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All Journal Jurnal Teknologi dan Manajemen Informatika CommIT (Communication & Information Technology) Jurnal TIMES Sinkron : Jurnal dan Penelitian Teknik Informatika JIKO (Jurnal Informatika dan Komputer) Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control JOURNAL OF APPLIED INFORMATICS AND COMPUTING METHOMIKA: Jurnal Manajemen Informatika & Komputerisasi Akuntansi Jiko (Jurnal Informatika dan komputer) JURIKOM (Jurnal Riset Komputer) JURTEKSI Building of Informatics, Technology and Science Jurnal Sistem Komputer dan Informatika (JSON) Jurnal Ilmiah Smart Journal of Applied Computer Science and Technology (JACOST) Bulletin of Computer Science Research Journal of Artificial Intelligence and Engineering Applications (JAIEA) Majalah Ilmiah METHODA TAMIKA: Jurnal Tugas Akhir Manajemen Informatika & Komputerisasi Akuntansi Journal of Computing Theories and Applications QOSIM: Jurnal Pendidikan, Sosial & Humaniora Jurnal Ilmiah Smart Jurnal Ilmiah Teknik Mesin, Elektro dan Komputer
Robet Robet
STMIK TIME
Religion Agriculture, Biological Sciences & Forestry Humanities Automotive Engineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Languange, Linguistic, Communication & Media Mechanical Engineering Medicine & Pharmacology Public Health Social Sciences

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Perancangan Aplikasi Web Chatbot Multi-Bahasa Berbasis NPL Translator API Dengan Multibahasa Terjemahan Robet Robet; Johanes Terang Kita Perangin Angin; Randy Wilson
Jurnal Ilmiah Teknik Mesin, Elektro dan Komputer Vol. 5 No. 1 (2025): Maret
Publisher : Lembaga Pengembangan Kinerja Dosen

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/juritek.v5i1.4150

Abstract

Perangkat lunak komputer yang dapat meniru dan memproses interaksi manusia yang diucapkan atau ditulis disebut chatbot multibahasa, atau chatterbot. Dengan chatbot, orang dapat berkomunikasi dengan perangkat digital seolah-olah mereka berbicara dengan orang sungguhan. Tujuan dari penelitian pengembangan ini adalah untuk menggunakan teknik pembelajaran mesin untuk membangun dan membuat aplikasi chatbot multibahasa dengan kemampuan penerjemahan. Proses pengembangan sistem memerlukan sejumlah fase. Salah satu pendekatan untuk pengembangan perangkat lunak adalah Siklus Hidup Pengembangan Perangkat Lunak. Metode Transformer dipilih sebagai pendekatan pengembangan sistem untuk penelitian ini. Tujuan yang diantisipasi dari penelitian ini adalah: untuk membuat aplikasi chatbot multibahasa berbasis situs web menggunakan NPL Translator API. Pengguna yang mengalami kendala bahasa mungkin merasa lebih mudah untuk menggunakan penerjemah otomatis bawaan aplikasi obrolan. Karena sifatnya yang berbasis web, program obrolan ini memfasilitasi komunikasi pengguna dari lokasi mana pun.
Penerapan Algoritma Transformer dalam Aplikasi Parafrase Teks Otomatis Robet Robet; Kelvin Leonardi Kohsasih; Jenime Darwin
TAMIKA: Jurnal Tugas Akhir Manajemen Informatika & Komputerisasi Akuntansi Vol 5 No 1 (2025): TAMIKA: Jurnal Tugas Akhir Manajemen Informatika & Komputerisasi Akuntansi
Publisher : Universitas Methodist Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46880/tamika.Vol5No1.pp103-109

