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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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Klasifikasi Tweet E-Commerce dengan Menggunakan Metode Support Vector Machine Yusra, Yusra; Fikry, Muhammad
Jurnal CoreIT: Jurnal Hasil Penelitian Ilmu Komputer dan Teknologi Informasi Vol 4, No 2 (2018): Desember 2018
Publisher : Fakultas Sains dan Teknologi, Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (416.116 KB) | DOI: 10.24014/coreit.v4i2.5205

Abstract

Aktifitas belanja online telah menjadi kebutuhan masyarakat. Online shop di media sosial merupakan pilihan tempat berbelanja karena pembeli dapat berinteraksi dan berkonsultasi langsung dengan penjual. Tantangan dalam mengumpulkan informasi transaksi e‑commerce di media sosial adalah banyaknya pemilik online shop dan kerahasiaan data. Namun demikian, informasi transaksi e‑commerce di Twitter dapat ditemukan pada tweet yang dapat diakses publik. Tweet biasanya berisikan aktifitas sebelum pembelian, aktifitas pembelian, aktifitas pengiriman oleh penjual, atau aktifitas penerimaan oleh pembeli. Hal ini menjadi indikator adanya transaksi. Tantangan lainnya adalah teks di media sosial menggunakan bahasa alami manusia yang seringkali dituliskan secara tidak terstruktur. Dalam penelitian ini, diklasifikasikan apakah suatu tweet berkaitan dengan transaksi e‑commerce atau tidak. Oleh karena itu, tweet yang telah dikumpulkan dan diberi label perlu dipraproses, meliputi case folding, cleaning, tokenisasi, normalisasi kata, stopword removal dan stemming. Selanjutnya, dilakukan ekstraksi fitur berdasarkan nilai document frequency dan threshold minimum bagi kata untuk dipilih sebagai fitur. Untuk setiap tweet, ditentukan nilai fitur dengan term frequency-inverse document frequency. Setelah dilakukan cross-validation dengan menggunakan kernel RBF, diketahui parameter terbaik adalah pasangan parameter C=0,9 dan γ=0,8 dengan rataan akurasi sebesar 96,1%. Model terbaik merupakan model yang menghasilkan nilai akurasi tertinggi. Akhirnya dilakukan pengujian dengan hasil akurasi sebesar 94%.
Chatbot Deteksi Awal Gangguan Kecemasan Menggunakan Dialogflow Rahmat Rizki Hidayat; Muhammad Fikry; Yusra Yusra
Jurnal Teknologi Terpadu Vol 11, No 2 (2023): JTT (Jurnal Teknologi Terpadu)
Publisher : Pusat Penelitian dan Pengabdian Kepada Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32487/jtt.v11i2.1867

Abstract

Nowadays, anxiety disorders are experienced by many individuals, making a significant impact on one's quality of life. Some people are unaware of the symptoms of anxiety disorders, making anxiety disorders trivial. This situation can cause serious physical and emotional discomfort, in some cases, leading to more severe impacts if not treated appropriately. One of the first steps in overcoming anxiety disorders is early detection. The earlier the disorder is detected, the better the chances of providing effective treatment and reducing its impact. The development of artificial intelligence technology has opened up new opportunities to address this problem. This research proposes an innovation in the form of a chatbot. The purpose of this study is to determine the feasibility and acceptability of a chatbot to identify and provide information related to symptoms of anxiety disorders. The research methodology includes Data Collection, conversation formation, model formation, implementation using Dialogflow, testing and results. The results of UAT testing on respondents consisting of students and psychologists obtained results of 84% and 74%, respectively.
Algoritme Logistic Regression untuk Mendeteksi Ujaran Kebencian dan Bahasa Kasar Multilabel pada Twitter Berbahasa Indonesia Ayu Fransiska; Surya Agustian; Fitri Insani; Muhammad Fikry; Pizaini Pizaini
Jurnal Nasional Komputasi dan Teknologi Informasi (JNKTI) Vol 5, No 4 (2022): Agustus 2022
Publisher : Program Studi Teknik Komputer, Fakultas Teknik. Universitas Serambi Mekkah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32672/jnkti.v5i4.4524

