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All Journal Teknika SITEKIN: Jurnal Sains, Teknologi dan Industri JTT (Jurnal Teknologi Terpadu) Jurnal CoreIT Seminar Nasional Teknologi Informasi Komunikasi dan Industri Jurnal Informatika Universitas Pamulang Jurnal Nasional Komputasi dan Teknologi Informasi Krea-TIF: Jurnal Teknik Informatika Komputasi: Jurnal Ilmiah Ilmu Komputer dan Matematika JSAI (Journal Scientific and Applied Informatics) Building of Informatics, Technology and Science Jurnal Sosial Humaniora Sigli INFORMASI (Jurnal Informatika dan Sistem Informasi) Journal of Computer System and Informatics (JoSYC) Jurnal Sistem Komputer dan Informatika (JSON) JUKI : Jurnal Komputer dan Informatika Jurnal Restikom : Riset Teknik Informatika dan Komputer Jurnal Computer Science and Information Technology (CoSciTech) SINTA Journal (Science, Technology, and Agricultural) Bulletin of Computer Science Research KLIK: Kajian Ilmiah Informatika dan Komputer Multidiciplinary Output Research for Actual and International Issue (Morfai Journal) J-Intech (Journal of Information and Technology) Jurnal Indonesia Raya Jurnal Dehasen Mengabdi SATIN - Sains dan Teknologi Informasi Journal Of Artificial Intelligence And Software Engineering Jurnal Malikussaleh Mengabdi Jurnal Indonesia : Manajemen Informatika dan Komunikasi Seminar Nasional Riset dan Teknologi (SEMNAS RISTEK)
Muhammad Fikry
Universitas Islam Negeri Sultan Syarif Kasim Riau
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Peningkatan Performa Klasifikasi Sentimen Tweet Kaesang Menggunakan Naïve Bayes dengan PSO pada Dataset Kecil Muhammad Ravil; Agustian, Surya; Fikry, Muhammad; Insani, Fitri
KLIK: Kajian Ilmiah Informatika dan Komputer Vol. 4 No. 6 (2024): Juni 2024
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/klik.v4i6.1939

Abstract

After the news of Kaesang's appointment as the Chairman of the Indonesian Solidarity Party (PSI), various speculations emerged on social media, particularly on Twitter (X). This study aims to classify sentiments regarding Kaesang's appointment as PSI Chairman using the Naïve Bayes algorithm optimized with Particle Swarm Optimization (PSO). The data used in this study consists tweets about Kaesang and tweets related to COVID-19. The text preprocessing process includes cleaning, case folding, tokenizing, stemming, and stopword removal. TF-IDF is used to represent words in vector form. In the initial experiment, Naïve Bayes performed classification using Kaesang data combined with COVID-19 data, with 300 data points for each label. Particle Swarm Optimization was used to improve the performance of the Naïve Bayes algorithm. The experiment results showed that the model tested with test data achieved the highest f1-score of 50%.
Klasifikasi Sentimen Terhadap Topik Pindah Ibu Kota Negara Pada Twitter Menggunakan Metode Naïve Bayes Classifier Dermawan, Jozu; Yusra, Yusra; Fikry, Muhammad; Agustian, Surya; Oktavia, Lola
Jurnal Sistem Komputer dan Informatika (JSON) Vol 5, No 3 (2024): Maret 2024
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/json.v5i3.7475

Abstract

Towards the middle of 2019, President Joko Widodo announced plans to relocate Indonesia's capital city. This caused pros and cons in the community, which were widely observed in various social media. To quickly measure the level of public sentiment towards the policy of moving the National Capital City (IKN), whose construction is already underway, a classification system that has good performance is needed. This research proposes a classification of public sentiment on the topic using the Naïve Bayes Classifier method. The data used in this study amounted to 4000 tweets that have been classified into two classes, namely 2000 positive class data and 2000 negative class data. The purpose of this research is how to apply the Naïve Bayes Classifier method in classifying sentiment on the topic of moving the nation's capital and determine the accuracy level of the method. The application of the Naïve Bayes classification method using TF-IDF features to classify 10% of the data as testing data resulted in an accuracy of 77.00%, for a precision value of 77.06%, recall 77.08% and f1-score of 77.00%. Based on the results achieved, the Naïve Bayes Classifier method is good at text classification tasks, with a fairly good accuracy rate.
Aplikasi Web Question Answering Menggunakan Langchain OpenAI Tentang Peraturan Perundang-undangan Bidang Pendidikan Saputra, Ikhsan Dwi; Harahap, Nazruddin Safaat; Agustian, Surya; Fikry, Muhammad; Oktavia, Lola
Journal of Computer System and Informatics (JoSYC) Vol 6 No 1 (2024): November 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josyc.v6i1.6182

