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Pengembangan Game 2D CyberQuest: Sarana Pembelajaran Perakitan CPU Ramadhaniasari, Dwi; Syafaaty, Fifit; Angelita, Rizqi Cahya; Putri, Muvidha Fatmawati; Hidayah, Alvin Noor; Dermawan, Dodik Arwin; Syahputra, Dimas Novian Aditia
JURNAL PETISI (Pendidikan Teknologi Informasi) Vol 5 No 1 (2024): JURNAL PETISI (Pendidikan Teknologi Informasi)
Publisher : Universitas Pendidikan Muhammadiyah Sorong

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36232/jurnalpetisi.v5i1.4978

Kemajuan teknologi informasi yang signifikan berperan penting dalam memajukan ilmu pengetahuan dan meningkatkan sumber daya manusia. Hal ini merupakan kabar baik sekaligus tantangan bagi pendidikan perakitan komputer yang relevan. Riset ini bertujuan untuk membuat sebuah media pembelajaran simulasi perakitan komputer yang lebih mudah dan mengasyikkan melalui Game 2D. Game ini menjadi pilihan untuk belajar merakit komputer secara mandiri bagi seseorang yang ingin belajar merakit komputer. Metode penyelesaian yang dipakai dalam pengembangan website ini adalah Game Development Life Cycle (GDLC), dengan tahapan mulai dari Initiation, Pre - Production, Production, Testing, Beta, dan terakhir Release . Hasil dari penelitian ini berupa aplikasi edukasi merakit CPU dengan fitur, menu website, materi, setting, informasi aplikasi, dan membangun aplikasi menggunakan sebuah mobile. Diharapkan game ini dapat meningkatkan pembelajaran kepada kita semua tentang merakit CPU menggunakan ponsel dan dapat menginspirasi masyarakat umum untuk terlibat dalam belajar mandiri.

Sistem Multi Agen untuk Pelayanan Drone pada Groundbase Docking Station SYAHPUTRA, DIMAS NOVIAN ADITIA; DEWANTO, RADEN SANGGAR; PRAMADIHANTO, DADET
ELKOMIKA: Jurnal Teknik Energi Elektrik, Teknik Telekomunikasi, & Teknik Elektronika Vol 10, No 4: Published October 2022
Publisher : Institut Teknologi Nasional, Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26760/elkomika.v10i4.859

ABSTRAKMulti-Agent System (MAS) diajukan sebagai solusi untuk mengatasi permasalahan pada groundbase sebuah DDS, di mana pada groundbase terdapat AGV yang bertugas untuk membantu Drone beraktifitas di DDS hingga kemudian berangkat kembali menuju DDS lain. Metode auction serta contract antar agent digunakan dalam pemrosesan request dari Drone dan pembagian sumber daya. Pada MAS diterapkan algoritma prioritas sebagai solusi apabila terjadi konflik antar agen. Pengujian dengan simulasi pada CoppeliaSim dan ROS (Robot Operating System) menunjukkan bahwa penggunaan algoritma prioritas berdampak positif pada MAS yang dibuat. Pada DDS dengan skenario 11 AGV, terjadi peningkatan kemampuan DDS dalam menerima dan memproses request yang datang dari 57.9% menjadi 100%, serta pemecahan deadlock yang terjadi pada DDS dari 10 menjadi 0 sehingga seluruh request dapat terselesaikan.Kata kunci: Multi-Agent System, Algoritma Prioritas, Drone Docking Station, AGVÂ ABSTRACTThe Multi-Agent System (MAS) was proposed as a solution to overcome problems in the groundbase of a DDS, where on the groundbase there is an AGV whose job is to help drones carry out activities in DDS and then depart for another DDS. Auction methods and contracts between agents are used in processing requests from drones and sharing resources. In MAS, a priority algorithm is applied as a solution in the event of a conflict between agents. Tests with simulations on CoppeliaSim and ROS (Robot Operating System) show that the use of priority algorithms has a positive impact on the created MAS. In DDS with 11 AGV scenario, there is an increase in DDS ability to receive and process incoming requests from 57.9% to 100%, as well as solving deadlocks that occur in DDS from 10 to 0 so that all requests can be resolved.Keywords: Multi-Agent System, Priority Algorithm, Drone Docking Station, AGV

Pengembangan Game 2D CyberQuest: Sarana Pembelajaran Perakitan CPU Dwi Ramadhaniasari; Fifit Syafaaty; Rizqi Cahya Angelita; Muvidha Fatmawati Putri; Alvin Noor Hidayah; Dodik Arwin Dermawan; Dimas Novian Aditia Syahputra
JURNAL PETISI (Pendidikan Teknologi Informasi) Vol. 5 No. 1 (2024): Jurnal PETISI (Pendidikan Teknologi Informasi)
Publisher : Universitas Pendidikan Muhammadiyah Sorong