Abstract

The development of Natural Language Processing (NLP) technology has enabled the creation of automated text manipulation applications, one of which is text paraphrasing. This study aims to implement a Transformer architecture with a focus on Indonesian text for automatic text paraphrasing applications. The model used is a pre-trained Text-to-Text Transfer Transformer (T5), which is fine-tuned using an Indonesian text corpus called the Indo-T5 model. During the training process, the model is trained to understand language structure and context in order to generate paraphrases that are not only grammatically correct but also semantically preserved. Evaluation was conducted using BLEU and ROUGE metrics to measure the similarity between the generated paraphrased texts and manual references. The evaluation results show that the model is capable of producing coherent, relevant paraphrased texts with a good level of lexical variation with a BLEU score of 50.1, and ROUGE-L of 61.7. Thus, this study demonstrates that Transformer-based models can be effectively applied to the task of text paraphrasing in Indonesian.
Aplikasi Deteksi Usia Berbasis Citra Menggunakan Model Deep Learning dengan Arsitektur CNN Robet Robet; Chandra Chandra; Jerico Setiawan
TAMIKA: Jurnal Tugas Akhir Manajemen Informatika & Komputerisasi Akuntansi Vol 5 No 1 (2025): TAMIKA: Jurnal Tugas Akhir Manajemen Informatika & Komputerisasi Akuntansi
Publisher : Universitas Methodist Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46880/tamika.Vol5No1.pp97-102

Abstract

This research aims to design and implement an age detection application based on facial images using a deep learning approach with a Convolutional Neural Network (CNN) architecture. The model is built to recognize and extract facial features in order to estimate an individual’s age automatically. Facial image datasets were obtained from public sources and enhanced through augmentation techniques such as rotation, flipping, and lighting adjustment to increase data variability. The training process involved splitting the data into training, validation, and testing sets. The model was evaluated using accuracy, precision, recall, and F1-score metrics. The gender detection system achieved an accuracy of 82.99% with a precision of 80.95% for males and 84.47% for females. Recall scores were 85.15% for males and 80.12% for females. For age detection, precision, recall, and F1-score varied across different age groups. Overall, the model demonstrates exemplary performance in age prediction, though it still faces challenges in distinguishing closely spaced age categories.
Comparative Performance of Machine Learning Algorithms for Detecting Online Gambling Promotional Comments on Youtube Michael Angelo; Robet; Jackri Hendrik
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.
Application of Bagging and Boosting Methods for Heart Disease Classification Yehezkiel E.A Parapak; Robet Robet; Jackri Hendrik
Journal of Applied Computer Science and Technology Vol. 6 No. 2 (2025): Desember 2025
Publisher : Indonesian Society of Applied Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52158/we9asn06

Abstract

Cardiovascular disease remains a primary contributor to global mortality, underscoring the urgent need for accurate and early diagnostic tools. This study aims to develop a robust classification model for heart disease by conducting a comparative analysis of six ensemble machine learning algorithms, comprising three from the Bagging family (Random Forest, Bagged Decision Tree, Extra Trees) and three from the Boosting family (AdaBoost, Gradient Boosting, XGBoost). The research utilizes the publicly available UCI Cleveland Heart Disease dataset, which exhibits a mild class imbalance. To address this, the Synthetic Minority Over-sampling Technique (SMOTE) was strategically applied to the training data. The performance of each model was rigorously evaluated using accuracy, precision, recall, and F1-score. Experimental results revealed that the Extra Trees algorithm, when combined with SMOTE, achieved the highest overall performance with 90% accuracy, 96% precision, 82% recall, and an 88% F1-score. The primary contribution of this work lies in its comprehensive analysis demonstrating that the randomization strategy of Extra Trees provides a superior and more reliable framework for this classification task compared to other common ensemble techniques, particularly after data balancing. These findings confirm that an integrated approach of ensemble learning and proper data balancing can significantly enhance the development of fair and effective diagnostic tools to support medical professionals.
Akurasi K-Means dengan Menggunakan Cluster dan Titik Grid Terbaik pada Pemetaan Grid Interatif K-Means Johanes Terang Kita Perangin Angin; Ari Rizkita; Robet Robet; Octara Pribadi
METHOMIKA: Jurnal Manajemen Informatika & Komputerisasi Akuntansi Vol. 9 No. 1 (2025): METHOMIKA: Jurnal Manajemen Informatika & Komputersisasi Akuntansi
Publisher : Universitas Methodist Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46880/jmika.Vol9No1.pp127-129

