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All Journal JTT (Jurnal Teknologi Terpadu) RABIT: Jurnal Teknologi dan Sistem Informasi Univrab JURNAL MEDIA INFORMATIKA BUDIDARMA INOVTEK Polbeng - Seri Informatika ILKOM Jurnal Ilmiah Voice Of Informatics AL-ULUM: JURNAL SAINS DAN TEKNOLOGI JOISIE (Journal Of Information Systems And Informatics Engineering) EDUMATIC: Jurnal Pendidikan Informatika Jurnal Manajemen Informatika dan Sistem Informasi Jurnal Sistem informasi dan informatika (SIMIKA) JSR : Jaringan Sistem Informasi Robotik Jurnal Tekinkom (Teknik Informasi dan Komputer) Jurnal Ilmu Komputer Journal of Applied Data Sciences Jurnal Computer Science and Information Technology (CoSciTech) Jurnal J-PEMAS JAIA - Journal of Artificial Intelligence and Applications SATIN - Sains dan Teknologi Informasi The Indonesian Journal of Computer Science
Hadi Asnal, Hadi
STMIK Amik Riau
Agriculture, Biological Sciences & Forestry Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Energy Engineering Materials Science & Nanotechnology Mechanical Engineering

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Aplikasi Chatbot Interaktif Pembelajaran Bahasa Pemrograman PHP dengan Algoritma NLP berbasis BERT Waleska, Rangga Febrio; Asnal, Hadi; Rahmiati, Rahmiati; Gunadi, Gunadi
Jurnal Pendidikan Informatika (EDUMATIC) Vol 9 No 2 (2025): Edumatic: Jurnal Pendidikan Informatika
Publisher : Universitas Hamzanwadi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29408/edumatic.v9i2.31427

Abstract

The digitalization of education facilitates access to information; however, beginners still face challenges in understanding programming languages such as PHP. This study aims to develop a chatbot based on Natural Language Processing (NLP) using the Sentence-BERT model (all-MiniLM-L6-v2) to understand user questions in natural language contextually. The research follows a prototyping development method, consisting of several stages: needs identification to determine relevant features for users; interface design to create an intuitive and user-friendly layout; web-based system implementation to realize system functions; and testing using the black-box method to ensure each feature works as specified, along with usability evaluation using the System Usability Scale (SUS) to assess user comfort and ease of use. The result is a chatbot application capable of matching user questions with a Q&A database using semantic similarity. All testing scenarios ran as expected. The SUS evaluation yielded a score of 89.58, indicating a very high level of user satisfaction. This research demonstrates that the integration of NLP and BERT can enhance the effectiveness and convenience of independent programming learning and has the potential to be applied to other educational platforms.
SISTEM REKOMENDASI VIDEO GAME BERBASIS USIA SEBAGAI ALAT PENGAWASAN ORANG TUA DI PLATFORM STEAM MENGGUNAKAN CONTENT-BASED FILTERING Oktavianda, Oktavianda; Efrizoni, Lusiana; Fatdha, Eiva; Asnal, Hadi
Jurnal Sistem Informasi dan Informatika (Simika) Vol. 8 No. 2 (2025): Jurnal Sistem Informasi dan Informatika (Simika)
Publisher : Program Studi Sistem Informasi, Universitas Banten Jaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47080/2wj54451

Abstract

Video recreations are a prevalent shape of amusement, particularly among children. In any case, numerous parents in Indonesia still need understanding of age appraisals for video recreations, driving to less viable supervision. This could uncover children to unseemly substance. This think about points to create an age-based video amusement suggestion framework utilizing the Content-Based Filtering strategy on the Steam dataset. The framework is planned to help guardians in selecting recreations suitable for their children. Evaluation results show the model performs very well, achieving a precision of 0.98 and a recall of 1.00. Additionally, the model records a Mean Absolute Error (MAE) of 0.469236, Mean Squared Error (MSE) of 6.440935, and Root Mean Squared Error (RMSE) of 2.537900. These findings highlight how well the system filters and suggests age-appropriate video games, assisting parents in better monitoring their kids' gaming habits.
ANALISIS PERBANDINGAN ALGORITMA C4.5 DAN NAIVE BAYES UNTUK MEMPREDIKSI KETERCAPAIAN TARGET PO DALAM MEMBANGUN PROJECT FTTH (FIBER TO THE HOME) Pratama, Ahmad Tara; Deni, Rahmad; Agustin, Agustin; Asnal, Hadi
Jurnal Teknik Informasi dan Komputer (Tekinkom) Vol 8 No 1 (2025)
Publisher : Politeknik Bisnis Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37600/tekinkom.v8i1.2309

