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All Journal Teknika Syntax Jurnal Informatika Jurnal Ilmiah Rekayasa dan Manajemen Sistem Informasi Jurnal Informatika Jurnal CoreIT JURNAL MEDIA INFORMATIKA BUDIDARMA JIEET (Journal of Information Engineering and Educational Technology) Indonesian Journal of Artificial Intelligence and Data Mining Seminar Nasional Teknologi Informasi Komunikasi dan Industri INOVTEK Polbeng - Seri Informatika Sebatik Jurnal Nasional Komputasi dan Teknologi Informasi Krea-TIF: Jurnal Teknik Informatika JURIKOM (Jurnal Riset Komputer) JOISIE (Journal Of Information Systems And Informatics Engineering) Building of Informatics, Technology and Science Jurnal Teknologi Informasi dan Multimedia Jurnal Informatika dan Rekayasa Elektronik Jurnal Teknologi Dan Sistem Informasi Bisnis Zonasi: Jurnal Sistem Informasi JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) Journal of Computer System and Informatics (JoSYC) International Journal of Recent Technology and Applied Science (IJORTAS) Jurnal Sistem Komputer dan Informatika (JSON) JUKI : Jurnal Komputer dan Informatika Jurnal Inovasi Teknik Informatika TIN: TERAPAN INFORMATIKA NUSANTARA Jurnal Ilmu Komputer Jurnal Teknik Informatika (JUTIF) Jurnal Computer Science and Information Technology (CoSciTech) Bulletin of Computer Science Research KLIK: Kajian Ilmiah Informatika dan Komputer Bulletin of Information Technology (BIT) Malcom: Indonesian Journal of Machine Learning and Computer Science Jurnal Sains dan Informatika : Research of Science and Informatic SATIN - Sains dan Teknologi Informasi Journal Of Artificial Intelligence And Software Engineering Jurnal Indonesia : Manajemen Informatika dan Komunikasi
Febi Yanto
Teknik Informatika, Universitas Islam Negeri Sultan Syarif Kasim Riau, Indonesia
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Optimasi Pertanyaan Menggunakan Refined Query Dalam Sistem Tanya Jawab Kitab Hadis Wijaya, Andy Huang; Harahap, Nazruddin Safaat; Irsyad , Muhammad; Yanto, Febi
SATIN - Sains dan Teknologi Informasi Vol 10 No 1 (2024): SATIN - Sains dan Teknologi Informasi
Publisher : STMIK Amik Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33372/stn.v10i1.1116

Abstract

This research aims to enhance a Question-Answering System for Hadith texts by incorporating Refined Query techniques and Large Language Models (LLMs), specifically OpenAI's GPT-4. Utilizing a dataset of 62,169 Hadith from nine significant books, the study follows a comprehensive methodology that covers data collection, analysis and preprocessing, and the integration of LangChain and OpenAI's Chat Model for optimized querying. The evaluation of the system's performance was conducted through comparative analysis before and after the application of Refined Query, BERTScore for text quality, and user-based quality assessments. Results demonstrate that Refined Query significantly improves the system's capacity to produce accurate and contextually relevant responses. Implementing Refined Query not only enhanced answer precision but also facilitated the generation of responses where none were previously available. The average BERTScore of 0.80351 and the quality of user responses with an average score of 87.3% for the student test and 90.3% for the hadith expert test further validate the efficacy of the system. This research advances the domain of Islamic information systems by demonstrating the fruitful integration of advanced computational techniques with religious texts, offering a fundamental step towards better access to the understanding of Islamic jurisprudence.
Penerapan Langchain Retriever dengan Model Chat Openai dalam Pengembangan Sistem Chatbot Hadis Berbasis Telegram Niken Aisyah Maharani Herwanza; Nazruddin Safaat Harahap; Febi Yanto; Fitri Insani
Jurnal Teknologi Informasi dan Multimedia Vol. 6 No. 1 (2024): May
Publisher : Sekawan Institut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35746/jtim.v6i1.514

