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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) JURTEKSI Jutisi: Jurnal Ilmiah Teknik Informatika dan Sistem Informasi Jusikom: Jurnal Sistem Informasi Ilmu Komputer Jurnal Ilmu Komputer dan Bisnis 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 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 Abdi Citra Jurnal Ilmiah Pengabdian Kepada Masyarakat (Nyiur-Dimas) Informasi interaktif : jurnal informatika dan teknologi informasi Jurnal PETISI (Pendidikan Teknologi Informasi) Journal of Information Technology Jurnal Publikasi Teknik Informatika
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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Implementasi Merdeka Belajar Pendidikan Agama Islam di Tingkat Sekolah Dasar/Madrasah Ibtidaiyah Dengan Memanfaatkan Teknologi Informasi Masruroh, Siti; Haerudin, Haerudin; Priyatna, Bayu
Jurnal Ilmiah Pengabdian Kepada Masyarakat (Nyiur-Dimas) Vol 3 No 1 (2023)
Publisher : Lembaga Penelitian dan Pengabdian Kepada Masyarakat (LP2M) IAIN Manado

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30984/nyiur.v3i1.424

Abstract

The purpose of study program-based community service is as an effort by lecturers to carry out the tridarma of higher education. In this case the Directorate General of Islamic Higher Religious Education (Diktis) provides a national level grant program for all lecturers under the ministry of religion. On this occasion, we propose a service with the theme Implementation of Freedom to Learn Islamic Religious Education at the Elementary School/Madrasah Ibtidaiyah Level by utilizing Information Technology, namely making an application "Islamic Smart Learning" for Madrasah Ibtidaiyah Al I'anah Dusun Kalihurip Duren Village, Klari District, Karawang Regency. This service activity is a contribution of the PAI Study Program to the world of elementary/Madrasah Ibtidaiyah education as a means of utilizing information technology in the independent learning program with the principle of independent learning, it is assumed that students are given freedom to access learning media that is more intelligent and enjoyable. So that the learning potential of students will grow more measurably and directed because there are more interactive learning media.
Analisis Sentimen Terhadap Opini Proyek Kereta Cepat Menggunakan Metode Naïve Bayes Classifier Jabar Sanjaya; Tukino; Priyatna, Bayu; Shofia Hilabi, Shofa
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.v14i1.6598

Abstract

Twitter merupakan satu dari banyaknya media sosial yang digunakan oleh hampir Sebagian besar masyarakat. Penggunaaan twitter umumnya digunakan untuk memposting sesuatu, seperti beropini tentang infrastruktur negara misalnya. Adapun salah satu infrastruktur negara yang sedang ramai diperbincangkan adalah proyek kereta cepat, masyarakat memiliki banyak pandangan tentang proyek ini. Untuk mengetahui opini masyarakat, tentang hal itu maka akan dilakukan Sentimen Analisis. Penelitian ini bertujuan untuk mengetahui penerapan Sentimen Analisis menggunakan metode Naïve Nayes Classifier pada opini masyarakat tentang proyek kereta cepat. Penelitian dimulai dari proses pengambilan data menggunakan teknik crawling dan mendapatkan kurang lebih 2007 tweet yang membahas proyek kereta cepat. Yang kemudian data dari hasil sentimen di preprocessing¸ lalu setiap data di labeling. Dari hasil labeling diterapkan metode naïve bayes classifier. Dari hasil penelitian ini menunjukan 57,23% masyarakat lebih dominan berkomentar atau beropini positif tentang proyek kereta cepat. Dan sisanya 42,77% beropini negatif. Adapun hasil evaluasi model Naïve Bayes yaitu, Presisi 81%, Recall 81%, F1-score 81%. dan kemudian Akurasi sebesar 81%.
Classification of Starling Images Using a Bayesian Network Hananto, April Lia; Rahman, Aviv Yuniar; Paryono, Tukino; Priyatna, Bayu; Hananto, Agustia; Huda, Baenil
Journal of Applied Data Sciences Vol 6, No 1: JANUARY 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i1.423

