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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) EXPERT: Jurnal Manajemen Sistem Informasi dan Teknologi JOIV : International Journal on Informatics Visualization RABIT: Jurnal Teknologi dan Sistem Informasi Univrab SMARTICS Journal Syntax Literate: Jurnal Ilmiah Indonesia JURNAL TEKNIK INFORMATIKA DAN SISTEM INFORMASI Edukasi Islami: Jurnal Pendidikan Islam JURIKOM (Jurnal Riset Komputer) Jurnal Riset Informatika Journal of Information System, Applied, Management, Accounting and Research METIK JURNAL Jurnal Informatika Kaputama (JIK) Jutisi: Jurnal Ilmiah Teknik Informatika dan Sistem Informasi Jurnal Ilmiah ILKOMINFO - Ilmu Komputer & Informatika Jusikom: Jurnal Sistem Informasi Ilmu Komputer Jurnal Ilmu Komputer dan Bisnis Jurnal Teknologi Informasi dan Multimedia Jurnal Ekonomi Manajemen Sistem Informasi Systematics Techno Xplore : Jurnal Ilmu Komputer dan Teknologi Informasi Jurnal Teknologi Dan Sistem Informasi Bisnis Zonasi: Jurnal Sistem Informasi Jurnal Informasi dan Teknologi Buana Information Technology and Computer Sciences (BIT and CS) JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) JIKA (Jurnal Informatika) Infotek : Jurnal Informatika dan Teknologi Journal of Applied Data Sciences Jurnal Cahaya Mandalika Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer) International Journal of Computer and Information System (IJCIS) International Journal of Engineering, Science and Information Technology Djtechno: Jurnal Teknologi Informasi Jurnal Tika Instal : Jurnal Komputer Dirgamaya: Jurnal Manajemen dan Sistem Informasi Jurnal Minfo Polgan (JMP) Jurnal Teknik Mesin Mechanical Xplore Jurnal Informatika Teknologi dan Sains (Jinteks) Abdimas Jurnal Sistem Informasi STORAGE: Jurnal Ilmiah Teknik dan Ilmu Komputer Jurnal Ilmiah Teknik Informatika dan Komunikasi Innovative: Journal Of Social Science Research Bulletin of Network Engineer and Informatics (BUFNETS) Jitu: Jurnal Informatika Utama VIDHEAS: Jurnal Nasional Abdimas Multidisiplin Jurnal Accounting Information System (AIMS) INTERNAL (Information System Journal) Masyarakat Berkarya: Jurnal Pengabdian dan Perubahan Sosial Jurnal PETISI (Pendidikan Teknologi Informasi) JUSTINDO (Jurnal Sistem dan Teknologi Informasi Indonesia) Journal of Information Technology
Hananto, Agustia
Fakultas Ilmu Komputer, Program Studi Sistem Informasi, Universitas Buana Perjuangan Karawang, Karawang, Indonesia
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ANALISIS USER SENTIMENT APLIKASI GOOGLE MAPS, MAPS.ME DAN WAZE MENGGUNAKAN METODE SUPPORT VECTOR MACHINE Ilham Fariz Asya Mubarok; Baenil Huda; Agustia Hananto; Tukino Tukino; Huban Kabir
Rabit : Jurnal Teknologi dan Sistem Informasi Univrab Vol 8 No 1 (2023): Januari
Publisher : LPPM Universitas Abdurrab

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36341/rabit.v8i1.3020

Abstract

Nowadays, the routing app is often used by many people, this app is very useful for users to find the best route by just entering the address code, this app can provide travel routes which can be taken by different kinds of vehicles. In Indonesia itself, there are several widely used route guidance apps with various positive and negative reviews. In this study, different types of apps namely Google Maps, Maps.me and Waze were used and the data is from user feedback through an online survey. The purpose of this study is to find out the users' ratings for each application which was used as the material for the study. Support Vector Machine method was used to process the data. For each app, 750 comments were received and the final result of maps.me was the app with the highest score based on 86.40% accuracy, 86.55% precision and 99.69% recall. The maps.me app received 68% positive reviews, followed by Waze with 29% and Google Maps with 3%. This makes maps.me the app with the highest score based on positive reviews.
Air quality prediction using boosting-based machine learning models for sustainable environment Ahmad Fauzi; Maharina Maharina; Jamaludin Indra; Ayu Ratna Juwita; Agustia Hananto; Euis Nurlaelasari
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 1: February 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i1.pp515-523

