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All Journal Jurnal Sistem dan Informatika JURNAL TEKNIK INFORMATIKA DAN SISTEM INFORMASI Jurnal Nasional Komputasi dan Teknologi Informasi @is The Best [Accounting Information System & Information Technology Business Enterprise] bit-Tech JOURNAL OF INFORMATION SYSTEM MANAGEMENT (JOISM) TIN: TERAPAN INFORMATIKA NUSANTARA Jurnal Teknologi dan Bisnis Jurnal Janitra Informatika dan Sistem Informasi Jurnal AbdiMU Jurnal Pengabdian Kepada Masyarakat Babakti Aksi Nyata : Jurnal Pengabdian Sosial dan Kemanusiaan Green Engineering: Journal of Engineering and Applied Science Big Data Analytics and Data Science
zulkifli, ridwan
Universitas Ma’soem
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Model Hybrid SDLC - DevOps dalam Pengembangan Sistem Informasi Akademik: Tinjauan Literatur Andryadi, Aan Ansen; Zulkifli, Ridwan; Nurahman, Ilmal Yakin; Amrullah, Fikri; Pratama, Frinska Putra; Manafi, Ahmad; Arachman, Taufik Arya Yasin
Jurnal Nasional Komputasi dan Teknologi Informasi (JNKTI) Vol 9, No 1 (2026): Februari 2026
Publisher : Program Studi Teknik Komputer, Fakultas Teknik. Universitas Serambi Mekkah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32672/jnkti.v9i1.10451

Abstract

Abstrak - Pengembangan sistem informasi akademik menghadapi tantangan yang rumit karena perlu memastikan bahwa data administratif tetap stabil dan bahwa layanan pengguna dapat berubah secara konstan. Sementara DevOps dapat mengabaikan standar dokumentasi formal yang diperlukan untuk audit institusi pendidikan, pendekatan tradisional Software Development Life Cycle (SDLC) sering dianggap terlalu kaku untuk menanggapi perubahan yang cepat. Tujuan dari penelitian ini adalah untuk membuat model hybrid konseptual yang mensinergikan disiplin struktural SDLC dengan agilitas teknis DevOps. Penelitian ini menganalisis dan mensintesis literatur mengenai fitur, kelebihan, dan kelemahan kedua metode dengan menggunakan metode peninjauan literatur sistematis (SLR). Hasil studi menunjukkan bahwa model hybrid SDLC–DevOps dapat menutupi kekurangan masing-masing metode dengan menggunakan fase perencanaan dan analisis Waterfall untuk memastikan kepatuhan regulasi dan dengan menerapkan praktik otomatisasi, integrasi, dan pengiriman berkelanjutan dari DevOps pada tahap konstruksi dan pemeliharaan. Studi ini membantu pengembang perangkat lunak di sektor pendidikan membuat sistem yang tidak hanya efektif dan fleksibel tetapi juga akuntabel dan terkendali secara manajerial.Kata kunci : Sistem Informasi Akademik; SDLC; DevOps; Model Hybrid; Systematic Literature Review; Abstract - Academic information system development faces complex challenges because it requires ensuring that administrative data remains stable and that user services are constantly evolving. While DevOps can bypass formal documentation standards required for auditing educational institutions, the traditional Software Development Life Cycle (SDLC) approach is often considered too rigid to respond to rapid change. The purpose of this study is to create a conceptual hybrid model that synergizes the structural disciplines of SDLC with the technical agility of DevOps. This study analyzes and synthesizes literature on the features, advantages, and disadvantages of both methods using a systematic literature review (SLR). The study results indicate that the SDLC–DevOps hybrid model can address the shortcomings of each method by using the Waterfall planning and analysis phases to ensure regulatory compliance and by implementing DevOps automation, integration, and continuous delivery practices in the construction and maintenance phases. This study helps software developers in the education sector create systems that are not only effective and flexible but also accountable and managerially controlled.Keywords: Academic Information System; SDLC; DevOps; Hybrid Model; Systematic Literature Review;
Analisis Model Sistem Informasi Pembelajaran Adaptif Menggunakan Machine Learning untuk Optimalisasi Hasil Belajar Mahasiswa Andryadi, Ansen; Zulkifli, Ridwan; Ibrahim, Deva; Aulya, Syifa; Puspita, Novia Nurul; Amalia, Dhita Ayu; Agustina, Nadya Sri
Jurnal Nasional Komputasi dan Teknologi Informasi (JNKTI) Vol 9, No 1 (2026): Februari 2026
Publisher : Program Studi Teknik Komputer, Fakultas Teknik. Universitas Serambi Mekkah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32672/jnkti.v9i1.10540