Abstract

The classification of starling species is vital for biodiversity conservation, especially as some species are endangered. This research investigates the effectiveness of the Bayesian Network (BayesNet) for classifying starling species and compares its performance with Artificial Neural Networks (ANN) and Naive Bayes models. The dataset comprises 300 images of five starling species—Bali, Rio, Moon, Kebo, and Uret—captured under controlled conditions. Feature extraction focused on color, texture, and shape, while data augmentation through slight image rotations was applied to enhance model generalization. The BayesNet model achieved an accuracy of 96.29% using a 90:10 training-to-testing split, outperforming ANN (90.74%) and Naive Bayes variants. Precision, recall, F1-score, and AUC-ROC values further validated the robustness of the BayesNet model, with precision at 0.90, recall at 0.91, F1-score at 0.92, and AUC-ROC at 0.95. These results demonstrate the superior performance of multi-feature Bayesian Networks in starling classification compared to other machine learning models. The novelty of this study lies in its application of a probabilistic approach using Bayesian Networks, which enhances interpretability and performance, especially in scenarios with limited data. Future work may explore additional feature sets and advanced machine learning models to further improve classification accuracy and robustness.
Implementasi Algoritma K-Means Untuk Klasterisasi Data Obat Puskesmas Kotabaru Kurniawan, Muhamad Dicky; Priyatna, Bayu; Nurapriani, Fitria
J-SAKTI (Jurnal Sains Komputer dan Informatika) Vol 7, No 2 (2023): EDISI SEPTEMBER
Publisher : STIKOM Tunas Bangsa Pematangsiantar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/j-sakti.v7i2.693

Abstract

Drug management is one of the things needed to manage drug supplies. Proper planning of drug needs makes drug procurement efficient and effective so that drugs are available in sufficient types and quantities as needed and easily obtained when needed. The purpose of this study was to classify drug data at the Kotabaru Health Center which can be used as a reference in making decisions in planning and controlling drug needs at the Health Center. The data used in this study are the Kotabaru Health Center annual report data from 2019 to 2021. Data processing in this study uses the K-means clustering method with rapidminer tools which is a data grouping technique by dividing the existing data into one or two forms. more clusters. The results of this study divide the drug data into 4 clusters, namely the first cluster (C0) with very low usage consisting of 27 drugs, the second cluster (C2) with low usage consisting of 6 drugs, the third cluster (C3) with high usage consisting of 1 drug, and the fourth (C2) with the highest usage consisting of 1 drug.
Implementasi Metode K-Means Clustering Pada Penilaian Kinerja Karyawan PT Kopetri Citra Abadi: Metode K-Means Clustering Intan, Melani; Bayu Priyatna; Fitria Nurapriani; Shofa Shofia Hilabi
Informasi Interaktif : Jurnal Informatika dan Teknologi Informasi Vol 8 No 1 (2023): JII Volume 8, Number 1, Januari 2023
Publisher : Program Studi Informatika Fakultas Teknik Universitas Janabadra

