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All Journal Jurnal Informatika dan Teknik Elektro Terapan JOIV : International Journal on Informatics Visualization International Journal of Artificial Intelligence Research Journal of Information Technology and Computer Science (JOINTECS) Syntax Literate: Jurnal Ilmiah Indonesia JURNAL TEKNIK INFORMATIKA DAN SISTEM INFORMASI Conference on Innovation and Application of Science and Technology (CIASTECH) J-SAKTI (Jurnal Sains Komputer dan Informatika) JURTEKSI Jutisi: Jurnal Ilmiah Teknik Informatika dan Sistem Informasi Jusikom: Jurnal Sistem Informasi Ilmu Komputer Jurnal Ilmu Komputer dan Bisnis Jurnal Teknologi Informasi dan Multimedia Systematics Techno Xplore : Jurnal Ilmu Komputer dan Teknologi Informasi Jurnal Teknologi Dan Sistem Informasi Bisnis Buana Ilmu Buana Information Technology and Computer Sciences (BIT and CS) Jurnal Accounting Information System (AIMS) INTERNAL (Information System Journal) International Journal of Educational Review Journal of Applied Data Sciences Jurnal Cahaya Mandalika Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer) Djtechno: Jurnal Teknologi Informasi KLIK: Kajian Ilmiah Informatika dan Komputer Instal : Jurnal Komputer J-SAKTI (Jurnal Sains Komputer dan Informatika) Jurnal Mandiri IT Jurnal Minfo Polgan (JMP) Abdimas Jurnal Ilmiah Sistem Informasi dan Ilmu Komputer Jurnal Sistem Informasi STORAGE: Jurnal Ilmiah Teknik dan Ilmu Komputer Innovative: Journal Of Social Science Research Jurnal Accounting Information System (AIMS) INTERNAL (Information System Journal) Jurnal Ilmiah Pengabdian Kepada Masyarakat (Nyiur-Dimas) Informasi interaktif : jurnal informatika dan teknologi informasi Jurnal PETISI (Pendidikan Teknologi Informasi) Journal of Information Technology
Priyatna, Bayu
Universitas Buana Perjuangan Karawang
Religion Agriculture, Biological Sciences & Forestry Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Mathematics Public Health Social Sciences Other

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Klasterisasi Tingkat Penjualan Kedai Kopi Hallo Burjois Menggunakan Algoritma K-Medoids Sebagai Evaluasi Pradana Rizki Maulana; April Lia Hananto; Agustia Hananto; Bayu Priyatna
JURNAL FASILKOM Vol. 14 No. 1 (2024): Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer)
Publisher : Unversitas Muhammadiyah Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37859/jf.v14i1.6912

Abstract

Kedai Hallo Burjois yang sedang mengalami tantangan untuk memperluas jangkauan produk menu produk, penelitian ini menerapkan algoritma K-Medoids Clustering untuk meningkatkan strategi promosi dengan mengidentifikasi menu-menu yang memiliki tingkat minat pembeli rendah. Proses tersebut melibatkan pengolahan data penjualan harian yang diubah menjadi format bulanan, di mana algoritma K-Medoids digunakan untuk membentuk tiga kluster yang mewakili tingkat penjualan tinggi, sedang, dan rendah. Hasil klasterisasi menunjukkan adanya menu-menu dengan penjualan rendah sebanyak 6 item, antara lain Americano, Caffe Latte, Dark Choco Caramel, Dimsum, Hazelnut Latte, dan Pasta Carbonara. Lalu kami mengadopsi prinsip 4P (Product, Price, Place & Promotion) untuk mengevaluasi produk dengan tingkat penjualan terendah. Uji validitas dilakukan menggunakan Davies Boulding Index (DBI), menunjukkan keakuratan dan konsistensi hasil klasteriasasi sebesar 0,95 pada tiga kluster.
Implementasi Metode Agile Development Dalam Perancangan Sistem Informasi Pendaftaran KB MKJP Berbasis Website Handayani, Citra; Priyatna, Bayu; Hananto, Agustia; Tukino, Tukino
Jurnal Ilmu Komputer dan Bisnis Vol. 16 No. 1 (2025): Vol. 16 No. 1 Mei (2025)
Publisher : STMIK Dharmapala Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47927/jikb.v16i1.1039