Abstract

Abstrak-Perkembangan teknologi digital dan pembelajaran daring menuntut sistem pembelajaran yang mampu menyesuaikan dengan kebutuhan individual mahasiswa untuk meningkatkan hasil belajar. Namun, banyak institusi pendidikan masih menggunakan pendekatan pembelajaran yang kurang adaptif sehingga efektivitas belajar belum optimal. Penelitian ini mengangkat persoalan tersebut dengan menganalisis model sistem informasi pembelajaran adaptif yang memanfaatkan machine learning untuk mengoptimalisasi hasil belajar mahasiswa. Tujuan penelitian adalah merancang dan mengevaluasi model sistem informasi pembelajaran adaptif yang mampu memberikan rekomendasi pembelajaran personal secara dinamis berdasarkan data performa dan pola belajar mahasiswa. Metode penelitian yang digunakan adalah penelitian dan pengembangan (RD) dengan pendekatan kuantitatif untuk menguji efektivitas model menggunakan data praktikum mahasiswa. Alat utama dalam penelitian ini adalah algoritma machine learning yang diterapkan pada platform pembelajaran berbasis web. Temuan penelitian menunjukkan bahwa penggunaan model adaptif berbasis machine learning secara signifikan meningkatkan ketercapaian hasil belajar mahasiswa dibandingkan metode pembelajaran tradisional. Implikasi penelitian ini dapat menjadi referensi bagi pengembangan sistem pembelajaran modern yang lebih responsif dan personal di perguruan tinggi, serta memperkuat penerapan teknologi kecerdasan buatan dalam pendidikan. Rekomendasi untuk penelitian selanjutnya adalah memperluas aplikasi model ini ke konteks pembelajaran lain dan integrasi dengan fitur pembelajaran yang lebih variatif.Kata Kunci : Sistem Informasi Pembelajaran; Machine Learning; Optimalisasi Hasil Belajar; Personalisasi Pembelajaran; Pendidikan Tinggi; Abstract - The rapid development of digital technology and online learning has driven the need for learning systems that can adapt to individual student needs in order to improve learning outcomes. However, many educational institutions still rely on less adaptive learning approaches, resulting in suboptimal learning effectiveness. This study addresses this issue by analyzing an adaptive learning information system model that utilizes machine learning to optimize student learning outcomes. The purpose of this research is to design and evaluate an adaptive learning information system model capable of providing dynamic, personalized learning recommendations based on students’ performance data and learning behavior patterns. The research employs a Research and Development (RD) methodology with a quantitative approach to test the effectiveness of the model using student practicum data. The main tool in this study is a machine learning algorithm implemented within a web-based learning platform. The findings indicate that the use of a machine learning–based adaptive model significantly improves students’ learning achievement compared to traditional learning methods. The implications of this study suggest that the proposed model can serve as a reference for the development of more responsive and personalized modern learning systems in higher education, as well as strengthen the implementation of artificial intelligence technologies in education. Future research is recommended to expand the application of this model to other learning contexts and to integrate more diverse learning features.Keywords : Learning Information System; Machine Learning; Learning Outcome Optimization; Personalized Learning; Higher Education;
Analisis Sentimen Real-Time Media Sosial Menggunakan Edge Computing dan Apache Kafka Ridwan Zulkifli
bit-Tech Vol. 7 No. 3 (2025): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v7i3.2372

