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All Journal Information Technology and Telematics Dinamik Pixel : Jurnal Ilmiah Komputer Grafis Proceeding SENDI_U Jurnal Teknik Informatika UNIKA Santo Thomas INTECOMS: Journal of Information Technology and Computer Science J-SAKTI (Jurnal Sains Komputer dan Informatika) Progresif: Jurnal Ilmiah Komputer JURNAL MAHAJANA INFORMASI Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi) Community Engagement and Emergence Journal (CEEJ) JITU : Journal Informatic Technology And Communication Jurnal Tunas Dinamika Informatika: Jurnal Ilmiah Teknologi Informasi Jurasik (Jurnal Riset Sistem Informasi dan Teknik Informatika) JURPIKAT (Jurnal Pengabdian Kepada Masyarakat) J-SAKTI (Jurnal Sains Komputer dan Informatika) Jurnal Teknik Informatika Unika Santo Thomas (JTIUST) Joutica : Journal of Informatic Unisla Jurnal Pengabdian Masyarakat Intimas (Jurnal INTIMAS): Inovasi Teknologi Informasi Dan Komputer Untuk Masyarakat Media Abdimas Pelita: Jurnal Pengabdian kepada Masyarakat Jurnal Informatika Dan Tekonologi Komputer
Heribertus Yulianton
Universitas Stikubank
Arts Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Environmental Science Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Public Health Social Sciences Other

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Implementasi Chatbot Berbasis Dialogflow dengan Metode Natural Language Processing untuk Rekomendasi Tempat Wisata di Kabupaten Kulonprogo Khotama, Faizal Wahyu; Yulianton, Heribertus
Jurasik (Jurnal Riset Sistem Informasi dan Teknik Informatika) Vol 10, No 1 (2025): Edisi Februari
Publisher : STIKOM Tunas Bangsa Pematangsiantar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/jurasik.v10i1.878

Abstract

This research is based on the importance of providing quick and accurate tourism information in Kulonprogo Regency. The problem addressed in this study revolves around how to implement a Dialogflow-based chatbot utilizing Natural Language Processing (NLP) technology to effectively recommend tourist destinations. The objective of this research is to design and test a chatbot capable of understanding users' natural language and providing relevant tourism recommendations. The research methodology includes data collection through interviews, observations, and document studies, as well as system design based on conversational flows and intents. Testing results indicate that the chatbot achieved 100% accuracy in black-box testing and an average score of 85 on the System Usability Scale (SUS) evaluation, demonstrating high user satisfaction. In conclusion, this chatbot provides an interactive and responsive solution to promote local tourism and improve the accessibility of tourism information for users.
Optimalisasi Model Klasifikasi Diabetes Menggunakan Ensemble Learning Adaboost, Gradient Boosting, dan XGBoost Wibisono, Setyawan; Hadikurniawati, Wiwien; Yulianton, Heribertus; Lestariningsih, Endang; Cahyono, Taufiq Dwi
Jurnal Teknik Informatika UNIKA Santo Thomas Vol 9 No. 2 : Tahun 2024
Publisher : LPPM UNIKA Santo Thomas