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37159/jii.v8i1.18

Abstract

Dalam dunia pekerjaan terdapat istilah karyawan terbaik yang menjadi tolak ukur kemajuan perusahaan itu sendiri. Penentuan tersebut dapat dilakukan dengan cara melihat kinerja karyawan sehari-hari seperti kedisiplinan, kerajinan dan prestasi lainnya yang mendukung pekerjaan tersebut. Selain itu, banyak juga aspek-aspek lain yang dapat menjadi bahan pertimbangan atasan dalam memberikan penilaian kepada karyawan. Pentingnya sumber daya manusia (SDM) dalam suatu perusahaan sebagai subyek pelaksana kebijakan serta segala kegiatan operasional perusahaan menjadikan perusahaan harus tetap berani dalam menghadapi tantangan dan perubahan serta memenangkan persaingan. Penguasaan aset manusia secara substansial mempengaruhi beragam perspektif pemenuhan tenaga kerja dalam suatu instansi. sumber daya manusia tersebut sangat mempengaruhi berbagai aspek penentu dalam keberhasilan kerja di suatu perusahaan. PT Kopetri Citra Abadi merupakan perusahaan jasa keamanan yang konsisten melakukan penilaian kinerja setiap bulannya, kemudian dilakukan rekapitulasi setiap tahun dengan akumulasi nilai yang diambil dari laporan penilaian bulanan. Dengan banyaknya karyawan dari perusahaan ini membutuhkan sebuah metode perhitungan yang akurat dan perlunya klasifikasi nilai berdasarkan beberapa kategori penilaian, melihat pentingnya hal tersebut maka proses penilaian karyawan perlu dilakukan secara rasional. Evaluasi ini akan berdampak pada pemberian  feed back antara atasan dan bawahan secara sinergis. Sehingga karyawan akan memahami objektivitas kerja dan mampu mendorong produktivitas usaha perusahaan. Maka dalam penelitian ini akan membahas perhitungan menggunakan metode K-Means Clustering secara manual dan menggunakan tools pada aplikasi Orange data mining dalam penilaian kinerja karyawan PT Kopetri Citra Abadi. Dari perhitungan tersebut menghasilkan pengelompokkan data 92 orang karyawan berdasarkan 3 kriteria yaitu sangat baik (C1) sebanyak 15 data, cukup baik (C2) sebanyak 44 data, dan kurang baik (C3) sebanyak 33 data. Keywords : K-Means, clustering,penilaian kinerja karyawan, prestasi kerja, klasifikasi
Penggunaan Algoritma K-Means Clustering CRISP-DM Dalam Mengelompokkan Drama Korea Sebagai Rekomendasi Film Fatlun, Aulia; Hilabi, Shofa Shofiah; Priyatna, Bayu; Hananto, April Lia
JATISI Vol 12 No 1 (2025): JATISI (Jurnal Teknik Informatika dan Sistem Informasi)
Publisher : Universitas Multi Data Palembang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35957/jatisi.v12i1.11347

Abstract

This study classifies Korean dramas based on popularity and ratings using K-Means Clustering within the CRISP-DM framework. The dataset from Kaggle includes title, release year, rating, vote count, duration, and genre. The Elbow Method determined that 2 clusters were optimal, with a Silhouette Score of 0.35, indicating a fairly good grouping. Recommended dramas have high ratings and vote counts, showing strong popularity, while less recommended dramas have lower ratings and fewer votes, indicating limited appeal. This model can enhance recommendation system accuracy, assist viewers in content selection, and help streaming platforms understand user trends and marketing strategies. Future improvements may involve alternative clustering methods (DBSCAN, Hierarchical Clustering) and additional features like actors, directors, and release year to refine accuracy.
Klasifikasi Genre dan Rating Konten di Netflix : Wawasan dari Analisis Chi-Square dan Clustering alzahra, alika aziza; Hananto, April Lia; Hananto, Agustia; Priyatna, Bayu
JATISI Vol 12 No 1 (2025): JATISI (Jurnal Teknik Informatika dan Sistem Informasi)
Publisher : Universitas Multi Data Palembang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35957/jatisi.v12i1.11354

Abstract

Streaming platforms like Netflix rely on genre classification and content rating to enhance user experience and develop more effective marketing strategies. This study aims to analyze the relationship between genre and content rating on Netflix using Chi-Square analysis and clustering techniques to group genres based on rating distribution patterns. The Chi-Square test results indicate a significant relationship between genre and content rating (p-value < 0.05), suggesting that certain types of content tend to receive specific ratings. Furthermore, clustering analysis identifies three main groups: genres with high ratings suitable for a broad audience, genres with varied ratings that are more subjective, and genres with lower ratings that often include adult or less popular content. These findings provide insights that Netflix can leverage to optimize its content catalog, develop personalized recommendations, and tailor marketing strategies based on user consumption patterns.
EVALUATION IT GOVERNANCE BASED ON COBIT 2019 FRAMEWORK AT BUANA PERJUANGAN UNIVERSITY Yazid, Muhammad Abi; Hananto, April Lia; Priyatna, Bayu; Paryono, Tukino
JURTEKSI (Jurnal Teknologi dan Sistem Informasi) Vol 11, No 2 (2025): Maret 2025
Publisher : Universitas Royal