Show Abstract | Download Original | Original Source | Check in Google Scholar

Kemajuan teknologi informasi yang signifikan berperan penting dalam memajukan ilmu pengetahuan dan meningkatkan sumber daya manusia. Hal ini merupakan kabar baik sekaligus tantangan bagi pendidikan perakitan komputer yang relevan. Riset ini bertujuan untuk membuat sebuah media pembelajaran simulasi perakitan komputer yang lebih mudah dan mengasyikkan melalui Game 2D. Game ini menjadi pilihan untuk belajar merakit komputer secara mandiri bagi seseorang yang ingin belajar merakit komputer. Metode penyelesaian yang dipakai dalam pengembangan website ini adalah Game Development Life Cycle (GDLC), dengan tahapan mulai dari Initiation, Pre - Production, Production, Testing, Beta, dan terakhir Release . Hasil dari penelitian ini berupa aplikasi edukasi merakit CPU dengan fitur, menu website, materi, setting, informasi aplikasi, dan membangun aplikasi menggunakan sebuah mobile. Diharapkan game ini dapat meningkatkan pembelajaran kepada kita semua tentang merakit CPU menggunakan ponsel dan dapat menginspirasi masyarakat umum untuk terlibat dalam belajar mandiri.

DESIGN AND DEVELOPMENT OF A TONG GLINDING SIMULATOR BASED ON VIRTUAL REALITY AS A MEDIUM FOR PHYSICS EDUCATION Syahputra, Dimas Novian Aditia; Panduman, Yohanes Yohanie Fridelin; Dermawan, Dodik Arwin; Sidhimantra, I Gde Agung Sri; Hakim, Septian Rahman
Jurnal INSTEK (Informatika Sains dan Teknologi) Vol 10 No 2 (2025): OCTOBER
Publisher : Department of Informatics Engineering, Faculty of Science and Technology, Universitas Islam Negeri Alauddin, Makassar, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24252/instek.v10i2.61811

Physics learning about motion concepts is often abstract, while traditional games with embedded scientific principles are increasingly overlooked. This study addresses both issues by developing a Virtual Reality (VR)–based physics simulator inspired by the traditional Tong Glinding game. Designed as an interactive virtual laboratory, the simulator allows users to adjust the incline angle (1°–6°), choose a smooth or rough surface, and observe the motion of a rolling barrel along a 20-meter track. Developed using the Research and Development (R&D) method with the ADDIE model and implemented in Unity 3D, the system integrates cultural heritage with immersive VR experimentation. Functional testing confirmed 100% conformity to design specifications. Simulation verification demonstrated high accuracy, with errors of only 1.8%–2.5% between simulation and analytical calculations on the smooth surface, while the rough surface correctly produced no motion at low angles. User experience testing with 30 participants yielded a high overall score of 87.5 out of 100, supported by strong reliability across all dimensions (Cronbach’s α = 0.85–0.92). These results show that the simulator is technically reliable, physically accurate, and well-received as a learning medium. The combination of cultural preservation and interactive VR-based experimentation represents the novelty of this work.

Optimization of Personalized Fashion Recommendations for H&M: A Collaborative Filtering Algorithm Approach with Temporal Time Interval Analysis Moch Deny Pratama; Dimas Novian Aditia Syahputra; M Adamu Islam Mashuri; Binti Kholifah; Rifqi Abdillah; Adinda Putri Pratiwi
Journal of Applied Informatics Research Vol. 1 No. 1 (2025): July
Publisher : Universitas Negeri Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26740/jair.v1i1.44593

This study presents a personalized fashion recommendation system for the H&M dataset, utilizing a cosine similarity-based collaborative filtering algorithm. This study investigates the effect of temporal segmentation on recommendation performance by conducting three experiments using datasets divided into two-week, one-month, and two-year time intervals. The experimental results show that the two-year interval achieves the best performance, producing a Mean Average Precision (MAP) of 0.02254 with a computational time of 2741.7 seconds. In contrast, the two-week interval achieves a MAP of 0.00915 in 1609.2 seconds, while the one-month interval produces a MAP of 0.00554 with a computational time of 3118.9 seconds. The main contribution of this study lies in the optimization of data structure transformation through dictionary-based modeling, which significantly improves training efficiency. These findings underscore the crucial role of temporal granularity in improving the accuracy and computational efficiency of collaborative filtering-based personalized fashion recommendation systems.