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Diabetes mellitus adalah penyakit kronis yang memengaruhi jutaan orang secara global dan membutuhkan metode diagnosis dini untuk mencegah komplikasi. Penelitian ini bertujuan untuk mengoptimalkan prediksi diabetes dengan membandingkan tiga metode ensemble learning: AdaBoost, Gradient Boosting, dan XGBoost. Dataset yang digunakan adalah Diabetes Health Indicators, yang menggabungkan indikator kesehatan seperti tekanan darah, kolesterol, dan kebiasaan gaya hidup. Tahapan penelitian meliputi pemrosesan data, pengembangan model, serta eval_uasi performa menggunakan metrik akurasi, presisi, recall, F1-score, dan AUC (Area Under the Curve). Hasil menunjukkan bahwa Gradient Boosting unggul dalam akurasi dan AUC, menandakan kemampuan yang lebih baik dalam mendeteksi diabetes secara konsisten dibandingkan dengan dua metode lainnya. AdaBoost memperlihatkan keseimbangan yang baik antara presisi dan recall, menjadikannya cocok untuk skenario yang memerlukan pengendalian kesalahan positif dan negatif secara proporsional. Sementara itu, XGBoost menawarkan efisiensi pemrosesan yang optimal dengan performa yang kompetitif. Gradient Boosting direkomendasikan untuk aplikasi klinis yang membutuhkan akurasi tinggi, sedangkan AdaBoost dapat menjadi alternatif ketika keseimbangan prediksi menjadi prioritas. Penelitian ini berkontribusi dalam pengembangan alat prediksi diabetes yang lebih akurat, efektif, dan dapat diterapkan di sektor kesehatan untuk mendukung upaya deteksi dini.
Optimalisasi Model Klasifikasi Diabetes Menggunakan Ensemble Learning Adaboost, Gradient Boosting, dan XGBoost Wibisono, Setyawan; Hadikurniawati, Wiwien; Yulianton, Heribertus; Lestariningsih, Endang; Cahyono, Taufiq Dwi
Jurnal Teknik Informatika UNIKA Santo Thomas Vol 9 No. 2 : Tahun 2024
Publisher : LPPM UNIKA Santo Thomas

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Diabetes mellitus adalah penyakit kronis yang memengaruhi jutaan orang secara global dan membutuhkan metode diagnosis dini untuk mencegah komplikasi. Penelitian ini bertujuan untuk mengoptimalkan prediksi diabetes dengan membandingkan tiga metode ensemble learning: AdaBoost, Gradient Boosting, dan XGBoost. Dataset yang digunakan adalah Diabetes Health Indicators, yang menggabungkan indikator kesehatan seperti tekanan darah, kolesterol, dan kebiasaan gaya hidup. Tahapan penelitian meliputi pemrosesan data, pengembangan model, serta eval_uasi performa menggunakan metrik akurasi, presisi, recall, F1-score, dan AUC (Area Under the Curve). Hasil menunjukkan bahwa Gradient Boosting unggul dalam akurasi dan AUC, menandakan kemampuan yang lebih baik dalam mendeteksi diabetes secara konsisten dibandingkan dengan dua metode lainnya. AdaBoost memperlihatkan keseimbangan yang baik antara presisi dan recall, menjadikannya cocok untuk skenario yang memerlukan pengendalian kesalahan positif dan negatif secara proporsional. Sementara itu, XGBoost menawarkan efisiensi pemrosesan yang optimal dengan performa yang kompetitif. Gradient Boosting direkomendasikan untuk aplikasi klinis yang membutuhkan akurasi tinggi, sedangkan AdaBoost dapat menjadi alternatif ketika keseimbangan prediksi menjadi prioritas. Penelitian ini berkontribusi dalam pengembangan alat prediksi diabetes yang lebih akurat, efektif, dan dapat diterapkan di sektor kesehatan untuk mendukung upaya deteksi dini.
Pendampingan dan Pelatihan Peningkatan Pengetahuan Tata Kelola Administrasi Keuangan Santi, Rina Candra Noor; Yulianton, Heribertus; Eniyati, Sri; Sutanto, Felix Andreas; Hadiono, Kristophorus
Pelita: Jurnal Pengabdian kepada Masyarakat Vol. 3 No. 3 (2023): Pelita: Jurnal Pengabdian kepada Masyarakat
Publisher : Perkumpulan Kualitama Edukatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51651/pjpm.v3i3.353

