Article Per Year (5 Year)
p-Index From 2021 - 2026
3.296
P-Index
This Author published in this journals
All Journal Semantik Jurnal Simetris Jurnal Informatika Seminar Nasional Informatika (SEMNASIF) Jurnal Informatika Upgris SISFOTENIKA METHOMIKA: Jurnal Manajemen Informatika & Komputerisasi Akuntansi JTIK (Jurnal Teknik Informatika Kaputama) Jurnal Tekinkom (Teknik Informasi dan Komputer) Idealis : Indonesia Journal Information System SKANIKA: Sistem Komputer dan Teknik Informatika Prosiding Konferensi Nasional PKM-CSR Abdimas Altruis: Jurnal Pengabdian Kepada Masyarakat KOMMAS: Jurnal Pengabdian Kepada Masyarakat Publikasi Hasil Pengabdian Kepada Masyarakat. Journal Of Informatics And Busisnes Jurnal Rekayasa Sistem Informasi dan Teknologi Journal of Data Analytics, Information, and Computer Science (JDAICS) Jurnal Computer and Technology
Hendrik Fery Herdiyatmoko
Unknown Affiliation
Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Engineering Environmental Science Mathematics Nursing Public Health Social Sciences Other

Published : 23 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 23 Documents
Search

 1   2   3 
Optimasi Support Vector Machine Menggunakan Feature Selection Chi-Square untuk Klasifikasi Sentimen Program Makan Siang Gratis Darmawan, I Komang; Prayogo, Aji; Chan, Steven; Herdiatmoko, Hendrik Fery
Journal Of Informatics And Busisnes Vol. 3 No. 4 (2026): Januari - Maret
Publisher : CV. ITTC INDONESIA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47233/jibs.v3i4.3943

Abstract

The Free Nutritious Lunch program policy has triggered massive public discourse on social media X, reflecting diverse public perceptions toward government policy effectiveness. This study aims to optimize the performance of the Support Vector Machine (SVM) algorithm by implementing Chi-Square Feature Selection to address high data dimensionality and noise challenges in social media text. A dataset of 10,524 tweets was acquired and processed through preprocessing, TF-IDF weighting, and lexicon-based automatic labeling. The results show that Chi-Square feature selection integration successfully reduced dimensions from 16,394 to the 1,000 best features without degrading accuracy. The linear kernel SVM model achieved an optimal accuracy rate of 91.12%. However, this study identifies that this high accuracy is heavily influenced by the dominance of the positive class, whereas performance on the negative and neutral classes remains limited due to data imbalance. Overall, feature optimization proved to increase computational efficiency while maintaining accuracy stability in mapping public responses to strategic national policies.
Analisis Sentimen Ulasan Aplikasi Bibit Menggunakan TF-IDF dan Support Vector Machine Adeodatus, Marselinus Dewadaru Bayu; Nopitasari, Sepiyana; Meivia, Wirda Arta; Herdiatmoko, Hendrik Fery
Journal Of Informatics And Busisnes Vol. 3 No. 4 (2026): Januari - Maret
Publisher : CV. ITTC INDONESIA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47233/jibs.v3i4.3973

Abstract

The rapid growth of financial technology (fintech) applications has increased the number of user reviews on digital platforms. These reviews contain valuable information regarding application quality, yet they are unstructured and difficult to analyze manually. This study aims to classify user review sentiments of the Bibit investment application into positive and negative categories using the Term Frequency–Inverse Document Frequency (TF-IDF) method and the Support Vector Machine (SVM) algorithm. The dataset was obtained from Kaggle, consisting of user reviews of the Bibit application collected from Google Play Store. The data were processed through several preprocessing stages, including cleaning, case folding, tokenization, stopword removal, and stemming. Feature extraction was performed using TF-IDF, and classification was conducted using SVM with a linear kernel. Model performance was evaluated using accuracy, precision, recall, and F1-score metrics. The results indicate that the combination of TF-IDF and SVM provides good performance in classifying the sentiment of Bibit application user reviews.
Penerapan Algoritma Decision Tree C4.5 Pada Test MBTI Berbasis Web: Studi Kasus: Universitas Katolik Musi Charitas Elvira, Redempta; Herdiatmoko, Fery
METHOMIKA: Jurnal Manajemen Informatika & Komputerisasi Akuntansi Vol. 9 No. 2 (2025): METHOMIKA: Jurnal Manajemen Informatika & Komputersisasi Akuntansi
Publisher : Universitas Methodist Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46880/jmika.Vol9No2.pp316-322

Abstract

A major problem in student personality assessment is the manual process of completing and interpreting test results, which leads to subjective bias and delays in counseling services. To address this, this study applies the Decision Tree C4.5 algorithm to a web-based MBTI test to produce an objective and efficient personality type classification. This study discusses the implementation of the Decision Tree C4.5 algorithm in a web-based Myers-Briggs Type Indicator (MBTI) test to classify students’ personality types at Musi Charitas Catholic University. The research objectives are (1) to apply and evaluate the Decision Tree C4.5 algorithm in personality classification based on MBTI test results, and (2) to develop a counseling support system capable of providing automatic, objective, and easy-to-understand classification results. The research method employed is development research (Research and Development) using the Waterfall model, including requirement analysis, system design, implementation, testing, and evaluation. The C4.5 algorithm was implemented to construct a classification model based on decision rules, which was then integrated into the web application. System testing using Black-Box and White-Box methods ensured that the system operates according to specifications and that all logical paths have been tested. Evaluation results indicate a classification accuracy of approximately 86% with consistent precision, recall, and F1-score values, demonstrating the effectiveness of the C4.5 algorithm in personality type classification. The system improves efficiency, accessibility, and objectivity in personality assessment compared to manual methods and can support sustainable student counseling and development services.
Co-Authors Achmad Alfian Adeodatus, Marselinus Dewadaru Bayu Adha , M. Fathir Adi Prananda, Patrik Galih Alvaro, Hadi Anselmus Agung Pramudito Arini, Diana Putri Cevin, Jery Chan, Steven Darmawan, I Komang Devi, Natalia Regina Diana Putri Arini Dwi Normawati, Dwi Elvira, Redempta Fransiska Soejono Gultom, David Hadi Alvaro Hendri Syaputra Herdiyanto, H.Y. Ary Indriasari, Bernadine Ajeng J.M.Sri Narhadi Khoirunnisaa, Alifah M. Fathir Adha Meivia, Wirda Arta Meylinda Mulyati Muhamad Septian Natalia Regina Devi Nopitasari, Sepiyana Orlando, Nikolas Patrik Galih Adi Prananda Prasetya, Heronimus Diego Pratama, Jeremia Sandy Pratama, Yohanes Dicka Prayogo, Aji Putiastanti, Stevani Fatrisia Rendy Saputra riyanto, agustinus Saputra, Laurensius Sirwanti Septian , Muhamad Stevani Fatrisia Putiastanti Theresia Sunarni wawan nurmansyah Windriani, H.Y. Wayan Windriani, Wayan Yohanes Dika Pratama