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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) JITU : Journal Informatic Technology And Communication Idealis : Indonesia Journal Information System SKANIKA: Sistem Komputer dan Teknik Informatika HOAQ (High Education of Organization Archive Quality) : Jurnal Teknologi Informasi 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 Jurnal Pelayanan Masyarakat Pelayanan Unggulan: Jurnal Pengabdian Masyarakat Terapan
Hendrik Fery Herdiyatmoko
Prodi Informatika, Universitas Katolik Musi Charitas Palembang
Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Engineering Environmental Science Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Nursing Public Health Social Sciences Other

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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.
Implementasi Hierarchical Clustering untuk Analisis FDMC Narrative Crypto Berbasis Web M. Fathir Adha; Hendrik Fery Herdiatmoko
JITU Vol 10 No 1 (2026)
Publisher : Universitas Boyolali

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36596/jitu.v10i1.2255

Abstract

This study implements the Hierarchical Clustering algorithm with Ward linkage and Euclidean distance methods to analyze 26 crypto narratives based on the Fully Diluted Market Cap (FDMC) metric. Using a hybrid method that integrates Waterfall, Cross-Industry Standard Process for Data Mining (CRISP-DM), and Knowledge Discovery in Databases (KDD), data was obtained from the CoinGecko API, manually clustered, and aggregated per narrative. Pre-processing involved logarithmic transformation (log-10) and Z-Score normalization to address power-law distributions and outliers, resulting in a more stable cluster structure. The clustering results mapped the market into five clusters: Bluechip (L1 with FDMC $2.76T), Growth (PAY, MEME, CEX, DEX, DeFi totaling $468.22B), Growth (AI, DePIN, DAO, L2, RWA, ORC, GameFi, XCH, DID, PRC, LST with $192.91B), Speculative (NFT, MET, SocialFi, BTC Eco, W3I with $17.55B), and Speculative (LPD, GambleFi, FTO, SEC with $2.34B). The model was validated with a Silhouette Score of 0.650 and a Cophenetic Correlation Coefficient of 0.647, indicating cohesive and representative clusters. A web-based implementation using Django, D3.js, and Chart.js provides interactive visualizations and portfolio recommendations. Contributions include a novel fundamental valuation approach, an adaptive clustering model, and practical analytical tools for investors, with potential expansion to multidimensional metrics in the future.
Edukasi Interaktif : Literasi Digital dan Etika Bermedia Sosial bagi Peserta Didik Baru SMK Xaverius Palembang Klaudius Jevanda BS; Latius Hermawan; Wawan Nurmansyah; Hendrik Fery Herdiatmoko; Fransiska Ninda Permata Nugraha
Pelayanan Unggulan : Jurnal Pengabdian Masyarakat Terapan Vol. 3 No. 2 (2026): Mei: Pelayanan Unggulan : Jurnal Pengabdian Masyarakat Terapan
Publisher : Lembaga Pengembangan Kinerja Dosen

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62951/unggulan.v3i2.3220

Abstract

In today’s world, social media has become an everyday necessity for interacting and socializing with others, but it has both positive and negative effects, particularly on the younger generation. The roles of parents and educators are crucial in building the digital resilience of the younger generation. The solution is to provide early education to the younger generation, particularly new students for the 2025/2026 school year, during the School Orientation Program conducted by the UKMC Community Service Team. The purpose of this activity is to educate new students at SMK Xaverius Palembang to improve their understanding of digital literacy and online etiquette and to use social media more responsibly. The PkM Team served as guest speakers, presenting material on digital literacy and social media ethics, as well as real-life case studies, on Thursday, July 17, 2025, in the auditorium of SMK Xaverius Palembang. This activity is a community service initiative that uses interactive educational methods, in which the team presents five questions related to online gambling, fake news, cyberbullying, explicit content, and ending human trafficking in the form of digital posters, and the students explain them. The team also provided real-life examples and explained their causes and effects. Based on the results of the program, students demonstrated increased awareness of the risks associated with online gambling, fake news, cyberbullying, and explicit content, as well as the importance of combating human trafficking, and were more likely to adopt positive social media etiquette.
PENERAPAN ALGORITMA RANDOM FOREST REGRESSION DALAM PREDIKSI HARGA SAHAM BBRI: IMPLEMENTATION OF THE RANDOM FOREST REGRESSION ALGORITHM FOR PREDICTING BBRI STOCK PRICES Winardi, Kevin; Nugroho, Yulianus Febry Tri; Johannes; Herdiatmoko, Hendrik Fery
HOAQ (High Education of Organization Archive Quality) : Jurnal Teknologi Informasi Vol. 17 No. 1 (2026): Jurnal HOAQ - Teknologi Informasi
Publisher : STIKOM Uyelindo Kupang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52972/hoaq.vol17no1.p9-18