Abstract

Penelitian ini mengusulkan sistem analisis sentimen real-time untuk data media sosial dengan mengintegrasikan arsitektur edge computing dan Apache Kafka. Seiring dengan pesatnya perkembangan teknologi informasi dan big data, analisis opini publik dari platform media sosial menuntut pemrosesan data yang cepat, efisien, dan akurat. Sistem yang dikembangkan dalam penelitian ini memanfaatkan perangkat edge untuk melakukan pre-processing data dan ekstraksi fitur secara lokal, yang bertujuan untuk mengurangi latensi dan beban jaringan sebelum data dikirim ke pusat analisis menggunakan Apache Kafka sebagai message broker. Metode ini dibandingkan dengan pendekatan berbasis cloud tradisional, dengan harapan dapat meningkatkan kecepatan dan akurasi analisis sentimen secara signifikan. Implikasi praktis dari penelitian ini adalah penyediaan solusi yang dapat diskalakan dan dioperasikan secara real-time untuk pemantauan opini publik, yang berguna dalam berbagai sektor seperti pemasaran digital, pemantauan isu sosial, deteksi krisis, dan analisis tren sosial. Hasil pengujian sistem menunjukkan bahwa integrasi antara edge computing dan Apache Kafka berhasil mempercepat proses analisis sentimen dengan peningkatan throughput yang signifikan dan penurunan latensi, tanpa mengorbankan akurasi analisis. Model analisis sentimen yang digunakan mencapai tingkat akurasi yang tinggi pada data uji. Penelitian ini juga mengidentifikasi beberapa tantangan terkait sinkronisasi data dan isu keamanan dalam sistem terdistribusi, yang dapat menjadi fokus penelitian lanjutan. Temuan ini memberikan kontribusi terhadap pengembangan sistem analitik berbasis big data untuk aplikasi real-time di dunia nyata, serta membuka peluang untuk penelitian lebih lanjut dalam mengoptimalkan kinerja dan skalabilitas sistem ini.
Success Factors for Implementing Robotic Process Automation (RPA) in Accounting Information Systems within the Trade Sector: a Systematic Literature Review Firdaus, Dony Waluya; Zulkifli, Ridwan; Nawawi, Muhamad; Afrianto, Irawan; Rijanto, Estiko; Sumitra, Irfan
@is The Best : Accounting Information Systems and Information Technology Business Enterprise Vol 10 No 2 (2025): @is The Best : Accounting Information Systems and Information Technology Busines
Publisher : Labkat Press KA FTIK UNIKOM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34010/aisthebest.v10i2.18687

Abstract

Digital transformation requires more operational efficiency in the trade sector, with RPA turning out to be one of the key technologies for modernizing Accounting Information Systems. This paper seeks to identify the adoption patterns and dominant factors determining the success of RPA implementation in finance and accounting functions within the trade sector-a domain which, so far, has been underexplored as compared to the banking sector. By means of a Systematic Literature Review with PRISMA 2020 guidelines, along with the bibliometric analysis of 72 Scopus-indexed articles published between 2020 and 2025, this study maps the evolution in technology adoption. The study portrays that RPA's adoption has moved beyond the automation of mere back-office repetitive tasks, such as reconciliation and data entry, toward the orchestration of end-to-end trade processes spanning procurement and logistics, while integrating with AI for handling unstructured data. The study further finds evidence that successful implementation relies on a maturity path leading from accurate selection of routine and high-volume processes to standardization of data input. The sustainability of RPA advantages is defined by organizational capabilities through CoE and successful change management, while supported by strategic governance through alignment of business objectives with information technology.   The result of this study provides a theoretical framework and practical guidance for trading firms in ensuring investments in RPA result in performance metrics and increased compliance.
Pemanfaatan Virtual Reality sebagai Media Pembelajaran Perakitan Komputer Zulkifli, Ridwan; Nurhakim, Egi Lukman; Nur Faidah, Rika Setiani; Nur Fadillah, Rike Setiani; Fadilah, Umi Lutfiatul; Sophiawati, Nur Kesha; Perdiansyah, Devi; Derismana, Derismana; Alfathurrahman, Dzikri
Jurnal Nasional Komputasi dan Teknologi Informasi (JNKTI) Vol 9, No 1 (2026): Februari 2026
Publisher : Program Studi Teknik Komputer, Fakultas Teknik. Universitas Serambi Mekkah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32672/jnkti.v9i1.10573