Abstract

High levels of air pollution are extremely harmful to humans and the environment. They increase the risk of respiratory infections and lung cancer, especially among vulnerable populations. Therefore, developing effective pollution control measures is crucial for mitigating these negative impacts. We need to implement effective methods to predict and manage air quality for the sake of public health and a healthier environment. In recent years, machine learning (ML) methods have been increasingly utilized in air quality prediction due to their ability to analyze datasets and identify complex patterns. However, the reliability and accuracy of air quality prediction models remain a challenge. This study proposes a boosting-based ML model for predicting air quality. We implemented three stages in the proposed method. In the first stage, we conducted data preprocessing and analysis to eliminate noise, remove redundant data, and encode categorical features. In the second stage, we predicted air quality categories by leveraging 25 ML models, dividing them into three distinct categories. The results show that the extreme gradient boosting (XGBoost), light gradient boosting machine (LGBM), and adaptive boosting (AdaBoost) models outperform the others in air quality prediction, achieving an accuracy of 99%. Finally, we compared these three models using 10-fold cross validation to ensure they generalize well in last stage.
Advancing Secure Communication in the Quantum Era through the Integration of Artificial Intelligence and Quantum Cryptographic Techniques: Author's Country: India Hemant N Chaudhari; Bayu Priyatna; Agustia Hananto
Buana Information Technology and Computer Sciences (BIT and CS) Vol. 7 No. 1 (2026): Buana Information Technology and Computer Sciences (BIT and CS)
Publisher : Information System; Universitas Buana Perjuangan Karawang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36805/fze4t734

Abstract

Utilizing the ideas of quantum physics, quantum cryptography is quickly becoming a vital defense against the growing cybersecurity risks of the contemporary day, especially in light of developing quantum computing. This essay investigates the complex field of quantum cryptography and looks at how it can transform network security and protect private data. We examine the fundamental ideas of quantum cryptography, such as Quantum Key Distribution (QKD) protocols like BB84 and E91, which use quantum features like superposition and entanglement to provide potentially indestructible secure communication channels. We also discuss the urgent need for quantum-resistant solutions in view of the developing "quantum threat" to well-known cryptographic algorithms like RSA and AES. The potential benefits and difficulties of using artificial intelligence (AI) techniques to boost quantum cryptography systems' resilience and efficiency are also examined. The creation of effective quantum repeater networks and enhanced security proofs are among the outstanding research topics, future difficulties, and present implementations in quantum cryptography that are covered in this study. We stress how crucial quantum cryptography is to protecting sensitive communications in the quantum era for a variety of industries, including the military, government, financial industry, and healthcare. We come to the conclusion that quantum cryptography has enormous potential for protecting vital information systems from future cyberattacks that are becoming more complex, even if we acknowledge the technology's early stages of development.
IMPLEMENTASI MACHINE LEARNING MELALUI PENDEKATAN ALGORITMA RANDOM FOREST DALAM PREDIKSI TINGKAT STRES BERDASARKAN POLA GAYA HIDUP Anita Khansa Ramadanti; April Lia Hananto; Bayu Priyatna; Agustia Hananto
Djtechno: Jurnal Teknologi Informasi Vol 7, No 1 (2026): April
Publisher : Universitas Dharmawangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46576/djtechno.v7i1.8457