Abstract

Program Keluarga Berencana (KB) merupakan salah satu upaya pemerintah Indonesia dalam mengendalikan pertumbuhan penduduk dan meningkatkan kesejahteraan masyarakat. Salah satu metode yang dianjurkan dalam program ini adalah Kontrasepsi Jangka Panjang (MKJP), seperti implan, IUD, dan sterilisasi. Namun, partisipasi masyarakat, terutama di daerah pedesaan seperti Kecamatan Karang Bahagia, Kabupaten Bekasi, masih tergolong rendah. Proses pencatatan dan pengelolaan data di BKKBN Kecamatan Karang Bahagia masih dilakukan secara manual, sehingga rawan mengalami kesalahan, kerusakan, atau kehilangan data. Untuk menjawab permasalahan tersebut, penelitian ini mengembangkan sistem informasi pendaftaran KB MKJP berbasis web dengan menggunakan metode Agile Development yang memungkinkan pengembangan dilakukan secara bertahap dan fleksibel. Sistem ini dirancang untuk meningkatkan efisiensi dalam proses pendaftaran, validasi data, serta pelaporan secara real-time. Berdasarkan hasil pengujian black box, seluruh fitur dalam sistem berfungsi sesuai dengan harapan. Diharapkan dengan adanya sistem ini, tingkat partisipasi masyarakat terhadap program MKJP dapat meningkat dan pengelolaan data menjadi lebih akurat serta terintegrasi.
IMPLEMENTASI MACHINE LEARNING MELALUI PENDEKATAN ALGORITMA RANDOM FOREST DALAM PREDIKSI TINGKAT STRES BERDASARKAN POLA GAYA HIDUP Ramadanti, Anita Khansa; Hananto, April Lia; Priyatna, Bayu; Hananto, Agustia
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 Andini, Vina; Hananto, April Lia; Priyatna, Bayu; Hananto, Agustia
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
Improved Hybrid GoogLeNet-Based Deep Learning Optimization for Standardized Straw Mushroom Quality Classification in Indonesia Priyatna, Bayu; Abdurahman, Titik Khawa; Miskon, Muhammad Fahmi; Hananto, April Lia; Hananto, Agustia Tia; Rahman, Aviv Yuniar
Journal of Applied Data Sciences Vol 7, No 2: May 2026
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v7i2.1206

Abstract

Deep learning plays a crucial role in modern computer vision due to its ability to automatically extract hierarchical features from large-scale image data. Among various architectures, Convolutional Neural Networks (CNNs) have been extensively utilized for image pattern interpretation, including in agricultural product inspection. Straw mushrooms (Volvariella volvacea) are important agro-industrial commodities in Indonesia; however, their quality assessment still relies on subjective manual evaluation based on the Indonesian National Standard (SNI:01-6945-2003), leading to inconsistency in grading results. To address this limitation, this research proposes an Improved Hybrid GoogLeNet model integrated with a YOLO-based detection framework and hybrid preprocessing to enhance feature clarity and classification robustness. The system is capable of conducting object detection, 3-class morphological quality classification (Pure White, Oval, and Black Spot/Defect), and automatic diameter measurement using calibrated pixel-to-centimeter conversion. Performance evaluation is carried out by benchmarking the proposed model against several popular deep learning architectures including YOLOv5, LeNet, AlexNet, VGGNet, and ResNet. Experimental results demonstrate that the Improved Hybrid GoogLeNet achieves the highest performance with precision of 97.99%, recall of 96.07%, and F1-score of 96.98%, along with low misclassification rates across all classes. These results indicate that the proposed method provides accurate, reliable, and efficient quality assessment that supports standardized automated grading in industrial applications. Therefore, this study contributes to the advancement of intelligent computer vision solutions for digital transformation in the Indonesian mushroom agro-industry.
Penerapan E-CRM Berbasis Web Menggunakan Metode RAD pada Showroom Wali Sanga Motor Arifin, Jihan Salsabila; Priyatna, Bayu; Nurapriani, Fitria; Hananto, April Lia
Jurnal Teknologi Informasi dan Multimedia Vol. 8 No. 2 (2026): May
Publisher : Sekawan Institut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35746/jtim.v8i2.986

