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All Journal Jurnal Pendidikan Islam KOMPUTIKA - Jurnal Sistem Komputer KOMPUTA : Jurnal Ilmiah Komputer dan Informatika Majalah Ilmiah UNIKOM Jurnal Abdimas PHB : Jurnal Pengabdian Masyarakat Progresif Humanis Brainstorming Jurnal Tata Kelola dan Kerangka Kerja Teknologi Informasi Indonesian Community Service and Empowerment Journal (IComSE) Jurnal Digit : Digital of Information Technology ASEAN Journal of Science and Engineering Education (AJSEE) Indonesian Journal of Multidiciplinary Research (IJoMR) Indonesian Journal of Community and Special Needs Education (IJCSNE) Indonesian Journal of Teaching in Science International Journal of Entrepreneurship & Technopreneur (INJETECH) International Journal of Informatics, Information System and Computer Engineering (INJIISCOM) International Journal of Research and Applied Technology (INJURATECH) Academic Journal of Computer Science Research (AJCSR) Abdi Teknoyasa Jurnal Pengabdian Teknik dan Ilmu Komputer (PETIK) Prosiding SEMNAS INOTEK (Seminar Nasional Inovasi Teknologi) ASEAN Journal for Science and Engineering in Materials
Maulana, Hanhan
Program Studi Teknik Informatika, Fakultas Teknik Dan Ilmu Komputer, Universitas Komputer Indonesia
Arts Humanities Automotive Engineering Chemical Engineering, Chemistry & Bioengineering 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 Materials Science & Nanotechnology Mathematics Mechanical Engineering Public Health Social Sciences Transportation Other

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Integrasi Gemini AI Berbasis Retrieval-Augmented Generation (RAG) pada Moodle untuk Penilaian Esai Otomatis dengan Pendekatan Human-in-the-Loop di Pendidikan Tinggi Adha, Rizki; Lusianto, Lusianto; Syaripudin, Dodi; Kurniawan S, Bobi; Bachtiar, Adam Mukharil; Maulana, Hanhan; Rainarli, Ednawati
Academic Journal of Computer Science Research Vol 8, No 1 (2026): Academic Journal of Computer Science Research (AJCSR)
Publisher : Institut Teknologi dan Bisnis Bina Sarana Global

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.38101/ajcsr.v8i1.16253

Abstract

Penilaian esai merupakan komponen penting dalam evaluasi pembelajaran di pendidikan tinggi, namun proses penilaian manual oleh dosen membutuhkan waktu yang besar dan berpotensi menimbulkan inkonsistensi, terutama pada kelas dengan jumlah mahasiswa yang besar. Penelitian ini bertujuan untuk mengembangkan dan mengevaluasi sistem penilaian esai otomatis berbasis Gemini AI yang terintegrasi dengan Learning Management System (LMS) Moodle menggunakan pendekatan Retrieval-Augmented Generation (RAG) dan mekanisme Human-in-the-Loop (HIL). Penelitian menggunakan metode Research and Development (R&D) dengan model ADDEI, yang meliputi tahap analisis, perancangan, pengembangan, evaluasi, dan implementasi sistem. Evaluasi sistem dilakukan melalui pengujian fungsional (black-box testing), serta kuesioner Human-in-the-Loop yang melibatkan 24 dosen dari 14 perguruan tinggi swasta di wilayah Banten, DKI Jakarta, dan Jawa Barat. Hasil pengujian menunjukkan bahwa seluruh fungsi sistem berjalan sesuai dengan spesifikasi. Evaluasi HIL menunjukkan tingkat penerimaan yang tinggi hingga sangat tinggi, terutama pada indikator peran AI sebagai decision-support system dan tanggung jawab akademik dosen. Selain itu, hasil validasi dosen menunjukkan bahwa sebagian besar rekomendasi skor dan umpan balik yang dihasilkan oleh sistem dapat diterima, dengan dosen tetap memiliki kendali penuh dalam menentukan nilai akhir. Temuan ini menunjukkan bahwa integrasi Gemini AI berbasis RAG dengan mekanisme Human-in-the-Loop efektif sebagai sistem pendukung penilaian esai yang efisien, akuntabel, dan sesuai dengan kebutuhan penilaian akademik di pendidikan tinggi.
DREAM: Design of Higher Education Curriculum Based on Spiritual Values Senny Luckyardi; Hanhan Maulana; Bagus Hary Prakoso; Benny Widaryanto; Chepi Nur Albar; Silvi Munawaroh; Juliana Karin
Jurnal Pendidikan Islam ARTICLE IN PRESS
Publisher : The Faculty of Tarbiyah and Teacher Training associated with PSPII