Abstract

Stres yang tidak terkelola berisiko berkembang menjadi gangguan serius seperti depresi hingga risiko bunuh diri. Machine learning dapat dioptimalkan sebagai solusi deteksi dini berdasarkan kombinasi faktor gaya hidup. Penelitian ini bertujuan untuk mengembangkan model prediksi tingkat stres melalui pendekatan algoritma Random Forest dengan dataset yang diperoleh platform Kaggle. Tahapan penelitian meliputi data preprocessing, penanganan ketidakseimbangan kelas menggunakan SMOTE, hingga evaluasi dan integrasi model.  Hasil evaluasi menunjukkan bahwa model mencapai akurasi sebesar 0.80, dengan nilai precision, recall, dan F1-Score secara keseluruhan berada pada angka 0,80. Performa terbaik diperoleh pada klasifikasi tingkat stres kategori High dengan F1-Score sebesar 0.86. Model yang telah tervalidasi kemudian diintegrasikan ke dalam antarmuka melalui Streamlit, sehingga mampu memberikan hasil prediksi secara real-time berdasarkan input data pengguna. Penelitian ini membuktikan bahwa algoritma Random Forest efektif dalam mengidentifikasi tingkat stres, dan implementasinya dalam bentuk aplikasi web berpotensi menjadi alat bantu deteksi dini yang fungsional dan sederhana.
Pendekatan Data Mining Dengan Algoritma K-Means Untuk Klasterisasi Faktor Perceraian Di Jawa Barat Vina Andini; April Lia Hananto; Bayu Priyatna; Agustia Hananto
JURNAL PETISI (Pendidikan Teknologi Informasi) Vol. 7 No. 2 (2026): JURNAL PETISI (Pendidikan Teknologi Informasi)
Publisher : Universitas Pendidikan Muhammadiyah Sorong

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36232/jurnalpetisi.v7i2.5560

Abstract

Abstrak: Penelitian ini dilatarbelakangi oleh tingginya angka perceraian di Provinsi Jawa Barat yang menunjukkan variasi faktor penyebab antarwilayah, sehingga diperlukan analisis kuantitatif berbasis data untuk mengidentifikasi pola pengelompokan determinannya. Tujuan penelitian ini adalah menganalisis konfigurasi klaster faktor penyebab perceraian serta mengidentifikasi faktor dominan pada masing-masing kelompok wilayah menggunakan pendekatan data mining. Metode yang digunakan adalah K-Means Clustering berbasis unsupervised learning terhadap data sekunder Open Data Jabar periode 2017–2024, dengan enam variabel utama yaitu ekonomi, KDRT, kawin paksa, zina, madat, dan cacat badan. Penentuan jumlah klaster dilakukan menggunakan Elbow Method, evaluasi model menggunakan Silhouette Coefficient, serta visualisasi pola dilakukan melalui Principal Component Analysis (PCA). Hasil penelitian menunjukkan terbentuknya tiga klaster dengan nilai Silhouette sebesar 0,61 yang mengindikasikan kualitas pemisahan klaster yang baik. Cluster pertama didominasi faktor ekonomi, cluster kedua menonjol pada faktor zina dan madat, sedangkan cluster ketiga menunjukkan kombinasi tekanan ekonomi, KDRT, dan kawin paksa. Temuan ini menegaskan bahwa perceraian di Jawa Barat dipengaruhi oleh pola determinan yang berbeda antarwilayah. Penelitian ini menyimpulkan bahwa pendekatan K-Means efektif dalam mengidentifikasi struktur laten faktor perceraian dan merekomendasikan kebijakan pencegahan yang disesuaikan dengan karakteristik klaster serta pengayaan variabel dan metode pada penelitian selanjutnya
Design of an Enterprise Architecture for Monitoring IT Services and Infrastructure Using TOGAF ADM at PT Fratama Kencana Gemilang Karina; April Lia Hananto; Bayu Priyatna; Agustia Hananto
J-INTECH ( Journal of Information and Technology) Vol 14 No 01 (2026): Journal of Information and Technology
Publisher : LPPM Universitas Bhinneka Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32664/j-intech.v14i01.2275