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33330/jurteksi.v11i2.3791

Abstract

Abstract: The utilization of Information Technology (IT) in higher education institutions is crucial for supporting academic and administrative activities. The Data and Information Center (PUSDATIN) of UBP Karawang manages various IT services, such as Sistem Informasi Perguruan Tinggi (SIPT), e-learning Buana Online Course (BOC), and others. This study aims to evaluate the maturity level of IT governance at UBP Karawang to ensure alignment with the university's strategic goals and identify areas requiring improvement. The research employs a quantitative descriptive method based on COBIT 2019, with data collected from 92 respondents, analyzed through goals cascade mapping and maturity level measurement. The evaluation results across 14 COBIT 2019 domains indicate that the IT governance maturity level at UBP Karawang is at Level 4 (Quantitatively Managed) with a score of 3.86 and an average gap of 1.13 from the expected level. The findings suggest that while IT governance at UBP Karawang is well-managed, there is still room for improvement. Therefore, several recommendations are proposed to optimize IT governance effectiveness, ensure regulatory compliance, and support the achievement of the university's strategic objectives.            Keywords: COBIT 2019; IT evaluation; IT governance; maturity level. Abstrak: Pemanfaatan Teknologi Informasi (TI) di perguruan tinggi sangat krusial untuk mendukung aktivitas akademik dan administratif. Pusat Data dan Informasi (PUSDATIN) UBP Karawang mengelola berbagai layanan TI, seperti Sistem Informasi Perguruan Tinggi (SIPT), e-learning Buana Online Course (BOC) dan lain-lain. Penelitian ini bertujuan untuk mengevaluasi tingkat kematangan tata kelola TI di UBP Karawang guna memastikan keselarasan dengan tujuan universitas serta mengidentifikasi area yang memerlukan perbaikan. Penelitian ini menerapkan metode deskriptif kuantitatif berbasis COBIT 2019, dengan data diperoleh dari 92 responden, dianalisis melalui pemetaan goals cascade dan pengukuran maturity level. Hasil evaluasi pada 14 domain COBIT 2019 menunjukkan tingkat kematangan TI UBP Karawang berada di Level 4 (Terkelola secara Kuantitatif) dengan skor 3.86, serta rata-rata gap 1.13 dari tingkat yang diharapkan. Kesimpulan dari penelitian ini mengindikasikan bahwa meskipun tata kelola TI di UBP Karawang telah terkelola dengan baik, masih terdapat ruang untuk perbaikan. Oleh karena itu, beberapa rekomendasi diajukan guna mengoptimalkan efektivitas tata kelola TI, menjamin kepatuhan terhadap regulasi, serta mendukung pencapaian tujuan strategis universitas. Kata kunci: COBIT 2019; evaluasi TI; maturity level; tata kelola TI. 
Perancangan Sistem Point of Sale Berbasis Web Menggunakan Framework CodeIgniter: Studi Kasus Toko Agen Satria Zahra, Nabila; Tukino, Tukino; Priyatna, Bayu; Hilabi, Shofa Shofia
Jutisi : Jurnal Ilmiah Teknik Informatika dan Sistem Informasi Vol 14, No 1: April 2025
Publisher : STMIK Banjarbaru

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35889/jutisi.v14i1.2710

Abstract

The Satria Agent Store still carries out transactions and manages goods data manually, causing the risk of errors, delays and difficulties in checking sales reports. This condition hampers operational efficiency and is not in accordance with developments in information technology. This research aims to develop a web-based Point of Sale (POS) system to support store operations. The system was developed using the CodeIgniter framework with MVC architecture, use of the PHP language, and MySQL as the database. The development method uses the Waterfall model which includes requirements analysis, system design, implementation and testing. The resulting system is able to support transactions, goods management and real-time sales reports. Testing using Black Box Testing ensures that the function runs according to plan, while the System Usability Scale (SUS) gives a score of 73.22, indicating the system is in the “good” category. This system is expected to increase the efficiency and operational efficiency of the Satria Agent Store.Keywords: Point of Sale; Website; CodeIgniter; Waterfalls AbstrakToko Agen Satria masih melakukan transaksi dan pengelolaan data barang secara manual, menyebabkan risiko kesalahan, keterlambatan, dan kesulitan pengecekan laporan penjualan. Kondisi ini menghambat efisiensi operasional dan tidak sesuai dengan perkembangan teknologi informasi. Penelitian ini bertujuan mengembangkan sistem Point of Sale (POS) berbasis web untuk mendukung operasional toko. Sistem dikembangkan menggunakan framework CodeIgniter dengan arsitektur MVC, penggunaan bahasa PHP, dan MySQL sebagai basis data. Metode pengembangan menggunakan model Waterfall yang meliputi analisis kebutuhan, perancangan sistem, implementasi, dan pengujian. Sistem yang dihasilkan mampu mendukung transaksi, pengelolaan barang, dan laporan penjualan secara real-time. Pengujian menggunakan Black Box Testing memastikan fungsi berjalan sesuai rencana, sedangkan System Usability Scale (SUS) memberikan skor 73,22, menandakan sistem memiliki kategori Good. Sistem ini diharapkan meningkatkan efisiensi dan efisiensi operasional Toko Agen Satria. 
Komparasi Kinerja Model Naive Bayes, SVM, dan BERT dalam Klasifikasi Sentimen Ulasan Pada Aplikasi YUMMY Sabrina Amanda Salsabila; Bayu Priyatna; Agustia Hananto; Tukino
STORAGE: Jurnal Ilmiah Teknik dan Ilmu Komputer Vol. 4 No. 2 (2025): Mei
Publisher : Yayasan Literasi Sains Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55123/storage.v4i2.5120

