using tokenization, stemming, stopwords removal, and TF-IDF feature extraction techniques. The hybrid model combines machine learning algorithms such as Naive Bayes, Logistic Regression, and Random Forest with CNN's ability to extract complex spatial patterns from text data. The evaluation results show that this model is able to achieve up to 95% accuracy, with consistently high precision, recall, and F1-score. This approach not only improves the accuracy of sentiment analysis, but also provides an effective solution for handling non-standard language variations, making it relevant for application in digital opinion analysis on social media.
Co-Authors Adam Prayogo Kuncoro Aditya Pratama Afrig Aminuddin Agus Pramono Al Haura, Adzkiyatun Nisa Alamsyah, Rizki Albana, Ilham Amalina, Siti Nahla Amin, M. Syaiful Ammar Fauzan, Ammar Andik Wijanarko, Andik Andina, Anisa Nur Anditya Putri, Shifa Anisa, Kholifatun ANNISA HANDAYANI Apitiadi, Satyo Dwi Aprilia, Kharisma Arief Adhy Kurniawan Arsi, Primandani Baetisalamah, Nadiva Amelia Berlilana Berlilana Dewi Cantika, Nourma Islam Dewi Fortuna Diningrum, Dwi Fatma Efendi, Alvin Junio Ilham Eldas Puspita Rini, Eldas Puspita Ely Purnawati, Ely Fadly Yashari Soumena Fariha, Zulfia Nur Ferdianto, Dwi Angga Hafshah, Luqyana Nida Hellik Hermawan Hendra Sudarso Hidayat, Muhammad Taufik Nur Hiiyatin, Dewi LaeIa I Putu Dody Suarnatha Ilham, Fatah Imam Tahyudin Indarto, Debi Iriane, Rara Irma Darmayanti Junianto, Haris Khoirudin, Muhamad Affan Kuat Indartono Kusuma, Bagus Adhi Kusuma, Velizha S Kusuma, Velizha Sandy Maghfira, Rahajeng Sasi Mahardika, Fajar Mahendra, Duta Aditya Marhalatun, Viva Miftahus Surur, Miftahus Muhammad Afif Muliasari Pinilih, Muliasari Muratno, Muratno Murjiatiningsih, Lilis Mustofa, Dinar Najibulloh, Imam Kharits Nandang Hermanto Nanjar, Agi Nugroho, Bagus Aji Nur Hasanah Nuraini, Eka Nurul Hidayati Pandega, Dimas Marsus Prayoga, Agung Priangga, Melaya Puji Hastuti Pujianto , Dimas Eko Purwadi Purwadi Puspitaningrum, Indar Putra, Mifthah Putranto, Aldrian Firmansyah Rahayu, Dania Gusmi Rahman Rosyidi Ramadhan, Muhammad Bintang Ranggi Praharaningtyas Aji Riesna, Deby Mega Rizkia Riny, Riny Riyanto Riyanto Riyanto Rujianto Eko Saputro Saekhu, Ahmad Saputra, Alfin Nur Aziz Saputri, Febryka Wulan Saputri, Inka Setiawan, Endri Sitaresmi Wahyu Handani, Sitaresmi Wahyu Sri Widiastuti, Sri Subarkah, Pungkas Taqwa Hariguna Udianti, Asih Utomo, Anwar Tri Waluyo, Retno Wijaya, Anugerah Bagus Winanto, Deden Wirasto, Anggit Wiwik Handayani Yusmedi Nurfaizal Zhafira, Alya