Abstract

This growing technology, lots of applications and notifications that are growing every year. With the existence of increasingly developing technology, there are many problems that exist. The problem that often occurs in the finance department is that when recording financial data, it often experiences delays in calculating deficiencies, excesses or the percentage profit obtained. Likewise, when searching for data, it often encounters problems if there are some data that need to be revised. This is what often happens to savings and loans at Dawis Melati, because the records are still manual.
Mentorship dan Pengembangan Desain Kemasan Produk Bagi Warga Tambakaji Semarang Santi, Rina Candra Noor; Eniyati, Sri; Sulastri, Sulastri; Yulianton, Heribertus; Hadionod, Kristophorus; Sutanto, Felix Andreas
Community Engagement and Emergence Journal (CEEJ) Vol. 5 No. 2 (2024): Community Engagement & Emergence Journal (CEEJ)
Publisher : Yayasan Riset dan Pengembangan Intelektual

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37385/ceej.v5i2.5732

Abstract

Desain kemasan Produk merupakan salah satu elemen penting dalam kesuksesan memasarkan produk. Namun, saat ini beberapa pelaku bisnis, terutama para ibu rumah tangga yang sedang belajar berbisnis, banyak yang belum berpikir tentang desain kemasan. Umumnya pelaku usaha baru yang berasal dari para ibu rumah tangga lebih memikirkan kualitas produk daripada desain produk. Padahal desain produk memegang peran penting.
Analysis of E-Government Implementation in Semarang City Based on Mayor Decree No.50/571 of 2023 on SPBE Architecture Determination Eka Ardhianto; Siti Sholihah Ari Susanti; Heribertus Yulianton; Widiyanto Tri Handoko; Kristiawan Nugroho
Jurnal Informatika Dan Tekonologi Komputer (JITEK) Vol. 6 No. 1 (2026): Maret : Jurnal Informatika dan Tekonologi Komputer
Publisher : Pusat Riset dan Inovasi Nasional

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55606/jitek.v6i1.8802

Abstract

This study provides an in-depth analysis of the implementation of e-government in Semarang City, driven by Mayor Decree No.50/571 of 2023 concerning the establishment of the Electronic-Based Government System (SPBE) framework. The research evaluates the alignment of Semarang City's e-government strategies with national regulations, including PerPres No.95/2018 on SPBE, PermenPANRB No.5/2018 on SPBE evaluation criteria, and PerPres No.132/2022 on national SPBE architecture. Employing qualitative methods through literature reviews, interviews, surveys, and observations, this study examines e-government readiness, smart governance, digital service integration, and benchmarking practices. The results highlight significant progress in enhancing public service efficiency and transparency, though challenges such as system interoperability, cybersecurity risks, and public engagement remain. Recommendations include strengthening infrastructure, improving human resource capacity, and fostering citizen involvement for sustainable e-government development.
Perbandingan Kinerja Model Klasifikasi dalam Memprediksi Intensi Pembelian Pengunjung E-Commerce Eniyati, Sri; Noor Santi, Rina Candra; Yulianton, Heribertus; Sunardi, Sunardi; Sulastri, Sulastri; Sugiyamta, Sugiyamta
Dinamik Vol 30 No 2 (2025)
Publisher : Universitas Stikubank

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35315/dinamik.v30i2.10593

Abstract

This study aims to analyze and compare the performance of the Naive Bayes, K-Nearest Neighbors (KNN), and Decision Tree algorithms in predicting the purchase intention of e-commerce visitors using the Online Shoppers Purchasing Intention Dataset, which consists of 12,330 records and 18 variables, with the Revenue variable serving as the classification target. The preprocessing stage involved transforming categorical and boolean variables into numerical form, standardizing features using StandardScaler, and splitting the dataset into 80 percent training data and 20 percent testing data. Model evaluation was conducted using accuracy, precision, recall, F1-score, and ROC-AUC metrics, and was further strengthened by 10-fold cross-validation to obtain more stable results. The findings indicate that KNN achieved the highest accuracy of 0.866180, while Naive Bayes produced the highest recall value of 0.690998 and the highest ROC-AUC value of 0.821696. Meanwhile, Decision Tree demonstrated relatively balanced performance with an accuracy of 0.857259 and an F1-score of 0.571776, whereas the cross-validation results identified KNN as the model with the highest average accuracy of 0.8770. These findings suggest that the selection of a classification model for purchase intention prediction cannot rely solely on a single evaluation metric, as each algorithm possesses different strengths. Therefore, a comparative approach among algorithms can help determine the most suitable model for supporting consumer behavior analysis on e-commerce platforms.
Analisis Kuantitatif Eksploitasi Akun Google Pasca Phishing Berbasis Konsistensi Jaringan Haq, Muhammad Syahrul; Yulianton, Heribertus
JITU Vol 10 No 1 (2026)
Publisher : Universitas Boyolali