Abstract

In Islamic studies, the Hadiths of Prophet Muhammad (SAW) hold significant value as guides for behavior and faith. However, access to understanding Hadiths often presents challenges, espe-cially for those who are not Hadith experts. The digitalization of Hadiths is still limited, making it time-consuming to find answers by sifting through the vast amount of available information. This research aims to create an efficient chatbot that provides answers related to Hadiths, including the original sources, quickly. The proposed solution is a technology-based approach through the development of a Hadith chatbot on Telegram, integrated with the LangChain Retriever and the GPT-4-1106-preview chat model from OpenAI. Using LangChain Retriever helps the chatbot find accurate answers by matching user questions with relevant Hadith databases, enhancing the ac-curacy of the chatbot's responses. The GPT-4-1106-preview chat model enables the chatbot to generate natural and context-appropriate responses, improving user interaction. The Rapid Ap-plication Development (RAD) method is applied in system development, through stages of Re-quirement Planning, User Design, Construction, and Cut-Over, including data analysis of Hadiths from the Nine Imam Hadith Books, totaling 62,169 Hadiths. The chatbot's performance evaluation uses the Scoring Evaluator framework with an average evaluation score of 0.97 and quality answer evaluation testing by five Hadith experts with an accuracy percentage of 90%. The Scoring Eval-uator test results indicate that the responses are highly accurate and aligned with Hadith refer-ences, and the quality answer evaluation test on a Likert scale shows respondents strongly agree with the system's answers. This research contributes to laypersons wanting to learn Hadiths by utilizing the chatbot as an interactive and innovative learning medium. Further research can expand the focus to complex interpretations of Musykil al-Hadith and asbab al-wurud to address deeper questions about Hadith interpretation.
Implementasi Metode RBMT dalam Penerjemahan Bahasa Indonesia ke Bahasa Makassar Hanif, Wan Muhammad; Yusra, Yusra; Muhammad Fikry; Febi Yanto; Siska Kurnia Gusti
Bulletin of Computer Science Research Vol. 6 No. 1 (2025): December 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v6i1.935

Abstract

?This research was conducted to address the limited availability of linguistic resources for regional languages, particularly Makassar Language, which does not yet have adequate automatic translation support. The main problem addressed in this study is the absence of a reliable automatic translation system for Makassar Language. The objective of this research is to apply a rule-based translation method to translate text from Indonesian into Makassar Language. This study focuses on the implementation of the Rule-Based Machine Translation (RBMT) method for translating Indonesian text into Makassar Language using the Python programming language. The RBMT implementation involves tokenization, morphological analysis, vocabulary matching, and the application of grammatical rules, including the identification of prefixes and suffixes. The data used consist of a bilingual dictionary compiled from various sources and a set of test sentences representing everyday sentence structures. Translation evaluation was carried out using the Word Error Rate (WER) method, yielding a result of 0.289, and the Character Error Rate (CER) method, with a result of 0.21, which fall into the “Good” category based on the evaluation scale. The main findings indicate that the application of the RBMT method is capable of producing reasonably accurate translations at both the word and character levels. These findings demonstrate that a rule-based approach can be effectively applied to regional languages with limited digital data and provide an initial overview of the potential use of rule-based methods to support the development and preservation of regional languages.
Comparison of Various Deep Learning Techniques to Obtain the Best Technique for Detecting Brain Cancer Yanto, Febi; Budianita, Elvia; Wang, Shir Li
Jurnal CoreIT: Jurnal Hasil Penelitian Ilmu Komputer dan Teknologi Informasi Vol 11, No 2 (2025): December 2025
Publisher : Fakultas Sains dan Teknologi, Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/coreit.v11i2.38599

Abstract

This study aims to address the difficulty of comparing deep learning–based brain cancer detection methods due to differences in datasets and parameter settings, which limits the generalizability of previous findings. The purpose of this research is to evaluate the performance of several convolutional neural network (CNN) architectures using identical datasets and experimental configurations to determine the most effective technique for early brain cancer detection. The study builds a comparative framework using the Keras API on TensorFlow, supported by libraries such as NumPy, Pandas, Matplotlib, and Seaborn. All datasets were split into stratified training, validation, and test sets, and preprocessing included resizing images to 224×224 pixels, converting them to 3-channel RGB, normalizing the inputs, and applying data augmentation. CNN architectures, including VGG16, ResNet50, GoogleNet, and AlexNet, were trained with consistent parameter settings, including epoch count, batch size, learning rate optimization, and training protocols. Performance evaluation using accuracy, precision, recall, and F1-score shows that GoogleNet and ResNet50 achieve the highest results across datasets (average >94%), with GoogleNet slightly outperforming ResNet50. AlexNet performs poorly on the Kaggle dataset but shows potential on the private dataset, while VGG16 demonstrates moderate but less consistent performance. The originality of this study lies in providing a unified evaluation framework that enables fair comparison across CNN models, offering valuable insights for selecting optimal architectures for brain cancer detection.
Klasifikasi Citra Daging Babi dan Daging Sapi Menggunakan Deep Learning Arsitektur ResNet-50 dengan Augmentasi Citra Sarah Lasniari; Jasril Jasril; Suwanto Sanjaya; Febi Yanto; Muhammad Affandes
Jurnal Sistem Komputer dan Informatika (JSON) Vol. 3 No. 4 (2022): Juni 2022
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/json.v3i4.4167