Abstract

Abstrak - Ujaran kebencian semakin meningkat bersamaan dengan banyaknya pengguna media sosial. Twitter merupakan salah satu media sosial yang membantu penyeberan ujaran ujaran melalui fitur twit-nya yang dilakukan berulang-ulang. Penelitian ini dilakukan untuk mengklasifikasi apakah sebuah twit mengandung ujaran kebencian atau bahasa kasar, dan jika terdeteksi mengandung ujaran kebencian maka akan diukur tingkatannya. Dataset yang digunakan diambil dari twitter sebanyak 13.126 twit asli. Klasifikasi menggunakan Algoritma logistic Regression dan fitur teks word embedding. Dilakukan beberapa kali percobaan untuk mendapatkan model terbaik agar pengujian didapatkan secara optimal. Rata-rata akurasi yang dari ketiga kelas sebesar 75,59%, untuk kelas hate speech 75,86%,kelas abusive 80,05%, kelas level 70,86% dengan komposisi 90:10.Kata kunci: Klasifikasi, Logistic Regression, Ujaran Kebencian, Twitter. Abstract - Hate speech is increasing along with the number of social media users. Twitter is one of the social media that helps spread utterances through its repeated tweet features. This study was conducted to classify whether a tweet contains hate speech or abusive language, and if it is detected to contain hate speech, the level will be measured. The dataset used was taken from twitter as many as 13,126 original tweets. Classification using Logistic Regression Algorithm and word embedding text feature. Several experiments were carried out to get the best model so that the test was obtained optimally. The average accuracy of the three classes is 75.59%, for the hate speech class is 75.86%, the abusive class is 80.05%, the level class is 70.86% with a composition of 90:10.Keyword : Classification, Logistic Regression, Hate Speech, Twitter.
Klasifikasi Sentimen Tweet Masyarakat terhadap Kendaraan Listrik Menggunakan Support Vector Machine Nuari Ananda; Muhammad Fikry; Yusra Yusra; Lestari Handayani; Iwan Iskandar
Jurnal Informatika Universitas Pamulang Vol 8 No 4 (2023): JURNAL INFORMATIKA UNIVERSITAS PAMULANG
Publisher : Teknik Informatika Universitas Pamulang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32493/informatika.v8i4.36754

Abstract

Sentiment analysis involves using classification algorithms to analyze public opinions and feelings in text. Within the automobile industry, electric vehicles (EVs) stem from the circular economy and represent a novel technology under investigation in sentiment classification studies. The Support Vector Machine (SVM) algorithm is commonly used in this research due to its superior accuracy compared to other algorithms. The goal of this study is to apply SVM variable selection techniques to enhance sentiment analysis quality. Python is the programming language used to build the sentiment classification model, which involves feature selection using TF-IDF, training with cross-validation and grid search, evaluation using a confusion matrix, and storing the dataset in a MySQL database. The research focuses on the sentiment classification of 3000 public tweets about electric vehicles on Twitter. Through various scenarios, it was observed that the accuracy of sentiment classification varied depending on factors such as randomizing data, handling negation, and using different types of features like unigrams or bigrams. The highest accuracy achieved was 84% using a scenario with random data, negation handling, and unigram features. Overall, this research highlights the impact of randomizing data and selecting appropriate features on sentiment classification accuracy for electric vehicles on Twitter.
Chatbot PTIPD Customer Care Center Service using Dialogfow Ndruru, Arlan Joliansa; Fikry, Muhammad; -, Yusra
Komputasi: Jurnal Ilmiah Ilmu Komputer dan Matematika Vol 20, No 2 (2023): Komputasi: Jurnal Ilmiah Ilmu Komputer dan Matematika
Publisher : Ilmu Komputer, FMIPA, Universitas Pakuan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33751/komputasi.v20i2.8281