Abstract

The management of information technology infrastructure at PT Fratama Kencana Gemilang currently faces significant operational challenges in its day-to-day operations. This is due to the device monitoring mechanisms currently in place, which remain manual and fragmented across units, resulting in the IT team often only becoming aware of technical issues after receiving user complaints. This reactive approach inevitably hinders company productivity, particularly regarding server services that form the core of the business. Therefore, this study aims to design a more proactive, automated, and integrated enterprise system monitoring architecture using the TOGAF ADM (The Open Group Architecture Framework Architecture Development Method) framework. Through this approach, it is expected that all of the company’s technology assets can be centrally monitored and aligned with long-term strategic business objectives. This research employs a qualitative descriptive approach conducted through direct observation of the existing system infrastructure and in-depth architectural modeling. This design process covers various key domains in a structured manner, ranging from the vision domain, business architecture, information system architecture, to the supporting technology infrastructure. The research results indicate that the proposed open-source-based monitoring system design has successfully met the company’s functional and technical requirements comprehensively. This is evidenced by the results of the expert validation process (expert review), which yielded an average score of 4.5 out of 5.0. These results confirm that the designed system is highly effective in providing real-time and accurate visibility into infrastructure performance. This study concludes that the proposed architecture and resulting blueprint are highly suitable to serve as the primary reference for company management in enhancing the reliability of their IT services. The implementation of this design is expected to accelerate the troubleshooting process and minimize the risk of future system failures.
Implementation of The Seasonal Autoregressive Integrated Moving Average Predictive Model on Raw Material Usage Data at PT. Plastik Karawang Flexindo Muhammad Rindra Alfiansyah; Tukino Tukino; Agustia Hananto; Elfina Novalia
International Journal of Engineering, Science and Information Technology Vol 5, No 3 (2025)
Publisher : Malikussaleh University, Aceh, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52088/ijesty.v5i3.867

Abstract

Fluctuations in raw material utilization in the manufacturing industry significantly impact production process efficiency, operational costs, and supply chain stability. Inaccurate planning and management of raw material inventories can lead to two extreme conditions: excess stock, which increases storage costs and the risk of expiration, or stock shortages, which could halt the production process and reduce productivity. To improve the accuracy of raw material consumption planning, this study applies the Seasonal Autoregressive Integrated Moving Average (SARIMA) model to predict raw material needs periodically based on historical data. The dataset used includes the consumption of Polyethylene (PE), High Density Polyethylene (HDPE), and Polypropylene (PP) from 2019 to 2025. The data is analyzed using a time series forecasting approach to identify trends and seasonal patterns. The SARIMA model is developed and optimized using three methods to search for the best parameters: Grid Search, Random Search, and Bayesian Optimization, to enhance prediction performance. The model's evaluation calculates the Mean Absolute Percentage Error (MAPE) as an accuracy indicator. The evaluation results show that although SARIMA can recognize seasonal patterns in raw material consumption, the prediction accuracy varies, with the best MAPE value being 16% and the highest being 34%. This indicates that external factors, such as market dynamics, government policies, global price fluctuations, and internal variables such as production schedules and customer demand, need to be considered to improve the model's precision. Overall, the application of SARIMA in this context provides a strategic contribution to supply chain management in the manufacturing industry, particularly in anticipating raw material needs, reducing uncertainty, and supporting more efficient and adaptive data-driven decision-making.
Analisis dan Pemodelan Proses Bisnis Katering pada UMKM Menggunakan BPMN April Lia Hananto; Elsa Rosalina; Agustia Hananto; Baenil Huda
INTERNAL (Information System Journal) Vol. 7 No. 1 (2024)
Publisher : Masoem University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32627/internal.v7i1.938

