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All Journal Jurnal Teknologi dan Manajemen Informatika Jurnal TIMES Sinkron : Jurnal dan Penelitian Teknik Informatika Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Jurnal Sistem Komputer dan Informatika (JSON) Bulletin of Computer Science Research Journal of Artificial Intelligence and Engineering Applications (JAIEA)
Robet
Sekolah Tinggi Manajemen Informatika Dan Komputer (STMIK) TIME
Automotive Engineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Energy Engineering

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Game Development "Kill Corona Virus" for Education About Vaccination Using Finite State Machine and Collision Detection Andi; Juan Charles; Octara Pribadi; Carles Juliandy; Robet
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Vol. 7, No. 4, November 2022
Publisher : Universitas Muhammadiyah Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22219/kinetik.v7i4.1470

Abstract

COVID-19 is a disease caused by the coronavirus and causes the main symptoms in the form of respiratory problems. One way to overcome the COVID-19 pandemic is through the vaccination process. However, in practice, the public is still not educated about the importance of vaccination in preventing coronavirus infection, so it is necessary to develop a game that provides education to the public to vaccinate. This study chose games as educational media because there are many game enthusiasts and the delivery of education through games is more memorable than on other platforms. This study uses the Game Development Life Cycle (GDLC) method in the game development stage. In addition, to create intelligent coronavirus enemy NPC characters in this study, Finite State Machine (FSM) and Collision Detection methods will be implemented to detect the accuracy of players' shots. The results were obtained in the form of a game "Kill Corona Virus" which is used as a medium of education for the public about the importance of vaccination. Based on the results of the tests carried out, it was found that the implementation of the Collision Detection method in the game in detecting collisions was appropriate and quite accurate and the Finite State Machine method succeeded in creating coronavirus enemy NPCs with appropriate states. In addition, based on the results of processing respondents' answers, it is known that the ”Kill Corona Virus” game that was built can convey vaccination education messages well and make people interested in vaccinating.
Penerapan Metode Webqual 4.0 Dalam Pengukuran Kualitas Website Awicoffee Robet; Agus Maringan Siahaan; Satriya Miharja
Bulletin of Computer Science Research Vol. 4 No. 2 (2024): Februari 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v4i2.339

Abstract

The measurement of Awicoffee’s website quality was conducted with the aim of enabling the website owner to develop aspects that have low value, so that they can continue to develop in line with the times. The data used for this research is the result of questionnaire distribution given to 100 respondents. Website quality measurement is carried out using the webqual 4.0 method with 3 aspects assessed, namely Usability, Information Quality, and Service Interaction Quality. The purpose of this research is to determine the satisfaction value of Awicoffee website users and to determine the most significant instrument in determining  user satisfaction. Based on the research results, it was found that these three variables have an influence of 76.59% on user satisfaction, while 23.41% is influenced by variables that were not examined. Also, the variable that has the most significant impact on user satisfaction is Information Quality with a value of 5.97, whereas the Usability variable has a value of 3.42 and the Service Interaction Quality variable has a value of 3.30.
Pemanfaatan Dana Desa Untuk Meningkatkan Kesejahteraan Masyarakat Melalui Promosi Produk UMKM Menggunakan Collaborative Filtering Berbasis Android Siahaan, Agus Maringan; Robet; Jonathan Kevin Fernando; Donna Natalia
Bulletin of Computer Science Research Vol. 5 No. 1 (2024): December 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v5i1.377

Abstract

Utilizing village funds through Badan Usaha Milik Desa (BUMDes) is a strategic effort to improve the welfare of rural communities. However, its implementation often faces challenges such as suboptimal management of local economic potential and limited promotion of Micro, Small, and Medium Enterprises (MSMEs) products. This study aims to develop an Android-based application utilizing the Collaborative Filtering method as an innovative solution to support the promotion of village MSME products, enhance the effective use of village funds, and drive economic growth in rural areas. The application is designed to provide MSME product recommendations based on user preferences. Integrating the Collaborative Filtering method, the application analyzes user interaction data to offer relevant product suggestions. Its key features include product search, personalized recommendations, and a user-friendly interface that is easy for rural communities to operate. The black-box method testing results show that this application works according to the designed specifications with a system interface functional success rate of 100% of 31 functional interfaces. increased the promotion and sales of local MSME products.
Improving Resnet Model In Safety Gear Classification Using Finest Optimizer Robet; Johanes Terang Kita Perangin Angin; Edi Wijaya
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 4 No. 2 (2025): February 2025
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v4i2.703