Abstract

In the rapid development of information technology over the past few years, the ease of accessing information has been one of the significant achievements. Artificial intelligence (AI) has emerged as a potential tool in bringing innovative solutions in various sectors of human life. This research aims to develop a web application capable of answering questions related to educational legislation using the LangChain framework and BERT model. The primary issue addressed is the complexity and volume of legal documents that are challenging for lay users to access and understand. The methodology involves converting legal documents from PDF to text, segmenting the text using LangChain, and evaluating system performance with BERTScore and ROUGE Score. The results indicate that BERTScore is superior in measuring the alignment between the system’s answers and reference answers, with some questions achieving a score of 100%. However, there are limitations, such as the manual effort required for document conversion and the substantial computational resources needed for text processing. This research significantly contributes to facilitating access and comprehension of educational legal documents and opens opportunities for further development with more advanced conversion techniques and AI models.
Klasifikasi Sentimen Masyarakat di Twitter terhadap Puan Maharani dengan Metode Modified K-Nearest Neighbor Putra, Wahyu Eka; Fikry, Muhammad; Yusra; Yanto, Febi; Cynthia, Eka Pandu
Jurnal Indonesia : Manajemen Informatika dan Komunikasi Vol. 6 No. 1 (2025): Januari
Publisher : Lembaga Penelitian dan Pengabdian Kepada Masyarakat (LPPM) STMIK Indonesia Banda Aceh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/jimik.v6i1.1211

Abstract

This study aims to address the challenges in classifying sentiment on Twitter regarding Puan Maharani by implementing the Modified K-Nearest Neighbor (MK-NN) method, supplemented with feature weighting and feature selection techniques. This method is designed to improve accuracy by assigning higher weights to important features and reducing data dimensions to avoid overfitting. Data is collected using a crawling technique on Indonesian-language tweets, which are then manually labeled and processed through a preprocessing stage. The testing results using the modified K-Nearest Neighbor (MK-NN) method with confusion matrices show the model's performance at three different values of K (3, 5, and 7) and data ratios of 90:10, 80:20, and 70:30. With a 90:10 data ratio and K=3, the method achieved the highest accuracy of 89.0%. These results indicate that the combination of MK-NN and related techniques is highly effective in sentiment classification, offering an innovative solution to the limitations of conventional methods. These findings have potential applications in public opinion analysis, particularly for supporting data-driven strategic decision-making.
Sistem Pendukung Keputusan Pemain Pingpong Terbaik di PTM Saung 14 Menggunakan Metode SAW Fikry, Muhammad; Lestari, Mei; Anggraeni, Ni Ketut Pertiwi
Semnas Ristek (Seminar Nasional Riset dan Inovasi Teknologi) Vol 9, No 1 (2025): SEMNAS RISTEK 2025
Publisher : Universitas Indraprasta PGRI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30998/semnasristek.v9i1.7759

Abstract

PTM Saung 14 adalah pusat pelatihan pingpong di Cibubur dengan 104 anggota aktif. Tingginya jumlah anggota menyebabkan kesulitan dalam menentukan pemain terbaik. Penelitian ini bertujuan merancang sistem pendukung keputusan untuk membantu pelatih memilih pemain pingpong terbaik di PTM Saung 14. Metode yang digunakan adalah Simple Additive Weighting (SAW), yang dipilih karena kemampuannya dalam mempermudah dan mempercepat proses penilaian. Dengan menggunakan SAW, sistem ini dapat menghitung dan menganalisis data pemain berdasarkan kriteria yang ditentukan secara efisien. Hasil penelitian menunjukkan bahwa sistem pendukung keputusan yang dirancang dapat membantu pelatih dalam menentukan pemain terbaik secara objektif, serta menyediakan fitur penyimpanan data pemain secara terstruktur untuk keperluan evaluasi di masa mendatang.
Klasifikasi Sentimen Ulasan Aplikasi WhatsApp di Play Store Menggunakan Naive Bayes Classifier Yolanda, Khovifah; Yusra, Yusra; Fikry, Muhammad
J-INTECH (Journal of Information and Technology) Vol 11 No 1 (2023): J-Intech : Journal of Information and Technology
Publisher : LPPM STIKI MALANG