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Abstract

This study aimed to develop an alternative curriculum design to meet the increasing demand for high-quality graduates in today’s dynamic economy. This research used a mixed-method approach for data collection and analysis to ensure comprehensive, reliable, and objective findings. The results highlighted a growing need to cultivate strong leadership traits, emphasizing the development of holistic and spiritual leadership that integrates ethics, decision-making, and practical actions. Individuals with prophetic leadership qualities were found to be highly dependable due to their strong sense of responsibility, spiritual grounding, and ability to make wise decisions based on available resources. In response, the DREAM curriculum was designed to nurture graduates with these attributes, equipping them to meet the evolving needs of modern industries. Graduates of the DREAM curriculum are expected to excel not only in hard and soft skills but also as inspirational leaders who motivate others. This research is projected to have several significant impacts, including bridging the skills gap, fostering leadership character development, enhancing graduate quality, equipping students with relevant technological knowledge and expertise, and promoting a curriculum rooted in Islamic spiritual values.
Predictive Modelling of Electronic Materials: A Review of Deep Learning Techniques in Computer Engineering Agis Abhi Rafdhi; Hanhan Maulana; Senny Luckyardi; Eddy Soeryanto Soegoto; Dostnazar Ximmataliyev; Goh Kang Wen; Tomáš Chochole; Hewa Majeed Zangana
ASEAN Journal for Science and Engineering in Materials Vol 5, No 3 (2026): AJSEM: Volume 5, Issue 3, December 2026
Publisher : Bumi Publikasi Nusantara

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Abstract

This review evaluates the application of deep learning (DL) for the predictive modeling of electronic materials in computer engineering. We analyzed peer-reviewed literature across four major databases, focusing exclusively on advanced architectures like Graph Neural Networks (GNNs) and Generative models. Results indicate these models accurately predict critical properties, such as band gaps and thermal conductivity, for next-generation semiconductors, 2D materials, and memristors. These high accuracies are achieved because architectures like GNNs effectively capture complex 3D spatial interactions without requiring manual feature engineering. However, practical fabrication remains hindered by data scarcity, algorithmic opacity, and a profound "Sim-to-Real Gap". While DL accelerates predictive design, sustaining Moore's Law ultimately requires developing autonomous "Self-Driving Labs" and Large Material Models to bridge digital predictions with physical synthesis.
Hyperparameter Optimization of Random Forest for Multiclass Classification of Student Academic Performance Using Multidimensional Factors Sri Nurhayati; Diana Effendi; Bobi Kurniawan Soegoto; Adam Mukharil Bachtiar; Hanhan Maulana; Ednawati Rainarli
Komputika : Jurnal Sistem Komputer Vol. 15 No. 1 (2026): Komputika: Jurnal Sistem Komputer
Publisher : Computer Engineering Departement, Universitas Komputer Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34010/komputika.v15i1.18885

Abstract

Classification for academic performances among students in a multi-class scenario is a challenging task due to its dependencies on multiple factors and characteristics, particularly in the medium academic performance category. This scenario makes it a problem for some models with their conventional settings in terms of their ability to optimally distinguish categories of academic performances while being used in classification tasks, thus leading to the need for optimization techniques in enhancing their performances. This research paper will design an optimization strategy for improving the performances of the Random Forest algorithm in a multi-class academic performance classification among students. This will help in enhancing decision-making systems in education. The research method used is a machine learning approach with a Random Forest algorithm optimized through hyperparameter tuning using RandomizedSearchCV. This study utilizes secondary student data obtained from the Kaggle public repository, consisting of 6,607 data points with 20 determining factors covering academic, behavioral, social, environmental, and health aspects. The results showed that Random Forest hyperparameter optimization was able to improve model performance from a baseline accuracy of 79.56% to 81.08% on the validation data, and achieved an accuracy of 81.69% on the test data. In addition, there was an improvement in performance in the Medium category classification, as indicated by an increase in the F1-score value from 0.69 to 0.72. Therefore, the optimization of Random Forest proved to be good in enhancing the performance and stability of multiclass classification of student academic performance.
Smart Notification System with the Integration of Robotic Process Automation and Reinforcement Learning Andri Heryandi; Sufa Atin; Hani Irmayanti; Adam Mukharil Bachtiar; Hanhan Maulana; Bobi Kurniawan Soegoto; Ednawati Rainarli
Komputika : Jurnal Sistem Komputer Vol. 15 No. 1 (2026): Komputika: Jurnal Sistem Komputer
Publisher : Computer Engineering Departement, Universitas Komputer Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34010/komputika.v15i1.18951