Abstract

Fafiras Kitchen is a family business that focuses on food catering services. This business involves three main operational stages, namely the ordering process, procurement of raw materials, and delivery of products to customers. From the results of observations and interviews, several problems were identified such as the absence of good records in the ordering process resulting in errors in order fulfillment and loss of customer data, dependence on one raw material supplier increases the risk of delays and disruptions in the supply chain and inefficient delivery management caused by human error and no real time tracking, so this has an impact on customer loyalty. This research aims to analyze and model the current business process and provide recommendations for improvement. Modeling is done using Business Process Model and Notation (BPMN) with a qualitative approach to investigate existing business processes. The results of this research can identify areas of improvement to increase the efficiency and effectiveness of MSMEs operations so that MSMEs can better understand, manage, and optimize their business processes. In addition, this research also conducted simulations using the Bizagi Modeler application to test the validity of the business process modeling that has been made. The simulation results show an increase in time in the implementation of business processes.
Co-Authors Abdul Hafiz Adila Rahmawati Afra, Alfina Fadhilah Agneresa Agneresa Ahmad Fauzi ali, agus alzahra, alika aziza Amir Amir Amri Abdulah Anggi Octa Fadilah Angraeni, Rahmah Nur Anita Khansa Ramadanti Annam, Dyno Syaiful Apriade Voutama Apriani, Fitria April Lia Hananto April Lia Hananto Arief Wibowo Arip Solehudin Asep Permana atikah, dwi Atmaja, Rashelin Zahra Aulia, Aldi Aviv Yuniar Rahman Aviv Yuniar Rahman Awal, Elsa Elvira Ayu Ratna Juwita Azizah, Fathin Putri Baenil Huda Baenil Huda Baenil Huda Baenil Huda Bayu Priyatna Bayu Yoga Astario Bilqis Amalia Utari Deva Defrina Aldeana Dodi Mulyadi Dodi Mulyadi Dyno Syaiful Annam Eko Pramono Elfina Novalia Elfinanovalia , Elfinanovalia Elsa Rosalina Emilia Sukmawati, Cici Erlyta Hares Euis Nurlaelasari Fatmanisa Mumpuni Delta Maharani Ferdiansyah, Indra FIKRI HAIKAL Fitria Nur Apriani Fitria Nurafriani Fitria Nurapriani Fitria Nurapriani Fizra Firdaus Nillan Goenawan Brotosaputro Hadaya Abhista Reswara Handayani, Citra Hemant N Chaudhari Herda Andriana Heryana, Nono Hilabi, Shofa Shofia Hilabi, Shofa Shofiah Hilabi, Shofa Shofiah Huban Kabir Huda , Baenil Huda, Baenil Ikhsan, Muhammad Daffa Ilham Fariz Asya Mubarok Indra Kurniawan Indra, Jamaludin Jasmine Dina Sabila Karina Karyadi Karyadi Khoirudin Khoirudin Khoirudin, Khoirudin Kusnadi, Akhmad Maharina Maharina Melisa Mubarok, Piky Muhamad Mammun Muhamad Rizky Arfani Muhamad Rizky Arfani Muhammad Difa Prakoso Fuadi Muhammad Khaerudin Muhammad Rindra Alfiansyah Novalia, Elfina Nur ‘Azah Nurajizah, Dhea Nurapriani, Fitria Nurfajria, Dera Nurhayati Paryono, Tukino Pradana Rizki Maulana Pratama, Tito Chaerul Priyatna, Bayu Priyatna, Bayu Puspita Sari, Desti Rahmatiani, Lusiana Rati Ratnasari Rini Mayasari Sabrina Amanda Salsabila Saefil Aripiyanto Salsabila, Nasya Setiawan, Pratama Wahyu Setiawan, Pratama Wahyu Shofa Shofia Hilab Shofa Shofia Hilabi Shofa Shofia Hilabi Shofa Shofia Hilabi Shofa Shofiah Hilabi Shofa Shofiah Hilabi Shofia Hilabi, Shofa Shofiah Hilabi, Shofa Sidqi Awaludin Sifa, Sifa Rismawati Sigit Budi Nugroho Silvana Nazuah Siti Masruroh Sri Wahyuni Sukarman Sukarman Sukarman Sukarman Sunarya, Edwin Yohanes Tamala, Evi TARMUJI TARMUJI, TARMUJI Taufik Ulhakim, Muhamad Thoyib, Imam Nurhuda Tikamori, Ghazi Tukino Tukino Tukino , Tukino Tukino Tukino Tukino Tukino Tukino Tukino, Tukino Tukino, Tukino Tukino, Tukino Utomo, Ainur Alam Budi Vina Andini Wahyu, Pratama Widyanti, Tyas Witulas Ambang Cahyati Yoga Astario, Bayu Zein, Selmia Aulia