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32664/j-intech.v11i1.867

Abstract

Currently, people can easily make remote contact with the WhatsApp application which makes it easier for users to communicate such as sending text messages, pictures, videos, voice messages, sharing files to make voice and video calls for free with internet access. WhatsApp is a digital application that can be used by the public for free, users can also provide reviews through the Google Play Store. The reviews on the Google Play Store are the opinions of users in providing input to application developers. This test aims to ascertain consumer opinions or emotions towards the WhatsApp application by applying the Naïve Bayes Classifier method in the process of classifying consumer reviews which will be used to solve for the highest score. The reviews are divided into two labels, namely positive reviews and negative reviews. Based on the tests that have been carried out, the highest accuracy results are obtained at a ratio of 90:10 with an accuracy of 81%, 74% precision and 54% recall with an unbalanced number of datasets, namely 669 positive reviews and 331 negative reviews. negative sentiment.
Pengaruh Kualitas Produk, Pelayanan, Emosi, dan Harga Terhadap Kepuasan Konsumen Martabak Pecenongan 78 Bengkulu Dengan Keunggulan Bersaing Sebagai Variabel Intervening Fikry, Muhammad; Sumartono, Eko; Yumiati, Yossie
SINTA Journal (Science, Technology, and Agricultural) Vol. 6 No. 1 (2025)
Publisher : Perkumpulan Dosen Muda (PDM) Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37638/sinta.6.1.23-30

Abstract

Penelitian ini bertujuan untuk meganalisis pengaruh kualitas produk, pelayanan, emosional, dan harga terhadap kepuasan konsumen Martabak Pecenongan 78 Bengkulu dengan keunggulan  bersaing sebagai variabel insstervening. Penelitian ini menggunakan pendekatan kuantitatif. Pengumpulan data dilakukan melalui kuesioner secara Accidental Sampling terhadap 280 responden. Analisis data menggunakan Structural Equation Modelling (SEM) dengan software SmartPLS 4. Hasil penelitian ini menunjukkan secara parsial kualitas produk, pelayanan, emosioal, harga, dan keunggulan bersaing berpengaruh positif dan signifikan terhadap kepuasan konsumen. Hasil uji mediasi menunjukkan hanya kualitas produk yang berpengaruh positif dan signifikan terhadap kepuasan konsumen melalui keunggulan bersaing. Variabel lainnya seperti pelayanan, emosional, dan harga tidak berpengaruh signifikan terhadap kepuasan konsumen melalui keunggulan bersaing.
PERBANDINGAN METODE SIMPLE ADDATIVE WEIGTHING (SAW) DENGAN METODE ANALIYTIC HIERARCHY PROCESS (AHP) DALAM MENGANALISA PENENTUAN PENERIMA BEASISWA KIP KULIAH DI UNIVERSITAS JABAL GHAFUR Sari, Cut Jora; Taufiq, Taufiq; Muthalib, Muchlis Abdul; Nurdin, Nurdin; Fikry, Muhammad
Jurnal Sosial Humaniora Sigli Vol 7, No 1 (2024): Juni 2024
Publisher : Universitas Jabal Ghafur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47647/jsh.v7i1.2442

Abstract

Penelitian ini dilakukan untuk perkembangan Sistem Pendukung Keputusan (SPK) dalam menentukan penerima beasiswa KIP Kuliah di Universitas Jabal Ghafur (Unigha), dengan menggunakan metode SAW dan AHP. Efisiensi dan objektivitas dalam proses seleksi beasiswa sangat penting untuk memastikan bahwa beasiswa diberikan kepada mahasiswa yang layak. Penelitian ini menggunakan berbagai metode, termasuk studi literatur, melakukan observasi dan wawancara, serta analisis data. Hasil penelitian menunjukkan bahwa metode SAW dan AHP sama-sama efektif dalam proses seleksi beasiswa, masing-masing memiliki keunggulan tersendiri dalam aspek penilaian yang berbeda.
Analisis Sentimen Ulasan Aplikasi Indodax Pada Google Play Store Dengan Algoritma Random Forest Muhammad Iqbal Maulana; Yusra; Muhammad Fikry; Surya Agustian; Siti Ramadhani
Bulletin of Computer Science Research Vol. 5 No. 4 (2025): June 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v5i4.626

Abstract

Crypto assets have become a global phenomenon with a significant increase in the number of investors in Indonesia. Indodax, as the largest crypto asset trading platform in Indonesia, has contributed to the growth of this ecosystem and received many user reviews through the Google Play Store. With more than 5 million downloads and 100 thousand reviews, sentiment analysis is an important tool to understand user perceptions of Indodax services. The results of manual labeling show that the majority of reviews are positive (3989 reviews), while neutral and negative sentiments are 477 and 534 reviews respectively. From the research and testing that has been carried out using the Random Forest method and optimizing with Hyperparameter Tuning GridSearchCV on 4 test scenarios. The best results were obtained in Scenario 4 (3 Preprocessing Stages (Cleaning, Case Folding, and Tokenization) + Random Forest & Hyperparameter Tuning) producing the best value, with Precision 81%, Recall 64%, F1-Score 70% and Accuracy 89%. With the best parameter values ??{'criterion': 'entropy', 'max_depth': None, 'max_features': 'sqrt', 'min_samples_leaf': 1, 'min_samples_split': 2, 'n_estimators': 100}. This study shows that every experimental model that is optimized produces a higher value than experimental model that is not optimized.
Retrieval-Augmented Generation in a Web-Based Question Answering System for Fiqh Books Ahadi, Ridho; Harahap, Nazruddin Safaat; Fikry, Muhammad; Kurnia, Fitra
Journal of Artificial Intelligence and Software Engineering Vol 5, No 2 (2025): Juni On-Progress
Publisher : Politeknik Negeri Lhokseumawe