Abstract

Pergerakan harga saham dipengaruhi oleh berbagai faktor dan bersifat fluktuatif, sehingga diperlukan metode prediksi yang mampu menangkap pola data yang kompleks. Penelitian ini bertujuan untuk memprediksi harga saham menggunakan metode Random Forest Regression. Data yang digunakan dibagi menjadi data pelatihan dan data pengujian untuk mengevaluasi kinerja model. Kinerja model dievaluasi menggunakan beberapa metrik, yaitu Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), dan koefisien determinasi (R²). Hasil penelitian menunjukkan bahwa model Random Forest Regression setelah optimasi menghasilkan nilai MAE sebesar 64,02,  RMSE sebesar 84,51, dan R² sebesar 0,8484. Nilai-nilai tersebut mengindikasikan bahwa model memiliki tingkat kesalahan prediksi yang rendah dan mampu menjelaskan 84,84% variasi pada data harga saham. Berdasarkan hasil tersebut, dapat disimpulkan bahwa Random Forest memiliki kinerja yang baik dan cukup andal dalam memprediksi harga saham.   Stock price movements are influenced by various factors and exhibit high volatility, making accurate prediction a challenging task. This study aims to predict stock prices using the Random Forest Regression method. The dataset is divided into training and testing sets to evaluate the model’s performance. The performance of the model is assessed using Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and the coefficient of determination (R²). The results show that the Random Forest Regression model after optimization achieves an MAE of 64,02, an RMSE of 84,51, and an R² value of 0.8484. These results indicate a low prediction error and demonstrate that the model is able to explain 84.84% of the variance in stock price data. Therefore, it can be concluded that Random Forest is an effective and reliable method for stock price prediction.
Edukasi Interaktif: Literasi Digital dan Etika Bermedia Sosial Bagi Peserta Didik Baru SMK Xaverius Palembang Jevanda BS, Klaudius; Hermawan, Latius; Nurmansyah, Wawan; Fery Herdiyatmoko, Hendrik; Ninda Permata Nugraha, Fransiska
Jurnal Pelayanan Masyarakat Vol. 3 No. 2 (2026): Juni: JPM :Jurnal Pelayanan Masyarakat
Publisher : Lembaga Pengembangan Kinerja Dosen

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62951/jpm.v3i2.3175

Abstract

In today’s world, social media has become an everyday necessity for interacting and socializing with others, but it has both positive and negative effects, particularly on the younger generation. The roles of parents and educators are crucial in building digital resilience among the younger generation, such as the new students at SMK Xaverius Palembang. One solution is to provide early education to new students during the School Environment Orientation Program. The UKMC Community Service Team served as guest speakers, presenting material on digital literacy and social media ethics, along with real-life case studies, to the incoming students for the 2025/2026 academic year on Thursday, July 17, 2025, in the auditorium of SMK Xaverius Palembang. The method used involved an interactive educational approach, in which the team presented five questions related to online gambling, fake news, cyberbullying, explicit content, and ending human trafficking in the form of digital posters, and the students explained them. The team also provided real-life examples and explained the causes and effects. The purpose of this activity is to educate new students at SMK Xaverius Palembang to improve their understanding of digital literacy and social media ethics, and to encourage them to use social media more wisely. According to the research findings, the activity proceeded as planned, and the students were very enthusiastic about participating in the discussion and answering questions using digital posters.
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 Ninda Permata Nugraha Fransiska Soejono Gultom, David Hadi Alvaro Hendri Syaputra Herdiyanto, H.Y. Ary Indriasari, Bernadine Ajeng J.M.Sri Narhadi Jevanda BS, Klaudius Johannes Khoirunnisaa, Alifah Klaudius Jevanda BS Latius Hermawan M. Fathir Adha Meivia, Wirda Arta Meylinda Mulyati Muhamad Septian Natalia Regina Devi Ninda Permata Nugraha, Fransiska Nopitasari, Sepiyana Nugroho, Yulianus Febry Tri 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 Wawan Nurmansyah, Wawan Winardi, Kevin Windriani, H.Y. Wayan Windriani, Wayan Yohanes Dika Pratama