Abstract

Ulasan pada Google Play Store memainkan peran penting dalam membentuk persepsi publik terhadap sebuah aplikasi. Penelitian ini bertujuan untuk melakukan sentimen ulasan dengan mengklasifikasikan mejadi kategori positif dan negatif di aplikasi Yummy, serta membandingkan performa tiga metode klasifikasi teks: Naive Bayes, Support Vector Machine (SVM), dan Bidirectional Encoder Representations from Transformers (BERT). Sebanyak 6.773 data ulasan didapatkan melalui scraping dan diproses dengan tahapan pra-pemrosesan seperti pembersihan teks, tokenisasi, stopword removal, dan stemming. Representasi fitur dilakukan menggunakan TF-IDF untuk Naive Bayes dan SVM, sedangkan BERT memanfaatkan token embedding dari model pra-latih. Hasil eksperimen menunjukkan bahwa SVM menghasilkan performa sangat efektif dengan akurasi 94%, diikuti oleh Naive Bayes dan BERT yang keduanya mencapai akurasi 90%. SVM juga menunjukkan keseimbangan yang lebih baik dalam mengklasifikasikan teks sentimen positif dan negatif. Temuan ini menegaskan SVM direkomendasikan sebagai solusi yang stabil dan efisien dalam analisis sentimen teks berbahasa Indonesia.
Co-Authors Aang Alim Murtopo Abdullah Abdullah Adittia Agustian Afra, Alfina Fadhilah Agustia Tia Hananto Ahmad Fauzi alzahra, alika aziza Ammar Zuhair Angraeni, Rahmah Nur Ani Ani Anita Khansa Ramadanti April Lia Hananto April Lia Hananto Aprilia Putri Nardilasari Arip Solehudin Asep Haris Aulia, Aldi Aviv Yuniar Rahman Aviv Yuniar Rahman Baenil Huda Cahya Diningrat Desfianthy, Fatiya Hanifah Fatlun, Aulia Fitria Nurapriani Fitria Ratna Sari Florida Ivonia Manek Guntur, Muhamad Haerudin Haerudin Hananto, Agustia Handayani, Citra Hemant N Chaudhari 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 Mega Rianita Kartika Mohamad Ricky Firdaus Muhammad Fahmi Miskon Muhammad Nova Naufal Zubdi Ahnaf Novalia, Elfina Nur ‘Azah Nurapriani, Fitria Nursyifa Paryono, Tukino Pradana Rizki Maulana Rini Mayasari Sabrina Amanda Salsabila Saepul Aripiyanto Satria Dawas Zhalifunas Septiana Rahayu Setiawan, Revi Shofa Shofia Hilabi Shofa Shofia Hilabi Shofa Shofiah Hilabi Shofa Shofiah Hilabi Shofia Hilabi, Shofa Sifa, Sifa Rismawati Siregar, Amril Mutoi Siti Masruroh Siti Masruroh Sofia Ramadanti Az-Zahra Sutan Faisal Syahri Susanto Tatang Rohana Teresa Abuya Titik Khawa Abdurahman Topan Setiawan Topan Trianto Tukino tukino, tukino Tukino, Tukino Tukino, Tukino Vina Andini Wahyu Aziz Ramadhani Yazid, Muhammad Abi Zahra, Nabila Zein, Selmia Aulia