Abstract

Beef is an example of an animal protein-rich food. The consumption of meat in Indonesia is increasing year after year, in tandem with the country's growing population. Many traders purposefully combine beef and pork in order to maximize profits. With the naked eye, it's difficult to tell the difference between pork and beef. In Muslim-majority countries, the assurance of halal meat is crucial. This study uses Deep Learning with the Convolutional Neural Network (CNN) method and ResNet-50 with data augmentation to classify images of beef and pork. The original meat picture databases contain 457 images, however following the data augmentation process, there are 2742 images in total, divided into three classes. The distribution of training and test data is 90 percent:10 percent in the comparison test scenario between the two original data schemes and supplemented data. With an average of 87.64 % accuracy, 87.59 % recall, and 90.90 % precision, the Confusion Matrix is the best classification performance model. There was no evidence of overfitting based on observations from the visualization of the training and testing process.
Klasifikasi Sentiment Review Aplikasi MyPertamina Menggunakan Word Embedding FastText dan SVM (Support Vector Machine) Mustasaruddin Mustasaruddin; Elvia Budianita; M Fikry; Febi Yanto
Jurnal Sistem Komputer dan Informatika (JSON) Vol. 4 No. 3 (2023): Maret 2023
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/json.v4i3.5695

Abstract

The MyPertamina application is a requirement for buying subsidized fuel oil (BBM), namely pertalite and diesel, the goal is that subsidized (BBM) purchases are right on target. The MyPertamina application has received many ratings and comments from the public, both positive and negative, with these comments and ratings expected to help the government as a benchmark in implementing a program. Therefore, this research aims to assess the MyPertamina application by grouping sentiment classes 90:10, 80:20 and 70:30. In this study, the method used is Fasttext and Support Vector Machine (SVM) to review the MyPertamina application. This research uses 8000 data, the data is grouped into three portions of data, with portions of 90:10, 80:20 and 70:30. The best SVM model was obtained with a data portion of 90:10 with a total of 7200 training data and 800 testing data, obtained 80% accuracy, 50% recall and 84% precision without undersampling. Meanwhile, if the amount of data is balanced (undersampling) with the number of positive data 1325, neutral 1325 and negative 1325, that is, with the benchmark of the lowest data value from the sentiment class, an accuracy of 67% is obtained, recall is 69% and precision is 57%. The highest number of sentiment classes from the 90:10 portion of the data is negative, namely 4300, neutral 1575 and positive 1325, because many users found reviews of the MyPertamina application, namely "after updating the MyPertamina application the bugs are getting worse".
Klasifikasi Sentimen Transformasi dan Reformasi Sepak Bola Indonesia Pada Twitter Menggunakan Algoritma Bernoulli Naïve Bayes Destri Putri Yani; Siska Kurnia Gusti; Febi Yanto; Muhammad Affandes
Jurnal Sistem Komputer dan Informatika (JSON) Vol. 4 No. 3 (2023): Maret 2023
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/json.v4i3.5829

Abstract

Federation Internationale de Football Association (FIFA) carried out Transformations and Reformations to Indonesian Football with one of them Indonesia was chosen as the Host of the U-20 World Cup in 2023. The transformations and reformations carried out cause people to often provide opinions through social media Twitter. Opinions given by the public can be positive or negative. The research uses Text Mining to classify sentiment in 2 categories with the Bernoulli Naïve Bayes algorithm. This research aims to classify positive and negative sentiments and determine the level of accuracy value of the sentiment classification results of Indonesian Football Transformation and Reformation. The research stages carried out are data collection, text preprocessing, data labeling, TF-IDF weighting, Bernoulli Naïve Bayes classification, and evaluation. Based on the research results from 4907 data there is duplicate data and only uses 2125 data which is divided into 90% training data and 10% testing data, so as to get accuracy with a high category value of 88%. The classification results show that many tweets are positive sentiments.
Klasifikasi Sentimen Tragedi Kanjuruhan Pada Twitter Menggunakan Algoritma Naïve Bayes Iqbal Salim Thalib; Siska Kurnia Gusti; Febi Yanto; Muhammad Affandes
Jurnal Sistem Komputer dan Informatika (JSON) Vol. 4 No. 3 (2023): Maret 2023
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/json.v4i3.5852