Abstract

The Occupational accidents that occur in the work environment are increasing day by day. This is caused by workers' non-compliance with the established work safety equipment. Although the supervision of the use of work safety equipment has been carried out, it is still done manually involving less effective human resources. Therefore, it is necessary to develop an intelligent model that can classify the use of work safety equipment more accurately. This study uses the pre-trained ResNet50 model and is combined with the best optimization model to improve accuracy. The results of the study showed that the RMSProp optimization model has better performance with an accuracy value of 97.01% in the 17th epoch of 50 epochs of data training and with training loss and validation loss values ​​of 0.3268 and 0.145, respectively. Testing of 20 images with each image, 10 images using safety equipment, and 10 images not using safety equipment can be classified correctly.
IMPLEMENTASI METODE PROTOTYPING DALAM PERANCANGAN UI/UX DESIGN PADA MEDIA DIGITAL TERANG KITA Rizkita, Ari; Perangin Angin, Johanes; Robet; Pribadi, Octara
Jurnal TIMES Vol 14 No 1 (2025): Jurnal TIMES
Publisher : STMIK TIME

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51351/jtm.14.1.2025834

Abstract

Perkembangan teknologi informasi pada era saat ini bergerak sangat pesat dan mencakup seluruh aspek kehidupan manusia, termasuk pada bidang media. Media digital terangkita.com, sebuah platform yang bergerak di bidang edukasi dan penyebaran konten positif berbasis nilai-nilai sosial. Proses perancangan dilakukan melalui tahapan metode prototyping, mulai dari pengumpulan kebutuhan pengguna, pembuatan sketsa awal, hingga pengujian desain interaktif. Pendekatan ini memungkinkan kolaborasi yang dinamis antara desainer dan pengguna, serta memberikan fleksibilitas untuk melakukan revisi berdasarkan umpan balik secara iteratif. Hasil penelitian menunjukkan bahwa metode prototyping mampu meningkatkan kualitas desain UI/UX secara signifikan, ditinjau dari aspek keterpahaman, kemudahan navigasi, dan kepuasan pengguna terhadap antarmuka. Temuan ini memberikan kontribusi terhadap pengembangan desain media digital yang lebih human-centered, serta menjadi acuan dalam proses desain interaktif berbasis kebutuhan pengguna. Kata kunci: UI/UX Design, Prototyping, Media Digital, Desain Interaktif, Terang Kita
Comparison of XGBoost and Naive Bayes Models in Type 2 Diabetes Prediction with RFE Feature Selection Barus, Hanisa putri; Robet; Feriani Astuti Tarigan
Sinkron : jurnal dan penelitian teknik informatika Vol. 10 No. 1 (2026): Article Research January 2026
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v10i1.15509

Abstract

Type 2 diabetes mellitus is a chronic disease with an increasing prevalence rate that can cause serious complications if not detected early. The application of machine learning algorithms can aid prediction, but selecting the right model and features greatly determines the accuracy of the results. This study aims to compare the performance of the Extreme Gradient Boosting (XGBoost) and Naive Bayes algorithms in predicting type 2 diabetes with and without Recursive Feature Elimination (RFE) feature selection. The data used were from the UCI Machine Learning Repository, comprising 768 samples and eight clinical features. The research process included data preprocessing, dividing the data into 614 training data and 154 testing data, applying RFE to select the most influential features, model training, and evaluation using accuracy, precision, recall, F1-score, and AUC. The results show that Naive Bayes without RFE achieves 70.77% accuracy, 0.57377 precision, 0.648148 recall, F1-score 0.608696, and 0.772778 AUC, while Naive Bayes with RFE increases the accuracy to 74.02% and the AUC to 0.793333. Meanwhile, XGBoost with RFE provided the best results with an accuracy of 74.67%, precision of 0.653061, recall of 0.592593, F1-score of 0.621359, and the highest AUC of 0.804259. Besides, applying RFE also improves the computational efficiency. These findings indicate that applying RFE significantly improves classification and computation time performance. The practical implication is that this model could aid early detection of diabetes in clinical settings. Further research can be conducted by optimizing parameters and using more diverse datasets.
Klasifikasi Multikelas Tingkat Diabetes Berdasarkan Indikator Kesehatan Pasien Menggunakan Strategi One-vs-Rest Panjaitan, Tabitha Martha Agustine; Robet; Octara Pribadi
Jurnal Sistem Komputer dan Informatika (JSON) Vol. 7 No. 2 (2025): Desember 2025
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/json.v7i2.8985