Abstract

Chatbot research is a unique innovation in the development of Artificial Intelli- gence and has promising prospects in the field of Education. One form of information service available at the university is the Customer Care Center (C3) PTIPD UIN Suska Riau, which is responsible for handling problems submitted by students. However, with so many questions or problems submitted to the PTIPD Customer Care Center, it is difficult for the PTIPD Cus- tomer Care Center to respond to student questions submitted, the service becomes ineffective and the response to the answers to the problems submitted becomes late. To overcome this problem, chatbot development was carried out for PTIPD UIN Suska Riau Customer Care Center Services using Dialogflow to improve services and overcome existing problems. Di- alogflow as conversation development platform that uses natural language processing (NLP) to understand and interpret user intent in conversations. Through User Acceptance Test (UAT) testing, the chatbot managed to achieve an acceptance rate of 84% overall. This shows that users, in this case, students respond positively to the use of chatbots in PTIPD Customer Care Center services. In addition, Usability Testing was also conducted to evaluate the level of usability of the chatbot. Based on this test, the chatbot achieved a score of 76, which indicates a good level of usability in interaction with users. The test results illustrate that the chatbot at the Customer Care Center PTIPD UIN Suska Riau has provided a positive user experience.
Klasifikasi Sentimen Masyarakat di Twitter terhadap Ganjar Pranowo dengan Metode Naïve Bayes Classifier Ritonga, Sinta Wahyuni; ., Yusra; Fikry, Muhammad; Cynthia, Eka Pandu
Building of Informatics, Technology and Science (BITS) Vol 5 No 1 (2023): June 2023
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v5i1.3535

Abstract

Indonesia is a country with a Democratic political system. The public is given freedom of speech, collaboration and public criticism. In the modern era, the use of social media is growing rapidly at the community level. One of the social media trends in Indonesia is Twitter which is used to convey aspirations to the government and as a means to convey daily activities, opinions, culture and get the latest information or news from Indonesia and abroad. Public opinion taken from Twitter can be positive, negative and neutral. The number of tweets on Twitter one of the trend topics in Indonesia is Ganjar Pranowo, can be used as a source of data in the assessment of sentiment classification which is processed to produce accuracy values. This study aims to classify public opinion on social media Twitter about Ganjar Pranowo using Naïve Bayes Classifier method. In the classification processing using a dataset of 4000 tweet data with two labeling classes, positive and negative to determine the efficiency of NBC performance combined with TF-IDF weighting, feature selection using supervised learning approach techniques. The results of the test on the classification of public sentiment research on Twitter about Ganjar Pranowo using NBC method using 10% of the test data from the dataset used to produce an accuracy value of 83.0%.
Edukasi Pemanfaatan Pekarangan Dengan Budidaya Sayuran Di Lingkungan Panti Sosial Bina Remaja Harapan Bengkulu Andini, Nanda; Fikry, Muhammad; Yunita, Rahma; Bahari, Bayu Dwi Prasetya; Zukhruf, Muhammad Firmansyah
Jurnal Dehasen Mengabdi Vol 3 No 1 (2024): Maret-Agustus
Publisher : Universitas Dehasen Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37676/jdm.v3i1.5725

Abstract

The Bina Remaja Harapan Social Shelter in the city of Bengkulu serves as a residence and learning center for children who are mostly abandoned by their parents. Through observation, it is evident that the utilization of backyard land is not yet optimal despite having sufficient space. This service project aims to teach the process and benefits of utilizing backyard land for vegetable cultivation. The method employed is participatory learning, where the team teaches and engages the participants in practical activities. The results show an improvement in the practical understanding of the shelter's children regarding planting techniques, maintenance, and the benefits of urban agriculture. Selected vegetable crops such as eggplants, mustard greens, spinach, water spinach, tomatoes, and chilies can be produced at an affordable cost.
Pemberdayaan Kelompok Wanita Tani Dengan Budidaya Sayuran Menggunakan Polibag Di Pekarangan Rumah Fikry, Muhammad; Hasugian, Leonardo; Sagala, Ruflica; Sumartono, Eko
Jurnal INDONESIA RAYA (Pengabdian pada Masyarakat Bidang Sosial, Humaniora, Kesehatan, Ekonomi dan Umum) Vol 5, No 1 (2024)
Publisher : Perkumpulan Dosen Muda Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37638/indonesiaraya.5.1.25-30