Abstract

This study proposes the development of an intelligent academic notification system by integrating Robotic Process Automation (RPA) and Reinforcement Learning (RL) to improve the effectiveness of delivering information to students and parents. RPA is utilized to automate the process of sending notifications across various channels, such as email and WhatsApp, ensuring fast, consistent, and hands-free message distribution. RL is implemented to determine the optimal communication channel based on delivery history, message status (sent, failed, read), and the cost associated with each channel. Each student is represented as a state, while the selection of a communication channel becomes an action evaluated using Q-learning. The system learns from recipient behavior and updates the Q-table to enhance the accuracy of channel selection for future notifications. Additionally, the system applies an automatic escalation mechanism to parents as the deadline approaches. The result of this research is a smart notification system that can be implemented within academic information systems to enhance operational efficiency and student engagement.
IMPLEMENTASI METODE K-NEAREST NEIGHBOR (K-NN) DAN FORWARD CHAINING UNTUK MONITORING TUMBUH KEMBANG BALITA Petrus Sokibi Sukanto; Rifqi Fahrudin; Ridho Taufiq Subagio; Ednawati Rainarli; Adam Mukharil Bachtiar; Hanhan Maulana; Bobi Kurniawan
Jurnal Digit : Digital of Information Technology Vol 16, No 1 (2026)
Publisher : Universitas Catur Insan Cendekia (CIC) Cirebon

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51920/jd.v16i1.460

Abstract

Pelayanan pelaporan hasil pemeriksaan balita di Posyandu seringkali menghadapi kendala akurasi dan keterlambatan informasi, yang menyulitkan kader serta orang tua dalam memantau tumbuh kembang anak secara efektif. Penelitian ini bertujuan untuk merancang bangun model sistem informasi berbasis website yang mampu menentukan status gizi dan perkembangan motorik balita secara akurat. Sistem ini mengintegrasikan dua metode kecerdasan buatan: K-Nearest Neighbor (K-NN) untuk klasifikasi status gizi berdasarkan antropometri, dan Forward Chaining untuk mendeteksi tahap perkembangan kemampuan motorik balita. Pengembangan model perangkat lunak dilakukan menggunakan framework CodeIgniter dengan pemodelan sistem menggunakan Unified Modelling Language (UML). Hasil penelitian menunjukkan bahwa model website ini memiliki performa yang sangat baik dengan tingkat akurasi sebesar 85,71% untuk penentuan status gizi melalui metode K-NN, dan tingkat akurasi mencapai 100% untuk identifikasi perkembangan motorik menggunakan Forward Chaining. Model ini diharapkan dapat menjadi alat monitoring yang handal bagi tenaga kesehatan dan orang tua. Sebagai pengembangan di masa depan, disarankan penambahan fitur switch akun bagi orang tua yang memiliki lebih dari satu balita untuk mempermudah manajemen data perkembangan anak secara personal.Kata kunci: Posyandu, Status Gizi, Perkembangan Balita, K-Nearest Neighbor, Forward Chaining.
Co-Authors A Nurpalah Adam Mukharil Bachtiar Adha, Rizki Agis Abhi Rafdhi Agustia, Richi Dwi Albar, C. N. Aliska, M Angki Ana Hadiana Andri Heryandi Asifa, V. Atin, Sufa Aziz, M. A. Bachtiar, Adam Mukharil Bagus Hary Prakoso Benny Widaryanto Bobi Kurniawan Soegoto Bobi Kurniawan, Bobi BR Ginting, Selvia Lorena Chepi Nur Albar D D Rhamdhan D. A. Fairuz Dedeng Hirawan, Dedeng Dessy Puspita Sari Dewi, A. C. Diana Effendi Dodi Syaripudin Dostnazar Ximmataliyev Dwi Agustia, Richi E S Soegoto Eddy Soeryanto Soegoto Ednawati Rainarli Erik Wirawan Fauzzan, Deran Deriyana Finandhita, Alif Goh Kang Wen Gusriyani Nur Azizah Hani Irmayanti Hermiyah, M. Herry Saputra Hewa Majeed Zangana I. Kusumaningrat Imelda Pangaribuan Irawan Afrianto Irfan D. Sumitra Juliana Karin Kankan Kasmana Khaira Ummah Kurnia Tsani Alamsyah Kurniawan S, Bobi L. Albion Lusianto, Lusianto M S Pasha M. Putra M. R. Kaira Mubarok, S. Muhamad Restu Fadillah Petrus Sokibi Sukanto Pramanda Aryan Praymoedya Alramadhan Bahroun Priatna, Riffa Alfaridzi R Syahputra Rainarli, Ednawati Ridho Taufiq Subagio Rifqi Fahrudin Rubi Nurajmi Kamal Salim, Michael Nagaku Milenn Salsadilla Mutiara Lestari Selvia Lorena Br Ginting Senny Luckyardi Setiawan, Devidli Setiyadi, Angga Silvi Munawaroh Sri Nurhayati Sufa Atin Sufa'atin Suryana, Taryana Tatan Tawami Tomas Chochole Wibowo, Intan Mutiara Widiansyah, M. Givaldi