Abstract

Diabetes is a non-communicable disease with a steadily increasing global prevalence. It often remains undiagnosed in its early stages, particularly during the prediabetic phase, which typically lacks noticeable symptoms. This study aims to develop a multi-class classification model to predict diabetes severity levels non-diabetic, prediabetic, and diabetic based on patient health indicators. A One-vs-Rest (OvR) strategy was employed, training each class against a combination of the others. The dataset was derived from the 2015 National Health Survey, comprising over 250,000 patient records with features such as blood pressure, body mass index, cholesterol levels, history of heart disease, and physical activity. Two machine learning algorithms, Logistic Regression and Random Forest, were applied to train the models. Class imbalance was addressed using the Synthetic Minority Over-sampling Technique (SMOTE). Evaluation metrics included accuracy, precision, recall, F1-score, and confusion matrix. The results show that the Random Forest model achieved an average accuracy of 93% and consistently high F1-scores, particularly for the prediabetic class of 98%. The most influential predictors were high blood pressure, obesity, and insufficient physical activity. This study contributes to the development of a reliable and efficient data-driven system for early diabetes risk detection.
Comparative Performance of Machine Learning Algorithms for Detecting Online Gambling Promotional Comments on Youtube Michael Angelo; Robet; Hendrik, Jackri
Jurnal Teknologi dan Manajemen Informatika Vol. 11 No. 2 (2025): Desember 2025
Publisher : Universitas Merdeka Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26905/jtmi.v11i2.16286

Abstract

Online-gambling promoters increasingly exploit YouTube comment sections, using text obfuscation, Unicode characters, emojis, irregular spacing, and symbols to evade automated moderation. This study aims to identify the most effective machine-learning algorithm for detecting such promotional comments by comparing models on standard metrics (precision, recall, F1-score, accuracy). We employ semi-supervised pseudo-labelling to expand the labelled set from 1,648 to 9,111 comments without additional manual annotation, admitting only high-confidence predictions. The pipeline includes customised character normalization, selective cleaning, tokenization, stopword removal, and Nazief–Adriani stemming, followed by TF–IDF feature extraction. Four algorithms are evaluated: Multinomial Naive Bayes, Logistic Regression, Random Forest, and Support Vector Machine, with hyperparameter optimization and class balancing via SMOTE. On a 1,823-sample test set, all models achieve over 98% accuracy; SVM yields the most balanced performance, resulting in the highest F1-score for the promotion class (0.9908). Confusion matrices and learning curves indicate stable behavior without overfitting or underfitting. We therefore recommend SVM for operational deployment in automated moderation of gambling-promotion comments on YouTube. These findings provide practical guidance for platform safety teams and suggest methodological baselines for similar NLP moderation tasks. Future work should explore ensemble and deep learning approaches, incorporate character and subword-level features, and further evaluate robustness under adversarial obfuscation and domain shift.
Co-Authors Agus Maringan Siahaan Andi Andi Barus, Hanisa putri Carles Juliandy Donna Natalia Edi Wijaya Feriani Astuti Tarigan Jackri Hendrik Johanes Terang Kita Perangin Angin Jonathan Kevin Fernando Juan Charles Michael Angelo Octara Pribadi Panjaitan, Tabitha Martha Agustine Perangin Angin, Johanes Rizkita, Ari Satriya Miharja Siahaan, Agus Maringan Teta, Bayu