Abstract

Traditional K-Means face 2 (two) main problems, namely: Determination of Initial Centroid and poor initial cluster. Determining the initial centroid using random numbers is one of the main problems in classical K-Means which results in low accuracy and long computation time. Likewise, determining the good centroid of each cluster without being accompanied by a process of paying attention to the performance of each cluster can also cause the accuracy value obtained is not good. This study will contribute to how the performance obtained by determining a good initial centroid is combined with the use of a good cluster. Determination of a good initial centroid is done by using the K-Means Grid Mapping which divides the determination of the centroid into several Grid Points. The result of this research is a combination of Iterative K-Means with Grid Mapping K-Means to become Iterative Grid Mapping K-Means which will get a good initial centroid and also a good cluster shown in the table of iris and abalone, comparison of the variables in the iris and abalone affecting the best cluster as a result.
DETEKSI BERITA HOAKS BAHASA INDONESIA MENGGUNAKAN KOMBINASI TF-IDF DAN K-NEAREST NEIGHBOR Ari Rizkita; Johanes Terang Kita Perangin Angin; Robet Robet; Octara Pribadi; Iqbal Giffari Ritonga
Jurnal TIMES Vol 15 No 1 (2026): Jurnal TIMES
Publisher : STMIK TIME

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51351/jtm.15.1.2026933

Abstract

Penyebaran berita hoaks di media digital Indonesia telah menjadi tantangan serius yang mengancam stabilitas sosial dan ketertiban publik. Berdasarkan data Kementerian Komunikasi dan Informatika, jumlah isu hoaks terus meningkat secara signifikan setiap tahunnya, sehingga diperlukan sistem deteksi otomatis yang cepat dan akurat. Penelitian ini bertujuan untuk mengimplementasikan dan menganalisis performa kombinasi metode ekstraksi fitur Term Frequency-Inverse Document Frequency (TF-IDF) dan algoritma K-Nearest Neighbor (KNN) dalam mengklasifikasikan berita hoaks berbahasa Indonesia. Dataset yang digunakan berjumlah 1.000 entri seimbang yang bersumber dari TurnBackHoax.id sebagai representasi hoaks, serta Antaranews, Kompas, dan Detik sebagai representasi berita valid. Eksperimen dilakukan dengan pembagian data latih dan uji sebesar 80:20 serta pengujian iteratif pada nilai parameter K ganjil (3, 5, 7, 9, dan 11). Hasil penelitian menunjukkan bahwa model mencapai performa maksimal dengan nilai akurasi, presisi, dan recall sebesar 100% pada seluruh skenario nilai K. Hal ini mengindikasikan bahwa pembobotan statistik TF-IDF mampu membedakan pola kosakata antara klarifikasi hoaks dan teks jurnalistik secara sempurna. Kesimpulannya, algoritma KNN terbukti sangat efektif dan efisien secara komputasi untuk digunakan sebagai sistem penyaring misinformasi pada media digital di Indonesia.
Co-Authors Agus Maringan Siahaan Alvin Alvin, Alvin Andi Andi Andy Andy Ari Rizkita Ari Rizkita Arnold Adimabua Ojugo Barus, Hanisa putri Bayu Teta Bimo Haryo Setyoko Br Tarigan, Widia CANDRA GUNAWAN Carles Juliandy Chandra Chandra De Rosal Ignatius Moses Setiadi Didik Aryanto Donna Natalia edi edi Edi Wijaya Edi Wijaya Edi Wijaya Eferhire Valentine Ugbotu Etika Kartikadarma Feriani Astuti Tarigan Feriani Astuti Tarigan Feriani, Feriani Astuti Tarigan Haria Wirawan Hendri Hendri Hendri Hendri Hoki, Leony Hose, Fernando Iqbal Giffari Ritonga Jackri Hendrik Jenime Darwin Jerico Setiawan Johanes Terang Kita Perangin Angin Jonathan Kevin Fernando Juan Charles Kelvin Leonardi Kohsasih Lase, Wisriani Liecero, Marcus Md Kamruzzaman Sarker Michael Angelo Nicholas Tio Nurhayati Harahap Octara Pribadi Panjaitan, Tabitha Martha Agustine Perangin Angin, Johanes Poppy Amalia Putri, Jihan Hidayah Randy Wilson Rizkita, Ari Robin Robin Robin Robin, Robin Robin Zheng Satriya Miharja Siahaan, Agus Maringan Simanjuntak, Martina Sances Sinaga, Asra Gretya Siregar, Tarq Hilmar Suyud Widiono Suyud Widiono Tabitha Chukwudi Aghaunor Vic Devlin Yehezkiel E.A Parapak Yessica Yamin Yuliana Yuliana