Abstract

Customer service at Wali Sanga Motor Showroom is still conducted manually, causing delays in information delivery, unstructured customer data management, and less optimal handling of or-ders and customer complaints. This study aims to develop a web-based Electronic Customer Rela-tionship Management (E-CRM) system using the Rapid Application Development (RAD) method to improve service effectiveness and customer relationship management. The RAD method was applied through stages of requirements planning, prototyping design, system construction, and implementation. The system was developed using the Laravel framework and MySQL database with main features including product information management, online motorcycle ordering, cus-tomer complaint services, and administrative data management. System testing was carried out using User Acceptance Test (UAT) and White Box Testing on all functional requirements that had been designed. The test results show that the system runs 100% in accordance with the UAT sce-narios and all main modules function according to user needs without any significant logical er-rors. The implementation of this E-CRM system is expected to improve service efficiency, acceler-ate responses to customers, and support integrated and sustainable management of customer ser-vice data at Wali Sanga Motor Showroom.
Design of an Enterprise Architecture for Monitoring IT Services and Infrastructure Using TOGAF ADM at PT Fratama Kencana Gemilang Karina; Hananto, April Lia; Priyatna, Bayu; Hananto, Agustia
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.
Inventory control system using threshold method for automotive industry Fadillah, Arya; Priyatna, Bayu; Nurapriani, Fitria; Tukino, Tukino
Jurnal Mandiri IT Vol. 14 No. 4 (2026): April: Computer Science and Field.
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/mandiri.v14i4.528

Abstract

Inventory control is a critical aspect in manufacturing companies to ensure the availability of materials and support smooth production processes. Ineffective inventory management can lead to stock shortages or overstock conditions, which may disrupt operational activities. This study aims to develop a web-based inventory control system using the Reorder Point (ROP) method to optimize stock management in a manufacturing environment. The system is designed to monitor stock levels, calculate Average Daily Usage (ADU), safety stock, and reorder points automatically. When stock reaches a predefined threshold, the system provides notifications to assist decision-making in replenishment processes. The development method used in this study is the Waterfall model, including analysis, design, implementation, and testing stages. The system is implemented using the CodeIgniter framework and MySQL database. Testing results show that the system can accurately calculate ROP values and effectively provide early warnings for low stock conditions. Therefore, the proposed system can improve efficiency, reduce the risk of stock shortages, and support better inventory management in manufacturing companies. The system achieved an accuracy rate of 100% in calculating ROP values based on Black Box testing results, and successfully generated real-time notifications for all critical stock conditions. The novelty of this study lies in the integration of threshold-based logic with automated notification features in a web-based system, which provides a more responsive and practical solution compared to previous inventory control approaches that rely on manual monitoring.
Co-Authors AA Sudharmawan, AA Aang Alim Murtopo Abdullah Abdullah Abdurahman, Titik Khawa Adittia Agustian Afra, Alfina Fadhilah Ahnaf, Naufal Zubdi alzahra, alika aziza Andini, Vina Angraeni, Rahmah Nur April Lia Hananto Aprilia Putri Nardilasari Arifin, Jihan Salsabila Arip Solehudin Asep Haris Aulia, Aldi Aviv Yuniar Rahman Baenil Huda Desfianthy, Fatiya Hanifah Diningrat, Cahya Fadillah, Arya Fatlun, Aulia Fitria Nurapriani Florida Ivonia Manek Guntur, Muhamad Haerudin Haerudin Hananto, Agustia Hananto, Agustia Tia Handayani, Citra Heryana, Nono Hilabi, Shofa Shofia Hilabi, Shofa Shofia Hilabi, Shofa Shofiah Hoerudin Hoerudin Huda, Baenil Intan, Melani Ismail, Dennisa Avriel Jabar Sanjaya Julia, Ina Julifer P Manurung Juniardi Karina Kurniawan, Muhamad Dicky Lutfiah, Siti Miskon, Muhammad Fahmi Mohamad Ricky Firdaus Muhammad Nova Novalia, Elfina Nur ‘Azah Nurapriani, Fitria Paryono, Tukino Pradana Rizki Maulana Ramadanti, Anita Khansa Reswara, Hadaya Abhista Rini Mayasari Sabrina Amanda Salsabila Saepul Aripiyanto Sari, Fitria Ratna Setiawan, Revi Shofa Shofia Hilabi Shofa Shofia Hilabi Shofa Shofiah Hilabi Shofia Hilabi, Shofa Sifa, Sifa Rismawati Siregar, Amril Mutoi Siti Masruroh Siti Masruroh Sutan Faisal Syahri Susanto Tatang Rohana Teresa Abuya Topan Setiawan Topan Trianto Tukino Tukino, Tukino tukino, tukino Tukino, Tukino Wahyu Aziz Ramadhani Yazid, Muhammad Abi Zahra, Nabila Zein, Selmia Aulia Zhalifunas, Satria Dawas