Abstract

In the digital era, the demand for high-speed and stable internet has become essential to support communication and information access. Fiber to the Home (FTTH) is one of the main solutions implemented by internet service providers such as MyRepublic. A critical component in FTTH network development is the issuance of Purchase Orders (PO) to vendors, which directly impacts the achievement of sales targets. This study aims to compare the performance of the C4.5 and Naïve Bayes classification algorithms in predicting PO target achievement to assist project planning and decision-making. The research uses historical data from FTTH projects and applies data partitioning scenarios of 70:30, 80:20, and 90:10 for model training and testing. Evaluation was conducted using accuracy as the main performance metric. The results show that the Naïve Bayes algorithm achieved the highest accuracy of 85.64% with a 70:30 data split, while C4.5 obtained 83.54% accuracy with a 90:10 data split. Based on these findings, the Naïve Bayes algorithm is considered more effective and consistent in predicting PO target achievement and is recommended for implementation in similar project scenarios.
Adaptive Neural Collaborative Filtering with Textual Review Integration for Enhanced User Experience in Digital Platforms Efrizoni, Lusiana; Ali, Edwar; Asnal, Hadi; Junadhi, Junadhi
Journal of Applied Data Sciences Vol 6, No 4: December 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i4.944

Abstract

This research proposes a hybrid rating prediction model that integrates Neural Collaborative Filtering (NCF), Long Short-Term Memory (LSTM), and semantic analysis through Natural Language Processing (NLP) to enhance recommendation accuracy. The main objective is to improve alignment between system predictions and actual user preferences by leveraging multi-source information from the Amazon Movies and TV dataset, which includes explicit user–item ratings and textual reviews. The core idea is to combine three complementary processing paths—(1) user–item interaction modeling via NCF, (2) temporal dynamics capture through LSTM, and (3) semantic understanding of reviews using NLP—into a unified deep learning-based adaptive architecture. Experimental evaluation demonstrates that this multi-input approach outperforms the baseline collaborative filtering model, with the Mean Absolute Error (MAE) reduced from 1.3201 to 1.2817 (a 2.91% improvement) and the Mean Squared Error (MSE) reduced from 2.2315 to 2.1894 (a 1.89% improvement). Training metrics visualization further shows a stable convergence pattern, with the MAE gap between training and validation consistently below 0.03, indicating minimal overfitting. The findings confirm that integrating cross-dimensional signals significantly enhances predictive performance and can contribute to increased user satisfaction and engagement in recommendation platforms. The novelty of this work lies in the simultaneous integration of interaction, temporal, and semantic dimensions into a single adaptive recommendation framework, a configuration not jointly explored in prior studies. Moreover, the flexible architecture enables adaptation to other domains such as e-commerce, music, or online learning, broadening its practical applicability.
Analisis Sentimen Layanan Hotel Menggunakan Algoritma Extra Trees: Studi Kasus pada Ulasan Pelanggan Aprilita, Windi; Junadhi; Agustin; Hadi Asnal
The Indonesian Journal of Computer Science Vol. 13 No. 3 (2024): The Indonesian Journal of Computer Science (IJCS)
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v13i3.4014

Abstract

This research aims to analyze the sentiment of hotel services based on customer reviews using the Extra Trees algorithm. This method was tested on a dataset containing customer reviews about hotel services. The evaluation is done by taking into account the accuracy, precision, recall, and F1 score of the developed model. The results showed that the Extra Trees algorithm was able to achieve an accuracy of 85.05%, with a precision of 84.46%, a recall of 97.00%, and an F1 score of 90.17%. These findings indicate that the Extra Trees algorithm has good performance in analyzing hotel service sentiment based on customer reviews. The implication of this research is to provide guidance to hotels to understand and improve their service quality based on feedback from customers. In addition, this research can also be the basis for further development in the field of sentiment analysis and customer service in the tourism industry.
Co-Authors Agus Alamsyah Agustin Agustin Agusviyanda Agusviyanda Aldi Syahputra Anam, M Khairul Aprilita, Windi Arifandi, Fajar Bayu Yuliansyah Bunga Nanti Pikir Deni, Rahmad Didik Sudyana Edwar Ali Efrizoni, Lusiana Elisawati, Elisawati Erlinda, Susi Fatdha, Eiva Fransiskus Zoromi Fransiskus Zoromi Fransiskus Zoromi, Fransiskus Gunadi Gunadi Hamdani Hamdani - Hartomi, Zupri Henra Helda Yenni, Helda Ivan Gunawan Jamaris, Muhamad Jamaris, Muhammad Junadhi Junadhi Junadhi, Junadhi Kadek Mirnawati Mardainis Mardainis Marni, Prina Mitrin, Abdullah Mohd Rinaldi Amartha Muhammad Bambang Firdaus Muhammad Febri Devara muhammad Fuad Muhammad Muadin Muhammad Nur Cahyadi Murtadho Habibi Habibi Nasrul Sani Nora Lizarti Nurhadi Oktavianda, Oktavianda Pratama, Ahmad Tara Prilly Maya Gita Rahmaddeni Rahmaddeni Rahmiati Rahmiati Rahmiati renaldi, reno Rini Yanti Rometdo Muzawi, Rometdo Supahri, Hafid Azis Susanti, Mega Susanti, Susanti Susi Erlinda T. Sy. Eiva Fatdha Tashid Torkis Nasution Triyani Arita Fitri Ulfah, Aniq Noviciate Waleska, Rangga Febrio Yansyah Saputra Wijaya Yuda Irawan Zaki Mubarok G