Abstract

Abstrak - Keterbatasan fasilitas Teknologi Informasi dan Komunikasi di sekolah menengah kejuruan menjadi kendala dalam pembelajaran perakitan komputer yang bersifat praktik. Penelitian ini bertujuan mengembangkan media pembelajaran perakitan komputer berbasis Virtual Reality sebagai solusi alternatif untuk meningkatkan keterlibatan dan pemahaman siswa. Aplikasi dikembangkan menggunakan game engine Unity dengan metode Multimedia Development Life Cycle yang meliputi tahap konsep, perancangan, pengumpulan bahan, perakitan, pengujian, dan distribusi. Media pembelajaran ini dijalankan pada perangkat Android dan didukung oleh VR Box sehingga memungkinkan siswa berinteraksi dengan komponen komputer secara virtual dalam lingkungan tiga dimensi. Evaluasi sistem dilakukan melalui alpha testing menggunakan black box testing serta beta testing dengan User Acceptance Testing menggunakan skala Likert terhadap 27 siswa jurusan Teknik Komputer dan Jaringan. Hasil pengujian menunjukkan tingkat penerimaan pengguna sebesar 81% dengan kategori sangat diterima. Hal ini menunjukkan bahwa media pembelajaran berbasis Virtual Reality mampu menjadi alternatif pembelajaran praktikum yang efektif serta membantu siswa memahami proses dan komponen perakitan komputer meskipun tanpa perangkat fisik secara langsung.Kata kunci : Virtual Reality; Media Pembelajaran; Perakitan Komputer; Sekolah Menengah Kejuruan; Abstract - Lecturer research management in higher education institutions often faces complex administrative challenges that are time-consuming and prone to documentation errors. These problems include difficulties in tracking proposal status, inconsistent progress reporting, and lack of transparency in the review process. The purpose of this study is to evaluate the effectiveness of a throwaway prototyping-based development approach in building a lecturer research management information system that is usable, efficient, and capable of improving administrative productivity. The system is designed to simplify research administration activities, such as proposal submission, reviewer assignment, progress reporting, and integrated research output tracking. The study employs a mixed method design that combines quantitative approaches through usability and time efficiency measurements, as well as qualitative approaches through in-depth interviews. The development process applies a throwaway prototyping cycle through stages: (1) initial requirements identification through stakeholder analysis, (2) rapid prototype creation for concept validation, (3) iterative evaluation and validation with end users, (4) prototype disposal and final system implementation based on feedback. This approach was chosen to accelerate requirements elicitation, validate interface flows before full development, and minimize rework before final implementation. The system was implemented as a web application using PHP (Laravel) and a relational database. Usability evaluation was conducted using the System Usability Scale (SUS), complemented by task-based efficiency measurements and brief interviews with end users. The research results show an SUS score of 85.5, indicating excellent usability and high user acceptance. Task completion time for core administrative activities decreased by 35% compared to manual processes. Qualitative feedback shows improved status transparency, reduced administrative burden, and more consistent documentation. Overall, these findings demonstrate that the prototyping model is effective for developing research management systems in higher education, particularly when user feedback is integrated from the outset and conducted iteratively.Keywords: Virtual Reality; Learning Media; Computer Assembly; Vocational High School;
Artificial Intelligence-Based Early Warning System for Disaster Management: A Literature Review Systematic and Bibliometric Analysis Ridwan Zulkifli; Zainal Arifin Hasibuan; Irawan Afrianto; Bella Hardiyana; Sri Supatmi
Big Data Analytics and Data Science Vol. 1 No. 2 (2026): June: Big Data Analytics and Data Science
Publisher : Asosiasi Pengelola Jurnal Informatika dan Komputer Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.66472/bdas.v1i2.392