Abstract

The Kanjuruhan Malang incident occurred on October 1 and resulted in 132 deaths, 96 serious injuries and 484 minor injuries. The cause of the riot occurred due to provocation between Arema Malang supporters and Persebaya Surabaya supporters who mentioned harsh words and other provocative actions that caused anger on both sides. Sentiment analysis of the Kanjuruhan tragedy using the Naive Bayes method was conducted through tweets taken through Twitter to understand the public's perception of the incident. The Naïve Bayes algorithm is performed for the sentiment classification of tweet data which is applied by processing the tweet text and classifying it into positive, negative, and neutral. In this study using data as much as 4843 data and carried out with tweet data that has been crawled resulting in 2,042 data. This research aims to classify sentiment and determine the level of accuracy in the Multinomial Naïve Bayes algorithm in the Kanjuruhan tragedy using a dataset in the form of tweets from twitter social media. The processed tweet data is divided into two types, namely 90% training data and 10% test data.  The results of this classification get a Naïve Bayes accuracy of 75% with a precission of 73%, recall of 75%, and f1-score value of 74%. The results of the tweet data used in this study can be concluded that the Naïve Bayes algorithm has a fairly good accuracy value.
Klasifikasi Sentiment Ulasan Aplikasi Sausage Man Menggunakan VADER Lexicon dan Naïve Bayes Classifier M Ikhsan Maulana; Elvia Budianita; Muhammad Fikry; Febi Yanto
Jurnal Sistem Komputer dan Informatika (JSON) Vol. 4 No. 3 (2023): Maret 2023
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/json.v4i3.5854

Abstract

Battle Royale games are games that mix adventure and survival elements with last man standing game modes. One of the most popular battle royale games is the Sausage Man game. The number of complaints such as bugs, cheaters, and FPS which continues to decrease makes the game annoying. The solution is that developers must improve and improve game security so that users feel comfortable playing the game. There are many opinions or reviews from users regarding problems in the game, sentiment analysis will be carried out on the Sausage Man application review data on the Google play store as a process to produce categorization of opinions through reviews. The purpose of the researcher is to carry out a sentiment analysis to see positive, neutral or negative opinions from Sausage Man game users. The stages carried out in this study were data collection using web scraping, data labeling, text preprocessing, document weighting, classification, and evaluation. The results of data labeling using the VADER Lexicon obtained 1089 reviews (36.3%) for positive sentiment, 912 reviews for neutral sentiment (30.4%), and 999 reviews for negative sentiment (33.3%). Classification using the Naïve Bayes Classifier. Evaluation using the Confusion Matrix by dividing 90% training data and 10% test data produces an accuracy of 75%, 79% precision, and 75% recall. For the division of 80% training data 20% of the test data produces an accuracy of 73%, 76% precision and 73% recall. Positive sentences are found more often, but the accuracy is still below 80%.
Klasifikasi Citra Daging Sapi dan Daging Babi Menggunakan CNN Arsitektur EfficientNet-B6 dan Augmentasi Data M. Fadil Martias; Jasril Jasril; Suwanto Sanjaya; Lestari Handayani; Febi Yanto
Jurnal Sistem Komputer dan Informatika (JSON) Vol. 4 No. 4 (2023): Juni 2023
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/json.v4i4.6195