Abstract

Kelompok Wanita Tani adalah wadah yang memberikan kesempatan bagi perempuan untuk ikut berpartisipasi dalam memajukan sektor pertanian. Kehadiran KWT merupakan pendorong dalam meningkatkan kesadaran dan inovasi petani, serta meningkatkan hasil lahan dan tanaman bagi anggotanya dan masyarakat sekitar. Kelompok Wanita Tani Golf yang ada di Kota Bengkulu diketuai oleh Ibu Esmi Kartika yang beranggotakan 20 orang. Masalah yang muncul pada Kelompok Wanita Tani Golf Partisipasi anggota kelompok yang rendah dan kesulitan dalam menjalankannya dikarenakan Kelompok Wanita Tani Golf baru terbentuk. Tujuan dari kegiatan ini adalah untuk menggerakan anggota Kelompok Wanita Tani dan membantu menjalankan Kelompok Wanita Tani ini supaya bisa berkembang. Kegiatan yang telah dilakukan mencakup pengenalan, penanaman dan pemeliharaan. Hasil dari kegiatan ini diharapkan dapat menambah semangat gotong – royong antar anggota.Kata Kunci: Inovasi Petani, Kelompok Wanita Tani, Pemberdayaan Masyarakat.
Analisa sentimen terhadap kenaikan bbm di twitter (x) menggunakan naive bayes classifier Muhammad Abdillah; Fikry, Muhammad; Yusra; Nazir, Alwis; Insani, Fitri
Computer Science and Information Technology Vol 5 No 1 (2024): Jurnal Computer Science and Information Technology (CoSciTech)
Publisher : Universitas Muhammadiyah Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37859/coscitech.v5i1.6954

Abstract

In early September 2022, there was a shock from the news of the rise in fuel prices. The government decided to increase the price of fuel due to the surge in world oil prices. PT Pertamina (Persero) officially raised the price of Fuel Oil (BBM) one-third of September 2022, at 2:30 PM WIB (Western Indonesia Time). Since the decision, it has sparked opinions from the public. Many people expressed their responses through the social media platform Twitter, both in positive and negative ways. This resulted in both positive and negative sentiments from the public. The data used consisted of 3,000 tweets with the keyword "FUEL PRICE INCREASE," collected from November 1, 2022, to December 1, 2022. This research utilized the Naive Bayes Classifier method, conducted with three comparisons using thresholds ranging from 0.001 to 0.007. The experiment was conducted with three types of data testing: opinion data, mixed data (opinion-non-opinion), and balanced data. Here are the test results: for opinion data, the highest accuracy obtained was 80% with a ratio of 90:10, for mixed data, the accuracy obtained was 67.7% with a ratio of 70:30, and for balanced data, the accuracy obtained was 63.6% with a ratio of 90:10.
Analisis Sentimen Terhadap Sebuah Figur Publik di Twitter Menggunakan Metode K-Nearest Neighbor Yenggi Putra Dinata; Yusra; Fikry, Muhammad; Yanto, Febi; Cynthia, Eka Pandu
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.v4i6.1904

Abstract

The development of online media, particularly through social media platforms like Twitter, has created a vast stage for various activities, including political campaigns and public opinion on public figures. When information technology advances rapidly, public opinion can be conveyed without time constraints through social media. Twitter, with its character limitations and the use of hashtags by users, is considered easier to gather information about existing opinions and sentiments. Currently, social media is widely used for communication and making friends, but also for other activities. Advertising products, buying and selling anything, including advertising political parties and campaigning for members of Congress or presidential candidates. This research focuses on sentiment analysis towards Puan Maharani, the Speaker of the Indonesian House of Representatives (DPR RI), using data from the social media platform Twitter. Twitter, as a platform that allows users to express opinions in a concise format, is used as the main source of information in this research. The K-Nearest Neighbor algorithm for sentiment analysis technique is utilized to classify individual tweets into positive or negative categories regarding views on Puan Maharani. The methods used in this research include data crawling, labeling, and data preprocessing, which involve case folding, cleaning, tokenizing, negation handling, normalization, stopword removal, and stemming. For the classification process, the K-Nearest Neighbor method, feature weighting (TF-IDF), and feature selection (thresholding) are employed, with a threshold value of 0.001. The data used comprises 9,000 tweets in the Indonesian language. The results of the testing conducted in the K-Nearest Neighbor method, using confusion matrices, with 6 different values of K (3, 5, 7, 9, 11, 13), with comparison mechanisms of 90:10, 80:20, and 70:30 achieved the highest accuracy of 90.00% with K = 11 from the comparison using the 90:10 ratio
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