Abstract

The increasing frequency and intensity of natural disasters globally demands the development of more accurate and responsive Early Warning Systems (EWS). In recent years, Artificial Intelligence (AI) has been increasingly applied in natural disaster mitigation, but the approaches used are still diverse and spread across various domains. This study aims to present a systematic literature review on the application of AI and deep learning in natural disaster early warning systems. This review was conducted following the PRISMA 2020 guidelines by analyzing literature published during the 2020–2025 period. The selection process resulted in 102 studies meeting the inclusion criteria, with 30 full-text articles being analyzed in depth to map disaster types, AI methods, data sources, and characteristics of early warning systems developed in various regions, including Asia and Africa. The review results show the dominance of deep learning approaches, particularly time series-based models such as Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU), particularly in flood forecasting and land deformation prediction. More advanced architectures, such as Transformer, are beginning to be adopted to capture long-term temporal patterns, while the combination of convolutional neural networks (CNN) with remote sensing data is widely used for spatial mapping of disaster events. Furthermore, the integration of sensor data and the Internet of Things (IoT) shows potential in supporting more responsive early warning systems. However, most research remains limited to the modeling or simulation stage, with little discussion of the real-time and operational implementation of EWS. This review highlights the gap between AI model development and the implementation of reliable early warning systems and provides a conceptual foundation for the future development of more integrated AI-based disaster mitigation systems.
Co-Authors Afriliani, Sri Agustina, Nadya Sri Alfathurrahman, Dzikri Amalia, Dhita Ayu Amalia, Saebah Nur Amrullah, Fikri Andriansyah, Rendi Andryadi, Aan Ansen Andryadi, Ansen Anggit Suryopratomo Arachman, Taufik Arya Yasin Arida Murti Martikasari Aulya, Syifa Awan Setiawan Bella Hardiyana Beny Riswanto Bobi Kurniawan, Bobi Derismana, Derismana Estiko Rijanto Fadilah, Umi Lutfiatul Fadillah, Rike Setiani Nur Faidah, Rika Setiani Nur Fikri, M.Nurul Firdaus, Dony Waluya Ginanjar, Rega Gymnastiar, Prayoga Hanifah, Syahda Lidini Hendra Purnama Ibarahim, Deva Ibrahim, Deva Indra Indra Irawan Afrianto Irwan Yulianto Jamil, Ihsan Surya Kurnia Sastradipraja, Cecep Lita Lestari Utami Manafi, Ahmad Mansur, Ahmad Gojali Mochammad Hasymi Somaida Mufida, Luthfiah Mukarom, Hasna Maulani Nawawi, Muhamad Nur Fadillah, Rike Setiani Nur Faidah, Rika Setiani Nurahman, Ilmal Yakin Nurfadhilah, Dila Siti Nurhakim, Egi Lukman Perdiansyah, Devi Pratama, Frinska Putra Puspita, Novia Nurul Putri, Neng Pita Lestia Rahmandhita, Awaluna Nurdilan Raihansyah, Muhammad Dhafa Ratnasari Ratnasari Rohmana, Randi Pajar Septiani, Rohmawati Septianti, Hanifah Siti Nurfadhilah, Dila Sophiawati, Nur Kesha Sri Supatmi Sri Supatmi Sumitra, Irfan Syarifudin, Herry Zainal Arifin Hasibuan Zainal Arifin Hasibuan