Enhancing Clickbait Headline Identification Performance Without Preprocessing Through Feature Reduction and Sentiment Analysis Moch Deny Pratama; Anisa Nur Azizah; Misbachul Falach Asy'ari; Dimas Novian Aditia Syahputra; M Adamu Islam Mashuri; Binti Kholifah; Rifqi Abdillah; Adinda Putri Pratiwi; Dina Zatusiva Haq
Journal of Applied Informatics Research Vol. 1 No. 1 (2025): July
Publisher : Universitas Negeri Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26740/jair.v1i1.44659

This study addresses the challenge of identifying clickbait headlines without relying on conventional text preprocessing, which can be resource-intensive and may degrade contextual integrity. To enhance detection performance, we examine three feature extraction methods: TF-IDF, Word2Vec, and Headline2Vec, an embedding technique designed for short texts like headlines. These features are optimized using feature selection algorithms, including Pearson Correlation Coefficient (PCC), Neighborhood Component Analysis (NCA), and Relief, to reduce dimensionality and enhance relevant signal retention. Sentiment polarity is also integrated as a complementary feature. A comparative evaluation is conducted using several machine learning classifiers, namely Support Vector Classifier (SVC), Random Forest, LightGBM, and XGBoost, across all combinations of feature extraction and selection methods. Results show that the optimal configuration Headline2Vec with Relief and SVC achieves the highest accuracy at 94.40%, outperforming other approaches. This demonstrates the effectiveness of combining semantic vectorization and feature selection for clickbait detection in the absence of traditional preprocessing. The findings support the development of streamlined and scalable classification models capable of maintaining high accuracy while reducing preprocessing overhead, making the proposed method particularly suitable for real-time and large-scale content moderation and news verification systems.

Pembuatan Sistem Informasi Pendaftaran Santri Baru di Pondok Pesantren Putri KH. Ahmad Basthomi Dimas Novian Aditia Syahputra; Dodik Arwin Dermawan; I Gde Agung Sri Sidhimantra; M Adamu Islam Mashuri; Andi Iwan Nurhidayat; Binti Khalifah; Salamun Rohman Nudin Nudin; Ari Kurniawan
Jurnal Altifani Penelitian dan Pengabdian kepada Masyarakat Vol. 5 No. 5 (2025): September 2025 - Jurnal Altifani Penelitian dan Pengabdian kepada Masyarakat
Publisher : Indonesian Scientific Journal

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59395/altifani.v5i5.837

Pondok Pesantren Putri KH. Ahmad Basthomi menghadapi tantangan administrasi akibat proses pendaftaran santri baru yang masih manual. Sistem ini tidak efisien, memakan waktu, dan rentan kesalahan data di tengah meningkatnya jumlah pendaftar setiap tahun. Sebagai solusi, sebuah program pengabdian masyarakat mengembangkan sistem informasi pendaftaran berbasis web. Sistem ini dirancang dengan fitur unggulan seperti unggah dokumen digital, validasi data terintegrasi, notifikasi otomatis, dan monitoring real-time melalui antarmuka yang ramah pengguna. Program ini juga mencakup pelatihan bagi staf administrasi dan sosialisasi kepada wali santri. Tujuannya adalah untuk mewujudkan manajemen administrasi yang lebih modern, efisien, dan akurat. Dengan implementasi ini, pesantren diharapkan dapat menjadi contoh pemanfaatan teknologi bagi institusi pendidikan keagamaan lain dalam menghadapi tantangan era digital.

Hybrid Transformer-XGBOOST Model Optimized with Ant Colony Algorithm for Early Heart Disease Detection: A Risk Factor-Driven and Interpretable Method Moch Deny Pratama; Faris Abdi El Hakim; Dimas Novian Aditia Syahputra; Dodik Arwin Dermawan; Asmunin Asmunin; Salamun Rohman Nudin; Andi Iwan Nurhidayat
Journal of Applied Data Sciences Vol 7, No 1: January 2026
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v7i1.969

Cardiovascular diseases (CVDs) remain the leading cause of death worldwide, with significant socioeconomic consequences due to premature death and chronic disability. Although clinical screening techniques have evolved, early and accurate prediction of heart disease is still partial due to the limited capacity of conventional machine learning algorithms to model the complex nonlinear interactions among various contributing risk factors e.g., hypertension, diabetes, hyperlipidemia, and genetic predisposition. To address these challenges, this research introduces a hybrid framework that combines the Transformer architecture known for its robust self-attention mechanism and high representational capabilities with Ant Colony Optimization (ACO), a nature-inspired metaheuristic algorithm modeled on the foraging behavior of ants, to enable adaptive and efficient hyperparameter optimization. The proposed model processes structured clinical data by encoding categorical variables into embeddings and normalizing numerical features, resulting in a unified tabular representation suitable for transformer-based analysis. ACO improves model efficiency by optimizing key parameters e.g., embedding configuration, learning rate, and depth, reducing manual intervention and computational overhead. The proposed Hybrid Transformer-ACO model focuses on interpretable clinical features to provide actionable risk stratification. Model evaluation was performed using classification metrics e.g., accuracy, precision, recall, F1 score, and time complexity to measure predictive performance and computational efficiency during the training and inference phases. These evaluation criteria provide evidence of the model's diagnostic reliability, generalizability, and practical feasibility for clinical application.. The model achieved 100% accuracy, sensitivity, specificity, and F1-score, outperforming several models. Time complexity analysis demonstrated efficient training and testing, while the model interpretability supports transparency and trust.

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