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36596/jitu.v10i1.2246

Abstract

Phishing attacks experienced a significant increase during the COVID-19 pandemic, with over 160,000 phishing domains identified quarterly in 2020. This research analyzes login success using phishing-derived data through residential proxies to identify critical factors affecting attack effectiveness against Google authentication systems. Quantitative methodology with controlled experiments utilized 150 Gmail accounts created specifically for this research, with a maximum of 15 login attempts per account. Results demonstrate a 90.7% success rate (136 of 150 cases), with three dominant factors: IP address accuracy (100% match = 97.8% success rate), tier-1 Malaysia ISP/ASN matching (AS4818 DiGi 92.3%, AS9534 Maxis 91.9%, AS4788 TM 90.3%), and geographic location consistency (Kuala Lumpur 59.3% with 91% success rate). Critical findings reveal systemic vulnerabilities in Google's 7-day old password validity policy, creating a window of vulnerability where 22.1% of attacks succeeded on days 3-6 post-password change. This research contributes to cybersecurity literature by providing a quantitative framework for measuring residential proxy effectiveness in post-phishing exploitation and recommending mandatory 2FA implementation and reduction of old password validity period to maximum 48 hours.
Co-Authors Anak Agung Istri Sri Wiadnyani Antesia, Tiara Ariska, Fera Bachtiar, Ridwan Miftachul Baehaqi Brawijaya, Bagas Dewi Handayani Untari Ningsih Dian Kristiawan Nugroho Dwi Agus Diartono Dwi Budi Santoso Dwi Setiawan Eddy Nurraharjo Edy Supriyanto Eka Ardhianto Eko Nur Wahyudi Endang Lestariningsih Fajar Ghulam Darussalam Felix Andreas Sutanto Felix Andreas Sutanto Fera Ariska Hadionod, Kristophorus Haq, Muhammad Syahrul Hersatoto Listiyono Itmamur Roghib Luthfiyan Izzathohir, Khamid Miftakhul Jeffri Alfa Razaq Khotama, Faizal Wahyu Kristophorus Hadiono Kusuma, Aditya Chandra Mardi Siswo Utomo Maulana Maulana, Maulana Mudiyanto, Agus Muhammad Arif Muhammad Bariq Al Mukasyah MUHAMMAD, Yordan Famelio M Famelio Nugrahaeni, Pipit Fitriana PRABAWA, SETYA AJI Praharantyasto, Bonny PRASTYA NUGRAHA, FENDI Priambodo, Ilyas Purwatiningtyas Purwatiningtyas Retnowati Retnowati Rina Candra Noor Santi Rina Candra NS Rohmah, Noviyani Purwaning Sanubari, Johan Hikmah SATRIAWAN, HB, SIGIT SATRIAWAN Setyawan Wibisono Siti Sholihah Ari Susanti Sri Eniyati Sri Mulyani Sri Mulyani Sugiyamta Sugiyamta Sulastri Sulastri Sunardi Sunardi Sunardi Sunardi Taufiq Dwi Cahyono Widiyanti, Regina Elsa Widiyanto Tri Handoko Wijaya, Mikhael Putra Wismarini, Th Dwiati Wiwien Hadikurniawati Zidan Rafindra Utomo