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30811/jaise.v5i2.7005

Abstract

Mayoritas masyarakat Indonesia yang beragama Islam cenderung tidak mengetahui madzhab yang mereka anut, padahal pemahaman madzhab sangat penting untuk menjalankan ibadah secara benar dan sah. Salah satu madzhab yang berkembang di Indonesia, terutama melalui ulama Hadramaut, adalah madzhab Imam Asy-Syafi’i. Kurangnya akses terhadap literatur fikih otentik serta metode pembelajaran interaktif menyebabkan masyarakat kesulitan memahami fikih secara mendalam. Perkembangan teknologi kecerdasan buatan (AI), memberikan dampak signifikan dalam berbagai aspek, termasuk pendidikan dan keagamaan. Salah satu implementasi AI yang berkembang adalah chatbot, sistem interaktif berbasis percakapan yang mampu memahami dan merespons pertanyaan secara alami. Dalam konteks pembelajaran keislaman, khususnya fikih, penggunaan chatbot AI menghadirkan peluang baru untuk pembelajaran yang lebih personal dan interaktif. Fikih sebagai cabang ilmu hukum Islam menuntut pemahaman mendalam terhadap sumber klasik dan aplikasinya dalam kehidupan modern. Penelitian ini memiliki tujuan untuk mengembangkan sistem tanya jawab fikih berbasis AI dan Natural Language Processing menggunakan Large Language Model (LLM), framework LangChain, serta metode Retrieval-Augmented Generation (RAG). Sistem ini dirancang memberikan jawaban relevan berdasarkan konteks fikih. Evaluasi menggunakan metrik BERTScore menghasilkan precision 86,50%, recall 84,76%, dan F1-score 85,52%, hasil ini menunjukkan akurasi tinggi dalam menjawab pertanyaan fikih.
Co-Authors -, Yusra Agustian, Surya Ahadi, Ridho Alwis Nazir Ananda, Silvia Andini, Nanda Angela, Angela Anggraeni, Ni Ketut Pertiwi Annisa Annisa Ayu Fransiska Bahari, Bayu Dwi Prasetya Damayanti, Elok Dermawan, Jozu Detha Yurisna Dimas Pratama, Dimas Eka Pandu Cynthia Eka Pandu Cynthia, Eka Pandu Eko Sumartono, Eko Elin Haerani Elin Haerani Elin Haerani Elvia Budianita Fadhilah Syafria Febi Yanto, Febi Fitri Insani Fitri Insani Hasugian, Leonardo Hutagalung, Yorio Arwandi Wisdom Ibnu Surya Ida Wahyuni Inggih Permana Iwan Iskandar kurnia, fitra Lestari Handayani Lola Oktavia Lutfi, Raihansyah Mardiansyah, M Rizki Mei Lestari, Mei Muchlis Abdul Muthalib Muhammad Abdillah Muhammad Iqbal Maulana Muhammad Irsyad Muhammad Ravil Nanda Sepriadi Nazir, Alwis Nazruddin Safaat H Ndruru, Arlan Joliansa Nuari Ananda Nurdin Nurdin nuryana nuryana, nuryana Oktavia, Lola Pizaini Pizaini Pizaini Pizaini Pizaini, Pizaini Prananda, Alga Putra, Wahyu Eka Putri Mardatillah Rahma Yunita, Rahma Rahmat Rizki Hidayat Ramadanu Putra Razi, Ar Reski Mai Candra Rinaldi Syarfianto Ritonga, Sinta Wahyuni Sagala, Ruflica Sapriadi, Muhammad Saputra, Ikhsan Dwi Sari, Cut Jora Sayed Omas Tutus Arifta Sayed Siti Ramadhani Sofiah Surya Agustian Surya Agustian Suwanto Sanjaya Tarigan, Anggun Kinanti Taufik Hidayat Taufiq Taufiq Tiara Dwi Arista Wirdiani, Putri Syakira Yani, Susmi Syahfrida Yenggi Putra Dinata Yolanda, Khovifah Yossie Yumiati Yuda Zafitra Fadhlan Yulinazira, Ulfa Yusra Yusra Yusra . YUSRA YUSRA Yusra, Yusra Yusriyana, Yusriyana Zukhruf, Muhammad Firmansyah