Abstract

In daily life, beef often serves as a staple food for humans. However, the high and expensive price of beef has prompted traders to adulterate it with pork for the sake of profit. Such adulteration has serious implications in the Islamic religion, where not all types of meat are considered halal (permissible for consumption), such as pork. As a result, consumers often remain unaware that the beef they purchase has been adulterated with pork. At a glance, both types of meat exhibit similar appearance and texture, making them difficult to differentiate. This research aims to classify beef and pork using a deep learning model with the Convolutional Neural Network (CNN) method, combined with data augmentation. The model used is EfficientNet-B6 with variations in the testing scenario. The variations include the ratio of training and testing data, learning rates, and optimizer for EfficientNet-B6. Data augmentation is performed using techniques such as random rotation, shifting, image scaling, vertical and horizontal flipping, and nearest pixel filling. Evaluation results using the confusion matrix show that the model with data augmentation achieves the highest accuracy for the classes of beef, pork, and adulterated samples at 92.00%, while the model without augmentation achieves an accuracy of 91.67%. However, from this experiment, the best scenario to avoid misclassifying pork and adulterated samples as beef can be obtained. This scenario involves a model with data augmentation, a 90:10 data split, SGD optimizer, and a learning rate of 0.01, which achieves the highest precision for the beef class at 96.05%. The research findings demonstrate that the use of data augmentation on images can improve the model's performance, and the model with data augmentation, a 90:10 data split, SGD optimizer, and a learning rate of 0.01 exhibits the best performance in classifying beef images.
Co-Authors Abdul Haris Abdussalam Al Masykur Adha, Martin Afiana Nabilla Zulfa Afriyanti, Liza Afroni, Hallend Agustina, Auliyah Alfitra Salam Alwis Nazir Andri Andri Aprilia, Risma Arif Mudi Priyatno Ariq At-Thariq Putra Baehaqi Benny Sukma Negara citra ainul mardhia putri Dafwen Toresa Dea Ropija Sari Destri Putri Yani Dewi, Nurika Dicky Abimanyu Dimas Ferarizki Dzaky Abdillah Salafy Edriyansyah Eka Pandu Cynthia Eka Pandu Cynthia Eka Pandu Cynthia, Eka Pandu Elin Haerani Elvia Budianita Fadhilah Syafria Fajar Febriyadi Fajri Fahreza Azeta Faris Apriliano Eka Fardianto Faris Fauzan Ray T Fauziyyah, Laila Nurul Fitra Kurnia Fitri Insani Fitri Insani Gusman, Deddy Gusti, Gogor Putra Hafi Puja Gusti, Siska Kurnia Hallend Afroni Hanif, Wan Muhammad Harni, Yulia Hatta, M Ilham Hidayat, Rizki Ichsan Permana Putra Idhafi, Zaky Iis Afrianty Iis Afrianty Ikhsan Hidayat Ikhwanul Akhmad DLY Illahi, Ridho Inggih Permana Iqbal Salim Thalib Irma Welly, Irma Irsyad , Muhammad Isnan Mellian Ramadhan Iwan Iskandar Iwan Jannata, Nanda Jasril Jasril Jasril Jasril Jasril Jasril Jeki Dwi Arisandi Kurniansyah, Juliandi Lestari Handayani Lestari Handayani Lisnawita Lisnawita M Fikry M Ikhsan Maulana M. Afdal M. Fadil Martias M. Nabil Dawami Masaugi, Fathan Fanrita Mazdavilaya, T Kaisyarendika mohamad samuri, suzani Morina Lisa Pura Muhammad Affandes Muhammad Affandes Muhammad Affandes Muhammad Fahri Muhammad Fikry Muhammad Fikry Muhammad Fikry Muhammad Fikry Muhammad Haiqal Dani Muhammad Irsyad Muhammad Irsyad Muhammad Irsyad Muhammad Irsyad Mustasaruddin Mustasaruddin Nabyl Alfahrez Ramadhan Amril Nadila Handayani Putri Nazruddin Safaat H Nazruddin Safaat H Negara, Benny Sukma Niken Aisyah Maharani Herwanza Nining Erlina Novriyanto Novriyanto Nurika Dewi Okta Silvia M Permata, Rizkiya Indah Pizaini Pizaini Prananda, Alga Pratama, Dandi Irwayunda Putra, Wahyu Eka Putri Ayuni, Desy Putri Zahwa Rahma Shinta Rahmad Abdillah Rahman, Muhammad Taufikur Rahmat Al Hafiz Raja Joko Musridho Reski Mai Candra Reski Mai Candra Reski Mai Candra Rometdo Muzawi, Rometdo Roni Setyawan RR. Ella Evrita Hestiandari Saeed, Alabbas Hussein Sandy Ilham Hakim Syasri Sarah Lasniari Sarah Lasniari Shahira, Fayza Shir Li, Wang Siti Ramadhani Sofiyah, Wan Sugandi, Hatami Karsa Surya Agustian Suwanto Sanjaya Syafria, Fadhillah Ulfah Adzkia Wang, Shir Li Wijaya, Andy Huang Wirdiani, Putri Syakira Yenggi Putra Dinata Yuli Novita Sari, Yuli Novita Yusra Yusra Yusra Yusra Yusra Yusra Yusra Yusra Yusra Yusra Yusra, Yusra